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Uncovering the spatial landscape of molecular interactions within the tumor microenvironment through latent spaces

ABSTRACT: Uncovering the spatial landscape of molecular interactions within the tumor microenvironment through latent spaces

PROVIDER: PRJNA930887 | ENA |

REPOSITORIES: ENA

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Uncovering the spatial landscape of molecular interactions within the tumor microenvironment through latent spaces

Project description:Recent advances in spatial transcriptomics (ST) enable gene expression measurements from a tissue sample while retaining its spatial context. This technology enables unprecedented in situ resolution of the regulatory pathways that underlie the heterogeneity in the tumor and its microenvironment (TME). The direct characterization of cellular co-localization with spatial technologies facilities quantification of the molecular changes resulting from direct cell-cell interaction, as occurs in tumor-immune interactions. We present SpaceMarkers, a novel bioinformatics algorithm to infer molecular changes from cell-cell interaction from latent space analysis of ST data. We apply this approach to infer molecular changes from tumor-immune interactions in Visium spatial transcriptomics data of metastasis, invasive and precursor lesions, and immunotherapy treatment. Further transfer learning in matched scRNA-seq data enabled further quantification of the specific cell types in which SpaceMarkers are enriched. Altogether, SpaceMarkers can identify the location and context-specific molecular interactions within the TME from ST data.

2023-04-19 | GSE224411 | GEO

Uncovering the spatial landscape of molecular interactions within the tumor microenvironment through latent spaces.

Project description: Not available

| S-EPMC10523348 | biostudies-literature

SPARROW reveals microenvironment zone-specific cell states in healthy and diseased tissues.

Project description:Spatially resolved transcriptomics technologies have significantly enhanced our ability to understand cellular characteristics within tissue contexts. However, current analytical tools often treat cell type inference and cellular neighbourhood identification as separate and hard clustering processes, resulting in models that are not comparable across tissue feature scales and samples, thus hindering a unified understanding of tissue features. Our computational framework, SPARROW, addresses these challenges by representing cell types and cellular organization patterns as latent embeddings learned through an interconnected neural network architecture. SPARROW integrates clustering directly into the learning of these latent embeddings, enabling feature extraction specific to clustering while ensuring comparability across samples through shared latent spaces. When applied to diverse datasets, SPARROW outperformed state-of-the-art methods in cell type inference and microenvironment zone delineation and uncovered microenvironment zone-specific fine cell states that reveal underlying biology. Furthermore, SPARROW algorithmically achieves single cell spatial resolution and whole transcriptome coverage---an experimental challenge---by integrating spatially resolved transcriptomics and scRNA-seq data in a shared latent space. This formulation enabled SPARROW to uncover both established and novel microenvironment zone-specific ligand-receptor interactions in human tonsils---discoveries not possible with either data modality alone. Overall, SPARROW provides a comprehensive characterization of tissue features across scales, samples and conditions.

2024-11-24 | GSE282714 | GEO

RNA Infrastructure Profiling Illuminates Transcriptome Structure in Crowded Spaces

Project description:RNA Infrastructure Profiling Illuminates Transcriptome Structure in Crowded Spaces

| PRJNA1018899 | ENA

UNCOVERING VULVAR CARCINOMA: AN INTEGRATIVE APPROACH

Project description:UNCOVERING VULVAR CARCINOMA: AN INTEGRATIVE APPROACH

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Macrophages are metabolically heterogenous within the tumor microenvironment

Project description:Macrophages are metabolically heterogenous within the tumor microenvironment

| PRJNA779399 | ENA

Defining the immunophenotype within the clear cell renal cell carcinoma tumour microenvironment to guide immune checkpoint blockade

Project description:We performed spatial transcriptome profiling (ST-seq) on nine fresh frozen tissue sections to understand the immunophenotypes; immune cell types, states and their spatial location within the clear cell renal cell carcinoma (ccRCC) tumour microenvironment (TME).

2023-08-16 | E-MTAB-12767 | biostudies-arrayexpress

Analysis of the microRNA that involved the tuberculosis or latent TB infection

Project description:To get to know the different levels of the microRNA among the pulmonary tuberculosis patients, latent-infected individuals and the complete healthy people, we have employed the Agilent miRNA microarray (version 2.0) as a discovery platform to identify microRNA with the potential to diagnose the TB patients and the latent infected persons. Each group have 4 volunteers. The Peripheral Blood Mononuclear Cells (PBMCs) of each group were made up of equally mixed volumes. The results showed that several microRNAs were found up- or low regulated in PBMCs of TB patients versus latent-infected individuals and similarly for latent-infected individuals versus complete healthy ones. Each group have 4 volunteers. The PBMCs of each group were made up of equally mixed volumes after being purified.

2010-05-19 | E-GEOD-15977 | biostudies-arrayexpress

Gene expression profiling identifies candidate biomarkers for active and latent tuberculosis

Project description:Tuberculosis (TB) is a serious infectious disease, but current methods of detection require improvement in sensitivity, efficiency or specificity. We conducted a microarray experiment, comparing the gene expression profiles in peripheral blood mononuclear cells among individuals with active TB, latent infection, and healthy conditions in a Taiwanese population. These differentially expressed genes may be potential biomarkers that can differentiate between active TB and latent infection. We isolated total RNA from the PBMC from 7 active TB, 7 latent infection, and 7 healthy individuals and profiled their transcriptional profiles to identify signficantly differentially expressed geens that differ among these three groups

2014-10-21 | E-GEOD-62525 | biostudies-arrayexpress

The Deciphering of Mouse Uterine Microenvironment during Early Pregnancy [spatial transcriptomics]

Project description:The mouse uterine microenvironment at pregnancy D7.5 was investigated by Visium spatial transcriptome. Ten distinct uterine microenvironments were identified with diverse biological functions, different endocrine regulations and intensive interactions. The single cell RNA-seq of D7.5 mouse uterus has been intergrated with the spatial visium results and predicted the presence of mutiple types of cells in each microenvironment.

2022-07-30 | GSE181169 | GEO

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