OSCR

Dominant clones leverage developmental epigenomic states to drive ependymoma.

Code ↔ Paper

8 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 8 matches · 1 of them tie a paragraph to a whole file without given lines: a weak match, whose lines are not tinted
  1. [1] § Methods › snMultiome data integration ↔ funcs.R, lines 178 to 219 · score 0.87 · RunPCA, RunUMAP, SCTransform, integrated LSI, graph, Weighted · Report a mismatch
  2. [2] § Methods › Processing of TrackerSeq barcode reads ↔ TrackerSeq/1_pre_process.sh, lines 1 to 51 · score 0.81 · UMI tools, pre processed, cell barcodes, R2, whitelist, trimming · Report a mismatch
  3. [3] § Methods › Classification of malignant cells ↔ 9.calMalignantCells.integ.v4.R, lines 1 to 61 · score 0.77 · filtered CNV regions, malignant cells, CNV genes, loss, ratio · Report a mismatch
  4. [4] § Methods › Cell cycling and non-cycling signatures ↔ 6.cellcycling.R, the whole file · a weak match · score 0.67 · CellCycleScoring, cell cycle score, G2M, cc, Seurat, genes · Report a mismatch
  5. [5] § Methods › Processing of TrackerSeq barcode reads ↔ 15.extract_lineage_barcodes.py, lines 25 to 161 · score 0.65 · CTG, GAC, TGA, conserved, bp, FASTQ · Report a mismatch
  6. [6] § Methods › RNA-seq and ATAC-seq analyses ↔ funcs.R, lines 4 to 34 · score 0.51 · call peaks, MACS2, mm10, quantification, ATAC, genomes · Report a mismatch
  7. [7] § Methods › snMultiome data processing ↔ 2.multiomeIntegration.R, lines 1 to 53 · score 0.51 · cellranger arc, mm10, Seurat, genome, ATAC, filter · Report a mismatch
  8. [8] § Embryonic chromatin accessibility and cancer ↔ 10.zr_binding_peaks.ipynb, lines 11 to 21 · score 0.51 · peak signal, ZR binding sites, neurons, RGCs, accessibility, gene · Report a mismatch

Paper

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The authors' code

R · 225 lines · 6.6 KB · MIT · 2 matches

  1. # Hua Sun
  4. CallPeaksUsingMACS2 <- function(obj=NULL, macs2='MACS2', annotation=NULL)
  5. {
  6. DefaultAssay(obj) <- "ATAC"
  7. ref <- unique(obj$ref)
  9. effective_genome_size <- 2.3e+09
  11. if (ref == 'hg38'){ effective_genome_size <- 2.7e+09 }
  13. peaks <- CallPeaks(obj, macs2.path = macs2, effective.genome.size = effective_genome_size)
  15. # remove peaks on nonstandard chromosomes and in genomic blacklist regions
  16. peaks <- keepStandardChromosomes(peaks, pruning.mode = "coarse")
  17. if (ref == 'mm10'){ peaks <- subsetByOverlaps(x = peaks, ranges = blacklist_mm10, invert = TRUE) }
  18. if (ref == 'hg38'){ peaks <- subsetByOverlaps(x = peaks, ranges = blacklist_hg38_unified, invert = TRUE) }
  20. # quantify counts in each peak
  21. macs2_counts <- FeatureMatrix(
  22. fragments = Fragments(obj),
  23. features = peaks,
  24. cells = colnames(obj)
  25. )
  27. obj[["peaks"]] <- CreateChromatinAssay(
  28. counts = macs2_counts,
  29. fragments = Fragments(obj),
  30. annotation = annotation
  31. )
  33. return(obj)
  34. }
  38. ObjectListConvertBedToGRanges <- function(snatac_macs2_list)
  39. {
  40. atac_peak <- c()
  41. for (obj in snatac_macs2_list){
  42. name <- unique(obj$Sample)
  44. # make peak bed for multiome data
  45. peaks <- as.data.frame(obj@assays$peaks@ranges)[,1:3]
  46. colnames(peaks) <- c('chr', 'start', 'end')
  48. # convert to genomic ranges
  49. gr <- GenomicRanges::makeGRangesFromDataFrame(as.data.frame(peaks))
  50. atac_peak[[name]] <- gr
  51. }
  52. print(names(atac_peak))
  54. # Create a unified set of peaks to quantify in each dataset
  55. combined.peaks <- GenomicRanges::reduce(x = unlist(GRangesList(atac_peak)))
  57. # Filter out bad peaks based on length
  58. peakwidths <- width(combined.peaks)
  59. combined.peaks <- combined.peaks[peakwidths < 10000 & peakwidths > 20]
  61. snatac_peak_list <- lapply(X = snatac_macs2_list, FUN = function(x) { x <- MakeNewATACObject(x, combined.peaks, annotation) })
  63. return(snatac_peak_list)
  64. }
  67. RenameObjectListCells <- function(objectlist)
  68. {
  69. objectlist <- base::lapply(X = objectlist, FUN = function(x) { x <- RenameCells(x, new.names = paste0(as.character(unique(x$orig.ident)), "_", Cells(x=x))) })
  71. return(objectlist)
  72. }
  76. ComputeLSI <- function(obj=NULL, assay='peaks', min_cutoff=10)
  77. {
  78. DefaultAssay(obj) <- assay
  79. obj <- FindTopFeatures(obj, min.cutoff=min_cutoff)
  80. obj <- RunTFIDF(obj)
  81. obj <- RunSVD(obj)
  83. return(obj)
  84. }
  87. MergeObjects <- function(objectlist)
  88. {
  89. samples <- c()
  90. for (x in objectlist){ samples <- append(samples, unique(x$Sample)) }
  91. combined_object <- merge(x=objectlist[[1]], y=objectlist[2:length(objectlist)], add.cell.ids = samples)
  93. return(combined_object)
  94. }
  97. RunUMAP_Plus <- function(
  98. obj=NULL,
  99. method='uwot',
  100. reduc='pca',
  101. reduc_name='umap',
  102. reduc_key='UMAP_',
  103. min_dim=1,
  104. max_dim=30,
  105. seed=47,
  106. res_clus=0.4
  107. ){
  108. obj <- RunUMAP(obj, umap.method=method, reduction = reduc, reduction.name = reduc_name, reduction.key = reduc_key, dims = min_dim:max_dim, seed.use = seed)
  109. obj <- FindNeighbors(obj, reduction = reduc, dims = min_dim:max_dim)
  110. obj <- FindClusters(obj, resolution = res_clus)
  112. return(obj)
  113. }
  118. ATACIntegration <- function(snatac_list=NULL, assay_use='peaks', new_reduc='integrated_lsi', min_cutoff=10, max_dim=40)
  119. {
  120. combined_snatac <- MergeObjects(snatac_list)
  121. DefaultAssay(combined_snatac) <- assay_use
  123. combined_snatac <- ComputeLSI(combined_snatac, assay_use, min_cutoff)
  124. snatac_list <- RenameObjectListCells(snatac_list)
  126. integration.anchors <- FindIntegrationAnchors(
  127. object.list = snatac_list,
  128. anchor.features = rownames(snatac_list[[1]]),
  129. reduction = "rlsi",
  130. dims = 2:max_dim
  131. )
  133. integrated <- IntegrateEmbeddings(
  134. anchorset = integration.anchors,
  135. reductions = combined_snatac[["lsi"]],
  136. new.reduction.name = new_reduc,
  137. dims.to.integrate = 1:max_dim
  138. )
  140. integrated[['lsi']] <- combined_snatac[['lsi']]
  142. return(integrated)
  143. }
  146. ATACIntegration <- function(snatac_list=NULL, assay_use='peaks', new_reduc='integrated_lsi', min_cutoff=10, max_dim=40, res_clus=0.4, seed=47)
  147. {
  148. combined_snatac <- MergeObjects(snatac_list)
  149. DefaultAssay(combined_snatac) <- assay_use
  151. combined_snatac <- ComputeLSI(combined_snatac, assay_use, min_cutoff)
  152. snatac_list <- RenameObjectListCells(snatac_list)
  154. integration.anchors <- Seurat::FindIntegrationAnchors(
  155. object.list = snatac_list,
  156. anchor.features = rownames(snatac_list[[1]]),
  157. reduction = "rlsi",
  158. dims = 2:max_dim
  159. )
  161. integrated <- Seurat::IntegrateEmbeddings(
  162. anchorset = integration.anchors,
  163. reductions = combined_snatac[["lsi"]],
  164. new.reduction.name = new_reduc,
  165. dims.to.integrate = 1:max_dim
  166. )
  168. integrated[['lsi']] <- combined_snatac[['lsi']]
  169. integrated <- RunUMAP_Plus(obj=integrated, reduc=new_reduc, reduc_name='atac.umap', reduc_key='atacUMAP_', min_dim=2, max_dim=max_dim, seed=seed, res_clus=res_clus)
  171. return(integrated)
  172. }
  177. MultiomeIntegration_v1 <- function(
  178. obj_list=NULL,
  179. snatac_obj=NULL,
  180. sct_ver = "v2",
  181. regress = NULL,
  182. var_feature=3000,
  183. reduc_rna = 'pca',
  184. rna_umap = 'rna.umap',
  185. reduc_atac='integrated_lsi',
  186. dim_max_rna=30,
  187. dim_max_atac=30,
  188. weight_name = "RNA.weight",
  189. res_clus=0.4,
  190. seed=42
  191. ){
  192. multiome <- snatac_obj
  194. merged_obj <- MergeObjects(obj_list)
  195. multiome[['RNA']] <- merged_obj[['RNA']]
  196. DefaultAssay(multiome)<-"RNA"
  198. multiome <- SCTransform(multiome, vst.flavor = sct_ver, vars.to.regress = regress, variable.features.n = var_feature, verbose = FALSE)
  199. multiome <- RunPCA(multiome, seed.use = seed, verbose = TRUE)
  200. multiome <- RunUMAP(multiome, dims = 1:dim_max_rna, reduction = reduc_rna, reduction.name = rna_umap, seed.use = seed)
  201. multiome <- FindMultiModalNeighbors(
  202. object = multiome,
  203. reduction.list = list(reduc_rna, reduc_atac),
  204. dims.list = list(1:dim_max_rna, 2:dim_max_atac),
  205. modality.weight.name = weight_name,
  206. verbose = TRUE
  207. )
  208. multiome <- RunUMAP(
  209. object = multiome,
  210. nn.name = "weighted.nn",
  211. reduction.name = "wnn.umap",
  212. reduction.key = "wnnUMAP_",
  213. seed.use = seed,
  214. verbose = TRUE
  215. )
  216. multiome <- FindClusters(multiome, graph.name="wsnn", resolution=res_clus)
  218. return(multiome)
  219. }

funcs.R at commit 0e77b93, under MIT · at the source

Overview

Authors: Alisha S. Kardian1,2,3, Hua Sun1,2, Siri Ippagunta1,2, Nicholas Laboe1,2, Srinidhi Varadharajan1,2, Kwanha Yu4,5, Hsiao-Chi Chen4,5, Erik Emanus1,2, Tuyu Zheng1,2, Riley M. Deneen4,5, Jon P. Connelly6,7, Yong-Dong Wang6,7, Jiangshan Zhan8,9, Hengxi Liu3,9, Kimberley Lowe2, Taylor Bugbee1,2,10, Rakesh Pathak1,2, Amanda Bland1,2, Sanya Mehta1,2,10, Sophie Cochiolo1,2,10
and 28 other authorsAmir Arabzade1,2, Blake Holcomb1,2,10, Kaitlin M. Budd1,2,10, Gabriele Kembuan1,2, Tristen Wright1,2, Emma Caesar1,2, Maxwell Park1,2, Amelia Hancock11, David Gee11, Joel Murdoch11, Yi Xiao12, Samuel K. McBrayer12, Thomas E. Merchant13, Jun Qi14, Adam D. Durbin15, Lindsay A. Schwarz2, Li Wang16,17, Andrew M. Donson18, Nicholas K. Foreman18, Sameer Agnihotri19,20, Alfonso Lavado7,21, Suzanne J. Baker1,2, David W. Ellison22, Hyun Kyoung Lee3,5,8,9, Shondra M. Pruett-Miller6,7, Kelsey C. Bertrand23, Benjamin Deneen3,4,5, Stephen C. Mack1,2
ORCID iDs: Alisha S. Kardian, Hua Sun, Srinidhi Varadharajan, Hsiao-Chi Chen, Tuyu Zheng, Jon P. Connelly, Yong-Dong Wang, Jiangshan Zhan, Hengxi Liu, Taylor Bugbee, Sanya Mehta, Gabriele Kembuan, Yi Xiao, Samuel K. McBrayer, Thomas E. Merchant, Jun Qi, Adam D. Durbin, Li Wang, Suzanne J. Baker, David W. Ellison, Hyun Kyoung Lee, Shondra M. Pruett-Miller, Kelsey C. Bertrand, Benjamin Deneen, Stephen C. Mack
23 affiliations
  1. Center of Excellence in Neuro-Oncology Sciences, St Jude Children’s Research Hospital,Memphis, TN USA
  2. Department of Developmental Neurobiology, St Jude Children’s Research Hospital,Memphis, TN USA
  3. Cancer and Cell Biology Program, Baylor College of Medicine,Houston, TX USA
  4. Cell and Gene Therapy Program, Baylor College of Medicine,Houston, TX USA
  5. Center for Cancer Neuroscience, Baylor College of Medicine,Houston, TX USA
  6. Center for Advanced Genome Engineering, St Jude Children’s Research Hospital,Memphis, TN USA
  7. Department of Cell and Molecular Biology, St Jude Children’s Research Hospital,Memphis, TN USA
  8. Department of Pediatrics, Section of Neurology, Baylor College of Medicine,Houston, TX USA
  9. Jan and Dan Duncan Neurological Research Institute, Texas Children’s Hospital,Houston, TX USA
  10. St Jude Children’s Research Hospital Graduate School of Biomedical Sciences,Memphis, TN USA
  11. University of Bath,Bath, UK
  12. Children’s Medical Center Research Institute, UT Southwestern,Dallas, TX USA
  13. Radiation Oncology Department, St Jude Children’s Research Hospital,Memphis, TN USA
  14. Cancer Biology Department, Dana-Farber Cancer Institute,Boston, MA USA
  15. Division of Molecular Oncology, Department of Oncology, St Jude Children’s Research Hospital,Memphis, TN USA
  16. Department of Neurology, University of California San Francisco,San Francisco, CA USA
  17. The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California San Francisco,San Francisco, CA USA
  18. Department of Pediatrics, University of Colorado Anschutz Medical Campus,Aurora, CO USA
  19. Department of Neurological Surgery, University of Pittsburg,Pittsburg, PA USA
  20. Children’s Hospital of Pittsburg,Pittsburg, PA USA
  21. Center for Pediatric Neurological Disease Research, St Jude Children’s Research Hospital,Memphis, TN USA
  22. Department of Pathology, St Jude Children’s Research Hospital,Memphis, TN USA
  23. Department of Oncology, St Jude Children’s Research Hospital,Memphis, TN USA
Institutions: Khon Kaen University (Thailand)
Journal: Nature, volume 652, issue 8111, pages 1027-1037
Dates: received 9 August 2024; accepted 11 February 2026; published online 25 March 2026; in print 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41586-026-10270-8 · PMID 41882368 · PMCID PMC13102692 · OpenAlex W7140289669
Open access: hybrid, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: genetics / omics (modality), human (organism), mouse (organism), other condition (population)
Methods: Smoothing, state filtering, decompositions, Evoked potentials
Keywords: CNS cancer, Differentiation, Tumour heterogeneity
MeSH: Cell Transformation, Neoplastic*, Ependymoma*, Epigenome*, Supratentorial Neoplasms*, Animals, Cell Differentiation, Cell Lineage, Chromatin, Clone Cells, DNA-Binding Proteins, Epigenomics, Female, Humans, Male, Mice, Oncogene Proteins, Fusion, Prosencephalon, Transcription Factors (* major topic)
Topic: Glioma Diagnosis and Treatment (Oncology, Medicine), according to OpenAlex
Funding: NCI NIH HHS (F31 CA265285, P01 CA096832, P01 CA298963, U01 CA281823, K99 CA277576, R01 CA284455, U01 CA217842, P30 CA021765, R01 CA280203, U01 CA294103, R01 CA223388, U01 CA281902); NINDS NIH HHS (R35 NS132230, R01 NS126287, R01 NS110859, R01 NS116361, R01 NS124093)
Citations: not cited yet (Europe PMC); 59 references in the paper

Abstract

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Repositories

Its files are read in the Code ↔ Paper reader above, with 8 matches between paragraphs and lines of code.

combiozone/CellTypeEstimate

License: GPL-3.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 67af17bb348c4b501e84af80495cb1f1e59fd1c2, 25 June 2026
Languages: R (3)
Size: 9 files, 3 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file
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Tools: Seurat (3 files), tidyverse (2 files), ggplot2 (1 file)
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At the source: github.com/combiozone/CellTypeEstimate

IanevskiAleksandr/sc-type

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Commit: 630e15cf1e51f2612eda4ad0406dfb17503fa8c9, 10 November 2024
Languages: JavaScript (6), R (4)
Size: 117 files, 10 scripts
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At the source: github.com/IanevskiAleksandr/sc-type

mayer-lab/Bandler-et-al_lineage

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Commit: 2916ae607658050e229e531dbebc6813848736c0, 6 May 2022
Languages: R (4), Jupyter (4), Python (1), Shell (1)
Size: 16 files, 10 scripts
Software Heritage: not archived
Found in: the text, “Processing of TrackerSeq barcode reads”
Holds: README, license file, environment (Lineage Coupling Analysis/requirements.txt), 5 notebooks
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: Matplotlib (4 files), NumPy (4 files), pandas (3 files), data.table (2 files), ggplot2 (2 files), seaborn (2 files), tidyverse (2 files), cowplot (1 file), ggpubr (1 file), Harmony (1 file), NetworkX (1 file), reshape2 (1 file), SciPy (1 file), Seurat (1 file)
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At the source: github.com/mayer-lab/Bandler-et-al_lineage

combiozone/PROJ-Multiome-EPN-ZR

License: MIT
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Commit: 0e77b93e8ac5e90111cfa51f1dd1acefff399142, 25 June 2026
Languages: R (10), Shell (4), Jupyter (1), Python (1)
Size: 25 files, 16 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, 1 notebook
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Seurat (7 files), tidyverse (4 files), data.table (3 files), ggplot2 (3 files), ggpubr (2 files), Monocle 3 (1 file), pandas (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
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18 files
At the source: github.com/combiozone/PROJ-Multiome-EPN-ZR

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Recorded: type, language, journal, volume, issue, pages, dates, 48 authors, 3 keywords, 18 MeSH terms, 2 funders, 55 references.

Cite

This paper

Kardian, A. S., Sun, H., Ippagunta, S., Laboe, N., Varadharajan, S., Yu, K., Chen, H.-C., Emanus, E., Zheng, T., Deneen, R. M., Connelly, J. P., Wang, Y.-D., Zhan, J., Liu, H., Lowe, K., Bugbee, T., Pathak, R., Bland, A., Mehta, S., . . . Mack, S. C. (2026). Dominant clones leverage developmental epigenomic states to drive ependymoma. Nature, 652(8111), 1027-1037. https://doi.org/10.1038/s41586-026-10270-8

BibTeX

@article{kardian2026dominant,
author = {Kardian, Alisha S. and Sun, Hua and Ippagunta, Siri and Laboe, Nicholas and Varadharajan, Srinidhi and Yu, Kwanha and Chen, Hsiao-Chi and Emanus, Erik and Zheng, Tuyu and Deneen, Riley M. and Connelly, Jon P. and Wang, Yong-Dong and Zhan, Jiangshan and Liu, Hengxi and Lowe, Kimberley and Bugbee, Taylor and Pathak, Rakesh and Bland, Amanda and Mehta, Sanya and Cochiolo, Sophie and Arabzade, Amir and Holcomb, Blake and Budd, Kaitlin M. and Kembuan, Gabriele and Wright, Tristen and Caesar, Emma and Park, Maxwell and Hancock, Amelia and Gee, David and Murdoch, Joel and Xiao, Yi and McBrayer, Samuel K. and Merchant, Thomas E. and Qi, Jun and Durbin, Adam D. and Schwarz, Lindsay A. and Wang, Li and Donson, Andrew M. and Foreman, Nicholas K. and Agnihotri, Sameer and Lavado, Alfonso and Baker, Suzanne J. and Ellison, David W. and Lee, Hyun Kyoung and Pruett-Miller, Shondra M. and Bertrand, Kelsey C. and Deneen, Benjamin and Mack, Stephen C.},
title = {{Dominant clones leverage developmental epigenomic states to drive ependymoma}},
journal = {Nature},
year = {2026},
month = mar,
volume = {652},
number = {8111},
pages = {1027--1037},
publisher = {Nature Portfolio},
issn = {0028-0836},
doi = {10.1038/s41586-026-10270-8},
url = {https://doi.org/10.1038/s41586-026-10270-8},
pmid = {41882368},
pmcid = {PMC13102692}
}

RIS

TY - JOUR
AU - Kardian, Alisha S.
AU - Sun, Hua
AU - Ippagunta, Siri
AU - Laboe, Nicholas
AU - Varadharajan, Srinidhi
AU - Yu, Kwanha
AU - Chen, Hsiao-Chi
AU - Emanus, Erik
AU - Zheng, Tuyu
AU - Deneen, Riley M.
AU - Connelly, Jon P.
AU - Wang, Yong-Dong
AU - Zhan, Jiangshan
AU - Liu, Hengxi
AU - Lowe, Kimberley
AU - Bugbee, Taylor
AU - Pathak, Rakesh
AU - Bland, Amanda
AU - Mehta, Sanya
AU - Cochiolo, Sophie
AU - Arabzade, Amir
AU - Holcomb, Blake
AU - Budd, Kaitlin M.
AU - Kembuan, Gabriele
AU - Wright, Tristen
AU - Caesar, Emma
AU - Park, Maxwell
AU - Hancock, Amelia
AU - Gee, David
AU - Murdoch, Joel
AU - Xiao, Yi
AU - McBrayer, Samuel K.
AU - Merchant, Thomas E.
AU - Qi, Jun
AU - Durbin, Adam D.
AU - Schwarz, Lindsay A.
AU - Wang, Li
AU - Donson, Andrew M.
AU - Foreman, Nicholas K.
AU - Agnihotri, Sameer
AU - Lavado, Alfonso
AU - Baker, Suzanne J.
AU - Ellison, David W.
AU - Lee, Hyun Kyoung
AU - Pruett-Miller, Shondra M.
AU - Bertrand, Kelsey C.
AU - Deneen, Benjamin
AU - Mack, Stephen C.
TI - Dominant clones leverage developmental epigenomic states to drive ependymoma
T2 - Nature
J2 - Nature
PY - 2026
DA - 2026/03/25
VL - 652
IS - 8111
SP - 1027
EP - 1037
SN - 0028-0836
PB - Nature Portfolio
DO - 10.1038/s41586-026-10270-8
UR - https://doi.org/10.1038/s41586-026-10270-8
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41586-026-10270-8",
"type": "article-journal",
"title": "Dominant clones leverage developmental epigenomic states to drive ependymoma",
"container-title": "Nature",
"author": [
{
"family": "Kardian",
"given": "Alisha S."
},
{
"family": "Sun",
"given": "Hua"
},
{
"family": "Ippagunta",
"given": "Siri"
},
{
"family": "Laboe",
"given": "Nicholas"
},
{
"family": "Varadharajan",
"given": "Srinidhi"
},
{
"family": "Yu",
"given": "Kwanha"
},
{
"family": "Chen",
"given": "Hsiao-Chi"
},
{
"family": "Emanus",
"given": "Erik"
},
{
"family": "Zheng",
"given": "Tuyu"
},
{
"family": "Deneen",
"given": "Riley M."
},
{
"family": "Connelly",
"given": "Jon P."
},
{
"family": "Wang",
"given": "Yong-Dong"
},
{
"family": "Zhan",
"given": "Jiangshan"
},
{
"family": "Liu",
"given": "Hengxi"
},
{
"family": "Lowe",
"given": "Kimberley"
},
{
"family": "Bugbee",
"given": "Taylor"
},
{
"family": "Pathak",
"given": "Rakesh"
},
{
"family": "Bland",
"given": "Amanda"
},
{
"family": "Mehta",
"given": "Sanya"
},
{
"family": "Cochiolo",
"given": "Sophie"
},
{
"family": "Arabzade",
"given": "Amir"
},
{
"family": "Holcomb",
"given": "Blake"
},
{
"family": "Budd",
"given": "Kaitlin M."
},
{
"family": "Kembuan",
"given": "Gabriele"
},
{
"family": "Wright",
"given": "Tristen"
},
{
"family": "Caesar",
"given": "Emma"
},
{
"family": "Park",
"given": "Maxwell"
},
{
"family": "Hancock",
"given": "Amelia"
},
{
"family": "Gee",
"given": "David"
},
{
"family": "Murdoch",
"given": "Joel"
},
{
"family": "Xiao",
"given": "Yi"
},
{
"family": "McBrayer",
"given": "Samuel K."
},
{
"family": "Merchant",
"given": "Thomas E."
},
{
"family": "Qi",
"given": "Jun"
},
{
"family": "Durbin",
"given": "Adam D."
},
{
"family": "Schwarz",
"given": "Lindsay A."
},
{
"family": "Wang",
"given": "Li"
},
{
"family": "Donson",
"given": "Andrew M."
},
{
"family": "Foreman",
"given": "Nicholas K."
},
{
"family": "Agnihotri",
"given": "Sameer"
},
{
"family": "Lavado",
"given": "Alfonso"
},
{
"family": "Baker",
"given": "Suzanne J."
},
{
"family": "Ellison",
"given": "David W."
},
{
"family": "Lee",
"given": "Hyun Kyoung"
},
{
"family": "Pruett-Miller",
"given": "Shondra M."
},
{
"family": "Bertrand",
"given": "Kelsey C."
},
{
"family": "Deneen",
"given": "Benjamin"
},
{
"family": "Mack",
"given": "Stephen C."
}
],
"container-title-short": "Nature",
"volume": "652",
"issue": "8111",
"page": "1027-1037",
"DOI": "10.1038/s41586-026-10270-8",
"PMID": "41882368",
"PMCID": "PMC13102692",
"ISSN": "0028-0836",
"publisher": "Nature Portfolio",
"URL": "https://doi.org/10.1038/s41586-026-10270-8",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
25
]
]
}
}

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