Developmental transcriptional control of mitochondrial homeostasis is required for activity-dependent synaptic connectivity.

During neuronal circuit formation, local control of axonal organelles ensures proper synaptic connectivity. Whether this process is genetically encoded is unclear and if so, its developmental regulatory mechanisms remain to be identified. We hypothesized that developmental transcription factors regulate critical parameters of organelle homeostasis that contribute to circuit wiring. We combined cell type-specific transcriptomics with a genetic screen to discover such factors. We identified Telomeric Zinc finger-Associated Protein (TZAP) as a temporal developmental regulator of neuronal mitochondrial homeostasis genes, including Pink1 . In Drosophila , loss of dTzap function during visual circuit development leads to loss of activity-dependent synaptic connectivity, that can be rescued by Pink1 expression. At the cellular level, loss of dTzap/TZAP leads to defects in mitochondrial morphology, attenuated calcium uptake and reduced synaptic vesicle release in fly and mammalian neurons. Our findings highlight developmental transcriptional regulation of mitochondrial homeostasis as a key factor in activity-dependent synaptic connectivity.

Shared enhancer gene regulatory networks between wound and oncogenic programs.

Wound response programs are often activated during neoplastic growth in tumors. In both wound repair and tumor growth, cells respond to acute stress and balance the activation of multiple programs, including apoptosis, proliferation, and cell migration. Central to those responses are the activation of the JNK/MAPK and JAK/STAT signaling pathways. Yet, to what extent these signaling cascades interact at the cis-regulatory level and how they orchestrate different regulatory and phenotypic responses is still unclear. Here, we aim to characterize the regulatory states that emerge and cooperate in the wound response, using the Drosophila melanogaster wing disc as a model system, and compare these with cancer cell states induced by rasV12scrib-/- in the eye disc. We used single-cell multiome profiling to derive enhancer gene regulatory networks (eGRNs) by integrating chromatin accessibility and gene expression signals. We identify a 'proliferative' eGRN, active in the majority of wounded cells and controlled by AP-1 and STAT. In a smaller, but distinct population of wound cells, a 'senescent' eGRN is activated and driven by C/EBP-like transcription factors (Irbp18, Xrp1, Slow border, and Vrille) and Scalloped. These two eGRN signatures are found to be active in tumor cells at both gene expression and chromatin accessibility levels. Our single-cell multiome and eGRNs resource offers an in-depth characterization of the senescence markers, together with a new perspective on the shared gene regulatory programs acting during wound response and oncogenesis.

Fly Cell Atlas: A single-nucleus transcriptomic atlas of the adult fruit fly.

For more than 100 years, the fruit fly Drosophila melanogaster has been one of the most studied model organisms. Here, we present a single-cell atlas of the adult fly, Tabula Drosophilae, that includes 580,000 nuclei from 15 individually dissected sexed tissues as well as the entire head and body, annotated to >250 distinct cell types. We provide an in-depth analysis of cell type-related gene signatures and transcription factor markers, as well as sexual dimorphism, across the whole animal. Analysis of common cell types between tissues, such as blood and muscle cells, reveals rare cell types and tissue-specific subtypes. This atlas provides a valuable resource for the Drosophila community and serves as a reference to study genetic perturbations and disease models at single-cell resolution.

Decoding gene regulation in the fly brain.

The Drosophila brain is a frequently used model in neuroscience. Single-cell transcriptome analysis1-6, three-dimensional morphological classification7 and electron microscopy mapping of the connectome8,9 have revealed an immense diversity of neuronal and glial cell types that underlie an array of functional and behavioural traits in the fly. The identities of these cell types are controlled by gene regulatory networks (GRNs), involving combinations of transcription factors that bind to genomic enhancers to regulate their target genes. Here, to characterize GRNs at the cell-type level in the fly brain, we profiled the chromatin accessibility of 240,919 single cells spanning 9 developmental timepoints and integrated these data with single-cell transcriptomes. We identify more than 95,000 regulatory regions that are used in different neuronal cell types, of which 70,000 are linked to developmental trajectories involving neurogenesis, reprogramming and maturation. For 40 cell types, uniquely accessible regions were associated with their expressed transcription factors and downstream target genes through a combination of motif discovery, network inference and deep learning, creating enhancer GRNs. The enhancer architectures revealed by DeepFlyBrain lead to a better understanding of neuronal regulatory diversity and can be used to design genetic driver lines for cell types at specific timepoints, facilitating their characterization and manipulation.

Analysis of long and short enhancers in melanoma cell states.

Understanding how enhancers drive cell-type specificity and efficiently identifying them is essential for the development of innovative therapeutic strategies. In melanoma, the melanocytic (MEL) and the mesenchymal-like (MES) states present themselves with different responses to therapy, making the identification of specific enhancers highly relevant. Using massively parallel reporter assays (MPRAs) in a panel of patient-derived melanoma lines (MM lines), we set to identify and decipher melanoma enhancers by first focusing on regions with state-specific H3K27 acetylation close to differentially expressed genes. An in-depth evaluation of those regions was then pursued by investigating the activity of overlapping ATAC-seq peaks along with a full tiling of the acetylated regions with 190 bp sequences. Activity was observed in more than 60% of the selected regions, and we were able to precisely locate the active enhancers within ATAC-seq peaks. Comparison of sequence content with activity, using the deep learning model DeepMEL2, revealed that AP-1 alone is responsible for the MES enhancer activity. In contrast, SOX10 and MITF both influence MEL enhancer function with SOX10 being required to achieve high levels of activity. Overall, our MPRAs shed light on the relationship between long and short sequences in terms of their sequence content, enhancer activity, and specificity across melanoma cell states.

Robust gene expression programs underlie recurrent cell states and phenotype switching in melanoma.

Melanoma cells can switch between a melanocytic and a mesenchymal-like state. Scattered evidence indicates that additional intermediate state(s) may exist. Here, to search for such states and decipher their underlying gene regulatory network (GRN), we studied 10 melanoma cultures using single-cell RNA sequencing (RNA-seq) as well as 26 additional cultures using bulk RNA-seq. Although each culture exhibited a unique transcriptome, we identified shared GRNs that underlie the extreme melanocytic and mesenchymal states and the intermediate state. This intermediate state is corroborated by a distinct chromatin landscape and is governed by the transcription factors SOX6, NFATC2, EGR3, ELF1 and ETV4. Single-cell migration assays confirmed the intermediate migratory phenotype of this state. Using time-series sampling of single cells after knockdown of SOX10, we unravelled the sequential and recurrent arrangement of GRNs during phenotype switching. Taken together, these analyses indicate that an intermediate state exists and is driven by a distinct and stable 'mixed' GRN rather than being a symbiotic heterogeneous mix of cells.

A scalable SCENIC workflow for single-cell gene regulatory network analysis.

This protocol explains how to perform a fast SCENIC analysis alongside standard best practices steps on single-cell RNA-sequencing data using software containers and Nextflow pipelines. SCENIC reconstructs regulons (i.e., transcription factors and their target genes) assesses the activity of these discovered regulons in individual cells and uses these cellular activity patterns to find meaningful clusters of cells. Here we present an improved version of SCENIC with several advances. SCENIC has been refactored and reimplemented in Python (pySCENIC), resulting in a tenfold increase in speed, and has been packaged into containers for ease of use. It is now also possible to use epigenomic track databases, as well as motifs, to refine regulons. In this protocol, we explain the different steps of SCENIC: the workflow starts from the count matrix depicting the gene abundances for all cells and consists of three stages. First, coexpression modules are inferred using a regression per-target approach (GRNBoost2). Next, the indirect targets are pruned from these modules using cis-regulatory motif discovery (cisTarget). Lastly, the activity of these regulons is quantified via an enrichment score for the regulon's target genes (AUCell). Nonlinear projection methods can be used to display visual groupings of cells based on the cellular activity patterns of these regulons. The results can be exported as a loom file and visualized in the SCope web application. This protocol is illustrated on two use cases: a peripheral blood mononuclear cell data set and a panel of single-cell RNA-sequencing cancer experiments. For a data set of 10,000 genes and 50,000 cells, the pipeline runs in <2 h.

Identification of genomic enhancers through spatial integration of single-cell transcriptomics and epigenomics.

Single-cell technologies allow measuring chromatin accessibility and gene expression in each cell, but jointly utilizing both layers to map bona fide gene regulatory networks and enhancers remains challenging. Here, we generate independent single-cell RNA-seq and single-cell ATAC-seq atlases of the Drosophila eye-antennal disc and spatially integrate the data into a virtual latent space that mimics the organization of the 2D tissue using ScoMAP (Single-Cell Omics Mapping into spatial Axes using Pseudotime ordering). To validate spatially predicted enhancers, we use a large collection of enhancer-reporter lines and identify ~ 85% of enhancers in which chromatin accessibility and enhancer activity are coupled. Next, we infer enhancer-to-gene relationships in the virtual space, finding that genes are mostly regulated by multiple, often redundant, enhancers. Exploiting cell type-specific enhancers, we deconvolute cell type-specific effects of bulk-derived chromatin accessibility QTLs. Finally, we discover that Prospero drives neuronal differentiation through the binding of a GGG motif. In summary, we provide a comprehensive spatial characterization of gene regulation in a 2D tissue.

Peritumoral activation of the Hippo pathway effectors YAP and TAZ suppresses liver cancer in mice.

The Hippo signaling pathway and its two downstream effectors, the YAP and TAZ transcriptional coactivators, are drivers of tumor growth in experimental models. Studying mouse models, we show that YAP and TAZ can also exert a tumor-suppressive function. We found that normal hepatocytes surrounding liver tumors displayed activation of YAP and TAZ and that deletion of Yap and Taz in these peritumoral hepatocytes accelerated tumor growth. Conversely, experimental hyperactivation of YAP in peritumoral hepatocytes triggered regression of primary liver tumors and melanoma-derived liver metastases. Furthermore, whereas tumor cells growing in wild-type livers required YAP and TAZ for their survival, those surrounded by Yap- and Taz-deficient hepatocytes were not dependent on YAP and TAZ. Tumor cell survival thus depends on the relative activity of YAP and TAZ in tumor cells and their surrounding tissue, suggesting that YAP and TAZ act through a mechanism of cell competition to eliminate tumor cells.

A Single-Cell Transcriptome Atlas of the Aging Drosophila Brain.

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.