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

Time-specific modulation of gene expression during differentiation by transcription factors promotes cell diversity. However, estimating their dynamic regulatory activity at the single-cell level and in a high-throughput manner remains challenging. We present FateCompass, an integrative approach that utilizes single-cell transcriptomics data to identify lineage-specific transcription factors throughout differentiation. By combining a probabilistic framework with RNA velocities or differentiation potential, we estimate transition probabilities, while a linear model of gene regulation is employed to compute transcription factor activities. Considering dynamic changes and correlations of expression and activities, FateCompass identifies lineage-specific regulators. Our validation using in silico data and application to pancreatic endocrine cell differentiation datasets highlight both known and potentially novel lineage-specific regulators. Notably, we uncovered undescribed transcription factors of an enterochromaffin-like population during in vitro differentiation toward ß-like cells. FateCompass provides a valuable framework for hypothesis generation, advancing our understanding of the gene regulatory networks driving cell-fate decisions.

Extensive NEUROG3 occupancy in the human pancreatic endocrine gene regulatory network.

OBJECTIVE: Mice lacking the bHLH transcription factor (TF) Neurog3 do not form pancreatic islet cells, including insulin-secreting beta cells, the absence of which leads to diabetes. In humans, homozygous mutations of NEUROG3 manifest with neonatal or childhood diabetes. Despite this critical role in islet cell development, the precise function of and downstream genetic programs regulated directly by NEUROG3 remain elusive. Therefore, we mapped genome-wide NEUROG3 occupancy in human induced pluripotent stem cell (hiPSC)-derived endocrine progenitors and determined NEUROG3 dependency of associated genes to uncover direct targets. METHODS: We 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) that were differentiated from this hiPSC line. We integrated NEUROG3 occupancy data with chromatin status and gene expression in PEPs as well as their NEUROG3-dependence. In addition, we investigated whether NEUROG3 binds type 2 diabetes mellitus (T2DM)-associated variants at the PEP stage. RESULTS: CUT&RUN revealed a total of 863 NEUROG3 binding sites assigned to 1263 unique genes. NEUROG3 occupancy was found at promoters as well as at distant cis-regulatory elements that frequently overlapped 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 or RFX transcription factors. We found that 22% of the genes downregulated in NEUROG3-/- PEPs, and 10% of genes enriched in NEUROG3-Venus positive endocrine cells were bound by NEUROG3 and thus likely to be directly regulated. NEUROG3 binds to 138 transcription factor genes, some with important roles in islet cell development or function, such as NEUROD1, PAX4, NKX2-2, SOX4, MLXIPL, LMX1B, RFX3, and NEUROG3 itself, and many others with unknown islet function. Unexpectedly, we uncovered that NEUROG3 targets genes critical for insulin secretion in beta cells (e.g., GCK, ABCC8/KCNJ11, CACNA1A, CHGA, SCG2, SLC30A8, and PCSK1). Thus, analysis of NEUROG3 occupancy suggests that the transient expression of NEUROG3 not only promotes islet destiny in uncommitted pancreatic progenitors, but could also initiate endocrine programs essential for beta cell function. Lastly, we identified eight T2DM risk SNPs within NEUROG3-bound regions. CONCLUSION: Mapping NEUROG3 genome occupancy in PEPs uncovered unexpectedly broad, direct control of the endocrine genes, raising novel hypotheses on how this master regulator controls islet and beta cell differentiation.

Genes expression profiling of alveolar macrophages exposed to non-functionalized, anionic and cationic multi-walled carbon nanotubes shows three different mechanisms of toxicity.

Functionalized multi-walled carbon nanotubes (MWCNT) have become the focus of increased research interest, particularly in their application as tools in different areas, such as the biomedical field. Despite the benefits associated with functionalization of MWCNT, particularly in overcoming issues relating to solubility, several studies have demonstrated that these functionalized nanoparticles display different toxicity profiles. For this study, we aim to compare NR8383 cells responses to three well-characterized MWCNT with varying functional groups. This study employed cytotoxicity assays, transcriptomics and proteomics to assess their toxicity using NR8383 rat alveolar macrophages as an in vitro model. The study findings indicated that all MWCNT altered ribosomal protein translation, cytoskeleton arrangement and induced pro-inflammatory response. Only functionalized MWCNT alter mTOR signaling pathway in conjunction with increased Lamtor gene expression. Furthermore, the type of functionalization was also important, with cationic MWCNT activating the transcription factor EB and inducing autophagy while the anionic MWCNT altering eukaryotic translation initiation factor 4 (EIF4) and phosphoprotein 70 ribosomal protein S6 kinase (p70S6K) signaling pathway as well as upregulation Tlr2 gene expression. This study proposes that MWCNT toxicity mechanisms are functionalization dependent and provides evidence that inflammatory response is a key event of carbon nanotubes toxicity.