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Neuropsychiatric genome-wide association studies (GWASs), including those for autism spectrum disorder and schizophrenia, show strong enrichment for regulatory elements in the developing brain. However, prioritizing risk genes and mechanisms is challenging without a unified regulatory atlas. Across 672 diverse developing human brains, we identified 15,752 genes harboring gene, isoform, and/or splicing quantitative trait loci, mapping 3739 to cellular contexts. Gene expression heritability drops during development, likely reflecting both increasing cellular heterogeneity and the intrinsic properties of neuronal maturation. Isoform-level regulation, particularly in the second trimester, mediated the largest proportion of GWAS heritability. Through colocalization, we prioritized mechanisms for about 60% of GWAS loci across five disorders, exceeding adult brain findings. Finally, we contextualized results within gene and isoform coexpression networks, revealing the comprehensive landscape of transcriptome regulation in development and disease.

Nucleotide changes in gene regulatory elements are important determinants of neuronal development and diseases. Using massively parallel reporter assays in primary human cells from mid-gestation cortex and cerebral organoids, we interrogated the cis-regulatory activity of 102,767 open chromatin regions, including thousands of sequences with cell type-specific accessibility and variants associated with brain gene regulation. In primary cells, we identified 46,802 active enhancer sequences and 164 variants that alter enhancer activity. Activity was comparable in organoids and primary cells, suggesting that organoids provide an adequate model for the developing cortex. Using deep learning we decoded the sequence basis and upstream regulators of enhancer activity. This work establishes a comprehensive catalog of functional gene regulatory elements and variants in human neuronal development.

The human genome is tightly packed into the 3D environment of the cell nucleus. Rapidly evolving and sophisticated methods of mapping 3D genome architecture have shed light on fundamental principles of genome organization and gene regulation. The genome is physically organized on different scales, from individual genes to entire chromosomes. Nuclear landmarks such as the nuclear envelope and nucleoli have important roles in compartmentalizing the genome within the nucleus. Genome activity (for example, gene transcription) is also functionally partitioned within this 3D organization. Rather than being static, the 3D organization of the genome is tightly regulated over various time scales. These dynamic changes in genome structure over time represent the fourth dimension of the genome. Innovative methods have been used to map the dynamic regulation of genome structure during important cellular processes including organism development, responses to stimuli, cell division and senescence. Furthermore, disruptions to the 4D genome have been linked to various diseases, including of the kidney. As tools and approaches to studying the 4D genome become more readily available, future studies that apply these methods to study kidney biology will provide insights into kidney function in health and disease., (© 2024. Springer Nature Limited.)

The cell cycle governs the proliferation, differentiation, and regeneration of all eukaryotic cells. Profiling cell cycle dynamics is therefore central to basic and biomedical research spanning development, health, aging, and disease. However, current approaches to cell cycle profiling involve complex interventions that may confound experimental interpretation. To facilitate more efficient cell cycle annotation of microscopy data, we developed CellCycleNet, a machine learning (ML) workflow designed to simplify cell cycle staging with minimal experimenter intervention and cost. CellCycleNet accurately predicts cell cycle phase using only a fluorescent nuclear stain (DAPI) in fixed interphase cells. Using the Fucci2a cell cycle reporter system as ground truth, we collected two benchmarking image datasets and trained two ML models-a support vector machine (SVM) and a deep neural network-to classify nuclei as being in either the G1 or S/G2 phases of the cell cycle. Our results suggest that CellCycleNet outperforms state-of-the-art SVM models on each dataset individually. When trained on two image datasets simultaneously, CellCycleNet achieves the highest classification accuracy, with an improvement in AUROC of 0.08-0.09. The model also demonstrates excellent generalization across different microscopes, achieving an AUROC of 0.95. Overall, using features derived from 3D images, rather than 2D projections of those same images, significantly improves classification performance. We have released our image data, trained models, and software as a community resource.

The accuracy of crucial nuclear processes such as transcription, replication, and repair, depends on the local composition of chromatin and the regulatory proteins that reside there. Understanding these DNA-protein interactions at the level of specific genomic loci has remained challenging due to technical limitations. Here, we introduce a method termed "DNA O-MAP", which uses programmable peroxidase-conjugated oligonucleotide probes to biotinylate nearby proteins. We show that DNA O-MAP can be coupled with sample multiplexed quantitative proteomics and next-generation sequencing to quantify DNA-protein and DNA-DNA interactions at specific genomic loci., Competing Interests: Competing Interest Statement D.K.S. is a collaborator with Thermo Fisher Scientific, Genentech, Calico Labs, and AI Proteins. C.K.C., A.F.T., E.K., D.M.S., and B.J.B. have filed a patent application covering aspects of this work. B.J.B. is listed as an inventor on patent applications related to the SABER technology related to this work.

Fluorescent in situ hybridization (FISH) enables the visualization of the position and abundance of nucleic acid molecules in fixed cell and tissue samples. Many FISH-based methods employ sets of synthetic, computationally designed DNA oligonucleotide (oligo) FISH probes, including massively multiplexed imaging spatial transcriptomics and spatial genomics technologies. Oligo probes can either be designed de novo or accessed from an existing database of pre-discovered probe sequences. This chapter describes the use of PaintSHOP, a user-friendly, web-based platform for the design of sets of oligo-based FISH probes. PaintSHOP hosts large collections of pre-discovered probes from many model organisms and also provides collections of functional sequences such as primers and readout domains and interactive tools to add these functional sequences to selected probes. Detailed examples are provided for three common experimental scenarios., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)