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<i>Hex</i>-MASP for mapping the whole-tissue spatial proteome and the intrabrain distribution of monoclonal antibodies.

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 › Generation of Protein Distribution Maps by MAsP App. ↔ R/MyDistriPlot.R, lines 1–123 · score 0.63 · protein abundances, protein distribution maps, map generation, score, location
  2. [2] § Materials and Methods › Generation of Protein Distribution Maps by MAsP App. ↔ inst/shiny/MAsP/app.R, lines 29–96 · score 0.59 · MAsP, protein abundances, distribution maps, score, app, location

Paper

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The authors' code

R · 140 lines · 6.4 KB · Apache-2.0 · 1 match

  1. #' Distribution map generation of proteins
  2. #'
  3. #' Distribution map generation of proteins
  4. #'
  5. #' @param df the quantitative protein result
  6. #' @param location the original location information
  7. #' @param target_name the protein in the map
  8. #' @param limit a logical value, if setting the value limits of protein abundance/ratio in the distribution map
  9. #' @param scale a logical value, if converting data to z-score
  10. #' @param upperL the upperbound of the protein abundance/ratio the map can show
  11. #' @param lowerL the lowerbound of the protein abundance/ratio the map can show
  12. #' @param color1 the lower color, color name and HTML code are both acceptable
  13. #' @param color2 the middle color, color name and HTML code are both acceptable
  14. #' @param color3 the upper color, color name and HTML code are both acceptable
  15. #' @param transparent a logical value, if the map is with a transparent background while downloading
  16. #' @param mask a logical value, if adding a brain-shape cover on the map
  17. #' @param Legend a logical value, if showing the legend
  18. #' @param g the brain-shape cover
  19. #' @return a protein distribution map
  20. #' @export
  21. MyDistriPlot <- function(df, location, target_name, limit, scale = TRUE, upperL = 2, lowerL = -2,
  22. color1, color2, color3, transparent = FALSE, mask = FALSE, Legend = TRUE, g = g){
  23. #outputs <- list()
  24. new_loc <- loc_convert(location)
  25. rownames(df) <- toupper(rownames(df))
  26. target_name <- toupper(target_name)
  27. target <- df[grep(target_name, rownames(df)),]
  28. target <- target[1,]
  29. target_name <- toupper(rownames(target))
  30. legend_label <- 'Protein Relative Abundance'
  31. if(scale){
  32. tmp_value <- as.numeric(target)
  33. tmp_value <- (tmp_value-mean(tmp_value, na.rm = TRUE))/sd(tmp_value, na.rm = TRUE)
  34. target[1,] <- tmp_value
  35. legend_label = "Protein Z-score"
  36. }
  37. new_loc$value <- NA
  38. for (i in 1:nrow(new_loc)) {
  39. tmp <- which(new_loc$Name[i] == names(target))
  40. if(length(tmp) != 0)
  41. new_loc$value[i] <- target[tmp]
  42. }
  43. new_loc$value <- as.numeric(new_loc$value)
  44. if(limit == 'none'){
  45. range_tmp <- range(as.numeric(new_loc$value), na.rm = TRUE)
  46. UpperLimit <- range_tmp[2]
  47. LowerLimit <- range_tmp[1]
  48. }
  49. if(limit == 'outlierRM'){
  50. target <- target[!is.na(target)]
  51. outliers <- boxplot.stats(as.numeric(target))$out
  52. dataRange <- target[!as.numeric(target) %in% outliers]
  53. UpperLimit <- range(dataRange)[2]
  54. LowerLimit <- range(dataRange)[1]
  55. }
  56. if(limit == 'customized'){
  57. UpperLimit <- as.numeric(upperL)
  58. LowerLimit <- as.numeric(lowerL)
  59. }
  61. for (i in 1:nrow(new_loc)) {
  62. tmp <-new_loc$value[i]
  63. if(!is.na(tmp)){
  64. if(tmp > UpperLimit){
  65. new_loc$value[i] <- UpperLimit
  66. }
  67. if(tmp < LowerLimit){
  68. new_loc$value[i] <- LowerLimit
  69. }
  70. }
  71. }
  73. if(transparent) transparent2 <- "transparent" else transparent2 <- NA
  74. if(Legend) legend2 <- 'right' else legend2 <- 'none'
  77. # read png mask
  79. PlotTitle <- paste('Heatmap for Protein', target_name)
  80. new_loc$row <- factor(new_loc$row, levels = base::sort(unique(new_loc$row), decreasing = TRUE))
  81. new_loc$column <- factor(new_loc$column, levels = base::sort(unique(new_loc$column), decreasing = FALSE))
  83. if(mask){
  84. output <- ggplot(new_loc, aes(column, row, fill= value)) +
  85. geom_tile() + xlab('Columns') + ylab('Rows') + labs(fill = legend_label) +
  86. scale_fill_gradientn(colors = c(color1,color2,color3),na.value = '#A9A9A9') +
  87. scale_x_discrete(breaks = 1:(ncol(location)+1)) +
  88. scale_y_discrete(breaks = 1:(nrow(location)+1)) +
  89. annotation_custom(g, xmin = -Inf, xmax = Inf, ymin = -Inf, ymax = Inf) +
  90. coord_fixed() +
  91. ggtitle(PlotTitle) +
  92. theme(plot.title = element_text(hjust = 0.5)) +
  93. theme(legend.position=legend2,panel.grid.minor=element_blank(),
  94. panel.border=element_rect(size=1,fill=NA,colour="black"),
  95. panel.background = element_rect(fill = "#A9A9A9",colour = NA),
  96. plot.background = element_rect(fill = transparent2,colour = NA),
  97. panel.grid.major = element_blank()) +
  98. theme(axis.text=element_text(size=14, family="Helvetica", colour="black"),
  99. axis.title=element_text(size=14, family="Helvetica",colour="black"),
  100. legend.text=element_text(size=14, family="Helvetica",colour="black"),
  101. legend.title=element_text(size=14, family="Helvetica", colour="black"))
  102. }else{
  103. output <- ggplot(new_loc, aes(column, row, fill= value)) +
  104. geom_tile() + xlab('Columns') + ylab('Rows') + labs(fill = legend_label) +
  105. scale_fill_gradientn(colors = c(color1,color2,color3),na.value = '#A9A9A9') +
  106. scale_x_discrete(breaks = 1:(ncol(location)+1)) +
  107. scale_y_discrete(breaks = 1:(nrow(location)+1)) +
  108. coord_fixed() +
  109. ggtitle(PlotTitle) +
  110. theme(plot.title = element_text(hjust = 0.5)) +
  111. theme(legend.position=legend2,panel.grid.minor=element_blank(),
  112. panel.border=element_rect(size=1,fill=NA,colour="black"),
  113. panel.background = element_rect(fill = "#A9A9A9",colour = NA),
  114. plot.background = element_rect(fill = transparent2,colour = NA),
  115. panel.grid.major = element_blank()) +
  116. theme(axis.text=element_text(size=14, family="Helvetica", colour="black"),
  117. axis.title=element_text(size=14, family="Helvetica",colour="black"),
  118. legend.text=element_text(size=14, family="Helvetica",colour="black"),
  119. legend.title=element_text(size=14, family="Helvetica", colour="black"))
  120. }
  122. return(output)
  123. }
  125. #' @export
  126. MyDistriPlot_batch <- function(df, location, proteins_csv, directory, limit, scale = TRUE, upperL = 2, lowerL = -2, mask = FALSE,
  127. color1, color2, color3, transparent = FALSE, Legend = TRUE, width, height, g = g){
  128. withProgress(message = 'Batch Plots', value = 0, {
  129. Proteins <- unname(unlist(proteins_csv))
  130. current_dir <- getwd()
  131. setwd(directory)
  132. for (i in 1:length(Proteins)) {
  133. incProgress(i/length(Proteins), detail = 'generating...')
  134. output <- MyDistriPlot(df = df,location = location, target_name = Proteins[i], limit = limit, scale = scale, upperL = upperL, lowerL = lowerL,
  135. mask = mask, color1 = color1, color2 = color2, color3 = color3,
  136. transparent = transparent, Legend = Legend, g = g)
  137. ggsave(filename = paste('Distribution_of_', Proteins[i],'.png',sep = ''), plot = output, device='png', width = width, height = height, dpi = 300)
  138. }
  139. })
  140. }

MyDistriPlot.R at commit 6e534b3, under Apache-2.0 · at the source

Overview

Authors: Shihan Huo1, Min Ma1,2,3, Shuo Qian1,2, Ming Zhang1,3, Jie Pu1, Xiaoyu Zhu1, Sailee Rasam4, Tara Barone5, Robert Plunkett5, Chi Zhou6, Jun Qu1,2,3
ORCID iDs: Shuo Qian, Tara Barone, Robert Plunkett, Jun Qu
  1. Department of Pharmaceutical Sciences, State University of New York at Buffalo, Buffalo, NY 14214
  2. Department of Cell Stress Biology, Roswell Park Comprehensive Cancer Center, Buffalo, NY 14203
  3. Center of Excellence in Bioinformatics & Life Sciences, Buffalo, NY 14203
  4. Department of Biochemistry, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY 14203
  5. Department of Neuro-Oncology, Roswell Park Comprehensive Cancer Center, Buffalo, NY 14203
  6. Department of Industrial and Systems Engineering, State University of New York at Buffalo, Buffalo, NY 14214
Institutions: State University of New York (United States); Roswell Park Comprehensive Cancer Center (United States)
Journal: Proceedings of the National Academy of Sciences of the United States of America, volume 123, issue 36, article e2532946123
Dates: received 5 December 2025; accepted 22 July 2026; published online 3 September 2026; in print 8 September 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1073/pnas.2532946123 · PMID 42691083 · PMCID PMC13552876 · OpenAlex W7160417836
Open access: hybrid, a free copy (OpenAlex)
Preprint: doi:10.64898/2026.05.01.722341
Status: code verified
Categories: genetics / omics (modality), mouse (organism), cellular / molecular (subfield)
Methods: Preprocessing, Connectivity, fMRI & imaging
Keywords: spatial proteomics, whole-tissue mapping, accurate protein distribution, mAb intrabrain distribution
MeSH: Antibodies, Monoclonal*, Brain*, Proteome*, Proteomics*, Animals, Liquid Chromatography-Mass Spectrometry, Mice (* major topic)
Journal subjects: Biological Sciences, Pharmacology
Topic: Advanced Proteomics Techniques and Applications (Spectroscopy, Chemistry), according to OpenAlex
Funding: National Institute of Diabetes and Digestive and Kidney Diseases (DK137113)
Citations: not cited yet (Europe PMC); 40 references in the paper

Abstract

Whole-tissue level spatial proteomics provides critical insights into region-specific biological regulations but remains challenging. Previously, we introduced the micro-scaffold assisted spatial proteomics (MASP) concept for whole-tissue mapping. However, this prototype required substantial development in spatial resolution, practicality, and throughput for practical application. Here we present a next-generation MASP technique (hex-MASP) featuring i) a new design of hexagonal-micro-wells fabricated with optimized projection micro-stereolithography 3D-printing, achieving high spatial resolution, sampling robustness, and mechanical strength for reproducibly compartmentalizing even tough tissues; ii) enhanced throughput/effectiveness in sample preparation and LC–MS analysis with high quantitative quality. Applied to mouse brain, hex-MASP achieved in-depth, whole-tissue mapping for >6,000 proteins in mouse brains, with high spatial accuracy and excellent data quality. The substantially improved resolution revealed critical regional details across the entire brain, that were not previously captured, enabling precise depiction of protein distribution heterogeneity. This technique enabled the identification of many unreported regionally enriched proteins across brain structures. We further applied hex-MASP to investigate the intrabrain distribution of intracerebroventricularly dosed antibody therapeutics and related proteins, which enabled whole-tissue mapping of protein drugs revealed insights into antibody brain penetration and distribution. Hex-MASP represents a robust, scalable platform for whole-tissue spatial proteomics.

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 2 matches between paragraphs and lines of code.

JunQu-Lab/MAsP

License: Apache-2.0
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 6e534b32f3c687cf99f57c8e1fb971ec4e305bfd, 8 January 2026
Languages: R (8)
Size: 30 files, 8 scripts
Software Heritage: not checked
Found in: the text, “Generation of Protein Distribution Maps by MAsP ”
Holds: README, license file, environment (DESCRIPTION), documentation
Not found: CITATION.cff, tests, continuous integration
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
10 files
At the source: github.com/JunQu-Lab/MAsP

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;
  • 8 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, Materials, and Software Availability

Raw files have been deposited in ProteomeXchange Consortium (PXD068767 (https://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD068767)) (40).

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, 11 authors, 4 keywords, 7 MeSH terms, 1 funder, 35 references.

Cite

This paper

Huo, S., Ma, M., Qian, S., Zhang, M., Pu, J., Zhu, X., Rasam, S., Barone, T., Plunkett, R., Zhou, C., & Qu, J. (2026). &lt;i&gt;Hex&lt;/i&gt;-MASP for mapping the whole-tissue spatial proteome and the intrabrain distribution of monoclonal antibodies. Proceedings of the National Academy of Sciences of the United States of America, 123(36), e2532946123. https://doi.org/10.1073/pnas.2532946123

BibTeX

@article{huo2026lt,
author = {Huo, Shihan and Ma, Min and Qian, Shuo and Zhang, Ming and Pu, Jie and Zhu, Xiaoyu and Rasam, Sailee and Barone, Tara and Plunkett, Robert and Zhou, Chi and Qu, Jun},
title = {{\&lt;i\&gt;Hex\&lt;/i\&gt;-MASP for mapping the whole-tissue spatial proteome and the intrabrain distribution of monoclonal antibodies}},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
year = {2026},
month = sep,
volume = {123},
number = {36},
pages = {e2532946123},
publisher = {National Academy of Sciences},
issn = {0027-8424},
doi = {10.1073/pnas.2532946123},
url = {https://doi.org/10.1073/pnas.2532946123},
pmid = {42691083},
pmcid = {PMC13552876}
}

RIS

TY - JOUR
AU - Huo, Shihan
AU - Ma, Min
AU - Qian, Shuo
AU - Zhang, Ming
AU - Pu, Jie
AU - Zhu, Xiaoyu
AU - Rasam, Sailee
AU - Barone, Tara
AU - Plunkett, Robert
AU - Zhou, Chi
AU - Qu, Jun
TI - &lt;i&gt;Hex&lt;/i&gt;-MASP for mapping the whole-tissue spatial proteome and the intrabrain distribution of monoclonal antibodies
T2 - Proceedings of the National Academy of Sciences of the United States of America
J2 - Proc Natl Acad Sci U S A
PY - 2026
DA - 2026/09/03
VL - 123
IS - 36
SP - e2532946123
SN - 0027-8424
PB - National Academy of Sciences
DO - 10.1073/pnas.2532946123
UR - https://doi.org/10.1073/pnas.2532946123
LA - en
ER -

CSL-JSON

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"id": "10.1073/pnas.2532946123",
"type": "article-journal",
"title": "&lt;i&gt;Hex&lt;/i&gt;-MASP for mapping the whole-tissue spatial proteome and the intrabrain distribution of monoclonal antibodies",
"container-title": "Proceedings of the National Academy of Sciences of the United States of America",
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"volume": "123",
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"DOI": "10.1073/pnas.2532946123",
"PMID": "42691083",
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