scholarly journals Re-investigation of classic T cell subsets and identification of novel cell subpopulations by single-cell RNA sequencing

2021 ◽  
Author(s):  
Xuefei Wang ◽  
Xiangru Shen ◽  
Shan Chen ◽  
Hongyi Liu ◽  
Ni Hong ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
Cell Surface ◽  
Single Cell ◽  
Rna Sequencing ◽  
T Cell Activation ◽  
Expression Profiles ◽  
T Cell Subsets ◽  
Cell Subpopulation ◽  
Single Cell Rna Sequencing

AbstractClassic T cell subsets are defined by a small set of cell surface markers, while single cell RNA sequencing (scRNA-seq) clusters cells using genome-wide gene expression profiles. The relationship between scRNA-seq Clustered-Populations (scCPops) and cell surface marker-defined classic T cell subsets remain unclear. Here, we interrogated 6 bead-enriched T cell subsets with 62,235 single cell transcriptomes and re-grouped them into 9 scCPops. Bead-enriched CD4 Naïve and CD8 Naïve were mainly clustered into their scCPop counterparts, while cells from the other T cell subsets were assigned to multiple scCPops including mucosal-associated invariant T cells and natural killer T cells. The multiple T cell subsets that form a single scCPop exhibited similar expression pattern, but not vice versa, indicating scCPops are much homogeneous cell populations with similar cell states. Interestingly, we discovered and named IFNhi T, a new T cell subpopulation that highly expressed Interferon Signaling Associated Genes (ISAGs). We further enriched IFNhi T by FACS sorting of BST2 for scRNA-seq analyses. IFNhi T cluster disappeared on tSNE plot after removing ISAGs, while IFNhi T cluster showed up by tSNE analyses of ISAGs alone, indicating ISAGs are the major contributor of IFNhi T cluster. BST2+ T cells and BST2− T cells showing different efficiencies of T cell activation indicates high level of ISAGs may contribute to quick immune responses.

scholarly journals Reclassification of classic T cell subsets and identification of novel cell types by single-cell RNA sequencing

2020 ◽  
Author(s):  
Xiangru Shen ◽  
Xuefei Wang ◽  
Shan Chen ◽  
Hongyi Liu ◽  
Ni Hong ◽  
...  
Keyword(s):  
T Cell ◽  
Single Cell ◽  
Rna Sequencing ◽  
T Cell Activation ◽  
Cell Activation ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cell Types ◽  
T Cell Subsets ◽  
Single Cell Rna Sequencing

Abstract Single cell RNA sequencing (scRNA-seq) clusters cells using genome-wide gene expression profiles. The relationship between scRNA-seq Clustered-Populations (scCPops) and cell surface marker-defined classic T cell subsets is unclear. Here, we interrogated 6 bead-enriched T cell subsets with 62,235 single cell transcriptomes and re-grouped them into 9 scCPops. Bead-enriched CD4 Naïve, CD8 Naïve and CD4 memory were mainly clustered into their scCPop counterparts, while the other T cell subsets were clustered into multiple scCPops including unexpected mucosal-associated invariant T cell and natural killer T cell. Most interestingly, we discovered a new T cell type that highly expressed Interferon Signaling Associated Genes (ISAGs), namely IFNhi T. We further enriched IFNhi T for scRNA-seq analyses. IFNhi T cluster disappeared on tSNE after removing ISAGs, and IFNhi T cluster showed up by tSNE analyses of ISAGs alone, indicating ISAGs are the major contributor of IFNhi T cluster. BST2+ cells and BST2- cells showing different efficiencies of T cell activation indicates high ISAGs may contribute to quick immune responses.

scholarly journals Cite-Seq Profiling of T Cells in Multiple Myeloma Patients Undergoing BCMA Targeting CAR-T or Bites Immunotherapy

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 11-12
Author(s):  
Noemie Leblay ◽  
Ranjan Maity ◽  
Elie Barakat ◽  
Sylvia McCulloch ◽  
Peter Duggan ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
Cell Surface ◽  
Single Cell ◽  
T Cell Activation ◽  
Cell Activation ◽  
Surface Protein ◽  
T Cell Subsets ◽  
Cell Surface Protein ◽  
Car T

Adaptive T cell therapy using chimeric antigen receptor (CAR) T cells and bispecific T cell engagers (BiTEs) have demonstrated encouraging responses in heavily pre-treated multiple myeloma (MM) patients. However, the cellular and molecular predictors of clinical response are not fully understood as well as the mediators of acquired resistance remain elusive. Local immune suppression and T cell exhaustion are important mediators of responses therefore, it is plausible to speculate that a tolerant tumor microenvironment and the expansion of specific T cell populations may dictate clinical responses. In this study, we performed at the single cell level a broad immunophenotypic and transcriptomic characterization of the blood and bone marrow (BM) T cells of sensitive and resistant MM patients treated with adaptive T cell therapies. Using cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) we measured the expansion of variable T cell subsets, T cell specific activation and inhibitor markers and their functional states in order to identify cellular mediators of resistance to these adoptive immune therapies. Serial blood samples and BM aspirates (n=12) were collected from patients treated with anti-BCMA CAR-T or BCMA-CD3 BiTEs at variable time points, prior and post initiation of therapy and at relapse. Bone marrow mononuclear fractions were isolated through ficoll density gradients coupled with magnetic sorting of CD3pos T cells. Unbiased mRNA profiling coupled with feature barcoding technology for cell surface protein (TotalSeq-B) of BM CD3pos T cells was then performed by using the chromium single cell (10x Genomics). Paired-end sequencing was performed on Illumina platform. Cell Ranger and Seurat pipeline were used for sample de-multiplexing, barcode processing, single-cell 3′ gene counting, cell surface protein expression and data analysis. CAR-T cells were identified by the expression of the chimeric CAR-T cell transcript. The parallel measurement of transcripts and cell surface protein phenotypes of CD3pos T cells using a panel of 19 immune surface markers underlined the T cell repertoire diversity and identified different T cell subsets among the CD8pos and CD4pos T cells. Notably, the cell surface protein information overlaid on the transcript-generated UMA allowed accurate identification of all main immune clusters, in particular for the CD45RA and CD45RO positive cells. Comparison of CITE-Seq features revealed that the T cells composition of the blood and BM niches differed significantly between sensitive and resistant patients. As such an enrichment of CD4pos T cells with a higher CD4:CD8 ratio was noted in responding patients. Phenotypic (CD45RA, CD45RO, CD95, CCR7, CD62L, CD28, CD27) and transcriptional signatures (TCF7, LEF1, GATA3, EOMES, TBX21, PRDM1) also identified a higher proportion of memory like T cells (Tscm, Tcm) in responding patients. In contrast, T cells of resistant patients were enriched with terminally exhausted (Tex) and senescent cells with loss of CD28, high GMZHand GMZB, CD57pos, CD69pos and CD160pos as well as upregulation of TBX21. Expression of T cell checkpoint inhibitors such as LAG3, TIGIT and PD1 was high in these Tex cells as well as in some Tem. Of note, ex vivo T cell activation studies with TIGIT blockade demonstrated T cell activation in an autologous MM and T cell co-culture system with enhanced MM cells death. An expanded cluster of regulatory T cells (Treg) FOXP3pos,CD25pos was also observed in two resistant patients. Of note, no loss of BCMA transcript or surface expression was noted in MM cells at the time of acquired resistance. Single cell transcriptome of primary MM cells and chromatin accessibility (ATAC-seq) analyses of T cells of these patients are ongoing to investigate the transcriptional programs and epigenetic factors underlying the immune escape. Combined single cell features profiling of the transcriptome and surface protein expression of T cells from MM patients receiving BCMA targeted CAR-T or BiTEs therapies revealed potential mediators of resistance. In particular, T cells composition (low CD4:CD8 ratio and reduced population of Tscm, Tcm) along with an enrichment of terminally exhausted T cells are the main features observed in resistant patients. Delineating these mechanisms will guide future T cells engineering studies to enhance the efficacy and response durability of adoptive immunotherapy in MM. Disclosures McCulloch: Amgen: Honoraria; Sanofi: Honoraria; Celgene: Honoraria; Janssen: Honoraria. Duggan:Jannsen: Consultancy; Amgen: Consultancy; Novartis: Honoraria; Celgene: Consultancy; Astra Zeneca: Consultancy. Jimenez-Zepeda:Janssen, Celgene, Amgen, Takeda: Honoraria. Bahlis:AbbVie: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; GSK: Consultancy, Honoraria; Genentech: Consultancy, Honoraria; BMS/Celgene and Janssen: Consultancy, Honoraria, Other: Travel, Accomodations, Research Funding; Karyopharm Therapeutics: Consultancy, Honoraria; Sanofi: Consultancy, Honoraria. Neri:Celgene/BMS: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Amgen: Consultancy, Honoraria.

scholarly journals The differential immune responses to COVID-19 in peripheral and lung revealed by single-cell RNA sequencing

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Gang Xu ◽  
Furong Qi ◽  
Hanjie Li ◽  
Qianting Yang ◽  
Haiyan Wang ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
Single Cell ◽  
Rna Sequencing ◽  
Mononuclear Cells ◽  
Immune Cell ◽  
Close Association ◽  
T Cell Subsets ◽  
Peripheral T Cells ◽  
Single Cell Rna Sequencing

Abstract Understanding the mechanism that leads to immune dysfunction in severe coronavirus disease 2019 (COVID-19) is crucial for the development of effective treatment. Here, using single-cell RNA sequencing, we characterized the peripheral blood mononuclear cells (PBMCs) from uninfected controls and COVID-19 patients and cells in paired broncho-alveolar lavage fluid (BALF). We found a close association of decreased dendritic cells (DCs) and increased monocytes resembling myeloid-derived suppressor cells (MDSCs), which correlated with lymphopenia and inflammation in the blood of severe COVID-19 patients. Those MDSC-like monocytes were immune-paralyzed. In contrast, monocyte-macrophages in BALFs of COVID-19 patients produced massive amounts of cytokines and chemokines, but secreted little interferons. The frequencies of peripheral T cells and NK cells were significantly decreased in severe COVID-19 patients, especially for innate-like T and various CD8+ T cell subsets, compared to healthy controls. In contrast, the proportions of various activated CD4+ T cell subsets among the T cell compartment, including Th1, Th2, and Th17-like cells were increased and more clonally expanded in severe COVID-19 patients. Patients’ peripheral T cells showed no sign of exhaustion or augmented cell death, whereas T cells in BALFs produced higher levels of IFNG, TNF, CCL4, CCL5, etc. Paired TCR tracking indicated abundant recruitment of peripheral T cells to the severe patients’ lung. Together, this study comprehensively depicts how the immune cell landscape is perturbed in severe COVID-19.

scholarly journals EPEN-22. SINGLE-CELL RNA SEQUENCING IDENTIFIES UPREGULATION OF IKZF1 IN PFA2 MYELOID SUBPOPULATION DRIVING AN ANTI-TUMOR PHENOTYPE

2020 ◽  
Vol 22 (Supplement_3) ◽  
pp. iii312-iii312
Author(s):  
Andrea Griesinger ◽  
Eric Prince ◽  
Andrew Donson ◽  
Kent Riemondy ◽  
Timothy Ritzman ◽  
...  
Keyword(s):  
T Cell ◽  
Single Cell ◽  
T Cell Activation ◽  
Expression Profiles ◽  
Myeloid Cells ◽  
Cell Subset ◽  
Lineage Tracing ◽  
Machine Learning Techniques ◽  
Cell Subpopulation ◽  
Immune Gene

Abstract We have previously shown immune gene phenotype variations between posterior fossa ependymoma subgroups. PFA1 tumors chronically secrete IL-6, which pushes the infiltrating myeloid cells to an immune suppressive function. In contrast, PFA2 tumors have a more immune activated phenotype and have a better prognosis. The objective of this study was to use single-cell(sc) RNAseq to descriptively characterize the infiltrating myeloid cells. We analyzed approximately 8500 cells from 21 PFA patient samples and used advanced machine learning techniques to identify distinct myeloid and lymphoid subpopulations. The myeloid compartment was difficult to interrupt as the data shows a continuum of gene expression profiles exist within PFA1 and PFA2. Through lineage tracing, we were able to tease out that PFA2 myeloid cells expressed more genes associated with an anti-viral response (MHC II, TNF-a, interferon-gamma signaling); while PFA1 myeloid cells had genes associated with an immune suppressive phenotype (angiogenesis, wound healing, IL-10). Specifically, we found expression of IKZF1 was upregulated in PFA2 myeloid cells. IKZF1 regulates differentiation of myeloid cells toward M1 or M2 phenotype through upregulation of either IRF5 or IRF4 respectively. IRF5 expression correlated with IKZF1, being predominately expressed in the PFA2 myeloid cell subset. IKZF1 is also involved in T-cell activation. While we have not completed our characterization of the T-cell subpopulation, we did find significantly more T-cell infiltration in PFA2 than PFA1. Moving forward these studies will provide us with valuable information regarding the molecular switches involved in the tumor-immune microenvironment and to better develop immunotherapy for PFA ependymoma.

scholarly journals Heterogeneity of T Cells in Atherosclerosis Defined by Single-Cell RNA-Sequencing and Cytometry by Time of Flight

2020 ◽  
Author(s):  
Holger Winkels ◽  
Dennis Wolf
Keyword(s):  
T Cells ◽  
T Cell ◽  
Single Cell ◽  
Rna Sequencing ◽  
T Regulatory Cells ◽  
Foam Cell ◽  
Regulatory Cells ◽  
T Helper ◽  
Antigen Specificity ◽  
Single Cell Rna Sequencing

The infiltration and accumulation of pro- and anti-inflammatory leukocytes within the intimal layer of the arterial wall is a hallmark of developing and progressing atherosclerosis. While traditionally perceived as macrophage- and foam cell-dominated disease, it is now established that atherosclerosis is a partial autoimmune disease that involves the recognition of peptides from ApoB (apolipoprotein B), the core protein of LDL (low-density lipoprotein) cholesterol particles, by CD4 + T-helper cells and autoantibodies against LDL and ApoB. Autoimmunity in the atherosclerotic plaque has long been understood as a pathogenic T-helper type-1 driven response with proinflammatory cytokine secretion. Recent developments in high-parametric cell immunophenotyping by mass cytometry, single-cell RNA-sequencing, and in tools exploring antigen-specificity have established the existence of several unforeseen layers of T cell diversity with mixed T H 1 and T regulatory cells transcriptional programs and unpredicted fates. These findings suggest that pathogenic ApoB-reactive T cells evolve from atheroprotective and immunosuppressive CD4 + T regulatory cells that lose their protective properties over time. Here, we discuss T cell heterogeneity in atherosclerosis with a focus on plasticity, antigen-specificity, exhaustion, maturation, tissue residency, and its potential use in clinical prediction.

scholarly journals OMIC-11. SINGLE CELL RNA SEQUENCING FROM THE CSF OF SUBJECTS WITH H3K27M+ DIPG/DMG TREATED WITH GD2 CAR T-CELLULAR THERAPY

2021 ◽  
Vol 23 (Supplement_1) ◽  
pp. i39-i39
Author(s):  
Aaron Mochizuki ◽  
Sneha Ramakrishna ◽  
Zina Good ◽  
Shabnum Patel ◽  
Harshini Chinnasamy ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
Single Cell ◽  
Rna Sequencing ◽  
Cell Product ◽  
Myeloid Suppressor Cells ◽  
Car T Cells ◽  
Car T Cell ◽  
Car T ◽  
Single Cell Rna Sequencing

Abstract Introduction We are conducting a Phase I clinical trial utilizing chimeric antigen receptor (CAR) T-cells targeting GD2 (NCT04196413) for H3K27M-mutant diffuse intrinsic pontine glioma (DIPG) and spinal cord diffuse midline glioma (DMG). Cerebrospinal fluid (CSF) is collected for correlative studies at the time of routine intracranial pressure monitoring via Ommaya catheter. Here we present single cell RNA-sequencing results from the first 3 subjects. Methods Single cell RNA-sequencing was performed utilizing 10X Genomics on cells isolated from CSF at various time points before and after CAR T-cell administration and on the CAR T-cell product. Output was aligned with Cell Ranger and analyzed in R. Results As detailed in the Majzner et al. abstract presented at this meeting, three of four subjects treated at dose-level one exhibited clear radiographic and/or clinical benefit. We have to date completed single cell RNA-sequencing for three of these four subjects (two with benefit, one without). After filtering out low-quality signals and doublets, 89,604 cells across 3 subjects were analyzed. Of these, 4,122 cells represent cells isolated from CSF and 85,482 cells represent CAR T-cell product. Two subjects who demonstrated clear clinical and radiographic improvement exhibited fewer S100A8+S100A9+ myeloid suppressor-cells and CD25+FOXP3+ regulatory T-cells in the CSF pre-infusion compared to the subject who did not derive a therapeutic response. In one subject with DIPG who demonstrated improvement, polyclonal CAR T-cells detectable in CSF at Day +14 demonstrated enrichment of CD8A, GZMA, GNLY and PDCD1 compared to the pre-infusion CAR T-cells by trajectory analysis, suggesting differentiation toward a cytotoxic phenotype; the same subject exhibited increasing numbers of S100A8+S100A9+ myeloid cells and CX3CR1+P2RY12+ microglia over time. Further analyses will be presented as data become available. Conclusions The presence of immunosuppressive myeloid populations, detectable in CSF, may correlate to clinical response in CAR T cell therapy for DIPG/DMG.

scholarly journals Targeted reconstruction of T cell receptor sequence from single cell RNA-sequencing links CDR3 length to T cell differentiation state

2016 ◽  
Author(s):  
Shaked Afik ◽  
Kathleen B. Yates ◽  
Kevin Bi ◽  
Samuel Darko ◽  
Jernej Godec ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
Single Cell ◽  
Rna Sequencing ◽  
Cell Response ◽  
T Cell Response ◽  
Effector Memory ◽  
Cell State ◽  
Tcr Repertoire ◽  
Single Cell Rna Sequencing

ABSTRACTThe T cell compartment must contain diversity in both TCR repertoire and cell state to provide effective immunity against pathogens1,2. However, it remains unclear how differences in the TCR contribute to heterogeneity in T cell state at the single cell level because most analysis of the TCR repertoire has, to date, aggregated information from populations of cells. Single cell RNA-sequencing (scRNA-seq) can allow simultaneous measurement of TCR sequence and global transcriptional profile from single cells. However, current protocols to directly sequence the TCR require the use of long sequencing reads, increasing the cost and decreasing the number of cells that can be feasibly analyzed. Here we present a tool that can efficiently extract TCR sequence information from standard, short-read scRNA-seq libraries of T cells: TCR Reconstruction Algorithm for Paired-End Single cell (TRAPeS). We apply it to investigate heterogeneity in the CD8+T cell response in humans and mice, and show that it is accurate and more sensitive than previous approaches3,4. We applied TRAPeS to single cell RNA-seq of CD8+T cells specific for a single epitope from Yellow Fever Virus5. We show that the recently-described "naive-like" memory population of YFV-specific CD8+T cells have significantly longer CDR3 regions and greater divergence from germline sequence than do effector-memory phenotype CD8+T cells specific for YFV. This suggests that TCR usage contributes to heterogeneity in the differentiation state of the CD8+T cell response to YFV. TRAPeS is publicly available, and can be readily used to investigate the relationship between the TCR repertoire and cellular phenotype.

scholarly journals STARTRAC analyses of scRNAseq data from tumor models reveal T cell dynamics and therapeutic targets

2021 ◽  
Vol 218 (6) ◽  
Author(s):  
Dev Bhatt ◽  
Boxi Kang ◽  
Deepali Sawant ◽  
Liangtao Zheng ◽  
Kristy Perez ◽  
...  
Keyword(s):  
T Cell ◽  
Single Cell ◽  
Rna Sequencing ◽  
Target Genes ◽  
Cellular Heterogeneity ◽  
T Cell Subsets ◽  
Mouse Tumor ◽  
Tumor Models ◽  
Antibody Treatment ◽  
Single Cell Rna Sequencing

Single-cell RNA sequencing is a powerful tool to examine cellular heterogeneity, novel markers and target genes, and therapeutic mechanisms in human cancers and animal models. Here, we analyzed single-cell RNA sequencing data of T cells obtained from multiple mouse tumor models by PCA-based subclustering coupled with TCR tracking using the STARTRAC algorithm. This approach revealed various differentiated T cell subsets and activation states, and a correspondence of T cell subsets between human and mouse tumors. STARTRAC analyses demonstrated peripheral T cell subsets that were developmentally connected with tumor-infiltrating CD8+ cells, CD4+ Th1 cells, and T reg cells. In addition, large amounts of paired TCRα/β sequences enabled us to identify a specific enrichment of paired public TCR clones in tumor. Finally, we identified CCR8 as a tumor-associated T reg cell marker that could preferentially deplete tumor-associated T reg cells. We showed that CCR8-depleting antibody treatment provided therapeutic benefit in CT26 tumors and synergized with anti–PD-1 treatment in MC38 and B16F10 tumor models.

Abstract 119: Donor Macrophages Modulate Cellular Rejection After Heart Transplantation

2021 ◽  
Vol 129 (Suppl_1) ◽  
Author(s):  
Benjamin Kopecky ◽  
Junedh Amrute ◽  
Hao Dun ◽  
C. Corbin Frye ◽  
DANIEL KREISEL ◽  
...  
Keyword(s):  
T Cells ◽  
Heart Transplantation ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Transplant Rejection ◽  
Gene Expression Profiles ◽  
Single Cell Rna Sequencing ◽  
Monocytes And Macrophages ◽  
Antigen Presenting

Heart transplant rejection is common and is associated with significant morbidity and mortality. Current immunosuppressive therapies primarily target recipient T-cells and have a multitude of untoward effects including infections, malignancies, and end-organ damage. Recent studies implicate the roles of antigen presenting cells towards pathogenesis of allograft rejection through recruitment and activation of T-cells. The importance of antigen presenting cell origin, identity, and functional importance remains unknown. Using complimentary imaging and single cell RNA sequencing techniques, we show that donor and recipient monocytes and macrophages co-exist after heart transplantation. These myeloid populations have diverse transcriptional signatures that evolve throughout ongoing rejection. Donor macrophages can be defined ontologically and based on their expression of C-C chemokine receptor 2 (CCR2) and expression of MHC-II. Donor CCR2+ and CCR2- populations can be further defined based on their gene expression profiles, highlighting the marked heterogeneity in the donor macrophage population. Selective depletion of CCR2+ macrophages result in prolonged allograft survival. We use longitudinal single cell RNA sequencing to show that donor CCR2+ and CCR2- macrophages have distinct activation mechanisms such that donor CCR2+ macrophages signal through MyD88/NF-kB. Conditional depletion of MyD88 in donor macrophages recapitulates the donor CCR2+ depletion phenotype. Further interrogation of MyD88 conditionally depleted allografts shows reduced T-cell alloreactivity, holding promise for a potential therapeutic target pathway. Together, we show the molecular identity, diversity, and evolution of donor and recipient monocytes and macrophages as well as the functional relevance and activation pathways of donor macrophages in cardiac allografts.

scholarly journals Impact of sequencing depth and read length on single cell RNA sequencing data: lessons from T cells

2017 ◽  
Author(s):  
Simone Rizzetto ◽  
Auda A. Eltahla ◽  
Peijie Lin ◽  
Rowena Bull ◽  
Andrew R. Lloyd ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Sequencing Depth ◽  
Read Length ◽  
Single Cell Rna Sequencing

ABSTRACTSingle cell RNA sequencing (scRNA-seq) has shown great potential in measuring the gene expression profiles of heterogeneous cell populations. In immunology, scRNA-seq allowed the characterisation of transcript sequence diversity of functionally relevant sub-populations of T cells, and notably the identification of the full length T cell receptor (TCRαβ), which defines the specificity against cognate antigens. Several factors, such as RNA library capture, cell quality, and sequencing output have been suggested to affect the quality of scRNA-seq data, but these factors have not been systematically examined.We studied the effect of read length and sequencing depth on the quality of gene expression profiles, cell type identification, and TCRαβ reconstruction, utilising 1,305 publically available scRNA-seq datasets, and simulation-based analyses. Gene expression was characterised by an increased number of unique genes identified with short read lengths (<50 bp), but these featured higher technical variability compared to profiles from longer reads. TCRαβ were detected in 1,027 cells (79%), with a success rate between 81% and 100% for datasets with at least 250,000 (PE) reads of length >50 bp.Sufficient read length and sequencing depth can control technical noise to enable accurate identification of TCRαβ and gene expression profiles from scRNA-seq data of T cells.

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