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Early glymphatic failure in AppNL-F knock-in mice is linked to parenchymal border macrophages loss.

Code ↔ Paper

2 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 2 matches
  1. [1] § Materials and methods › Statistical analysis ↔ Shanbhag_et_al_Efflux_manus_kod/Fluoroscence_Flow_Rate/20231107_Nasal_efflux_Statistics.R, lines 95–152 · score 0.53 · Kruskal Wallis, way ANOVA, Shapiro Wilk
  2. [2] § Materials and methods › Statistical analysis ↔ Shanbhag_et_al_Efflux_manus_kod/Blaze_Figure/20231107_SCLN_DCLN_Blaze_Statistics.R, lines 47–117 · score 0.52 · Kruskal Wallis, way ANOVA, Shapiro Wilk

Paper

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The authors' code

R · 152 lines · 9.5 KB · no license · 1 match

  1. library('tidyverse')
  2. library('ggplot2')
  3. library('dplyr')
  4. library('ggpubr')
  5. library('see')
  6. library('cowplot')
  7. library('rstatix')
  8. library('R.utils')
  9. library('psych')
  10. library('nlme')
  11. library('purrr')
  12. library('multcomp')
  14. # Create folder for analysis outputs
  15. directory_name = 'Exp_1' # Should change for each experiment type
  16. dir.create(directory_name)
  18. # Read in experiment data file
  19. setwd('C://Users//em6050jo//Documents//Lundgaard_Labb_Swanberg//Efflux_Paper//Data//For_Manuscript//Excel_Files//Fluoroscopy_Flow_Rate//')
  20. data <- read.csv(file = 'C://Users//em6050jo//Documents//Lundgaard_Labb_Swanberg//Efflux_Paper//Data//For_Manuscript//Excel_Files//Fluoroscopy_Flow_Rate//20241107_Static_Fluoroscopy_KMS_Exp_1.csv', header=TRUE)
  23. # PART I ################################ DATA PREPROCESSING #################################
  25. # Summarize group statistics for all variables
  26. data_by_group <- data %>%
  27. group_by(Group) %>%
  28. summarize(across(where(is.numeric), list(mean=mean, sd=sd, count= ~ n(), se = ~ sd(.x) / sqrt(n()))), .groups = 'drop')
  30. data_by_group = arrange(data_by_group, desc(Group))
  31. data$Group <- as.factor(data$Group)
  32. #data$Group <- factor(data$Group, levels=rev(levels(data$Group)))
  34. # PART IIA ################################ DATA PLOTTING #################################
  36. num_columns = ncol(data)
  37. data_for_plotting = data
  39. for (i in 9:num_columns){
  41. data_for_plotting$Group = as.factor(data_for_plotting$Group)
  42. data_for_plotting$y = data[,i]
  43. ylabelplot = paste(colnames(data)[i], "\n")
  44. ylabelplot = gsub("_", " ", ylabelplot)
  45. ylabel = colnames(data)[i]
  47. # Plot NTe enhancement volume
  48. enhancement_volume_hist <- ggdensity(data_for_plotting, x = "y", xlab ="\nGroup", ylab =ylabelplot,
  49. add = "mean", rug = TRUE,
  50. color = "Group", fill = "Group",
  51. palette = c("#00AFBB", "#E7B800", '#A4115c')) +
  52. theme(plot.margin = unit(c(1, 1, 1, 1), "cm"), text = element_text(size = 12), axis.text = element_text(size = 12))
  54. filename_png = paste0(directory_name,'//', ylabel, '_hist.png')
  55. ggsave(filename_png, enhancement_volume_hist, device = 'png', width = 6, height = 6, dpi = 600)
  56. filename_eps = paste0(directory_name,'//', ylabel, '_hist.pdf')
  57. ggsave(filename_eps, plot = print(enhancement_volume_hist), device = pdf, width = 6, height = 6, dpi = 600)
  59. enhancement_volume_violin <- ggviolin(data_for_plotting, x = "Group", y = "y", fill = "Group", xlab ="\nGroup", ylab =ylabelplot)+
  60. geom_dotplot(binaxis='y', stackdir='center', dotsize=1.5)
  61. theme(plot.margin = unit(c(1, 1, 1, 1), "cm"), text = element_text(size = 12), axis.text = element_text(size = 12))
  63. filename_png = paste0(directory_name,'//', ylabel, '_violin.png')
  64. ggsave(filename_png, enhancement_volume_violin, device = 'png', width = 6, height = 6, dpi = 600)
  65. filename_eps = paste0(directory_name,'//', ylabel, '_violin.pdf')
  66. ggsave(filename_eps, plot = print(enhancement_volume_violin), device = pdf, width = 6, height = 6, dpi = 600)
  67. }
  69. # Plot select figures for grid
  70. NT_enhancement_violin <- ggviolin(data, x = "Group", y = "Nasal_40min_Mean_Intensity_12b", fill = "Group", xlab ="\nGroup", ylab ="NT mean pixel intensity (a.u.)", legend="none")+
  71. geom_dotplot(binaxis='y', stackdir='center', dotsize=1.5)
  72. theme(plot.margin = unit(c(1, 1, 1, 1), "cm"), text = element_text(size = 12), axis.text = element_text(size = 12))
  74. OFC_enhancement_violin <- ggviolin(data, x = "Group", y = "Olfactory_Cistern_40min_Mean_Intensity_12b", fill = "Group", xlab ="\nGroup", ylab = "OFC mean pixel intensity (a.u.)", legend="none")+
  75. geom_dotplot(binaxis='y', stackdir='center', dotsize=1.5)
  76. theme(plot.margin = unit(c(1, 1, 1, 1), "cm"), text = element_text(size = 12), axis.text = element_text(size = 12))
  78. Skullcap_Dura_enhancement_violin <- ggviolin(data, x = "Group", y = "Skullcap_Dura_Mean_Intensity_12b", fill = "Group", xlab ="\nGroup", ylab = "Skullcap and dura mean pixel intensity (a.u.)", legend="none")+
  79. geom_dotplot(binaxis='y', stackdir='center', dotsize=1.5)
  80. theme(plot.margin = unit(c(1, 1, 1, 1), "cm"), text = element_text(size = 12), axis.text = element_text(size = 12))
  82. Isolated_Dura_enhancement_violin <- ggviolin(data, x = "Group", y = "Dura_Isolated_Mean_Intensity_12b", fill = "Group", xlab ="\nGroup", ylab = "Isolated dura mean pixel intensity (a.u.)", legend="none")+
  83. geom_dotplot(binaxis='y', stackdir='center', dotsize=1.5)
  84. theme(plot.margin = unit(c(1, 1, 1, 1), "cm"), text = element_text(size = 12), axis.text = element_text(size = 12))
  86. SCLNs_enhancement_violin <- ggviolin(data, x = "Group", y = "SCLNsPostPFA_Mean_Intensity_12b", fill = "Group", xlab ="\nGroup", ylab = "SCLN mean pixel intensity (a.u.)", legend="none")+
  87. geom_dotplot(binaxis='y', stackdir='center', dotsize=1.5)
  88. theme(plot.margin = unit(c(1, 1, 1, 1), "cm"), text = element_text(size = 12), axis.text = element_text(size = 12))
  90. DCLNs_enhancement_violin <- ggviolin(data, x = "Group", y = "DCLNsPostPFA_Mean_Intensity_12b", fill = "Group", xlab ="\nGroup", ylab = "DCLN mean pixel intensity (a.u.)", legend="none")+
  91. geom_dotplot(binaxis='y', stackdir='center', dotsize=1.5)
  92. theme(plot.margin = unit(c(1, 1, 1, 1), "cm"), text = element_text(size = 12), axis.text = element_text(size = 12))
  95. ## Plot final grids for manuscript
  96. grid_fluoroscopy_enhancement <- plot_grid(NT_enhancement_violin, OFC_enhancement_violin, Skullcap_Dura_enhancement_violin, Isolated_Dura_enhancement_violin, SCLNs_enhancement_violin, DCLNs_enhancement_violin, ncol=3, align="v")
  97. filename_png = paste0(directory_name,'//Master_Grid_Fluoroscopy_Enhancement.png')
  98. ggsave(filename_png, grid_fluoroscopy_enhancement, device = 'png', width = 9, height = 9, dpi = 600)
  99. filename_eps = paste0(directory_name,'//Master_Grid_Fluoroscopy_Enhancement.pdf')
  100. ggsave(filename_eps, plot = print(grid_fluoroscopy_enhancement), device = pdf, width = 9, height = 9, dpi = 600)
  102. # PART IIIA ################################ INFERENTIAL STATISTICS ON PREPROCESSED DATA #################################
  104. #Define filename
  105. filename_txt = paste0(directory_name,'//Final_Inferential_Statistics.txt')
  106. filename_txt = file(filename_txt, 'w')
  108. ############################################# BETWEEN-GROUP STATISTICS #############################################
  110. for (i in 9:num_columns){
  111. data_for_plotting$Group = as.factor(data_for_plotting$Group)
  112. data_for_plotting$y = data[,i]
  113. ylabelplot = paste(colnames(data)[i], "\n")
  114. ylabelplot = gsub("_", " ", ylabelplot)
  115. ylabel = colnames(data)[i]
  117. # Plot NTe enhancement volume
  118. desc_mean_enhancement <- describeBy(y ~ Group, data = data_for_plotting, mat = TRUE)
  119. aov_mean_enhancement = 0
  120. aov_mean_enhancement <- try(aov(data = data_for_plotting, formula = y ~ Group), silent = TRUE)
  121. summary(aov_mean_enhancement)
  122. aov_mean_enhancement_shapiro_residuals = 0
  123. aov_mean_enhancement_shapiro_residuals = try(shapiro.test(residuals(aov_mean_enhancement)), silent = TRUE)
  124. kw_mean_enhancement = kruskal.test(y ~ Group, data_for_plotting)
  127. glht_posthoc <-glht(aov_mean_enhancement, linfct = mcp(Group ="Tukey"))
  128. glht_posthoc_reported <- summary(glht_posthoc, test = adjusted("none"))
  130. posthoc_parametric <- pairwise.t.test(data_for_plotting$y, data_for_plotting$Group, paired = FALSE, p.adjust.method = "BH")
  131. posthoc_nonparametric <- pairwise.wilcox.test(data_for_plotting$y, data_for_plotting$Group, paired = FALSE, p.adjust.method = "BH")
  134. cat("############################################# BETWEEN-GROUP STATISTICS #############################################\n", file = filename_txt, append=TRUE)
  135. captureOutput(ylabel, file = filename_txt, append = TRUE)
  136. cat("############################################# ######################## #############################################\n", file = filename_txt, append=TRUE)
  137. cat("\n\n Descriptives\n", file = filename_txt, append=TRUE, sep = "\n")
  138. captureOutput(desc_mean_enhancement, file = filename_txt, append = TRUE)
  139. cat("\n\n One-Way ANOVA\n", file = filename_txt, append=TRUE, sep = "\n")
  140. captureOutput(summary(aov_mean_enhancement), file = filename_txt, append = TRUE)
  141. cat("\n\n One-Way ANOVA Residual Shapiro-Wilk Test\n", file = filename_txt, append=TRUE, sep = "\n")
  142. captureOutput(aov_mean_enhancement_shapiro_residuals, file = filename_txt, append = TRUE)
  143. cat("\n\n Kruskal-Wallis Test\n", file = filename_txt, append=TRUE, sep = "\n")
  144. captureOutput(kw_mean_enhancement, file = filename_txt, append = TRUE)
  145. cat("\n\n Parametric post hoc pairwise comparisons (Tukey-corrected p-values)\n", file = filename_txt, append=TRUE, sep = "\n")
  146. captureOutput(glht_posthoc_reported, file = filename_txt, append = TRUE)
  147. cat("\n\n Parametric post hoc pairwise comparisons (Benjamini-Hochberg-corrected p-values)\n", file = filename_txt, append=TRUE, sep = "\n")
  148. captureOutput(posthoc_parametric, file = filename_txt, append = TRUE)
  149. cat("\n\n Nonparametric post hoc pairwise comparisons (Benjamini-Hochberg-corrected p-values)\n", file = filename_txt, append=TRUE, sep = "\n")
  150. captureOutput(posthoc_nonparametric, file = filename_txt, append = TRUE)
  151. }
  152. close(filename_txt)

20231107_Nasal_efflux_Statistics.R at commit d8d8f63, no license · at the source

Overview

Authors: Na Liu1,2, Yiyi Yang1, Marios Kritsilis1,2, Kelley M Swanberg1,2, Chenchen Liu1,2,3, Xuanhui Liu1,2, Brecht Moonen1,2,4, Tomas Deierborg1, Per Nilsson5, Stina Syvänen6, Dag Sehlin6, Iben Lundgaard1,2
ORCID iDs: Yiyi Yang, Marios Kritsilis, Kelley M Swanberg, Tomas Deierborg
  1. Department of Experimental Medical Science, Lund University, Lund 22184, Sweden
  2. Wallenberg Centre for Molecular Medicine, Lund University, Lund 22184, Sweden
  3. Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
  4. Department of Immunology and Infection, Biomedical Research Institute, Hasselt University, Diepenbeek 3590, Belgium
  5. Division of Neurogeriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm 17177, Sweden
  6. Department of Public Health and Caring Sciences, Uppsala University, Uppsala 75185, Sweden
Institutions: Lund University (Sweden); Tongji Hospital (China); Huazhong University of Science and Technology (China); Hasselt University (Belgium); Karolinska Institutet (Sweden); Uppsala University (Sweden)
Journal: Brain : a journal of neurology, volume 149, issue 7, pages 2422-2437
Dates: received 28 July 2025; accepted 30 January 2026; published online 28 February 2026; in print July 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1093/brain/awag080 · PMID 41762118 · PMCID PMC13337242 · OpenAlex W7132837748
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism), Alzheimer's / dementia (population), cellular / molecular (subfield)
Methods: Spectral & time-frequency, Statistics, Connectivity
Keywords: App knock-in Alzheimer’s mice, glymphatic system, parenchymal border macrophages
MeSH: Alzheimer Disease*, Amyloid beta-Protein Precursor*, Glymphatic System*, Macrophages*, Parenchymal Tissue*, Amyloid beta-Peptides, Animals, Brain, Disease Models, Animal, Gene Knock-In Techniques, Mice, Mice, Inbred C57BL, Mice, Transgenic, Plaque, Amyloid (* major topic)
Topic: Cerebrospinal fluid and hydrocephalus (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: Leif Lundblad Family; Olle Engkvist Foundation; Vetenskapsrådet (VR)
Citations: cited by 3 papers (Europe PMC); 80 references in the paper
Notices: A comment on this paper has been published (42244340, from Europe PMC)

Abstract

Amyloid-β (Aβ) accumulation is a hallmark of Alzheimer’s disease. Cerebral Aβ deposition is attenuated by a functional glymphatic system, in which perivascular entry of CSF and its exchange with interstitial fluid mediate solute clearance. Parenchymal border macrophages (PBMs), positioned along glymphatic pathways, are emerging as important players for glymphatic clearance. However, how glymphatic function and PBMs are affected in App knock-in models of Alzheimer’s disease is unknown.

In this study, we used two App knock-in mouse models that develop progressive Aβ pathology, AppNL-F and AppNL-G-F. AppNL-F mice showed reductions in glymphatic influx and clearance at 6 months, preceding substantial Aβ plaque deposition. The decrease in glymphatic function in AppNL-F mice was correlated with a loss of PBMs and altered marker expression. Acute administration of Aβ into the CSF decreased the number of PBMs and impaired glymphatic transport in wild-type mice, thus recapitulating the pre-plaque stage. In contrast, the number of PBMs was not reduced in AppNL-G-F mice, possibly owing to an enhanced Aβ phagocytic capacity in PBMs. Four weeks of systemic anti-Aβ antibody treatment efficiently reduced Aβ plaque load and rescued PBMs in some brain regions; however, the treatment did not restore glymphatic function in the AppNL-F model.

These findings suggest that glymphatic dysfunction in App knock-in models of Alzheimer’s disease is not driven by parenchymal Aβ plaque load but is closely linked to pre-plaque Aβ-induced loss of PBMs. Preservation of PBM abundance and their normal marker expression might be important for maintaining glymphatic function and mitigating early progression of Alzheimer’s disease.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

kswanberg/osv

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: d8d8f6352694a624e0b0bae8779f08d9c43e8a1b, 21 April 2026
Languages: R (30)
Size: 32 files, 30 scripts
Software Heritage: not checked
Found in: the text, “CSF tracer imaging and quantifications”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (30 files), cowplot (28 files), easystats (28 files), ggplot2 (28 files), ggpubr (28 files), nlme (28 files), psych (28 files), rstatix (28 files), car (27 files), lme4 (27 files), multcomp (8 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
30 files
At the source: github.com/kswanberg/Osv/blob/main/20240419_Image_Processing_Methods.pdf

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;
  • 30 scripts, each with its path and the digest of its content;
  • 2 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

The data that supports the findings of this study are available from the corresponding author, upon reasonable request.

Reproduced under the paper's license (CC BY-NC), 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, 12 authors, 3 keywords, 14 MeSH terms, 3 funders, 80 references, 1 integrity notice.

Cite

This paper

Liu, N., Yang, Y., Kritsilis, M., Swanberg, K. M., Liu, C., Liu, X., Moonen, B., Deierborg, T., Nilsson, P., Syvänen, S., Sehlin, D., & Lundgaard, I. (2026). Early glymphatic failure in AppNL-F knock-in mice is linked to parenchymal border macrophages loss. Brain : a journal of neurology, 149(7), 2422-2437. https://doi.org/10.1093/brain/awag080

BibTeX

@article{liu2026early,
author = {Liu, Na and Yang, Yiyi and Kritsilis, Marios and Swanberg, Kelley M and Liu, Chenchen and Liu, Xuanhui and Moonen, Brecht and Deierborg, Tomas and Nilsson, Per and Syvänen, Stina and Sehlin, Dag and Lundgaard, Iben},
title = {{Early glymphatic failure in AppNL-F knock-in mice is linked to parenchymal border macrophages loss}},
journal = {Brain : a journal of neurology},
year = {2026},
month = jul,
volume = {149},
number = {7},
pages = {2422--2437},
publisher = {Oxford University Press},
issn = {0006-8950},
doi = {10.1093/brain/awag080},
url = {https://doi.org/10.1093/brain/awag080},
pmid = {41762118},
pmcid = {PMC13337242}
}

RIS

TY - JOUR
AU - Liu, Na
AU - Yang, Yiyi
AU - Kritsilis, Marios
AU - Swanberg, Kelley M
AU - Liu, Chenchen
AU - Liu, Xuanhui
AU - Moonen, Brecht
AU - Deierborg, Tomas
AU - Nilsson, Per
AU - Syvänen, Stina
AU - Sehlin, Dag
AU - Lundgaard, Iben
TI - Early glymphatic failure in AppNL-F knock-in mice is linked to parenchymal border macrophages loss
T2 - Brain : a journal of neurology
J2 - Brain
PY - 2026
DA - 2026/07/01
VL - 149
IS - 7
SP - 2422
EP - 2437
SN - 0006-8950
PB - Oxford University Press
DO - 10.1093/brain/awag080
UR - https://doi.org/10.1093/brain/awag080
LA - en
ER -

CSL-JSON

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