pyTCR: A comprehensive and scalable solution for TCR-Seq data analysis to facilitate reproducibility and rigor of immunogenomics research

Peng, Kerui; Moore, Jaden; Vahed, Mohammad; Brito, Jaqueline J.; Kao, Guoyun; Burkhardt, Amanda M.; Alachkar, Houda; Mangul, Serghei

doi:10.3389/fimmu.2022.954078

Cited by 3 publications

(3 citation statements)

References 17 publications

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“… 72 reference immunophenotyping, pattern learning, and transfer learning ProjectTILs Azimuth SingleR patternMarkers projectR cFIT Andreatta et al., 66 Hao et al., 67 Aran et al., 73 Stein-O’Brien et al., 74 Sharma et al., 75 Peng et al. 76 receptor-ligand modeling Domino LIANA NicheNet CellPhoneDB Cherry et al., 77 Dimitrov et al., 78 Browaeys et al., 79 Efremova et al. 80 TCR/BCR sequencing TCR-BCR read alignment to chains, clone read extraction MiXCR Trust4 10xVDJ ImmunoSEQ Bolotin et al., 81 Song et al., 82 10

Section: Computationally Dissecting Immune Cell States and Trajectoriesmentioning

confidence: 99%

“… 76 receptor-ligand modeling Domino LIANA NicheNet CellPhoneDB Cherry et al., 77 Dimitrov et al., 78 Browaeys et al., 79 Efremova et al. 80 TCR/BCR sequencing TCR-BCR read alignment to chains, clone read extraction MiXCR Trust4 10xVDJ ImmunoSEQ Bolotin et al., 81 Song et al., 82 10

Genomics Adaptive Biotech T/B cell repertoires, clonotypes, peptides, exome clonotyping, diversity estimation, expansion, compositionality scRepertoire Immunarch Immcantation pyTCR scirpy Borcherding et al., 83 Nazarov et al, 84 Vander Heiden et al., 85 Peng et al., 76 Sturm et al. 86 neoepitope specificity/MHC prediction NetTCR-2.0 TCRconv TCRrex NetMHCpan MHCFurry MixMHCpred MixMHC2pred Montemurro et al., 87 Jokinen et al., 88 Gielis et al., 89 Reynisson et al., 90 O’Donnell et al., 91 Gfeller et al., 92 Racle et al.…”

Section: Computationally Dissecting Immune Cell States and Trajectoriesmentioning

confidence: 99%

See 1 more Smart Citation

Systems immunology spanning tumors, lymph nodes, and periphery

Sidiropoulos,

Ho,

Jaffee

et al. 2023

Cell Reports Methods

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Section: Computationally Dissecting Immune Cell States and Trajectoriesmentioning

confidence: 99%

Section: Computationally Dissecting Immune Cell States and Trajectoriesmentioning

confidence: 99%

Systems immunology spanning tumors, lymph nodes, and periphery

Sidiropoulos,

Ho,

Jaffee

et al. 2023

Cell Reports Methods

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“…The target audience for most of these tools are computational biology experts, and therefore can require a steep learning curve for many experimental immunologists. Programs are either written in python (ConGA [6], pyTCR [7], Dandelion [8]) or R (scRepertoire [9]). Data generated through the 10x Genomics technologies can be analysed using their own software platforms, including CellRanger and Loupe Browser v6.…”

Section: Introductionmentioning

confidence: 99%

T cell receptor-centric perspective to multimodal single-cell data analysis

Mullan,

Ha,

Valkiers

et al. 2023

Preprint

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IntroductionThe hypervariable T cell receptor (TCR), created through somatic recombination, allows for recognition of a diverse array of antigens. Single sequencing technologies allow capture of both the single cell expression data (scRNA-seq) with the paired single cell TCR sequencing (scTCR-seq). However, the current analytical pipelines have limited capacity to integrate both data levels. To overcome these limitations, we developed STEGO (Single cell TCR and Expression Grouped Ontologies) Shiny R application to facilitate the complex analysis required for understanding T cells role in various conditions.Program parametersSTEGO.R application includes the Seurat quality control (QC) process, merging with Harmony, followed by semi-supervised cellular annotations with scGate. The scRNA-seq with scTCR-seq is broken down into four sections: top clonotype, expanded clonotypes, clustering (ClusTCR2) and target epitopes from TCRex predictions. The Shiny R interface also facilitates the program’s accessibility to novice R coders. The application can be found athttps://github.com/KerryAM-R/STEGO.R.Preliminary analysisOut of 22 selected public datasets, 12 could be processed with STEGO.R. We re-interrogated the dataset concerning colon inflammations following melanoma therapies, as original studies did not integrate the scRNA-seq with scTCR-seq analysis. From one study, our novel process identified that the colitis expanded T cells were cytotoxic CD8+ T cells with over-represented transcripts including IFNG, GNLY, PFR1, GZMB, NKG7, HLA-DR, KLRD1 transcripts relative to both the non-expanded clonotypes, non-colitis cases and healthy colon donors. The analysis also identified a TRGV4 cluster associated with melanoma cases as well as two TRBV6-2 clusters specific to colitis.DiscussionSTEGO.R facilitates fast and reproducible analysis of complex scRNA-seq with TCR repertoire data. We have demonstrated its utility by extracting novel biologically relevant insights into T-cells. We anticipate this program will facilitate the identification of subtle T population differences and if these are specific to a TCR clone and/or the expanded repertoire.

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Rigorous benchmarking of T-cell receptor repertoire profiling methods for cancer RNA sequencing

Peng

Nowicki

Campbell

et al. 2023

Briefings in Bioinformatics

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The ability to identify and track T-cell receptor (TCR) sequences from patient samples is becoming central to the field of cancer research and immunotherapy. Tracking genetically engineered T cells expressing TCRs that target specific tumor antigens is important to determine the persistence of these cells and quantify tumor responses. The available high-throughput method to profile TCR repertoires is generally referred to as TCR sequencing (TCR-Seq). However, the available TCR-Seq data are limited compared with RNA sequencing (RNA-Seq). In this paper, we have benchmarked the ability of RNA-Seq-based methods to profile TCR repertoires by examining 19 bulk RNA-Seq samples across 4 cancer cohorts including both T-cell-rich and T-cell-poor tissue types. We have performed a comprehensive evaluation of the existing RNA-Seq-based repertoire profiling methods using targeted TCR-Seq as the gold standard. We also highlighted scenarios under which the RNA-Seq approach is suitable and can provide comparable accuracy to the TCR-Seq approach. Our results show that RNA-Seq-based methods are able to effectively capture the clonotypes and estimate the diversity of TCR repertoires, as well as provide relative frequencies of clonotypes in T-cell-rich tissues and low-diversity repertoires. However, RNA-Seq-based TCR profiling methods have limited power in T-cell-poor tissues, especially in highly diverse repertoires of T-cell-poor tissues. The results of our benchmarking provide an additional appealing argument to incorporate RNA-Seq into the immune repertoire screening of cancer patients as it offers broader knowledge into the transcriptomic changes that exceed the limited information provided by TCR-Seq.

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pyTCR: A comprehensive and scalable solution for TCR-Seq data analysis to facilitate reproducibility and rigor of immunogenomics research

Cited by 3 publications

References 17 publications

Systems immunology spanning tumors, lymph nodes, and periphery

Systems immunology spanning tumors, lymph nodes, and periphery

T cell receptor-centric perspective to multimodal single-cell data analysis

Rigorous benchmarking of T-cell receptor repertoire profiling methods for cancer RNA sequencing

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