Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

The covariance environment defines cellular niches for spatial inference

ABSTRACT: The tsunami of new multiplexed spatial profiling technologies has opened a range of computational challenges focused on leveraging these powerful data for biological discovery. A key challenge underlying computation is a suitable representation for features of cellular niches. Here, we develop the covariance environment (COVET), a representation that can capture the rich, continuous multivariate nature of cellular niches by capturing the gene-gene covariate structure across cells in the niche, which can reflect the cell-cell communication between them. We define a principled optimal transport-based distance metric between COVET niches and develop a computationally efficient approximation to this metric that can scale to millions of cells. Using COVET to encode spatial context, we develop environmental variational inference (ENVI), a conditional variational autoencoder that jointly embeds spatial and single-cell RNA-seq data into a latent space. Two distinct decoders either impute gene expression across spatial modality, or project spatial information onto dissociated single-cell data. We show that ENVI is not only superior in the imputation of gene expression but is also able to infer spatial context to disassociated single-cell genomics data.

ORGANISM(S): Mus musculus

PROVIDER: GSE246395 | GEO | 2024/03/08

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

High resolution spatially resolved transcriptomic atlas of kidney injury and repair by RNA hybridization-based in situ sequencing [10X Visium]

Project description:Spatially resolved transcriptomics technologies allow for the measurement of gene expression in situ. We applied direct RNA hybridization-based in situ sequencing (ISS, Cartana) to compare male and female healthy mouse kidneys and the male kidneys injury and repair timecourse of ischemic reperfusion injury (IRI). A pre-selected panel of 200 genes were used to identify the dynamics of cell states and their spatial distributions during injury and repair. We developed a new computational pipeline, CellScopes, for the rapid analysis, multi-omic integration and visualization of spatially resolved transcriptomic datasets. The resulting atlas allowed us to resolve distinct kidney niches, dynamic alterations in cell state over the course of injury and repair and cell-cell interactions between leukocytes and kidney parenchyma. Projection of snRNA-seq dataset from the same injury and repair samples allowed us to impute the spatial localization of genes not directly measured by Cartana.

2024-01-17 | GSE227045 | GEO

High resolution spatially resolved transcriptomic atlas of kidney injury and repair by RNA hybridization-based in situ sequencing [ISS, Cartana]

2024-01-17 | GSE227044 | GEO

Spatial proteogenomics reveals distinct and evolutionarily-conserved hepatic macrophage niches (spatial)

Project description:Spatial proteogenomics reveals distinct and evolutionarily-conserved hepatic macrophage niches (spatial)

| PRJNA793008 | ENA

Employing fingerprinting of medicinal plants by means of LC-MS and machine learning for species identification task

Project description:A dataset of liquid chromatography-mass spectrometry measurements of medicinal plant extracts from 76 species was generated and used for training and validating plant species identification algorithms. Various strategies for data handling and feature space selection were tested. Constrained Tucker decomposition, large-scale (more than 1500 variables) discrete Bayesian Networks and autoencoder based dimensionality reduction coupled with continuous Bayes classifier and logistic regression were optimized to achieve the best accuracy. Classification algorithms based on Tucker decomposition of original data and logistic regression on representation learned with autoencoder showed identification accuracy of up to 96%, outperforming various implementations of Bayesian Networks. Benefits and drawbacks of used approaches were discussed. Tolerance to changes in data created by using different extraction methods and equipment was tentatively tested. Main study is reported in the current study MTBLS688Helianthus tuberosus assay is reported in <a href='https://www.ebi.ac.uk/metabolights/MTBLS759/' rel='noopener noreferrer' target='_blank'>MTBLS759</a>

2022-04-15 | MTBLS688 | MetaboLights

Pol II preferentially regulates ribosomal protein expression by trapping disassociated subunits

Project description:Pol II has been recognized as a passively regulated holoenzyme. However, whether Pol II plays specific regulatory roles remain unclear. Here, fractions containing disassociated RPB3 (dRPB3) were identified by size exclusion chromatography in various cells. Through a unique strategy, Specific Degradation of Disassociated Subunits (SDDS), we demonstrated that dRPB3 functions as a regulatory component of Pol II to enable the preferential control of 3’ end processing of ribosomal protein genes directly through its N-terminal domain. Machine learning analysis of large-scale genomic features revealed that the little elongation complex helps to specialize the functions of dRPB3. Mechanistically, dRPB3 facilitates CBC-PCF11 axis activity to increase the efficiency of 3’ end processing. Furthermore, RPB3 is dynamically regulated during development and diseases. These findings suggest that Pol II gains specific regulatory functions by trapping disassociated subunits.

2023-02-14 | PXD038474 | Pride

Spatial transcriptomics from murine inguinal and mesenteric lymph nodes

Project description:B cell-interacting reticular cells (BRC) form transcriptionally and topologically stable immune-interacting microenvironments that direct efficient humoral immunity. While several immune niche factors have been elucidated, the cues sustaining BRC function and topology across activation states remain unclear. Here, we employed spatial transcriptome analysis of murine ingunal and mesenteric lymph nodes to examine co-localization of distinct BRC subsets and immune cells complementing BRC-immune cell interaction analysis. Spatial analysis supported predicted feedforward BRC-immune cell circuits that sustain topologically-organized, functional niches across inflammatory states, lymphoid organs and species.

2023-03-29 | E-MTAB-12468 | biostudies-arrayexpress

Spatial architecture of high-grade glioma reveals tumor heterogeneity within distinct domains

Project description:High-grade gliomas are aggressive primary brain cancers with poor response to standard regimens, driven by immense heterogeneity. In isocitrate dehydrogenase (IDH) wild-type high-grade glioma (glioblastoma, GBM), increased intra-tumoral heterogeneity is associated with more aggressive disease. Recently, spatial technologies have emerged to dissect this complex heterogeneity within the tumor ecosystem by preserving cellular organization in situ. Here, we construct a high- resolution molecular landscape of GBM and IDH-mutant high-grade glioma patient samples to investigate the cellular subtypes and spatial communities that compose high-grade glioma using digital spatial profiling and spatial molecular imaging. This uncovered striking diversity of the tumor and immune microenvironment, that is embodied by the heterogeneity of the inferred copy- number alterations in the tumor. Reconstructing the tumor architecture revealed brain-intrinsic niches, composed of tumor cells reflecting brain cell types and microglia; and brain-extrinsic niches, populated by mesenchymal tumor cells and monocytes. We further reveal that cellular communication in these niches is underpinned by specific ligand-receptor pairs. This primary study reveals high levels of intra-tumoral heterogeneity in high-grade gliomas, associated with a diverse immune landscape within spatially localized regions.

2023-12-13 | GSE232469 | GEO

The covariance environment defines cellular niches for spatial inference

Project description:The covariance environment defines cellular niches for spatial inference

| PRJNA1032867 | ENA

Human Early Postnatal Thymus [CITE-seq]

Project description:Within the thymus, regulation of the cellular cross-talk directing T cell development is dependent on spatial interactions within specialized niches. To create a holistic, spatially defined map of tissue niches guiding postnatal T cell development we employed the multidimensional imaging platform CO-detection by indEXing (CODEX), as well as Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) and Assay for Transposable-Accessible Chromatin (ATAC-seq). We generated age-matched 4–5-month-old postnatal thymus datasets for both male and female donors, and describe signaling and cell-cell interaction networks in sequential thymocyte developmental niches. Together, these data represent a unique age-matched spatial multiomic resource to investigate how sex-based differences in thymus regulation and T cell development arise, and provide an essential resource to understand the mechanisms underlying immune function and dysfunction in males and females.

2024-08-30 | GSE264587 | GEO

Human Early Postnatal Thymus [ATAC-seq]

2024-08-30 | GSE264585 | GEO