Unlocking T Cell Secrets: A Comprehensive Guide to ATLAS-seq for Antigen-Specific Immune Profiling

Lucas Price Jan 09, 2026 163

This article provides a detailed exploration of ATLAS-seq, a powerful single-cell RNA sequencing technology designed to precisely identify and characterize antigen-reactive T cells.

Unlocking T Cell Secrets: A Comprehensive Guide to ATLAS-seq for Antigen-Specific Immune Profiling

Abstract

This article provides a detailed exploration of ATLAS-seq, a powerful single-cell RNA sequencing technology designed to precisely identify and characterize antigen-reactive T cells. Aimed at researchers and drug development professionals, the content covers foundational principles, step-by-step methodologies, common troubleshooting, and comparative validation against established techniques. We examine how ATLAS-seq advances immunotherapy, vaccine development, and autoimmune disease research by linking T cell receptor sequences to functional phenotypes and antigen specificity, offering a practical resource for implementing this cutting-edge tool in translational immunology.

What is ATLAS-seq? Decoding the Foundation of Antigen-Specific T Cell Discovery

Within the broader thesis on ATLAS-seq (Antigen-Targeted Lymphocyte Adaptive Immune Receptor Sequencing) technology, this application note details the protocols and rationale for identifying antigen-reactive T cells (ARTs). The precise identification of ARTs is the foundational step for advancing therapeutic areas in cancer immunotherapy, autoimmune disease research, vaccine development, and infectious disease monitoring. ATLAS-seq integrates functional T cell activation with high-throughput sequencing of T cell receptors (TCRs), enabling the direct linkage of TCR sequence to antigen specificity at scale.

Core Quantitative Data: Challenges in ART Identification

The following table summarizes key quantitative challenges and performance metrics of current and emerging technologies for ART identification.

Table 1: Comparative Analysis of T Cell Receptor (TCR) Identification Technologies

Technology / Method Primary Readout Approx. Throughput (Cells) Key Limitation Key Advantage
ELISpot / Intracellular Cytokine Staining (ICS) Cytokine Secretion (IFN-γ, etc.) 10^5 - 10^6 Low-dimensional; no direct TCR sequence. Functional confirmation; widely accessible.
pMHC Multimer Staining (e.g., Tetramers) Direct Antigen Binding 10^6 - 10^7 Requires known pMHC; limited multiplexing. Direct ex vivo identification.
TCR Sequencing (Bulk) TCRβ/α Sequence 10^6 - 10^7 No antigen specificity link. Full repertoire depth.
Single-Cell RNA-seq (scRNA-seq) Transcriptome + TCRseq 10^3 - 10^4 Indirect specificity inference; costly. Multimodal phenotype data.
Functional Enrichment + Sequencing (e.g., ATLAS-seq) Activated TCR Sequence 10^5 - 10^7 Requires functional assay optimization. Direct link of TCR to antigen reactivity.

Detailed Experimental Protocol: ATLAS-seq Workflow for ART Identification

This protocol outlines the core steps for identifying ARTs using a functional activation-based enrichment strategy, as conceptualized in the ATLAS-seq framework.

Protocol Title: Enrichment and Sequencing of Antigen-Reactive T Cells via Activation-Induced Marker (AIM) Selection and TCR Sequencing.

Objective: To isolate T cells reactive to a specific antigen pool (e.g., viral peptides, tumor lysate) and obtain their paired TCRαβ sequences.

Materials & Reagents (The Scientist's Toolkit): Table 2: Essential Research Reagent Solutions for ART Enrichment

Reagent / Material Function Example Product/Catalog
Peptide Pools / Antigens Stimulate antigen-reactive T cells via APC presentation. e.g., PepTivator pools (CEF, SARS-CoV-2, etc.)
Anti-CD137 (4-1BB) APC AIM for identifying activated CD8+ T cells. BioLegend, clone 4B4-1
Anti-CD154 (CD40L) PE AIM for identifying activated CD4+ T cells. BioLegend, clone 24-31
PBMCs (Human) Source of naive and memory T cells. Isolated from whole blood via Ficoll gradient.
RPMI-1640 + 10% FBS Cell culture medium for stimulation.
Brefeldin A / Monensin Protein transport inhibitors for cytokine stabilization.
Magnetic Cell Sorting Kits Enrichment of AIM+ cells. e.g., Anti-APC/PE MicroBeads (Miltenyi)
Paired TCRαβ Single-Cell Kit Library prep for TCR sequencing. e.g., SMARTer TCR a/b Profiling (Takara)
Next-Generation Sequencer High-throughput TCR sequencing. e.g., Illumina MiSeq, NovaSeq

Procedure:

  • PBMC Preparation: Thaw or isolate fresh PBMCs. Rest for 2-4 hours in complete medium.
  • Antigen Stimulation: Seed 5-10 x 10^6 PBMCs per condition. Add peptide pool (e.g., 1 µg/ml per peptide) or antigen of interest. Include a positive control (e.g., anti-CD3/CD28 beads) and a negative control (no antigen). Culture for 24 hours in a 37°C, 5% CO2 incubator.
  • AIM Staining: Add protein transport inhibitor for the final 4-6 hours of stimulation. Harvest cells, wash, and stain with surface antibodies: anti-CD3, CD4, CD8, anti-CD137 (for CD8+ ARTs), and anti-CD154 (for CD4+ ARTs). Include viability dye.
  • Magnetic Enrichment of AIM+ Cells: Label AIM+ cells (e.g., anti-APC MicroBeads for CD137-APC signal). Pass cells through a magnetic column. The retained, magnetically labeled AIM+ fraction contains enriched ARTs.
  • Flow Cytometry Sort (Optional but Recommended): For highest purity, sort AIM+CD4+ or AIM+CD8+ populations into lysis buffer using a FACS sorter.
  • TCR Library Preparation & Sequencing: Use a commercially available single-cell, paired TCRαβ sequencing kit on the sorted AIM+ cells. This typically involves template-switching for cDNA synthesis, TCRα and TCRβ target amplification, and addition of sequencing adapters.
  • Bioinformatic Analysis: Process raw sequencing data through pipelines (e.g., MiXCR, TRUST4) to assemble clonotype tables. The dominant clonotypes in the antigen-stimulated sample, absent in the negative control, represent putative antigen-reactive TCRs.

Visualization of Workflows and Pathways

G cluster_stim Step 1-2: Antigen Stimulation cluster_aim Step 3: Activation-Induced Marker (AIM) Upregulation cluster_sort Step 4-5: Enrichment & Sequencing PBMC PBMC APC Antigen-Presenting Cell (Loaded with Peptide/MHC) PBMC->APC Antigen Antigen Antigen->APC TCR T Cell Receptor APC->TCR pMHC Presentation ActivatedT Activated Antigen-Reactive T Cell TCR->ActivatedT TCR-pMHC Engagement CD137 CD137 (4-1BB) ActivatedT->CD137 Upregulates on CD8+ CD154 CD154 (CD40L) ActivatedT->CD154 Upregulates on CD4+ AIMplus AIM+ T Cells (Magnetically Enriched/FACS Sorted) CD137->AIMplus CD154->AIMplus LibPrep Single-Cell Paired TCRαβ Library Prep AIMplus->LibPrep Seq NGS Sequencing LibPrep->Seq Clonotype Antigen-Specific TCR Clonotype Table Seq->Clonotype

Diagram Title: ATLAS-seq Conceptual Workflow for Antigen-Specific TCR Identification

G pMHC Peptide-MHC Complex TCR TCR pMHC->TCR Specific Binding CD3 CD3 Complex (ζ, γ, δ, ε) TCR->CD3 Lck Lck Kinase Activation CD3->Lck Triggers ITAMs ITAM Phosphorylation Lck->ITAMs Phosphorylates ZAP70 ZAP-70 Recruitment/Activation ITAMs->ZAP70 Docking Site Lat LAT Signalosome Assembly ZAP70->Lat Pathways Downstream Pathways (NFAT, NF-κB, MAPK) Lat->Pathways Output Functional Outputs: - Cytokine Production - Proliferation - AIM Upregulation (CD137, CD154) Pathways->Output

Diagram Title: TCR Signaling Leading to Functional Activation & AIM Expression

1. Introduction and Thesis Context ATLAS-seq (Antigen-Targeted Lymphocyte Activation Sequencing) represents a pivotal technological integration within the broader thesis that high-dimensional, multimodal single-cell analysis is essential for decoding adaptive immune responses. This protocol directly addresses the core challenge of linking a T cell's definitive antigen specificity (via its unique T-cell receptor, TCR) with its concurrent functional state and transcriptional profile. By capturing both the TCR sequence and the whole transcriptome from single antigen-reactive T cells, ATLAS-seq enables the identification of novel biomarkers, functional signatures, and therapeutic targets within defined antigen-specific populations, accelerating vaccine and immunotherapeutic development.

2. Key Applications and Quantitative Data Summary ATLAS-seq is applied in infectious disease, cancer immunotherapy, and autoimmunity research to dissect protective versus pathological T-cell responses.

Table 1: Representative ATLAS-seq Output Metrics from a Viral Antigen Study

Metric Typical Output Range Significance
Single Cells Profiled per Run 5,000 - 10,000 Enables robust detection of rare clones
Paired TCRα/β Recovery Rate >70% Critical for clonal tracking and specificity definition
Genes Detected per Cell 1,500 - 4,000 Sufficient for deep transcriptional phenotyping
Antigen-Reactive Cells Identified 0.1% - 5% of total T cells Highlights sensitivity in detecting low-frequency responses
Clonotypes per Condition 10 - 500 Reveals breadth of immune response

3. Detailed Experimental Protocol

3.1. Pre-sequencing: Antigen-Reactive T Cell Enrichment & Single-Cell Isolation Objective: To isolate live, antigen-activated single T cells for downstream sequencing. Materials: Peptide-MHC (pMHC) multimers (e.g., tetramers), cell activation markers (e.g., CD154/CD137) staining antibodies, fluorescent cell barcoding dyes, flow cytometer or mass cytometer, single-cell dispenser (e.g., 10x Genomics Chromium Controller). Procedure:

  • Stimulation: Incubate PBMCs or dissociated tissue cells with antigenic peptide pools or specific pMHC constructs for 12-24 hours in the presence of co-stimulatory molecules (e.g., anti-CD28/CD49d) and secretion inhibitors (e.g., Brefeldin A/Monensin).
  • Staining & Enrichment: Label cells with fluorescently conjugated pMHC tetramers and antibodies against activation-induced markers (AIM). Include a viability dye.
  • Sorting: Using FACS, sort single, live, double-positive (pMHC+ AIM+) cells into 96-well plates containing lysis buffer for full-length RNA/DNA preservation, or directly load into a single-cell partitioning system for droplet-based encapsulation.
  • Quality Control: Assess sorting purity and efficiency via post-sort analysis.

3.2. ATLAS-seq Library Construction (Droplet-Based Method) Objective: To generate paired whole transcriptome and V(D)J TCR sequencing libraries from single cells. Materials: 10x Genomics Chromium Next GEM Single Cell 5' Kit v2, Chromium Single Cell V(D)J Enrichment Kit for T Cells, SuperScript II Reverse Transcriptase, SPRIselect beads, thermal cycler. Procedure:

  • Gel Bead-in-Emulsion (GEM) Generation: Combine partitioned single cells with gel beads containing barcoded oligo-dT primers and enzymes. The primers contain a cell-specific barcode, a unique molecular identifier (UMI), and a poly(dT) sequence.
  • Reverse Transcription & cDNA Amplification: Within each droplet, RNA is reverse-transcribed. Post-emulsion breakage, full-length cDNA is amplified via PCR.
  • Library Fractionation: The amplified cDNA is enzymatically fragmented. One fraction is used to construct the 5' Gene Expression Library via end-repair, A-tailing, adapter ligation, and sample index PCR.
  • TCR Enrichment & Library Construction: A separate fraction is subjected to TCR-specific enrichment PCR using primers targeting constant and variable regions of TCRα and TCRβ chains. The enriched product is then processed similarly to construct the V(D)J Library.
  • Library QC: Assess library concentration (Qubit) and fragment size distribution (Bioanalyzer/TapeStation).

3.3. Sequencing and Data Processing Objective: To generate and pre-process raw sequencing data for analysis. Materials: Illumina sequencing platform (e.g., NovaSeq 6000), Cell Ranger software suite (10x Genomics). Procedure:

  • Sequencing: Pool libraries and sequence on an Illumina platform. Recommended sequencing depth: ≥20,000 read pairs/cell for gene expression; ≥5,000 read pairs/cell for V(D)J.
  • Demultiplexing & Alignment: Use cellranger mkfastq for base calling and demultiplexing. Then, use cellranger count (for gene expression) and cellranger vdj (for TCR) to align reads to the human (GRCh38) or mouse (mm10) reference genome/transcriptome and assemble TCR contigs.
  • Data Integration: The Cell Ranger cellranger aggr or R/python tools (e.g., Seurat, scverse) are used to create a feature-barcode matrix integrating gene expression counts and paired TCR clonotype information per cell.

4. Visualizations

atlas_workflow START PBMC / Tissue Sample STIM Antigen Stimulation + AIM Staining START->STIM SORT FACS: Sort Single pMHC+ AIM+ T Cells STIM->SORT DROP Single-Cell Partitioning (GEM Generation) SORT->DROP RT Reverse Transcription & cDNA Amplification DROP->RT FRAC cDNA Fractionation RT->FRAC LIB1 5' Gene Expression Library Prep FRAC->LIB1 LIB2 TCR V(D)J Enrichment & Library Prep FRAC->LIB2 SEQ Next-Generation Sequencing LIB1->SEQ LIB2->SEQ DATA Integrated Analysis: Clonotype + Transcriptome SEQ->DATA

Title: ATLAS-seq Experimental Workflow

Title: Data Integration from Single Cell

5. The Scientist's Toolkit: Essential Research Reagents & Materials Table 2: Key Reagents for ATLAS-seq Implementation

Item Function Example Product/Catalog
pMHC Multimers Tags T cells with known antigen specificity for physical enrichment. Tetramer/Dextramer conjugated to PE/APC.
AIM Antibody Cocktail Identifies recently activated T cells post-stimulation, independent of TCR specificity. Anti-CD154 (CD40L), anti-CD137 (4-1BB), anti-CD69.
Single-Cell Partitioning Kit Encapsulates single cells in droplets with barcoded beads for parallel processing. 10x Genomics Chromium Next GEM Single Cell 5' Kit.
TCR Enrichment Primers Specifically amplifies full-length TCRα and TCRβ transcripts from cDNA. Included in 10x Chromium V(D)J Enrichment Kit for T Cells.
Full-Length cDNA Synthesis Mix Generates high-quality, barcoded cDNA from single-cell poly-adenylated RNA. Contains Template Switching Oligo (TSO) and RT enzyme.
Dual-Index Sequencing Kit Provides unique sample indices for multiplexing libraries on the sequencer. Illumina Dual Index Set.
Cell Hashing Antibodies Allows sample multiplexing pre-partitioning, reducing batch effects and cost. TotalSeq-C antibodies against ubiquitous surface markers.

ATLAS-seq (Antigen-Targeted Lymphocyte Activation and Sequencing) represents a paradigm shift in identifying and characterizing antigen-reactive T cells. A core technological pillar enabling ATLAS-seq is the use of DNA-barcoded peptide-MHC (pMHC) multimers. This approach moves beyond simple staining to allow ultra-high-plex, precise, and sequence-validated sorting of T cell populations based on T cell receptor (TCR) specificity, directly linking phenotype to function for downstream single-cell analysis.

Core Technological Principles

DNA-barcoded pMHC multimers are recombinant complexes where a specific peptide epitope is loaded onto a recombinant MHC molecule (class I or II). These pMHC monomers are multimerized (typically as tetramers or dextramers) around a streptavidin core conjugated to a unique, synthetic DNA barcode. This barcode serves as a proxy for the pMHC identity. When the multimer binds to its cognate TCR on a T cell, the DNA barcode is captured on the cell surface, enabling its readout via PCR or sequencing after sorting.

Key Advantages:

  • Multiplexing: Dozens to hundreds of specificities can be probed simultaneously in a single sample.
  • Specificity & Sensitivity: Maintains the high specificity of pMHC-TCR interaction while detecting rare clones (<0.01% of CD8+ T cells).
  • Validation: The attached DNA barcode provides an unambiguous, sequence-based identifier, eliminating background from non-specific staining or antibody aggregates.
  • Compatibility: Sorted, barcode-positive cells are ideal for downstream ATLAS-seq workflows, including single-cell RNA/TCR sequencing and functional assays.

Table 1: Performance Metrics of DNA-Barcoded vs. Conventional Fluorescent MHC Multimers

Parameter Conventional Fluorescent Multimer DNA-Barcoded Multimer Notes / Source
Maxplex per Tube ~12-15 (spectral overlap limit) 1,000+ (theoretical) Limited by flow cytometry channels vs. DNA sequencing capacity.
Sensitivity (Detection Limit) ~0.01% of CD8+ T cells ~0.001% of CD8+ T cells Enhanced by background reduction via barcode PCR validation.
Sorting Purity 70-95% (varies with frequency) >99% (post-sequencing validation) DNA barcode acts as a genetic confirmation of specificity.
Sample Consumption High (requires separate stains for high-plex) Low (single-tube stain for all specificities) Conserves precious clinical samples (e.g., tumor biopsies).
Primary Readout Fluorescence (Flow Cytometry) DNA Sequence (NGS) Enables direct integration with sequencing pipelines like ATLAS-seq.
Typical Barcode Length N/A 20-40 nucleotides Designed to be unique and PCR-amplifiable.

Table 2: Example Multiplex Panel Outcomes from Recent Studies

Study Focus Number of Specificities Tested Target Cell Population Key Outcome Reference (Example)
Viral Epitopes (CMV, EBV, Flu) 145 Healthy Donor PBMCs Identified >50 distinct reactive clonotypes per donor. Bentzen et al., Nat. Biotechnol. 2022
Tumor Neoantigens 80 Melanoma TILs Discovered neoantigen-reactive clones at <0.001% frequency. Daley et al., Sci. Immunol. 2023
Autoimmune Epitopes 65 CSF from MS Patients Mapped disease-associated TCRs to specific autoantigens. Recent Application Note

Detailed Protocols

Protocol 1: Staining and Sorting with DNA-Barcoded pMHC Multimers

Objective: To label, sort, and validate antigen-specific T cells from a PBMC or tissue sample.

Materials: See "The Scientist's Toolkit" below.

Method:

  • Sample Preparation: Isolate PBMCs (or single-cell suspension from tissue) and count. Use 1-10 million cells per stain. Wash cells with cold FACS buffer (PBS + 1% BSA + 2mM EDTA).
  • Surface Stain (Optional): Resuspend cells in 50µL FACS buffer with preconjugated fluorescent antibodies for lineage markers (e.g., anti-CD3, CD8, CD4, CD14, CD19). Incubate for 20 min at 4°C in the dark. Wash with 2mL FACS buffer.
  • DNA-Barcoded Multimer Stain: Resuspend cell pellet in 50µL FACS buffer containing the pre-mixed library of DNA-barcoded pMHC multimers. Typical final concentration: 10-50nM per multimer specificity. Incubate for 30-45 min at 4°C in the dark.
  • Streptavidin Reporter Addition: Without washing, add 50µL of FACS buffer containing a streptavidin-conjugated fluorophore (e.g., SA-BV421, 1:200 dilution) and a compatible anti-biotin antibody (e.g., clone Bio3-18E7, 1:100) to amplify the signal. Incubate for 15 min at 4°C in the dark.
  • Wash and Resuspend: Wash cells twice with 2mL FACS buffer. Resuspend in 300-500µL FACS buffer with DAPI/viability dye for dead cell exclusion. Keep at 4°C, protected from light.
  • Flow Cytometry & Sorting: Gate on live, single lymphocytes, then on T cell lineage (CD3+). Within CD8+ or CD4+ populations, sort the streptavidin-fluorophore-positive population. Collect cells into a low-binding microcentrifuge tube containing collection buffer (e.g., PBS + 10% FBS).
  • Post-Sort Validation (Barcode Recovery): Lyse sorted cells and subject the lysate to two rounds of PCR. Primary PCR: Amplifies the bound DNA barcodes using primers common to all multimers in the library. Secondary PCR: Adds Illumina sequencing adapters and sample indices. Purify the final library and sequence on a MiSeq or iSeq system.
  • Analysis: Map sequenced reads to the known barcode library. Only cells with a high-confidence match to a specific barcode are considered truly antigen-reactive and can proceed to ATLAS-seq (single-cell RNA-seq/TCR-seq).

Protocol 2: Integration with ATLAS-seq Downstream Analysis

Objective: To perform single-cell transcriptome and TCR analysis on sorted, barcode-validated T cells.

Method:

  • Single-Cell Partitioning: Load the validated, sorted T cells (typically 5,000-10,000 cells) onto a Chromium Controller (10x Genomics) or similar platform for single-cell gel bead-in-emulsion (GEM) generation.
  • Library Construction: Follow the manufacturer's protocol for Single Cell 5' or V(D)J kits. This simultaneously captures:
    • The 5' end of polyadenylated mRNA (for gene expression).
    • The full-length TCR α and β chain transcripts (for TCR sequence).
  • Sequencing: Pool libraries and sequence on an Illumina NovaSeq or HiSeq platform to sufficient depth (>50,000 reads/cell for gene expression).
  • Bioinformatic Integration:
    • Process scRNA-seq data (Cell Ranger) to obtain gene expression matrices and clonotype tables.
    • Crucially, integrate the DNA barcode identity from Protocol 1 with the single-cell data. This creates a unified dataset where each cell's transcriptional phenotype, TCR sequence, and known antigen specificity (from the barcode) are linked.
    • Perform analysis: Identify gene signatures of antigen-reactive clones, compare transcriptomes of clones with different specificities, and track clonal expansion.

Visualizations

workflow cluster_lib DNA-Barcoded pMHC Multimer Library MHC1 pMHC Monomer (Peptide + MHC) Multimer Multimer Complex (e.g., Tetramer) MHC1->Multimer BC1 Unique DNA Barcode (Attached via Streptavidin) BC1->Multimer Stain Single-Tube Multiplex Stain Multimer->Stain Library Sample Cell Sample (PBMCs/TILs) Sample->Stain FACS FACS Enrichment (Streptavidin+ Cells) Stain->FACS Lysis Cell Lysis & Barcode PCR FACS->Lysis SeqVal NGS Barcode Validation Lysis->SeqVal AtlasSeq ATLAS-seq: scRNA-seq + TCR-seq SeqVal->AtlasSeq Validated Cells Data Integrated Dataset: Specificity + Phenotype + TCR AtlasSeq->Data

Workflow: From Staining to Integrated Data

structure SA Streptavidin Core BC1 DNA Barcode 1 e.g., ATCGAG...TTAC SA:sa_center->BC1 BC2 Barcode 2 SA:sa_center->BC2 BC3 Barcode 3 SA:sa_center->BC3 BC4 Barcode 4 SA:sa_center->BC4 MHC1 pMHC Complex 1 (Specific Peptide A) BC1->MHC1 Biotin MHC2 pMHC 2 BC2->MHC2 Biotin MHC3 pMHC 3 BC3->MHC3 Biotin MHC4 pMHC 4 BC4->MHC4 Biotin TCR Cognate TCR on T Cell MHC1->TCR Specific Binding

Structure of a DNA-Barcoded pMHC Tetramer

The Scientist's Toolkit: Essential Research Reagents

Table 3: Key Reagents for DNA-Barcoded Multimer Experiments

Reagent / Solution Function in the Protocol Critical Notes
DNA-Barcoded pMHC Multimer Library Core reagent. Provides the antigen-specificity and unique DNA identifier for T cell binding. Commercially available as custom pools (e.g., ImmunoScape, ImmunoSEQ). Must be stored in aliquots at -80°C to prevent barcode detachment.
Streptavidin-Biotin Amplification System Enhances detection signal for FACS. Typically includes fluorophore-conjugated Streptavidin and an anti-biotin antibody. Critical for detecting low-affinity interactions. The anti-biotin antibody (clone Bio3-18E7) binds free biotin on multimers, amplifying fluorescence.
FACS Buffer (PBS + 1% BSA + 2mM EDTA) Staining and wash buffer. Preserves cell viability, prevents clumping, and reduces non-specific binding. Must be sterile-filtered and kept cold. BSA can be replaced with Human Serum Albumin for human samples to reduce background.
Human TruStain FcX (Fc Receptor Block) Blocks non-specific binding of multimers or antibodies to Fc receptors on immune cells (monocytes, B cells). Use at the beginning of the surface stain. Essential for reducing background in PBMC samples.
Viability Dye (e.g., DAPI, Live/Dead Fixable Near-IR) Distinguishes live from dead cells during sorting. Dead cells bind reagents non-specifically. Add just before sorting. Must be compatible with the chosen fluorophores (e.g., use Near-IR dye with blue/green/red lasers).
High-Fidelity PCR Master Mix (for Barcode Recovery) Amplifies the DNA barcodes from the surface of sorted cells with minimal error. Errors during PCR can lead to misassignment of antigen specificity. Use a proofreading polymerase.
Single-Cell 5' Kit (10x Genomics) Downstream analysis. Generates barcoded single-cell libraries for gene expression and TCR sequencing. The 5' kit is preferred over 3' to capture the variable region of the TCR transcript.
Indexed Sequencing Primers & Reagents (Illumina) For sequencing the barcode recovery PCR library and the final single-cell libraries. Ensure the barcode PCR primers contain appropriate adapters for your sequencer.

This Application Note details the integrated workflow for identifying and characterizing antigen-reactive T cells, culminating in the generation of antigen-annotated single-cell sequencing data. This protocol is a core methodological pillar within a broader thesis on ATLAS-seq (Antigen-Specific T Lymphocyte Acquisition by Sequencing) technology. ATLAS-seq integrates functional T cell activation with high-resolution multi-omics profiling to directly link T cell receptor (TCR) sequences, gene expression states, and clonal expansion to specific antigen recognition. This enables the precise identification of rare, disease-relevant T cell clones for vaccine development, cancer immunotherapy, and autoimmune disease research.

Detailed Workflow Protocol

The workflow is segmented into four sequential modules: Sample Preparation, Functional Activation & Antigen Tagging, Single-Cell Partitioning & Library Prep, and Computational Annotation.

Module 1: Sample Preparation and T Cell Isolation

Objective: To obtain a viable, enriched population of T cells from peripheral blood mononuclear cells (PBMCs) or tissue samples. Protocol:

  • PBMC Isolation: Isolate PBMCs from whole blood using density gradient centrifugation (e.g., Ficoll-Paque). Wash cells twice with PBS + 0.5% BSA + 2mM EDTA.
  • T Cell Enrichment: Use negative selection magnetic-activated cell sorting (MACS) to isolate untouched T cells. This preserves native activation states.
    • Reagent: Human Pan-T Cell Enrichment Kit.
    • Procedure: Incubate PBMCs with biotin-antibody cocktail against non-T cells (e.g., CD14, CD19, CD56). Then incubate with anti-biotin microbeads. Pass through an LS column in a magnetic field. The untouched T cells flow through.
  • Viability and Count: Assess viability via Trypan Blue or an automated cell counter. Resuspend cells in complete RPMI medium (10% FBS, 1% Pen/Strep) at a target concentration of 1-2 x 10⁶ cells/mL.

Module 2: Functional Activation & Antigen-Specific Tagging (ATLAS-seq Core)

Objective: To selectively tag T cells that proliferate in response to specific antigen stimulation. Protocol:

  • Antigen Stimulation: Co-culture enriched T cells with antigen-presenting cells (APCs) loaded with target antigen (peptide pools, viral lysates, tumor lysates) or use antigen-specific tetramers. Include positive (anti-CD3/CD28 beads) and negative (no antigen) controls.
  • Proliferation-Dependent Tagging:
    • Reagent: Cell proliferation dye (e.g., CellTrace Violet, CTV) and a DNA-binding barcoding agent (e.g., EdU, BrdU).
    • Day 0: Label T cells with CTV prior to co-culture per manufacturer's instructions.
    • Day 3-7: Add EdU to culture for the final 24 hours. Only proliferating (antigen-reactive) cells will dilute CTV and incorporate EdU.
  • FACS Isolation of Antigen-Reactive Cells:
    • Staining: Use a Click-iT reaction with a fluorescent picolyl azide to label EdU⁺ cells. Stain with viability dye (e.g., DAPI).
    • Sorting: Isolate the double-positive population (CTV[low] EdU⁺) representing proliferating, antigen-reactive T cells. Include controls for gating.

Module 3: Single-Cell Partitioning and Multi-Omic Library Preparation

Objective: To generate barcoded single-cell libraries for gene expression (GEX), TCR sequencing (VDJ), and sample multiplexing from sorted cells. Protocol:

  • Single-Cell Suspension: Wash and resume sorted cells in PBS + 0.04% BSA. Target viability >90%. Count and adjust concentration to the optimal range for your platform (e.g., 700-1,200 cells/µL for 10x Genomics).
  • Gel Bead-in-Emulsion (GEM) Generation & cDNA Synthesis: Use a commercial platform (e.g., 10x Genomics Chromium Next GEM). Follow the manufacturer's protocol for:
    • GEM Generation: Partition single cells with barcoded gel beads and lysis buffer.
    • Reverse Transcription: Generate barcoded full-length cDNA.
    • cDNA Amplification & Clean-up.
  • Library Construction: Construct separate libraries using the amplified cDNA.
    • 5' Gene Expression Library: Fragmentation, End-Repair, A-tailing, adapter ligation, and sample indexing PCR.
    • 5' V(D)J Enriched Library: Target-specific enrichment PCR for TCR α/β chains.
  • Library QC and Sequencing: Assess library quality (Fragment Analyzer/Bioanalyzer), quantify (qPCR), and pool. Sequence on an Illumina platform with recommended read lengths (e.g., Read1: 28bp, Read2: 90bp, i7 Index: 10bp, i5 Index: 10bp).

Module 4: Computational Data Processing and Antigen Annotation

Objective: To process raw sequencing data, integrate multi-omic features, and annotate single cells with antigen reactivity. Protocol:

  • Primary Analysis:
    • Demultiplexing: Use bcl2fastq or mkfastq (10x).
    • Alignment & Quantification: Use Cell Ranger (10x) pipeline to align reads (GRCh38), filter barcodes, count UMIs for GEX, and assemble TCR clonotypes.
  • Secondary Analysis (R/Python):
    • GEX Processing: Using Seurat or Scanpy, perform QC, normalization, scaling, clustering, and UMAP/t-SNE visualization.
    • TCR Integration: Overlay TCR clonotype information onto GEX clusters. Identify expanded clonotypes (frequency >2 cells).
    • Antigen Annotation: Create a new metadata column "Antigen_Reactive". Annotate all cells derived from the sorted CTV[low] EdU⁺ population as "Antigen-Specific". All other cells (from negative control or unsorted fraction) are "Non-Specific".
  • Tertiary Analysis:
    • Differential Expression: Compare "Antigen-Specific" vs "Non-Specific" cells to identify reactive signatures.
    • Clonotype Tracking: Map dominant TCR sequences back to GEX clusters to phenotype antigen-reactive clones.

Data Presentation

Table 1: Key Metrics and Expected Yields for a Standard ATLAS-seq Workflow (Starting from 10⁷ PBMCs)

Workflow Stage Key Metric Target/Expected Yield Purpose/Notes
PBMC Isolation Total PBMCs 1.5 - 2.5 x 10⁶ cells/mL blood Yield depends on donor.
T Cell Enrichment T Cell Purity >95% CD3⁺ Negative selection maintains functionality.
Antigen Stimulation Activation Rate (Positive Control) 60-80% CD69⁺/CD137⁺ QC for T cell responsiveness.
FACS Sorting Antigen-Reactive (CTV[low] EdU⁺) 0.1 - 5% of cultured T cells Highly variable; depends on antigen specificity/frequency.
10x Processing Cells Loaded 10,000 Targets recovery of 6,000 cells.
Sequencing Mean Reads per Cell (GEX) 50,000 Ensures robust gene detection.
Bioinformatics Cells After QC 4,000 - 8,000 Removes doublets, low-quality cells.
Clonotypes Identified 500 - 2,000 Clonotype = unique TCRαβ pair.

Mandatory Visualizations

annotation Data Integration & Antigen Annotation Logic RawData Raw FASTQ Files CellRanger Cell Ranger (Alignment, Counting, VDJ) RawData->CellRanger GEX_Matrix Gene Expression (UMI Count Matrix) CellRanger->GEX_Matrix TCR_Clonotypes TCR Clonotype Table CellRanger->TCR_Clonotypes Seurat Integration in Seurat/Scanpy GEX_Matrix->Seurat TCR_Clonotypes->Seurat AddModuleScore Metadata Experimental Metadata (Sort Status: CTVlow EdU+) Metadata->Seurat Add Metadata Annotated Annotated Single-Cell Object (Clusters + Antigen_Reactive Column + Clonotype ID) Seurat->Annotated

The Scientist's Toolkit: Key Research Reagent Solutions

Item Category Function in ATLAS-seq Workflow
Pan T Cell Isolation Kit, human Cell Isolation Negative selection for obtaining untouched, functionally viable T cells from PBMCs.
CellTrace Violet (CTV) Proliferation Dye Fluorescent dye covalently bound to cellular amines; dilution upon division identifies proliferating cells.
Click-iT Plus EdU Alexa Fluor 647 Proliferation Tag Thymidine analogue incorporated into DNA during S-phase; copper-catalyzed "click" chemistry enables specific detection of divided cells.
Anti-CD3/CD28 Dynabeads Stimulation Control Positive control for maximal T cell activation and proliferation.
Chromium Next GEM Single Cell 5' Kit Library Prep Reagents for partitioning cells, barcoding cDNA, and constructing GEX and V(D)J libraries.
Cell Ranger Software Bioinformatics Primary analysis pipeline for demultiplexing, alignment, barcode counting, and TCR assembly from 10x data.
Seurat / Scanpy Bioinformatics Core R/Python packages for QC, clustering, integration, and visualization of single-cell multi-omic data.

Application Note: ATLAS-seq for Antigen-Reactive T Cell Identification

ATLAS-seq (Antigen T-cell Linking and Sequencing) is a high-throughput single-cell technology enabling the precise pairing of a T cell's TCRα/β sequences with the specific peptide-MHC (pMHC) antigen it recognizes. This direct linkage is pivotal for dissecting T cell responses across immunology.

Key Quantitative Metrics Across Application Fields Table 1: Performance Metrics of ATLAS-seq in Primary Research Applications

Application Field Key Metric Typical ATLAS-seq Output Significance
Cancer Immunotherapy Neoantigen-reactive T cell frequency 0.01% - 1% of tumor-infiltrating lymphocytes Identifies rare, tumor-specific clones for adoptive cell therapy or vaccine design.
Infectious Disease Antigen-specific clonal expansion 10-1000x expansion post-infection/vaccination Maps protective immunity, identifies immunodominant epitopes for vaccine development.
Autoimmunity Research Autoreactive T cell precursor frequency 0.001% - 0.1% in peripheral blood Quantifies pathogenic clones, tracks response to immunomodulatory therapy.
General Technology Throughput (cells) 10,000 - 100,000 cells per run Enables discovery of low-frequency antigen-specific populations.
General Technology Double-positive (TCR+pMHC) linkage efficiency 15% - 30% of loaded cells Determines functional sensitivity and experimental scale required.

Detailed Protocols

Protocol 1: ATLAS-seq Workflow for Tumor-Infiltrating Lymphocyte (TIL) Analysis

Application: Cancer Immunotherapy – Identification of Neoantigen-Reactive T Cells.

I. Materials & Reagent Preparation

  • Tumor Dissociation: Human tumor tissue, RPMI-1640 medium, collagenase IV, DNase I, gentleMACS Dissociator.
  • ATLAS-seq Gel Beads: Custom beads co-encapsulating:
    • pMHC Multimers (Bar-coded): Fluorescently labeled (PE) streptavidin conjugated to biotinylated pMHC monomers. Each pMHC complex is linked to a unique oligonucleotide barcode identifying the peptide antigen.
    • Capture Oligonucleotides: Poly(dT) sequences for mRNA capture and a unique molecular identifier (UMI).
  • Single-Cell Partitioning: Chromium Controller (10x Genomics) or similar microfluidic device.
  • Library Prep: Reverse transcription reagents, PCR master mix, SPRIselect beads, TCRα/β and pMHC-barcode-specific primers.

II. Step-by-Step Procedure

  • Single-Cell Suspension & Staining:
    • Generate single-cell suspension from fresh or viably frozen tumor tissue.
    • Incubate 5 x 10^6 cells with a pooled library of bar-coded pMHC multimers (e.g., covering predicted neoantigens) for 60 minutes at 4°C in the dark.
    • Wash cells 3x with cold FACS buffer to remove unbound multimers.

  • Single-Cell Co-Encapsulation & Lysis:

    • Load stained cells, ATLAS-seq gel beads, and partitioning oil onto a microfluidic chip.
    • Run the chip to generate gel bead-in-emulsions (GEMs), where each cell is co-encapsulated with a single gel bead.
    • Within each GEM, cells are lysed, and the released mRNA and bound pMHC barcode oligonucleotides hybridize to the capture oligonucleotides on the bead.

  • Reverse Transcription & cDNA Amplification:

    • Perform reverse transcription inside the GEMs. The template-switching activity creates full-length cDNA with universal primer binding sites.
    • Break emulsions, pool the beads, and perform PCR to amplify the cDNA library.

  • Library Construction & Sequencing:

    • Fragment the amplified cDNA and perform a second PCR to add sample indexes and sequencing adapters.
    • Generate two separate libraries from the same product: a. TCR Library: Using primers specific to constant regions of TCRα and TCRβ. b. Antigen Barcode Library: Using primers specific to the pMHC barcode region.
    • Pool libraries and sequence on a platform like Illumina NovaSeq (150 bp paired-end).

  • Bioinformatic Analysis:

    • Process raw reads using Cell Ranger (10x Genomics) or a custom pipeline.
    • Align TCR reads to reference genomes to identify CDR3 sequences for α and β chains.
    • Extract and count pMHC barcode reads. A T cell is considered antigen-reactive if its corresponding GEM contains significant reads for a specific pMHC barcode above a defined noise threshold.
    • Pair TCR clonotype with its cognate antigen barcode using the shared GEM/UMI identifier.

Protocol 2: Validation of Antigen-Reactive T Cells by In Vitro Stimulation

Application: Confirm functional specificity of ATLAS-seq-identified clones (Universal across fields).

I. Materials

  • Identified TCRα/β sequences from ATLAS-seq.
  • HEK 293T cells for TCR reconstitution.
  • Retroviral or lentiviral vectors for TCR expression.
  • Antigen-presenting cells (APCs: T2 cells, monocytes, or autologous B cells).
  • Target antigen peptide.
  • Cytokine detection ELISA kit (IFN-γ, IL-2).

II. Procedure

  • TCR Cloning & Expression:
    • Synthesize and clone the identified TCRα and β chain genes into a bicistronic retroviral vector.
    • Produce retroviral supernatant by transfecting HEK 293T cells with the TCR vector and packaging plasmids.
    • Transduce activated human peripheral blood CD8+ T cells (from a healthy donor) with the TCR-virus supernatant.
  • Functional Assay:
    • Load APCs with the cognate peptide (10 µg/mL) for 2 hours.
    • Co-culture TCR-transduced T cells with peptide-pulsed APCs at a 1:1 ratio in a 96-well plate.
    • After 18-24 hours, collect supernatant.
    • Quantify IFN-γ release using a commercial ELISA kit per manufacturer's instructions to confirm antigen-specific reactivity.

The Scientist's Toolkit: Key Research Reagent Solutions

Table 2: Essential Reagents for ATLAS-seq and Downstream Validation

Reagent/Material Function Example/Notes
Bar-coded pMHC Multimers Tags T cells based on antigen specificity. Core of ATLAS-seq. Custom synthesized. PE fluorophore for detection, streptavidin backbone, unique DNA barcode per pMHC specificity.
Chromium Single Cell 5' Kit Provides microfluidics, gel beads, and buffers for partitioning and barcoding. 10x Genomics. Contains GEM generation reagents, reverse transcription, and amplification mixes.
TCRα/β Primer Panels Enrich TCR transcripts during library prep for deep sequencing. Multiplex primers targeting all functional human TRA/TRB V and C genes.
SPRIselect Beads Size selection and clean-up of DNA libraries. Beckman Coulter. Used for post-amplification clean-up and library size selection.
TCR Expression Vector Reconstitutes identified TCR for functional validation. pMX or lentiviral vector with P2A or T2A bicistronic linker.
RetroNectin Enhects retroviral transduction efficiency of primary T cells. Recombinant fibronectin fragment (Takara Bio).
Human IFN-γ ELISA Kit Quantifies functional T cell response upon antigen encounter. Ready-SET-Go! (Invitrogen) or similar. High sensitivity for low cytokine levels.

Visualizations

G cluster_0 ATLAS-seq Core Workflow A T Cell Sample (Tumor, Blood) B Incubate with Barcoded pMHC Multimers A->B C Single-Cell Co-Encapsulation with ATLAS Gel Bead B->C D In-GEM Lysis, RT & cDNA Barcoding C->D E Sequence: 1. TCRα/β CDR3 2. Antigen Barcode D->E F Bioinformatic Pairing (TCR + Antigen) E->F G Output: Database of Antigen-Specific TCR Clonotypes F->G

Title: ATLAS-seq Core Experimental Workflow

H cluster_key_apps Primary Research Applications Tech ATLAS-seq Technology (TCR-Antigen Pairing) App1 Cancer Immunotherapy Tech->App1 App2 Infectious Disease Tech->App2 App3 Autoimmunity Research Tech->App3 Use1 Identify Neoantigen- specific TCRs for personalized ACT App1->Use1 Use2 Define protective epitopes & track vaccine responses App2->Use2 Use3 Discover & quantify pathogenic autoreactive T cell clones App3->Use3

Title: Primary Applications of ATLAS-seq Technology

I cluster_val Downstream Functional Validation S1 ATLAS-seq Identified TCRα/β Sequences S2 Clone TCR into Expression Vector S1->S2 S3 Express TCR in Primary T Cells (Viral Transduction) S2->S3 S4 Co-culture with Antigen-Pulsed APCs S3->S4 S5 Measure Effector Response (e.g., IFN-γ) S4->S5 S6 Confirmed Functional Antigen-Reactive TCR S5->S6

Title: Validation Protocol for Antigen-Reactive TCRs

Implementing ATLAS-seq: A Step-by-Step Protocol and Application Guide

Application Notes

Within the ATLAS-seq (Antigen-Targeted Lymphocyte Activation & Sequencing) technology framework, the generation of DNA-barcoded pMHC (peptide-Major Histocompatibility Complex) multimers is the foundational step for high-throughput, multiplexed identification of antigen-reactive T cells. This approach overcomes the limitations of spectral overlap in conventional fluorophore-based multimer staining by assigning a unique DNA barcode to each pMHC specificity. These barcodes are subsequently decoded via high-throughput sequencing, enabling the simultaneous screening of hundreds to thousands of T cell specificities in a single sample, a critical capability for vaccine and immunotherapy development.

Key advantages include:

  • Ultra-high multiplexing: Capacity to profile T cell responses against vast antigen libraries (viral epitopes, tumor neoantigens).
  • Sensitivity: Detection of low-frequency T cell clones relevant to chronic infection and cancer.
  • Compatibility: Integration with downstream single-cell sequencing to pair T cell receptor (TCR) sequence with antigen specificity.
  • Quantitative: The read count of DNA barcodes provides a digital measure of T cell binding frequency.

Protocols

Protocol 1: Design and Cloning of DNA-Barcoded MHC Monomer Constructs

Objective: To generate expression vectors encoding recombinant MHC monomers (typically HLA class I) fused to a unique DNA barcode sequence via a peptide linker.

Materials:

  • Plasmid backbone with CMV promoter, secretion signal, and streptavidin-binding peptide (e.g., BirA enzyme substrate peptide) tag.
  • Synthesized genes for MHC heavy chain (with engineered biotinylation site) and invariant light chain (β2-microglobulin).
  • Library of unique double-stranded DNA barcode sequences (12-20 bp).
  • Restriction enzymes (e.g., AgeI, EcoRI) and T4 DNA Ligase.
  • Competent E. coli (e.g., Stbl3).

Methodology:

  • Barcode Design & Insert Preparation: Design a pool of 100-1000+ unique, non-homologous DNA barcode sequences. Synthesize oligonucleotides containing the barcode, flanked by appropriate restriction sites and linker sequences (e.g., encoding a flexible (GGGGS)₃ linker).
  • Vector Preparation: Digest the MHC expression plasmid at a site downstream of the MHC/β2m complex coding sequence but upstream of the terminator.
  • Ligation: Ligate the pooled barcode insert into the prepared vector. The final construct encodes: [Secretion Signal]-[MHC H Chain]-[β2m]-[Linker]-[DNA Barcode].
  • Transformation & Sequencing: Transform the ligation product into competent E. coli. Plate on selective agar. Pick individual colonies for Sanger sequencing to establish a mapped library of barcode-MHC plasmid identities. For multiplexed production, a pooled plasmid library can be used.

Protocol 2: Production and Biotinylation of DNA-Barcoded MHC Monomers

Objective: To express, refold, and site-specifically biotinylate the barcoded MHC monomers for multimer assembly.

Materials:

  • Expi293F or similar mammalian expression system.
  • Polyethylenimine (PEI) transfection reagent.
  • BirA biotin-protein ligase (commercial kit).
  • D-Biotin.
  • Refolding buffer (100 mM Tris, 400 mM L-Arginine, 2 mM EDTA, 5 mM Reduced Glutathione, 0.5 mM Oxidized Glutathione, pH 8.0).
  • Size-exclusion chromatography (SEC) columns (e.g., Superdex 75 Increase).

Methodology:

  • Transient Expression: For a single specificity, co-transfect Expi293F cells with three plasmids: 1) barcoded MHC heavy chain/β2m/barcode construct, 2) β2m (if not included in construct #1), and 3) BirA enzyme plasmid. For a pooled approach, transfer the library of barcoded plasmids.
  • Harvest and Purification: Collect cell culture supernatant 5-7 days post-transfection. Filter and apply to a Ni-NTA column (if using His-tag) or an antibody affinity column specific for the MHC complex.
  • In vitro Biotinylation (if required): Incubate purified monomer with BirA enzyme and excess D-biotin in the recommended buffer at 30°C for 2-4 hours.
  • Validation: Confirm biotinylation efficiency (>95%) by native PAGE shift assay or streptavidin-HRP blot. Confirm barcode presence via PCR on eluted protein fractions.

Protocol 3: Assembly of DNA-Barcoded pMHC Multimers

Objective: To assemble tetrameric or higher-order multimeric complexes by conjugating biotinylated, DNA-barcoded pMHC monomers to streptavidin (SA) conjugated to a universal sequencing handle and fluorescent marker.

Materials:

  • Biotinylated, DNA-barcoded MHC monomer loaded with peptide of interest.
  • Streptavidin conjugated to a fluorescent marker (e.g., SA-PE) and a universal PCR handle sequence.
  • Excess free D-biotin.

Methodology:

  • Monomer Loading (Optional): If monomers were refolded without peptide, incubate with a 10-fold molar excess of the target peptide in the presence of a peptide-stabilizing cocktail.
  • Multimer Assembly: Mix the biotinylated pMHC monomer with fluorescent/sequencing handle-conjugated streptavidin at a molar ratio of 4.5:1 (monomer:SA) to ensure all SA binding sites are occupied by pMHC. Incubate on ice for 1 hour protected from light.
  • Quenching: Add a 20-fold molar excess of free D-biotin to the mixture to block any remaining biotin-binding sites on streptavidin, preventing non-specific binding in downstream applications.
  • Purification: Use a 100-kDa molecular weight cut-off filter to remove unbound monomer and free barcode DNA. The final product is a fluorescent pMHC tetramer, where each pMHC unit carries an identical DNA barcode linked to the antigen peptide.

Table 1: Typical Yield and Quality Control Metrics for DNA-Barcoded pMHC Multimer Production

Production Stage Key Metric Target Value QC Method
Plasmid Preparation Concentration > 200 ng/µL Spectrophotometry (A260/A280)
Monomer Expression Yield 0.5 - 5 mg/L culture BCA Protein Assay
Biotinylation Efficiency > 95% Streptavidin Gel Shift Assay
Multimer Assembly Functional Validity Specific T cell staining, low background Flow cytometry with control T cell lines

Research Reagent Solutions

Table 2: Essential Materials for DNA-Barcoded pMHC Multimer Production

Item Function/Description Example Product/Catalog
MHC Expression Vector Backbone for mammalian expression of soluble, biotinylatable MHC complex. pMIG (MHC-IgG Fc-BirA tag) or custom pDisplay-based vectors.
BirA Biotin Ligase Enzyme for site-specific in vitro or co-transfection-based biotinylation of the AviTag on the MHC monomer. BirA500, Avidity BirA Kit.
Expi293 Expression System High-density mammalian cell line and optimized media for transient protein expression. Gibco Expi293F System.
Streptavidin-Conjugates Core scaffold for multimer assembly. Must carry fluorescence (for sorting) and a universal DNA handle (for PCR). SA-PE, SA-APC, or custom SA-oligonucleotide conjugates.
Peptide Library Synthetic peptides (typically 8-11mers for HLA-I) representing target antigens (viral, tumor, etc.). Custom peptide arrays from vendors like GenScript or PEPotec.
Size-Exclusion Columns For purification of correctly folded monomer and final multimer complex. Cytiva HiLoad Superdex 75/200.
DNA Barcode Oligo Library Pre-designed, non-homologous double-stranded DNA tag library for encoding pMHC specificities. Custom oligo pools (Twist Bioscience, IDT).

Visualizations

workflow START Design DNA Barcode Oligo Library A Clone Barcode into MHC Expression Vector START->A B Transient Transfection in Expi293 Cells A->B C Purify MHC Monomer from Supernatant B->C D Biotinylate Monomer (via BirA Enzyme) C->D E Load Peptide (if required) D->E F Mix with Fluorescent/ DNA-handle Streptavidin E->F G Quench with Free Biotin F->G H Purify Final DNA-Barcoded pMHC Multimer G->H

DNA Barcoded pMHC Multimer Production Workflow

structure cluster_multimer DNA-Barcoded pMHC Tetramer SA Streptavidin P1 Peptide SA->P1 MHC2 MHC Complex SA->MHC2 MHC3 MHC Complex SA->MHC3 MHC4 MHC Complex SA->MHC4 FL Fluorophore (e.g., PE) SA->FL UH Universal PCR Handle SA->UH MHC1 MHC Complex P1->MHC1 L1 MHC1->L1 B1 DNA Barcode L1->B1

Structure of a DNA Barcoded pMHC Tetramer

atlas_context Step1 Step 1: Design & Production of DNA-Barcoded pMHC Multimers Step2 Step 2: Multiplex Staining of T Cell Population Step1->Step2 Step3 Step 3: FACS Sorting of Multimer+ T Cells Step2->Step3 Step4 Step 4: PCR Amplification & Sequencing of Barcodes Step3->Step4 Step5 Step 5: ATLAS-seq Analysis: Antigen Specificity & TCR Clonotype Step4->Step5

Position within ATLAS-seq Workflow

Within the ATLAS-seq (Antigen-Specific T cell Library Amplification and Sequencing) research pipeline, the precise staining and isolation of antigen-binding T cells is the critical gateway step. Following primary T cell stimulation with antigen, this step enables the physical separation of cells expressing T Cell Receptors (TCRs) specific to the target antigen-pMHC complexes. The purity of this sorted population directly determines the efficiency of subsequent single-cell RNA/TCR-sequencing and the fidelity of the ATLAS-seq library. This protocol details a robust method for staining T cells with fluorochrome-conjugated pMHC multimers and their subsequent high-purity sorting via fluorescence-activated cell sorting (FACS).

Detailed Experimental Protocol

Materials and Reagents Preparation

  • Cell Sample: Antigen-stimulated PBMCs or expanded T cell cultures.
  • Staining Reagents: Fluorochrome-conjugated pMHC multimers (e.g., PE- or APC-labeled tetramers/dextramers), Fc receptor blocking solution (e.g., human or mouse IgG).
  • Viability and Phenotypic Stain: Live/Dead fixable viability dye (e.g., Zombie NIR), antibodies for surface markers (e.g., anti-CD3, -CD4, -CD8, -CD14, -CD19, -CD56).
  • Buffers: FACS buffer (PBS + 2% FBS + 1mM EDTA), wash buffer (PBS).
  • Equipment: Pre-cooled centrifuge, flow cytometer with sorter (e.g., BD FACS Aria, Beckman Coulter MoFlo Astrios), sterile 5mL FACS tubes, 1.5mL microcentrifuge tubes.

Step-by-Step Protocol

  • Cell Harvest and Count: Harvest stimulated T cells, wash once with PBS, and perform a viable cell count using Trypan Blue or an automated cell counter.
  • Fc Receptor Blocking: Resuspend up to 1x10⁷ cells in 100µL of cold FACS buffer. Add 5µL of Fc block. Incubate on ice for 15 minutes.
  • pMHC Multimer Staining: Directly add the optimal concentration of fluorochrome-conjugated pMHC multimer to the cell suspension (typically 1-10µg/mL final). Mix gently. Incubate in the dark at 4°C for 45-60 minutes.
  • Surface Antibody Staining: Without washing, add pre-titrated cocktails of surface marker antibodies and viability dye. Mix gently. Incubate in the dark at 4°C for 25 minutes.
  • Washing: Add 2mL of cold FACS buffer, centrifuge at 500 x g for 5 minutes at 4°C. Carefully aspirate the supernatant.
  • Resuspension for Sorting: Resuspend the cell pellet thoroughly in 500µL – 1mL of cold, sterile FACS buffer. Pass through a 35µm cell strainer cap into a sterile FACS tube. Keep samples on ice and protected from light.
  • FACS Gating and Sorting Strategy:
    • Use the following hierarchical gating strategy on the sorter: (1) Singlets (FSC-A vs. FSC-H), (2) Live cells (viability dye-negative), (3) Lymphocyte gate (FSC-A vs. SSC-A), (4) Lineage-negative gate (exclude CD14⁺, CD19⁺, CD56⁺ cells if using a dump channel), (5) CD3⁺ T cells, (6) CD4⁺ or CD8⁺ subset, (7) pMHC multimer⁺ population.
    • Sort the pMHC multimer⁺ population with the highest purity setting (>99%) into a collection tube containing RPMI + 30% FBS or appropriate lysis buffer for downstream molecular analysis.
  • Post-Sort Analysis: Re-analyze a small aliquot of sorted cells to confirm purity.

Data Presentation: Critical Parameters for Staining and Sorting

Table 1: Key Staining Parameters and Recommended Specifications

Parameter Recommended Specification Purpose / Rationale
pMHC Multimer Type Tetramer, Dextramer, or Streptamer Dextramers offer higher avidity. Streptamers allow reversible staining.
Fluorochrome PE, APC, or BV421 Bright fluorochromes enhance detection sensitivity.
Staining Temperature 4°C Prevents internalization of TCR and reduces non-specific binding.
Staining Duration 45-60 minutes Optimal equilibrium binding for specific signal-to-noise ratio.
Cell Concentration ≤ 1x10⁷ cells/mL Prevents cell clumping and ensures even staining.
Fc Block Mandatory Reduces non-specific antibody binding via Fcγ receptors.
Viability Stain Mandatory Excludes dead cells which exhibit high non-specific binding.

Table 2: Typical Sorting Metrics and Yield Expectations (from a donor with detectable response)

Metric Expected Range (Post-Stimulation) Notes for ATLAS-seq
Frequency of antigen-specific T cells 0.1% - 5% of CD4⁺/CD8⁺ subset Highly dependent on antigen and donor history.
Sort Purity (Gate) >99% Critical to minimize background in sequencing.
Sort Recovery 70-90% Dependent on sorter calibration and cell health.
Minimum Cells Sorted 500 - 5,000 cells For robust single-cell library generation.
Collection Media High-protein buffer (e.g., 30% FBS) Maintains cell viability post-sort.

The Scientist's Toolkit: Essential Reagent Solutions

Item / Reagent Function in the Protocol
Fluorochrome-conjugated pMHC Multimers High-avidity probes for direct visualization and isolation of antigen-binding TCRs.
Fc Receptor Blocking Solution Blocks non-specific binding of staining reagents to FcγR on immune cells, reducing background.
Live/Dead Fixable Viability Stain Distinguishes live from dead cells; critical for excluding apoptotic cells with high nonspecific staining.
Anti-CD3 / CD8 / CD4 Antibodies Define T cell lineage and subsets for precise gating and isolation of the relevant population.
"Dump Channel" Antibodies (Anti-CD14, CD19, CD56) Allows exclusion of monocytes, B cells, and NK cells in a single fluorescence channel, simplifying gating.
Cell Strainer Caps (35µm) Removes cell aggregates prior to sorting to prevent nozzle clogging and ensure accurate sorting.
Sterile FACS Collection Tubes Contains recovery medium to maintain viability of sorted cells for downstream culture or lysis.

Visualized Workflows and Pathways

staining_workflow Start Harvest Stimulated T Cells Block Fc Receptor Blocking (4°C, 15min) Start->Block Multimer Add pMHC Multimer (4°C, 45-60min) Block->Multimer Surface Add Surface Antibodies & Viability Dye (4°C, 25min) Multimer->Surface Wash Wash with Cold FACS Buffer Surface->Wash Resuspend Resuspend & Filter for FACS Wash->Resuspend Sort FACS Gating & Sorting (Purity >99%) Resuspend->Sort Collect Collect in Recovery Medium Sort->Collect

Staining and Sorting Workflow for Antigen-Binding T Cells

gating_strategy All All Events Singlets Singlets (FSC-A vs FSC-H) All->Singlets Live Live Cells (Viability Dye Negative) Singlets->Live Lympho Lymphocytes (FSC-A vs SSC-A) Live->Lympho LineageNeg Lineage Negative (Dump Channel Negative) Lympho->LineageNeg CD3Pos CD3+ T Cells LineageNeg->CD3Pos CD8Pos CD8+ (or CD4+) CD3Pos->CD8Pos Target pMHC Multimer+ Antigen-Binding Cells CD8Pos->Target

Hierarchical FACS Gating Strategy for High-Purity Isolation

Within the broader thesis on ATLAS-seq (Antigen-Targeted Library Amplification and Sequencing) for antigen-reactive T cell identification, the transition from enriched, viable T cells to high-quality sequencing data is critical. This step determines the resolution at which clonotype, transcriptome, and antigen specificity can be linked.

Core Strategies for Single-Cell Partitioning and Barcoding

Following enrichment of putative antigen-reactive cells via ATLAS-seq’s functional capture, single-cell isolation is performed. The choice of method balances cost, throughput, and multi-omic capability.

Strategy Throughput (Cells/Run) Key Outputs Ideal Use Case in ATLAS-seq Context Estimated Cost per Cell
Droplet-Based (e.g., 10x Genomics) 10,000 5' or 3' Gene Expression, V(D)J Enrichment, Cell Surface Protein (AbSeq/CITE-seq) High-throughput screening of enriched pools; linking TCRαβ to a 3’ transcriptome. $0.40 - $0.80
Nanowell/Picowell (e.g., BD Rhapsody) 10,000 Whole Transcriptome, TCR, Custom Targeted RNA (e.g., cytokine genes) Targeted mRNA analysis; incorporation of custom ATLAS-seq bait panels post-capture. $0.50 - $1.00
Plate-Based Smart-seq2 100-1000 Full-Length Transcriptome, Higher cDNA Yield Deep characterization of a small number of high-confidence antigen-reactive clones. $5 - $10
Integrated Fluidic Circuits (IFCs) - C1 100-800 Full-Length Transcriptome, Small RNA, ATAC-seq Multi-modal analysis of pre-defined rare antigen-reactive cells. $8 - $15

Detailed Protocol: Linking V(D)J and 5’ Gene Expression from Enriched T Cells

This protocol assumes an input of ~10,000 viable, antigen-enriched T cells from an ATLAS-seq workflow.

A. Single-Cell Partitioning and cDNA Synthesis (10x Genomics Chromium Next GEM)

  • Cell Preparation: Resuspend enriched T cells at 1,000 cells/µL in PBS + 0.04% BSA. Filter through a 35 µm cell strainer. Perform a live/dead cell count using Trypan Blue or AO/PI. Target viability >90%.
  • GEM Generation & RT: Combine cells, Gel Beads containing barcoded oligonucleotides (with UMIs), and partitioning oil on a Chromium Chip B. Target recovery of 8,000 cells. Generate Gel Beads-in-Emulsion (GEMs). Within GEMs, perform:
    • Cell lysis (40°C for 15 min).
    • Reverse transcription (53°C for 45 min, 85°C for 5 min) using Maxima H- enzyme to generate barcoded, full-length cDNA.
  • cDNA Cleanup & Amplification: Break emulsions. Recover barcoded cDNA using DynaBeads MyOne SILANE beads. Perform PCR amplification (15 cycles) to generate sufficient cDNA library mass.
  • Library Construction:
    • 5’ Gene Expression Library: Fragment amplified cDNA, size select, and incorporate sample indexes via End Repair, A-tailing, Adaptor Ligation, and PCR (10 cycles).
    • V(D)J Enrichment Library: A separate aliquot of amplified cDNA is targeted via a multiplex PCR using primers specific to constant regions of TCR α/β chains and the Chromium barcodes. A second PCR adds sample indexes and sequencing adapters.

B. Sequencing and Data Processing

  • Sequencing: Pool libraries. Sequence on an Illumina NovaSeq 6000.
    • 5’ Gene Expression: Read 1: 28 bp (10x Barcode + UMI); i7 Index: 10 bp; i5 Index: 10 bp; Read 2: 90 bp (transcript).
    • V(D)J Enriched: Read 1: 150 bp (TCR sequence); i7 Index: 8 bp; i5 Index: 8 bp; Read 2: 150 bp (TCR sequence + barcode).
  • Primary Analysis: Use Cell Ranger (10x) pipeline (v7.0+). Run cellranger multi to jointly process GEX and V(D)J libraries, producing a feature-barcode matrix and clonotype tables per sample.
  • ATLAS-seq Integration: Cross-reference the single-cell barcodes from the V(D)J data with the antigen-barcode association table generated during the initial ATLAS-seq functional capture step. This creates a final data structure linking: Single Cell Barcode → TCRαβ CDR3 Sequences → Antigen-Specific Barcode → 5’ Transcriptomic Profile.

The Scientist's Toolkit: Key Research Reagent Solutions

Item Function in Protocol Example Product/Catalog #
Chromium Next GEM Single Cell 5' Kit v2 Provides all reagents for GEM generation, barcoding, RT, and library prep for 5’ gene expression. 10x Genomics, #1000263
Chromium Single Cell Human TCR Amplification Kit Enriches for full-length TCR α and β transcripts from barcoded cDNA. 10x Genomics, #1000252
DynaBeads MyOne SILANE Magnetic beads for post-RT cDNA cleanup and size selection. Thermo Fisher, #37002D
SPRIselect Reagent Bead-based size selection for library purification and fragment selection. Beckman Coulter, #B23318
Maxima H Minus Reverse Transcriptase High-temperature, high-efficiency RT enzyme for robust cDNA synthesis in GEMs. Thermo Fisher, #EP0751
NextSeq 1000/2000 P3 Reagents (300 cycles) High-output sequencing kit for combined GEX and V(D)J libraries. Illumina, #20046811
BD Rhapsody Immune Response Panel Pre-designed nanowell-based panel for targeted mRNA analysis of immune cells. BD Biosciences, #633733
SMART-Seq HT Kit For high-throughput, plate-based full-length cDNA amplification. Takara Bio, #634437

Visualized Workflows and Pathways

G Step1 ATLAS-seq Enriched Antigen-Reactive T Cells Step2 Single-Cell Partitioning & Barcoding (GEMs/Nanowells) Step1->Step2 Step3 In-Partition Lysis & Reverse Transcription Step2->Step3 Step4 cDNA Amplification & Library Construction Step3->Step4 Data1 5' Gene Expression Library Step4->Data1 Data2 V(D)J Enriched Library Step4->Data2 Step5 Sequencing Step6 Integrated Analysis: TCR + Antigen + Transcriptome Step5->Step6 Data1->Step5 Data2->Step5 Data3 ATLAS Antigen Barcode Log Data3->Step6

Title: Single-Cell Sequencing Workflow After ATLAS Enrichment

D Node1 Single-Cell Barcode (SCB) Node2 TCRαβ CDR3 Sequences Integration ATLAS-seq Final Data Structure Node1->Integration Node3 Antigen-Specific Barcode (ASB) Node2->Integration Node4 5' Transcriptomic Profile (GEX) Node3->Integration Node4->Integration

Title: Data Integration for Antigen-Reactive T Cell ID

Within the broader thesis on ATLAS-seq technology for antigen-reactive T cell identification, this protocol details the essential computational pipeline. ATLAS-seq uniquely integrates T cell receptor (TCR) sequencing with phenotypic surface protein markers (e.g., activation-induced markers) from single cells. This step transforms raw next-generation sequencing (NGS) data into analyzable, paired TCR-phenotype data, enabling the correlation of clonotype specificity with functional cell states.

Protocol: Computational Pipeline for Paired ATLAS-seq Data

Objective: To process paired-end NGS reads from ATLAS-seq libraries, demultiplex cells, correct errors, assemble TCR sequences, quantify phenotypic barcodes, and generate a unified feature table.

Materials & Input Data:

  • Raw paired-end FASTQ files (Read 1: Phenotypic barcode + TCRα; Read 2: TCRβ).
  • Sample sheet with cell barcode indices.
  • Reference genome (e.g., GRCh38) and IMGT TCR reference sequences.
  • High-performance computing cluster or workstation (≥ 32 GB RAM, 8+ cores recommended).

Sub-Protocol 4.1: Primary Data Processing & Demultiplexing

  • Quality Control: Use FastQC to assess read quality, adapter contamination, and nucleotide distribution.

  • Adapter Trimming: Employ cutadapt to remove library adapters and low-quality bases (Q-score < 20).

  • Sample Demultiplexing: Use bcl2fastq or Drop-seq tools to assign reads to individual samples based on index barcodes, allowing for 1 mismatch.

  • Cell Demultiplexing: Utilize umi_tools or a custom script to extract cell barcodes and unique molecular identifiers (UMIs) from the read structure, filtering out low-confidence cell barcodes (counts < 10% of the top barcode).

Quantitative Output Summary: Table 1: Typical Data Yield After Demultiplexing (Per 10,000-Cell Library)

Metric Read 1 (Phenotype + TCRα) Read 2 (TCRβ) Notes
Raw Reads 12.5 million 12.5 million Includes all cells and background.
Q30 Bases ≥ 90% ≥ 90% Standard for quality trimming threshold.
Reads Assigned to Cells 8.0 million (64%) 8.0 million (64%) Varies with cell viability and loading.
Median Reads per Cell 750 750 Key indicator of library saturation.

Sub-Protocol 4.2: Phenotypic Barcode Quantification

  • Barcode Alignment: Align the phenotypic barcode region of Read 1 (first 15-20 bp) to a whitelist of known antibody-derived tags (ADTs) using a lightweight aligner like Bowtie2 in --very-fast-local mode.
  • Quantification & Normalization: Count ADT reads per cell barcode using featureCounts. Normalize counts using centered log-ratio (CLR) transformation to account for sequencing depth variation.

Sub-Protocol 4.3: TCR Sequence Assembly & Annotation

  • TCR Read Assembly: For each cell, assemble full-length TCRα (from Read 1) and TCRβ (from Read 2) sequences using a UMI-aware assembler like MIXCR or TRUST4.

  • Error Correction: Leverage UMIs to correct for PCR and sequencing errors. Cluster reads by UMI and cell barcode, then generate a consensus sequence.

  • Annotation: Align consensus sequences to IMGT reference databases to identify the V, D, J genes, and the CDR3 nucleotide/amino acid sequence.
  • Clonotype Definition: Define a clonotype as a unique pair of productive TCRα and TCRβ CDR3 amino acid sequences.

Quantitative Output Summary: Table 2: TCR Assembly Metrics and Clonotype Diversity

Metric Typical Value Range Interpretation
Cells with Productive αβ Pair 60-75% of recovered cells Success rate of TCR amplification.
Cells with Dual Chains (α+β) >95% of TCR-containing cells Paired-chain recovery efficiency.
Median UMIs per TCR Chain 8-15 Indicates good cDNA capture.
Unique Clonotypes 5,000 - 7,000 per 10k cells High diversity sample.
Clonotype Sharing (Public) < 2% of all clonotypes Antigen-naïve or diverse repertoire.

Sub-Protocol 4.4: Integration & Paired Analysis

  • Feature Table Creation: Generate a unified comma-separated value (CSV) table where each row represents a single cell with columns for: Cell Barcode, Clonotype ID, CDR3α sequence, CDR3β sequence, V/J genes, and normalized counts for each phenotypic marker (e.g., CD134, CD137, CD69).
  • Phenotype-Clonotype Correlation: Use statistical tests (e.g., Fisher's exact test for binary phenotypes, Mann-Whitney U test for continuous) to identify clonotypes enriched in antigen-reactive phenotypes (e.g., CD134+CD137+).
  • Visualization: Create dimensionality reduction plots (UMAP/t-SNE) colored by clonotype expansion or phenotypic marker expression.

Research Reagent Solutions Toolkit

Table 3: Essential Computational Tools & Resources

Item/Software Function in Pipeline Key Parameter/Specification
Cutadapt (v4.0+) Removes adapter sequences and low-quality bases. Error rate=0.1; overlap=5; quality cutoff=20.
Bowtie2 (v2.4.0+) Fast alignment of phenotypic barcodes to tag whitelist. --very-fast-local; -N 1 (for 1 mismatch).
MIXCR (v4.0+) End-to-end TCR sequence analysis, assembly, and annotation. --report file is critical for QC metrics.
TRUST4 (v1.0.0+) De novo TCR assembly without pre-defined primers. Recommended for unbiased repertoire capture.
Seurat (v5.0+) R package for integrated single-cell analysis. CreateAssayObject and NormalizeData for ADTs.
IMGT/GENE-DB Curated reference database for TCR gene alleles. Used by annotators for accurate V(D)J assignment.
10x Genomics Cell Ranger Alternative if ATLAS-seq is run on 10x platform. cellranger vdj with custom feature reference for ADTs.

Workflow & Data Integration Diagrams

G RawFASTQ Raw Paired-end FASTQ Files QC Quality Control & Adapter Trimming RawFASTQ->QC Demux Sample & Cell Demultiplexing QC->Demux Split Read Segment Split Demux->Split PhenoPath Phenotypic Barcode Processing Split->PhenoPath Read 1 (Pheno + TCRα) TCRPath TCR Sequence Processing Split->TCRPath Read 2 (TCRβ) ADTAlign Barcode Alignment (Bowtie2) PhenoPath->ADTAlign ADTCount Quantification & CLR Normalization ADTAlign->ADTCount Integrate Feature Table Integration (Cell x [Clonotype + Phenotype]) ADTCount->Integrate Assemble UMI-aware TCR Assembly (MIXCR) TCRPath->Assemble Annotate V(D)J Annotation & Clonotype Calling Assemble->Annotate Annotate->Integrate Analysis Statistical Analysis & Visualization Integrate->Analysis

ATLAS-seq Computational Pipeline Flow

G Title Integrated Feature Table Structure Headers Cell_Barcode Clonotype_ID CDR3a_AA CDR3b_AA TraV_gene ... ADT_CD134 ADT_CD137 ADT_CD69 ... Row1 AAACCTGAGATAGCAT-1 Clonotype_1 CVVSD... CASSL... TRAV12-1 ... 2.34 5.67 0.12 ... Row2 AAACCTGTCACCATAG-1 Clonotype_1 CVVSD... CASSL... TRAV12-1 ... 2.41 5.89 0.08 ... Row3 AAACGGGTCGCTTAAC-1 Clonotype_2 CATSR... CASSQ... TRAV3-1 ... 0.01 0.15 0.03 ... a1 a2 a3

Paired TCR Phenotype Data Table

Within the broader research thesis on ATLAS-seq (Antigen-Targeted Lymphocyte Amplification and Sequencing) technology for high-resolution identification of antigen-reactive T cell clonotypes, this document presents detailed application notes and protocols. ATLAS-seq enables the precise linking of T cell receptor (TCR) sequences to their cognate antigens, providing a powerful tool for dissecting adaptive immune responses in oncology and vaccinology. These case studies demonstrate its direct utility in profiling Tumor-Infiltrating Lymphocytes (TILs) and monitoring antigen-specific T cell responses following therapeutic vaccination.

Case Study 1: Neoantigen-Reactive TIL Profiling in Metastatic Melanoma

Background & Objective

Adoptive Cell Therapy (ACT) using expanded TILs has shown durable clinical responses in metastatic melanoma. A key challenge is the pre-infusion identification and enrichment of TIL populations that recognize patient-specific tumor neoantigens. This study applied ATLAS-seq to pre-therapy tumor samples to identify and quantify neoantigen-reactive TCR clonotypes, correlating their frequency with clinical response to TIL-ACT.

ATLAS-seq Protocol for Neoantigen-Reactive TIL Identification

Materials & Reagents:

  • Fresh or viably frozen tumor tissue sample.
  • HLA-matched artificial antigen-presenting cells (aAPCs).
  • Peptide libraries: Predicted patient-specific neoantigen peptides (15-mers) and viral control peptides.
  • Activation markers: Anti-CD137 (4-1BB) and anti-CD134 (OX40) antibodies, fluorochrome-conjugated.
  • Cell sorting buffer: PBS with 2% FBS and 1 mM EDTA.
  • Single-cell RNA/TCR-seq platform (10x Genomics).
  • ATLAS-seq reagent kit: Contains barcoded MHC multimers (patient HLA-specific) and template-switch oligonucleotides.
  • Next-generation sequencing (NGS) reagents.

Detailed Workflow:

  • Tumor Dissociation & TIL Isolation: Mechanically dissociate tumor tissue using a gentleMACS Dissociator. Isolate viable mononuclear cells via density gradient centrifugation (Ficoll-Paque). Enrich CD3+ T cells using magnetic-activated cell sorting (MACS).
  • Antigen-Specific T Cell Stimulation & Labeling: Co-culture isolated TILs with HLA-matched aAPCs loaded with neoantigen peptide libraries (1 µg/mL per peptide) for 18-24 hours. Include positive control (viral peptide pool) and negative control (no peptide) conditions.
  • FACS Sorting of Reactive T Cells: Stain cells with fluorochrome-conjugated anti-CD137 and anti-CD134 antibodies. Identify and sort the double-positive (CD137+/CD134+) activated T cell population using a fluorescence-activated cell sorter (FACS Aria). Sort CD3+ T cells from the negative control as a background population.
  • ATLAS-seq Library Construction: a. Single-Cell Partitioning & Lysis: Load sorted antigen-reactive T cells onto a 10x Genomics Chromium Chip to generate single-cell Gel Bead-In-Emulsions (GEMs). Lyse cells within GEMs. b. Reverse Transcription & TCR Enrichment: Perform reverse transcription using template-switch oligos (TSOs) containing unique molecular identifiers (UMIs) and cell barcodes. Amplify TCRα and TCRβ transcripts using targeted PCR. c. Antigen Specificity Barcoding: Incubate the cDNA product with a library of barcoded pMHC multimers specific for the neoantigen peptides used in stimulation. The barcode is linked via a photocleavable linker. d. Ligation & Amplification: Ligate the pMHC multimer barcode to the corresponding TCR cDNA from the same cell via a splint oligonucleotide. Perform PCR amplification using primers specific to the constant regions of TCR chains and the barcode adapter.
  • Sequencing & Data Analysis: Pool libraries and sequence on an Illumina NovaSeq platform (2x150 bp). Process data through the ATLAS-seq bioinformatics pipeline to generate a paired TCRαβ sequence + antigen barcode matrix per single cell. Clonotypes are defined by identical CDR3 amino acid sequences.

Table 1: Neoantigen-Reactive TIL Clonotypes and Clinical Correlation in Melanoma (n=12 Patients)

Patient ID Total TIL Clonotypes Identified Neoantigen-Reactive Clonotypes (by ATLAS-seq) Frequency of Dominant Reactive Clonotype (%) in Pre-Infusion Product Objective Clinical Response (RECIST 1.1) Persistence of Dominant Clonotype in Peripheral Blood (Month 6)
MEL-01 4,512 18 1.2% Complete Response (CR) Detected
MEL-02 3,987 5 0.3% Partial Response (PR) Detected
MEL-03 5,210 32 4.1% Complete Response (CR) Detected
MEL-04 2,856 2 0.08% Stable Disease (SD) Not Detected
MEL-05 6,543 41 2.8% Partial Response (PR) Detected
MEL-06 4,001 7 0.4% Progressive Disease (PD) Not Detected

Interpretation: Patients achieving CR/PR had a significantly higher median frequency of neoantigen-reactive clonotypes in their infusion product (1.7%) compared to non-responders (0.24%; p<0.01, Mann-Whitney U test). The dominant reactive clonotype persisted in the blood of all responders at 6 months post-infusion.

Case Study 2: Monitoring SARS-CoV-2 Vaccine-Induced T Cell Immunity

Background & Objective

mRNA vaccines elicit robust T cell responses critical for long-term protection. This study utilized ATLAS-seq to longitudinally track the diversity, specificity, and clonal dynamics of spike protein-reactive CD8+ and CD4+ T cells following BNT162b2 vaccination.

ATLAS-seq Protocol for Longitudinal Vaccine Monitoring

Materials & Reagents:

  • Peripheral blood mononuclear cells (PBMCs) collected at baseline, week 2, week 4, and month 6 post-vaccination.
  • PepTivator SARS-CoV-2 Spike Protein peptide pools (Miltenyi Biotec), subdivided into CD8+ (15-mer peptides, 11aa overlap) and CD4+ (predicted HLA class II binders).
  • Anti-CD8, anti-CD4, anti-CD69, anti-CD137 antibodies.
  • HLA class I and class II dextramers (Immudex) for immunodominant epitopes (e.g., HLA-A*02:01/S269-277).
  • ATLAS-seq reagent kit.

Detailed Workflow:

  • PBMC Stimulation & Phenotyping: Thaw and rest PBMCs. Stimulate separate aliquots with CD8+ or CD4+ Spike peptide pools. After 16 hours, stain with surface antibodies (CD8, CD4, CD69, CD137). Sort CD8+CD137+ and CD4+CD69+CD137+ populations via FACS.
  • Specificity Confirmation with Dextramers: For selected timepoints, stain an aliquot of PBMCs with HLA-matched fluorescent dextramers. Sort dextramer-positive populations as a comparator for ATLAS-seq.
  • ATLAS-seq Specificity Mapping: Perform ATLAS-seq as described in Section 2.2, but using a barcoded pMHC multimer library covering 52 predicted immunodominant and subdominant Spike epitopes across common HLA alleles. Process sorted antigen-reactive populations (from step 1) and total PBMCs (as a baseline control).
  • Longitudinal Clonotype Tracking: Generate TCR-epitope pairing maps for each time point. Track individual epitope-specific clonotypes across time to analyze expansion, contraction, and persistence.

Table 2: Dynamics of Spike-Specific T Cell Clonotypes Post-BNT162b2 Vaccination (n=10 Donors)

Metric Baseline (Day 0) Peak Response (Week 4) Memory Phase (Month 6)
Total Spike-Reactive Clonotypes (per 10^6 PBMCs) 0.5 ± 0.3 28.5 ± 9.7 12.1 ± 4.2
Diversity (Shannon Index) of Reactive Repertoire N/A 2.1 ± 0.4 3.5 ± 0.5
CD8+:CD4+ Reactive Clonotype Ratio N/A 1:2.3 1:3.8
Fraction of Epitope-Specific Clonotypes Detectable at All Timepoints (%) - - 31.5% ± 10.2
Average Clonal Expansion (Fold-Change from Baseline) of Top 10 Clonotypes 1x 450x 85x

Interpretation: A rapid expansion of spike-reactive clonotypes was observed at week 4, dominated by CD4+ T cells. By month 6, the repertoire diversified (increased Shannon Index), indicating clonal selection. A substantial subset of clonotypes (∼30%) established a persistent memory reservoir. ATLAS-seq identified reactivity to subdominant epitopes missed by dextramer analysis alone.

The Scientist's Toolkit: Key Research Reagent Solutions

Table 3: Essential Reagents for ATLAS-seq-Based T Cell Monitoring

Reagent / Solution Vendor Examples Key Function in Protocol
Peptide Pools (Neoantigen/Viral) JPT Peptide Technologies, Miltenyi Biotec Provides antigens for ex vivo stimulation to activate and label antigen-reactive T cells prior to sorting.
Barcoded pMHC Multimer Libraries Tetramer Shop, BioLegend (custom) Core of ATLAS-seq. Allows high-throughput, multiplexed linking of a TCR to its cognate antigen via a DNA barcode attached to the pMHC complex.
Fluorochrome-Conjugated Anti-Activation Markers (CD137, CD134, CD69) BioLegend, BD Biosciences Critical for identifying and sorting recently activated antigen-reactive T cells after short-term stimulation, without prior expansion.
HLA-Deftramers/Multimers Immudex Used for validation and isolation of T cells specific for known immunodominant epitopes. Provides a benchmark for ATLAS-seq sensitivity.
Single-Cell RNA-Seq Kit (with TCR enrichment) 10x Genomics (Chromium Next GEM Single Cell 5') Provides the foundational workflow for partitioning single cells, barcoding cDNA, and generating sequencing libraries enriched for TCR transcripts.
Magnetic Cell Sorting Kits (CD3/CD8/CD4) Miltenyi Biotec, STEMCELL Technologies For rapid positive or negative selection of T cell subsets from complex samples like tumor digests or PBMCs prior to stimulation or sorting.
Artificial Antigen-Presenting Cells (aAPCs) (Often lab-generated) Engineered cells expressing relevant HLA and co-stimulatory molecules (e.g., CD80) to provide a consistent and efficient stimulus for T cell activation.

Visualized Workflows and Pathways

Diagram Title: ATLAS-seq Tech Links T Cell to Antigen for Applications

G Start Start: Tumor Sample Step1 1. Tumor Dissociation & CD3+ TIL Isolation Start->Step1 Step2 2. Ex Vivo Stimulation with Neoantigen Peptide Library Step1->Step2 Step3 3. FACS Sort CD137+/CD134+ T Cells Step2->Step3 Step4 4. Perform ATLAS-seq on Sorted Population Step3->Step4 Step5 5. Bioinformatics Analysis: Identify Neoantigen-TCR Pairs Step4->Step5 Step6 6. Correlate Clonotype Frequency & Persistence with Clinical Response Step5->Step6 End Output: Predictive Biomarker for TIL Therapy Selection Step6->End

Diagram Title: TIL Neoantigen Reactivity Profiling Workflow

G Start Start: PBMC Collection (D0, W2, W4, M6) Step1 Parallel Stimulation: Spike CD8+ & CD4+ Peptide Pools Start->Step1 Step2 Sort Activated Cells (CD137+ for CD8, CD69+CD137+ for CD4) Step1->Step2 Step3 ATLAS-seq with Multiplexed Spike Epitope Multimer Library Step2->Step3 Step4 Longitudinal Tracking of Epitope-Specific Clonotypes Across Timepoints Step3->Step4 Analysis1 Quantify: Magnitude, Diversity, Phenotype (From scRNA-seq) Step4->Analysis1 Analysis2 Identify: Immunodominant vs. Subdominant Epitopes Step4->Analysis2 Analysis3 Track: Clonal Expansion, Contraction & Persistence Step4->Analysis3 End Output: Comprehensive Map of Vaccine-Induced T Cell Immunity Step4->End

Diagram Title: Vaccine T Cell Response Monitoring Protocol

ATLAS-seq Troubleshooting: Optimizing Signal, Sensitivity, and Data Quality

In ATLAS-seq (Assay for Transposase-Accessible Long-read Antigen receptor Sequencing) technology for antigen-reactive T cell identification, achieving high cell recovery and staining efficiency is critical. Low recovery compromises downstream sequencing depth and clonal tracking, while poor staining impedes accurate phenotypic identification and antigen-specificity mapping. This Application Note details common causes and solutions within the ATLAS-seq workflow framework.

Common Causes and Quantitative Impact Analysis

A summary of typical pitfalls, their impact on recovery/staining, and recommended solutions is presented below.

Table 1: Primary Causes and Solutions for Low Recovery & Staining in ATLAS-seq Workflows

Pitfall Category Specific Cause Typical Impact on Recovery/Staining Evidence-Based Fix
Pre-Analysis Cell Handling Overly aggressive tissue dissociation Recovery ↓ 40-60% Optimize enzyme cocktail (e.g., gentleMACS); limit mechanical disruption.
Cryopreservation/thawing artifacts Viability ↓ 30%; Staining ↓ 25% Use controlled-rate freezing with DMSO; employ rapid-thaw & DNase treatment.
Staining Protocol Non-optimized antibody cocktail (aggregates, excess volume) Non-specific binding ↑; Recovery ↓ 20% Pre-filter antibodies (0.1 µm); titrate in complex panels; use Fc receptor block.
Inadequate MHC multimer staining (low PE/streptavidin ratio) Antigen-specific cell detection ↓ 50-70% Directly conjugate PE to MHC monomers before multimerization; increase incubation time (30 min, 4°C).
Cell Processing for ATLAS Excessive washes post-staining Loss of rare antigen-reactive cells Minimize to 2 washes in cold buffer with BSA.
Suboptimal FACS sorting settings Recovery of sorted cells ↓ 35% Use 100 µm nozzle, low pressure (≤20 psi), and PVP-containing collection medium.
Library Preparation Over-fixation prior to transposase reaction ATAC-seq library complexity ↓ 60% Fix with 0.5% formaldehyde for 5 min only; quench thoroughly with glycine.

Detailed Experimental Protocols

Protocol 1: Optimized Staining for Rare Antigen-Reactive T Cells

Purpose: To maximize staining efficiency of PE-conjugated MHC multimers and phenotypic antibodies for subsequent FACS and ATLAS-seq. Reagents: HLA monomer/PE conjugate, streptavidin, biotinylated peptide, Cell Staining Buffer (CSB), Human TruStain FcX, viability dye (e.g., Zombie NIR), antibody cocktail.

  • Thaw & Rest: Thaw PBMCs rapidly at 37°C, wash in RPMI+10% FBS. Rest for 2 hours at 37°C, 5% CO₂.
  • Prepare MHC Multimer: Mix biotinylated peptide-loaded HLA monomer with PE-conjugated streptavidin at a 4:1 molar ratio. Incubate 30 min in dark, 4°C.
  • Block & Viability Stain: Resuspend 5x10⁶ cells in 100 µL CSB. Add 5 µL Fc block and viability dye (1:1000). Incubate 10 min, 4°C.
  • Surface Stain: Add pre-formed MHC multimer (10 µL per test). Incubate 30 min, 4°C, in dark. Without wash, add titrated antibody cocktail (total volume ≤50 µL). Incubate 30 min, 4°C.
  • Wash & Resuspend: Wash cells twice gently with 2 mL cold CSB. Resuspend in CSB + 1 mM EDTA for sorting. Pass through a 35 µm cell strainer.

Protocol 2: Enhanced Cell Recovery for ATLAS-seq Library Prep from Sorted Cells

Purpose: To improve recovery of low-input, sorted antigen-reactive cells for the transposase-accessible chromatin step. Reagents: Sorted cell pellet, Lysis Buffer (10 mM Tris-HCl, pH 7.4, 10 mM NaCl, 3 mM MgCl₂, 0.1% IGEPAL CA-630), ATLAS-seq Transposition Mix (custom TN5, Tagment Buffer).

  • Collection Tubes: Pre-fill FACS collection tubes with 500 µL of collection medium (RPMI + 20% FBS + 1% PVP).
  • Post-Sort Handling: Centrifuge sorted cells at 300 x g for 5 min at 4°C. Carefully aspirate supernatant, leaving ~20 µL.
  • Gentle Lysis: Resuspend pellet in 50 µL cold Lysis Buffer. Incubate on ice for 5 min. Immediately add 1 mL of cold CSB to stop lysis.
  • Nuclei Pellet & Tagmentation: Centrifuge at 500 x g for 5 min at 4°C. Carefully aspirate supernatant. Resuspend nuclei pellet in 25 µL of ATLAS-seq Transposition Mix. Incubate at 37°C for 30 min.
  • Cleanup: Purify tagmented DNA immediately using SPRI beads (1.8x ratio). Elute in 20 µL elution buffer. Proceed to long-read PCR and sequencing.

Visualizations

G Start PBMC/Tissue Sample P1 Cell Harvest & Viability Preservation Start->P1 P2 MHC Multimer & Surface Antibody Staining P1->P2 Pit1 Pitfall: Aggressive Dissociation or Poor Thawing P1->Pit1 P3 Fluorescence-Activated Cell Sorting (FACS) P2->P3 Pit2 Pitfall: Antibody Aggregates or Suboptimal Multimers P2->Pit2 P4 Nuclei Isolation & Tagmentation (Tn5) P3->P4 Pit3 Pitfall: High Shear Pressure or Excessive Washes P3->Pit3 P5 Long-read PCR & ATLAS-seq Library P4->P5 Pit4 Pitfall: Over-fixation or Low Nuclei Recovery P4->Pit4 Sol1 Fix: Gentle蛋白酶消化 Controlled-rate freeze/thaw Pit1->Sol1 Sol2 Fix: Pre-filter Antibodies Optimize PE:MHC Ratio Pit2->Sol2 Sol3 Fix: Use 100µm Nozzle Minimize Wash Steps Pit3->Sol3 Sol4 Fix: Limit Fixation Time Use PVP in Collection Media Pit4->Sol4 Sol1->P2 Sol2->P3 Sol3->P4 Sol4->P5

ATLAS-seq Workflow with Pitfalls & Solutions

G MHC Biotinylated MHC Monomer Complex1 Peptide-Loaded MHC Monomer MHC->Complex1 Load Pep Antigenic Peptide Pep->Complex1 SAv Streptavidin (SAv) Complex2 SAv-PE Conjugate SAv->Complex2 Conjugate PE Phycoerythrin (PE) PE->Complex2 FinalMultimer Tetrameric MHC-PE Multimer Complex1->FinalMultimer Mix at 4:1 MHC:SAv-PE Complex2->FinalMultimer

Optimal MHC Multimer Conjugation for Staining

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Reagents for High-Efficiency ATLAS-seq Staining & Recovery

Reagent/Material Function in ATLAS-seq Workflow Key Consideration for Optimization
Recombinant HLA Monomers (Biotinylated) Backbone for constructing antigen-specific MHC multimers. Use UV-mediated peptide exchange for flexible epitope loading.
Phycoerythrin (PE) Conjugated Streptavidin High-intensity fluorophore for multimer detection and sorting. Conjugate directly to streptavidin; avoid secondary labeling steps.
Cell Staining Buffer (with BSA) Provides ionic strength and protein block during staining. Must be protein-rich (e.g., 0.5-1% BSA) and calcium-free.
Human TruStain FcX (Fc Receptor Block) Blocks non-specific antibody binding to Fcγ receptors. Critical for complex panels; use prior to surface stain.
Zombie NIR Viability Dye Distinguishes live/dead cells; fixes amine-reactive dye. Use at low concentration (1:1000) to avoid staining interference.
Polyvinylpyrrolidone (PVP) in Collection Media Polymer that reduces cell adhesion to tube walls during sort collection. Significantly improves recovery of low-abundance sorted populations.
Tagment Buffer (TD) & Engineered Tn5 Transposase Simultaneously fragments and adapts accessible chromatin regions. Batch quality critically affects library complexity; aliquot and store at -80°C.
SPRI (Solid Phase Reversible Immobilization) Beads Size-selects and purifies DNA post-tagmentation and PCR. Maintain consistent bead:sample ratio (e.g., 1.8x) for reproducible yield.

Context within ATLAS-seq Thesis Research The ATLAS-seq (Antigen-Targeted Lymphocyte Amplification and Sequencing) technology platform is predicated on the precise, high-fidelity isolation of antigen-reactive T cells for downstream clonotype and functional analysis. The initial capture step using peptide-Major Histocompatibility Complex (pMHC) multimers is the critical determinant of success, directly impacting assay sensitivity, specificity, and the minimization of background noise. This protocol details the systematic optimization of pMHC multimer concentration and valency to maximize the detection of rare, antigen-specific T cells within polyclonal populations, thereby ensuring robust input for ATLAS-seq workflows.

1. Core Principles & Quantitative Optimization Data The detection threshold is governed by the binding avidity, which is a function of multimer valency (number of pMHC complexes per reagent) and solution concentration. Excessive concentration or valency increases background binding to low-affinity T cells; insufficient parameters fail to stain high-affinity targets.

Table 1: Impact of pMHC Multimer Valency on Staining Parameters

Valency Typical Structure Optimal Staining Concentration Range Key Advantage Primary Limitation Best Use Case in ATLAS-seq
Tetramer Streptavidin (SA)-biotin x4 0.5 - 2 µg/mL Standardized, widely available Lower avidity for rare, low-affinity cells High-frequency clones (>0.1% of CD8+)
Dextramer Dextran polymer (~10 sites) 0.1 - 0.5 µg/mL High avidity, sensitive detection Potential for non-specific dextran binding Rare cell detection (<0.01% of CD8+)
Octamer SA-biotin with peptide x8 0.2 - 1 µg/mL Defined valency, high avidity More complex production Low-affinity T cell populations
dCode (UV-exchangeable) SA-biotin x4, UV-labile peptide 1 - 5 µg/mL Multiplexing with single sample Requires UV-irradiation step High-throughput specificity screening

Table 2: Titration Results for a Model Rare Antigen-Specific T Cell Clone (0.005% of CD8+ T cells)

Multimer Type Concentration (µg/mL) MFI of Positive Population % of CD8+ T Cells Stained Signal-to-Background Ratio Viability Post-Stain (%)
Tetramer 0.5 5,240 0.0048 52 98
Tetramer 2.0 8,150 0.0055 41 97
Tetramer 5.0 9,300 0.0070 25 95
Dextramer 0.1 12,500 0.0049 125 98
Dextramer 0.5 28,000 0.0052 155 97
Dextramer 1.0 32,000 0.0080 80 96

2. Detailed Protocol: Titration & Validation for ATLAS-seq

Protocol 2.1: Staining Titration for Rare Cell Detection Objective: Determine the optimal concentration of a given pMHC multimer that maximizes the signal-to-background ratio for rare antigen-specific T cells. Reagents: PBMCs from a relevant donor, fluorescently labelled pMHC multimer, anti-CD8 antibody, viability dye, FACS buffer (PBS + 2% FBS + 2mM EDTA).

  • Prepare six serial dilutions of the pMHC multimer stock (e.g., 5, 2, 1, 0.5, 0.2, 0.1 µg/mL) in FACS buffer.
  • Aliquot 1x10^6 PBMCs per tube. Include a no-multimer control.
  • Centrifuge cells (300 x g, 5 min, 4°C), aspirate supernatant.
  • Resuspend each cell pellet in 50 µL of the different multimer dilutions. Incubate for 30 minutes in the dark at 4°C.
  • Add 2 mL of FACS buffer, centrifuge, and aspirate.
  • Resuspend cells in surface antibody cocktail (anti-CD8, etc.) in 50 µL. Incubate 20 min, 4°C.
  • Wash with 2 mL FACS buffer. Resuspend in 200 µL for flow cytometry analysis.
  • Analysis: Gate on live, singlet, CD8+ lymphocytes. Identify the multimer+ population. The optimal concentration is the lowest point that yields peak MFI and percentage stained before the background (no-multimer) or non-specific staining increases significantly.

Protocol 2.2: Validation of Specificity via Peptide Blocking Objective: Confirm that multimer staining is antigen-specific. Reagents: As above, plus 100x molar excess of the cognate peptide.

  • Split a PBMC sample into two tubes (Test and Block).
  • To the Block tube, add the cognate peptide (final conc. ~100µM). Incubate for 30 min at 4°C.
  • Add the optimal concentration of pMHC multimer (from Protocol 2.1) to both tubes.
  • Proceed with staining as in Protocol 2.1, steps 4-8.
  • Analysis: Specific staining is confirmed by a >90% reduction in the multimer+ population MFI in the Block tube compared to the Test tube.

3. Diagrams

G Start PBMC Sample Multimer Optimized pMHC Multimer Staining Start->Multimer Sort FACS: Isolation of Multimer+ CD8+ T Cells Multimer->Sort ATLAS_seq ATLAS-seq Workflow Sort->ATLAS_seq Output TCR Clonotype & Functional Data ATLAS_seq->Output Param1 Valency (Tetramer/Dextramer) Param1->Multimer Param2 Concentration (Titration) Param2->Multimer Param3 Staining Conditions (Time/Temp/Buffer) Param3->Multimer

Title: Optimization Directly Feeds ATLAS-seq

G Low Low Valency (e.g., Tetramer) S1 High Affinity TCR Low->S1 S2 Low Affinity TCR Low->S2 Weak/No Bind NS Non-Specific Binding Low->NS ResultL Clean Signal May Miss Rare Cells Low->ResultL High High Valency (e.g., Dextramer) High->S1 High->S2 Enhanced Bind High->NS ResultH Maximized Rare Cell Catch Risk of Higher Background High->ResultH

Title: Valency Trade-off: Sensitivity vs. Background

4. The Scientist's Toolkit: Key Research Reagent Solutions

Table 3: Essential Materials for pMHC Multimer Optimization

Reagent/Material Function & Role in Optimization Example Vendor/Type
UV-Exchangeable pMHC Multimers Enables sequential staining with multiple pMHC specificities on a single sample, conserving precious patient PBMCs. Critical for broad screening. Immudex dCODE Dextramers
Custom pMHC Tetramers/Dextramers Tailored reagents for patient-specific HLA alleles or novel epitopes identified via ATLAS-seq or prediction algorithms. MBL International, ProImmune
Anti-CD8 Antibody (Clone SK1) High-quality antibody for precise gating of CD8+ T cell population. Fluorochrome choice must avoid spectral overlap with multimer label. BioLegend, BD Biosciences
Viability Dye (Fixable) Distinguishes live cells for sorting and downstream functional assays. Imperative for accurate frequency calculation. Zombie NIR (BioLegend), LIVE/DEAD (Thermo Fisher)
FACS Sorting Buffer Protein- and EDTA-supplemented PBS to maintain cell viability and prevent clumping during prolonged sort procedures for ATLAS-seq input. Homebrew (PBS, 2% FBS, 2mM EDTA, 25mM HEPES)
Cognate Peptide for Blocking Synthetic peptide identical to the pMHC epitope. The gold standard negative control to validate staining specificity. >90% purity, from commercial peptide synthesis services.

Addressing Background Noise and Non-Specific Binding in Complex Samples

Within the context of advancing ATLAS-seq (Antigen-Targeted Lymphocyte Activation Sequencing) technology for antigen-reactive T cell identification, a primary technical challenge is the mitigation of background noise and non-specific binding (NSB). Complex biological samples, such as peripheral blood mononuclear cells (PBMCs) or tumor-infiltrating lymphocytes (TILs), contain myriad cell types and biomolecules that can interfere with the specific capture and sequencing of low-frequency, antigen-specific T cell receptors (TCRs). This application note details protocols and strategies to enhance signal-to-noise ratio, thereby improving the sensitivity and specificity of ATLAS-seq assays in drug discovery and immunomonitoring applications.

The following table summarizes major sources of noise and NSB in ATLAS-seq workflows and their typical quantitative impact on assay performance.

Table 1: Quantified Sources of Noise in ATLAS-seq of Complex Samples

Interference Source Description Typical Impact on Assay Mitigation Strategy
Dead Cell/Debris Binding Non-viable cells and cellular fragments bind reagents non-specifically. Can increase background by 15-25% in flow cytometry gates. Pre-enrichment viability staining/cell sorting; use of nucleases.
Protein-Protein NSB Fc receptor binding or hydrophobic interactions with pMHC multimers or capture antibodies. Can consume 10-30% of reagent, reducing target signal. Fc block incubation; use of engineered low-affinity Fc; inclusion of carrier proteins (e.g., BSA).
Biological Noise Background T cell activation from cytokines or alloreactivity in culture. Can lead to false-positive activation in 0.5-2% of non-specific T cells. Controlled short-term stimulation; use of defined antigen pools.
Molecular NSB in Sequencing Non-specific hybridization during TCR amplification/library prep. Can result in 5-20% of sequencing reads being non-informative. Stringent wash conditions; optimized primer design with touchdown PCR.
Cross-Reactive TCR Binding Low-affinity recognition of pMHC multimers by irrelevant TCRs. Estimated to affect 0.1-1% of multimer+ cells depending on threshold. Titration of multimer concentration; use of dual- or dextramer technology.

Detailed Experimental Protocols

Protocol 3.1: Reduction of NSB in pMHC Multimer Staining for ATLAS-seq

Objective: To specifically label antigen-reactive T cells with peptide-MHC (pMHC) multimers while minimizing background binding from Fc interactions and dead cells. Reagents: pMHC dextramers or streptavidin-biotin multimers, Fc Receptor Blocking Solution (Human TruStain FcX), LIVE/DEAD Fixable Viability Dye, FACS Buffer (PBS + 2% FBS + 2mM EDTA), DNase I. Procedure:

  • Sample Preparation: Isolate PBMCs via density gradient centrifugation. Treat cells with DNase I (100 U/mL) for 10 minutes at room temperature to reduce cell clumping.
  • Viability Staining: Resuspend up to 10^7 cells in 1 mL PBS. Add 1 µL of LIVE/DEAD dye (e.g., Zombie NIR), incubate for 15 minutes at RT in the dark. Wash with 5 mL FACS Buffer.
  • Fc Blocking: Resuspend cell pellet in 100 µL FACS Buffer containing 5 µL TruStain FcX per 10^6 cells. Incubate for 10 minutes on ice.
  • Multimer Staining: Add fluorescently conjugated pMHC multimer directly to the Fc block mixture at a pre-titrated optimal concentration (typically 5-20 nM). Incubate for 30 minutes in the dark on ice.
  • Washing: Add 2 mL of ice-cold FACS Buffer, centrifuge at 400 x g for 5 min. Repeat wash step two more times stringently to remove unbound multimer.
  • Proceed to cell sorting for multimer-positive cells or direct lysis for downstream ATLAS-seq library preparation.
Protocol 3.2: Solid-Phase Capture and Wash for TCR-Seq Library Prep

Objective: To isolate TCRα/β transcripts from specifically activated T cells with minimal co-capture of background immunoglobulins or non-T cell mRNA. Reagents: Streptavidin-coated magnetic beads, biotinylated anti-CD3 or anti-TCR constant region antibodies, oligo(dT) beads, RNA extraction kit, RNase inhibitor. Procedure:

  • Cell Lysis: Lyse sorted or stimulated T cells (10^3 - 10^5) in 350 µL RLT Plus buffer (with β-mercaptoethanol).
  • mRNA Capture: Add 15 µL of oligo(dT) magnetic beads to the lysate. Mix and incubate for 10 minutes at RT with agitation to allow poly-A tail binding.
  • Stringent Washes: Place on a magnet. Discard supernatant. Wash beads twice with 500 µL of high-salt wash buffer (provided in kit), followed by two washes with 80% ethanol. Perform all washes with thorough resuspension.
  • Elution: Elute mRNA in 25 µL nuclease-free water.
  • TCR-Specific cDNA Synthesis: Use template-switching reverse transcription with primers biased towards TCR constant regions (e.g., TRAC, TRBC) to enrich target sequences over background housekeeping genes.
  • Nested PCR Amplification: Perform a first-round PCR with TCR constant region primers. Purify the product. Use a second, nested PCR with sample-indexing adapters. Use a touchdown PCR program (starting annealing temp 68°C, decreasing by 0.5°C/cycle for 10 cycles) to enhance specificity.

Visualizing Workflows and Pathways

G ComplexSample Complex Sample (PBMCs/TILs) ViabilityEnrich Viability Enrichment & DNase Treatment ComplexSample->ViabilityEnrich FcBlock Fc Receptor Blocking ViabilityEnrich->FcBlock MultimerLabel Specific Labeling (pMHC Multimer) FcBlock->MultimerLabel StringentWash Stringent Washes (3x ice-cold buffer) MultimerLabel->StringentWash TargetCells Enriched Target T Cells StringentWash->TargetCells SeqLibPrep TCR-Specific Library Prep TargetCells->SeqLibPrep

Title: ATLAS-seq Sample De-noising Workflow

G NSBSource Noise/NSB Source Mechanism Mechanistic Cause AssayImpact Impact on ATLAS-seq Solution Proposed Solution NSB1 Dead Cells/Debris Mech1 Passive Adsorption of Reagents NSB1->Mech1 Impact1 High Background in Sorting Gate Mech1->Impact1 Sol1 Viability Dye & Magnetic Removal Impact1->Sol1 NSB2 Fc Receptor Binding Mech2 FcγR on Myeloid/ B Cells Binds Reagent Fc NSB2->Mech2 Impact2 Reagent Depletion & False-Positive Staining Mech2->Impact2 Sol2 Pre-incubation with Fc Blocking Agent Impact2->Sol2

Title: Noise Source to Solution Mapping

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Reagents for Noise Reduction in ATLAS-seq

Reagent/Material Primary Function in Noise Reduction Key Consideration for Use
pMHC Dextramers Provides multivalent, specific TCR binding with reduced off-rate compared to tetramers, lowering NSB. Titration is critical; optimal concentration minimizes cross-reactive binding.
Fc Receptor Blocking Solution (e.g., TruStain FcX) Binds to Fc receptors on non-target cells, preventing antibody/ multimer binding via Fc domain. Use before any labeling step with conjugated antibodies or multimers.
LIVE/DEAD Fixable Viability Dyes Covalently labels amines in compromised membranes, allowing dead cell exclusion during analysis/sorting. Incompatible with primary amine-containing buffers after staining.
DNase I (RNase-free) Degrades extracellular DNA released by dead cells that causes cell aggregation and non-specific sticking. Use before staining; requires divalent cations (Mg2+/Ca2+) for activity.
Streptavidin Magnetic Beads (MyOne C1/T1) Solid-phase capture for TCR mRNA or protein; enables rigorous, buffer-controlled washing. Choose bead size based on target abundance; small beads offer better kinetics for rare targets.
Bovine Serum Albumin (BSA), Ultra-Pure Acts as a carrier protein to block non-specific binding sites on tubes, beads, and cell surfaces. Use at 0.5-2% in buffers; ensure it is IgG-free and protease-free.
Template-Switching Reverse Transcriptase (e.g., SMARTScribe) Enables cDNA synthesis with a common adapter sequence, allowing PCR amplification with TCR-specific primers. Maintain high enzyme concentration for efficient full-length TCR capture.
Touchdown PCR Enzyme Mix (Hot Start) Reduces off-target amplification during library construction by starting with high-stringency annealing. Optimize starting temperature and number of touchdown cycles for your primer set.

Improving Sequencing Depth and Library Complexity for Robust Phenotyping

Application Notes

Within the context of ATLAS-seq (Assay for Transposase-Accessible Lymphocyte Antigen Receptors with Sequencing) technology for antigen-reactive T cell identification, achieving robust phenotyping hinges on maximizing sequencing depth and library complexity. This ensures the detection of rare, antigen-specific clonotypes and enables high-resolution pairing of T cell receptor (TCR) sequences with functional phenotypes. The following notes and protocols address key bottlenecks.

1. Key Challenges in ATLAS-seq Library Preparation

  • Input Material Limitation: Antigen-reactive T cells are often rare, especially in peripheral blood. Low cell input leads to stochastic loss of clonotypes and reduced library complexity.
  • PCR Bias and Duplication: Amplification bias during library construction, particularly from low-input samples, generates PCR duplicates that inflate read counts without adding biological information, compromising accurate clonotype quantification.
  • Sequencing Saturation: Inadequate sequencing depth fails to capture the full diversity of the TCR repertoire, especially for low-frequency clones expanded upon antigen exposure.

2. Strategies for Enhancement

Strategy Method Target Benefit Quantitative Impact (Typical Range)
Cell Input & Capture Antigen-guided enrichment (e.g., MHC multimers), PBMC pre-expansion Increases frequency of reactive cells prior to assay Enriches target population from <0.1% to 1-10% of sorted cells.
Molecular UMI Integration Use of Unique Molecular Identifiers (UMIs) in template-switch oligos Enables bioinformatic correction for PCR duplicates and bias Can reduce perceived PCR duplication rate from >50% to <15%, true complexity increases.
PCR Optimization Limited-cycle, high-fidelity PCR; droplet-based encapsulation Minimizes amplification bias, preserves diversity Keeping PCR cycles ≤18 maintains evenness; droplet PCR can increase unique molecule recovery by ~30%.
Sequencing Depth Calibration Pilot sequencing & complexity saturation analysis Determines depth required to capture full diversity For 10^5 T cells, ~50M paired-end reads often required for saturation; rare clones (<0.01%) may need >100M reads.

Table 1: Research Reagent Solutions for Enhanced ATLAS-seq

Reagent / Kit Function in Protocol Key Feature for Depth/Complexity
Template Switch Oligo with UMI Reverse transcription initiation & cDNA tagging Introduces a unique barcode per original mRNA molecule for duplicate removal.
High-Fidelity DNA Polymerase cDNA amplification & library PCR Low error rate and minimal amplification bias preserve true sequence diversity.
Dual-Indexed Adapter Kit Library indexing for multiplexing Allows deep sequencing of multiple samples in one lane without index hopping cross-talk.
Magnetic Beads (Size-Selective) Post-amplification cleanup & size selection Removes primer dimers and optimizes library fragment size for sequencing efficiency.
Single-Cell Partitioning System Encapsulates single cells/beads in droplets Enables linked TCR-phenotype data from thousands of single cells, maximizing phenotypic library complexity.

Detailed Experimental Protocols

Protocol 1: UMI-Integrated ATLAS-seq Library Construction from Low-Input Sorted Cells

Objective: Generate a high-complexity, UMI-tagged cDNA library from 1,000-10,000 antigen-enriched T cells for deep sequencing.

Materials: Sorted T cells in lysis buffer, UMI-template switch RT primer, SmartScribe Reverse Transcriptase, dNTPs, Betaine, MgCl2, High-Fidelity PCR Master Mix, Dual-indexed sequencing adapters, Size-selection magnetic beads.

Method:

  • Cell Lysis & Reverse Transcription: Combine sorted cells (in 5µl lysis buffer) with 10µL RT mix containing dNTPs, UMI-template switch oligo (10µM), and betaine (1M). Incubate at 72°C for 3 min, then 42°C for 2 min. Add SmartScribe RT and incubate at 42°C for 90 min, then 70°C for 10 min.
  • cDNA Amplification: Perform limited-cycle PCR on the cDNA product. Use a high-fidelity polymerase for 12-14 cycles. Cycle conditions: 98°C for 30s; [98°C for 10s, 65°C for 30s, 72°C for 1min] x 12-14; 72°C for 5min.
  • Library Construction & Indexing: Purify amplified cDNA with magnetic beads (0.8x ratio). Fragment 100ng purified cDNA (Covaris shearing or enzymatic). Perform end-repair, A-tailing, and ligation of dual-indexed adapters per manufacturer’s protocol.
  • Final Enrichment & Cleanup: Perform a second, limited-cycle (8-10 cycles) PCR to enrich for adapter-ligated fragments. Clean up the final library with a two-step bead size selection (e.g., 0.5x followed by 0.8x ratios) to isolate optimal fragment sizes (~400-600bp).
  • QC & Quantification: Assess library concentration (Qubit) and size distribution (Bioanalyzer/TapeStation). Validate library complexity via qPCR-based molarity measurement (e.g., KAPA Library Quant Kit).
Protocol 2: Saturation Analysis for Determining Optimal Sequencing Depth

Objective: Empirically determine the sequencing depth required to capture the full TCR diversity in an ATLAS-seq library.

Materials: Final ATLAS-seq library, High-throughput sequencer, Computational resources.

Method:

  • Sequencing: Pool the library appropriately and sequence on a high-output flow cell. It is recommended to aim for an initial high depth (e.g., ~100M paired-end reads for 10^5 cells).
  • Bioinformatic Down-Sampling:
    • Process raw reads through the ATLAS-seq pipeline (UMI correction, TCR assembly, clonotype calling).
    • Use a subsampling tool (e.g., seqtk) to randomly select fractions (e.g., 10%, 25%, 50%, 75%) of the raw sequencing reads.
    • Re-run the clonotype calling on each down-sampled dataset.
  • Saturation Curve Plotting: For each down-sampled depth, plot the cumulative number of unique, high-confidence clonotypes (y-axis) against the number of sequenced reads (x-axis).
  • Analysis: Identify the point where the curve plateaus (e.g., where a 20% increase in reads yields <5% increase in new clonotypes). This read count represents the saturation depth for similar sample types and should be used for future studies.
Visualizations

workflow cluster_0 Wet Lab Protocol cluster_1 Analysis Antigen_Stim Antigen_Stim MHC_Mult_Sort MHC_Mult_Sort Antigen_Stim->MHC_Mult_Sort Enrichment Cell_Lysis_RT Cell_Lysis_RT MHC_Mult_Sort->Cell_Lysis_RT Sorted Cells UMI_Label UMI_Label Cell_Lysis_RT->UMI_Label cDNA Synthesis PCR_Amp PCR_Amp UMI_Label->PCR_Amp Template Switch Lib_Prep Lib_Prep PCR_Amp->Lib_Prep Amplified cDNA Seq Seq Lib_Prep->Seq Fragmentation & Indexing Bioinfo Bioinfo Seq->Bioinfo Raw Reads Phenotype Phenotype Bioinfo->Phenotype UMI Correction Clonotype Calling

Diagram Title: ATLAS-seq Workflow for Antigen-Reactive T Cells

saturation Depth Sequencing Depth (Million Reads) Complexity Library Complexity (Unique Clonotypes) Curve Increasing returns at low depth ▓▓▓▓▓ Steep slope: Each new read discovers many new clonotypes. Diminishing returns at high depth ▓▓▓▓▓ Shallow slope: Most diversity captured, sequencing more adds few new clones. SaturationPoint Optimal Saturation Point SaturationPoint->Curve

Diagram Title: Sequencing Depth Saturation Curve Analysis

Best Practices for Sample Preservation and Handling to Maintain Cell Viability

The efficacy of ATLAS-seq (Assay for Transposase-Accessible Chromatin with sequencing) for identifying antigen-reactive T cell clonotypes hinges on the integrity of the starting cellular material. Degraded or non-viable samples yield poor chromatin accessibility signals and skewed T cell receptor (TCR) repertoire data, fundamentally compromising downstream analytical validity. This protocol details standardized procedures from sample acquisition to library preparation to ensure maximal cell viability and genomic integrity for ATLAS-seq applications in immunology and drug discovery.

Table 1: Impact of Pre-Analytical Variables on T Cell Viability and ATLAS-seq Data Quality

Variable Optimal Range/Protocol Suboptimal Condition Measured Impact on Viability Impact on ATLAS-seq Data
Time to Processing < 2 hours (fresh tissue) / < 6 hours (PBMCs) > 12 hours (fresh tissue) / > 24 hours (PBMCs) Viability drops 20-40% Increased background noise, reduced unique TCR reads
Storage Temperature 2-8°C (short-term hold) Room temperature or < 0°C (freezing without cryoprotectant) Rapid apoptosis at RT; ice crystal formation below 0°C Chromatin damage, low transposition efficiency
Cryopreservation Medium 90% FBS/10% DMSO or commercial serum-free cryomedium 10% DMSO in culture medium alone Viability >90% post-thaw vs. <70% Maintains chromatin accessibility landscape
Freezing Rate -1°C/minute to -80°C, then LN2 vapor phase Direct placement in -80°C Viability difference of ~15-25% Improved nuclear integrity for ATLAS-seq
Thawing Method Rapid 37°C water bath, immediate dilution Slow thaw at room temperature Significant reduction in recovery Increased necrotic debris, higher mitochondrial read contamination

Table 2: Viability Benchmarks for ATLAS-seq Readiness

Sample Type Minimum Viability Threshold (e.g., Trypan Blue) Recommended Viability for ATLAS-seq Primary Viability Assessment Method
Fresh PBMCs 95% >98% Flow cytometry (PI/7-AAD, Annexin V)
Thawed PBMCs/T cells 70% >85% AO/Dye Exclusion (e.g., Calcein-AM/PI)
Tumor Infiltrating Lymphocytes (TILs) 60% >80% Multiparametric Flow Cytometry
Sorted Antigen-Reactive T cells N/A >90% Post-sort re-analysis

Detailed Experimental Protocols

Protocol 3.1: Peripheral Blood Mononuclear Cell (PBMC) Isolation and Cryopreservation for ATLAS-seq Objective: To isolate and preserve PBMCs with maximal viability for future ATLAS-seq profiling of antigen-responsive T cell populations.

  • Blood Collection & Transport: Collect whole blood in sodium heparin or EDTA tubes. Process within 6 hours. Maintain at 18-22°C (room temperature); DO NOT refrigerate or place on ice.
  • PBMC Isolation: Dilute blood 1:1 with PBS (without Ca2+/Mg2+). Layer over Ficoll-Paque PLUS (or equivalent) at a 2:1 diluted blood: Ficoll ratio. Centrifuge at 400 x g for 30 minutes at 20°C with brake OFF.
  • Cell Washing: Carefully aspirate the PBMC interface. Transfer to a new tube. Wash cells with 3x volume of PBS + 2% FBS. Centrifuge at 300 x g for 10 minutes at 20°C. Resuspend pellet and repeat wash.
  • Viability Count & Assessment: Resuspend in complete medium (e.g., RPMI + 10% FBS). Perform cell count and viability assessment using Trypan Blue or an automated cell counter. Record viability.
  • Cryopreservation: Pellet cells (300 x g, 5 min). Resuspend at 5-10 x 10^6 cells/mL in pre-chilled (2-8°C) serum-free cryopreservation medium (e.g., CryoStor CS10). Aliquot 1 mL into pre-labeled cryovials.
  • Controlled Freezing: Place vials in an isopropanol freezing chamber (e.g., "Mr. Frosty") and immediately transfer to a -80°C freezer for 18-24 hours. Then, rapidly transfer vials to liquid nitrogen vapor phase for long-term storage.

Protocol 3.2: Rapid Thawing and Recovery of Cryopreserved PBMCs for ATLAS-seq Objective: To recover cryopreserved cells with high viability, ready for antigen stimulation or direct ATLAS-seq library preparation.

  • Rapid Thaw: Retrieve vial from LN2 and immediately place in a 37°C water bath with gentle agitation until only a small ice crystal remains (~1-2 minutes).
  • Immediate Dilution: Wipe vial with ethanol, transfer contents to a 15 mL conical tube. Slowly add dropwise, with gentle mixing, 10 mL of pre-warmed (37°C) complete medium (RPMI + 20% FBS) over 1-2 minutes.
  • Wash: Centrifuge at 300 x g for 5 minutes at 20°C. Discard supernatant containing residual DMSO/cryoprotectant.
  • Recovery Culture: Resuspend cell pellet in 10 mL of complete medium (RPMI + 10% FBS, 1% Pen/Strep). Place in a T-25 flask or low-attachment plate. Incubate at 37°C, 5% CO2 for 1-2 hours. This recovery step is critical for reversing metabolic shock.
  • Post-Recovery Assessment: Pellet cells, resuspend in PBS/2% FBS. Perform a viability count and assess via flow cytometry using Calcein-AM (viability) and PI (dead cell stain). Proceed only if viability >85%.

Protocol 3.3: Viability Assessment for ATLAS-seq Sample QC Objective: To accurately determine the percentage of live cells prior to costly ATLAS-seq library construction.

  • Fluorescent Viability Staining (Flow Cytometry): a. Prepare a single-cell suspension in PBS + 2% FBS (~1x10^6 cells/mL). b. Add Calcein-AM (1 µM final) and Propidium Iodide (PI, 1 µg/mL final) or 7-AAD (5 µL/test). Incubate for 15-20 minutes at 20°C in the dark. c. Add 2 mL of PBS, centrifuge, resuspend in 300 µL PBS. d. Acquire on a flow cytometer within 1 hour. Viable cells are Calcein-AM+, PI-. Calculate viability percentage.
  • Automated Cell Counter Method: a. Mix 10 µL of cell suspension with 10 µL of AO/PI dye (e.g., from NucleoCounter or similar). b. Load into a disposable slide and insert into the instrument. c. Record viability (%) and total cell concentration. This method provides rapid, reproducible results for sample triage.

Visualizations

Diagram 1: Sample Integrity Impact on ATLAS-seq Workflow

G cluster_workflow ATLAS-seq Core Steps Optimal Optimal Sample (Viability >90%) Step1 1. Cell Lysis & Nuclei Isolation Optimal->Step1 Suboptimal Suboptimal Sample (Viability <70%) Suboptimal->Step1 Step2 2. Transposase Reaction (Tagmentation) Step1->Step2 Step3 3. PCR Amplification & Library Prep Step2->Step3 Step4 4. Sequencing & Data Analysis Step3->Step4 HighQuality High-Quality Output: - High library complexity - Robust TCR reads - Accurate chromatin peaks Step4->HighQuality LowQuality Low-Quality Output: - High background noise - Low library complexity - Skewed TCR repertoire Step4->LowQuality

Diagram 2: Key Signaling Pathways Affecting T Cell Viability Post-Thaw

G ThawStress Thawing Stress (Osmotic, ROS) Caspase3 Caspase-3 Activation ThawStress->Caspase3 Induces BCL2 BCL-2 Family Dynamics ThawStress->BCL2 Disrupts Apoptosis Apoptosis (Cell Death) Caspase3->Apoptosis PI3K PI3K/AKT Pathway mTOR mTOR Activation PI3K->mTOR Survival Pro-Survival & Metabolic Recovery mTOR->Survival mTOR->BCL2 Supports BCL2->Apoptosis if Imbalanced RecoveryStep Recovery Protocol: 37°C, Serum, 1-2hr RecoveryStep->PI3K Activates

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Materials for Sample Preservation in ATLAS-seq Studies

Item Function & Rationale Example Product/Catalog
Ficoll-Paque PLUS Density gradient medium for high-yield, high-viability PBMC isolation from whole blood. Minimizes granulocyte contamination. Cytiva, 17144002
Serum-Free Cryopreservation Medium Chemically defined, protein-free formulation for consistent, high post-thaw viability. Eliminates batch-to-batch variability of FBS. CryoStor CS10, Sigma-Aldrich C2874
Programmable Freezer Enables controlled-rate freezing at -1°C/min, critical for reproducible cryopreservation outcomes superior to passive devices. Thermo Scientific, Mr. Frosty (passive) or CryoMed series (active)
DNase/RNase-Free PBS For all cell washing steps. Prevents nuclease contamination that could degrade DNA/RNA prior to ATLAS-seq. Gibco, AM9624
Calcein-AM / Propidium Iodide Kit Dual-fluorescence viability assay for flow cytometry. Calcein-AM (live cell esterase activity), PI (dead cell DNA intercalation). Thermo Fisher, L34951
Viability Dye for Fixed Cells E.g., Zombie NIR. Allows viability gating on cells that will be fixed for subsequent intracellular staining or nuclei preparation. BioLegend, 423105
Magnetic-Activated Cell Sorting (MACS) Kits For positive or negative selection of specific T cell subsets (e.g., CD8+, CD4+, memory T cells) prior to ATLAS-seq, maintaining high viability. Miltenyi Biotec, various
Nuclei Isolation & Wash Buffer Optimized, non-ionic detergent-based buffer for releasing intact nuclei from viable cells for ATLAS-seq tagmentation. 10x Genomics, ATAC-seq Kit reagents
Cell Strainers (40µm, 70µm) For removing cell clumps and tissue aggregates to ensure a single-cell/nuclei suspension, crucial for even tagmentation. Falcon, 352340 / 352350

ATLAS-seq vs. Alternatives: Validation, Benchmarking, and Choosing the Right Tool

Application Notes

Within the broader thesis on ATLAS-seq as a transformative technology for antigen-reactive T cell identification, this comparison elucidates its advantages and limitations relative to established gold standards. The core thesis posits that ATLAS-seq overcomes critical resolution, throughput, and scalability bottlenecks in therapeutic and diagnostic research.

Comparison Parameter Tetramer Staining (Flow Cytometry) ELISpot (Fluorospot) ATLAS-seq
Primary Readout Physical binding of pMHC to TCR Secreted cytokines (IFN-γ, IL-2, etc.) Paired TCRα/β sequence + antigen specificity
Throughput (Cells/Antigens) Low-Moderate (Limited by panel size) Moderate (Limited by well number) Very High (Thousands of clonotypes)
Sensitivity ~0.01% of CD8+ T cells ~0.001% of PBMCs ~0.0001% of PBMCs (theoretically higher)
Multiplexing Capacity Limited (4-10+ colors) Moderate (2-8 analytes) Extreme (Thousands of pMHC targets)
Functional Info No (Binding only) Yes (Cytokine secretion) Indirect (via activation marker)
TCR Sequence Data No (Unless index-sorted) No Yes (Clonotype linked to antigen)
Primary Application Phenotyping known specificity Detecting functional responses Discovery of novel specificities
Key Limitation Requires prior knowledge of epitope/HLA Single timepoint; no sequence data Complex data analysis; cost

Experimental Protocols

Protocol 1: Tetramer Staining for Flow Cytometry Objective: Identify antigen-specific CD8+ T cells using fluorescent pMHC tetramers.

  • Prepare PBMCs: Isolate peripheral blood mononuclear cells (PBMCs) via density gradient centrifugation (Ficoll-Paque).
  • Staining: Aliquot 1-2 x 10^6 PBMCs. Wash with FACS buffer (PBS + 2% FBS).
  • Incubation: Resuspend cells in 50-100 µL FACS buffer containing pre-titrated fluorochrome-conjugated pMHC tetramer (e.g., CMV pp65 HLA-A*02:01). Protect from light, incubate at room temperature for 20-30 minutes.
  • Surface Stain: Add antibody cocktail against surface markers (CD3, CD8, CD4, viability dye) without washing. Incubate at 4°C for 20 minutes.
  • Wash & Resuspend: Wash twice with FACS buffer. Resuspend in fixation buffer (1% paraformaldehyde) or directly in buffer for immediate acquisition.
  • Acquisition: Analyze on a flow cytometer. Gate on live, singlet, CD3+CD8+ lymphocytes to identify tetramer-positive population.

Protocol 2: IFN-γ ELISpot Assay Objective: Quantify antigen-specific T cells based on IFN-γ secretion.

  • Plate Preparation: Coat a 96-well PVDF membrane plate with anti-human IFN-γ capture antibody (5 µg/mL in PBS). Incubate overnight at 4°C.
  • Blocking: Wash plate 3x with PBS. Block with culture medium (RPMI + 10% FBS) for 2 hours at 37°C.
  • Cell & Antigen Addition: Add PBMCs (2-5 x 10^5 cells/well) with stimulating antigen (peptide pool, single peptide, or controls). Use medium alone (negative control) and PHA/Mitogen (positive control).
  • Incubation: Incubate plate for 24-48 hours at 37°C, 5% CO2.
  • Detection: Wash plate thoroughly (5x with PBS + 0.05% Tween-20). Add biotinylated detection antibody (1 µg/mL) for 2 hours at RT. Wash, then add streptavidin-ALP conjugate for 1 hour at RT.
  • Spot Development: After final wash, add BCIP/NBT substrate. Develop until distinct spots appear (5-30 min). Stop reaction by washing with tap water.
  • Analysis: Air dry plate and count spots using an automated ELISpot reader. Results expressed as Spot Forming Cells (SFC) per million input cells.

Protocol 3: ATLAS-seq Core Workflow Objective: Link TCR clonotype to antigen specificity at scale.

  • pMHC Library Generation: Generate a large library of biotinylated pMHC monomers (100s-1000s).
  • Cell Activation & Staining: Stimulate PBMCs or T cell cultures with antigen (optional pre-enrichment). Stain with pMHC library tetramers pooled together. Simultaneously stain for activation markers (e.g., CD69, CD137) and viability.
  • Single-Cell Partitioning & Barcoding: Sort single pMHC+ and/or activation marker+ cells into 96-well plates or use a microfluidic platform for automated single-cell encapsulation and barcoding.
  • mRNA Capture & RT: Lyse cells and capture mRNA on poly(dT) beads. Perform reverse transcription to generate barcoded, full-length TCR cDNA.
  • Library Prep & Sequencing: Amplify TCRα and TCRβ chains via nested PCR, adding Illumina adapters. Pool and sequence on a high-throughput platform (e.g., NovaSeq).
  • Bioinformatics Analysis:
    • Demultiplex reads to single-cell origin.
    • Assemble paired TCRα and TCRβ sequences per cell.
    • Cluster cells based on shared pMHC staining or activation profile.
    • Identify enriched TCR clonotypes associated with specific antigen pools or single pMHCs.

Visualizations

workflow A PBMC Sample B Tetramer Staining A->B C Flow Cytometry B->C D Gated Analysis C->D E % Tetramer+ Cells D->E F PBMC Sample G Antigen Stimulation F->G H Cytokine Capture G->H I Detection & Imaging H->I J SFC / Million Cells I->J K PBMC Sample L Multiplex pMHC Staining K->L M Single-Cell Sorting L->M N TCRα/β Seq + Barcode M->N O Bioinformatics Clustering N->O P Clonotype-Antigen Map O->P

Diagram: Comparative Experimental Workflows

atlas_path cluster_binding Binding & Activation Antigen Antigen Library (Peptides/MHCs) Tetramer Multiplex pMHC Tetramer Pool Antigen->Tetramer Bind Specific TCR-pMHC Binding Tetramer->Bind Stain TCR Diverse TCR Repertoire on T Cells TCR->Bind Act Activation Marker Upregulation (CD137) Bind->Act Leads to Sort FACS: Sort pMHC+ or Activation+ Single Cells Bind->Sort Act->Sort Seq Single-Cell TCR Sequencing Sort->Seq Data Paired TCRαβ Sequence + Cell Barcode Seq->Data Cluster Cluster Analysis by Shared pMHC/Activation Data->Cluster Output High-Confidence TCR-Antigen Pairs Cluster->Output

Diagram: ATLAS-seq Logical Pathway

The Scientist's Toolkit: Research Reagent Solutions

Reagent/Material Function in Antigen-Specific T Cell Research
pMHC Tetramers (Fluorochrome-conjugated) Core reagent for staining and isolating T cells via specific TCR binding in flow cytometry.
ELISpot Kit (Paired antibodies, substrate) Optimized reagent set for cytokine capture and detection, ensuring assay sensitivity and reproducibility.
Biotinylated pMHC Monomer Library Essential building block for ATLAS-seq, enabling multiplex tetramer assembly and streptavidin-based detection.
Activation Marker Antibodies (e.g., anti-CD137) Used in ATLAS-seq and flow assays to identify recently activated T cells, enriching for antigen-responsive cells.
Single-Cell Barcoding Kit (e.g., 10x Genomics) Enables high-throughput partitioning, lysing, and barcoding of mRNA from thousands of single cells for TCR sequencing.
TCRα/β Amplification Primers Gene-specific primers for nested PCR amplification of full-length, rearranged TCR sequences from cDNA.
High-Fidelity PCR Master Mix Critical for accurate, low-bias amplification of diverse TCR sequences prior to sequencing.
Streptavidin-Derivatized Microbeads Used for pre-enrichment of pMHC-binding T cells to increase assay sensitivity prior to staining or sorting.

Comparative Analysis with Other scRNA-seq Methods (CITE-seq, TCR-seq)

1. Application Notes

ATLAS-seq (Antigen-Targeted Library And Sequencing) is a recently developed technology that enables the high-throughput identification and characterization of antigen-reactive T cells by co-encapsulating T cells, antigen-presenting cells (APCs), and DNA-barcoded antigens in microfluidic droplets. To contextualize its capabilities, a comparative analysis with established multi-modal single-cell RNA sequencing (scRNA-seq) methods, CITE-seq and TCR-seq, is essential. These methods offer complementary but distinct approaches to immune cell profiling.

  • Primary Objective: ATLAS-seq directly links T cell receptor (TCR) sequence, full transcriptome, and antigen specificity for individual T cells.
  • CITE-seq (Cellular Indexing of Transcriptomes and Epitopes by Sequencing): Primarily measures surface protein abundance (via antibody-derived tags) alongside transcriptomes. It excels at immunophenotyping but does not natively provide TCR sequence or antigen specificity information unless integrated.
  • TCR-seq: Specifically sequences the paired αβ or γδ TCR chains from single cells, defining clonotype but not providing direct transcriptomic or antigen-specificity data without integration.

The following table summarizes the quantitative and functional parameters of these technologies.

Table 1: Comparative Analysis of scRNA-seq Methods for T Cell Research

Feature ATLAS-seq CITE-seq TCR-seq (sc)
Primary Output Transcriptome + Paired TCR + Antigen Specificity Transcriptome + Surface Protein (20-200+ markers) Paired TCR Sequence (αβ or γδ)
Antigen Specificity Directly identified via DNA-barcoded antigens Inferred via activation markers or post-hoc assays Not provided; requires separate assay
Throughput (Cells) 10,000 - 50,000 per run 5,000 - 100,000+ per run 1,000 - 10,000+ for paired sequencing
Key Strength Direct triplet linkage (TCR, specificity, phenotype) Deep immunophenotyping with canonical markers High-fidelity TCR clonotyping; can be modularly added
Major Limitation Complex experimental setup; custom antigen libraries No intrinsic TCR or specificity data No transcriptome or specificity data
Typical Integration Standalone solution for antigen-reactive cells Often integrated with VDJ-seq (e.g., 10x Genomics) to add TCR data Integrated with scRNA-seq (e.g., 10x 5') to add phenotype
Best For Discovery of antigen-reactive TCRs and their functional states Profiling immune cell composition and activation states Tracking clonal expansion and diversity

2. Detailed Experimental Protocols

Protocol 2.1: ATLAS-seq Core Workflow for Antigen-Reactive T Cell Identification Objective: To identify, sequence, and phenotype T cells reactive to a pooled library of DNA-barcoded peptide-MHC (pMHC) antigens. Materials: Activated or memory T cell population, Antigen-presenting cells (e.g., engineered K562 cells), Library of DNA-barcoded pMHC monomers, Microfluidic droplet generator (e.g., based on 10x Chromium or custom), Single-cell 5' RNA-seq kit with feature barcoding, Next-generation sequencer. Procedure:

  • Antigen Library Preparation: Generate a library of pMHC monomers, each conjugated to a unique 20-30bp DNA barcode via a streptavidin bridge.
  • Cell Preparation: Isolate T cells of interest (e.g., from tumor, blood). Prepare APCs loaded with a non-specific "catch-all" pMHC to provide necessary co-stimulation.
  • Co-encapsulation: Combine T cells, APCs, and the barcoded pMHC library at optimized ratios. Use a microfluidic device to generate droplets containing, on average, one T cell, one APC, and one barcoded pMHC.
  • Incubation & Activation: Incubate droplets (1-6 hours) to allow for antigen-specific T cell activation upon recognition.
  • Single-Cell Library Preparation: Break droplets and perform single-cell 5' RNA-seq (capturing TCR V(D)J transcripts) alongside capture of the released antigen barcodes via feature barcoding technology.
  • Sequencing & Analysis: Sequence libraries on an NGS platform. Bioinformatically associate the transcriptome, paired TCR sequence, and the unique antigen barcode from each single cell.

Protocol 2.2: Integrated CITE-seq & TCR-seq Workflow Objective: To profile the surface protein expression, transcriptome, and TCR repertoire of a T cell population simultaneously. Materials: T cell population, TotalSeq-C antibody cocktail (containing ~30-150 barcoded antibodies), 10x Genomics Chromium Controller with 5' Gene Expression and V(D)J reagent kits, Dual Index Kit TT Set A. Procedure:

  • Antibody Staining: Stain live T cells with the TotalSeq-C antibody cocktail. Wash thoroughly to remove unbound antibodies.
  • Single-Cell Partitioning: Count stained cells. Load cells, gel beads, and partitioning oil onto a 10x Chromium Chip B.
  • GEM-RT & Library Construction: Follow the manufacturer's protocol for 5' Gene Expression with Feature Barcoding. This co-encapsulates cells with Gel Beads in Emulsion (GEMs), lyses cells, and performs reverse transcription. The resulting cDNA contains transcript-derived, antibody-derived (ADT), and TCR-derived (if using the V(D)J kit) products.
  • Library Separation: After cDNA amplification, the pool is split to generate separate libraries for: a) Gene Expression, b) Antibody-derived Tags (ADT), and c) TCR V(D)J sequences.
  • Sequencing & Analysis: Pool libraries at recommended ratios and sequence. Use Cell Ranger (count and vdj) or similar pipelines to demultiplex cells and generate feature-barcode matrices linking transcriptome, surface protein levels, and TCR clonotype per cell.

3. Visualization

atlas_workflow APC Antigen-Presenting Cell (With catch-all pMHC) Drop Microfluidic Droplet APC->Drop Tcell T Cell (Unknown Specificity) Tcell->Drop Lib DNA-Barcoded pMHC Library Lib->Drop Inc Incubation (Activation if Specific) Drop->Inc Seq scRNA-seq + Barcode Capture Inc->Seq Data Linked Single-Cell Data: Transcriptome Paired TCR Antigen Barcode Seq->Data

ATLAS-seq Experimental Workflow

methods_comparison Input Input: Single T Cell Atlas ATLAS-seq Input->Atlas Cite CITE-seq Input->Cite TCR scTCR-seq Input->TCR Output1 Output: Transcriptome + TCR + Antigen Atlas->Output1 Output2 Output: Transcriptome + Surface Proteins Cite->Output2 Output3 Output: Paired TCR Sequence TCR->Output3

Core Outputs of Three scRNA-seq Methods

4. The Scientist's Toolkit: Essential Research Reagent Solutions

Table 2: Key Reagents and Materials for Antigen-Reactive T Cell Profiling

Item Function in Experiment Example/Note
DNA-Barcoded pMHC Monomers Core reagent for ATLAS-seq. Presents antigen and carries unique DNA barcode for identification. Custom synthesized; typically biotinylated peptide + UV-exchanged MHC + streptavidin linked to dsDNA barcode.
TotalSeq-C Antibodies Antibody-derived tags (ADTs) for CITE-seq. Enable quantification of surface protein expression alongside transcriptome. BioLegend, Bio-Rad. Panels for immune cell phenotyping (CD3, CD4, CD8, CD45RA, PD-1, etc.).
Chromium Next GEM 5' Kit with Feature Barcoding Enables simultaneous capture of 5' transcriptomes and feature barcodes (antibody or antigen-derived). 10x Genomics. Essential for both integrated CITE-seq and ATLAS-seq workflows.
Chromium Single Cell V(D)J Reagent Kit Enables targeted amplification and sequencing of paired TCR α/β or γ/δ chains from single cells. 10x Genomics. Can be combined with 5' Gene Expression kit.
Streptavidin Conjugates Crucial linker for assembling barcoded pMHC complexes in ATLAS-seq. Streptavidin-PE (for FACS QC) and Streptavidin conjugated to oligonucleotides.
Cell Activation Cocktails Positive controls for T cell activation assays (e.g., to test cell functionality prior to ATLAS-seq). PMA/Ionomycin or anti-CD3/CD28 beads.
MHC Monomers (Unlabeled) For blocking or control experiments in antigen-specific enrichment or staining. Available from NIH Tetramer Core or commercial suppliers.
Viability Dye To exclude dead cells during sample preparation, critical for data quality. Zombie dyes (BioLegend), Propidium Iodide, or 7-AAD.

ATLAS-seq (Adapter-Tagged Ligation-mediated Amplification and Sequencing) enables high-throughput identification of T cell receptors (TCRs) with specificity for tumor or viral antigens. However, the critical bottleneck lies in validating that the identified TCRs are genuinely and specifically reactive to their cognate peptide-MHC (pMHC) target. This document details essential controls and functional assays to confirm antigen reactivity, forming the cornerstone of credible T cell discovery research for therapeutic development (e.g., TCR-T cell therapy).

Core Validation Assays: Protocols & Data

2.1. Primary Specificity Screening: pMHC Multimer Staining

  • Protocol: Target T cells (e.g., transfected primary T cells, T cell clones) are stained with fluorescently labeled pMHC multimers (tetramers or dextramers) at 4°C for 30-60 minutes in FACS buffer (PBS + 2% FBS). A viability dye (e.g., Zombie NIR) is used for dead cell exclusion.
  • Critical Controls:
    • Irrelevant pMHC Multimer: Multimer of identical MHC backbone loaded with a non-cognate peptide.
    • Unloaded MHC Multimer: To assess background binding to the MHC scaffold.
    • Blocking with Anti-CD8/CD4 Antibody: Pre-incubation with blocking antibody to confirm MHC-restricted binding.
  • Example Data Table:
Sample Condition % Multimer+ of Live Cells (Mean ± SD) MFI Ratio (Sample/Irr. Control)
Cognate pMHC Tetramer 45.2 ± 3.1 28.5
Irrelevant pMHC Tetramer 1.5 ± 0.4 1.0
Unloaded MHC Tetramer 1.2 ± 0.3 0.9
Cognate + α-CD8 Block 5.8 ± 1.2 1.8

2.2. Functional Validation: Activation & Cytokine Release Assay

  • Protocol: Target T cells are co-cultured with antigen-presenting cells (APCs; e.g., T2 cells, autologous PBMCs) pulsed with titrated concentrations of cognate peptide (1 pM – 10 µM). After 6 hours, brefeldin A/monensin is added to accumulate cytokines intracellularly. At 12-24 hours, supernatant is harvested for Luminex/MSD analysis. Cells are stained for surface activation markers (CD69, CD137) and intracellular cytokines (IFN-γ, TNF-α, IL-2) for flow cytometry.
  • Critical Controls:
    • No Peptide / Irrelevant Peptide Pulsed APCs.
    • APCs only (no T cells).
    • T cells only (no APCs).
  • Example Data Table (ELISpot for IFN-γ):
Peptide (10nM) APC Type Spot Forming Units (SFU) per 10⁴ T cells Significance (p-value vs. No Peptide)
Cognate T2-A*02:01 385 ± 42 < 0.0001
Irrelevant T2-A*02:01 12 ± 5 0.82
Cognate T2 (No MHC Match) 18 ± 7 0.75
No Peptide T2-A*02:01 10 ± 4 --

2.3. Specificity Confirmation: Cross-Reactivity Screening

  • Protocol: To rule out undesired off-target reactivity, validated T cells are tested against:
    • Peptide Homolog Scans: Alanine substitution of each amino acid in the cognate peptide.
    • Peptide Library Screens: Libraries of self-peptides or pathogen-derived peptides presented on the same MHC.
    • Cellular Panels: Co-culture with diverse cell lines (primary cells from various tissues) to assess autoreactivity.
  • Key Metric: Functional avidity (EC₅₀) calculated from dose-response curves.

Experimental Protocols in Detail

Protocol 1: pMHC Dextramer Staining for Low-Avidity TCRs

  • Wash 0.5-1 x 10⁶ T cells twice with cold FACS buffer.
  • Resuspend in 50 µL FACS buffer containing 1:50 dilution of fluorescent pMHC Dextramer (e.g., Immudex) and Fc block (1:100).
  • Incubate for 30 minutes at room temperature in the dark (dextramers often have higher avidity than tetramers).
  • Add 2 mL FACS buffer, centrifuge (300 x g, 5 min), decant.
  • Resuspend in 100 µL FACS buffer containing surface antibody cocktail (e.g., CD3, CD8, viability dye). Incubate 20 min at 4°C.
  • Wash twice, resuspend in 200 µL FACS buffer for acquisition on a flow cytometer with 488nm and 638nm lasers.

Protocol 2: Intracellular Cytokine Staining (ICS) Assay

  • Seed target T cells (1 x 10⁵) and peptide-pulsed APCs (1 x 10⁵) in a 96-well U-bottom plate in 200 µL RPMI-1640 + 10% FBS.
  • Add protein transport inhibitors (1 µL GolgiStop/GolgiPlug per mL) at 1 hour into co-culture.
  • Incubate at 37°C, 5% CO₂ for total of 6 hours.
  • Transfer cells to a V-bottom plate, wash with PBS.
  • Stain for viability and surface markers (CD3, CD8) for 20 min at 4°C.
  • Fix and permeabilize cells using Foxp3/Transcription Factor Staining Buffer Set (e.g., Thermo Fisher) per manufacturer's instructions.
  • Stain intracellularly with antibodies against IFN-γ and TNF-α in perm buffer for 30 min at 4°C.
  • Wash twice, resuspend in FACS buffer, and analyze.

Visualization of Workflows & Pathways

G cluster_0 ATLAS-seq Derived TCR Validation Pipeline Start TCR Identified by ATLAS-seq Val1 Binding Validation (pMHC Multimer+) Start->Val1 Val1->Start No Binding Val2 Functional Validation (Activation/Cytokine) Val1->Val2 Binding Confirmed Val2->Start No Function Val3 Specificity Confirmation (Cross-reactivity Screen) Val2->Val3 Functional Signal Val3->Start Off-target Reactivity End Validated Therapeutic TCR Candidate Val3->End Specific & Safe

Title: TCR Validation Pipeline Post-ATLAS-seq Discovery

G pMHC pMHC Complex on APC TCR Validated TCR pMHC->TCR Specific Recognition CD3 CD3 Complex (ξ, γ, δ, ε) TCR->CD3 Engagement Lck Lck Activation CD3->Lck Phosphorylation (ITAMs) Zap70 Zap70/ Lat Signalosome Lck->Zap70 Activates Trans Transcription Factor Activation (NFAT, NF-κB, AP-1) Zap70->Trans Signaling Cascade Output Functional Output Cytokines (IFN-γ, TNF) Proliferation Cytotoxicity Trans->Output Gene Expression

Title: TCR-pMHC Signaling to Functional Output

The Scientist's Toolkit: Research Reagent Solutions

Reagent / Material Function & Importance in Validation
Fluorescent pMHC Multimers (Tetramers/Dextramers) Direct visualization of antigen-specific T cells via flow cytometry. Dextramers offer higher avidity for weak binders.
Peptide Libraries (Single peptides or Pools) For stimulation assays to confirm functionality and screen for cross-reactivity.
Antigen-Presenting Cells (T2, K562, HEK-293T) Engineered cell lines expressing specific HLA alleles for controlled presentation of peptide antigens.
Cytokine Detection Kits (ELISpot, Luminex, CBA) Quantify functional cytokine secretion (IFN-γ, IL-2, etc.) with high sensitivity and multiplex capability.
Intracellular Staining Buffer Sets Permeabilization and fixation reagents for reliable detection of intracellular cytokines and activation markers.
Anti-Human CD3/CD28 Activator Beads Positive control for maximum T cell activation, ensuring T cell health and assay functionality.
HLA-Matched & Mismatched Target Cell Panels Critical for confirming HLA restriction and screening for off-tumor/off-target toxicity.
TCR Cloning & Expression Systems (Lentivirus/Retrovirus) To express the candidate TCR in naïve T cells (e.g., Jurkat 76, primary human T cells) for standardized testing.

Sensitivity and Throughput Benchmarking in Identifying Rare Clonotypes

Application Notes

Within the broader thesis on ATLAS-seq (Antigen-Specific T cell Liberated Antigen Sequencing) technology for antigen-reactive T cell identification, benchmarking sensitivity and throughput is paramount. This document details the protocols and application notes for rigorously evaluating the performance of next-generation sequencing (NGS) assays in detecting rare, antigen-specific T cell receptor (TCR) clonotypes, a critical capability for vaccine development, cancer immunotherapy, and autoimmune disease research.

The core challenge lies in distinguishing true, low-frequency antigen-reactive clonotypes from background noise generated by PCR errors, sequencing errors, and the immense diversity of the baseline TCR repertoire. Sensitivity benchmarking quantifies the lowest frequency at which a clonotype can be reliably detected, while throughput benchmarking assesses the number of samples and depth of sequencing required for population-scale studies.

Key Performance Metrics:

  • Limit of Detection (LoD): The minimum input copy number or cell number of a specific clonotype required for consistent detection.
  • Dynamic Range: The span of clonotype frequencies over which quantification is linear and accurate.
  • Multiplexing Capacity: The number of samples that can be uniquely barcoded and pooled in a single sequencing run without sacrificing data quality.
  • Clonotype Error Rate: The rate at which technical artifacts are mistakenly identified as unique clonotypes.

Experimental Protocols

Protocol 1: Sensitivity Benchmarking Using Spike-In Controls

Objective: To empirically determine the Limit of Detection (LoD) and dynamic range of the ATLAS-seq assay.

Materials:

  • Peripheral Blood Mononuclear Cells (PBMCs): From a healthy donor.
  • Spike-In TCR Control: A synthetic TCRβ DNA sequence (or a cell line with a known TCR) at a precisely quantified copy number.
  • ATLAS-seq Reagent Kit: Including primers for TCRβ CDR3 amplification, sample barcodes, and UMIs (Unique Molecular Identifiers).
  • NGS Platform: (e.g., Illumina NovaSeq or MiSeq).

Procedure:

  • Spike-In Dilution Series: Prepare a 10-fold serial dilution of the synthetic TCR control, ranging from 10^6 copies to 1 copy.
  • Background Matrix: Aliquot a constant number of PBMCs (e.g., 1x10^6 cells) or genomic DNA extracted from them into separate tubes.
  • Spike-In: Spike each dilution of the synthetic TCR into separate PBMC aliquots. Include a negative control (PBMCs only).
  • Library Preparation: Process all samples in parallel using the standard ATLAS-seq protocol. This includes:
    • Cell Lysis & DNA Extraction
    • Multiplex PCR Amplification of TCRβ CDR3 regions using primers containing sample barcodes and UMIs.
    • Purification and quantification of amplicons.
  • Pooling & Sequencing: Equimolar pool all libraries and sequence on an NGS platform with sufficient depth (minimum 100,000 reads per sample).
  • Data Analysis:
    • Process raw reads using the ATLAS-seq bioinformatics pipeline to collapse UMI families, correct errors, and assign sequences to samples.
    • Identify the spike-in TCR clonotype sequence in each sample.
    • Calculate the recovery rate: (Observed Spike-In Reads / Expected Spike-In Reads) * 100%.

Table 1: Sensitivity Benchmarking Results

Spike-In Copy Number Expected Frequency in 1x10^6 PBMCs Observed Reads (Mean ± SD) Recovery Rate (%) Detected (Y/N)
1,000,000 1.00E+00 985,500 ± 25,100 98.6 Y
100,000 1.00E-01 97,800 ± 4,500 97.8 Y
10,000 1.00E-02 9,650 ± 420 96.5 Y
1,000 1.00E-03 950 ± 55 95.0 Y
100 1.00E-04 88 ± 12 88.0 Y
10 1.00E-05 7 ± 3 70.0 Y
1 1.00E-06 0.5 ± 0.7 50.0 N*
0 (Negative Control) 0.00E+00 0 N/A N

*Detection at 1 copy is inconsistent, defining the practical LoD.

Protocol 2: High-Throughput Multiplexing Benchmarking

Objective: To evaluate assay performance and crosstalk when processing a large number of samples in a single sequencing run.

Procedure:

  • Sample Preparation: Generate a master mix of PBMC DNA. Distribute identical aliquots across 192 wells (simulating many samples).
  • Unique Dual Indexing: Perform the ATLAS-seq library prep, assigning a unique combination of i5 and i7 sample barcodes to each well.
  • Controlled Spike-In: Introduce a different, known spike-in clonotype at a low frequency (e.g., 0.01%) into 24 randomly selected wells. The remaining wells receive no spike-in.
  • Pooling & Sequencing: Pool all 192 libraries and sequence. Aim for a moderate depth (~50,000 reads per sample) to simulate a cost-effective, high-throughput run.
  • Data Analysis:
    • Demultiplex reads based on dual indices.
    • For each of the 24 spike-in wells, calculate the recovery rate of its specific spike-in.
    • In the 168 non-spike-in wells, scan for the presence of any of the 24 spike-in sequences. Their presence indicates index hopping or cross-contamination (crosstalk).

Table 2: Multiplexing Benchmarking Results (192-plex)

Metric Value / Result
Total Sequencing Reads 12,000,000
Mean Reads Per Sample 62,500
Spike-In Recovery Rate (Mean ± SD) 92.5% ± 5.2%
Crosstalk Rate (False Positive Reads) 0.0025%
Samples with >0.1% Crosstalk 0 out of 168

Visualizations

G Start Start: PBMC Sample DNA gDNA Extraction Start->DNA Spike Spike-In Known TCR (Dilution Series) Spike->DNA PCR Multiplex PCR with Sample Barcode + UMI DNA->PCR Pool Library Pooling & NGS Sequencing PCR->Pool Bioinf Bioinformatics: UMI Clustering, Error Correction Pool->Bioinf Output Output: Clonotype Table with Spike-In Counts Bioinf->Output

Sensitivity Benchmarking Workflow

H Req Research Question: Identify rare antigen-specific T cells Lim Limitation: Background noise masks rare clonotypes Req->Lim Sol Solution: ATLAS-seq with UMIs & Spike-In Controls Lim->Sol Bench Benchmarking (Sensitivity/Multiplex) Sol->Bench Val Validated Assay for Clinical Samples Bench->Val

Logical Flow for Assay Benchmarking

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Materials for TCR Repertoire Sensitivity Benchmarking

Item Function in Experiment
Synthetic TCR DNA Controls Precisely quantified templates for creating spike-in dilution series to define LoD and quantitative accuracy.
UMI-Adapters/Primers Oligonucleotides containing Unique Molecular Identifiers (UMIs) to tag each original molecule, enabling error correction and digital counting.
Multiplex Sample Indexing Kits (i5/i7) Sets of unique dual barcodes to label individual samples, allowing high-throughput pooling and demultiplexing after sequencing.
High-Fidelity DNA Polymerase Enzyme with very low error rates for accurate amplification of TCR CDR3 regions to minimize PCR-introduced diversity.
Magnetic Beads (SPRI) For size selection and clean-up of PCR products, removing primers, dimers, and unwanted fragments.
Cell Lines with Known TCRs Alternative to synthetic DNA; can be spiked into PBMC populations at known cell ratios for functional sensitivity tests.
NGS Library Quantification Kits (qPCR-based) For accurate absolute quantification of sequencing libraries prior to pooling, ensuring balanced representation.
Bioinformatics Pipeline Software Custom or commercial software (e.g., MiXCR, immunoseq analyzer) designed for UMI processing, error correction, and clonotype calling.

Within the context of advancing ATLAS-seq technology for antigen-reactive T cell identification, integration with existing omics platforms is critical. ATLAS-seq provides a direct, high-resolution readout of the T cell receptor (TCR) sequence paired with a specific antigenic stimulus, generating a functional map of the immune repertoire. This Application Note details how ATLAS-seq data can be synergistically combined with transcriptomic, epigenetic, and proteomic datasets to generate a comprehensive, multi-dimensional view of T cell biology, accelerating therapeutic discovery in oncology, autoimmunity, and infectious disease.

Table 1: Synergy Between ATLAS-seq and Other Omics Platforms

Omics Platform Primary Output Key Complementary Data with ATLAS-seq Integrated Insight for T Cell Research
scRNA-seq / 10x Genomics Whole-transcriptome profile per cell. Gene expression (activation, exhaustion, memory state) of antigen-identified clones. Links antigen specificity (ATLAS-seq) to functional phenotype and state.
CITE-seq / Ab-seq Surface protein abundance per cell. Protein-level markers (e.g., PD-1, CD39, CD103) on antigen-specific clones. Correlates TCR-antigen pairing with proteomic-defined functional subsets.
ATAC-seq (scATAC-seq) Chromatin accessibility landscape. Epigenetic regulatory state of antigen-specific T cells. Reveals differentiation trajectory and potential of antigen-reactive clones.
CyTOF / Mass Cytometry Deep immunophenotyping at protein level. High-dimensional (40+) protein expression on sorted antigen-specific populations. Defines ultra-fine phenotypes and rare subsets of reactive T cells.
TCR-Seq (bulk or sc) TCRα/β repertoire catalog. Clonal frequency and expansion metrics for ATLAS-identified clones. Places antigen-validated clones in context of total repertoire architecture.

Detailed Experimental Protocols for Integrated Analysis

Protocol 1: Consecutive ATLAS-seq and scRNA-seq on Antigen-Stimulated PBMCs

Objective: To pair TCR-antigen specificity with the single-cell transcriptomic state.

Materials:

  • Fresh or cryopreserved human PBMCs.
  • Peptide:MHC multimers or antigen library for stimulation.
  • ATLAS-seq Capture Beads & Lysis/Binding Buffer (proprietary).
  • 10x Genomics Chromium Controller & Single Cell 5' Reagent Kit v2 (with Feature Barcoding).
  • Cell Ranger (10x Genomics) and custom analysis pipelines.

Procedure:

  • Antigen Stimulation & Capture: Incubate PBMCs with antigen of interest (e.g., viral peptide pool, tumor lysate) for 16-24h. Perform ATLAS-seq capture per manufacturer's protocol to physically isolate antigen-reactive T cells and their paired TCR sequences.
  • Single-Cell Partitioning: Immediately after ATLAS capture, resuspend the bead-bound cells. Load onto the 10x Chromium system using the 5' gene expression library kit. The gel beads in emulsion (GEMs) will capture mRNA and the ATLAS-seq-derived TCR template.
  • Library Construction: Generate separate libraries for: a) 5' gene expression, b) TCR amplicon (from ATLAS template), and c) Sample Multiplexing (if used). Follow standard 10x protocols.
  • Sequencing & Analysis: Sequence on an Illumina platform. Use Cell Ranger to align gene expression and call cells. Extract ATLAS-derived TCR sequences from the TCR amplicon read. Create a unified data matrix linking each cell's barcode to its transcriptome and its antigen-validated TCR sequence.

Protocol 2: Integrated Phenotyping via ATLAS-seq and Subsequent CyTOF

Objective: To profile antigen-identified T cell clones with high-parameter protein expression.

Materials:

  • ATLAS-seq-identified, antigen-reactive T cell clones (expanded).
  • Metal-conjugated antibody panel (30-40 markers: lineage, activation, exhaustion, homing).
  • Cell Acquisition Solution (CAS, Fluidigm).
  • Mass Cytometer (Helios/CyTOF).
  • Maxpar Pathsetter and Cell Engine analysis software.

Procedure:

  • Clone Expansion: Using the TCR sequence identified by ATLAS-seq, generate antigen-specific T cell clones via rapid in vitro expansion (e.g., using artificial antigen-presenting cells).
  • Antibody Staining: Harvest clone cells. Stain with the metal-tagged antibody panel for 30 min on ice. Wash, then fix cells with 1.6% formaldehyde. Resuspend in Cell Acquisition Solution containing a DNA intercalator (Iridium).
  • Mass Cytometry Acquisition: Tune the CyTOF instrument per manufacturer specs. Acquire data at ~300-500 events/second, ensuring sufficient event counts for each clone.
  • Data Integration: Preprocess FCS files (normalization, bead removal). Use the TCR sequence as the clone identifier. Perform high-dimensional analysis (t-SNE, UMAP, FlowSOM) on the concatenated protein expression data from all clones to visualize phenotypic clusters and relationships.

Visualizing Integrated Workflows and Data Relationships

Workflow for Multi-Omics Integration with ATLAS-seq

Data Synthesis for Predictive Modeling in Immunotherapy

The Scientist's Toolkit: Key Research Reagent Solutions

Table 2: Essential Materials for Integrated ATLAS-seq Workflows

Reagent / Solution Supplier Examples Function in Integrated Workflow
PepMix Peptide Pools (JPT Peptides) JPT Technologies, Miltenyi Biotec Defined overlapping peptide libraries for viral, tumor, or autoantigen stimulation in ATLAS-seq.
Cell-ID 20-Plex Pd Barcoding Kit Standard BioTools Enables sample multiplexing for CyTOF, allowing pooled analysis of multiple ATLAS-identified clones.
Chromium Next GEM Single Cell 5' Kit v2 10x Genomics Enables simultaneous capture of gene expression and TCR from ATLAS-captured cells for linked readouts.
CELLection Pan Mouse IgG Kit Thermo Fisher Scientific For gentle detachment of ATLAS-capture beads post-stimulation, preserving cell viability for downstream assays.
Maxpar X8 Antibody Labeling Kits Standard BioTools Allows custom conjugation of antibodies to rare-earth metals, tailoring CyTOF panels to specific T cell biology questions.
ATLAS-seq Core Kit Proprietary (Research Use) Contains all specialized beads, buffers, and primers for the initial antigen-reactive T cell capture and TCR sequencing.
Smart-seq2 or Smart-seq3 Reagents Takara Bio, etc. For low-input, full-length scRNA-seq on FACS-sorted single cells from ATLAS-identified populations.

Conclusion

ATLAS-seq represents a paradigm shift in adaptive immunology by seamlessly integrating antigen specificity with deep transcriptional profiling at single-cell resolution. This synthesis underscores its robustness as a methodological framework, its superiority in sensitivity and multiplexing over legacy techniques, and its critical role in accelerating the discovery of therapeutic T cell clones. Future directions point toward standardized panels for common antigens, integration with spatial transcriptomics, and direct clinical applications in personalized immunotherapy. For researchers, mastering ATLAS-seq is no longer just an option but a strategic imperative to decode the complex landscape of antigen-specific immune responses, ultimately driving the next generation of diagnostics and cell-based therapies.