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Predicting Relevant Microglia-Associated Cell-Cell Communication Pathways in Alzheimer's Disease: A Role for SPP1.

Code ↔ Paper

10 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 10 matches · 2 of them tie a paragraph to a whole file without given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › snRNA Analysis Pipeline ↔ analysis_code/02_subclustering_analysis/02A_subclustering_template.R, lines 53 to 140 · score 0.92 · principal component, FindClusters, RunUMAP, ScaleData, variable features, harmonized · Report a mismatch
  2. [2] § Results › Generating a Complementary snRNAseq Dataset to Study the Cellular Interactome in Alzheimer's Disease ↔ analysis_code/02_subclustering_analysis/02A_subclustering_template.R, lines 1 to 51 · score 0.89 · CSF1R, SLC17A7, SLC1A2, inhibitory neuron, ADARB2, AQP4 · Report a mismatch
  3. [3] § Methods › snRNA Analysis Pipeline ↔ analysis_code/01_analysis_from_cellRanger7_outs/01D_per_sample_QC.R, lines 44 to 105 · score 0.85 · ambient RNA, FindClusters, RunUMAP, ScaleData, variable features, doublets · Report a mismatch
  4. [4] § Results › Generating a Complementary snRNAseq Dataset to Study the Cellular Interactome in Alzheimer's Disease ↔ analysis_code/01_analysis_from_cellRanger7_outs/01E_total_object_integration.R, lines 41 to 112 · score 0.83 · CSF1R, SLC17A7, SLC1A2, ADARB2, AQP4, BCAN · Report a mismatch
  5. [5] § Methods › snRNA Analysis Pipeline ↔ analysis_code/02_subclustering_analysis/02A_subclustering_template.R, lines 53 to 140 · score 0.79 · harmonized principal component, cumulative percent, variation associated, UMAP, neighbor, cell · Report a mismatch
  6. [6] § Results › Generating a Complementary snRNAseq Dataset to Study the Cellular Interactome in Alzheimer's Disease ↔ analysis_code/02_subclustering_analysis/02A_subclustering_template.R, lines 1 to 51 · score 0.72 · vascular leptomeningeal cells, endothelial cells, excitatory neurons, fibroblasts, lymphocytes, macrophages · Report a mismatch
  7. [7] § Methods › snRNA Analysis Pipeline ↔ analysis_code/01_analysis_from_cellRanger7_outs/01E_total_object_integration.R, lines 41 to 112 · score 0.63 · cumulative percent, variation associated, UMAP, neighbor, cell · Report a mismatch
  8. [8] § Methods › snRNA Analysis Pipeline ↔ analysis_code/04_CellChat/04A_template_CellChat_per_condition.R, the whole file · a weak match · score 0.60 · CellChatDB, database, diagnosis, subsets, interactions, pathways · Report a mismatch
  9. [9] § Results › Generating a Complementary snRNAseq Dataset to Study the Cellular Interactome in Alzheimer's Disease ↔ analysis_code/03_proportion_analysis/03A_proportion_analysis.R, the whole file · a weak match · score 0.57 · excitatory neurons, inhibitory neurons, PMD, age, sex, Propeller · Report a mismatch
  10. [10] § Results › Microglia–Microglia SPP1 Signaling in AD ↔ analysis_code/04_CellChat/04B_CellChat_compare.R, lines 87 to 138 · score 0.55 · ligand receptor, cell chat, aggregated, incoming, outgoing, widths · Report a mismatch

Paper

Loaded from Europe PMC by your browser; OSCR does not store it: doi.org · Europe PMC

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The authors' code

R · 140 lines · 6 KB · no license · 4 matches

  1. #performed on HPC
  2. # visualize different cell type markers to use for subset
  3. # for MTL object, total object was split into excitatory_neurons, inhibitory_neurons, and non_neurons
  4. # for combined MTL and EGE dataset, we split into all available cell types, namely excitatory_neurons, inhibitory_neurons, astrocytes, microglia, monocytes, endothelial cells, fibroblasts, vascular leptomeningeal cells, oligodendrocytes, OPCs, lymphocytes, and CNS macrophages.
  5. # during subclustering, we sometimes took step wise approaches to extracting cell types, e.g. first subset out and subcluster neurons, then identify and repeat analysis on the excitatory subset
  7. library(Seurat)
  8. library(dplyr)
  9. library(patchwork)
  10. library(ggplot2)
  11. library(harmony)
  12. library(qs2)
  14. sessionInfo()
  16. #define locations
  17. scratch.dir = "/link/to/HPC/directory/"
  18. Output.dir.plots = "./script_identifier_"
  19. Output.dir.objects = paste0(scratch.dir, "current_round_script_identifier_")
  20. prev.Output.dir.objects = "previous_round_script_identifier_" #either total object or subset object from another round
  22. #load object
  23. cells = readRDS(paste0(scratch.dir, prev.Output.dir.objects))
  24. Idents(cells) = "RNA_snn_res.0.08" #resolution of interest
  26. # visualize the cells we intend to subset (from full object) or remove (during subclustering rounds)
  27. pdf(paste0(Output.dir.plots, "visualize_cell_subsets.pdf"))
  29. markers <- c("RBFOX3",
  30. "SNAP25",
  31. "SLC17A7",
  32. "GAD2",
  33. "ADARB2",
  34. "LHX6",
  35. "GFAP","AQP4","SLC1A2",
  36. "PLP1","MOBP","OPALIN",
  37. "CSF1R","C3",
  38. "CLDN5","PECAM1",
  39. "DCN","COL1A1",
  40. "PDGFRB",
  41. "PDGFRA","BCAN",
  42. "CSPG4")
  44. DotPlot(object = cells, assay = "RNA",cluster.ident = T, features = markers, cols = c("green", "red"), dot.scale = 12) +
  45. theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
  46. aspect.ratio = 12/10) + xlab("") + ylab("")
  48. cells$highlight = cells$RNA_snn_res.0.08 %in% c(1, 8, 9, 10, 15) # detect which clusters to keep
  49. DimPlot(cells, group.by = "highlight", raster =T)
  51. dev.off()
  53. # example1: subsetting out nonNeuronal cells for first subclustering
  54. cells = subset(cells, highlight == TRUE)
  55. nrow([email hidden]) %>% print()
  56. [email hidden]$Sample %>% table() %>% print()
  58. # example2: subset out bad clusters, also applicable when removing predicted doublets
  59. cells = subset(cells, highlight == FALSE)
  60. cells = subset(cells, predicted_doublet == "False")
  63. # proceed to analysis
  64. # we use 2000 variable features for clustering different cell types, and 4000 variable features to subcluster within one cell type (e.g. neurons)
  65. cells <- NormalizeData(cells, verbose = FALSE)
  66. cells <- FindVariableFeatures(cells, selection.method = "vst",
  67. nfeatures = 4000, verbose = FALSE)
  69. # rescale object
  70. # we regress out 'percent.mito' and 'nCount_RNA' when clustering different cell types, and include 'percent.ribo' when subclustering within one cell type (e.g. neurons)
  71. print("rescaling object")
  72. cells <- ScaleData(object = cells, vars.to.regress = c("percent.mito", "nCount_RNA", "percent.ribo"))
  74. #dimensionality reduction with PCA
  75. cells <- RunPCA(cells, npcs = 40)
  77. #visualize PCA
  78. DimPlot(cells, reduction = "pca", group.by = "Sample", raster = T) + NoLegend()
  79. ggsave(paste0(Output.dir.plots, "scaled_pca.pdf"))
  80. ElbowPlot(cells, ndims = 40)
  81. ggsave(paste0(Output.dir.plots, "scaled_elbowPlot.pdf"))
  83. #harmonize principal components using Harmony
  84. print("starting harmony")
  85. cells <- RunHarmony(cells, "Sample", plot_convergence = TRUE)
  86. ggsave(paste0(Output.dir.plots, "harmony_convergencePlot.pdf"))
  87. print("harmony finished, processing object")
  89. # determine optimal PCs to load into UMAP
  90. # adapted from the approach here: https://hbctraining.github.io/scRNA-seq/lessons/sc_exercises_clustering_analysis.html
  91. # Determine percent of variation associated with each PC
  92. pct <- cells[["harmony"]]@stdev / sum(cells[["harmony"]]@stdev) * 100
  93. # Calculate cumulative percents for each PC
  94. cumu <- cumsum(pct)
  95. # Determine which PC exhibits cumulative percent greater than 90% and % variation associated with the PC as less than 5
  96. co1 <- which(cumu > 90 & pct < 5)[1]
  97. co2 <- sort(which((pct[1:length(pct) - 1] - pct[2:length(pct)]) > 0.1), decreasing = T)[1] + 1
  98. pcs_harmony <- min(co1, co2)
  99. print(paste0("Harmony PCs to load into UMAP: ", pcs_harmony))
  101. cells = cells %>%
  102. RunUMAP(reduction = "harmony", dims = 1:pcs_harmony) %>%
  103. FindNeighbors(reduction = "harmony", dims = 1:pcs_harmony)
  105. cells = cells %>%
  106. FindClusters(resolution = 0.08) %>%
  107. FindClusters(resolution = 0.2) %>%
  108. FindClusters(resolution = 0.15) %>%
  109. FindClusters(resolution = 0.1)
  111. print("Finish processing, saving and making plots")
  113. #switching to QS2 instead of saveRDS for speed
  114. qs_save(cells, paste0(Output.dir.objects, "integrated.qs2"))
  116. #plotting
  117. pdf(paste0(Output.dir.plots, "integration_visualization.pdf"))
  119. #total UMAP check
  120. DimPlot(cells, raster = T, label = T, group.by = "Donor") + NoLegend()
  121. DimPlot(cells, raster = T, label = T, group.by = "Sample") + NoLegend()
  122. DimPlot(cells, raster = T, label = T, group.by = "region") + NoLegend()
  123. DimPlot(cells, raster = T, label = T, group.by = "predicted_doublet") + NoLegend()
  125. #clustering check
  126. list_of_res = c("RNA_snn_res.0.08","RNA_snn_res.0.1","RNA_snn_res.0.15","RNA_snn_res.0.2")
  128. for (each in list_of_res){
  129. print(DimPlot(cells, group.by=each, raster = T, label = T) + NoLegend())
  130. print(VlnPlot(cells,group.by=each,features = c("nFeature_RNA", "percent.ribo", "percent.mito","nCount_RNA"),ncol = 2, pt.size = 0))
  131. print(VlnPlot(cells,group.by=each,features = c("scrublet_score"), pt.size = 0))
  132. print(DotPlot(object = cells, group.by=each, assay = "RNA",cluster.ident = T,
  133. features = markers, cols = c("green", "red"), dot.scale = 12) + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),aspect.ratio = 12/10) + xlab("") + ylab(""))
  134. }
  135. dev.off()
  137. # run quick marker testing for specific resolutions
  138. Idents(cells) = "RNA_snn_res.0.2"
  139. markers = FindAllMarkers(cells)
  140. write.csv(markers, paste0(Output.dir.plots, "res0.2_markers.csv"))

02A_subclustering_template.R at commit c6cc60e, no license · at the source

Overview

Authors: Janssen M. Kotah1, Marina Trombetta Lima1, Esmée C. Dragt1, Asimenia Voulgaroglou1, Nieske Brouwer1, Inge R. Holtman1, Susanne M. Kooistra1, Bart J. L. Eggen1
ORCID iDs: Janssen M. Kotah, Marina Trombetta Lima, Esmée C. Dragt, Asimenia Voulgaroglou, Inge R. Holtman, Susanne M. Kooistra, Bart J. L. Eggen
  1. Department of Biomedical Sciences University of Groningen, University Medical Center Groningen Groningen the Netherlands
Institutions: University Medical Center Groningen (Netherlands); University of Groningen (Netherlands)
Journal: Glia, volume 74, issue 10, article e70210
Dates: received 3 February 2026; accepted 24 July 2026; published online 4 August 2026; in print October 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1002/glia.70210 · PMID 42552722 · PMCID PMC13438122 · OpenAlex W7172522993
Open access: hybrid, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: genetics / omics (modality), human (organism), Alzheimer's / dementia (population), cellular / molecular (subfield)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, fMRI & imaging
Keywords: Alzheimer's disease, cell–cell communication, microglia, single nucleus RNA sequencing, SPP1
MeSH: Alzheimer Disease*, Brain*, Cell Communication*, Microglia*, Osteopontin*, Aged, Aged, 80 and over, Female, Humans, Male, Plaque, Amyloid, Signal Transduction (* major topic)
Topic: Neuroinflammation and Neurodegeneration Mechanisms (Neurology, Neuroscience), according to OpenAlex
Funding: Alzheimer Nederland (WE.03‐2025‐06, WE.03‐2020‐03); Stichting De Cock-Hadders; ZonMw (MODEM 10510032120006)
Citations: not cited yet (Europe PMC); 100 references in the paper

Abstract

Microglia play a key role in the pathophysiology of Alzheimer's Disease (AD) and their increased heterogeneity likely affects disease progression. We previously identified distinct microglial signatures that were enriched in AD donors and associated with amyloid and tau, respectively. Here we generated a snRNAseq dataset from postmortem control and AD cases and analyzed alterations in cell–cell communication pathways that might be relevant to AD pathophysiology. One signaling pathway perturbed in AD cases involved SPP1, and while this pathway was also present in control samples, microglia–microglia SPP1 signaling was restricted to AD donors. Further analyses within microglia–microglia signaling predict AD‐specific induction of GAS6‐AXL signaling (from inflammatory and ribosomal microglia), and SPP1‐ITGAV/ITGB5 signaling (from disease‐associated and inflammatory microglia, among others). Together, these findings might in part explain the increased microglia phagocytic profile described in AD. RNAscope confirmed enrichment of SPP1 expressing microglia near amyloid plaques in AD brain tissue samples. These data indicate altered cellular communications between microglia in the AD brain.

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 10 matches between paragraphs and lines of code.

jmkotah/AD_snRNAseq

License: none: the authors keep all their rights
State: the link answers, verified on 31 October 2026
Evidence: files inventoried
Commit: c6cc60e287885b6d535323d96339e759739bdba9, 11 August 2026
Languages: R (8), Python (1)
Size: 12 files, 9 scripts
Software Heritage: not archived
Found in: “Data Availability Statement”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (8 files), Seurat (8 files), tidyverse (8 files), patchwork (6 files), Harmony (2 files), data.table (1 file), NumPy (1 file), pandas (1 file), Scanpy (1 file)
Availability: 2 checks, the latest on 31 October 2026: the link answers
  • 31 October 2026: the link answers
  • 1 October 2026: the link answers
10 files
At the source: github.com/jmkotah/AD_snRNAseq

The paper's code and data availability statement is in the Data section.

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;
  • 9 scripts, each with its path and the digest of its content;
  • 10 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

No dataset and no data link were found in the paper.

Data Availability Statement

The snRNAseq data generated in this paper is available on the Gene Expression Omnibus with accession number GSE335667. The reanalyzed Gerrits et al. (2021) dataset is available on the Gene Expression Omnibus with accession number GSE148822. Code associated with snRNAseq clustering and CellChat analyses is available at https://github.com/jmkotah/AD_snRNAseq. Correspondence and requests for materials should be addressed to Bart Eggen ().

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

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Version 1, 1 October 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 5 keywords, 12 MeSH terms, 3 funders, 100 references.

Cite

This paper

Kotah, J. M., Lima, M. T., Dragt, E. C., Voulgaroglou, A., Brouwer, N., Holtman, I. R., Kooistra, S. M., & Eggen, B. J. L. (2026). Predicting Relevant Microglia-Associated Cell-Cell Communication Pathways in Alzheimer's Disease: A Role for SPP1. Glia, 74(10), e70210. https://doi.org/10.1002/glia.70210

BibTeX

@article{kotah2026predicting,
author = {Kotah, Janssen M. and Lima, Marina Trombetta and Dragt, Esmée C. and Voulgaroglou, Asimenia and Brouwer, Nieske and Holtman, Inge R. and Kooistra, Susanne M. and Eggen, Bart J. L.},
title = {{Predicting Relevant Microglia-Associated Cell-Cell Communication Pathways in Alzheimer's Disease: A Role for SPP1}},
journal = {Glia},
year = {2026},
month = oct,
volume = {74},
number = {10},
pages = {e70210},
publisher = {Wiley},
issn = {0894-1491},
doi = {10.1002/glia.70210},
url = {https://doi.org/10.1002/glia.70210},
pmid = {42552722},
pmcid = {PMC13438122}
}

RIS

TY - JOUR
AU - Kotah, Janssen M.
AU - Lima, Marina Trombetta
AU - Dragt, Esmée C.
AU - Voulgaroglou, Asimenia
AU - Brouwer, Nieske
AU - Holtman, Inge R.
AU - Kooistra, Susanne M.
AU - Eggen, Bart J. L.
TI - Predicting Relevant Microglia-Associated Cell-Cell Communication Pathways in Alzheimer's Disease: A Role for SPP1
T2 - Glia
J2 - Glia
PY - 2026
DA - 2026/10/01
VL - 74
IS - 10
SP - e70210
SN - 0894-1491
PB - Wiley
DO - 10.1002/glia.70210
UR - https://doi.org/10.1002/glia.70210
LA - en
ER -

CSL-JSON

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"id": "10.1002/glia.70210",
"type": "article-journal",
"title": "Predicting Relevant Microglia-Associated Cell-Cell Communication Pathways in Alzheimer's Disease: A Role for SPP1",
"container-title": "Glia",
"author": [
{
"family": "Kotah",
"given": "Janssen M."
},
{
"family": "Lima",
"given": "Marina Trombetta"
},
{
"family": "Dragt",
"given": "Esmée C."
},
{
"family": "Voulgaroglou",
"given": "Asimenia"
},
{
"family": "Brouwer",
"given": "Nieske"
},
{
"family": "Holtman",
"given": "Inge R."
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{
"family": "Kooistra",
"given": "Susanne M."
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{
"family": "Eggen",
"given": "Bart J. L."
}
],
"container-title-short": "Glia",
"volume": "74",
"issue": "10",
"page": "e70210",
"DOI": "10.1002/glia.70210",
"PMID": "42552722",
"PMCID": "PMC13438122",
"ISSN": "0894-1491",
"publisher": "Wiley",
"URL": "https://doi.org/10.1002/glia.70210",
"language": "en",
"issued": {
"date-parts": [
[
2026,
10,
1
]
]
}
}

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