Online single-cell data integration through projecting heterogeneous datasets into a common cell-embedding space (preprint)

ABSTRACT Computational tools for integrative analyses of diverse single-cell experiments are facing formidable new challenges including dramatic increases in data scale, sample heterogeneity, and the need to informatively cross-reference new data with foundational datasets. Here, we present SCALEX, a deep-learning method that integrates single-cell data by projecting cells into a batch-invariant, common cell-embedding space in a truly online manner ( i.e. , without retraining the model). SCALEX substantially outperforms online iNMF and other state-of-the-art non-online integration methods on benchmark single-cell datasets of diverse modalities, (e.g., scRNA-seq, scATAC-seq), especially for datasets with partial overlaps, accurately aligning similar cell populations while retaining true biological differences. We showcase SCALEX’s advantages by constructing continuously expandable single-cell atlases for human, mouse, and COVID-19 patients, each assembled from diverse data sources and growing with every new data. The online data integration capacity and superior performance makes SCALEX particularly appropriate for large-scale single-cell applications to build-upon previously hard-won scientific insights.

Construction of continuously expandable single-cell atlases through integration of heterogeneous datasets in a generalized cell-embedding space (preprint)

Single-cell RNA-seq and ATAC-seq analyses have been widely applied to decipher cell-type and regulation complexities. However, experimental conditions often confound biological variations when comparing data from different samples. For integrative single-cell data analysis, we have developed SCALEX, a deep generative framework that maps cells into a generalized, batch-invariant cell-embedding space. We demonstrate that SCALEX accurately and efficiently integrates heterogenous single-cell data using multiple benchmarks. It outperforms competing methods, especially for datasets with partial overlaps, accurately aligning similar cell populations while r,etaining true biological differences. We demonstrate the advantages of SCALEX by constructing continuously expandable single-cell atlases for human, mouse, and COVID-19, which were assembled from multiple data sources and can keep growing through the inclusion of new incoming data. Analyses based on these atlases revealed the complex cellular landscapes of human and mouse tissues and identified multiple peripheral immune subtypes associated with COVID-19 disease severity.

CD127 imprints functional heterogeneity to diversify monocyte responses in human inflammatory diseases (preprint)

Studies on human monocytes historically focused on characterization of bulk responses, whereas functional heterogeneity is largely unknown. Here, we identified an inducible population of CD127-expressing human monocytes under inflammatory conditions and named the subset M127. M127 is nearly absent in healthy individuals yet abundantly present in patients with infectious and inflammatory conditions such as COVID-19 and rheumatoid arthritis. Multiple genomic and functional approaches revealed unique gene signatures of M127 and unified anti-inflammatory properties imposed by the CD127-STAT5 axis. M127 expansion correlated with mild COVID-19 disease outcomes. Thereby, we phenotypically and molecularly characterized a human monocyte subset marked by CD127 that retained anti-inflammatory properties within the pro-inflammatory environments, uncovering remarkable functional diversity among monocytes and signifying M127 as a potential therapeutic target for human inflammatory disorders.