Integration of single-cell multimodal data to define the chromatin landscape of rheumatoid arthritis

Abstract

Rheumatoid arthritis (RA) is a prototypical tissue-mediated autoimmune disease and a major healthcare burden. It is characterized by synovial tissue inflammation. While there have been multiple efforts to identify tissue-resident pathogenic cell populations in the RA synovium using single-cell RNA-seq, how these cell populations are epigenetically regulated remains largely unexplored. Open chromatin, measured via single-nucleus assay for transpose-accessibility chromatin using sequencing (snATAC-seq), can reveal the underpinnings of transcriptional regulation across heterogeneous cell states. As with any single-cell technology, the computational integration of multiple snATAC-seq samples into one consistent dataset is a challenge that relies on correcting batch effects while maintaining biological phenotypes. Multimodal datasets, that measure both chromatin accessibility and gene expression, have given us the opportunity to both link transcriptional regulation to its output gene expression as well as measure the performance of snATAC-seq integration against a gold standard snRNA-seq integration. Utilizing 30 RA synovial tissue samples assayed using unimodal snATAC-seq and multimodal snATAC-seq + snRNA-seq, I developed a computational pipeline to amalgamate single-nucleus open chromatin datasets into 24 cohesive biological cell classes. I then identified putative transcription factors per class, such as STAT3 within a sublining fibroblast class. By integrating with an RA tissue transcriptional atlas, I proposed that these chromatin classes represented ‘superstates’ corresponding to multiple transcriptional cell states. I demonstrated the utility of this RA tissue chromatin atlas through the associations between disease phenotypes and chromatin class abundance as well as the nomination of classes mediating the effects of putatively causal RA genetic variants. Furthermore, I used these RA multimodal datasets and 2 published COVID-19 datasets to benchmark 57 additional computational pipelines encompassing 5 feature types, 7 integration methods, and 1 batch correction method to define effective strategies for multi-sample snATAC-seq integration. Using a command-line tool I developed and 2 novel multimodal metrics, I determined that SnapATAC2 using ATAC-specific features (peaks, cCRE, tiles) with Harmony correction performed best. This work demonstrates the value of chromatin accessibility studies to discern disease-specific transcriptional regulation. It also highlights how multiple modalities can complement each other. The benchmarking study shows how one modality can be used to assess another. The superstate hypothesis illustrates how both modalities can be integrated together to learn about the relationships between the underlying biological processes. The RA applications show how both modalities can give mechanistic insight into RA pathology. The pipelines I developed here, both for snATAC-seq and multimodal analysis as well as the benchmarking methodology, are broadly applicable to many diseases and will hopefully inspire more investigations into disease-specific chromatin accessibility.