OSCR

Comparative analysis of nuclei isolation methods for brain single-nucleus RNA sequencing.

Code ↔ Paper

7 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 7 matches · 3 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Results › Method-specific variations in glial gene expression ↔ scripts/analysis/Fig4.UCell.R, the whole file · a weak match · score 0.80 · disease associated microglia, C1qa, UCell, homeostatic microglia, Hexb, Mbp
  2. [2] § Results › Isolation technique influences the cell types captured in snRNA-seq ↔ scripts/analysis/Fig2.Dotplot.R, the whole file · a weak match · score 0.78 · Slc17a7, Sv2b, Slc1a3, Gad1, Gad2, Gja1
  3. [3] § Results › Method-specific variations in glial gene expression ↔ scripts/analysis/Fig4.UCell.R, the whole file · a weak match · score 0.75 · C1qa, UCell, disease associated, Glul, homeostatic, Tmem119
  4. [4] § STAR★Methods › Quantification and statistical analysis › snRNA-seq data analysis ↔ vignettes/SAHA_vignette.Rmd, lines 62–102 · score 0.69 · Allen Brain Atlas, Cell Atlas, FindAllMarkers, RStudio, pre, Mouse
  5. [5] § STAR★Methods › Quantification and statistical analysis › snRNA-seq data analysis ↔ scripts/preprocess/Step1_IsolationPreProcess_DFandSX.R, lines 485–574 · score 0.66 · scMCA, elbow, Seurat, prefix, scoring, mt
  6. [6] § Results › Isolation technique influences the cell types captured in snRNA-seq ↔ scripts/analysis/Fig2.PropsandROGUE.R, lines 56–107 · score 0.59 · excitatory neuron, inhibitory neuron, ROGUE, oligodendrocytes, assay, astrocytes
  7. [7] § Results › Differences in quality-control metrics among isolation protocols ↔ scripts/preprocess/Step1_IsolationPreProcess_DFandSX.R, lines 367–421 · score 0.55 · SoupX, ambient RNA, transformed, rho, doublets, subset

Paper

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The authors' code

R · 80 lines · 3.4 KB · no license · 2 matches

  1. set.seed(1221)
  2. setwd("/N/project/kim_lab/Kersey_Holly/IsolationCollab")
  4. library(UCell)
  5. library(Seurat)
  6. library(ggplot2)
  7. library(gridExtra)
  8. #load seurat object
  9. isolation <- readRDS("20231206_snIsolation_collab.rds")
  11. #subset object based on cell type
  12. Idents(isolation) <- "simple_anno"
  13. isolation.microglia <- subset(isolation, idents="Microglia")
  14. isolation.astrocyte <- subset(isolation, idents="Astrocyte")
  15. isolation.oligo <- subset(isolation, idents="Oligo")
  17. #define marker genes for astrocytes
  18. markers.as <- list()
  19. markers.as$HM_AS = c("Kcnj10","Glul","Slc1a2","Slc1a3","Slc6a11","Slc16a1","Ldha","Srebf1")
  20. markers.as$DA_AS = c("Hmgb1","Hmgb3","Hmgb2","Id3","Gfap","S100b","Cd81","Cebpa","Ptprg")
  21. #define marker genes for microglia
  22. markers.mg <- list()
  23. markers.mg$HM_MG = c("Tmem119","P2ry12","P2ry13","Cx3cr1", "C1qa", "Csf1r", "Hexb")
  24. markers.mg$DA_MG= c("Apoe", "Cst7", "Trem2", "Itgax", "B2m", "Cst7")
  25. #define marker genes for oligos
  26. markers.og <- list()
  27. markers.og$HM_OL = c("Olig1","Olig2","Mog","Mbp","Mobp","Plp1","Sox10","Gpr37","Mag","Cnp","Myrf")
  29. #set identity to isolation method
  30. Idents(isolation.microglia) <- "tech"
  31. #Compute UCell scores
  32. isolation.microglia <- AddModuleScore_UCell(isolation.microglia, features = markers.mg)
  33. signature.names.mg <- paste0(names(markers.mg), "_UCell")
  34. P <- VlnPlot(isolation.microglia, features = signature.names.mg, pt.size = FALSE)
  35. write.csv(P[[1]][['data']], "ucell.HM_MG.csv") #save homeostatic microglia scores
  36. write.csv(P[[2]][['data']], "ucell.DA_MG.csv") #save disease-associated scores
  39. Idents(isolation.astrocyte) <- "tech"
  40. isolation.astrocyte <- AddModuleScore_UCell(isolation.astrocyte, features = markers.as)
  41. signature.names.as <- paste0(names(markers.as), "_UCell")
  42. Q <- VlnPlot(isolation.astrocyte, features = signature.names.as, pt.size = FALSE)
  43. write.csv(Q[[1]][['data']], "ucell.HM_AS.csv") #save homeostatic microglia scores
  44. write.csv(Q[[2]][['data']], "ucell.DA_AS.csv") #save disease-associated microglia scores
  47. Idents(isolation.oligo) <- "tech"
  48. isolation.oligo <- AddModuleScore_UCell(isolation.oligo, features = markers.og)
  49. signature.names.og <- paste0(names(markers.og), "_UCell")
  50. R <- VlnPlot(isolation.oligo, features = signature.names.og, pt.size = FALSE)
  51. write.csv(R[[1]][['data']], "ucell.HM_OG.csv") #save homeostatic oligo scores
  53. #For visualization purposes
  54. p1 <- VlnPlot(isolation.oligo, features = "HM_OL_UCell", pt.size = FALSE)+
  55. stat_summary(fun.y = median, geom='point', size = 2, colour = "black")+
  56. ylim(0,0.7)+
  57. theme_classic()+NoLegend()
  59. p2 <- VlnPlot(isolation.microglia, features = "DA_MG_UCell", pt.size = FALSE)+
  60. stat_summary(fun.y = median, geom='point', size = 2, colour = "black")+
  61. ylim(0,0.7)+
  62. theme_classic()+NoLegend()
  63. p3 <- VlnPlot(isolation.microglia, features = "HM_MG_UCell", pt.size = FALSE)+
  64. stat_summary(fun.y = median, geom='point', size = 2, colour = "black")+
  65. ylim(0,0.7)+
  66. theme_classic()+NoLegend()
  68. p4 <- VlnPlot(isolation.astrocyte, features = "DA_AS_UCell", pt.size = FALSE)+
  69. stat_summary(fun.y = median, geom='point', size = 2, colour = "black")+
  70. ylim(0,0.7)+
  71. theme_classic()+NoLegend()
  72. p5 <- VlnPlot(isolation.astrocyte, features = "HM_AS_UCell", pt.size = FALSE)+
  73. stat_summary(fun.y = median, geom='point', size = 2, colour = "black")+
  74. ylim(0,0.7)+
  75. theme_classic()+NoLegend()
  77. pdf(file = "Ucell.pdf")
  78. grid.arrange(p5,p4,p3,p2,p1, nrow=1,ncol=5)
  79. dev.off()
  80. #Plots were edited in Adobe Illustrator for stylistic components, i.e., titles, colors...

Fig4.UCell.R at commit b39d9d3, no license · at the source

Overview

Authors: Holly N Kersey1,2, Dominic J Acri1,2, Luke C Dabin2,3, Kelly A Hartigan1,2, Richard Mustaklem2,3, Jung Hyun Park2,4, Jungsu Kim2,3
ORCID iDs: Luke C Dabin, Jungsu Kim
  1. Medical Neurosciences Graduate Program, Indiana University School of Medicine, Indianapolis, IN, USA
  2. Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA
  3. Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
  4. Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
Institutions: Indiana University Indianapolis (United States); Indiana University School of Medicine; Indiana University – Purdue University Indianapolis (United States); Indiana University Bloomington (United States); Indiana University (United States)
Journal: Cell reports methods, volume 6, issue 3, article 101337
Dates: received 23 March 2025; accepted 5 February 2026; published online 23 March 2026; in print March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.crmeth.2026.101337 · PMID 41875869 · PMCID PMC13030979 · OpenAlex W7140084071
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), mouse (organism), cellular / molecular (subfield)
Methods: Smoothing, state filtering, decompositions
Keywords: snRNA-seq, nuclei isolation, contamination, ambient RNA, data quality
MeSH: Brain*, Cell Nucleus*, Sequence Analysis, RNA*, Single-Cell Analysis*, Animals, Mice, Single-Cell Gene Expression Analysis (* major topic)
Topic: RNA Research and Splicing (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: National Institute on Aging (R21 AG072738, T32AG071444, U01AG076804, R01 AG077829, RF1 AG074543, R01 AG071281); International Business Machines Corporation; Indiana University; Eli Lilly and Company; Indiana University School of Medicine; Lilly Endowment; National Institutes of Health
Citations: cited by 7 papers (Europe PMC); 71 references in the paper
Research resources: C57BL/6J RRID:IMSR_JAX:000664

Abstract

Single-nucleus RNA sequencing (snRNA-seq) enables resolving cellular heterogeneity in complex tissues by using nuclei instead of cells, overcoming limitations of single-cell RNA sequencing and enabling analysis of frozen and hard-to-isolate tissues. Despite advances in isolation techniques, systematic evaluations of their effects on nuclear integrity and subsequent data quality remain lacking, a critical gap with profound implications for rigor and reproducibility. To address this, we compared three mechanistically distinct nuclei isolation strategies with brain tissue: a sucrose gradient centrifugation-based method, a spin column-based method, and a machine-assisted platform. All methods captured diverse cell types but revealed considerable protocol-dependent differences in cell type proportions, transcriptional homogeneity, and the preservation of cell-state-specific markers. Moreover, workflows differentially influenced contamination levels from ambient, mitochondrial, and ribosomal RNAs, with the machine-assisted method exhibiting the highest overall data quality. Our findings establish nuclei isolation methodology as a critical experimental variable shaping snRNA-seq data quality and biological interpretation.

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 7 matches between paragraphs and lines of code.

jungsukimlab/NucleiIsolation

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: b39d9d3c75f9413a41d8855f480d05633f4d1852, 5 April 2026
Languages: R (9)
Size: 12 files, 9 scripts
Software Heritage: not archived
Found in: “Data and code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Seurat (9 files), ggplot2 (8 files), tidyverse (3 files), cowplot (2 files), patchwork (2 files)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
10 files
At the source: github.com/jungsukimlab/NucleiIsolation

Zenodo 14040699

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: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (14 files), ComplexHeatmap (3 files), data.table (2 files), ggplot2 (2 files), circlize (1 file), ggpubr (1 file), Seurat (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
31 files
At the source:

neurogenetics/saha

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 0ced41731fb3200fd16d1e415b33d088ad42b695, 13 May 2026
Languages: R (32)
Size: 83 files, 32 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README, license file, environment (DESCRIPTION), tests, documentation, 1 notebook
Not found: CITATION.cff, continuous integration
Tools: tidyverse (15 files), ComplexHeatmap (3 files), Seurat (3 files), ggplot2 (2 files), circlize (1 file), data.table (1 file), ggpubr (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
34 files
At the source: github.com/neurogenetics/SAHA/tree/pre-release

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:

  • 3 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 70 scripts, each with its path and the digest of its content;
  • 7 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

The snRNA-seq data generated in this study have been deposited in the GEO database: GSE290858 (https://ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE290858) and are publicly available as of the date of publication.

The code for the snRNA-seq analysis in this study has been deposited at https://github.com/jungsukimlab/NucleiIsolation. An archival version is available at doi:10.5281/zenodo.18378837 (https://doi.org/10.5281/zenodo.18378837).

Any additional information required to reanalyze the data reported in this study 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 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 5 keywords, 7 MeSH terms, 7 funders, 66 references, 1 RRID.

Cite

This paper

Kersey, H. N., Acri, D. J., Dabin, L. C., Hartigan, K. A., Mustaklem, R., Park, J. H., & Kim, J. (2026). Comparative analysis of nuclei isolation methods for brain single-nucleus RNA sequencing. Cell reports methods, 6(3), 101337. https://doi.org/10.1016/j.crmeth.2026.101337

BibTeX

@article{kersey2026comparative,
author = {Kersey, Holly N and Acri, Dominic J and Dabin, Luke C and Hartigan, Kelly A and Mustaklem, Richard and Park, Jung Hyun and Kim, Jungsu},
title = {{Comparative analysis of nuclei isolation methods for brain single-nucleus RNA sequencing}},
journal = {Cell reports methods},
year = {2026},
month = mar,
volume = {6},
number = {3},
pages = {101337},
publisher = {Elsevier},
issn = {2667-2375},
doi = {10.1016/j.crmeth.2026.101337},
url = {https://doi.org/10.1016/j.crmeth.2026.101337},
pmid = {41875869},
pmcid = {PMC13030979}
}

RIS

TY - JOUR
AU - Kersey, Holly N
AU - Acri, Dominic J
AU - Dabin, Luke C
AU - Hartigan, Kelly A
AU - Mustaklem, Richard
AU - Park, Jung Hyun
AU - Kim, Jungsu
TI - Comparative analysis of nuclei isolation methods for brain single-nucleus RNA sequencing
T2 - Cell reports methods
J2 - Cell Rep Methods
PY - 2026
DA - 2026/03/01
VL - 6
IS - 3
SP - 101337
SN - 2667-2375
PB - Elsevier
DO - 10.1016/j.crmeth.2026.101337
UR - https://doi.org/10.1016/j.crmeth.2026.101337
LA - en
ER -

CSL-JSON

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