Proper cell fate determination is largely orchestrated by complex gene regulatory networks centered around transcription factors. However, experimental elucidation of key transcription factors that drive cellular identity is currently often intractable. Here, we present ANANSE (ANalysis Algorithm for Networks Specified by Enhancers), a network-based method that exploits enhancer-encoded regulatory information to identify the key transcription factors in cell fate determination. As cell type-specific transcription factors predominantly bind to enhancers, we use regulatory networks based on enhancer properties to prioritize transcription factors. First, we predict genome-wide binding profiles of transcription factors in various cell types using enhancer activity and transcription factor binding motifs. Subsequently, applying these inferred binding profiles, we construct cell type-specific gene regulatory networks, and then predict key transcription factors controlling cell fate transitions using differential networks between cell types. This method outperforms existing approaches in correctly predicting major transcription factors previously identified to be sufficient for trans-differentiation. Finally, we apply ANANSE to define an atlas of key transcription factors in 18 normal human tissues. In conclusion, we present a ready-to-implement computational tool for efficient prediction of transcription factors in cell fate determination and to study transcription factor-mediated regulatory mechanisms. ANANSE is freely available at https://github.com/vanheeringen-lab/ANANSE.

Proteorhodopsins are heptahelical membrane proteins which function as light-driven proton pumps. They use all-trans-retinal A1 as a ligand and chromophore and absorb visible light (520-540 nm). In the present paper, we describe modulation of the absorbance band of the proteorhodopsin from Monterey Bay SAR 86 gammaproteobacteria (PR), its red-shifted double mutant PR-D212N/F234S (PR-DNFS) and Gloeobacter rhodopsin (GR). This was approached using three analogues of all-trans-retinal A1, which differ in their electronic and conformational properties: all-trans-6,7-s-trans-locked retinal A1, all-trans-phenyl-retinal A1 and all-trans-retinal A2. We further probed the effect of these retinal analogues on the proton pump activity of the proteorhodopsins. Our results indicate that, whereas the constraints of the retinal-binding pocket differ for the proteorhodopsins, at least two of the retinal analogues are capable of shifting the absorbance bands of the pigments either bathochromically or hypsochromically, while maintaining their proton pump activity. Furthermore, the shifts implemented by the analogues add up to the shift induced by the double mutation in PR-DNFS. This type of chromophore substitution may present attractive applications in the field of optogenetics, towards increasing the flexibility of optogenetic tools or for membrane potential probes.

During vertebrate gastrulation, mesoderm is induced in pluripotent cells, concomitant with dorsal-ventral patterning and establishing of the dorsal axis. We applied single-cell chromatin accessibility and transcriptome analyses to explore the emergence of cellular heterogeneity during gastrulation in Xenopus tropicalis. Transcriptionally inactive lineage-restricted genes exhibit relatively open chromatin in animal caps, whereas chromatin accessibility in dorsal marginal zone cells more closely reflects transcriptional activity. We characterized single-cell trajectories and identified head and trunk organizer cell clusters in early gastrulae. By integrating chromatin accessibility and transcriptome data, we inferred the activity of transcription factors in single-cell clusters and tested the activity of organizer-expressed transcription factors in animal caps, alone or in combination. The expression profile induced by a combination of Foxb1 and Eomes most closely resembles that observed in the head organizer. Genes induced by Eomes, Otx2, or the Irx3-Otx2 combination are enriched for maternally regulated H3K4me3 modifications, whereas Lhx8induced genes are marked more frequently by zygotically controlled H3K4me3. Taken together, our results show that transcription factors cooperate in a combinatorial fashion in generally open chromatin to orchestrate zygotic gene expression.

Summary Retinoic acid (RA) signaling is an important and conserved pathway that regulates cellular proliferation and differentiation. Furthermore, perturbed RA signaling is implicated in cancer initiation and progression. However, the mechanisms by which RA signaling contributes to homeostasis, malignant transformation, and disease progression in the intestine remain incompletely understood. Here, we report, in agreement with previous findings, that activation of the Retinoic Acid Receptor and the Retinoid X Receptor results in enhanced transcription of enterocyte-specific genes in mouse small intestinal organoids. Conversely, inhibition of this pathway results in reduced expression of genes associated with the absorptive lineage. Strikingly, this latter effect is conserved in a human organoid model for colorectal cancer (CRC) progression. We further show that RXR motif accessibility depends on progression state of CRC organoids. Finally, we show that reduced RXR target gene expression correlates with worse CRC prognosis, implying RA signaling as a putative therapeutic target in CRC.

Gene regulatory networks (GRNs) serve as useful abstractions to understand transcriptional dynamics in developmental systems. Computational prediction of GRNs has been successfully applied to genome-wide gene expression measurements with the advent of microarrays and RNA-sequencing. However, these inferred networks are inaccurate and mostly based on correlative rather than causative interactions. In this review, we highlight three approaches that significantly impact GRN inference: (1) moving from one genome-wide functional modality, gene expression, to multi-omics, (2) single cell sequencing, to measure cell type-specific signals and predict context-specific GRNs, and (3) neural networks as flexible models. Together, these experimental and computational developments have the potential to significantly impact the quality of inferred GRNs. Ultimately, accurately modeling the regulatory interactions between transcription factors and their target genes will be essential to understand the role of transcription factors in driving developmental gene expression programs and to derive testable hypotheses for validation.

Advanced computational methods exploit gene expression and epigenetic datasets to predict gene regulatory networks controlled by transcription factors (TFs). These methods have identified cell fate determining TFs but require large amounts of reference data and experimental expertise. Here, we present an easy to use network-based computational framework that exploits enhancers defined by bidirectional transcription, using as sole input CAGE sequencing data to correctly predict TFs key to various human cell types. Next, we applied this Analysis Algorithm for Networks Specified by Enhancers based on CAGE (ANANSE-CAGE) to predict TFs driving red and white blood cell development, and THP-1 leukemia cell immortalization. Further, we predicted TFs that are differentially important to either cell line- or primary- associated MLL-AF9-driven gene programs, and in primary MLL-AF9 acute leukemia. Our approach identified experimentally validated as well as thus far unexplored TFs in these processes. ANANSE-CAGE will be useful to identify transcription factors that are key to any cell fate change using only CAGE-seq data as input.

The recent development of single-cell techniques is essential to unravel complex biological systems. By measuring the transcriptome and the accessible genome on a single-cell level, cellular heterogeneity in a biological environment can be deciphered. Transcription factors act as key regulators activating and repressing downstream target genes, and together they constitute gene regulatory networks that govern cell morphology and identity. Dissecting these gene regulatory networks is crucial for understanding molecular mechanisms and disease, especially within highly complex biological systems. The gene regulatory network analysis software ANANSE and the motif enrichment software GimmeMotifs were both developed to analyse bulk datasets. We developed scANANSE, a software pipeline for gene regulatory network analysis and motif enrichment using single-cell RNA and ATAC datasets. The scANANSE pipeline can be run from either R or Python. First, it exports data from standard single-cell objects. Next, it automatically runs multiple comparisons of cell cluster data. Finally, it imports the results back to the single-cell object, where the result can be further visualised, integrated, and interpreted. Here, we demonstrate our scANANSE pipeline on a publicly available PBMC multi-omics dataset. It identifies well-known cell type-specific hematopoietic factors. Importantly, we also demonstrated that scANANSE combined with GimmeMotifs is able to predict transcription factors with both activating and repressing roles in gene regulation.

Motivation Analyzing a functional genomics experiment, such as ATAC-, ChIP- or RNA-sequencing, requires genomic resources such as a reference genome assembly and gene annotation. These data can generally be retrieved from different organizations and in different versions. Most bioinformatic workflows require the user to supply this genomic data manually, which can be a tedious and error-prone process. Results Here we present genomepy, which can search, download, and preprocess the right genomic data for your analysis. Genomepy can search genomic data on NCBI, Ensembl, UCSC and GENCODE, and inspect available gene annotations to enable an informed decision. The selected genome and gene annotation can be downloaded and preprocessed with sensible, yet controllable, defaults. Additional supporting data can be automatically generated or downloaded, such as aligner indexes, genome metadata and blacklists. Availability Genomepy is freely available at https://github.com/vanheeringen-lab/genomepy under the MIT license and can be installed through pip or Bioconda. Supplementary information Supplementary data are available at Bioinformatics online.