Proteoform profiling of endogenous single cells from rat hippocampus at scale

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Data availability

A list of 165 identified proteoforms, .RAW files and processed single-cell data are available on MassIVE repositories under accession codes MSV000096472 and MSV000092360. The biological pathway database for GSVA was retrieved from MSigDB of the Broad Institute (https://www.gsea-msigdb.org/gsea/msigdb). Source data are provided with this paper.

Code availability

Compiled scripts used to create and process I2MS files are available on GitHub (https://github.com/NRTDP/scPiMS). Additional software and data that support and corroborate the findings of this study are available on GitHub (https://github.com/NRTDP/scPiMS).

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Acknowledgements

This study was funded by the National Institutes of Health (P41 GM108569 and UH3 CA246635, to N.L.K.; P30 DA018310, to N.L.K. and J.V.S.; K99 AI183290, to P.S.; P30 CA060553, to the Robert H. Lurie Comprehensive Cancer Center), The Investigator Program at the Chan Zuckerberg Biohub Chicago (to N.L.K. and J.V.S.) and Northwestern University.

Author information

Authors and Affiliations

  1. Department of Chemistry, Northwestern University, Evanston, IL, USA

    Pei Su & Neil L. Kelleher

  2. Proteomics Center of Excellence, Chemistry of Life Processes Institute, Northwestern University, Evanston, IL, USA

    Michael A. R. Hollas, Indira Pla, Fatma Ayaloglu Butun, Joseph B. Greer, Bryan P. Early, Ryan T. Fellers, Michael A. Caldwell, Jared O. Kafader & Neil L. Kelleher

  3. Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Stanislav Rubakhin & Jonathan V. Sweedler

  4. Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Stanislav Rubakhin & Jonathan V. Sweedler

  5. Chan Zuckerberg Biohub Chicago, Chicago, IL, USA

    Jonathan V. Sweedler & Neil L. Kelleher

  6. Department of Biochemistry and Molecular Genetics, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA

    Neil L. Kelleher

  7. Department of Molecular Biosciences, Northwestern University, Evanston, IL, USA

    Neil L. Kelleher

  8. Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, USA

    Neil L. Kelleher

Contributions

N.L.K. conceptualized the project. P.S., J.O.K. and S.R. developed the methodology. N.L.K. and J.V.S. channeled resources. P.S., M.A.R.H., I.P., J.B.G., B.P.E. and R.T.F. participated in software development. P.S., M.A.R.H., I.P., S.R., J.B.G., B.P.E. and J.O.K. participated in the investigation of the results. P.S., M.A.R.H., I.P. and R.T.F. participated in visualization of the figures. The original draft was written by P.S. and N.L.K. All authors participated in writing, review and editing. The work was supervised by J.O.K., N.L.K. and J.V.S.

Corresponding author

Correspondence to
Neil L. Kelleher.

Ethics declarations

Competing interests

J.O.K. and N.L.K. report a conflict of interest with I2MS technology, being commercialized by Thermo Fisher Scientific. R.T.F., J.B.G. and N.L.K. are involved in commercialization of software. N.L.K. is a paid consultant for Thermo Fisher Scientific. The other authors declare no competing interests.

Peer review

Peer review information

Nature Biotechnology thanks Nikolai Slavov and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Teaser

Intact protein sampling based on individual ion mass spectrometry enables a scalable approach to single-cell proteomics.

Extended data

Extended Data Fig. 1 Single cell feature selection demonstrated using the data shown in Fig. 1b-d.

From 752 optically-registered cells, 529 single cells were detected based on filtering of distance and scPiMS sampling conditions (annotated in panel (a)). Location of the 529 single cells are registered in the pixel-based graph in panel (a). (b) Cellogram single cell feature selection purely based on peak features (more in Methods). Using a peak threshold of 750 ions and a spacing of 7 scans, 529 single cell-like peak profiles were registered, which was well correlated with the optical registration and statistical modeling.

Extended Data Fig. 2 Mass spectrum of (a) the proteoform library obtained from hippocampal-specific PiMS experiment and (b) aggregated 5272-cell dataset I with proteoform annotations.

Mass spectrum of (a) the proteoform library obtained from hippocampal-specific PiMS experiment (a 2.5 mm by 2.5 mm hippocampal region of a 10-micron thin brain section with a total of 38 million ions and 15930 MS scans) and (b) aggregated 5272-cell dataset I with annotations of the 165 identified proteoforms in Supplementary Data 5.5 and the 23 MS/MS-identified proteoforms in Supplementary Data 4. The inset in (a) shows the detailed annotation of MBP (P02688-4) proteoforms at ~14.1 kDa. The proteoforms labeled in gray are abundant proteins found in hippocampal tissues but not part of the hippocampal-enriched biological pathways, therefore they were not included in the IMT database and not used for subsequent cell typing effort. However, these proteoforms were identified using our MS/MS approach and were confirmed to be ubiquitously-expressed brain proteins (for example, ubiquitin, ATP synthase subunits-ATP5E, myelin basic proteins-MBPs, brain acid soluble protein-BASP1) that only have a minor contribution to cell type classification of neurons and glial cells.

Source data

Extended Data Fig. 3 PAScore summary of the 5272-cell dataset I with 10% FDR and 10 decoy sequences for each proteoform matching event.

(a) pie chart of a total of 869,880 candidate matching events with 45.5% missing values, 49.5% FDR-filtered PAScores, and 5% non-zero PAScores assigned to 165 proteoforms identified from the rat hippocampus. (b) PAScore distribution of FDR-filtered matching events with an average of 1.86 ions matched to a proteoform (blue portion in the pie chart in panel a). (c) Non-zero PAScores assigned to the 165 identified proteoforms plotted according to number of ions (2-8) matched to the proteoforms (red portion comprising 5% in the pie chart in panel a). (d) Distribution of PAScores for the 5% of proteoforms assigned in single cells to one of the 165 identified proteoforms (that is, a total of 43.5k scProteoforms).

Extended Data Fig. 4 Principal component analysis of 4927 cells colored according to the cell type assigned.

The pink arrows are the ‘biplot’ vectors representing the coefficients of the biological pathway variables on the principal components shown in Fig. 2b. The Neu, Ast and Mgl labels show the locations of the single cells with optical images in Fig. 2b.

Extended Data Fig. 5 Total and proteoform-assigned ion count per cell distribution across cell types in the 5272-cell dataset I.

Total (a) and proteoform-assigned (b) ion count per cell distribution across cell types in the 5272-cell dataset I. Ast, astrocytes; Mgl, microglia; Neu, neurons.

Extended Data Fig. 7 Mass spectra of the monoacetylated ARP5L proteoform constructed from 50, 100, 200, and 300 single neurons and individual ion mass spectra from three single neurons showing assignment of the monoacetylated ARP5L proteoform.

(a) Mass spectra of the monoacetylated ARP5L proteoform constructed from 50, 100, 200, and 300 single neurons. The theoretical isotopic distribution is shown in the mirrored panel. (b) Individual ion mass spectra from three single neurons showing assignment of the monoacetylated ARP5L proteoform in its mass range with the PAScores in cells numbered #784, #1247 and #605.

Extended Data Fig. 8 Different representation of the mass spectrum of cell #179 shown in Fig. 2d.

Different representation of the mass spectrum of cell #179 shown in Fig. 2d (middle and bottom). The top panel shows a single-ion representation that shows the breakdown of the four-ion composite peak, of which two ions were matched to an ALDOA proteoform and two were not.

Extended Data Fig. 9 Bar and scatter plots of detected proteoforms involved in glucose metabolism (REACTOME Stable Identifier: R-HSA-70326) and glial fibril acidic protein (GFAP, a classical immunofluorescent astrocytic marker) across cell types.

Bar and scatter plots of detected proteoforms involved in glucose metabolism (REACTOME Stable Identifier: R-HSA-70326) and glial fibril acidic protein (GFAP, a classical immunofluorescent astrocytic marker) across cell types that showed consistent elevated PAScores in the astrocytic population. Each panel is labeled with the proteoform name and UniProt accession together with a proteoform descriptor. The average unadjusted PAScores are labeled using solid diamond symbols. The upper side of the whiskers indicate PAScore at 99% percentile.

Extended Data Fig. 10 Bar and scatter plots of detected proteoforms involved in HIV Infection (REACTOME Stable Identifier: R-HSA-162906) and Host Interactions of HIV Factors (REACTOME Stable Identifier: R-HSA-162909) across cell types.

Bar and scatter plots of detected proteoforms involved in HIV Infection (REACTOME Stable Identifier: R-HSA-162906) and Host Interactions of HIV Factors (REACTOME Stable Identifier: R-HSA-162909) across cell types that showed consistent elevated PAScores in the microglia population. Each panel is labeled with the proteoform name and UniProt accession together with a proteoform descriptor. The average unadjusted PAScores are labeled using solid diamond symbols. The upper side of the whiskers indicate PAScore at 99% percentile.

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Su, P., Hollas, M.A.R., Pla, I. et al. Proteoform profiling of endogenous single cells from rat hippocampus at scale.
Nat Biotechnol (2025). https://doi.org/10.1038/s41587-025-02669-x

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  • DOI: https://doi.org/10.1038/s41587-025-02669-x

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