Gradwohl, G. | bioRxiv Science

bioRxiv2022

Characterization of cell-fate decision landscapes by estimating transcription factor dynamics.

Modulation of gene expression during differentiation by transcription factors promotes cell diversity. Despite their role in cell fate decisions, no experimental assays estimate their regulatory activity in a high-throughput manner and at the single-cell resolution. We present FateCompass for identifying lineage-specific transcription factors across differentiation. It uses single-cell transcriptomics data to infer differentiation trajectories and transcription factor activities. We combined a probabilistic framework with RNA velocities or a differentiation potential to estimate transition probabilities and perform stochastic simulations. Also, we learned transcription factor activities using a linear model of gene regulation. Considering dynamic changes and correlations, we identified lineage-specific regulators. We applied FateCompass to an islet cell formation dataset from the mouse embryo, and we found known and novel potential cell-type drivers. Also, when applied to a differentiation protocol dataset towards beta-like cells, we pinpointed undescribed regulators of an off-target population, which were experimentally validated. Thus, as a framework for identifying lineage-specific transcription factors, FateCompass could have implications on hypothesis generation to increase the understanding of the gene regulatory networks driving cell fate choices. HighlightsO_LIWe developed FateCompass, a flexible pipeline to estimate transcription factor activities during cell-fate decision using single-cell RNA seq data. C_LIO_LIFateCompass outlines gene expression stochastic trajectories by infusing the direction of differentiation using RNA velocity or a differentiation potential when RNA velocity fails. C_LIO_LITranscription factor dynamics allow the identification of time-specific regulatory interactions. C_LIO_LIFateCompass predictions revealed known and novel cell-subtype-specific regulators of mouse pancreatic islet cell development. C_LIO_LIDifferential motif analysis predicts lineage-specific regulators of stem cell-derived human {beta}- cells and sheds light on the cellular heterogeneity of {beta}-cell differentiation protocols. C_LIO_LIExperimental validation supports the proposed GRN controlling SC-EC differentiation predicted by FateCompass. C_LI

systems biology↗

bioRxiv2021

Extensive NEUROG3 occupancy in the human pancreatic endocrine gene regulatory network

ObjectiveMice lacking the bHLH transcription factor (TF) Neurog3 do not form pancreatic islet cells, including insulin secreting beta cells, causing diabetes. In human, homozygous mutations of NEUROG3 manifest with neonatal or childhood diabetes. Despite this critical role in islet cell development, the precise function and downstream genetic programs regulated directly by NEUROG3 remain elusive. We therefore mapped genome-wide NEUROG3 occupancy in human induced pluripotent stem cell (iPSC)-derived endocrine progenitors and determined NEUROG3 dependency of associated genes to uncover direct targets. MethodsWe generated a novel hiPSC line (NEUROG3-HA-P2A-Venus), where NEUROG3 is HA-tagged and fused to a self-cleaving fluorescent VENUS reporter. We used the CUT&RUN technique to map NEUROG3 occupancy and epigenetic marks in pancreatic endocrine progenitors (PEP) differentiated from this hiPSC line. We integrated NEUROG3 occupancy data with chromatin status and gene expression in PEPs and their NEUROG3-dependence. In addition, we searched whether NEUROG3 binds type 2 diabetes mellitus (T2DM)-associated variants at the PEP stage. ResultsCUT&RUN revealed a total of 863 NEUROG3 binding sites assigned to 1268 unique genes. NEUROG3 occupancy was found at promoters as well as at distant cis-regulatory elements frequently overlapping within PEP active enhancers. De novo motif analyses defined a NEUROG3 consensus binding motif and suggested potential co-regulation of NEUROG3 target genes by FOXA, RFX or PBX transcription factors. Moreover, we found that 22% of the genes downregulated in NEUROG3-/- hESC-derived PEPs are bound by NEUROG3 and thus likely to be directly regulated. NEUROG3 targets include transcription factors known to have important roles in islet cell development or function, such as NEUROD1, PAX4, NKX2-2, SOX4, MLXIPL, LMX1B, RFX3, and NEUROG3 itself. Remarkably, we uncovered that NEUROG3 binds transcriptional regulator genes with enriched expression in human fetal pancreatic alpha (e.g., IRX1, IRX2), beta (e.g., NKX6-1, SMAD9, ISX, TFCP2L1) and delta cells (ERBB4) suggesting that NEUROG3 could control islets subtype programs. Moreover, NEUROG3 targets genes critical for insulin secretion in beta cells (e.g., GCK, ABCC8/KCNJ11, CACNA1A, CHGA, SCG2, SLC30A8 and PCSK1). In addition, we unveiled a panel of ncRNA potentially regulated by NEUROG3. Lastly, we identified several T2DM risk SNPs within NEUROG3 peaks suggesting a possible developmental role of NEUROG3 in T2DM susceptibility. ConclusionMapping of NEUROG3 genome occupancy in PEPs uncovers an unexpectedly broad, direct control of the endocrine gene regulatory network (GRN) and raises novel hypotheses on how this master regulator controls islet and beta cell differentiation. HighlightsO_LINEUROG3 CUT&RUN analysis revealed 1268 target genes in human pancreatic endocrine progenitors (PEPs) C_LIO_LINEUROG3 binding sites overlap with active chromatin regions in PEPs. C_LIO_LI1/5 of the genes downregulated in NEUROG3-/- hESC-derived PEPs are bound by NEUROG3. C_LIO_LINEUROG3 targets islet specific TFs and regulators of insulin secretion. C_LIO_LISeveral T2DM risk allelles lie within NEUROG3 bound regions. C_LI

genomics↗