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Spatially resolved transcriptomics in human brain metastases identifies macrophage-tumor interactions associated with survival.

Code ↔ Paper

4 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 4 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] § STAR★Methods › Method details › Human brain metastases single nuclei sequencing data processing ↔ data-raw/pbmc.R, the whole file · a weak match · score 0.82 · FindVariableFeatures, FindClusters, FindNeighbors, percent mt, sum, Seurat
  2. [2] § STAR★Methods › Method details › Human brain metastases single nuclei sequencing data processing ↔ R/RunBasicSeurat.R, lines 29–77 · score 0.74 · FindClusters, FindNeighbors, percent mt, regress, Seurat, mitochondrial
  3. [3] § Results › Transcriptional landscape of microenvironment cell types in brain metastatic disease ↔ vignettes/News.Rmd, lines 170–219 · score 0.57 · MS4A1, IL7R, CD8A, gene expression, smooth, cell
  4. [4] § Results › Transcriptional landscape of microenvironment cell types in brain metastatic disease ↔ vignettes/Visualization.Rmd, lines 300–313 · score 0.55 · MS4A1, IL7R, CD8A, cell

Paper

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

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The authors' code

R · 55 lines · 2.2 KB · GPL-3.0 · 1 match

  1. library(Seurat)
  2. pbmc <- Read10X("pbmc3k_10X/outs/filtered_feature_bc_matrix/")
  3. pbmc <- CreateSeuratObject(counts = pbmc, project = "pbmc3k", min.cells = 3, min.features = 200)
  4. pbmc[["percent.mt"]] <- PercentageFeatureSet(pbmc, pattern = "^MT-")
  5. pbmc <- subset(pbmc, subset = nFeature_RNA > 200 & nFeature_RNA < 2500 & percent.mt < 5)
  6. pbmc <- NormalizeData(pbmc)
  7. pbmc <- FindVariableFeatures(pbmc)
  8. pbmc <- ScaleData(pbmc)
  9. pbmc <- RunPCA(pbmc)
  10. pbmc <- FindNeighbors(pbmc, dims = 1:10)
  11. pbmc <- FindClusters(pbmc, resolution = 0.5)
  12. pbmc <- RunUMAP(pbmc, dims = 1:10)
  13. DimPlot(pbmc, reduction = "umap")
  14. new.cluster.ids <- c("CD4 T Naive", "Mono CD14", "CD4 T Memory", "B cell", "CD8 T cell", "Mono FCGR3A", "NK cell", "DC", "Platelet")
  15. pbmc$cluster <- new.cluster.ids[pbmc$seurat_clusters]
  16. DimPlot(pbmc, group.by = "cluster", label = T)
  17. # remove
  18. cells <- CellSelector(DimPlot(pbmc))
  19. cell.rm1 <- intersect(cells, colnames(pbmc)[pbmc$seurat_clusters == "0"])
  20. cells <- CellSelector(DimPlot(pbmc))
  21. cell.rm2 <- setdiff(cells, colnames(pbmc)[pbmc$seurat_clusters == "8"])
  22. cell.rm <- c(cell.rm1, cell.rm2)
  23. # keep enough number of DC and platelet
  24. table(pbmc$seurat_clusters)
  25. cell.kp <- colnames(pbmc)[pbmc$seurat_clusters %in% c("8")]
  26. cell.kp <- c(cell.kp, sample(colnames(pbmc)[pbmc$seurat_clusters %in% c("7")], 12))
  27. DimPlot(pbmc, cells.highlight = cell.rm)
  28. DimPlot(pbmc, cells.highlight = cell.kp)
  30. cells <- sample(setdiff(colnames(pbmc), c(cell.rm, cell.kp)), 500 - length(cell.kp))
  31. cells <- c(cells, cell.kp)
  32. DimPlot(pbmc, cells.highlight = cells)
  34. pbmc_sub <- subset(pbmc, cells = cells)
  35. pbmc_sub <- subset(pbmc_sub, features = rownames(pbmc_sub)[rowSums(GetAssayData(pbmc_sub)) >= 1] )
  36. DimPlot(pbmc_sub)
  37. table(pbmc_sub$cluster)
  38. [email hidden]$orig.ident <- sample(c("sample1","sample2","sample2"), size = 500, replace = T)
  39. [email hidden]$orig.ident <- factor(pbmc_sub$orig.ident)
  40. pbmc_sub$cluster <- factor(pbmc_sub$cluster)
  41. Idents(pbmc_sub) <- 'cluster'
  42. DimPlot(pbmc_sub, label = T)
  43. DimPlot(pbmc_sub, group.by = "orig.ident")
  44. pbmc <- pbmc_sub
  46. set.seed(42)
  48. pbmc$condition <- gsub("sample","condition",pbmc$orig.ident)
  49. pbmc$sample_id <- paste0(
  50. pbmc$condition, "_rep",
  51. sample(1:3, ncol(pbmc), replace = TRUE)
  52. )
  54. usethis::use_data(pbmc, overwrite = TRUE)

pbmc.R at commit c917a47, under GPL-3.0 · at the source

Overview

Authors: Aaditya Khatri1, Courtney M McKernan2, Amanda ED Van Swearingen3, Vaibhav Jain4, Hannah L Thrash2, Arianna Towne2, Jing Jin Gu2, Simon G Gregory4,5, Carey K Anders3, Ann Marie Pendergast2
ORCID iDs: Aaditya Khatri
  1. Department of Medicine, Division of Pulmonary, Allergy and Critical Care Medicine, Duke University School of Medicine, Durham, NC, USA
  2. Department of Pharmacology and Cancer Biology, Duke University School of Medicine, Durham, NC, USA
  3. Duke Center of Brain and Spine Metastasis, Duke Cancer Institute, Durham, NC, USA
  4. Duke Molecular Physiology Institute, Duke University, Durham, NC, USA
  5. Department of Neurosurgery, Duke University School of Medicine, Durham, NC, USA
Institutions: Duke University (United States); Duke Medical Center (United States); Duke Cancer Institute
Journal: iScience, volume 29, issue 8, article 116517
Dates: received 22 December 2025; accepted 8 June 2026; published online 12 August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.isci.2026.116517 · PMID 42666958 · PMCID PMC13523839 · OpenAlex W7202274613
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), other condition (population), clinical / translational (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning
Keywords: brain metastasis, tumor microenvironment, spatial transcriptomics
Topic: Single-cell and spatial transcriptomics (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: National Heart Lung and Blood Institute (1R38HL143612, 1K38HL180998-01, 5T32HL160494-03); National Institute of General Medical Sciences (5T32GM133352); National Institutes of Health National Cancer Institute (T32-CA009111, 1F31CA290692-01A1, R01CA246133); NCI NIH HHS (P30 CA014236, T32 CA009111, R01 CA246133, F31 CA290692); National Institutes of Health; NHLBI NIH HHS (T32 HL160494, R38 HL143612, K38 HL180998); US Department of Defense (W81XWH-22-1-0034, W81XWH-22-1-0033); Lung Cancer Research Foundation; NIGMS NIH HHS (T32 GM133352)
Citations: not cited yet (Europe PMC); 43 references in the paper
Research resources: Alpha Smooth Muscle Actin RRID:AB_2565041, CD163 RRID:AB_2800204, COL1A1 RRID:AB_2827934, CD68 RRID:AB_307338, Cytokeratin (pan reactive) RRID:AB_439775, HLA-DR RRID:AB_468639, MRC1 (CD206) RRID:AB_571923, RRID:SCR_017860

Abstract

Despite advances in treatment approaches, the mean survival for patients with brain metastases remains poor. The incidence of brain metastases continues to rise, and there remains a need to identify novel therapeutics targeting mechanisms critical for brain metastasis. We employed a multi-omic approach, including single nuclei and spatially resolved transcriptomic profiling across 23 brain metastatic samples, with the representation of lung, breast, and melanoma metastases, to identify tumor-microenvironment interactions associated with survival outcomes in brain metastasis. We found that the specific role of macrophages in disease progression is context-dependent. Activated HLA-DR+ inflammatory macrophages directly in contact with cancer cells at the tumor boundary are associated with responsiveness to therapies and improved patient survival. Conversely, reprogrammed macrophages expressing extracellular matrix proteins and TGFβ1 are associated with poor survival. These findings identify spatially distinct tumor cell-macrophage interactions associated with survival outcomes in patients with brain metastases and represent targets for immunotherapy strategies.

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 4 matches between paragraphs and lines of code.

huayc09/SeuratExtend

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: c917a47e20cab9fb3399ff7ed078d3e8139339f0, 13 June 2026
Languages: R (43), JavaScript (15)
Size: 670 files, 58 scripts
Software Heritage: not archived
Found in: the resources table
Holds: README, license file, environment (DESCRIPTION, inst/extdata/environment-linux.yml, inst/extdata/environment-mac-silicon.yml, inst/extdata/environment-mac.yml, inst/extdata/environment-windows.yml), documentation, 8 notebooks
Not found: CITATION.cff, tests, continuous integration
Tools: Seurat (21 files), tidyverse (18 files), ggplot2 (11 files), reshape2 (7 files), reticulate (5 files), ggpubr (4 files), cowplot (3 files), ComplexHeatmap (1 file), mgcv (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
60 files
At the source: github.com/huayc09/SeuratExtend

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;
  • 58 scripts, each with its path and the digest of its content;
  • 4 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 and code availability

All data associated with this study are present in the paper or the supplementary materials. The raw singl-cell RNA sequencing datasets are available at the NCBI Geo (GSE325196 (https://ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE325196)). Processed Seurat object files are available on Figshare (https://doi.org/10.6084/m9.figshare.30349819). This paper does not report original code. Any additional information required to re-analyze the data reported in this paper is available from the lead contact upon request.

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 3, 28 September 2026

  • Authors: added Aaditya Khatri (0000-0002-4372-2324); removed Aaditya Khatri

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 3 keywords, 9 funders, 43 references, 8 RRIDs.

Cite

This paper

Khatri, A., McKernan, C. M., Van Swearingen, A. E., Jain, V., Thrash, H. L., Towne, A., Gu, J. J., Gregory, S. G., Anders, C. K., & Pendergast, A. M. (2026). Spatially resolved transcriptomics in human brain metastases identifies macrophage-tumor interactions associated with survival. iScience, 29(8), 116517. https://doi.org/10.1016/j.isci.2026.116517

BibTeX

@article{khatri2026spatially,
author = {Khatri, Aaditya and McKernan, Courtney M and Van Swearingen, Amanda ED and Jain, Vaibhav and Thrash, Hannah L and Towne, Arianna and Gu, Jing Jin and Gregory, Simon G and Anders, Carey K and Pendergast, Ann Marie},
title = {{Spatially resolved transcriptomics in human brain metastases identifies macrophage-tumor interactions associated with survival}},
journal = {iScience},
year = {2026},
month = aug,
volume = {29},
number = {8},
pages = {116517},
publisher = {Elsevier},
issn = {2589-0042},
doi = {10.1016/j.isci.2026.116517},
url = {https://doi.org/10.1016/j.isci.2026.116517},
pmid = {42666958},
pmcid = {PMC13523839}
}

RIS

TY - JOUR
AU - Khatri, Aaditya
AU - McKernan, Courtney M
AU - Van Swearingen, Amanda ED
AU - Jain, Vaibhav
AU - Thrash, Hannah L
AU - Towne, Arianna
AU - Gu, Jing Jin
AU - Gregory, Simon G
AU - Anders, Carey K
AU - Pendergast, Ann Marie
TI - Spatially resolved transcriptomics in human brain metastases identifies macrophage-tumor interactions associated with survival
T2 - iScience
J2 - iScience
PY - 2026
DA - 2026/08/12
VL - 29
IS - 8
SP - 116517
SN - 2589-0042
PB - Elsevier
DO - 10.1016/j.isci.2026.116517
UR - https://doi.org/10.1016/j.isci.2026.116517
LA - en
ER -

CSL-JSON

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