ArticleClinical proteomics2025
Spatial top-down proteomics for the functional characterization of human kidney.
Article in Clinical proteomics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Review
- Towards Cellular Resolution of Tryptic Peptides in Tissue Sections by MALDI MS Imaging: A Focus on Enzyme Application and Reproducibility.Analytical chemistry · 2026Article
- Characterization of the biofilm landscape of Bacillus subtilis by spatial microproteomics.Analytical and bioanalytical chemistry · 2026Article
- Integrating Spatial Proteogenomics in Cancer Research.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Single-section multiplex spatial proteomics of immune microenvironments in kidney transplantation.Frontiers in immunology · 2026Article
- The evolving landscape of spatial proteomics technologies in the AI age.Fundamental research · 2026Review
- Single-cell epigenetics and multiomics analysis in kidney research.Clinical and experimental nephrology · 2025Review
- Characterization of cytokine treatment on human pancreatic islets by top-down proteomics.bioRxiv : the preprint server for biology · 2025Article
- Laser capture microdissection.Nature reviews. Methods primers · 2025Article
- Challenges in Spatial Metabolomics and Proteomics for Functional Tissue Unit and Single-Cell Resolution.Seminars in nephrology · 2024Review
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14 authors.
Funding
Abstract
backgroundThe Human Proteome Project has credibly detected nearly 93% of the roughly 20,000 proteins which are predicted by the human genome. However, the proteome is enigmatic, where alterations in amino acid sequences from polymorphisms and alternative splicing, errors in translation, and post-translational modifications result in a proteome depth estimated at several million unique proteoforms. Recently mass spectrometry has been demonstrated in several landmark efforts mapping the human proteoform landscape in bulk analyses. Herein, we developed an integrated workflow for characterizing proteoforms from human tissue in a spatially resolved manner by coupling laser capture microdissection, nanoliter-scale sample preparation, and mass spectrometry imaging.
resultsUsing healthy human kidney sections as the case study, we focused our analyses on the major functional tissue units including glomeruli, tubules, and medullary rays. After laser capture microdissection, these isolated functional tissue units were processed with microPOTS (microdroplet processing in one-pot for trace samples) for sensitive top-down proteomics measurement. This provided a quantitative database of 616 proteoforms that was further leveraged as a library for mass spectrometry imaging with near-cellular spatial resolution over the entire section. Notably, several mitochondrial proteoforms were found to be differentially abundant between glomeruli and convoluted tubules, and further spatial contextualization was provided by mass spectrometry imaging confirming unique differences identified by microPOTS, and further expanding the field-of-view for unique distributions such as enhanced abundance of a truncated form (1-74) of ubiquitin within cortical regions.
conclusionsWe developed an integrated workflow to directly identify proteoforms and reveal their spatial distributions. Of the 20 differentially abundant proteoforms identified as discriminate between tubules and glomeruli by microPOTS, the vast majority of tubular proteoforms were of mitochondrial origin (8 of 10) while discriminate proteoforms in glomeruli were primarily hemoglobin subunits (9 of 10). These trends were also identified within ion images demonstrating spatially resolved characterization of proteoforms that has the potential to reshape discovery-based proteomics because the proteoforms are the ultimate effector of cellular functions. Applications of this technology have the potential to unravel etiology and pathophysiology of disease states, informing on biologically active proteoforms, which remodel the proteomic landscape in chronic and acute disorders.
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