Evidence map›Paper›PMID 40796563›Full record

SynthesisNature communications2025

Improving reproducibility of differentially expressed genes in single-cell transcriptomic studies of neurodegenerative diseases through meta-analysis.

Nathan Nakatsuka, Drew Adler, Longda Jiang, Austin Hartman, Evan Cheng, Eric Klann, Rahul Satija

Abstract readMeta-Analysis
In one paragraph

Synthesis in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Nathan NakatsukaNew York Genome Center, New York, NY, USA. nnakatsuka@nygenome.org.ORCID http://orcid.org/0000-0002-5091-4914
Drew AdlerCenter for Neural Science, New York University, New York, NY, USA.
Longda JiangNew York Genome Center, New York, NY, USA.ORCID http://orcid.org/0000-0003-4964-6497
Austin HartmanNew York Genome Center, New York, NY, USA.ORCID http://orcid.org/0000-0001-7278-1852
Evan ChengCenter for Neural Science, New York University, New York, NY, USA.ORCID http://orcid.org/0000-0001-9036-1245
Eric KlannCenter for Neural Science, New York University, New York, NY, USA.ORCID http://orcid.org/0000-0001-7379-6802
Rahul SatijaNew York Genome Center, New York, NY, USA.ORCID http://orcid.org/0000-0001-9448-8833

Funding

Center for Integrated Cellular Analysis - Valeria A. Sanchez EstradaRM1HG011014 · NHGRI · NEW YORK GENOME CENTER · PI LANDAU, DAN, SATIJA, RAHUL · 2020 to 2025
$22.1M
Combining New Molecular and Informatic Strategies to Find Hidden Ways to Treat Brain DiseaseR35NS097404 · NINDS · ROCKEFELLER UNIVERSITY · PI DARNELL, ROBERT B · 2017 to 2024
$9.0M
Translational Control in Memory and Brain DisordersR35NS122316 · NINDS · NEW YORK UNIVERSITY · PI Eric Klann · 2021 to 2026
$6.5M
Training Systems and Integrative NeuroscienceT32MH019524 · NIMH · NEW YORK UNIVERSITY · PI KIORPES, LYNNE · 1992 to 2025
$5.2M
Modeling Gene Regulatory Networks for Early Cardiopharyngeal DevelopmentR01HD096770 · NICHD · NEW YORK UNIVERSITY · PI BONNEAU, RICHARD A, CHRISTIAEN, LIONEL · 2018 to 2022
$3.0M
Comprehensive reference map construction, geolocation and data integration for HuBMAP HIVEOT2OD026673 · OD · NEW YORK GENOME CENTER · PI MARIONI, JOHN CARLO, SATIJA, RAHUL · 2018 to 2021
$2.9M
Learning the metadata of the cell with single cell genomicsDP2HG009623 · NHGRI · NEW YORK GENOME CENTER · PI SATIJA, RAHUL · 2016 to 2016
$2.8M
Dysregulated Ribosomal Protein Synthesis in Amyloid and Tau Mouse ModelsR21NS121786 · NINDS · NEW YORK UNIVERSITY · PI KLANN, ERIC · 2021 to 2021
$436k
NHGRI NIH HHS DP2 HG009623NHGRI NIH HHS RM1 HG011014NICHD NIH HHS R01 HD096770NIH HHS OT2 OD026673NIMH NIH HHS T32 MH019524NINDS NIH HHS R21 NS121786NINDS NIH HHS R35 NS097404NINDS NIH HHS R35 NS122316
6 · The paper itself

Abstract

False positive claims of differentially expressed genes (DEGs) in scRNA-seq studies are of substantial concern. We found that DEGs from individual Parkinson's (PD), Huntington's (HD), and COVID-19 datasets had moderate predictive power for case-control status of other datasets, but DEGs from Alzheimer's (AD) and Schizophrenia (SCZ) datasets had poor predictive power. We developed a non-parametric meta-analysis method, SumRank, based on reproducibility of relative differential expression ranks across datasets, and found DEGs with improved predictive power. Specificity and sensitivity of these genes were substantially higher than those discovered by dataset merging and inverse variance weighted p-value aggregation methods. Up-regulated DEGs implicated chaperone-mediated protein processing in PD glia and lipid transport in AD and PD microglia, while down-regulated DEGs were in glutamatergic processes in AD astrocytes and excitatory neurons and synaptic functioning in HD FOXP2 neurons. Lastly, we evaluate factors influencing reproducibility of individual studies as a prospective guide for experimental design.

Indexed as

Gene Expression ProfilingNeurodegenerative DiseasesSingle-Cell AnalysisTranscriptomeAlzheimer DiseaseHumansHuntington DiseaseParkinson DiseaseReproducibility of Results

Identifiers

PMID40796563
PMCPMC12344038

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.