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Gpnmb defines a phagocytic state of microglia linked to cell death in prion disease mouse model.

Code ↔ Paper

5 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 5 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Methods › Immunofluorescence of Brain Slices ↔ Immunofluorescence/Colocalization_pipeline_F6h.R, lines 27–33 · score 0.66 · EBImage, local thresholding, binary, adaptive, Colocalization, immunofluorescence
  2. [2] § Methods › Single-cell RNA sequencing analysis › Prion-infected mouse dataset ↔ output/03st_deconvolution/ST_deconvolution.Rmd, lines 154–259 · score 0.60 · log2fc, fold change, gene expression, metadata, prion, transcriptomic
  3. [3] § Methods › Immunofluorescence of Brain Slices ↔ Immunofluorescence/Colocalization_pipeline_SF10a.R, lines 122–134 · score 0.60 · colocalized cells, positive cell, logical, mask, threshold, Immunofluorescence
  4. [4] § Results › Gpnmb expression is increased in microglia during PrD progression ↔ output/03st_deconvolution/Deconvolved_CellTypes/Cell_Type_Correlation/Cell_Types_Correlation.R, lines 46–116 · score 0.56 · Venn diagram, overlapping genes, ORA, scatter, correlation, deconvoluted
  5. [5] § Methods › Prion spatial transcriptomic (Visium 10X Genomics) › FASTQ File Handling ↔ code/01st_spaceranger.sh, the whole file · a weak match · score 0.53 · reference genome, FASTQ, SpaceRanger, metadata

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

R · 225 lines · 6.1 KB · no license · 1 match

  1. library(tidyverse)
  2. library(EBImage)
  3. library(ggpubr)
  4. library(tidyverse)
  5. library(ggpubr)
  8. data_dir <- getwd()
  9. replicates <- 1:6
  10. conditions <- c("sham", "stroke")
  12. read_gray <- function(path) {
  13. img <- readImage(path)
  14. if (colorMode(img) != 0) img <- channel(img, "gray")
  15. img
  16. }
  18. # Morphological background subtraction:
  19. # bg is estimated by a large opening (choose size bigger than objects)
  20. bg_subtract <- function(img, brush_size = 35) {
  21. bg <- opening(img, makeBrush(brush_size, shape = "disc"))
  22. out <- img - bg
  23. out[out < 0] <- 0
  24. out
  25. }
  27. # Adaptive/local threshold -> binary
  28. # w = window size (odd), offset tunes stringency
  29. adaptive_bin <- function(img, w = 51, offset = 0.02) {
  30. # EBImage::thresh returns 0/1 image
  31. m <- thresh(img, w = w, h = w, offset = offset)
  32. m > 0
  33. }
  35. # Clean small speckles
  36. clean_mask <- function(mask, min_size = 20) {
  37. mask <- opening(mask, makeBrush(3, "disc"))
  38. mask <- closing(mask, makeBrush(3, "disc"))
  39. # remove tiny components
  40. lab <- bwlabel(mask)
  41. tab <- table(lab)
  42. keep <- as.integer(names(tab)[tab >= min_size])
  43. mask & (lab %in% keep)
  44. }
  46. analyze_one <- function(rep, condition,
  47. iba1_otsu = TRUE,
  48. G_brush = 35, G_w = 51, G_offset = 0.02,
  49. L_brush = 25, L_w = 51, L_offset = 0.02,
  50. min_size = 20) {
  52. g_file <- file.path(data_dir, paste0("rep", rep, "_", condition, "_Gpnmb.tif"))
  53. i_file <- file.path(data_dir, paste0("rep", rep, "_", condition, "_Iba1.tif"))
  54. l_file <- file.path(data_dir, paste0("rep", rep, "_", condition, "_Lgals3.tif"))
  56. if (!all(file.exists(c(g_file, i_file, l_file)))) {
  57. message("Missing files for rep ", rep, " ", condition)
  58. return(NULL)
  59. }
  61. G <- read_gray(g_file)
  62. I <- read_gray(i_file)
  63. L <- read_gray(l_file)
  65. ## 1) Iba1 mask (reference compartment)
  66. if (iba1_otsu) {
  67. I_thr <- otsu(I)
  68. Ibin <- I > I_thr
  69. } else {
  70. Ibin <- adaptive_bin(I, w = 51, offset = 0.02)
  71. I_thr <- NA_real_
  72. }
  73. Ibin <- clean_mask(Ibin, min_size = min_size)
  75. ## 2) Gpnmb: background subtract + adaptive threshold
  76. Gbs <- bg_subtract(G, brush_size = G_brush)
  77. Gbin <- adaptive_bin(Gbs, w = G_w, offset = G_offset)
  78. Gbin <- clean_mask(Gbin, min_size = min_size)
  79. Gbin <- Gbin & Ibin
  81. ## 3) Lgals3: background subtract + adaptive threshold (works well for puncta)
  82. Lbs <- bg_subtract(L, brush_size = L_brush)
  83. Lbin <- adaptive_bin(Lbs, w = L_w, offset = L_offset)
  84. Lbin <- clean_mask(Lbin, min_size = min_size)
  85. Lbin <- Lbin & Ibin
  87. ## 4) Overlaps (inside Iba1)
  88. GL <- Gbin & Lbin
  89. triple <- Gbin & Lbin & Ibin # same as GL since already gated to Ibin
  91. I_pix <- sum(Ibin)
  92. G_pix <- sum(Gbin)
  93. L_pix <- sum(Lbin)
  94. GL_pix <- sum(GL)
  96. tibble(
  97. Replicate = rep,
  98. Condition = condition,
  100. # record parameters (so you can report them)
  101. I_thr = I_thr,
  102. G_brush = G_brush, G_w = G_w, G_offset = G_offset,
  103. L_brush = L_brush, L_w = L_w, L_offset = L_offset,
  104. min_size = min_size,
  106. # counts within Iba1
  107. I_pixels = I_pix,
  108. G_in_I_pixels = G_pix,
  109. L_in_I_pixels = L_pix,
  110. GL_in_I_pixels = GL_pix,
  112. # the % overlaps (pick what you mean by "% overlap")
  113. pct_I_covered_by_GL = ifelse(I_pix > 0, 100 * GL_pix / I_pix, NA_real_),
  114. pct_G_covered_by_L = ifelse(G_pix > 0, 100 * GL_pix / G_pix, NA_real_),
  115. pct_L_covered_by_G = ifelse(L_pix > 0, 100 * GL_pix / L_pix, NA_real_)
  116. )
  117. }
  119. ## Run batch
  120. results <- expand_grid(Replicate = 1:6, Condition = c("sham","stroke")) %>%
  121. pmap_dfr(~ analyze_one(..1, ..2))
  123. write_csv(results, file.path(data_dir, "overlap_Iba1_gated_adaptive.csv"))
  125. ## Plot (example: % of Iba1 area that is double-positive)
  126. ggplot(results, aes(x = Condition, y = pct_I_covered_by_GL, fill = Condition)) +
  127. geom_boxplot(outlier.shape = NA, alpha = 0.7) +
  128. geom_jitter(width = 0.12, size = 2) +
  129. stat_compare_means(method = "wilcox.test", label = "p.format") +
  130. theme_classic() +
  131. ylab("% Iba1 area that is Gpnmb+ AND Lgals3+")
  136. # ---- summarize mean ± SD + significance label ----
  137. data_summary <- results %>%
  138. group_by(Condition) %>%
  139. summarise(
  140. Mean = mean(pct_I_covered_by_GL, na.rm = TRUE),
  141. StdDev = sd(pct_I_covered_by_GL, na.rm = TRUE),
  142. .groups = "drop"
  143. )
  145. pval <- wilcox.test(pct_I_covered_by_GL ~ Condition, data = results)$p.value
  147. sig_label <- dplyr::case_when(
  148. pval < 0.001 ~ "***",
  149. pval < 0.01 ~ "**",
  150. pval < 0.05 ~ "*",
  151. TRUE ~ "ns"
  152. )
  154. data_summary <- data_summary %>%
  155. mutate(
  156. Significance = paste0("p = ", signif(pval, 2), " (", sig_label, ")"),
  157. y_text = Mean + StdDev + 0.05 * (max(results$pct_I_covered_by_GL, na.rm=TRUE) -
  158. min(results$pct_I_covered_by_GL, na.rm=TRUE))
  159. )
  161. # ---- plot: grey bars (mean), points, errorbars, p-text ----
  162. my_plot <- ggplot() +
  163. geom_bar(
  164. data = data_summary,
  165. aes(x = Condition, y = Mean, fill = Condition),
  166. stat = "identity",
  167. color = "black",
  168. width = 0.65
  169. ) +
  170. scale_fill_grey(start = 0.8, end = 0.2) +
  171. geom_errorbar(
  172. data = data_summary,
  173. aes(x = Condition, ymin = Mean - StdDev, ymax = Mean + StdDev),
  174. width = 0.18
  175. ) +
  176. geom_point(
  177. data = results,
  178. aes(x = Condition, y = pct_I_covered_by_GL, color = Condition),
  179. position = position_jitter(width = 0.12, height = 0),
  180. size = 2.2,
  181. show.legend = FALSE
  182. ) +
  183. geom_text(
  184. data = data_summary,
  185. aes(x = Condition, y = y_text, label = Significance),
  186. vjust = 0,
  187. size = 4
  188. ) +
  189. theme_minimal() +
  190. labs(
  191. x = "Condition",
  192. y = "% Iba1 area that is Gpnmb+ AND Lgals3+"
  193. ) +
  194. theme(
  195. legend.position = "none",
  196. axis.text.x = element_text(angle = 45, hjust = 1),
  197. strip.text = element_text(size = 10, face = "bold"),
  198. panel.spacing = unit(1, "lines")
  199. )
  201. my_plot
  205. my_plot <- my_plot +
  206. theme(
  207. axis.title = element_blank(),
  208. axis.text.x = element_blank(),
  209. axis.text.y = element_blank(),
  210. axis.ticks = element_blank()
  211. )
  213. write_csv(results, file.path(data_dir, "overlap_Iba1_gated_adaptive.csv"))
  216. # SVG (exact size: 54.5 x 18 mm)
  217. ggsave(
  218. filename = "Gpnmb_Lgals3_overlap_boxplot.svg",
  219. plot = my_plot,
  220. width = 18,
  221. height = 54.5,
  222. units = "mm"
  223. )

Colocalization_pipeline_F6h.R at commit 7c7e382, no license · at the source

Overview

Authors: Davide Caredio1, Giovanni Mariutti1, Lisa Polzer1, Beatrice Gatta2, Martina Cerisoli1, Yasmine Laimeche1, Giulia Miracca1, Jeanne Droux3, Mohamad El Amki3, Marc Emmenegger1,4, Marian Hruska-Plochan2, Susanne Wegener3, Magdalini Polymenidou2, Matthias Schmitz5, Inga Zerr5, Elena De Cecco1, Adriano Aguzzi6
ORCID iDs: Davide Caredio, Giovanni Mariutti, Lisa Polzer, Mohamad El Amki, Marian Hruska-Plochan, Susanne Wegener, Magdalini Polymenidou, Elena De Cecco, Adriano Aguzzi
  1. Institute of Neuropathology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
  2. Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
  3. Department of Neurology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
  4. Present Address: Medical Immunology, Department of Laboratory Medicine, University Hospital Basel, Basel, Switzerland
  5. Department of Neurology, National Reference Center for TSE, Georg-August University, Göttingen, Germany
  6. Institute for the Science of the Aging Brain (ISAB), St. Gallen, Switzerland
Institutions: University of Zurich (Switzerland); University Hospital Zurich (Switzerland); University of Basel (Switzerland); University Hospital of Basel (Switzerland); University of Göttingen (Germany); ISAB - Institute for the Science of the Aging Brain (Switzerland)
Journal: Nature communications, volume 17, issue 1, article 6138
Dates: received 19 August 2025; accepted 20 April 2026; published online 12 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-73003-5 · PMID 42120390 · PMCID PMC13365222 · OpenAlex W7160931621
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism), other condition (population), cellular / molecular (subfield)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Evoked potentials, fMRI & imaging
Keywords: Microglia, Cell death in the nervous system, Chronic inflammation, Prion diseases
MeSH: Eye Proteins*, Membrane Glycoproteins*, Microglia*, Phagocytosis*, Prion Diseases*, Animals, Brain, Cell Death, Disease Models, Animal, Lysosomes, Mice, Mice, Inbred C57BL (* major topic)
Topic: Neuroinflammation and Neurodegeneration Mechanisms (Neurology, Neuroscience), according to OpenAlex
Funding: Swiss National Science Foundation (207872, 179040); Michael J. Fox Foundation for Parkinson&apos;s Research; Candoc UZH fellowship CJD foundation; NOMIS Stiftung; CJD Foundation GELU Foundation; Human Frontier Science Program
Citations: cited by 1 paper (Europe PMC); 101 references in the paper

Abstract

Neurodegenerative disorders display brain region tropism accompanied by the emergence of distinct cellular states that contribute to disease pathogenesis, with molecular alterations occurring predominantly in glial cells. Here we show the emergence of a microglial state with distinct spatial distribution in the brains of terminally sick prion-infected mice characterized by high expression of Gpnmb (glycoprotein non-metastatic melanoma protein B), transcriptional signatures consistent with phagocytic activity, and increased expression of lysosomal genes in regions undergoing pronounced cell death. We find that this cellular state is not induced by pathological protein aggregates but by soluble factors released by dying cells regardless of the initiating insult. This work defines Gpnmb⁺ microglia as a distinct phagocytic state that links cell death to microglial activation and reveals a generalizable mechanism by which microglia respond to cell loss.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 5 matches between paragraphs and lines of code.

JEFworks-Lab/STdeconvolve

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 13b1953263a5ce34e2471c4defebedbda44c7d08, 3 August 2025
Languages: R (7)
Size: 33 files, 7 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, environment (DESCRIPTION), tests, continuous integration, documentation, 1 notebook
Not found: license file, CITATION.cff
Tools: ggplot2 (3 files), mgcv (1 file), reshape2 (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
8 files
At the source: github.com/JEFworks-Lab/STdeconvolve.git

dcare91/ST_prions

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 7c7e38278bcc504a2b62c8d0e00068e25749fd44, 13 February 2026
Languages: R (12), Shell (2)
Size: 261 files, 14 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, documentation, 6 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration
Tools: tidyverse (5 files), ggplot2 (4 files), Seurat (3 files), igraph (2 files), clusterProfiler (1 file), data.table (1 file), ggpubr (1 file), reshape2 (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
15 files
At the source: github.com/dcare91/ST_prions

Zenodo 18983911

License: CC-BY-4.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
At the source:

Code availability

Code used to reproduce the results reported in this study is available at https://github.com/dcare91/ST_prions and archived at Zenodo 10.5281/zenodo.18983911. The STdeconvolve algorithm used in this study is available at https://github.com/JEFworks-Lab/STdeconvolve.git.

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:

  • 3 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 21 scripts, each with its path and the digest of its content;
  • 5 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

The spatial transcriptomics data, as well as related processed data generated in this study, have been deposited in the GEO database under accession code GSE277577 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE277577). Raw immunofluorescent microscopy images have been deposited in the Zenodo database 10.5281/zenodo.18743243. The single-cell RNA-seq dataset reanalyzed in this study25 is available from the Broad Institute Single Cell Portal under study SCP1962. The single-cell RNA sequencing dataset from sorted microglia from 5XFAD mice and controls57 is available at GSE98969 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE98969). The spatial transcriptomics data presented in Fig. 6i, was obtained through the online platform strokemap.cn. The data values generated in this study are provided in the Source Data file. Source data are provided in this paper.

Code used to reproduce the results reported in this study is available at https://github.com/dcare91/ST_prions and archived at Zenodo 10.5281/zenodo.18983911. The STdeconvolve algorithm used in this study is available at https://github.com/JEFworks-Lab/STdeconvolve.git.

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, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 17 authors, 4 keywords, 12 MeSH terms, 6 funders, 99 references.

Cite

This paper

Caredio, D., Mariutti, G., Polzer, L., Gatta, B., Cerisoli, M., Laimeche, Y., Miracca, G., Droux, J., El Amki, M., Emmenegger, M., Hruska-Plochan, M., Wegener, S., Polymenidou, M., Schmitz, M., Zerr, I., De Cecco, E., & Aguzzi, A. (2026). Gpnmb defines a phagocytic state of microglia linked to cell death in prion disease mouse model. Nature communications, 17(1), 6138. https://doi.org/10.1038/s41467-026-73003-5

BibTeX

@article{caredio2026gpnmb,
author = {Caredio, Davide and Mariutti, Giovanni and Polzer, Lisa and Gatta, Beatrice and Cerisoli, Martina and Laimeche, Yasmine and Miracca, Giulia and Droux, Jeanne and El Amki, Mohamad and Emmenegger, Marc and Hruska-Plochan, Marian and Wegener, Susanne and Polymenidou, Magdalini and Schmitz, Matthias and Zerr, Inga and De Cecco, Elena and Aguzzi, Adriano},
title = {{Gpnmb defines a phagocytic state of microglia linked to cell death in prion disease mouse model}},
journal = {Nature communications},
year = {2026},
month = may,
volume = {17},
number = {1},
pages = {6138},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-73003-5},
url = {https://doi.org/10.1038/s41467-026-73003-5},
pmid = {42120390},
pmcid = {PMC13365222}
}

RIS

TY - JOUR
AU - Caredio, Davide
AU - Mariutti, Giovanni
AU - Polzer, Lisa
AU - Gatta, Beatrice
AU - Cerisoli, Martina
AU - Laimeche, Yasmine
AU - Miracca, Giulia
AU - Droux, Jeanne
AU - El Amki, Mohamad
AU - Emmenegger, Marc
AU - Hruska-Plochan, Marian
AU - Wegener, Susanne
AU - Polymenidou, Magdalini
AU - Schmitz, Matthias
AU - Zerr, Inga
AU - De Cecco, Elena
AU - Aguzzi, Adriano
TI - Gpnmb defines a phagocytic state of microglia linked to cell death in prion disease mouse model
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/05/12
VL - 17
IS - 1
SP - 6138
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-73003-5
UR - https://doi.org/10.1038/s41467-026-73003-5
LA - en
ER -

CSL-JSON

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Single-nucleus brain transcriptomics reveals microglia dysfunction in multiple system atrophy.
Journal: Nature communications
In common: clusterProfiler, reshape2, ggpubr, 3 other tools, cellular / molecular, 3 references

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