Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Ultra-fast and deep spatial proteomic technology enables topographic mapping of human brain tumors

ABSTRACT: Spatial proteomics is essential for resolving molecular heterogeneity and tissue microenvironment, yet it remains constrained by trade‑offs among spatial resolution, analytical throughput, and proteome depth. Here we present SMID‑MOSF, an ultra‑trace and rapid sample preparation strategy based on macroporous ordered siliceous foams-assisted single‑pot, miniaturized in‑solution digestion. Coupled with laser capture microdissection-based in situ sampling, SMID-MOSF enables high‑resolution, deep spatial proteomic profiling within a streamlined workflow. SMID‑MOSF identified over 3700 protein groups from single HEK 293T cells and more than 3000 protein groups from 50-μm mouse brain tissue voxels. The method reduces digestion time to 10 minutes, is compatible with high-resolution grid-based sampling formats, and completes within 1.5 hours, enabling scalable spatial analyses. Applied to human oligodendroglioma and brain metastasis tissues, SMID‑MOSF quantified over 4100 protein groups at 50-μm spatial resolution, delineating histopathological regions and mapping tumor heterogeneity. Integration with flow cytometry-derived immune cell proteomic references and spatial deconvolution further revealed distinct immune microenvironmental features between the two tumor types. Together, SMID‑MOSF provides a scalable framework for whole‑tissue spatial proteomics and broadens access to high‑depth in situ proteome analysis.

ORGANISM(S): Homo Sapiens Mus Musculus

SUBMITTER: Xiaohui Liu

PROVIDER: PXD076766 | iProX | Wed Apr 08 00:00:00 BST 2026

REPOSITORIES: iProX

ACCESS DATA

Json Xml

Similar Datasets

All-at-once spatial proteome profiling of complex tissue context with single-cell-type resolution by proximity proteomics

Project description:Spatial proteomics enables in-depth mapping of cellular components and tissue architectures, mostly achieved by laser microdissection-mass spectrometry (LMD-MS) and antibody-based imaging. However, the trade-offs among sampling resolution, throughput, and proteome coverage still limit the applicability of these strategies, especially toward deep proteome profiling with high spatial resolution. Here, we propose PSPro (Proximity labeling for Spatial Proteomics) by leveraging precise antibody-targeted biotinylation at three-dimension scale and efficient affinity purification for MS-based cell-type-specific proteome profiling from single tissue slice. With fine-tuned labeling parameters, PSPro achieves all-at-once cell-type proteome capture with sub-micrometer resolution and shows comparable performance to flow cytometry- and LMD-based proteomic workflows. We apply PSPro to fixed tumor and spleen slices, enriching thousands of proteins containing known markers from ten cell types. For tissue microenvironments with high heterogeneity, we further incorporate region-resolved LMD into PSPro to facilitate direct comparison of cell subpopulations from the same tissue slice, revealing spatial proteome heterogeneity of cancer cells and immune cells in pancreatic tumor. Collectively, PSPro converts traditional “antibody-epitope” paradigm to “antibody-cell type proteome” for spatial biology in a user-friendly manner.

2024-02-02 | MSV000093995 | MassIVE

High-density spatial transcriptomics arrays for in situ tissue profiling

Project description:Tissue function relies on the precise spatial organization of cells characterized by distinct molecular profiles. Single-cell RNA-Seq captures molecular profiles but not spatial organization. Conversely, spatial profiling assays to date have lacked global transcriptome information, throughput or single-cell resolution. Here, we develop High-Density Spatial Transcriptomics (HDST), a method for RNA-Seq at high spatial resolution. Spatially barcoded reverse transcription oligonucleotides are coupled to beads that are randomly deposited into tightly packed individual microsized wells on a slide. The position of each bead is decoded with sequential hybridization using complementary oligonucleotides providing a unique bead-specific spatial address. We then capture, and spatially in situ barcode, RNA from the histological tissue sections placed on the HDST array. HDST recovers hundreds of thousands of transcript-coupled spatial barcodes per experiment at 2 μm resolution. We demonstrate HDST in the mouse brain, use it to resolve spatial expression patterns and cell types, and show how to combine it with histological stains to relate expression patterns to tissue architecture and anatomy. HDST opens the way to spatial analysis of tissues at high resolution.

2019-09-09 | GSE130682 | GEO

In-depth Spatial Proteomics for Whole Tissue Profiling by Sparse Sampling Strategy

Project description:To achieve a deep quantitative proteomic profiling of whole tissue with the spatial localization preserved, we designed an untargeted spatial proteomics workflow with a sparse sampling strategy. We combined a computational approach and MS-based proteomics analysis to localize high-throughput proteome mapping within a tissue by microdissecting the slices into a series of parallel strips with different angles, that allow subsequent image reconstruction. We successfully reconstructed a whole-tissue mapping in a mouse brain, consisting of 8,645 protein distributions and expressions with a spatial resolution of approx. 300 μm.

2024-12-31 | PXD059321 |

Direct 3D sampling of the embryonic mouse head: Layer-wise nanosecond infrared laser (NIRL) ablation from scalp to cortex for spatially resolved proteomics

Project description:Common workflows in bottom-up proteomics require homogenization of tissue samples giving to get access to the biomole-cules within the cells. The homogenized tissue samples often contain many different cell types, thereby representing an average of the natural proteome composition and rare cell types are not sufficiently represented. To overcome this problem, small vol-ume sampling and spatial resolution is needed to maintain a better representation of the sample composition and their proteo-me signatures. Using nanosecond infrared laser ablation, the region of interest can be targeted in a three dimensional (3D) fash-ion whereby the spatial information is maintained during the simultaneous process of sampling and homogenization. In this study, we ablated 40 µm thick consecutive layers directly from the scalp through the cortex of the embryonic mouse heads and analyzed them by subsequent bottom-up proteomics. Extra- and intracranial ablated layers showed distinct proteome profiles comprising expected cell specific proteins. Additionally, known cortex markers like SOX2, KI67, NESTIN and MAP2 showed a layer specific spatial protein abundance distribution. We propose potential new marker proteins for cortex layers such as MTA1 and NMRAL1. The obtained data confirm that the new 3D -tissue sampling and homogenization method is well suited for investigating the spatial proteome signature of tissue samples in a layer-wise manner. Characterization of the proteome composition of embryonic skin and bone structures, meninges, and cortex lamination in situ, enables to a better understanding of molecular mechanisms of development during embryogenesis and disease pathogenesis.

2024-01-26 | PXD043011 | Pride

Sci-Space E14 Mouse Embryo Data

Project description:Spatial patterns of gene expression span many scales, and are shaped by both local (e.g. cell-cell interactions) and global (e.g. tissue, organ) context. However, most in situ methods for profiling gene expression either average local contexts or are restricted to limited fields of view. Here we introduce sci-Space, a scale-flexible method that retains single cell resolution while resolving spatial heterogeneity in gene expression at larger scales. As a proof-of-concept, we apply sci-Space to the developing mouse embryo (E14), capturing the approximate spatial coordinates of profiled cells from whole embryo serial sections.

2021-07-01 | GSE166692 | GEO

High resolution mapping of the tumor microenvironment using integrated single-cell, spatial and in situ analysis [Xenium]

Project description:Single-cell and spatial technologies that profile gene expression across a whole tissue are revolutionizing the resolution of molecular states in clinical tissue samples. Current commercially available technologies provide whole transcriptome single-cell, whole transcriptome spatial context, or high resolution in situ targeted gene expression analysis. Here, we combine these technologies to explore tissue heterogeneity in large, FFPE human breast cancer sections. In one sample, we identify three molecularly distinct tumor subtypes, enabling us to define cell neighborhoods and biomarkers in the progression towards invasive carcinoma. In another sample, we identify rare boundary cells that sit at the critical myoepithelial border confining the spread of malignant cells. We demonstrate that each technology alone provides information about molecular signatures relevant to understanding cancer heterogeneity. However, it is the integration of these technologies that leads to even deeper insights, ushering in discoveries that will progress oncology research and the development of diagnostics and therapeutics.

2023-10-09 | GSE243168 | GEO

High resolution mapping of the tumor microenvironment using integrated single-cell, spatial and in situ analysis [scRNA-seq and Visium]

2023-10-09 | GSE243275 | GEO

High Resolution Spatial Mapping of Microbiome-Host Interactions via in situ Polyadenylation and Spatial RNA Sequencing [Spatial transcriptomics]

Project description:Inter-microbial and host-microbial interactions are critical for the functioning of the gut microbiome, but few tools are available to measure these interactions in situ. Here, we report a method for broad spatial sampling of microbiome-host interactions in the gut at high resolution (1 µm). This method combines enzymatic in situ polyadenylation of both bacterial and host RNA with spatial RNA-sequencing to increase bacterial RNA recovery and enable transcriptomic analysis of low-abundance and spatially restricted microbial taxa. We benchmark the method against existing spatial transcriptomic workflows, demonstrating improved sensitivity and resolution. Application of this method in a mouse model of intestinal neoplasia revealed the biogeography of the mouse gut microbiome as function of location in the intestine, frequent strong inter-microbial interactions at short length scales, and tumor-associated changes in the architecture of the host-microbiome interface. This method is compatible with widely available commercial platforms for spatial RNA-sequencing and can therefore be readily adopted to study the role of short-range, bidirectional host-microbe interactions in microbiome health and disease.

2026-01-21 | GSE316608 | GEO

Mapping the total transcriptome at near cellular resolution

Project description:Spatial transcriptomics provides context to gene expression information, however most methods are only sensitive to a subset of the RNA species. Many noncoding and microbial RNA molecules lack the poly(A) tail which is used to capture RNAs in sequencing-based methods. These transcripts represent valuable analytes for studying gene regulation and host-microbe interactions, respectively. We and others recently showed that in situ polyadenylation combined with poly(dT) capture enables broad analysis of coding, noncoding, and non-host RNAs. However, current implementations neither attain single-cell resolution or retain spatial information. Recent commercialization of spatial transcriptomics tools have enabled broad adoption of these technologies. Several new tools have been developed which can attain near cellular resolution, including SlideSeq (Seeker, Takara/Curio Bioscience) and StereoSeq (STOmics, BGI). Here we adapt in situ polyadenylation to two commerical spatial transcriptomics platforms with near cellular resolution and show the spatial heterogeneity of the total transcriptome in the infected murine heart and testis.

2026-01-21 | GSE316962 | GEO

Neuropeptides in the Extracellular Space of the Mouse Cortex Measured by In Vivo Nanodialysis Probe

Project description:Neuropeptides are key neuromodulators in the central nervous system that shape sensory processing, yet their extracellular dynamics in the somatosensory cortex (S1) remain poorly understood. This study applies an innovative membrane-free silicon nanodialysis (ND) probe coupled with liquid chromatography-mass spectrometry (LC-MS) to analyze the extracellular neuropeptidome in the mouse S1 with spatial resolution down to 100 μm. Localized in vivo sampling identified extracellular peptides from secretogranin-1, ProSAAS, pro-opiomelanocortin (POMC), and others. Minimal tissue damage, enabled by probe dimensions of 75×15 μm², resulted in an absence of structural peptides in the dialysate indicating low intracellular contamination. Many detected secretory peptides correlated with strong local mRNA expression; however, the detection of POMC-derived peptides, despite negligible local expression, suggests long-distance peptide transport or extracellular processing. To expand peptide identification, a discovery-to-targeted peptidomic approach was developed, revealing 46 peptides from 24 proteins in dialysate samples, including 10 proteins with low local expression. Complementary S1 tissue analysis confirmed POMC peptides and showed that 17% of 304 prohormone-derived peptides had low local expression. These results uncover a complex extracellular peptide landscape shaped by both local and long-distance signaling. By overcoming the limitations of traditional microdialysis, this approach advances the understanding of neuropeptide signaling in cortical function.

2025-09-29 | PXD066497 | Pride

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