Roquilly, A. | bioRxiv Science

bioRxiv2023

A gene-expression module in circulating immune cells is associated with cell migration during immune diseases.

Circulating immune cells are critical mediators of inflammation upon recruitment to tissues, yet how their gene expression state influences this recruitment is not well understood. Here, we report longitudinal single-cell transcriptome profiling of peripheral blood mononuclear cells in patients undergoing kidney transplantation rejection. We identify a novel gene expression module, termed ALARM (early activation transcription factor module), associated with transcriptional regulation, homing, and immune activation across multiple immune cell types. Circulating cells expressing this module are significantly reduced in patients experiencing graft rejection, a finding confirmed in a pig model of acute kidney transplantation rejection. Correspondingly, module expression is markedly increased in kidney grafts undergoing rejection, indicating preferential recruitment of ALARM-expressing cells to the inflamed tissue. Within this module, we identify the receptor CXCR4 and its ligand CXCL12, expressed in the graft, as a likely mechanism for recruitment. In vitro transwell assays combined with scRNA-seq reveal that this CXCR4-CXCL12 interaction is critical for T cell migration and upregulation of CD69, an early activation marker, and is accompanied by a metabolic switch towards glycolysis. Further exploration of publicly available transcriptomic data demonstrates that this module is generally expressed in healthy individuals and is strongly associated with responses to infection, including SARS-CoV-2 infection. This finding is further supported by experiments in a pneumonia mouse model, which confirm the recruitment of CXCR4-expressing T cells during lung infection. Moreover, we find that module expression is predictive of immune-mediated diseases. In summary, we have identified a key gene expression module in circulating immune cells that orchestrates their preferential recruitment to inflamed tissues, metabolic reprogramming, promoting tissue residency and effector functions. These insights advance our understanding of immune cell recruitment and activation mechanisms in transplant rejection and infectious diseases, with potential implications for therapeutic interventions.

immunology↗

bioRxiv2022

Latent representation of single-cell transcriptomes enables algebraic operations on cellular phenotypes

Single-cell RNA-sequencing (scRNA-seq) coupled with robust computational analysis facilitates the characterization of phenotypic heterogeneity within tumors. Current scRNA-seq analysis pipelines are capable of identifying a myriad of malignant and non-malignant cell subtypes from single-cell profiling of tumors. However, given the extent of intra-tumoral heterogeneity, it is challenging to assess the risk associated with individual cell subpopulations, primarily due to the complexity of the cancer phenotype space and the lack of clinical annotations associated with tumor scRNA-seq studies. To this end, we introduce SCellBOW, a scRNA-seq analysis framework inspired by document embedding techniques from the domain of Natural Language Processing (NLP). SCellBOW is a novel computational approach that facilitates effective identification and high-quality visualization of single-cell subpopulations. We compared SCellBOW with existing best practice methods for its ability to precisely represent phenotypically divergent cell types across multiple scRNA-seq datasets, including our in-house generated human splenocyte and matched peripheral blood mononuclear cell (PBMC) dataset. For tumor cells, SCellBOW estimates the relative risk associated with each cluster and stratifies them based on their aggressiveness. This is achieved by simulating how the presence or absence of a specific cell subpopulation influences disease prognosis. Using SCellBOW, we identified a hitherto unknown and pervasive AR-/NElow (androgen-receptor-negative, neuroendocrine-low) malignant subpopulation in metastatic prostate cancer with conspicuously high aggressiveness. Overall, the risk-stratification capabilities of SCellBOW hold promise for formulating tailored therapeutic interventions by identifying clinically relevant tumor subpopulations and their impact on prognosis.

bioinformatics↗