Transcriptome-wide analysis of differential expression in perturbation atlases

Ajay Nadig^{1

2

3

4}, Joseph M Replogle^{5

6

7}, Angela N Pogson⁸, Mukundh Murthy⁹, Steven A McCarroll^{9

10

11}, Jonathan S Weissman^{8

11

12

13}, Elise B Robinson^{14

9

15}, Luke J O'Connor^{16

17}

Affiliations

¹ Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. anadig@broadinstitute.org.
² Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA. anadig@broadinstitute.org.
³ Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA. anadig@broadinstitute.org.
⁴ Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA. anadig@broadinstitute.org.
⁵ Department of Medicine, Massachusetts General Hospital, Boston, MA, USA. jreplogle@mgh.harvard.edu.
⁶ Medical Scientist Training Program, University of California San Francisco, San Francisco, CA, USA. jreplogle@mgh.harvard.edu.
⁷ Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, Cambridge, MA, USA. jreplogle@mgh.harvard.edu.
⁸ Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
⁹ Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
¹⁰ Department of Genetics, Harvard Medical School, Boston, MA, USA.
¹¹ Howard Hughes Medical Institute, Cambridge, MA, USA.
¹² David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
¹³ Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA.
¹⁴ Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
¹⁵ Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
¹⁶ Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. loconnor@broadinstitute.org.
¹⁷ Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA. loconnor@broadinstitute.org.

PMID: 40259084
DOI: 10.1038/s41588-025-02169-3

Transcriptome-wide analysis of differential expression in perturbation atlases

Ajay Nadig et al. Nat Genet. 2025 May.

. 2025 May;57(5):1228-1237.

doi: 10.1038/s41588-025-02169-3. Epub 2025 Apr 21.

Authors

Affiliations

¹ Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. anadig@broadinstitute.org.
² Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA. anadig@broadinstitute.org.
³ Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA. anadig@broadinstitute.org.
⁴ Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA. anadig@broadinstitute.org.
⁵ Department of Medicine, Massachusetts General Hospital, Boston, MA, USA. jreplogle@mgh.harvard.edu.
⁶ Medical Scientist Training Program, University of California San Francisco, San Francisco, CA, USA. jreplogle@mgh.harvard.edu.
⁷ Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, Cambridge, MA, USA. jreplogle@mgh.harvard.edu.
⁸ Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
⁹ Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
¹⁰ Department of Genetics, Harvard Medical School, Boston, MA, USA.
¹¹ Howard Hughes Medical Institute, Cambridge, MA, USA.
¹² David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
¹³ Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA.
¹⁴ Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
¹⁵ Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
¹⁶ Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. loconnor@broadinstitute.org.
¹⁷ Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA. loconnor@broadinstitute.org.

PMID: 40259084
DOI: 10.1038/s41588-025-02169-3

Abstract

Single-cell CRISPR screens such as Perturb-seq enable transcriptomic profiling of genetic perturbations at scale. However, the data produced by these screens are noisy, and many effects may go undetected. Here we introduce transcriptome-wide analysis of differential expression (TRADE)-a statistical model for the distribution of true differential expression effects that accounts for estimation error appropriately. TRADE estimates the 'transcriptome-wide impact', which quantifies the total effect of a perturbation across the transcriptome. Analyzing several large Perturb-seq datasets, we show that many transcriptional effects remain undetected in standard analyses but emerge in aggregate using TRADE. A typical gene perturbation affects an estimated 45 genes, whereas a typical essential gene affects over 500. We find moderate consistency of perturbation effects across cell types, identify perturbations where transcriptional responses vary qualitatively across dosage levels and clarify the relationship between genetic and transcriptomic correlations across neuropsychiatric disorders.

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Conflict of interest statement

Competing interests: J.S.W. declares outside interest in 5 AM Venture, Amgen, Chroma Medicine, KSQ Therapeutics, Maze Therapeutics, Tenaya Therapeutics, Tessera Therapeutics, Ziada Therapeutics and Third Rock Ventures. J.M.R. consults for Third Rock Ventures and Maze Therapeutics, and is a consultant for and equity holder in Waypoint Bio. The remaining authors declare no competing interests.

Update of

Transcriptome-wide characterization of genetic perturbations.
Nadig A, Replogle JM, Pogson AN, McCarroll SA, Weissman JS, Robinson EB, O'Connor LJ. Nadig A, et al. bioRxiv [Preprint]. 2024 Jul 3:2024.07.03.601903. doi: 10.1101/2024.07.03.601903. bioRxiv. 2024. Update in: Nat Genet. 2025 May;57(5):1228-1237. doi: 10.1038/s41588-025-02169-3. PMID: 39005298 Free PMC article. Updated. Preprint.

References

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1. Yang, J. et al. Common SNPs explain a large proportion of the heritability for human height. Nat. Genet. 42, 565–569 (2010). - PubMed - PMC - DOI

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Transcriptome-wide analysis of differential expression in perturbation atlases

Affiliations

Transcriptome-wide analysis of differential expression in perturbation atlases

Authors

Affiliations

Abstract

Conflict of interest statement

Update of

References

MeSH terms

Grants and funding

LinkOut - more resources

Full Text Sources