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SCENIC+ is a comprehensive toolbox for inferring and analyzing enhancer-driven gene regulatory networks using single-cell multiomic data.

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.

Stein Aerts, Jan Cools and colleagues report exome sequencing of T-cell acute lymphoblastic leukemia. They identify recurrent somatic mutations in CNOT3 and ribosome genes RPL5 and RPL10.

SCENIC enables simultaneous regulatory network inference and robust cell clustering from single-cell RNA-seq data.

A chromatin accessibility atlas of 240,919 cells in the adult and developing Drosophila brain reveals 95,000 enhancers, which are integrated in cell-type specific enhancer gene regulatory networks and decoded into combinations of functional transcription factor binding sites using deep learning.

Benchmarking of scATAC-seq protocols demonstrates the effects of data quality on downstream analyses.

As an unsupervised Bayesian framework, cisTopic classifies regions in scATAC-seq data into regulatory topics, which are used for clustering.

Bravo González-Blas et al. uncover enhancer-gene regulatory networks underlying hepatocyte identity and their zonation state by combining single-cell and spatial multiomics with massively parallel reporter assays and deep learning.

Deep learning models were used to design synthetic cell-type-specific enhancers that work in fruit fly brains and human cell lines, an approach that also provides insights into these gene regulatory elements.

SCENIC is a computational pipeline to predict cell-type-specific transcription factors through network inference and motif enrichment. Here the authors describe a detailed protocol for pySCENIC: a faster, container-based implementation in Python.

A genome-wide analysis of DNA and RNA sequences, gene expression and DNA modifications in 200 samples of acute myeloid leukaemia sets the stage for data integration and verification that will enhance our understanding of this cancer.

Enhancers are genomic elements critical for regulating gene expression. In this Review, the authors discuss how sequence-to-function models can be used to unravel the rules underlying enhancer activity and function as biological ‘oracles’ aiding the design of synthetic enhancers with tailored cell-type-specific or cell-state-specific activities.

Profiling phenotype switching in melanoma. Wouters et al. perform large-scale gene expression analysis in patient samples and identify transcriptional networks that modulate the intermediate cell state and ultimate mesenchymal switching in melanoma.

The brain cell types of Octopus vulgaris that control their sophisticated behavioral repertoire are still unknown. Here, authors use single-cell transcriptomics to profile neuronal and glial cell types and compare cell type relationships within the octopus brain and across species.

The key regulators that allow transition from proliferative to invasive phenotype in melanoma cells have not been identified yet. The authors perform chromatin and transcriptome profiling followed by comprehensive bioinformatics analysis identifying new candidate regulators for two distinct cell states of melanoma.

The genomic sequences of 12 Drosophila species for conserved elements and describe the relationship between conservation and function for many specific sequence motifs.

Genomic studies of paleopteran insects, such as mayflies, are needed to reconstruct early insect evolution. Here, Almudi and colleagues present the genome of the mayfly Cloeon dipterum and use transcriptomics to characterize its adaptations to distinct habitats and the origin of insect wings.

New computational method uses convolutional neural networks for cis-regulatory sequence analysis to analyze and cluster scATAC-seq data.

Mechanical forces, along with gene regulatory networks and cell-cell signalling, play an important role in the complex organization of tissues. Here the authors describe devices that actively apply mechanical force to developing neural tube, demonstrating that mechanical forces increase growth and enhance patterning.

A comprehensive single-cell analysis of the lung cancer microenvironment reveals marked heterogeneity of transcriptional networks that defines novel clinically relevant stromal cell populations.

Blanpain and colleagues use mouse models of activated Hedgehog signalling to analyse the temporal gene expression changes involved in basal cell carcinoma initiation. They show that tumour initiation involves activation of the Wnt pathway and reprogramming of the adult interfollicular epidermis tumour-initiating cells into a cell fate resembling that of embryonic hair follicle progenitor cells.

Silencing expression of the long noncoding RNA NEAT1 prevents paraspeckle formation and sensitizes neoplastic cells to DNA-damage-induced cell death. NEAT1 expression also predicts chemotherapy response in ovarian cancer patients.

This Primer on chromatin accessibility profiling methods discusses differences in the methods commonly used to determine chromatin states in different cell types, including ATAC-seq and ChIP–seq. The authors summarize applications in different areas of research, from single cells to tissues and whole organisms.

A known oncogene, MITF, resides in a region of chromosome 3 that is amplified in melanomas and associated with poor prognosis; now, a long non-coding RNA gene, SAMMSON, is shown to also lie in this region, to also act as a melanoma-specific survival oncogene, and to be a promising therapeutic target for anti-melanoma therapy.

The LifeTime initiative is an ambitious, multidisciplinary programme that aims to improve healthcare by tracking individual human cells during disease processes and responses to treatment in order to develop and implement cell-based interceptive medicine in Europe.