scANANSE gene regulatory network and motif analysis of single-cell clusters.

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.

Myeloid-derived suppressor cells and tolerogenic dendritic cells are distinctively induced by PI3K and Wnt signaling pathways.

Imbalanced immune responses are a prominent hallmark of cancer and autoimmunity. Myeloid cells can be overly suppressive, inhibiting protective immune responses or inactive not controlling autoreactive immune cells. Understanding the mechanisms that induce suppressive myeloid cells, such as myeloid-derived suppressor cells (MDSCs) and tolerogenic dendritic cells (TolDCs), can facilitate the development of immune-restoring therapeutic approaches. MDSCs are a major barrier for effective cancer immunotherapy by suppressing antitumor immune responses in cancer patients. TolDCs are administered to patients to promote immune tolerance with the intent to control autoimmune disease. Here, we investigated the development and suppressive/tolerogenic activity of human MDSCs and TolDCs to gain insight into signaling pathways that drive immunosuppression in these different myeloid subsets. Moreover, monocyte-derived MDSCs (M-MDSCs) generated in vitro were compared to M-MDSCs isolated from head-and-neck squamous cell carcinoma patients. PI3K-AKT signaling was identified as being crucial for the induction of human M-MDSCs. PI3K inhibition prevented the downregulation of HLA-DR and the upregulation of reactive oxygen species and MerTK. In addition, we show that the suppressive activity of dexamethasone-induced TolDCs is induced by ß-catenin-dependent Wnt signaling. The identification of PI3K-AKT and Wnt signal transduction pathways as respective inducers of the immunomodulatory capacity of M-MDSCs and TolDCs provides opportunities to overcome suppressive myeloid cells in cancer patients and optimize therapeutic application of TolDCs. Lastly, the observed similarities between generated- and patient-derived M-MDSCs support the use of in vitro-generated M-MDSCs as powerful model to investigate the functionality of human MDSCs.

Retinoic acid signaling drives differentiation toward the absorptive lineage in colorectal cancer.

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.

ANANSE: an enhancer network-based computational approach for predicting key transcription factors in cell fate determination.

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.