Biology subjects

Li, S. Y.

Publications and source records attributed to Li, S. Y..

4 recordsLinked to original sources

bioRxiv2026

Refinement of the Pathogenic T Cell Receptor Motif Suggests Type 17 CD8+ T Cells Initiate HLA-B*27-associated Autoimmunity

Human leukocyte antigen B*27 (HLA-B*27) is a major risk factor for autoimmune diseases such as axial spondyloarthritis (axSpA), acute anterior uveitis (AAU), and psoriatic arthritis (PsA). HLA-B*27 presents bacterial and self-antigens to disease-associated CD8+ T cells, however, how this leads to a pathogenic IL-17-mediated disease remains unclear. We performed mutational experiments in an established disease-associated TCR and demonstrated that the pathogenic motif encompasses a wider range of TCR sequences than previously appreciated. We next leveraged computational pairing of alpha/beta TCR sequencing (TCR-seq) to demonstrate that the broadened pathogenic motif distinguishes axSpA and AAU patients from healthy controls. Paired single-cell RNA sequencing and single-cell TCR sequencing datasets demonstrated that the pathogenic cells have a distinct transcriptional signature. We find a coupling of pathogenic TCRs to this transcriptional signature in public datasets from axSpA and PsA joint fluid and AAU ocular fluid. Finally, we detect the pathogenic CD8+ T cells in the gut and demonstrate that they express a Type 17 program. Our findings suggest that HLA-B*27+ axSpA, AAU, and PsA are initiated by HLA-B*27-restricted pathogenic CD8+ T cells that undergo Type 17 differentiation in response to intestinal antigens.

immunology↗

bioRxiv2026

Emergent frequency-dependent selection predicts mutation outcomes in complex ecological communities

Ecological interactions can dramatically alter evolutionary outcomes in complex communities. Yet, the framework of population genetics largely neglects interactions from a species-rich community. Here, we bridge this gap by using dynamical mean-field theory to integrate community ecology into classical population genetics models. We show that ecological interactions result in emergent frequency-dependent selection between parents and mutants, characterized by a single parameter measuring the strength of ecological feedbacks. This result generalizes classical population genetics models to highly diverse communities and enables predictions of mutation outcomes in these eco-evolutionary settings. We derive an analytic expression for fixation probability that extends Kimuras formula and reveals that ecological interactions strongly suppress the fixation of moderately beneficial mutations. This suppression arises because frequency-dependent selection leads to prolonged coexistence between parent and mutant lineages, which acts as a barrier to fixation. The strength of these effects increases with effective population size and the number of open niches in the ecosystem. Our study establishes a framework for integrating ecological interactions into population genetics, showing that evolutionary outcomes can be predicted using simple models even in the presence of complex community feedbacks.

evolutionary biology↗

bioRxiv2026

MapMyCells: High-performance mapping of unlabeled cell-by-gene data to reference brain taxonomies

Single-cell mapping methods convert raw, heterogeneous single-cell datasets into interpretable and comparable representations of biological identity. As reference cell-type taxonomies mature, mapping new datasets to shared references has become a central strategy for enabling cross-study integration, reproducible annotation, and cumulative biological knowledge. Here we present MapMyCells, an open-source framework designed to align diverse single-cell omics datasets to hierarchical reference taxonomies with minimal preprocessing. MapMyCells provides out-of-the-box support for an expanding set of high-quality brain cell-type references generated by the Allen Institute for Brain Science, the BRAIN Initiative, and the Seattle Alzheimers Disease Brain Cell Atlas, including whole-brain mouse and human atlases, aging and Alzheimers disease cohorts, and a cross-species consensus taxonomy initially focused on the basal ganglia. MapMyCells enables efficient mapping of hundreds of thousands of cells on standard workstations without specialized hardware, providing a deterministic, scalable, and modality-agnostic approach that is robust across species and molecular assays. The framework produces interpretable confidence metrics and quantitative summaries of mapping performance, allowing users to evaluate assignment precision and accuracy. We demonstrate the mapping of unlabeled transcriptomic, epigenomic, and spatial datasets to reference taxonomies and describe a general workflow for preparing arbitrary hierarchical taxonomies for reference-based mapping. As the ecosystem of single-cell reference atlases expands, MapMyCells offers a practical and reproducible solution for community-scale cell-type annotation and cross-dataset integration, supporting the development of unified and extensible brain cell atlases.

bioinformatics↗

bioRxiv2020

Partitioning gene-based variance of complex traits by gene score regression

The majority of genome-wide association studies (GWAS) loci are not annotated to known genes in the human genome, which renders biological interpretations difficult. Transcriptome-wide association studies (TWAS) associate complex traits with genotype-based prediction of gene expression deriving from expression quantitative loci(eQTL) studies, thus improving the interpretability of GWAS findings. However, these results can sometimes suffer from a high false positive rate, because predicted expression of different genes may be highly correlated due to linkage disequilibrium between eQTL. We propose a novel statistical method, Gene Score Regression (GSR), to detect causal gene sets for complex traits while accounting for gene-to-gene correlations. We consider non-causal genes that are highly correlated with the causal genes will also exhibit a high marginal association with the complex trait. Consequently, by regressing on the marginal associations of complex traits with the sum of the gene-to-gene correlations in each gene set, we can assess the amount of variance of the complex traits explained by the predicted expression of the genes in each gene set and identify plausible causal gene sets. GSR can operate either on GWAS summary statistics or observed gene expression. Therefore, it may be widely applied to annotate GWAS results and identify the underlying biological pathways. We demonstrate the high accuracy and computational efficiency of GSR compared to state-of-the-art methods through simulations and real data applications. GSR is openly available at https://github.com/li-lab-mcgill/GSR.

bioinformatics↗