The following study was conducted by Scientists from Department of Computer Science, University of California, Los Angeles, CA, USA; Department of Clinical Pharmacy, School of Pharmacy, University of Southern California, Los Angeles, CA, USA; Bioinformatics Interdepartmental Ph.D. Program, University of California, Los Angeles, CA, USA; Biomedical Sciences Graduate Program, University of California, San Francisco, CA, USA; Department of Molecular, Cell, and Developmental Biology, University of California, Los Angeles, CA, USA; Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, CA, USA; Program in Bioinformatics and Integrative Genomics, Harvard Medical School, Boston, MA, USA; Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK; Department of Computer Science, Georgia State University, Atlanta, GA, USA; The Laboratory of Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow, Russia; Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, CA, USA; Department of Genetics, The Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel; Department of Medicine, University of California, San Francisco, San Francisco, CA, USA; Immunogenetics Center, Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA; Sandler Asthma Basic Research Center, Department of Microbiology and Immunology, University of California, San Francisco, USA; Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA; Department of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA; Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA. Study is published in Nature Communications Journal as detailed below
Nature Communications; Volume 11, Article Number: 3126 (2020)
Profiling Immunoglobulin Repertoires across Multiple Human Tissues Using RNA Sequencing
Abstract
Profiling immunoglobulin (Ig) receptor repertoires with specialized assays can be cost-ineffective and time-consuming. Here we report ImReP, a computational method for rapid and accurate profiling of the Ig repertoire, including the complementary-determining region 3 (CDR3), using regular RNA sequencing data such as those from 8,555 samples across 53 tissues types from 544 individuals in the Genotype-Tissue Expression (GTEx v6) project. Using ImReP and GTEx v6 data, we generate a collection of 3.6 million Ig sequences, termed the atlas of immunoglobulin repertoires (TAIR), across a broad range of tissue types that often do not have reported Ig repertoires information. Moreover, the flow of Ig clonotypes and inter-tissue repertoire similarities across immune-related tissues are also evaluated. In summary, TAIR is one of the largest collections of CDR3 sequences and tissue types, and should serve as an important resource for studying immunological diseases.
Source:
Nature Communications
URL: https://www.nature.com/articles/s41467-020-16857-7
Citation:
Mandric, I., J. Rotman, et al. (2020). “Profiling immunoglobulin repertoires across multiple human tissues using RNA sequencing.” Nature Communications 11(1): 3126.
