Back

Single cell resolved spatial immune repertoire unveils spatial heterogeneity of lymphoid aggregates in human immune disorders

Zhan, X.; Liu, Y.; Guo, Y.; Zhou, W.; Yan, Y.; Zeng, H.; Dong, X.; Chen, X.; Ma, R.; Liu, Z.; Zhu, F.; Zheng, X.; Li, X.; Yin, J.; Chan, F. K.-m.; Liu, C.; Liu, L.; Xu, X.; Hou, Y.; Tao, H.; Dong, Y.; Zeng, T.; Li, Y.; Zhou, J.; Zeng, Z.; Feng, Y.

2025-01-19 immunology
10.1101/2025.01.16.630222 bioRxiv
Show abstract

Adaptive immunity, mediated by T and B cell responses, is essential for defending against infections and cancers while also being implicated in autoimmune diseases. Tracking T and B cell repertoires in situ at single-cell resolution is essential for understanding adaptive immune responses. To address the lack of tools for in situ single-cell T/BCR (XCR) sequencing, we developed Stereo-XCR-seq, an efficient strategy for retrieving and sequencing TCR and BCR from Stereo-seq cDNA libraries at subcellular resolution. Stereo-XCR-seq provides unbiased full-length XCR reads alongside spatial transcriptomics, enabling the identification of heterogeneous lymphoid aggregates with distinct clonal activities in cancers and inflammatory bowel disease (IBD). We identified plasma cell aggregates that differ from tertiary lymphoid structures (TLSs) in both transcriptomic profiles and clonal activities, with spatial positioning potentially mediating unique immune responses. Collectively, Stereo-XCR-seq enables in situ single-cell profiling of T and B cell clonal activities within tissue microenvironments, providing insights into lymphocyte adaption to environmental stimuli. This technology provides potential for advancing our understanding of tissue immunity and the development of therapeutic strategies for immune disorders.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.