Evidence map›Paper›PMID 40045235›Full record

ArticleClinical proteomics2025

Spatial top-down proteomics for the functional characterization of human kidney.

Kevin J Zemaitis, James M Fulcher, Rashmi Kumar, David J Degnan, Logan A Lewis, Yen-Chen Liao, Marija Veličković, Sarah M Williams, Ronald J Moore, Lisa M Bramer and 4 more

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Integrating Spatial Proteogenomics in Cancer Research.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Review
  5. Article
  6. Review
  7. Review
  8. Article
  9. Laser capture microdissection.Nature reviews. Methods primers · 2025
    Article
  10. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

14 authors.

Kevin J Zemaitis *Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA.ORCID http://orcid.org/0000-0002-3524-9776
James M Fulcher *Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA.ORCID http://orcid.org/0000-0001-9033-3623
Rashmi KumarEnvironmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA.ORCID http://orcid.org/0000-0001-8994-5091
David J DegnanBiological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA.ORCID http://orcid.org/0000-0001-5737-7173
Logan A LewisBiological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA.ORCID http://orcid.org/0000-0003-2301-300X
Yen-Chen LiaoEnvironmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA.ORCID http://orcid.org/0000-0002-8124-0457
Marija VeličkovićEnvironmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA.ORCID http://orcid.org/0000-0003-3664-5719
Sarah M WilliamsEnvironmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA.ORCID http://orcid.org/0000-0002-0175-466X
Ronald J MooreBiological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA.ORCID http://orcid.org/0000-0003-2806-2855
Lisa M BramerBiological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA.ORCID http://orcid.org/0000-0002-8384-1926
Dušan VeličkovićEnvironmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA.ORCID http://orcid.org/0000-0001-7945-9620
Ying ZhuEnvironmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA.ORCID http://orcid.org/0000-0002-5416-0566
Mowei ZhouEnvironmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA.ORCID http://orcid.org/0000-0003-3575-3224
Ljiljana Paša-TolićEnvironmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA. Ljiljana.pasatolic@pnnl.gov.ORCID http://orcid.org/0000-0001-9853-5457

Funding

Spatially resolved characterization of proteoforms for functional proteomicsUH3CA256959 · NCI · BATTELLE PACIFIC NORTHWEST LABORATORIES · PI PASA-TOLIC, LJILJANA · 2022 to 2023
$1.0M
Spatially resolved characterization of proteoforms for functional proteomicsUG3CA256959 · NCI · BATTELLE PACIFIC NORTHWEST LABORATORIES · PI PASA-TOLIC, LJILJANA · 2020 to 2021
$600k
NCI NIH HHS UG3 CA256959NCI NIH HHS UH3 CA256959NIH HHS UG3CA256959
6 · The paper itself

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.

Indexed as

GlomeruliIntact proteinsLaser capture microdissectionMALDIMedullary raysMicroPOTSSpatial proteomicsTop-down proteomicsTubules

Identifiers

PMID40045235
PMCPMC11881370

What OpenQuestion holds

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Registered trials

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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.