Analysis

Genes don’t function in isolation within an organism; their roles are intricately defined by their interactions with other genes, as manifested in gene regulatory networks (GRNs). Recent advancements in single-cell assays and computational methods have greatly enhanced our ability to infer GRNs with higher quality and quantity. This presents a unique opportunity to fundamentally recharacterize every gene through the lens of GRNs. On one hand, the individual gene regulations elucidated within the inferred GRNs can directly guide downstream validation and drug discovery efforts. On the other hand, the GRN as a whole can provide fundamental insights from both systems biology and network science perspectives.

As a lab deeply invested in GRN inference, we are well positioned to address these questions. Leveraging the growing single-cell datasets, we have inferred and analyzed cell-type specific co-expression networks [1]. Gene clusters within these networks were demonstrated highly informative for functional annotations. Furthermore, our work has encompassed comparative analyses of cell-type-specific and dynamic GRNs, leading to the identification of key regulators of developmental processes [2]. Our intuitive visualizations for dynamic GRNs enabled unique analyses and interpretations for biomedical researchers. Lately, we utilized population-scale single-cell datasets to infer cell-state specific GRNs, where we found genes other than transcription factors playing a major role as regulators [3]. These GRNs also rewire across different cell types and states, but primarily through the (in)activation of regulatory relations as opposed to changes in regulation strength or directionality.

We will continue along these directions and develop novel approaches for summarizing and comparing GRNs. As data availability and GRN inference methods continue to improve, we anticipate a plethora of opportunities ahead to link disease and other phenotypes to individual gene regulatory relations, instead of genes.

[1] Wang, L. Single-cell normalization and association testing unifying CRISPR screen and gene co-expression analyses with Normalisr. Nat Commun 12, 6395 (2021).

[2] Wang, L. et al. Dictys: dynamic gene regulatory network dissects developmental continuum with single-cell multiomics. Nat Methods 1–11 (2023).

[3] Wang, L. Airqtl dissects cell state-specific causal gene regulatory networks with efficient single-cell eQTL mapping. bioRxiv: 2025.01.15.633041.