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Spatial multi-omics identifies early synaptic pruning and context-specific dopaminergic vulnerability in synucleinopathies.

Code ↔ Paper

9 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 9 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Data processing and analysis ↔ 01_run_BASS.R, the whole file · a weak match · score 0.79 · raw probe, log normalized, expressed genes, BASS, barcode, batch
  2. [2] § Methods › Data processing and analysis ↔ 02_make_seurat.R, lines 1–80 · score 0.75 · Cluster assignments, raw probe, Spaceranger, BASS, barcode, Seurat
  3. [3] § Results › Spatial transcriptomic profiling reveals regional gene expression changes and cellular composition in Parkinson’s disease progression ↔ 02_make_seurat.R, lines 82–115 · score 0.63 · SNtrans, CerPed, SNpr, reticulata, nigra, SNpc
  4. [4] § Results › Impact of lewy body pathology on regional gene expression ↔ Figure03.Rmd, lines 229–260 · score 0.58 · SNtrans, CerPed, SNpr, SNpc, Fiber, LBP
  5. [5] § Results › Early synaptic dysfunction in iLBD: C1QC-associated tagging of inhibitory synapses in the SNpc ↔ Figure05.Rmd, lines 284–293 · score 0.58 · Inh_PAX5_VCAN, MG_TSPO_VIM, GAD1, iLBD
  6. [6] § Results › Spatial transcriptomic profiling reveals regional gene expression changes and cellular composition in Parkinson’s disease progression ↔ Figure03.Rmd, lines 229–260 · score 0.57 · SNtrans, CerPed, SNpr, SNpc, fibers, genes
  7. [7] § Results › Spatial transcriptomic profiling reveals regional gene expression changes and cellular composition in Parkinson’s disease progression ↔ 02_make_seurat.R, lines 82–115 · score 0.57 · SNtrans, CerPed, SNpr, reticulata, nigra, SNpc
  8. [8] § Results › Dopaminergic neuron loss and misfolded αSyn pathology across disease cohorts ↔ make_metad.R, the whole file · a weak match · score 0.54 · Braak tau, median AUC, SAA, cohorts, AD
  9. [9] § Results › Dopaminergic neuron loss and misfolded αSyn pathology across disease cohorts ↔ Figure03.Rmd, lines 40–164 · score 0.54 · Braak tau, median AUC, SAA, model, AD

Paper

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The authors' code

R · 115 lines · 5.5 KB · no license · 3 matches

  1. library(BASS)
  2. library(Seurat)
  3. library(tidyverse)
  4. library(sctransform)
  5. library(future)
  6. source('../ggplot_theme_FLS.R')
  7. plan("multisession", workers = 24)
  8. plan()
  9. set.seed(42)
  11. ##### Get metadata #####
  12. metad <- read_tsv('resources/essential_metad_combined_cohorts.txt') %>%
  13. mutate(case = case_when(
  14. group %in% c('AD', 'ADLBD') ~ paste0('NBB', str_replace(case, '-', '_')),
  15. TRUE ~ case
  16. ))
  17. excludesamples <- read_lines('resources/excludesamples.txt')
  18. filterprobes <- read_lines('resources/drop_probes.txt') %>% str_sub(., start = 17L)
  20. ##### Load BASS object and extract cluster assignments #####
  21. mybass <- readRDS('resources/mybass_batchcorr.rds')
  22. zlabels <- mybass@results$z # spatial domain labels
  23. znames <- lapply(mybass@xy, rownames)
  24. zlabels_named <- mapply(function(zlabel, zname) {
  25. res <- data.frame(barcode = zname, cluster = zlabel)
  26. return(res)
  27. }, zlabel = zlabels, zname = znames, SIMPLIFY = FALSE)
  28. names(zlabels_named) <- names(znames)
  29. zlabels_named <- data.table::rbindlist(zlabels_named, idcol = 'sample') %>%
  30. mutate(cluster = paste0('c', cluster)) %>%
  31. mutate(joincol = paste0(sample, '_', barcode))
  33. ##### Load Spaceranger output and make Seurat object list #####
  34. probefiles <- list.files('data', pattern = 'raw_probe_bc_matrix.h5', recursive = TRUE, full.names = TRUE)
  35. probefiles <- probefiles[grepl('outs', probefiles, ignore.case = FALSE)]
  36. barcodefiles <- list.files('data', pattern = 'barcodes.tsv.gz', recursive = TRUE, full.names = TRUE)
  37. barcodefiles <- barcodefiles[grepl('outs', barcodefiles, ignore.case = FALSE)]
  38. barcodefiles <- barcodefiles[grepl('filtered_feature_bc_matrix', barcodefiles)]
  39. filterprobes <- read_lines('resources/drop_probes.txt') %>% str_sub(., start = 17L)
  40. imagefiles <- str_sub(list.files('data', pattern = 'spatial', recursive = TRUE, full.names = TRUE), end = -24L)
  41. excludesamples <- read_lines('resources/excludesamples.txt')
  42. imagefiles <- imagefiles[grepl('outs', imagefiles, ignore.case = FALSE)]
  44. probefiles <- probefiles[!grepl(paste(excludesamples, collapse = '|'), probefiles)]
  45. imagefiles <- imagefiles[!grepl(paste(excludesamples, collapse = '|'), imagefiles)]
  46. imagefiles <- imagefiles[!grepl(paste(excludesamples, collapse = '|'), imagefiles)]
  47. barcodefiles <- barcodefiles[!grepl(paste(excludesamples, collapse = '|'), barcodefiles)]
  48. ## sanity check:
  49. test <- data.frame(image = imagefiles, probes = probefiles, barcodes = barcodefiles)
  51. seurat_list <- mapply(function(probes, barcodes, images) {
  52. myid <- str_sub(images, start = 6L, end = -14L) # extract sample ID
  53. print(paste0('----> working on: ', myid))
  54. bc <- read_tsv(barcodes, col_names = FALSE) %>% pull(X1)
  55. print(paste0(myid, ': Filtering out ', length(bc), ' spots'))
  56. mat <- Read10X_h5(probes)
  57. print(paste0(myid, ': dims before filtering :', paste0(dim(mat), collapse = ', ')))
  58. mat <- mat[!rownames(mat) %in% filterprobes,bc]
  59. mat <- mat[,colSums(mat) != 0]
  60. print(paste0(myid, ': dims before filtering :', paste0(dim(mat), collapse = ', ')))
  61. matched_probes <- str_sub(rownames(mat), end = -9L)
  62. mat_aggr <- list(Matrix.utils::aggregate.Matrix(mat, groupings = matched_probes, FUN = sum))
  63. assay.names <- 'spatial'
  64. slice.names <- myid
  65. image.list <- mapply(Read10X_Image, images, assay = assay.names, slice = slice.names, image.name = "tissue_hires_image.png")
  66. object.list <- mapply(CreateSeuratObject, mat_aggr, assay = assay.names)
  67. object.list <- mapply(function(.object, .image, .assay,
  68. .slice) {
  69. .image <- .image[Cells(.object)]
  70. .object[[.slice]] <- .image
  71. return(.object)
  72. }, object.list, image.list, assay.names, slice.names)
  74. seurat <- merge(object.list[[1]], y = object.list[-1])
  76. return(seurat)
  78. }, probes = probefiles, barcodes = barcodefiles, images = imagefiles, SIMPLIFY = FALSE)
  79. names(seurat_list) <- str_sub(names(seurat_list), start = 6L, end = -29L)
  80. saveRDS(seurat_list, 'resources/seurat_list_raw.rds', compress = FALSE)
  82. ##### Concatenate Seurat objects, normalize and add metadata #####
  83. seurat <- merge(seurat_list[[1]], seurat_list[2:28], add.cell.ids = c(names(seurat_list[1]), names(seurat_list[2:28])), merge.data = TRUE, merge.dr = FALSE, project = 'spatial_nigra')
  84. seurat$orig.ident <- str_sub(names(seurat$orig.ident), end = -20L)
  85. seurat$group <- metad[match(seurat$orig.ident, metad$case),]$group
  86. seurat$sex <- metad[match(seurat$orig.ident, metad$case),]$sex
  87. seurat <- seurat %>%
  88. NormalizeData(.) %>%
  89. ScaleData(.)
  90. table(colnames(seurat) %in% zlabels_named$joincol) # sanity check
  91. seurat$cluster <- zlabels_named[match(colnames(seurat), zlabels_named$joincol),]$cluster
  93. ##### Name clusters upon histological examination #####
  94. zlabels_named <- zlabels_named %>%
  95. dplyr::mutate(cluster_anno = case_when(
  96. cluster == 'c1' ~ 'Fibers',
  97. cluster == 'c2' ~ 'SNtrans',
  98. cluster == 'c3' ~ 'SNpr',
  99. cluster == 'c4' ~ 'CerPed',
  100. cluster == 'c5' ~ 'SNpc'
  101. ))
  103. ##### Run MAGIC #####
  104. reticulate::use_condaenv('~/miniforge3/bin/python3.10')
  105. library(Rmagic)
  106. seurat_magic <- magic(JoinLayers(seurat), n.jobs = 24, verbose = TRUE, seed = 42)
  107. saveRDS(seurat_magic, 'resources/seurat_clustered_annotated_4excludesamples_MAGIC.rds', compress = FALSE)
  108. seurat$cluster_anno <- zlabels_named[match(colnames(seurat), zlabels_named$joincol),]$cluster_anno
  111. ##### Get marker genes #####
  112. DefaultAssay(seurat) <- 'spatial'
  113. Idents(seurat) <- seurat$cluster_anno
  114. allmarkers <- FindAllMarkers(seurat, assay = 'spatial', logfc.threshold = 0.5)
  115. write_tsv(allmarkers, 'resources/seurat_spatial_allmarkers_4exludesamples.tsv')

02_make_seurat.R at commit 7ccda3f, no license · at the source

Overview

Authors: Svenja-Lotta Rumpf1,2, Felix L Strübing1,3, Karsten Nalbach1,4, Claudia Marina Vargiu5, Giacomo Berg1,3, Stefan F Lichtenthaler1,2,4, Piero Parchi5,6, Pan Gao1,3, Weilin Chen1,3, Matthias Brendel7, Johannes S Gnörich1,7, Alexander Bernhardt3, Léa Dias Rodrigues1,3, Günter U Höglinger1,2,3,8, Jochen Herms1,2,9, Thomas Koeglsperger1,3
ORCID iDs: Felix L Strübing, Karsten Nalbach, Claudia Marina Vargiu, Stefan F Lichtenthaler, Piero Parchi, Pan Gao, Matthias Brendel, Johannes S Gnörich, Alexander Bernhardt, Günter U Höglinger, Jochen Herms
  1. German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
  2. Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
  3. Department of Neurology, LMU University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
  4. Neuroproteomics, School of Medicine and Health, TUM University Hospital, Technical University of Munich, Munich, Germany
  5. Laboratorio di Neuropatologia, IRCCS Istituto delle Scienze Neurologiche, Ospedale Bellaria, Bologna, Italy
  6. Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
  7. Department of Nuclear Medicine, LMU University Hospital, LMU Munich, Munich, Germany
  8. Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, MD 20815 Chevy Chase, USA
  9. Centre for Neuropathology and Prion Research, LMU Munich, Munich, Germany
Institutions: German Center for Neurodegenerative Diseases (Germany); Munich Cluster for Systems Neurology (Germany); LMU Klinikum (Germany); Ludwig-Maximilians-Universität München (Germany); TUM Klinikum (Germany); Technical University of Munich (Germany); Istituto delle Scienze Neurologiche di Bologna (Italy); Ospedale Bellaria (Italy); University of Bologna (Italy); Aligning Science Across Parkinson's (United States); Institut für Neuropathologie (Germany)
Journal: Nature communications, volume 17, issue 1, article 6976
Dates: received 21 October 2025; accepted 12 June 2026; published online 21 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-74961-6 · PMID 42481480 · PMCID PMC13392254 · OpenAlex W7169858363
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), Alzheimer's / dementia (population), Parkinson's (population), cellular / molecular (subfield)
Methods: Statistics, fMRI & imaging, Smoothing, state filtering, decompositions
Keywords: Microglia, Parkinson's disease
MeSH: Dopaminergic Neurons*, Neuronal Plasticity*, Parkinson Disease*, Synucleinopathies*, Aged, Aged, 80 and over, alpha-Synuclein, Alzheimer Disease, Complement C1q, Female, Humans, Lewy Body Disease, Male, Mesencephalon, Multiomics, Proteomics, Spatial Transcriptomics, Substantia Nigra, Synapses (* major topic)
Topic: Parkinson's Disease Mechanisms and Treatments (Neurology, Medicine), according to OpenAlex
Funding: Fritz Thyssen Stiftung; Stichting ParkinsonFonds
Citations: not cited yet (Europe PMC); 132 references in the paper

Abstract

Parkinson’s disease (PD) is characterized by degeneration of dopaminergic neurons in the substantia nigra pars compacta, but the molecular events preceding neuronal loss remain unclear. Here, we combine spatial transcriptomics, spatial proteomics, and α-synuclein (αSyn) seed amplification assays to profile post-mortem midbrain tissue from controls, incidental Lewy body disease (iLBD), PD, Alzheimer’s disease (AD), and AD with Lewy body pathology (AD + LBP). We find that αSyn seeding activity correlates with dopaminergic neuron loss in PD-spectrum cases but not in AD-associated LBP, indicating disease-context dependent relationships between αSyn pathology and neurodegeneration. In iLBD, before overt substantia nigra Lewy pathology or detectable αSyn aggregation, we detect increased expression of the complement component C1QC together with loss of inhibitory synaptic markers. These findings support early complement-associated remodeling of inhibitory synapses as a potential pathogenic event preceding overt αSyn aggregation and neuronal degeneration in PD.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 9 matches between paragraphs and lines of code.

fstrueb/spatial_nigra

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 7ccda3f993399fb2f717db0db49eb7423d54064e, 4 November 2025
Languages: R (10)
Size: 11 files, 10 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, 4 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (10 files), Seurat (6 files), patchwork (5 files), data.table (2 files), edgeR (2 files), broom (1 file), cowplot (1 file), ggpubr (1 file), reticulate (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
11 files
At the source: github.com/fstrueb/spatial_nigra

Code availability

The code necessary to reproduce the findings is available at https://github.com/fstrueb/spatial_nigra.

Reproduced under the paper's license (CC BY), from the paper cited above.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 10 scripts, each with its path and the digest of its content;
  • 9 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data Availability Statement

Spatial transcriptomics data generated in this study have been deposited in the NCBI Sequence Read Archive (SRA) under accession code PRJNA1357890. Raw data accessions (fastq files) range from SRX31009153 to SRX31009184 and can be found under the associated BioProject accession ID PRJNA1357890 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1357890/). Proteomics data generated in this study have been deposited in the ProteomeXchange Consortium via the PRIDE partner repository under accession code PXD062998 (http://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD062998). Source data are provided with this paper.

The code necessary to reproduce the findings is available at https://github.com/fstrueb/spatial_nigra.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 16 authors, 2 keywords, 19 MeSH terms, 2 funders, 132 references.

Cite

This paper

Rumpf, S.-L., Strübing, F. L., Nalbach, K., Vargiu, C. M., Berg, G., Lichtenthaler, S. F., Parchi, P., Gao, P., Chen, W., Brendel, M., Gnörich, J. S., Bernhardt, A., Dias Rodrigues, L., Höglinger, G. U., Herms, J., & Koeglsperger, T. (2026). Spatial multi-omics identifies early synaptic pruning and context-specific dopaminergic vulnerability in synucleinopathies. Nature communications, 17(1), 6976. https://doi.org/10.1038/s41467-026-74961-6

BibTeX

@article{rumpf2026spatial,
author = {Rumpf, Svenja-Lotta and Strübing, Felix L and Nalbach, Karsten and Vargiu, Claudia Marina and Berg, Giacomo and Lichtenthaler, Stefan F and Parchi, Piero and Gao, Pan and Chen, Weilin and Brendel, Matthias and Gnörich, Johannes S and Bernhardt, Alexander and Dias Rodrigues, Léa and Höglinger, Günter U and Herms, Jochen and Koeglsperger, Thomas},
title = {{Spatial multi-omics identifies early synaptic pruning and context-specific dopaminergic vulnerability in synucleinopathies}},
journal = {Nature communications},
year = {2026},
month = jul,
volume = {17},
number = {1},
pages = {6976},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-74961-6},
url = {https://doi.org/10.1038/s41467-026-74961-6},
pmid = {42481480},
pmcid = {PMC13392254}
}

RIS

TY - JOUR
AU - Rumpf, Svenja-Lotta
AU - Strübing, Felix L
AU - Nalbach, Karsten
AU - Vargiu, Claudia Marina
AU - Berg, Giacomo
AU - Lichtenthaler, Stefan F
AU - Parchi, Piero
AU - Gao, Pan
AU - Chen, Weilin
AU - Brendel, Matthias
AU - Gnörich, Johannes S
AU - Bernhardt, Alexander
AU - Dias Rodrigues, Léa
AU - Höglinger, Günter U
AU - Herms, Jochen
AU - Koeglsperger, Thomas
TI - Spatial multi-omics identifies early synaptic pruning and context-specific dopaminergic vulnerability in synucleinopathies
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/07/21
VL - 17
IS - 1
SP - 6976
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-74961-6
UR - https://doi.org/10.1038/s41467-026-74961-6
LA - en
ER -

CSL-JSON

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"type": "article-journal",
"title": "Spatial multi-omics identifies early synaptic pruning and context-specific dopaminergic vulnerability in synucleinopathies",
"container-title": "Nature communications",
"author": [
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"family": "Rumpf",
"given": "Svenja-Lotta"
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{
"family": "Strübing",
"given": "Felix L"
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{
"family": "Nalbach",
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{
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{
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{
"family": "Chen",
"given": "Weilin"
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{
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"given": "Matthias"
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{
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{
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{
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"container-title-short": "Nat Commun",
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"DOI": "10.1038/s41467-026-74961-6",
"PMID": "42481480",
"PMCID": "PMC13392254",
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