Sara Aibar

SCENIC: single-cell regulatory network inference and clustering

We present SCENIC, a computational method for simultaneous gene regulatory network reconstruction and cell-state identification from single-cell RNA-seq data (http://scenic.aertslab.org). On a compendium of single-cell data from tumors and brain, we demonstrate that cis-regulatory analysis can be exploited to guide the identification of transcription factors and cell states. SCENIC provides critical biological insights into the mechanisms driving cellular heterogeneity.

A Single-Cell Transcriptome Atlas of the Aging Drosophila Brain

Summary The diversity of cell types and regulatory states in the brain, and how these change during aging, remains largely unknown. We present a single-cell transcriptome atlas of the entire adult Drosophila melanogaster brain sampled across its lifespan. Cell clustering identified 87 initial cell clusters that are further subclustered and validated by targeted cell-sorting. Our data show high granularity and identify a wide range of cell types. Gene network analyses using SCENIC revealed regulatory heterogeneity linked to energy consumption. During aging, RNA content declines exponentially without affecting neuronal identity in old brains. This single-cell brain atlas covers nearly all cells in the normal brain and provides the tools to study cellular diversity alongside other Drosophila and mammalian single-cell datasets in our unique single-cell analysis platform: SCope (http://scope.aertslab.org). These results, together with SCope, allow comprehensive exploration of all transcriptional states of an entire aging brain.

Comparative oncogenomics identifies tyrosine kinase FES as a tumor suppressor in melanoma

Identification and functional validation of oncogenic drivers are essential steps toward advancing cancer precision medicine. Here, we have presented a comprehensive analysis of the somatic genomic landscape of the widely used BRAFV600E- and NRASQ61K-driven mouse models of melanoma. By integrating the data with publically available genomic, epigenomic, and transcriptomic information from human clinical samples, we confirmed the importance of several genes and pathways previously implicated in human melanoma, including the tumor-suppressor genes phosphatase and tensin homolog (PTEN), cyclin dependent kinase inhibitor 2A (CDKN2A), LKB1, and others. Importantly, this approach also identified additional putative melanoma drivers with prognostic and therapeutic relevance. Surprisingly, one of these genes encodes the tyrosine kinase FES. Whereas FES is highly expressed in normal human melanocytes, FES expression is strongly decreased in over 30% of human melanomas. This downregulation correlates with poor overall survival. Correspondingly, engineered deletion of Fes accelerated tumor progression in a BRAFV600E-driven mouse model of melanoma. Together, these data implicate FES as a driver of melanoma progression and demonstrate the potential of cross-species oncogenomic approaches combined with mouse modeling to uncover impactful mutations and oncogenic driver alleles with clinical importance in the treatment of human cancer.

Phenotype molding of stromal cells in the lung tumor microenvironment

Cancer cells are embedded in the tumor microenvironment (TME), a complex ecosystem of stromal cells. Here, we present a 52,698-cell catalog of the TME transcriptome in human lung tumors at single-cell resolution, validated in independent samples where 40,250 additional cells were sequenced. By comparing with matching non-malignant lung samples, we reveal a highly complex TME that profoundly molds stromal cells. We identify 52 stromal cell subtypes, including novel subpopulations in cell types hitherto considered to be homogeneous, as well as transcription factors underlying their heterogeneity. For instance, we discover fibroblasts expressing different collagen sets, endothelial cells downregulating immune cell homing and genes coregulated with established immune checkpoint transcripts and correlating with T-cell activity. By assessing marker genes for these cell subtypes in bulk RNA-sequencing data from 1,572 patients, we illustrate how these correlate with survival, while immunohistochemistry for selected markers validates them as separate cellular entities in an independent series of lung tumors. Hence, in providing a comprehensive catalog of stromal cells types and by characterizing their phenotype and co-optive behavior, this resource provides deeper insights into lung cancer biology that will be helpful in advancing lung cancer diagnosis and therapy.A comprehensive single-cell analysis of the lung cancer microenvironment reveals marked heterogeneity of transcriptional networks that defines novel clinically relevant stromal cell populations.

Mapping gene regulatory networks from single-cell omics data

Abstract Single-cell techniques are advancing rapidly and are yielding unprecedented insight into cellular heterogeneity. Mapping the gene regulatory networks (GRNs) underlying cell states provides attractive opportunities to mechanistically understand this heterogeneity. In this review, we discuss recently emerging methods to map GRNs from single-cell transcriptomics data, tackling the challenge of increased noise levels and data sparsity compared with bulk data, alongside increasing data volumes. Next, we discuss how new techniques for single-cell epigenomics, such as single-cell ATAC-seq and single-cell DNA methylation profiling, can be used to decipher gene regulatory programmes. We finally look forward to the application of single-cell multi-omics and perturbation techniques that will likely play important roles for GRN inference in the future.

A single-cell catalogue of regulatory states in the ageing Drosophila brain

The diversity of cell types and regulatory states in the brain, and how these change during ageing, remains largely unknown. Here, we present a single-cell transcriptome catalogue of the entire adult Drosophila melanogaster brain sampled across its lifespan. Both neurons and glia age through a process of “regulatory erosion”, characterized by a strong decline of RNA content, and accompanied by increasing transcriptional and chromatin noise. We identify more than 50 cell types by specific transcription factors and their downstream gene regulatory networks. In addition to neurotransmitter types and neuroblast lineages, we find a novel neuronal cell state driven by datilografo and prospero. This state relates to neuronal birth order, the metabolic profile, and the activity of a neuron. Our single-cell brain catalogue reveals extensive regulatory heterogeneity linked to ageing and brain function and will serve as a reference for future studies of genetic variation and disease mutations.

Identification of novel direct targets of Drosophila Sine oculis and Eyes absent by integration of genome-wide data sets.

Drosophila eye development is a complex process that involves many transcription factors (TFs) and interactions with their cofactors and targets. The TF Sine oculis (So) and its cofactor Eyes absent (Eya) are highly conserved and are both necessary and sufficient for eye development. Despite their many important roles during development, the direct targets of So are still largely unknown. Therefore the So-dependent regulatory network governing eye determination and differentiation is poorly understood. In this study, we intersected gene expression profiles of so or eya mutant eye tissue prepared from three different developmental stages and identified 1731 differentially expressed genes across the Drosophila genome. A combination of co-expression analyses and motif discovery identified a set of twelve putative direct So targets, including three known and nine novel targets. We also used our previous So ChIP-seq data to assess motif predictions for So and identified a canonical So binding motif. Finally, we performed in vivo enhancer reporter assays to test predicted enhancers from six candidate target genes and find that at least one enhancer from each gene is expressed in the developing eye disc and that their expression patterns overlap with that of So. We furthermore confirmed that the expression level of predicted direct So targets, for which antibodies are available, are reduced in so or eya post-mitotic knockout eye discs. In summary, we expand the set of putative So targets and show for the first time that the combined use of expression profiling of so with its cofactor eya is an effective method to identify novel So targets. Moreover, since So is highly conserved throughout the metazoa, our results provide the basis for future functional studies in a wide variety of organisms.

The transcription factor Grainy head primes epithelial enhancers for spatiotemporal activation by displacing nucleosomes

Transcriptional enhancers function as docking platforms for combinations of transcription factors (TFs) to control gene expression. How enhancer sequences determine nucleosome occupancy, TF recruitment and transcriptional activation in vivo remains unclear. Using ATAC–seq across a panel of Drosophila inbred strains, we found that SNPs affecting binding sites of the TF Grainy head (Grh) causally determine the accessibility of epithelial enhancers. We show that deletion and ectopic expression of Grh cause loss and gain of DNA accessibility, respectively. However, although Grh binding is necessary for enhancer accessibility, it is insufficient to activate enhancers. Finally, we show that human Grh homologs—GRHL1, GRHL2 and GRHL3—function similarly. We conclude that Grh binding is necessary and sufficient for the opening of epithelial enhancers but not for their activation. Our data support a model positing that complex spatiotemporal expression patterns are controlled by regulatory hierarchies in which pioneer factors, such as Grh, establish tissue-specific accessible chromatin landscapes upon which other factors can act.The authors show that the transcription factor Grainy head (Grh) is necessary and sufficient for opening of epithelial enhancers, but not for their activation. Grh is shown to function as a pioneer factor, displacing nucleosomes and paving the way for other transcription factors to activate enhancers.

Toward Minimal Residual Disease-Directed Therapy in Melanoma.

Many patients with advanced cancers achieve dramatic responses to a panoply of therapeutics yet retain minimal residual disease (MRD), which ultimately results in relapse. To gain insights into the biology of MRD, we applied single-cell RNA sequencing to malignant cells isolated from BRAF mutant patient-derived xenograft melanoma cohorts exposed to concurrent RAF/MEK-inhibition. We identified distinct drug-tolerant transcriptional states, varying combinations of which co-occurred within MRDs from PDXs and biopsies of patients on treatment. One of these exhibited a neural crest stem cell (NCSC) transcriptional program largely driven by the nuclear receptor RXRG. An RXR antagonist mitigated accumulation of NCSCs in MRD and delayed the development of resistance. These data identify NCSCs as key drivers of resistance and illustrate the therapeutic potential of MRD-directed therapy. They also highlight how gene regulatory network architecture reprogramming may be therapeutically exploited to limit cellular heterogeneity, a key driver of disease progression and therapy resistance.

Cis-topic modelling of single cell epigenomes

Single-cell epigenomics provides new opportunities to decipher genomic regulatory programs from heterogeneous samples and dynamic processes. We present a probabilistic framework called cisTopic, to simultaneously discover “cis-regulatory topics” and stable cell states from sparse single-cell epigenomics data. After benchmarking cisTopic on single-cell ATAC-seq data, single-cell DNA methylation data, and semi-simulated single-cell ChIP-seq data, we use cisTopic to predict regulatory programs in the human brain and validate these by aligning them with co-expression networks derived from single-cell RNA-seq data. Next, we performed a time-series single-cell ATAC-seq experiment after SOX10 perturbations in melanoma cultures, where cisTopic revealed dynamic regulatory topics driven by SOX10 and AP-1. Finally, machine learning and enhancer modelling approaches allowed to predict cell type specific SOX10 and SOX9 binding sites based on topic specific co-regulatory motifs. cisTopic is available as an R/Bioconductor package at http://github.com/aertslab/cistopic.