Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

CITEgeist: Cellular Indexing of Transcriptomes and Epitopes for Guided Exploration of Intrinsic Spatial Trends

ABSTRACT: Spatial transcriptomics provides insights into tissue architecture by linking gene expression with spatial localization. Current deconvolution methods rely heavily on single-cell RNA sequencing (scRNA-seq) references, which are costly and often unavailable, especially if the tissue under evaluation is limited as in a core biopsy specimen. We present a novel tool, CITEgeist, that deconvolutes spatial transcriptomics data using antibody capture from the same slide as the reference, directly leveraging cell surface protein measurements from the same tissue section. This approach circumvents the limitations of scRNA-seq as a reference, offering a cost-effective and biologically grounded alternative. Our method employs mathematical optimization to estimate cell type proportions and gene expression profiles, incorporating sparsity constraints for robustness and interpretability. Benchmarks against state-of-the-art deconvolution methods show improved accuracy in cell type resolution, particularly in dense tumor microenvironments, while maintaining computational efficiency. This antibody-based tool advances spatial transcriptomics by providing a scalable, accurate, and reference-independent solution for deconvolution in complex tissues. We validate this tool by using a combined approach of simulated data and clinical samples by applying CITEgeist to translational pre-treatment and post-treatment ER+ breast tumors from an ongoing clinical trial, emphasizing the applicability and robustness of CITEgeist.

ORGANISM(S): Homo sapiens

PROVIDER: GSE289326 | GEO | 2026/03/19

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

scRNA-seq mouse splenocytes prior to and during blood-stage malaria infection.

Project description:Blood-stage malaria infection induces differentiation of several effector CD4 + T subsets including Tfh and Th1 cells. The cues and microarchitectural niches required in secondary lymphoid organs for their formation were previously uncharacterised. Here we used scRNA-seq to profile splenocyte transcriptomes at steady state or upon malaria infection as a reference dataset for deconvolution of spatial transcriptomic data.

2024-07-08 | GSE244804 | GEO

scRNA-seq + TCR-seq of mouse polyclonal CD4+ T cells during blood-stage malaria infection.

Project description:Blood-stage malaria infection induces differentiation of several effector CD4 + T subsets including Tfh and Th1 cells. The cues and microarchitectural niches required in secondary lymphoid organs for their formation were previously uncharacterised. Here we used scRNA-seq to profile CD4+ T cell transcriptomes upon malaria infection as a reference dataset for deconvolution of spatial transcriptomic data.

2024-07-08 | GSE244805 | GEO

Novel integration of spatial and single-cell omics data sets enables deeper insights into IPF pathogenesis

Project description:Idiopathic pulmonary fibrosis (IPF) is a progressive lung disease characterized by repetitive alveolar injuries with excessive deposition of extracellular matrix (ECM) proteins. A crucial need in understanding IPF pathogenesis is identifying cell types associated with histopathological regions, particularly local fibrosis centers known as fibroblast foci. To address this, we integrated published spatial transcriptomics and single-cell RNA sequencing (scRNA-seq) transcriptomics and adopted the Query method and the Overlap method to determine cell type enrichments in histopathological regions. Distinct fibroblast cell types are highly associated with fibroblast foci, and transitional alveolar type 2 and aberrant KRT5-/KRT17+ epithelial cells are associated with morphologically normal alveoli in human IPF lungs. Furthermore, we employed laser capture microdissection directed mass spectrometry to profile proteins. By comparing with another published similar dataset, common differentially expressed proteins and enriched pathways related to ECM structure organization and collagen processing were identified in fibroblast foci. Importantly, cell type enrichment results from innovative spatial proteomics and scRNA-seq data integration accord with those from spatial transcriptomics and scRNA-seq data integration, supporting the capability and versatility of the entire approach. In summary, we integrated spatial multi-omics with scRNA-seq data to identify disease-associated cell types and potential targets for novel therapies in IPF intervention. The approach can be further applied to other disease areas characterized by spatial heterogeneity.

2025-01-27 | PXD058805 | Pride

Multi-resolution deconvolution of spatial transcriptomics data reveals continuous patterns of inflammation (Spatial scRNA-Seq)

Project description:Multi-resolution deconvolution of spatial transcriptomics data reveals continuous patterns of inflammation (Spatial scRNA-Seq)

| PRJNA727008 | ENA

SDePER: a hybrid machine learning and regression method for cell-type deconvolution of spatial barcoding-based transcriptomic data

Project description:Spatial barcoding-based transcriptomic (ST) data require cell type deconvolution for cellular-level downstream analysis. Here we present SDePER, a hybrid machine learning and regression method, to deconvolve ST data using reference single-cell RNA sequencing (scRNA-seq) data. SDePER removes the systematic difference between the ST and scRNA-seq data (platform effects) explicitly and efficiently to ensure the linear relationship between ST data and cell type-specific expression profile. It also considers sparsity of cell types per capture spot and across-spots spatial correlation in cell type compositions. Based on the estimations, SDePER imputes for cell type compositions and gene expression at enhanced resolution. We assessed the performance of SDePER and six existing methods using simulations and four real datasets. All results showed that SDePER achieved significantly more accurate and robust results than the existing methods suggesting the importance of considering platform effects, sparsity and spatial correlation in cell type deconvolution.

2024-08-01 | GSE231385 | GEO

SCDC: Deconvolution of Bulk Gene Expression by Single-Cell RNA Sequencing Data

Project description:This paper describes SCDC, a deconvolution method for bulk RNA-seq that leverages cell-type specific gene expression profiles from multiple scRNA-seq reference datasets.

2019-08-30 | GSE136148 | GEO

DNA methylation-based prediction of response to immune checkpoint inhibition in metastatic melanoma

Project description:A novel approach of reference-free deconvolution of large-scale DNA methylation data enabled to develop a machine learning classifier based on CpG sites, specific for Latent Methylation Components (LMC), that allowed for patient allocation to prognostic clusters. DNA methylation data was processed using reference-free analyses (MeDeCom) and reference-based computational tumor deconvolution (MethylCIBERSORT, LUMP). These results demonstrate that LMC-based segregation of large-scale DNA methylation data is a promising tool for classifier development and treatment response estimation in cancer patients under targeted immunotherapy.

2021-07-22 | GSE175699 | GEO

Spatial transcriptomics of fetal skin

Project description:Spatial organization of different cell types within prenatal skin across various anatomical sites is not well understood. To address this, here we have generated spatial transcriptomics data from prenatal facial and abdominal skin obtained from a donor at 10 post conception weeks. This in combination with our prenatal skin scRNA-seq dataset has helped us map the location of various identified cell types.

2024-06-04 | E-MTAB-13024 | biostudies-arrayexpress

Multi-resolution deconvolution of spatial transcriptomics data reveals continuous patterns of inflammation (scRNA-Seq)

Project description:Multi-resolution deconvolution of spatial transcriptomics data reveals continuous patterns of inflammation (scRNA-Seq)

| PRJNA727005 | ENA

An interpretable and adaptive autoencoder for efficient tissue deconvolution

Project description:Deconvolution models are a powerful tool for extracting cell type-specific information from bulk gene expression profiles. Current methods leverage advanced machine learning models and high-resolution sequencing, like single-cell RNA-sequencing (scRNA-seq), showing promising results across diverese tissues and conditions. However, they still present important limitations: Many depend on selecting a robust reference, which can strongly affect the deconvolution. Secondly, pseudobulk data used for training and real bulk RNA-seq samples often exhibit strong distribution shifts, which are currently unaccounted for. Finally, most deconvolution approaches behave as black boxes, which can compromise the reliability of the results. Here, we present Sweetwater, an adaptive and interpretable autoencoder that efficiently deconvolves bulk samples leveraging multiple classes of reference data. Moreover, we propose an improved way of generating training data from a mixture of FACS-sorted FASTQ files, reducing platform-specific biases and outperforming current single-cell-based references. Furthermore, we introduce a gold standard dataset to facilitate fair and accurate evaluation of deconvolution approaches. Finally, we demonstrate that Sweetwater adapts effectively to deconvolved samples during training, uncovering biologically meaningful patterns and enhancing result's reliability. Sweetwater is available at https://github.com/ML4BM-Lab/Sweetwater, and we anticipate it will expedite the accurate examination of high-throughput clinical data across diverse applications.

2025-09-10 | GSE297720 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data