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Lookup NU author(s): Dr Joe InnsORCiD, Dr Andy Frey, Professor Matthias TrostORCiD, Professor Neil RajanORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2026 Inns et al.Single-cell proteomics (SCP) reveals cellular heterogeneity and biological insights inaccessible to bulk analysis. Existing limitations are cost, sample loss during processing, and accessibility to state-of-the-art instrumentation. We describe a label-free SCP methodology in human tissue, combining FACS, oil-immersion cell handling, mass spectrometry, and neural-network-derived spectral libraries, which address these issues. We tested this methodology in a skin tumor syndrome, CYLD cutaneous syndrome (CCS), assessing tumor heterogeneity. Using a Bruker timsTOF HT platform, we quantified >4,000 proteins, averaging ∼700 per cell, through a cost-effective pipeline without specialised liquid handling infrastructure. By using preexisting bioinformatic tools from the scRNA-seq field, we implemented a robust analysis methodology, discriminating between macrophages, dendritic cells, and tumor keratinocytes, in an unbiased analysis of 419 CCS tumor cells. We validated the biological accuracy of cell annotations by cross referencing with each cell's FACS markers. Furthermore, we identified a novel CCS tumor-associated macrophage population, which carried a tumor microenvironment remodelling signature. Our findings demonstrate an accessible SCP technology capable of yielding novel biological discoveries in clinical tissue.
Author(s): Inns J, Frey AM, Ng WW, Trost M, Rajan N
Publication type: Article
Publication status: Published
Journal: Life science alliance
Year: 2026
Volume: 9
Issue: 9
Print publication date: 01/09/2026
Online publication date: 29/06/2026
Acceptance date: 15/06/2026
Date deposited: 27/07/2026
ISSN (print): 2575-1077
Publisher: Life Science Alliance
URL: https://doi.org/10.26508/lsa.202603759
DOI: 10.26508/lsa.202603759
Data Access Statement: The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository (40) with the dataset identifier PXD073250.
PubMed id: 42373542
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