AI Insight
Researchers developed FOCAL, a new computational method that combines interpretable machine learning with gene regulatory networks to identify critical regulatory circuits controlling cell fate decisions. The approach identifies specific transcription factor-gene linkages rather than individual transcription factors, revealing both established cell states and transient regulatory events that precede differentiation. Applied to B and T cells, FOCAL uncovered a previously unknown cooperative interaction between transcription factors NFATC2 and IRF8 that restrains plasmablast formation while promoting germinal center B cell development, which was confirmed through genetic experiments.
Why it matters
This method addresses a fundamental limitation in analyzing gene regulatory networks by avoiding circular reasoning in prioritizing important regulatory connections. The ability to predict cell fate decisions before visible differentiation occurs could improve understanding of immune system development and potentially inform therapeutic strategies for immune-related diseases or cell engineering applications.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Gene regulatory networks (GRNs) model causal linkages that control cell fate decisions and differentiation transitions. Prioritizing regulatory subnetworks underlying cell state differences is of critical importance, but current methods including those reliant on topological metrics introduce circularity as the metrics prioritizing TFs are computed from the same networks whose assumptions they inherit. Separately, interpretable machine learning methods can identify latent factors (LFs) that discriminate cellular states with formal statistical guarantees but do not model regulatory linkages. Here, we present FOCAL (Factor-Outcome Coupling for Assessment of Linkages), a paradigm to prioritize regulatory subnetworks by coupling state-specific and dynamic GRNs with outcome-supervised LFs learned using interpretable machine learning without reference to network topology. This shifts GRN focus from macroscopic TF nodes to state-specific and dynamic TF-gene linkages. In B and T cells, FOCAL identified GIFs (GRNs coupled to Interpretable latent Factors), prioritized regulatory subnetworks underlying established states as well as transient regulatory episodes preceding them. By coupling LFs learnt from perturbation experiments of lineage-defining TFs, FOCAL identified transcriptional predisposition to alternative fates within progenitor cell populations before overt differentiation. This uncovered a novel NFATC2-IRF8 interplay in activated B cells, that was validated by in-vitro and in-vivo genetic perturbations. The two transcription factors act cooperatively to restrain extrafollicular plasmablast differentiation and promote germinal center B cell fate.