ReviewAnalytical methods : advancing methods and applications2026
MALDI spatial proteomics: a mini review of approaches and techniques.
Review in Analytical methods : advancing methods and applications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Visualizing the molecular landscape of skin: applications and perspectives of mass spectrometry imaging.Analytical and bioanalytical chemistry · 2026Review
- A Multimodal Imaging Pipeline for the Discovery of Molecular Markers of Cellular Neighborhoods.bioRxiv : the preprint server for biology · 2026Article
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Authors and funding
2 authors.
Funding
Abstract
Spatial context is becoming increasingly important in the omics disciplines. Spatial proteomics is a diverse field encompassing numerous techniques that provide both the location and identity of proteins in biological samples. Improving upon bulk analyses, spatial proteomics can map peptides and intact proteins within tissue. This review focuses on the application of matrix-assisted laser desorption/ionization (MALDI) in spatial proteomics. Approaches are grouped into two general categories and discussed: protein MALDI MSI and MSI-guided spatial proteomics. A discussion of the workflow for each method is presented, and challenges to each approach are discussed. Recent and technically interesting cases in the literature are presented for each category. This review aims to guide researchers interested in MALDI protein imaging through the strengths, weaknesses, and technical considerations of the many workflows available to them.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.