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Glioma-intrinsic MAPK/ERK signaling promotes immunotherapy efficacy through T cell infiltration and interferon responses.

Code ↔ Paper

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  1. [1] § Methods › MERFISH analysis ↔ scripts/184_prepare_doi_deposit.py, lines 80–217 · score 0.51 · GBmap, scRNA, transcriptomics, mapping, model, gene

Paper

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The authors' code

Python · 221 lines · 9 KB · MIT · 1 match

  1. """
  2. Prepare a Zenodo-ready DOI deposit package for the project analysis code + key results.
  4. Creates:
  5. results/21_doi_deposit/glioma_radiotranscriptomics_deposit/
  6. + zip archive
  7. + CITATION.cff
  8. + README_DEPOSIT.md
  9. + deposit_manifest.json
  11. If ZENODO_TOKEN is set in the environment, attempts a Zenodo Sandbox or production
  12. deposition via REST API and writes the resulting DOI/concept DOI.
  13. """
  14. from __future__ import annotations
  16. import hashlib
  17. import json
  18. import os
  19. import shutil
  20. import zipfile
  21. from datetime import date
  22. from pathlib import Path
  24. ROOT = Path(r"F:\Glioma Radiotranscriptomics")
  25. OUT = ROOT / "results/21_doi_deposit"
  26. PKG = OUT / "glioma_radiotranscriptomics_deposit"
  27. OUT.mkdir(parents=True, exist_ok=True)
  29. INCLUDE = [
  30. # gene sets / methods instruments
  31. "data/05_signatures/neurotransmitter_programs_prespecified.json",
  32. "data/04_atlases_neurotransmitter/neurotransmitter_roi_mapping_v1.json",
  33. # key result tables
  34. "results/00_cohort/discovery_master_table.csv",
  35. "results/02_scores/discovery_with_scores.csv",
  36. "results/03_associations/C_NT_to_TME_adj_imaging.csv",
  37. "results/05_cgga_validation/cgga_P1_meta.csv",
  38. "results/12_cibersortx_official/cibersortx_job_summary.json",
  39. "results/16_composition_controls/composition_control_summary.json",
  40. "results/16_composition_controls/p1_geneset_overlap.csv",
  41. "results/16_composition_controls/manuscript_table_composition_sensitivity.csv",
  42. "results/16_composition_controls/cgga_attenuation.csv",
  43. "results/17_survival/survival_cox_results.csv",
  44. "results/17_survival/survival_summary.json",
  45. "results/18_scrna_donor_pseudobulk/donor_level_enrichment_vs_other_types.csv",
  46. "results/18_scrna_donor_pseudobulk/donor_pseudobulk_summary.json",
  47. "results/19_visium_histology_defined/histology_section_contrasts.csv",
  48. "results/19_visium_histology_defined/visium_histology_summary.json",
  49. "results/20_pet_juspace_exposures/pet_exposure_summary.json",
  50. "results/20_pet_juspace_exposures/pet_exposure_associations.csv",
  51. "results/13_visium_core_edge/visium_summary.json",
  52. "results/07_cell_source/cell_source_summary.json",
  53. "docs/PREREGISTERED_ENDPOINTS.md",
  54. ]
  56. SCRIPTS_GLOB = [
  57. "scripts/30_score_nt_and_tme.py",
  58. "scripts/40_associations_mediation.py",
  59. "scripts/80_juspace_nuclei_distances.py",
  60. "scripts/90_scrna_cell_source.py",
  61. "scripts/140_visium_core_edge.py",
  62. "scripts/170_composition_controls.py",
  63. "scripts/180_survival_models.py",
  64. "scripts/181_scrna_donor_pseudobulk.py",
  65. "scripts/182_visium_histology_defined.py",
  66. "scripts/183_pet_juspace_exposures.py",
  67. "scripts/184_prepare_doi_deposit.py",
  68. "scripts/analysis_config.py",
  69. ]
  72. def sha256(path: Path) -> str:
  73. h = hashlib.sha256()
  74. with path.open("rb") as f:
  75. for chunk in iter(lambda: f.read(1 << 20), b""):
  76. h.update(chunk)
  77. return h.hexdigest()
  80. def main() -> None:
  81. if PKG.exists():
  82. shutil.rmtree(PKG)
  83. PKG.mkdir(parents=True)
  85. manifest = []
  86. for rel in INCLUDE + SCRIPTS_GLOB:
  87. src = ROOT / rel
  88. if not src.exists():
  89. manifest.append({"path": rel, "status": "missing"})
  90. continue
  91. dest = PKG / rel
  92. dest.parent.mkdir(parents=True, exist_ok=True)
  93. shutil.copy2(src, dest)
  94. manifest.append({"path": rel, "status": "copied", "bytes": src.stat().st_size, "sha256": sha256(src)})
  96. readme = f"""# Neurotransmitter–immune radiotranscriptomics of diffuse glioma — analysis deposit
  98. Date: {date.today().isoformat()}
  100. This package contains analysis code, pre-specified neurotransmitter gene sets, and key
  101. result tables supporting the manuscript. Primary parent datasets remain at their public
  102. sources (TCGA/TCIA Bakas, GDC STAR, CGGA, Dryad Visium DOI 10.5061/dryad.h70rxwdmj,
  103. GBmap, Hansen PET maps via netneurolab/hansen_receptors, Stanford CIBERSORTx).
  105. ## Contents
  106. - `data/05_signatures/` — neurotransmitter gene-set definitions (central instrument)
  107. - `scripts/` — scoring, associations, composition controls, survival, donor pseudobulk,
  108. histology-defined Visium, PET exposures
  109. - `results/` — summary tables required to reproduce manuscript claims
  111. ## License
  112. Code: MIT. Gene sets and derived tables: CC BY 4.0. Upstream data retain original licenses.
  113. """
  114. (PKG / "README_DEPOSIT.md").write_text(readme, encoding="utf-8")
  116. citation = f"""cff-version: 1.2.0
  117. title: "Neurotransmitter transcriptional programs and immune microenvironment states in diffuse glioma — analysis code and result tables"
  118. message: If you use this deposit, please cite it and the primary manuscript.
  119. type: software
  120. authors:
  121. - family-names: "[Author]"
  122. given-names: "[To be completed]"
  123. date-released: {date.today().isoformat()}
  124. version: "1.0.0"
  125. license: MIT
  126. repository-code: "local-deposit-package"
  127. abstract: >
  128. Analysis code, neurotransmitter gene-set definitions, composition-control outputs,
  129. survival models, donor-level scRNA pseudobulk tests, histology-defined Visium contrasts,
  130. and Hansen PET exposure associations for diffuse glioma radiotranscriptomics.
  131. """
  132. (PKG / "CITATION.cff").write_text(citation, encoding="utf-8")
  133. (ROOT / "CITATION.cff").write_text(citation, encoding="utf-8")
  135. (OUT / "deposit_manifest.json").write_text(json.dumps(manifest, indent=2), encoding="utf-8")
  137. zip_path = OUT / "glioma_radiotranscriptomics_deposit.zip"
  138. if zip_path.exists():
  139. zip_path.unlink()
  140. with zipfile.ZipFile(zip_path, "w", compression=zipfile.ZIP_DEFLATED) as zf:
  141. for path in PKG.rglob("*"):
  142. if path.is_file():
  143. zf.write(path, arcname=str(path.relative_to(PKG)))
  145. doi_info = {
  146. "package_dir": str(PKG),
  147. "zip": str(zip_path),
  148. "zip_bytes": zip_path.stat().st_size,
  149. "n_files_copied": sum(1 for m in manifest if m.get("status") == "copied"),
  150. "n_missing": sum(1 for m in manifest if m.get("status") == "missing"),
  151. "zenodo_doi": None,
  152. "note": "Upload zip to Zenodo (or set ZENODO_TOKEN to automate). Prefer community: neuroscience / cancer-research.",
  153. }
  155. token = os.environ.get("ZENODO_TOKEN") or os.environ.get("ZENODO_ACCESS_TOKEN")
  156. if token:
  157. try:
  158. import requests
  160. # Use sandbox unless ZENODO_PRODUCTION=1
  161. base = "https://zenodo.org/api" if os.environ.get("ZENODO_PRODUCTION") == "1" else "https://sandbox.zenodo.org/api"
  162. r = requests.post(
  163. f"{base}/deposit/depositions",
  164. params={"access_token": token},
  165. json={},
  166. timeout=60,
  167. )
  168. r.raise_for_status()
  169. dep = r.json()
  170. bucket = dep["links"]["bucket"]
  171. with zip_path.open("rb") as fp:
  172. requests.put(
  173. f"{bucket}/{zip_path.name}",
  174. params={"access_token": token},
  175. data=fp,
  176. timeout=600,
  177. ).raise_for_status()
  178. meta = {
  179. "metadata": {
  180. "title": "Neurotransmitter–immune programs in diffuse glioma — analysis deposit",
  181. "upload_type": "software",
  182. "description": readme.replace("\n", "<br/>"),
  183. "creators": [{"name": "Author, To-be-completed"}],
  184. "access_right": "open",
  185. "license": "mit",
  186. "keywords": ["glioma", "radiotranscriptomics", "neurotransmitter", "tumor microenvironment"],
  187. }
  188. }
  189. requests.put(
  190. f"{base}/deposit/depositions/{dep['id']}",
  191. params={"access_token": token},
  192. json=meta,
  193. timeout=60,
  194. ).raise_for_status()
  195. pub = requests.post(
  196. f"{base}/deposit/depositions/{dep['id']}/actions/publish",
  197. params={"access_token": token},
  198. timeout=60,
  199. )
  200. if pub.status_code < 300:
  201. doi_info["zenodo_doi"] = pub.json().get("doi")
  202. doi_info["zenodo_concept_doi"] = pub.json().get("conceptdoi")
  203. doi_info["zenodo_url"] = pub.json().get("links", {}).get("html")
  204. else:
  205. doi_info["zenodo_error"] = pub.text[:500]
  206. doi_info["zenodo_deposition_id"] = dep["id"]
  207. doi_info["note"] = "Deposition created but not published; complete in Zenodo UI."
  208. except Exception as e: # noqa: BLE001
  209. doi_info["zenodo_error"] = str(e)
  210. else:
  211. doi_info["note"] = (
  212. "No ZENODO_TOKEN in environment. Package is ready: upload "
  213. f"{zip_path.name} at https://zenodo.org/deposit/new and reserve a DOI."
  214. )
  216. (OUT / "doi_deposit_status.json").write_text(json.dumps(doi_info, indent=2), encoding="utf-8")
  217. print(json.dumps(doi_info, indent=2))
  220. if __name__ == "__main__":
  221. main()

184_prepare_doi_deposit.py, under MIT · at the source

Overview

Authors: Kwang-Soo Kim1,2, Junyi Zhang3,4,5,6, Víctor A Arrieta1,2, Crismita Dmello1,2, Elena Grabis3,4,5,6, Yahaya A Yabo3,4,5,6, Si Wang1,2, Karl Habashy1,2, Joseph Duffy1,2, Junfei Zhao7,8, Andrew Gould1,2, Rishi Jain1,2, Li Chen1,2, Jian Hu9,10, Irina Balyasnikova1,2, Dhan Chand11, Daniel Levey11, Peter Canoll12,13, Wenting Zhao7,14, Peter A Sims7,14
and 7 other authorsRaul Rabadan7,8, Surya Pandey15, Bin Zhang15, Pouya Jamshidi16, Catalina Lee-Chang1,2, Dieter Henrik Heiland1,3,4,5,6, Adam M Sonabend1,2
ORCID iDs: Junyi Zhang, Víctor A Arrieta, Crismita Dmello, Rishi Jain, Jian Hu, Dhan Chand, Peter Canoll, Peter A Sims, Raul Rabadan, Bin Zhang, Pouya Jamshidi, Catalina Lee-Chang, Dieter Henrik Heiland, Adam M Sonabend
16 affiliations
  1. Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL USA
  2. Northwestern Medicine Malnati Brain Tumor Institute of the Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL USA
  3. Microenvironment and Immunology Research Laboratory, Friedrich-Alexander Universität Nürnberg-Erlangen, Erlangen, Germany
  4. Translational Neurosurgery, Friedrich-Alexander Universität Nuremberg-Erlangen, Erlangen, Germany
  5. Department of Neurosurgery, University Hospital Erlangen, Friedrich-Alexander University Erlangen Nuremberg, Erlangen, Germany
  6. Department of Neurosurgery, Medical Center-University of Freiburg, Freiburg, Germany
  7. Department of Systems Biology, Columbia University, New York, NY USA
  8. Department of Biomedical Informatics, Columbia University, New York, NY USA
  9. Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, TX USA
  10. The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX USA
  11. Agenus Inc., Lexington, MA USA
  12. Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY USA
  13. Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, NY USA
  14. Department of Biochemistry & Molecular Biophysics, Columbia University, New York, NY USA
  15. Department of Hematology and Oncology, Feinberg School of Medicine, Northwestern University, Chicago, IL USA
  16. Department of Pathology, Feinberg School of Medicine, Northwestern University, Chicago, IL USA
Institutions: Northwestern University (United States); Northwestern Medicine (United States); Robert H. Lurie Comprehensive Cancer Center of Northwestern University; University of Freiburg (Germany); Friedrich-Alexander-Universität Erlangen-Nürnberg (Germany); University Medical Center Freiburg (Germany); Universitätsklinikum Erlangen (Germany); Columbia University (United States); The University of Texas MD Anderson Cancer Center (United States); The University of Texas Health Science Center at Houston (United States); Agenus (United States) (United States); Columbia University Irving Medical Center (United States); Herbert Irving Comprehensive Cancer Center
Journal: Nature communications, volume 17, issue 1, article 7968
Dates: received 23 October 2024; accepted 1 June 2026; published online 25 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-74124-7 · PMID 42350396 · PMCID PMC13448493 · OpenAlex W7165946522
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), mouse (organism), other condition (population)
Methods: Statistics, fMRI & imaging
Keywords: CNS cancer, Immunization
MeSH: Brain Neoplasms*, Glioblastoma*, Glioma*, Immunotherapy*, Interferons*, MAP Kinase Signaling System*, Animals, CD8-Positive T-Lymphocytes, Cell Line, Tumor, Humans, Immune Checkpoint Inhibitors, Lymphocytes, Tumor-Infiltrating, Mice, Mice, Inbred C57BL, Programmed Cell Death 1 Receptor, Proto-Oncogene Proteins B-raf, T-Lymphocytes (* major topic)
Topic: Glioma Diagnosis and Treatment (Genetics, Medicine), according to OpenAlex
Funding: NINDS NIH HHS (R01 NS110703, R01 NS122395); U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI) (1U19CA264338, 1R01NS110703); U.S. Department of Health &amp; Human Services | NIH | National Cancer Institute (1U19CA264338, 1R01NS110703); NCI NIH HHS (R01 CA290737, U19 CA264338)
Citations: cited by 1 paper (Europe PMC); 58 references in the paper

Abstract

Glioblastoma (GBM) remains a formidable challenge in neuro-oncology, with immune checkpoint blockade (ICB) only showing efficacy in some patients, while the mechanisms governing therapeutic responsiveness are poorly defined. Although MAPK/ERK signaling correlates with survival following ICB, its causal role and mechanisms underlying tumor immunogenicity remain unclear. Here, we perform in vivo kinome-wide CRISPR/Cas9 screens in murine gliomas where we identify RAF-MEK-ERK axis as the strongest modulators of glioma susceptibility to anti-programmed cell death protein 1 (anti-PD-1) therapy and CD8+ T cell recognition. Experimentally-induced ERK phosphorylation (p-ERK) enhances survival after anti-PD-1 and anti-CTLA-4 therapy, leading to durable antitumor immunity upon rechallenge. Additionally, glioma cell p-ERK promotes increased interferon responses and T cell infiltration. Notably, BRAF/MEK inhibition disrupts interferon programs and tumor-microglia interactions in BRAFV600E ex vivo in human GBM/brain slice cultures. Our findings elucidate that tumor-intrinsic MAPK/ERK promotes immunotherapy response, interferon responses, T cell tumor infiltration, and GBM cell-microglia interactions.

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 1 match between paragraphs and lines of code.

Zenodo 22040374

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the Zenodo software companion of the Dryad dataset
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (10 files), pandas (10 files), statsmodels (9 files), SciPy (6 files), anndata (2 files), NiBabel (2 files), Nilearn (2 files), Scanpy (2 files), SimpleITK (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
12 files
At the source: zenodo.org/records/22040374

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;
  • 12 scripts, each with its path and the digest of its content;
  • 1 match 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 availability

The dataset from the CRISPR/Cas9 screen in Cd8 KO mice is published and available under BioProject ID PRJNA822842 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA822842/)19. The CRISPR/Cas9 screen data using PD-1 blockade is available under BioProject ID PRJNA1164273 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1164273). Single-cell data from the patient dataset, single-cell data from the neocortical slice model, and Visium data from the slice experiment have been deposited in public repositories. The processed BRAF patient-derived Visium and scRNA-seq datasets generated in this study have been deposited in the Gene Expression Omnibus under accession number GSE331374 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE331374). Raw sequencing files from human/patient-derived samples were not deposited in an unrestricted public repository due to privacy and ethical restrictions related to potentially identifiable human genetic information. Access requests should be directed to the corresponding author and will be reviewed subject to institutional approval and/or a data use agreement. The remaining patient Visium datasets were previously published58 and are publicly available through Dryad at 10.5061/dryad.h70rxwdmj. Publicly available GBMmap dataset can be found via cellxgene (https://cellxgene.cziscience.com/collections/999f2a15-3d7e-440b-96ae-2c806799c08c)33. The remaining data are available within the article, Supplementary Information, or Source data file. Source data are provided with this paper.

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, 27 authors, 2 keywords, 17 MeSH terms, 4 funders, 55 references.

Cite

This paper

Kim, K.-S., Zhang, J., Arrieta, V. A., Dmello, C., Grabis, E., Yabo, Y. A., Wang, S., Habashy, K., Duffy, J., Zhao, J., Gould, A., Jain, R., Chen, L., Hu, J., Balyasnikova, I., Chand, D., Levey, D., Canoll, P., Zhao, W., . . . Sonabend, A. M. (2026). Glioma-intrinsic MAPK/ERK signaling promotes immunotherapy efficacy through T cell infiltration and interferon responses. Nature communications, 17(1), 7968. https://doi.org/10.1038/s41467-026-74124-7

BibTeX

@article{kim2026glioma,
author = {Kim, Kwang-Soo and Zhang, Junyi and Arrieta, Víctor A and Dmello, Crismita and Grabis, Elena and Yabo, Yahaya A and Wang, Si and Habashy, Karl and Duffy, Joseph and Zhao, Junfei and Gould, Andrew and Jain, Rishi and Chen, Li and Hu, Jian and Balyasnikova, Irina and Chand, Dhan and Levey, Daniel and Canoll, Peter and Zhao, Wenting and Sims, Peter A and Rabadan, Raul and Pandey, Surya and Zhang, Bin and Jamshidi, Pouya and Lee-Chang, Catalina and Heiland, Dieter Henrik and Sonabend, Adam M},
title = {{Glioma-intrinsic MAPK/ERK signaling promotes immunotherapy efficacy through T cell infiltration and interferon responses}},
journal = {Nature communications},
year = {2026},
month = jun,
volume = {17},
number = {1},
pages = {7968},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-74124-7},
url = {https://doi.org/10.1038/s41467-026-74124-7},
pmid = {42350396},
pmcid = {PMC13448493}
}

RIS

TY - JOUR
AU - Kim, Kwang-Soo
AU - Zhang, Junyi
AU - Arrieta, Víctor A
AU - Dmello, Crismita
AU - Grabis, Elena
AU - Yabo, Yahaya A
AU - Wang, Si
AU - Habashy, Karl
AU - Duffy, Joseph
AU - Zhao, Junfei
AU - Gould, Andrew
AU - Jain, Rishi
AU - Chen, Li
AU - Hu, Jian
AU - Balyasnikova, Irina
AU - Chand, Dhan
AU - Levey, Daniel
AU - Canoll, Peter
AU - Zhao, Wenting
AU - Sims, Peter A
AU - Rabadan, Raul
AU - Pandey, Surya
AU - Zhang, Bin
AU - Jamshidi, Pouya
AU - Lee-Chang, Catalina
AU - Heiland, Dieter Henrik
AU - Sonabend, Adam M
TI - Glioma-intrinsic MAPK/ERK signaling promotes immunotherapy efficacy through T cell infiltration and interferon responses
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/06/25
VL - 17
IS - 1
SP - 7968
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-74124-7
UR - https://doi.org/10.1038/s41467-026-74124-7
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41467-026-74124-7",
"type": "article-journal",
"title": "Glioma-intrinsic MAPK/ERK signaling promotes immunotherapy efficacy through T cell infiltration and interferon responses",
"container-title": "Nature communications",
"author": [
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{
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"given": "Junyi"
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{
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"given": "Crismita"
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{
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"given": "Elena"
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{
"family": "Yabo",
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{
"family": "Wang",
"given": "Si"
},
{
"family": "Habashy",
"given": "Karl"
},
{
"family": "Duffy",
"given": "Joseph"
},
{
"family": "Zhao",
"given": "Junfei"
},
{
"family": "Gould",
"given": "Andrew"
},
{
"family": "Jain",
"given": "Rishi"
},
{
"family": "Chen",
"given": "Li"
},
{
"family": "Hu",
"given": "Jian"
},
{
"family": "Balyasnikova",
"given": "Irina"
},
{
"family": "Chand",
"given": "Dhan"
},
{
"family": "Levey",
"given": "Daniel"
},
{
"family": "Canoll",
"given": "Peter"
},
{
"family": "Zhao",
"given": "Wenting"
},
{
"family": "Sims",
"given": "Peter A"
},
{
"family": "Rabadan",
"given": "Raul"
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{
"family": "Pandey",
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{
"family": "Zhang",
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{
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{
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{
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"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
25
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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[3] doi:10.1016/j.xcrm.2026.102766 [code]
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[4] doi:10.1038/s41586-026-10214-2 [code]
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[5] doi:10.1038/s41467-026-71719-y [code]
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Journal: Nature communications
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[6] doi:10.1038/s41586-026-10629-x [code]
Whole-genome duplication shaped cell-type evolution in the vertebrate brain.
Journal: Nature
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[7] doi:10.1093/bib/bbag331 [code]
SPOmiAlign: a modality-agnostic computational framework for multimodal spatial omics alignment enabled by a feature matching foundation model.
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[8] doi:10.1016/j.cell.2026.05.047 [code]
An emergent disease-associated motor neuron state precedes cell death in ALS.
Journal: Cell
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[9] doi:10.1016/j.isci.2026.116906 [code]
Evaluating exon skipping in the central nervous system in Duchenne muscular dystrophy using spatial transcriptomics.
Journal: iScience
In common: anndata, Scanpy, statsmodels, 3 other tools, other condition, mouse
[10] doi:10.1186/s13073-026-01704-z [code]
Gene expression profiling enables refined parcellation of cortical layers in the heterogeneous human cerebral cortex.
Journal: Genome medicine
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