计算生物学
仿形(计算机编程)
生物
转录组
DNA条形码
单细胞分析
外周血单个核细胞
基因表达谱
细胞
进化生物学
遗传学
计算机科学
基因
基因表达
操作系统
体外
作者
Ajay Sapre,Efthymia Papalexi,Alec Salvino,Simone Marrujo,Guillermo Gallareta-Olivares,Charles M. Roco,Alexander Rosenberg
出处
期刊:Journal of Immunology
[American Association of Immunologists]
日期:2024-05-01
卷期号:212 (1_Supplement): 0276_5971-0276_5971
标识
DOI:10.4049/jimmunol.212.supp.0276.5971
摘要
Abstract Many autoimmune diseases are characterized by aberrant B cells which play a central role in pathogenesis. Each B cell expresses a B cell receptor (BCR), which determines its specificity in binding to different epitopes. For autoimmune diseases such as Rheumatoid Arthritis, Lupus, and Type 1 Diabetes our current understanding of the role of unique BCR repertoires is poorly understood; however, recent advances in single-cell RNA sequencing has allowed the field to simultaneously profile both the BCR and whole transcriptome leading to the characterization of clonal diversity of B cell subtypes. Traditional methods using microfluidic barcoding are low throughput (1,000s-10,000s of cells) and costly. To overcome these limitations, we developed a method to simultaneously characterize the BCR repertoire and full transcriptomes of up to 1 million cells from up to 96 samples in one experiment. For proof of concept, we applied this method to simultaneously profile the BCR and whole transcriptome on multiple healthy and diseased samples. Using the whole transcriptome data, we detected all major subtypes of B cells in peripheral blood. From the BCR data, we defined BCR isotypes, captured a majority of cells with a paired chain, detected full length chains, and differentiated dominant & rare clones. This technology paves the way for high-throughput and sensitive screening of BCR repertoires to further the understanding of B cell biology and disease.
科研通智能强力驱动
Strongly Powered by AbleSci AI