Core Keyword · Spatial Omics

Spatial Omics

Spatial omics is a general term for techniques that perform in-situ sequencing or imaging analysis of molecules such as transcripts, proteins, and metabolites while preserving the tissue's spatial position information. It combines the molecular panorama of 'omics' with the spatial localization of 'histology', enabling researchers to answer both 'what molecules are present' and 'where they are' on the same section.
Table of Contents
Definition and Technical BranchesThe Role of Mass Spectrometry Imaging in Spatial OmicsComparison of Mainstream Spatial Omics TechniquesMulti-Omics Integration Trend
Spatial omics: one tissue section → transcript / protein / metabolite multi-omics spatial info Tissue section Transcriptome Proteome Metabolome MSI is the core approach for spatial metabolomics and spatial proteomics
Spatial Omics — schematic diagram

Definition and Technical Branches

Traditional omics (transcriptomics, proteomics, metabolomics) provide a 'mixture inventory' that loses spatial information; spatial omics anchors this inventory back to tissue coordinates. According to the molecular level measured, the main branches include: spatial transcriptomics (measuring in-situ mRNA distribution), spatial proteomics (measuring in-situ protein expression), and spatial metabolomics (measuring in-situ metabolite distribution). The three characterize tissue from the three levels of gene expression, protein function, and metabolic end products respectively.

Different branches use different technical means: spatial transcriptomics mostly relies on in-situ sequencing/probe hybridization (such as barcode-array-based methods); spatial proteomics includes both antibody-based imaging and mass-spectrometry-driven approaches; spatial metabolomics mainly relies on mass spectrometry imaging (MSI). Understanding branch differences helps select the right tool for the scientific question.

The Role of Mass Spectrometry Imaging in Spatial Omics

Mass spectrometry imaging is the core method for spatial metabolomics and also an important tool for spatial proteomics (especially at the protein/peptide level). Compared with antibody-dependent methods, MSI requires no prior targets and can simultaneously present the in-situ distribution of hundreds to thousands of metabolites/lipids/drugs, representing 'discovery-type' spatial analysis. It is also frequently integrated with spatial transcriptomics data, using 'gene expression changes' to correspond to 'metabolite/lipid end-product changes', forming a complete mechanistic picture.

In the spatial proteomics direction, MALDI imaging can present the tissue distribution of proteins/peptides; in the single-cell/subcellular direction, matrix-free sources such as Neo-Source LDPI (2–3 μm) provide new tools for single-cell maps at the metabolic level. It can be said that MSI is a key bridge connecting metabolic end products with spatial heterogeneity.

Comparison of Mainstream Spatial Omics Techniques

Spatial transcriptomics: based on in-situ sequencing or probe hybridization, resolution can reach subcellular to single-cell, excels at gene expression profiles, but requires known/designed probes and does not directly give metabolic information. Spatial proteomics: antibody imaging has high resolution but limited throughput; mass-spectrometry routes (MALDI) cover broadly but require sample preparation. Spatial metabolomics: MSI is label-free and parallel across multiple molecules, but quantification and molecular-weight annotation still need methodological support.

BranchMain technologyAdvantageLimitation
Spatial transcriptomicsIn-situ sequencing / probesGene expression, single-cellRequires probes, no metabolic info
Spatial proteomicsAntibody imaging / MALDIFunctional protein levelAntibody throughput / sample prep
Spatial metabolomicsMass spectrometry imaging (MSI)Label-free, multi-molecule parallelHard to quantify and annotate

Multi-Omics Integration Trend

A single omics can only see one link in the mechanistic chain, whereas real biological processes involve multi-layer linkage of transcription—protein—metabolism. Multi-omics integration (continuous sections of the same tissue or multi-modal on the same platform) has become the frontier of spatial omics: for example, overlaying spatial transcriptomics with spatial metabolomics can explain 'which genes are up-regulated corresponding to which metabolites accumulate'.

The bottleneck in achieving integration lies in registration (coordinate alignment of different sections/modalities), data volume, and analysis tool chains. Standard formats output by mass spectrometry imaging such as imzML are gradually being connected with transcriptomics and proteomics analysis workflows. As domestic matrix-free imaging sources (such as Neo-Source LDPI/DPI) lower the MSI barrier, the participation of spatial metabolomics in multi-omics integration will continue to rise.

Frequently Asked Questions (FAQ)

What is the relationship between spatial omics and mass spectrometry imaging?
Mass spectrometry imaging is one of the technical pillars of spatial omics, especially supporting spatial metabolomics and also serving as an important means for spatial proteomics; it provides label-free, multi-molecule parallel in-situ distribution information.
What are the main branches of spatial omics?
The three main branches are spatial transcriptomics, spatial proteomics, and spatial metabolomics, which respectively measure the in-situ distribution of mRNA, proteins, and metabolites, corresponding to the three layers of gene—function—end product.
Can mass spectrometry imaging be integrated with proteomics/transcriptomics?
Yes. By aligning coordinates (registration), the metabolite/lipid distribution of MSI can be integrated with spatial transcriptomics and proteomics data to explain multi-layer linkage mechanisms.
Why is mass spectrometry imaging commonly used for spatial metabolomics?
Because MSI is label-free and can simultaneously present the in-situ distribution of hundreds to thousands of metabolites/lipids/drugs, representing discovery-type spatial analysis.

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