OSCR

Adapted Smart-seq3xpress Facilitates Selective Microglial Transcriptomic Profiling From Frozen Brain Tissue.

Code ↔ Paper

19 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 19 matches
  1. [1] § Results › PU.1-Based Approach Preserves Microglial Heterogeneity in Frozen Tissue ↔ 2V_02_mg_cells_UMAP_dot_plot.Rmd, lines 244–254 · score 0.91 · C1qa, C1qb, P2ry12, Clec7a, Ccl4, Ifit3
  2. [2] § Methods › Data Analysis › 3’ Bias Metrics ↔ 1DP_06_library_qc.Rmd, lines 153–169 · score 0.91 · additional_sequence_annot.gtf, Gene body coverage, RSeQC, mm10_RefSeq.bed, usegalaxy.eu, mRNA
  3. [3] § Methods › Data Analysis › Data Filtering ↔ 1DP_05_all_cells_LC_FN_raw_data_to_seurat_merged_integrated_UMAP.Rmd, lines 236–268 · score 0.80 · UMIfraction, percent.spike, nCount_RNA, nFeature_RNA, ERCC, SIRV
  4. [4] § Methods › Data Analysis › Data Filtering ↔ 1DP_00_all_cells_raw_data_to_seurat_merged_integrated_UMAP.Rmd, lines 268–302 · score 0.80 · UMIfraction, percent.spike, nCount_RNA, nFeature_RNA, ERCC, SIRV
  5. [5] § Methods › Data Analysis › Cell–Cell Communication Analysis ↔ 1DP_04_CellChat.Rmd, lines 168–188 · score 0.80 · computeNetSimilarityPairwise, netEmbedding, mergeCellChat, netClustering, LiveNuclei, LiveCells
  6. [6] § Methods › Data Analysis › Normalization, Integration, and Clustering ↔ 1DP_01_mg_cells_raw_data_to_seurat_merged_integrated_UMAP.Rmd, lines 115–144 · score 0.79 · FindClusters, FindNeighbors, SCTransform, CCAIntegration, UMAP, resolution
  7. [7] § Methods › Data Analysis › Gene Ontology Enrichment Analysis ↔ 1DP_03_GO.Rmd, lines 10–47 · score 0.78 · clusterProfiler, Gene ontology, GO terms, functional pathways, biological process, LiveCells
  8. [8] § Methods › Data Analysis › Normalization, Integration, and Clustering ↔ 1DP_00_all_cells_raw_data_to_seurat_merged_integrated_UMAP.Rmd, lines 268–302 · score 0.78 · FindClusters, FindNeighbors, SCTransform, CCAIntegration, UMAP, resolution
  9. [9] § Methods › Data Analysis › Normalization, Integration, and Clustering ↔ 1DP_05_all_cells_LC_FN_raw_data_to_seurat_merged_integrated_UMAP.Rmd, lines 236–268 · score 0.78 · FindClusters, FindNeighbors, SCTransform, CCAIntegration, UMAP, resolution
  10. [10] § Results › PU.1-Based Enrichment Enhances snRNA-seq Profiling of Microglia in Frozen Brain Tissue ↔ 2V_00_all_cells_UMAP_dot_plot.Rmd, lines 156–168 · score 0.77 · Cx3cr1, endothelial cells, mural cells, neutrophils, astrocytes, monocytes
  11. [11] § Methods › Data Analysis › Normalization, Integration, and Clustering ↔ 1DP_01_mg_cells_raw_data_to_seurat_merged_integrated_UMAP.Rmd, lines 115–144 · score 0.77 · FindClusters, FindNeighbors, SCTransform, CCAIntegration, UMAP, resolution
  12. [12] § Results › PU.1-Based Protocol Enables Detection of Inflammatory Microglial States ↔ 2V_04_DEG.Rmd, lines 168–203 · score 0.69 · Hif1a, Clec7a, ARM population, Cd63, Ctsb, Apoe
  13. [13] § Methods › Data Analysis › Differential Gene Expression Analysis ↔ 1DP_02_DEG.Rmd, lines 158–243 · score 0.62 · FindMarkers, RNA assay, isolation protocol, HM, Seurat, ARM
  14. [14] § Methods › Data Analysis › Identification of Cluster Markers ↔ 2V_02_mg_cells_UMAP_dot_plot.Rmd, lines 623–666 · score 0.60 · module scores, AddModuleScore, Seurat, UMAP, genes
  15. [15] § Results › PU.1-Based Enrichment Enhances snRNA-seq Profiling of Microglia in Frozen Brain Tissue ↔ 2V_00_all_cells_UMAP_dot_plot.Rmd, lines 156–168 · score 0.57 · endothelial cells, mural cells, neutrophils, astrocytes, monocytes, PVM
  16. [16] § Results › PU.1-Based Protocol Enables Detection of Inflammatory Microglial States ↔ 2V_04_DEG.Rmd, lines 33–134 · score 0.55 · log2 fold change, Volcano, log10, overlap, HM, LiveNuclei
  17. [17] § Methods › Data Analysis › Differential Gene Expression Analysis ↔ 2V_04_DEG.Rmd, lines 33–134 · score 0.54 · log2 fold change, HM, isolation protocol, ARM, gene, cells
  18. [18] § Methods › Data Analysis › 3’ Bias Metrics ↔ 1DP_06_library_qc.Rmd, lines 138–150 · score 0.51 · prefixes, internal, ERCC, SIRV, header, BAM
  19. [19] § Results › PU.1-Based Profiling Captures Cell–Cell Interactions Among Microglia ↔ 2V_06_CellChat.Rmd, lines 131–191 · score 0.50 · CellChat, chord diagrams, interactions, LiveNuclei, signaling, LiveCells

Paper

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The authors' code

R Markdown · 561 lines · 21 KB · no license · 3 matches

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It can be read at the source: 2V_04_DEG.Rmd.

Overview

Authors: Dominika Dostalova1,2, Pavel Abaffy1,3, Eva Rohlova3, Jan Kriska4, Tomas Knotek4,5, Jana Tureckova4, Denisa Kirdajova1, Miroslava Anderova4, Lukas Valihrach1,3
ORCID iDs: Dominika Dostalova, Pavel Abaffy, Eva Rohlova, Jan Kriska, Tomas Knotek, Jana Tureckova, Denisa Kirdajova, Miroslava Anderova, Lukas Valihrach
  1. Laboratory of Glial Biology and Omics Technologies, Institute of Biotechnology of the Czech Academy of Sciences, Vestec, Czech Republic
  2. Faculty of Science, Charles University, Prague, Czech Republic
  3. GeneCore Facility, Institute of Biotechnology of the Czech Academy of Sciences, Vestec, Czech Republic
  4. Department of Cellular Neurophysiology, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czech Republic
  5. Second Faculty of Medicine, Charles University, Prague, Czech Republic
Institutions: Charles University (Czechia); Czech Academy of Sciences, Institute of Biotechnology (Czechia); Czech Academy of Sciences (Czechia); Czech Academy of Sciences, Institute of Experimental Medicine (Czechia)
Journal: Cellular and molecular neurobiology, volume 46, issue 1, article 113
Dates: received 11 December 2025; accepted 4 May 2026; published online 14 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1007/s10571-026-01743-5 · PMID 42133140 · PMCID PMC13357468 · OpenAlex W7161125067
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), mouse (organism), stroke (population), cellular / molecular (subfield)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions
Keywords: Enrichment, Frozen tissue, Microglia, PU.1, Single-nucleus RNA - sequencing, Stroke
MeSH: Brain*, Cryopreservation*, Freezing*, Gene Expression Profiling*, Microglia*, Transcriptome*, Animals, Male, Mice, Mice, Inbred C57BL, Sequence Analysis, RNA, Single-Cell Gene Expression Analysis (* major topic)
Topic: Neuroinflammation and Neurodegeneration Mechanisms (Neurology, Neuroscience), according to OpenAlex
Funding: Grantová Agentura, Univerzita Karlova (No. 2224); Grantová Agentura České Republiky (23-05327S); Ministerstvo Školství, Mládeže a Tělovýchovy (Financed by EU-Next Generation EU, LX22NPO5107, Financed by EU—Next Generation EU, MULTIOMICS_CZ CZ.02.01.01/00/23_020/0008540)
Citations: not cited yet (Europe PMC); 45 references in the paper
Research resources: RRID:AB_2149209, ACSA-2 RRID:AB_2727978, PU.1 RRID:AB_2799984, RRID:AB_3718051

Abstract

Single-cell transcriptomics has revealed the central role of microglia in brain development, homeostasis, and disease, particularly in the context of neuroinflammation. While single-cell RNA-sequencing enables targeted microglial analysis from fresh tissue, studying these cells in cryopreserved or archival samples remains challenging due to the lack of a protocol for their specific enrichment and RNA-sequencing. In this study, we developed a method for profiling microglial nuclei from fresh-frozen tissue using Smart-seq3xpress. This approach relies on PU.1-based enrichment, made possible by a brief formaldehyde fixation step to preserve the antigen. To ensure compatibility with Smart-seq3xpress, we utilized a Thermolabile Proteinase K treatment to reverse cross-links, thereby maintaining high transcriptomic sensitivity. We benchmarked the method in a mouse model of ischemic stroke, evaluating both technical performance and its ability to capture biologically meaningful microglial states. Compared to standard single-nucleus protocols, our approach yielded higher gene and UMI counts and a greater proportion of coding reads. Transcriptomic profiles closely matched those from whole-cell RNA-sequencing, including the detection of activation markers and diverse microglial subpopulations. This approach enables high-resolution transcriptomic analysis of microglia from fresh-frozen or archival brain samples, overcoming major limitations of single-nucleus RNA sequencing. It broadens access to cellular insights from biobanked material, supporting both basic research and translational studies of neuroinflammatory disease.

Graphical Abstract: A modified Smart-seq3xpress protocol was developed for microglial profiling by combining formaldehyde fixation and PU.1-based nuclei enrichment from fresh-frozen brain tissue. The method yields high gene and UMI counts alongside strong coding coverage, enabling high-resolution characterization of diverse microglial activation states following stroke.

Supplementary Information: The online version contains supplementary material available at 10.1007/s10571-026-01743-5.

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 19 matches between paragraphs and lines of code.

GliaOmicsLab/microglia-sc-snRNAseq

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 2e9446edb410619736e4773edbeeab4e5d34d5fc, 12 May 2026
Languages: R (17)
Size: 19 files, 17 scripts
Software Heritage: not archived
Found in: “Data Availability”
Holds: README, environment (renv.lock), 15 notebooks
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: ggplot2 (14 files), tidyverse (14 files), patchwork (13 files), Seurat (13 files), reshape2 (9 files), ComplexHeatmap (8 files), pheatmap (4 files), clusterProfiler (3 files), data.table (2 files), ggpubr (1 file), reticulate (1 file), SAMtools (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
18 files, not copied: shown from their source

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At the source: github.com/GliaOmicsLab/microglia-sc-snRNAseq

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;
  • 17 scripts, each with its path and the digest of its content;
  • 19 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

Datasets cited

Data Availability

The data that support the findings of this study are publicly available on Zenodo (https://zenodo.org/records/16737457), and the code for analysis is available on GitHub (https://github.com/GliaOmicsLab/microglia-sc-snRNAseq). Data from the main experiment (all cells (https://scarfweb.nygen.io/eu-central-1/public/q2acwcw8) and the subset of microglia (https://scarfweb.nygen.io/eu-central-1/public/wxvqmskh)) are available for online analysis via the Nygen portal. A detailed version of the optimized protocol is available at Protocols.io (https://www.protocols.io/private/E618BD14711011F08A690A58A9FEAC02).

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

Versions

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 6 keywords, 12 MeSH terms, 3 funders, 43 references, 4 RRIDs.

Cite

This paper

Dostalova, D., Abaffy, P., Rohlova, E., Kriska, J., Knotek, T., Tureckova, J., Kirdajova, D., Anderova, M., & Valihrach, L. (2026). Adapted Smart-seq3xpress Facilitates Selective Microglial Transcriptomic Profiling From Frozen Brain Tissue. Cellular and molecular neurobiology, 46(1), 113. https://doi.org/10.1007/s10571-026-01743-5

BibTeX

@article{dostalova2026adapted,
author = {Dostalova, Dominika and Abaffy, Pavel and Rohlova, Eva and Kriska, Jan and Knotek, Tomas and Tureckova, Jana and Kirdajova, Denisa and Anderova, Miroslava and Valihrach, Lukas},
title = {{Adapted Smart-seq3xpress Facilitates Selective Microglial Transcriptomic Profiling From Frozen Brain Tissue}},
journal = {Cellular and molecular neurobiology},
year = {2026},
month = may,
volume = {46},
number = {1},
pages = {113},
publisher = {Springer},
issn = {0272-4340},
doi = {10.1007/s10571-026-01743-5},
url = {https://doi.org/10.1007/s10571-026-01743-5},
pmid = {42133140},
pmcid = {PMC13357468}
}

RIS

TY - JOUR
AU - Dostalova, Dominika
AU - Abaffy, Pavel
AU - Rohlova, Eva
AU - Kriska, Jan
AU - Knotek, Tomas
AU - Tureckova, Jana
AU - Kirdajova, Denisa
AU - Anderova, Miroslava
AU - Valihrach, Lukas
TI - Adapted Smart-seq3xpress Facilitates Selective Microglial Transcriptomic Profiling From Frozen Brain Tissue
T2 - Cellular and molecular neurobiology
J2 - Cell Mol Neurobiol
PY - 2026
DA - 2026/05/14
VL - 46
IS - 1
SP - 113
SN - 0272-4340
PB - Springer
DO - 10.1007/s10571-026-01743-5
UR - https://doi.org/10.1007/s10571-026-01743-5
LA - en
ER -

CSL-JSON

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