A multi-omic single-cell atlas of human gynecologic malignancies
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ABSTRACT:
PROVIDER: PRJNA699347 | ENA |
REPOSITORIES: ENA
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Project description:A multi-omic single-cell atlas of human gynecologic malignancies
| PRJNA699345 | ENA
Project description:Tumors show substantial amounts of cellular heterogeneity by forming complex ecosystems of malignant and non-malignant cells. Herein, we present a comprehensive multi-omic cell atlas of matched single-cell transcriptome and single-cell chromatin accessibility profiles spanning over 150,000 cells from 11 human gynecologic tumors. By jointly analyzing these transcriptomic and chromatin accessibility profiles at single-cell resolution, we identify 115,734 total peak-to-gene links representing putative regulatory interactions. We find some of these regulatory interactions explain cell type-specific expression patterns of hallmark cancer pathway regulators such as the mTOR activator RHEB. We also leverage these data to infer differential transcription factor activity, such as ZEB1, across cell type-specific enhancers between two different fractions of the same patient tumor. Our work highlights the importance of precision medicine in the treatment of gynecologic cancers and we show that this resource will deepen our understanding of non-coding genomic regions in the context of tumor biology.
2021-11-04 | GSE173682 | GEO
Project description:Single-cell multi-omic and multi-region atlas of the Aging Mouse Brain
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Project description:A Time-resolved Multi-omic Atlas of the Developing Mouse Liver
| PRJNA545709 | ENA
Project description:A Time-resolved Multi-omic Atlas of the Developing Mouse Stomach
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Project description:Alzheimer’s disease (AD) is the leading cause of dementia, driven by complex molecular alterations that disrupt neural and glial cell function. However, the precise epigenomic and chromatin regulatory changes underlying AD pathogenesis across different brain regions remain incompletely understood. To address this, we generated a comprehensive single-cell multi-omic atlas of the human AD brain. We profiled 11 AD donors and 9 age-matched healthy donors, spanning three key brain regions implicated in AD pathology: the visual cortex (VC), temporal cortex (TC), and prefrontal cortex (PFC). Using single-nucleus methylome sequencing (snmC-seq3) and joint profiling of chromatin conformation and DNA methylation (snm3C-seq), we obtained extensive datasets enabling integrated analysis of transcriptional regulation, epigenetic remodeling, and 3D genome architecture. This resource allowed us to characterize AD-associated alterations in DNA methylation at promoters, enhancers, and transposable elements, as well as their consequences on transcription factor motif accessibility. At the chromatin level, we identified disease-related changes in higher-order genome organization, including compartments, topologically associating domains (TADs), and loops. By comparing multiple cortical regions, we also uncovered regional heterogeneity within matched cell types, highlighting both shared and region-specific epigenetic signatures of AD. Together, this atlas provides a systematic, multi-region view of single-cell epigenomic landscapes in AD and offers insights into regulatory mechanisms underlying disease progression.
2025-12-04 | GSE308132 | GEO
Project description:Deconvolution of regulatory mechanisms that drive transcriptional programs in cancer cells is key to understanding tumor biology. Herein, we present matched transcriptome (scRNA-seq) and chromatin accessibility (scATAC-seq) profiles at single-cell resolution from human ovarian and endometrial tumors processed immediately following surgical resection. This dataset reveals the complex cellular heterogeneity of these tumors and enabled us to quantitatively link variation in chromatin accessibility to gene expression. We show that malignant cells acquire previously unannotated regulatory elements to drive hallmark cancer pathways. Moreover, malignant cells from within the same patients show substantial variation in chromatin accessibility linked to transcriptional output, highlighting the importance of intratumoral heterogeneity. Finally, we infer the malignant cell type-specific activity of transcription factors. By defining the regulatory logic of cancer cells, this work reveals an important reliance on oncogenic regulatory elements and highlights the ability of matched scRNA-seq/scATAC-seq to uncover clinically relevant mechanisms of tumorigenesis in gynecologic cancers.
| S-EPMC8642316 | biostudies-literature
Project description:A Multi-omic Atlas of Human Embryonic Skeletal Development -Multiome GEX+ATAC
| PRJEB98619 | ENA
Project description:Understanding the gene regulatory mechanisms that establish and maintain cell type identities is a central goal in cellular and developmental biology. Single-cell RNA sequencing and multi-omic profiling have revolutionized this field, enabling high-resolution investigation of gene expression dynamics across differentiation stages. RNA velocity, which estimates gene expression changes using mechanistic models, has emerged as a powerful approach for trajectory inference. Recent advances in RNA velocity methods address key limitations such as steady-state assumptions and lack of support for multi-omic data but still fall short in multi-sample integration and differential testing. To overcome these challenges, we introduce MultiVeloVAE, a probabilistic framework for multi-sample RNA velocity inference that integrates single-cell RNA and multi-omic data. MultiVeloVAE models gene expression on a shared time scale, accounts for lineage bifurcations, and enables statistical testing of velocity parameters. Our approach achieves a good balance between batch correction and biological variance conservation and outperforms existing methods in trajectory reconstruction. Using newly generated 10X Multiome datasets from human embryoid bodies and hematopoietic cells, we demonstrate that MultiVeloVAE provides novel insights into chromatin accessibility and gene expression dynamics during development. These results highlight the potential of MultiVeloVAE as a comprehensive tool for de novo multi-omic trajectory analysis and biological discovery.
2025-10-01 | GSE284047 | GEO