Scirpy: A Scanpy extension for analyzing single-cell T-cell receptor sequencing data

Summary Advances in single-cell technologies have enabled the investigation of T cell phenotypes and repertoires at unprecedented resolution and scale. Bioinformatic methods for the efficient analysis of these large-scale datasets are instrumental for advancing our understanding of adaptive immune responses in cancer, but also in infectious diseases like COVID-19. However, while well-established solutions are accessible for the processing of single-cell transcriptomes, no streamlined pipelines are available for the comprehensive characterization of T cell receptors. Here we propose Scirpy, a scalable Python toolkit that provides simplified access to the analysis and visualization of immune repertoires from single cells and seamless integration with transcriptomic data. Availability and implementation Scirpy source code and documentation are available at https://github.com/icbi-lab/scirpy.

[1]  P. Bradley,et al.  Quantifiable predictive features define epitope-specific T cell receptor repertoires , 2017, Nature.

[2]  B. Binstadt,et al.  Dual TCR T Cells: Identity Crisis or Multitaskers? , 2019, The Journal of Immunology.

[3]  L. Bradbury,et al.  Altered Repertoire Diversity and Disease‐Associated Clonal Expansions Revealed by T Cell Receptor Immunosequencing in Ankylosing Spondylitis Patients , 2020, Arthritis & Rheumatology.

[4]  Z. Trajanoski,et al.  Next-generation computational tools for interrogating cancer immunity , 2019, Nature Reviews Genetics.

[5]  Jeff Daily,et al.  Parasail: SIMD C library for global, semi-global, and local pairwise sequence alignments , 2016, BMC Bioinformatics.

[6]  Thomas D. Wu,et al.  Peripheral T cell expansion predicts tumour infiltration and clinical response , 2020, Nature.

[7]  Joel Nothman,et al.  SciPy 1.0-Fundamental Algorithms for Scientific Computing in Python , 2019, ArXiv.

[8]  Anneliese O. Speak,et al.  T cell fate and clonality inference from single cell transcriptomes , 2016, Nature Methods.

[9]  Piyushkumar A. Mundra,et al.  Immune-awakening revealed by peripheral T cell dynamics after one cycle of immunotherapy , 2019, Nature Cancer.

[10]  Pornpimol Charoentong,et al.  Computational genomics tools for dissecting tumour–immune cell interactions , 2016, Nature Reviews Genetics.

[11]  Paul Hoffman,et al.  Integrating single-cell transcriptomic data across different conditions, technologies, and species , 2018, Nature Biotechnology.

[12]  Aditya Radhakrishnan,et al.  Reverse TCR repertoire evolution toward dominant low-affinity clones during chronic CMV infection , 2020, Nature Immunology.

[13]  Fabian J Theis,et al.  SCANPY: large-scale single-cell gene expression data analysis , 2018, Genome Biology.

[14]  L. Bradbury,et al.  Altered Repertoire Diversity and Disease‐Associated Clonal Expansions Revealed by T Cell Receptor Immunosequencing in Ankylosing Spondylitis Patients , 2020, Arthritis & rheumatology.