Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Spatial transcriptomics dataset of primary tumours from MDA-MB-231 xenograft model

ABSTRACT: Identifying functionally important cell states and structure within heterogeneous tumors remains a significant biological and computational challenge. Current clustering or trajectory-based models are ill-equipped to address the notion that cancer cells reside along a phenotypic continuum. We present Archetypal Analysis network (AAnet), a neural network that learns archetypal states within a phenotypic continuum in single-cell data. Unlike traditional archetypal analysis, AAnet learns archetypes in simplex-shaped neural network latent space. Using pre-clinical models and clinical breast cancers, AAnet resolves distinct cell states and processes, including cell proliferation, hypoxia, metabolism and immune interactions. Primary tumor archetypes are recapitulated in matched liver, lung and lymph node metastases. The dataset here comprises of the 10X genomics based spatial transcriptomics (Visium) on MDA-MB-231 xenografts to perform archetypal analysis and understand spatial aspects of tumour hetegeneity. scRNAseq datasets from matched models and metastasis is projected onto this spatial transcriptomics data to understand the spatial dependencies and characteriostics of these archetypes in the manuscript.

ORGANISM(S): Mus musculus Homo sapiens

PROVIDER: GSE300613 | GEO | 2025/06/24

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Single cell RNAseq dataset of MDA-MB-231 xenograft and matched metastasis

Project description:Identifying functionally important cell states and structure within heterogeneous tumors remains a significant biological and computational challenge. Current clustering or trajectory-based models are ill-equipped to address the notion that cancer cells reside along a phenotypic continuum. We present Archetypal Analysis network (AAnet), a neural network that learns archetypal states within a phenotypic continuum in single-cell data. Unlike traditional archetypal analysis, AAnet learns archetypes in simplex-shaped neural network latent space. Using pre-clinical models and clinical breast cancers, AAnet resolves distinct cell states and processes, including cell proliferation, hypoxia, metabolism and immune interactions. Primary tumor archetypes are recapitulated in matched liver, lung and lymph node metastases. This dataset comprises of the 10X genomics based single cell RNAseq on MDA-MB-231 xenografts and matched metastasis used to perform archetypal analysis to understand tumour hetegeneity as a phenotypic continuum.

2025-06-23 | GSE299393 | GEO

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

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 | GSE220571 | GEO

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

2023-08-08 | GSE220572 | GEO

Assessing rejection-related disease in kidney transplant biopsies based on archetypal analysis of molecular phenotypes

Project description:Molecular phenotyping of biopsies affords opportunities for increased precision and improved disease classification to address the limitations of conventional histologic diagnostic systems. We applied archetypal analysis, an unsupervised method similar to cluster analysis, to microarray data from 1208 prospectively collected kidney transplant biopsies from 13 centers. Seven machine learning-generated cross-validated classifier scores per biopsy were used as input for the archetypal analysis. Six archetypes representing extreme phenotypes were generated: no rejection; T cell-mediated rejection (TCMR); three phenotypes associated with antibody-mediated rejection (ABMR) - early-stage, fully-developed, and late-stage; and mixed rejection (TCMR plus early-stage ABMR). Each biopsy was assigned six scores, one for each archetype, that together represent a probabilistic assessment of that biopsy based on its rejection-related molecular properties. Viewed as clusters, the archetypes were similar to existing histologic Banff categories, but there was 32% disagreement, much of it probably reflecting the “noise” in the current histologic assessment system. Graft survival was worst for fully-developed and late-stage ABMR and was better predicted by molecular archetype scores than histologic diagnoses. The results provide a system for precision molecular assessment of biopsies and a new standard for recalibrating conventional diagnostic systems. (ClinicalTrials.gov NTC1299168) We applied archetypal analysis, an unsupervised method similar to cluster analysis, to microarray data from 1208 prospectively collected kidney transplant biopsies from 13 centers. This dataset is part of the TransQST collection.

2017-07-10 | E-GEOD-98320 | biostudies-arrayexpress

Assessing rejection-related disease in kidney transplant biopsies based on archetypal analysis of molecular phenotypes

2017-07-10 | GSE98320 | GEO

Denitrifying bacterial assemblages respond to environmental conditions of a shallow estuary

Project description:Molecular analysis of dissimilatory nitrite reductase genes (nirS) was conducted using a customized microarray containing 165 nirS probes (archetypes) to identify members of sedimentary denitrifying communities. The goal of this study was to examine denitrifying community responses to changing environmental variables over spatial and temporal scales in the New River Estuary (NRE), NC, USA. Multivariate statistical analyses revealed three denitrifier assemblages and uncovered “generalist” and “specialist” archetypes based on the distribution of archetypes within these assemblages. Generalists, archetypes detected in all samples during at least one season, were commonly world-wide found in estuarine and marine ecosystems, comprised 11-29% of the abundant NRE archetypes. Archetypes found in a particular site, “specialists”, were found to co-vary based on site specific conditions. Archetypes specific to the lower estuary in winter were designated Cluster I and significantly correlated by sediment Chl a and porewater Fe2+. A combination of specialist and more widely distributed archetypes formed Clusters II and III, which separated based on salinity and porewater H2S, respectively. The co-occurrence of archetypes correlated with different environmental conditions highlights the importance of habitat type and niche differentiation among denitrifying communities and supports the essential role of individual community members in overall ecosystem function.

2017-05-02 | GSE98190 | GEO

The molecular basis of socially-mediated phenotypic plasticity in a eusocial paper wasp

Project description:We report analyses the individual-level brain gene expression profiles of Polistes dominula paper wasps following the loss of a queen, a perturbation that induces some individuals to undergo a significant phenotypic shift and become queens while others maintain an apparently unchanged worker phenotype. We use a machine learning approach to map individual transcriptomes on a continuum between worker and queen states and then match these scores to detailed phenotypic data. We find a strong response of caste-associated gene expression to queen loss, with individuals’ expression profiles becoming intermediate between queen and worker states.

2020-12-23 | GSE153532 | GEO

Transcription profiling and proteomic profiling of mouse hippocampus

Project description:We have correlated transciptomics, proteomics and toponomics analyses of hippocampus tissue of inbred C57/BL6 mice to analyse the interrelationship of expressed genes and proteins at different levels of organization. We find that transcriptome and proteome levels of function are highly conserved between different mice, while the topological organization (the toponome) of protein clusters in synapses of the hippocampus is highly individual, with only few interindividual overlaps (0.15 %). In striking contrast, the overall spatial patterns of individual synaptic states, defined by protein clusters, have boundaries within a strict and non-individual spatial frame of the total synaptic network. The findings are the first to provide insight in the systems biology of gene expression on transcriptome, proteome and toponome levels of function in the same brain subregion. The approach may lay the ground for designing studies of neurodegeneration in mouse models and human brains. Experiment Overall Design: 4 biological replicates, all wild type

2008-06-19 | E-GEOD-8239 | biostudies-arrayexpress

scRNA-seq of triple knockout SCLC mouse models for subtyping

Project description:Small cell lung cancer (SCLC) tumors comprise heterogeneous mixtures of cell states, categorized into neuroendocrine (NE) and non-neuroendocrine (non-NE) transcriptional subtypes. NE to non-NE state transitions, fueled by plasticity, likely underlie adaptability to treatment and dismal survival rates. Here, we apply an archetypal analysis to model plasticity by recasting SCLC phenotypic heterogeneity through multi-task evolutionary theory. Cell line and tumor transcriptomics data fit well in a five-dimensional convex polytope whose vertices optimize tasks reminiscent of pulmonary NE cells, the SCLC normal counterparts. These tasks, supported by knowledge and experimental data, include proliferation, slithering, metabolism, secretion, and injury repair, reflecting cancer hallmarks. SCLC subtypes, either at the population or single-cell level, can be positioned in archetypal space by bulk or single-cell transcriptomics, respectively, and characterized as task specialists or multi-task generalists by the distance from archetype vertex signatures. In the archetype space, modeling single-cell plasticity as a Markovian process along an underlying state manifold indicates that task trade-offs, in response to microenvironmental perturbations or treatment, may drive cell plasticity. Stifling phenotypic transitions and plasticity may provide new targets for much-needed translational advances in SCLC.

2022-08-22 | GSE211751 | GEO

Spatial transcriptomics analysis of a panel of PDX models of GBM

Project description:Visium spatial transcriptomics was carried out on ten glioblastoma PDX models and one tumour free age-matched control brain.

2025-06-09 | GSE268312 | GEO