Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Reconstruction of 3-dimensional tissue organization at the single-cell resolution

ABSTRACT: Reconstruction of 3-dimensional tissue organization at the single-cell resolution

PROVIDER: PRJNA910215 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR22580422_1.fastq.gz	Fastqsanger.gz
	SRR22580422_2.fastq.gz	Fastqsanger.gz
	SRR22580423_1.fastq.gz	Fastqsanger.gz
	SRR22580423_2.fastq.gz	Fastqsanger.gz
	SRR22580823_1.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 6

Similar Datasets

Reconstruction of 3-dimensional tissue organization at the single-cell resolution [ST]

Project description:Reconstruction of 3-dimensional tissue organization at the single-cell resolution [ST]

| PRJNA910304 | ENA

Reconstruction of 3-dimensional tissue organization at the single-cell resolution [scRNA-seq]

Project description:Reconstruction of 3-dimensional tissue organization at the single-cell resolution [scRNA-seq]

| PRJNA910305 | ENA

Reconstruction of 3-dimensional tissue organization at the single-cell resolution [ST]

Project description:We designed a neural network-based computational method that learns transcriptome-to-space mapping and reconstructs 3D tissue organization by learning from scRNA-seq and spatial transcriptomic data.

2023-08-08 | GSE220572 | GEO

Reconstruction of 3-dimensional tissue organization at the single-cell resolution [scRNA-seq]

2023-08-08 | GSE220571 | GEO

Reconstruction of 3-dimensional tissue organization at the single-cell resolution.

Project description:Recent advances in spatial transcriptomics (ST) have allowed for the mapping of tissue heterogeneity, but this technique lacks the resolution to investigate gene expression patterns, cell-cell communications and tissue organization at the single-cell resolution. ST data contains a mixed transcriptome from multiple heterogeneous cells, and current methods predict two-dimensional (2D) coordinates for individual cells within a predetermined space, making it difficult to reconstruct and study three-dimensional (3D) tissue organization. Here we present a new computational method called scHolography that uses deep learning to map single-cell transcriptome data to 3D space. Unlike existing methods, which generate a projection between transcriptome data and 2D spatial coordinates, scHolography uses neural networks to create a high-dimensional transcriptome-to-space map that preserves the distance information between cells, allowing for the construction of a cell-cell proximity matrix beyond the 2D ST scaffold. Furthermore, the neighboring cell profile of a given cell type can be extracted to study spatial cell heterogeneity. We apply scHolography to human skin, human skin cancer and mouse brain datasets, providing new insights into gene expression patterns, cell-cell interactions and spatial microenvironment. Together, scHolography offers a computational solution for digitizing transcriptome and spatial information into high-dimensional data for neural network-based mapping and the reconstruction of 3D tissue organization at the single-cell resolution.

| S-EPMC9881965 | biostudies-literature

Reconstruction of 3-dimensional tissue organization at the single-cell resolution

Project description:This SuperSeries is composed of the SubSeries listed below.

2023-08-08 | GSE220573 | GEO

Super-resolution imaging reveals disrupted higher-order heterochromatin organization at early-stage carcinogenesis on pathological tissue

Project description:Abnormal chromatin structure is one of the hallmarks in cancer and has long been used for cancer diagnosis, but the observed aberrant chromatin structures characteristic of cancer cells is limited to microscale features, largely due to due to the diffraction-limited resolution of conventional light microscopy. Recent advances in super-resolution microscopy overcome the technical limitations of conventional light microscopy and enable the in-situ visualization of the higher-order chromatin structure down to ~30 nm. Here we present PathSTORM, a variant of stochastic optical reconstruction microscopy (STORM) optimized for super-resolution imaging of densely packed higher-order chromatin organization at different epigenomic states on pathological tissue. We then apply this tool to characterize the structural alteration of higher-order heterochromatin, enriched with H3K9me3 and satellite repeats, at various stages of carcinogenesis in mouse models and human tissue specimens. PathSTORM reveals that a significant disruption of higher-order heterochromatin structure occurs in early carcinogenesis when tissue still appears histologically normal and becomes progressively severe during neoplastic progression. The disrupted higher-order heterochromatin structure also appears to be a common feature that reflects the evolution of carcinogenesis independent of molecular pathways in multiple tumor types. Taken together, PathSTORM can serve as a powerful tool to visualize higher-order chromatin structure in the spatial context of tissue architecture on pathological tissue. Our findings provide new insights on the role of higher-order heterochromatin structure in carcinogenesis, especially at the early stage, and have the potential for improving cancer diagnosis, risk stratification and facilitating the development and evaluation of new preventive strategies.

2020-03-19 | GSE121800 | GEO

Hi-C map of the three-dimensional genome organization

Project description:The spatial arrangement of interphase chromosomes in the nucleus is important for gene expression and genome function in animals and in plants. The recently developed Hi-C technology is an efficacious method to investigate genome packing. Here we present a detailed Hi-C map of the three-dimensional genome organization of the plant Arabidopsis thaliana. We find that local chromatin packing differs from the patterns seen in animals, with kilobasepair-sized segments that have much higher intra-chromosome interaction rates than neighboring regions and which represent a dominant local structural feature of genome conformation in A. thaliana. These regions appear as positive strips on two-dimensional representations of chromatin interaction and they are enriched in epigenetic marks H3K27me3, H3.1 and H3.3. We also identify over 400 insulator-like regions. Furthermore, although topologically associating domains (TADs), which are prominent in animals, are not the dominant feature of A. thaliana genome packing, we found over 1,000 regions that have properties of TAD boundaries, and a similar number of regions similar to the interior of TADs. These insulator-like, TAD-boundary-like, and TAD-interior-like regions show strong enrichment for distinct epigenetic marks, and correlate with gene transcription levels. We conclude that epigenetic modifications, gene density, and transcriptional activity all contribute to shaping the local structure of the A. thaliana nuclear genome.

2014-09-24 | E-MTAB-4438 | biostudies-arrayexpress

Three-dimensional chromatin re-organization during muscle stem cell aging

Project description:Three-dimensional chromatin re-organization during muscle stem cell aging

| PRJNA883688 | ENA

The dynamic three-dimensional organization of the diploid yeast genome

Project description:The dynamic three-dimensional organization of the diploid yeast genome

| PRJNA349185 | ENA