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bioRxiv2025

Single Cell Transcriptome Signatures of Sarcoidosis in Lung Immune Cell Populations

RationaleTo identify cell specific molecular changes associated with sarcoidosis risk and progression, we aimed to characterize the cellular composition, gene expression patterns, and cell-cell interactions in BAL cells from patients with sarcoidosis (both progressive and non-progressive) and healthy controls. MethodsSingle cell RNA-seq data were collected on 12 sarcoidosis and 4 control participants. We combined scRNA-seq data from these participants with our previously collected data on 4 sarcoidosis and 10 control participants for a final sample size of 16 sarcoidosis cases (8 progressive and 8 non-progressive) and 14 controls. Following initial preprocessing in CellRanger, data were quality controlled, combined, and clustered in Seurat. We tested differences in cell proportions by disease group using F-tests on cell proportions and differences in gene expression using pseudobulk analysis. Cell to cell communication and pathway analysis were performed using CellChat. ResultsWe identified five macrophage populations: resident, high metallothionein (MT) resident, recruited, profibrotic recruited, and proliferating macrophages. Each subpopulation displayed unique gene expression profiles, with notable differential expression of genes and pathways linked to sarcoidosis in resident macrophages, recruited macrophages, and proliferating macrophages. We also observed changes in gene expression associated with disease progression in resident and recruited macrophages. In non-macrophages cells, we observed a significant reduction in the number of B cells in sarcoidosis patients compared to controls. Among T cell populations, we identified specific transcriptional alterations at gene and pathway level. Additionally, we observed distinct differences in cell-to-cell interactions of macrophages and T cells between sarcoidosis patients and healthy controls. ConclusionsThese findings underscore the complexity of immune cell involvement in sarcoidosis and highlight potential cellular and molecular targets for further investigation.

genomics↗

bioRxiv2023

Machine learning of enhancer-promoter specificity based on enhancer perturbation studies reveals a distinct class of enhancers.

MotivationUnderstanding the rules that govern enhancer-driven transcription remains a central unsolved problem in genomics. Now with multiple massively parallel enhancer perturbation assays published, there are enough data that we can utilize to learn to predict enhancer promoter relationships in a data driven manner. ResultsWe applied machine learning to one of the largest enhancer perturbation studies integrated with transcription factor and histone modification ChIP-seq. Based on the learned model, we confirmed previously reported rules governing enhancer driven transcription, and we gained some insights that generated new hypotheses, such as a novel role for protecting against replication-transcription conflict at the active enhancers in CHAMP1. We also identified a distinct class of enhancers that drives target promoter transcription, but is not in strong contact with the promoters. There were two clusters of such enhancers that regulated ATG2A and the histone 1 cluster respectively. These enhancers were different from other typical enhancers, in that they had other strong enhancers nearby, and they also had strong H3K4me3 marks at the target promoters, both patterns that typically predict reduced enhancer influence, but here contributing in the opposite way. In summary, we find that integrating genomic assays with enhancer perturbation studies increases the accuracy of the model, and provides novel insights into the understanding of enhancer driven transcription. Availabilitythe trained models and the source code are available at https://github.com/HanLabUNLV/abic. Contactmira.han@unlv.edu

bioinformatics↗