Nature CommunicationsNeurosciencePeer-reviewed journal article
Multi-omic characterization of human sural nerves acrosspolyneuropathies
Michael HemingiD, Anna‐Lena BörschiD, Jolien WolbertiD, Christian ThomasiD, Anne Kathrin MausbergiD, Fabian SzepanowskiiD, Bianca Eggert, I‐Na LuiD, Julia Tietz, Finja S.V. DienhartiD, Maja Meschnark, Jan‐Kolja StreckeriD, Michael Glatza, Carolina ThomasiD, Noemi Gmahl, Christine Anna DambietziD, Michael MütheriD, Anne‐Kathrin Uerschels, Kathy KeyvaniiD, Jens MinnerupiD, Kathrin DoppleriD, Nurcan ÜçeyleriD, Julieta ApreaiD, Andreas DahliD, Ruth Martha StassartiD, , Gerd Meyer zu HörsteiD
Abstract
Diseases of peripheral nerves termed polyneuropathies (PNPs) are common, mechanistically heterogeneous, and challenging to diagnose. Here, we integrated single nuclei transcriptomics of peripheral nerves from 33 human PNP patients and four controls (365,708 nuclei) with subcellular spatial transcriptomics. We identified novel and human-specific nerve cell type markers including unexpectedly heterogeneous perineurial fibroblasts. All PNPs shared a loss of myelinating and an increase in repair Schwann cells and endoneurial lipid-associated macrophages. Transcriptional changes affected multiple cells outside of the endoneurium across PNPs, suggesting PNPs as pan-nerve diseases. Spatially, PNPs showed a previously unknown perineurial hyperplasia and fibrotic dispersion and this was most pronounced in immune-mediated PNPs. Single cell transcriptomics supported the differential diagnosis of PNPs with potential for future unbiased diagnostic classification. One-sentence summaryThe first large-scale integrated single cell and spatial transcriptomic characterization of human peripheral nerves identifies novel cell markers and unexpected heterogeneity of perineurial cells, reveals polyneuropathies as pan-nerve diseases, and shows that single cell transcriptomics hold potential for unbiased nerve disease classification.
- organism: human
- assay: scRNA-seq
- assay: spatial transcriptomics
- research focus: cell type annotation
- research focus: differential expression
- approach: statistical model
- Citations
- 15
- FWCI
- 9.07
- Field percentile
- top 1%
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