Single-cell spatial multi-omics and deep learning dissect enhancer-driven gene regulatory networks in liver zonation.

In the mammalian liver, hepatocytes exhibit diverse metabolic and functional profiles based on their location within the liver lobule. However, it is unclear whether this spatial variation, called zonation, is governed by a well-defined gene regulatory code. Here, using a combination of single-cell multiomics, spatial omics, massively parallel reporter assays and deep learning, we mapped enhancer-gene regulatory networks across mouse liver cell types.

Decoding gene regulation in the fly brain.

The Drosophila brain is a frequently used model in neuroscience. Single-cell transcriptome analysis, three-dimensional morphological classification and electron microscopy mapping of the connectome 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.

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.

Interpretation of allele-specific chromatin accessibility using cell state-aware deep learning.

Genomic sequence variation within enhancers and promoters can have a significant impact on the cellular state and phenotype. However, sifting through the millions of candidate variants in a personal genome or a cancer genome, to identify those that impact -regulatory function, remains a major challenge. Interpretation of noncoding genome variation benefits from explainable artificial intelligence to predict and interpret the impact of a mutation on gene regulation.

Cross-species analysis of enhancer logic using deep learning.

Deciphering the genomic regulatory code of enhancers is a key challenge in biology because this code underlies cellular identity. A better understanding of how enhancers work will improve the interpretation of noncoding genome variation and empower the generation of cell type-specific drivers for gene therapy. Here, we explore the combination of deep learning and cross-species chromatin accessibility profiling to build explainable enhancer models.

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.

CNKSR1 gene defect can cause syndromic autosomal recessive intellectual disability.

The advent of high-throughput sequencing technologies has led to an exponential increase in the identification of novel disease-causing genes in highly heterogeneous diseases. A novel frameshift mutation in CNKSR1 gene was detected by Next-Generation Sequencing (NGS) in an Iranian family with syndromic autosomal recessive intellectual disability (ARID). CNKSR1 encodes a connector enhancer of kinase suppressor of Ras 1, which acts as a scaffold component for receptor tyrosine kinase in mitogen-activated protein kinase (MAPK) cascades.

Biallelic missense variants in ZBTB11 can cause intellectual disability in humans.

Exploring genes and pathways underlying intellectual disability (ID) provides insight into brain development and function, clarifying the complex puzzle of how cognition develops. As part of ongoing systematic studies to identify candidate ID genes, linkage analysis and next-generation sequencing revealed Zinc Finger and BTB Domain Containing 11 (ZBTB11) as a novel candidate ID gene. ZBTB11 encodes a little-studied transcription regulator, and the two identified missense variants in this study are predicted to disrupt canonical Zn2+-binding residues of its C2H2 zinc finger domain, leading to possible altered DNA binding.