Gene regulatory network inference with popInfer reveals dynamic regulation of hematopoietic stem cell quiescence upon diet restriction and aging

Abstract

Inference of gene regulatory networks (GRNs) to reveal how cell state transitions are controlled is possible with single-cell genomics data. However, obstacles to temporal inference from snapshot data are difficult to overcome. Single-nuclei multiomics data offer means to bridge this gap and derive temporal information from snapshot data using joint measurements of gene expression and chromatin accessibility in the same single cells. We developed popInfer to infer networks that characterize lineage-specific dynamic cell state transitions from joint gene expression and chromatin accessibility data. Benchmarking against alternative methods for GRN inference, we showed that popInfer achieves higher accuracy in the GRNs inferred. popInfer was applied to study single-cell multiomics data characterizing hematopoietic stem cells (HSCs) and the transition from HSC to a multipotent progenitor cell state during murine hematopoiesis across age and dietary conditions. From networks predicted by popInfer, we discovered gene interactions controlling entry to/exit from HSC quiescence that are perturbed in response to diet or aging.

Building and analyzing metacells in single-cell genomics data

“…Other tools use metacells as part of a broader pipeline or method designed for different purposes (see Supplementary Table 1). Some of them, including iCells 42 , msPHATE 44 , scCorr 47 , scWGCNA 48 , popInfer 49 , and C 50 , developed a dedicated method for metacell identification. Others use existing tools or compute metacells by excessive clustering of the data 43,51–56 .…”

“…This study was among the first to demonstrate that the concept of metacells, initially proposed for scRNA-seq data, can be applied to scATAC-seq, flow cytometry, CyTOF, AIR and even clinical data. Many of these studies utilize alternative nomenclature for metacells, including iCells 42 , supercells 43 , clusters 47 , pseudocells 48,49,52,55 , miniclusters 51 , micro-clusters 53 , micro-states 54 , groups 44 or cellstates 50 . To avoid ambiguities, we encourage future studies to use the well-established term ‘metacell’ originally coined in Baran et al 38 .…”