PreprintCited by 0Advanced Proteomics Techniques and ApplicationsMass Spectrometry Techniques and ApplicationsNeuroinflammation and Neurodegeneration Mechanisms

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Single-cell nanodroplet processing proteomics pipeline for analysis of human-derived microglia

Open access

Preprint — published as 10.1002/pmic.70112

Abstract

Single-cell omics tools provide unique insights into heterogeneous cell populations and their responses to stimuli. For example, single-cell RNA sequencing has identified several transcriptionally distinct populations of microglia, which are resident immune cells of the central nervous system (CNS) that are responsive to CNS injury, infection, and neurodegeneration. To date, single cell studies of microglia have focused on RNA-sequencing or cytometry by time of flight (CyTOF) which provide indirect readouts of protein abundance or quantification of a limited number of targets. Herein, we present a workflow based on FACS-assisted isolation, cryopreservation and nanodroplet-based processing for single-cell mass spectrometry proteomics analysis of the postmortem human brain cortex-derived microglia. From a single microglial cell, 1039 proteins could be identified on average. As a proof-of-principle we applied single-cell proteomics for exploring the heterogeneity of brain microglia at the cellular level. This pilot proteomics data partially recapitulates the prior microglia subtypes. Specifically, we determined mitochondrial proteins, in particular members of NADH dehydrogenase (Complex I), cytochrome b-c1 (Complex III), cytochrome c oxidase (Complex IV), F1-ATPase (Complex V), and Na+/K+-ATPase complex drive variation across microglia. This pipeline offers the potential for identifying functionally and analytically relevant protein targets for microglia in Alzheimer’s disease and other neurological disorders. Significance of Study: Microglia are a key brain cell type that may contribute to pathogenesis in neurodegenerative disease. Transcriptomic profiling of microglia from the central nervous system of humans and animal models has identified several subtypes of microglia, and complementary proteomic profiling of microglia is likely to provide functionally and therapeutically relevant targets, however single-proteomics studies of human-derived microglia are lacking. This work describes a label-free, single-cell proteomics approach for microglia isolated by fluorescence-activated cell sorting from a human donor that yields comparable numbers of identifications in comparison to prior single-cell RNA sequencing studies of microglia. This approach holds promise for enabling large-scale proteomics-based subtyping of microglia and studying their roles in neurodegenerative diseases.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1101/2025.10.02.680067

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 57

  1. 10.1146/annurev-immunol-051116-052358 · DOI · Paper page
  2. 10.1146/annurev-immunol-032713-120240 · DOI · Paper page
  3. 10.1016/j.celrep.2020.01.010 · DOI · Paper page
  4. 10.1038/s41467-020-19737-2 · DOI · Paper page
  5. 10.1038/s41467-018-02926-5 · DOI · Paper page
  6. 10.1016/j.neuron.2018.12.006 · DOI · Paper page
  7. 10.1038/s41593-019-0532-y · DOI · Paper page
  8. 10.1016/j.immuni.2018.11.004 · DOI · Paper page
  9. 10.1038/s41586-019-0924-x · DOI · Paper page
  10. 10.1186/s12974-023-02809-7 · DOI
  11. 10.15252/embr.201846171 · DOI · Paper page
  12. 10.1016/j.celrep.2017.09.039 · DOI · Paper page
  13. 10.1016/j.cell.2017.05.018 · DOI · Paper page
  14. 10.1002/pmic.202200183 · DOI
  15. 10.1038/s41586-024-07871-6 · DOI · Paper page
  16. 10.1038/s41593-024-01764-7 · DOI
  17. 10.3389/fnagi.2023.1201982 · DOI · Paper page
  18. 10.1083/jcb.201709069 · DOI · Paper page
  19. 10.1038/s41593-021-00999-y · DOI · Paper page
  20. 10.1038/s12276-020-00513-7 · DOI
  21. 10.1038/s41392-024-01911-3 · DOI · Paper page
  22. 10.21203/rs.3.rs-2987263/v1 · DOI
  23. 10.1038/s41593-018-0290-2 · DOI · Paper page
  24. 10.1038/s41593-018-0100-x · DOI · Paper page
  25. 10.1016/j.immuni.2018.01.011 · DOI · Paper page
  26. 10.1186/s40478-022-01465-x · DOI
  27. 10.3389/fnmol.2020.583811 · DOI
  28. 10.1002/pmic.202200183 · DOI
  29. 10.3389/fncel.2024.1379717 · DOI
  30. 10.1016/j.bbapap.2021.140658 · DOI
  31. 10.1016/j.tins.2021.11.001 · DOI
  32. 10.1016/0304-3940(94)11792-H · DOI · Paper page
  33. 10.1186/s12974-018-1288-0 · DOI
  34. 10.1038/s41467-018-03367-w · DOI · Paper page
  35. 10.1038/s41592-024-02559-1 · DOI
  36. 10.1038/s41598-022-16358-1 · DOI
  37. 10.1586/epr.12.50 · DOI
  38. 10.1021/acs.analchem.1c00990 · DOI
  39. 10.1038/s41467-024-54099-z · DOI
  40. 10.1021/acs.jproteome.9b00475 · DOI
  41. 10.1021/pr501138h · DOI · Paper page
  42. 10.1093/bioinformatics/btm069 · DOI
  43. 10.1093/bioinformatics/btz961 · DOI
  44. 10.1074/mcp.M113.031591 · DOI · Paper page
  45. 10.1093/biostatistics/kxj037 · DOI · Paper page
  46. 10.1021/acs.jproteome.3c00441 · DOI
  47. 10.1038/s41594-024-01353-z · DOI
  48. 10.12688/f1000research.24956.2 · DOI · Paper page
  49. 10.1186/s12974-020-02019-5 · DOI
  50. 10.1002/glia.23948 · DOI
  51. 10.1186/s40478-017-0418-8 · DOI
  52. 10.1016/j.celrep.2024.114908 · DOI
  53. 10.3389/fncel.2020.00198 · DOI · Paper page
  54. 10.1111/bph.13139 · DOI · Paper page
  55. 10.1002/glia.23484 · DOI
  56. 10.1046/j.1460-9568.2001.01418.x · DOI
  57. 10.1016/j.cub.2014.01.071 · DOI

Metadata

Authors
Ives, A. N., Fulcher, J. M., Bautista, A. R., Kitata, R. B., Williams, S. M., Bennett, D. A., De Jager, P. L., & Petyuk, V. A.
Affiliations
  • Pacific Northwest National Laboratory (US)
  • Columbia University Irving Medical Center (US)
  • Rush University Medical Center (US)
Corresponding author
Petyuk, Vladislav A.
Venue
PROTEOMICS
Publisher
openRxiv
Published
2025-10-04
Volume
26
Issue
5
Pages
59-68
ISSN
1615-9853 (ISSN-L)
OpenAlex
W7131820366
Cited by
0
References
1
FWCI
0.00
Type
journal article
Language
en
Open access
Locations
  • PROTEOMICS (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (submittedVersion, cc-by)
Funders
NIH National Institute of Health (U01 AG061356, R01 AG015819, R01 AG17917, P30 AG10161, P30 AG72975)
Topics
  • Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
  • Physical Sciences › Chemistry › Spectroscopy › Mass Spectrometry Techniques and Applications
  • Life Sciences › Neuroscience › Neurology › Neuroinflammation and Neurodegeneration Mechanisms
Keywords
microglia, single-cell proteomics, microglial heterogeneity, microglial subtypes, mitochondrial proteins, NADH dehydrogenase complex I, cytochrome c oxidase, Na+/K+-ATPase, Alzheimer's disease, neurodegenerative diseases, label-free quantitative proteomics

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review