T2T-CHM13 versus&#160;hg38: accurate identification of immunoglobulin isotypes from scRNA-seq requires a genome reference matched for ancestry

Nie, J; Tellier, J; Tarasova, I; Nutt, SL; Smyth, GK

T2T-CHM13 versus hg38: accurate identification of immunoglobulin isotypes from scRNA-seq requires a genome reference matched for ancestry

Author(s): Nie, J; Tellier, J; Tarasova, I; Nutt, SL; Smyth, GK;
Details: Publication Year 2025-06,Volume 7,Issue #2,Page lqaf074
Journal Title: NAR Genomics & Bioinformatics
Abstract: Antibody production by B cells is essential for protective immunity. The clonal selection theory posits that each mature B cell has a unique immunoglobulin receptor generated through random gene recombination and, when stimulated to differentiate into an antibody-secreting cell, has the capacity to produce only a single antibody specificity. It follows from this 'one-cell-one-antibody' dogma that single-cell RNA-seq profiling of antibody-secreting cells should find that each cell expresses only a single form of each of the immunoglobulin heavy and light chains. However, when using GRCh38 as the genome reference, we found that many antibody-secreting cells appeared to express multiple immunoglobulin isotypes. When the newly published T2T-CHM13 genome was used instead as the genome reference, every antibody-secreting cell was found to express a unique isotype, and read mapping quality was also improved. We show that the superior performance of T2T-CHM13 was due to its European origin matching the genetic background of the query samples. On the other hand, T2T-CHM13 failed to appropriately fit the 'one-cell-one-antibody' dogma when applied to data derived from East Asia. Our results show that read assignment to human immunoglobulin isotype genes is very sensitive to the ancestral origin of the genome reference.
Publisher: Oxford Academic
Keywords: Humans; *Genome, Human; *Immunoglobulin Isotypes/genetics; *Single-Cell Analysis/methods; *RNA-Seq/methods; B-Lymphocytes/immunology/metabolism; Reference Standards; Single-Cell Gene Expression Analysis
Research Division(s): Immunology; Bioinformatics and Computational Biology
PubMed ID: 40503052
Publisher's Version: https://doi.org/10.1093/nargab/lqaf074
Open Access at Publisher's Site: https://doi.org/10.1093/nargab/lqaf074
Terms of Use/Rights Notice: Refer to copyright notice on published article.

Creation Date: 2025-06-26 09:55:23

Last Modified: 2025-06-26 09:56:13