bioRxivImmunologyPreprint
Profiling immunoglobulin repertoires across multiple human tissues by RNA Sequencing
S. Mangul, I. Mandric, H. T. Yang, N. Strauli, D. Montoya, J. Rotman, W. V. Der Wey, J. R. Ronas, B. Statz, A. Zelikovsky, R. Spreafico, S. Shifman, N. Zaitlen, M. Rossetti, K. M. Ansel, E. Eskin
Abstract
Assay-based approaches provide a detailed view of the adaptive immune system by profiling T and B cell receptor repertoires. However, these methods come at a high cost and lack the scale of standard RNA sequencing (RNA-seq). Here we report the development of ImReP, a novel computational method for rapid and accurate profiling of the adaptive immune repertoire from regular RNA-Seq data. We applied it to 8,555 samples across 544 individuals from 53 tissues from the Genotype-Tissue Expression (GTEx v6) project. ImReP is able to efficiently extract TCR- and BCR- derived reads from the RNA-Seq data and accurately assemble the complementarity determining regions 3 (CDR3s), the most variable regions of B- and T-cell receptors determining their antigen specificity. Using ImReP, we have created the systematic atlas of immunological sequences for B- and T-cell repertoires across a broad range of tissue types, most of which have not been studied for B and T cell receptor repertoires. We have also examined the compositional similarities of clonal populations between the GTEx tissues to track the flow of T- and B- clonotypes across immune-related tissues, including secondary lymphoid organs and organs encompassing mucosal, exocrine, and endocrine sites. The atlas of T- and B-cell receptor receptors, freely available at https://sergheimangul.wordpress.com/atlas-immune-repertoires/, is the largest collection of CDR3 sequences and tissue types. We anticipate this recourse will enhance future studies in areas such as immunology and advance development of therapies for human diseases. ImReP is freely available at https://sergheimangul.wordpress.com/imrep/.
- organism: human
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: clustering
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: deep learning
- approach: statistical model
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