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A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles
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Abstract
We previously piloted the concept of a Connectivity Map (CMap), whereby genes, drugs, and disease states are connected by virtue of common gene-expression signatures. Here, we report more than a 1,000-fold scale-up of the CMap as part of the NIH LINCS Consortium, made possible by a new, low-cost, high-throughput reduced representation expression profiling method that we term L1000. We show that L1000 is highly reproducible, comparable to RNA sequencing, and suitable for computational inference of the expression levels of 81% of non-measured transcripts. We further show that the expanded CMap can be used to discover mechanism of action of small molecules, functionally annotate genetic variants of disease genes, and inform clinical trials. The 1.3 million L1000 profiles described here, as well as tools for their analysis, are available at https://clue.io.
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“The magnitude of gene expression changes has been reported to correlate with functional impact (Wawer et al. 2014; Subramanian et al. 2017)”
From Murine single-cell RNA-seq reveals cell-identity- and tissue-specific trajectories of aging (2019) · cites this paper as Subramanian et al. 2017
Experimental — accuracy not yet measuredIn the citing paper
“many transcriptional states could be faithfully inferred from a subset of gene expression measurements32,33”
From Combinatorial single-cell CRISPR screens by direct guide RNA capture and targeted sequencing (2020) · cites this paper as [33]
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“Alternatively, gene panels such as the L1000 panel34 could be used (Extended Data Figure 3), which has been shown to capture the majority of information contained in the full transcriptome across a wide range of perturbations34.”
From Targeted Perturb-seq enables genome-scale genetic screens in single cells (2020) · cites this paper as [34]
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“Perturbation modelling is underpinned by systematic databases of biological response to treatment (e.g. Connectivity Map [2], L1000, DRUG-seq [3]) and leverages the significant potential of machine learning (ML) to make predictive models, e.g., molecular targets or IC50 values.”
From A mini-review on perturbation modelling across single-cell omic modalities (2024) · cites this paper as [2]
In the citing paper
“Of note, the LINCS consortium, and in particular the L1000 Connectivity Map resource, provides a catalog of postperturbation transcriptomics profiles for thousands of compounds, offering a global map of the phenotypic response landscape of the medicinal chemistry space.205”
From Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence (2023) · cites this paper as [205]
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“Another caveat of our approach is the likely existence of null signatures corresponding to innocuous molecules with no actual bioactivity in a given CC data type38.”
From Bioactivity descriptors for uncharacterized chemical compounds (2021) · cites this paper as [38]
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Metadata
- Authors
- Subramanian, A., Narayan, R., Corsello, S. M., Peck, D. D., Natoli, T. E., Lu, X., Gould, J., Davis, J. F., Tubelli, A. A., Asiedu, J. K., Lahr, D. L., Hirschman, J. E., Liu, Z., Donahue, M., Julian, B., Khan, M., Wadden, D., Smith, I. C., Lam, D., Liberzon, A., Toder, C., Bagul, M., Orzechowski, M., Enache, O. M., Piccioni, F., Johnson, S. A., Lyons, N. J., Berger, A. H., Shamji, A. F., Brooks, A. N., Vrcic, A., Flynn, C., Rosains, J., Takeda, D. Y., Hu, R., Davison, D., Lamb, J., Ardlie, K., Hogstrom, L., Greenside, P., Gray, N. S., Clemons, P. A., Silver, S., Wu, X., Zhao, W.-N., Read-Button, W., Wu, X., Haggarty, S. J., Ronco, L. V., Boehm, J. S., Schreiber, S. L., Doench, J. G., Bittker, J. A., Root, D. E., Wong, B., & Golub, T. R.
- Affiliations
- Broad Institute (US)
- Corresponding author
- Golub, Todd R.
- ORCID
- Subramanian, Aravind
- Corsello, Steven M.
- Gould, Joshua
- Tubelli, Andrew A.
- Lahr, David L.
- Hirschman, Jodi E.
- Liu, Zihan
- Khan, Mariya
- Smith, Ian C.
- Orzechowski, Marek
- Enache, Oana M.
- Piccioni, Federica
- Johnson, Sarah A.
- Berger, Alice H.
- Brooks, Angela N.
- Vrcic, Anita
- Takeda, David Y.
- Ardlie, Kristin
- Gray, Nathanael S.
- Clemons, Paul A.
- Silver, Serena
- Wu, Xiaoyun
- Zhao, Wen-Ning
- Haggarty, Stephen J.
- Ronco, Lucienne V.
- Boehm, Jesse S.
- Schreiber, Stuart L.
- Doench, John G.
- Bittker, Joshua A.
- Root, David E.
- Golub, Todd R.
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2017-11
- Volume
- 171
- Issue
- 6
- Pages
- 1437-1452.e17
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC5990023
- OpenAlex
- W2612467560
- Cited by
- 3,515
- References
- 73
- Citations by year
- 2026: 436 · 2025: 479 · 2024: 493 · 2023: 458 · 2022: 507 · 2021: 421 · 2020: 320 · 2019: 261 · 2018: 122 · 2017: 12
- FWCI
- 80.27
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- NIH (U54HG006093, U54HL127366, U01HG008699, CA009172, TR001100)
- Conquer Cancer Foundation of ASCO Young
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Keywords
- Connectivity Map, L1000, gene expression signatures, functional annotation of genetic variants, reproducibility, clinical trial design
- MeSH terms
- Pharmaceutical Preparations, Humans, Neoplasms, Organ Specificity, Sequence Analysis, RNA, Drug Resistance, Neoplasm, Gene Expression Profiling, Cell Line, Tumor, Small Molecule Libraries
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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