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.
“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
The Results section, “Identifying bioactive subsets of small molecule screening libraries” (page unavailable), links a strong transcriptional response to biological activity: 92% of established drugs, versus 15% of unoptimized compounds, had high transcriptional activity scores. The Methods define that score partly by the number of differentially expressed genes. This supports using expression-response strength as an indicator of activity, but the paper does not establish a correlation between the magnitude of gene-expression changes and the degree of functional impact. (LLM, conf=0.84)
Experimental — accuracy not yet measuredA subset of expression measurements may be enough to infer many transcriptional states accurately.
“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]
The L1000 panel could be used and has been shown to capture most transcriptome information across many perturbations.
“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]
Perturbation modelling is underpinned by systematic databases of biological response to treatment and leverages machine learning to make predictive models such as molecular targets or IC50 values.
“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]
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.
“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]
There is likely existence of null signatures corresponding to innocuous molecules with no actual bioactivity in a given CC data type.
“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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Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation
Malta, T. M., Sokolov, A., Gentles, A. J., Burzykowski, T., Poisson, L., Weinstein, J. N., Kamińska, B., Huelsken, J., Omberg, L., Gevaert, O., et al. (2018) · Cell · doi:10.1016/j.cell.2018.03.034 · Cited by 2,180 · see citing works
Self-citationA Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade
Jerby-Arnon, L., Shah, P., Cuoco, M. S., Rodman, C., Su, M.-J., Melms, J. C., Leeson, R., Kanodia, A., Mei, S., Lin, J.-R., et al. (2018) · Cell · doi:10.1016/j.cell.2018.09.006 · Cited by 1,429 · see citing works
Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery
Yang, X. Y., Wang, Y., Byrne, R., Schneider, G., & Yang, S. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00728 · Cited by 1,045 · see citing works
Genetic and transcriptional evolution alters cancer cell line drug response
Ben-David, U., Siranosian, B., Ha, G., Tang, H., Oren, Y., Hinohara, K., Strathdee, C. A., Dempster, J., Lyons, N. J., Burns, R., et al. (2018) · Nature · doi:10.1038/s41586-018-0409-3 · Cited by 931 · see citing works
Self-citationDiscovering the anticancer potential of non-oncology drugs by systematic viability profiling
Corsello, S. M., Nagari, R. T., Spangler, R. D., Rossen, J., Kocak, M., Bryan, J. G., Humeidi, R., Peck, D., Wu, X., Tang, A. A., et al. (2020) · Nature Cancer · doi:10.1038/s43018-019-0018-6 · Cited by 878 · see citing works
Self-citationSynergy and antagonism in natural product extracts: when 1 + 1 does not equal 2
Caesar, L. K., & Cech, N. B. (2019) · Natural Product Reports · doi:10.1039/c9np00011a · Cited by 940 · see citing works
Proteogenomic Characterization Reveals Therapeutic Vulnerabilities in Lung Adenocarcinoma
Gillette, M. A., Satpathy, S., Cao, S., Dhanasekaran, S. M., Vasaikar, S. V., Krug, K., Petralia, F., Li, Y., Liang, W.-W., Reva, B., et al. (2020) · Cell · doi:10.1016/j.cell.2020.06.013 · Cited by 820 · see citing works
Self-citationArtificial Intelligence in Cancer Research and Precision Medicine
Bhinder, B., Gilvary, C., Madhukar, N. S., & Elemento, O. (2021) · Cancer Discovery · doi:10.1158/2159-8290.cd-21-0090 · Cited by 781 · see citing works
MULTI-seq: sample multiplexing for single-cell RNA sequencing using lipid-tagged indices
McGinnis, C. S., Patterson, D. M., Winkler, J., Conrad, D. N., Hein, M. Y., Srivastava, V., Hu, J. L., Murrow, L. M., Weissman, J. S., Werb, Z., et al. (2019) · Nature Methods · doi:10.1038/s41592-019-0433-8 · Cited by 643 · see citing works
HERB: a high-throughput experiment- and reference-guided database of traditional Chinese medicine
Fang, S., Dong, L., Liu, L., Guo, J., Zhao, L., Zhang, J.-Y., Bu, D., Liu, X., Huo, P., Cao, W., et al. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa1063 · Cited by 708 · see citing works
Proteogenomic and metabolomic characterization of human glioblastoma
Wang, L.-B., Karpova, A., Gritsenko, M. A., Kyle, J. E., Cao, S., Li, Y., Rykunov, D., Colaprico, A., Rothstein, J. H., Hong, R., et al. (2021) · Cancer Cell · doi:10.1016/j.ccell.2021.01.006 · Cited by 661 · see citing works
Deep learning improves prediction of drug–drug and drug–food interactions
Ryu, J. Y., Kim, H. U., & Lee, S. Y. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1803294115 · Cited by 662 · see citing works
TTD: Therapeutic Target Database describing target druggability information
Zhou, Y., Zhang, Y., Zhao, D., Yu, X., Shen, X., Zhou, Y., Wang, S., Qiu, Y., Chen, Y., & Zhu, F. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad751 · Cited by 667 · see citing works
Bressan, D., Battistoni, G., & Hannon, G. J. (2023) · Science · doi:10.1126/science.abq4964 · Cited by 606 · see citing works
Stroke genetics informs drug discovery and risk prediction across ancestries
Mishra, A., Malik, R., Hachiya, T., Jürgenson, T., Namba, S., Posner, D. C., Kamanu, F., Koido, M., Grand, Q. L., Shi, M., et al. (2022) · Nature · doi:10.1038/s41586-022-05165-3 · Cited by 591 · see citing works
Proteogenomic Landscape of Breast Cancer Tumorigenesis and Targeted Therapy
Krug, K., Jaehnig, E. J., Satpathy, S., Blumenberg, L. M., Karpova, A. Y., Anurag, M., Miles, G., Mertins, P., Geffen, Y., Tang, L. C., et al. (2020) · Cell · doi:10.1016/j.cell.2020.10.036 · Cited by 532 · see citing works
Trans effects on gene expression can drive omnigenic inheritance
Liu, X., Li, Y. I., & Pritchard, J. K. (2019) · Cell · doi:10.1016/j.cell.2019.04.014 · Cited by 523 · see citing works
The International League Against Epilepsy Consortium on Complex Epilepsies, Abou-Khalil, B., Auce, P., Avbersek, A., Bahlo, M., Balding, D. J., Bast, T., Baum, L., Becker, A. J., Becker, F., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-07524-z · Cited by 482 · see citing works
Targeting metabolic dysregulation for fibrosis therapy
Zhao, X. L., Kwan, J. Y. Y., Yip, K. W., Liu, P. P., & Liu, F. (2019) · Nature Reviews Drug Discovery · doi:10.1038/s41573-019-0040-5 · Cited by 516 · see citing works
The quest to slow ageing through drug discovery
Partridge, L., Fuentealba, M., & Kennedy, B. K. (2020) · Nature Reviews Drug Discovery · doi:10.1038/s41573-020-0067-7 · Cited by 468 · see citing works
Combinatorial single-cell CRISPR screens by direct guide RNA capture and targeted sequencing
Replogle, J. M., Norman, T. M., Xu, A., Hussmann, J. A., Chen, J., Cogan, J. Z., Meer, E. J., Terry, J. M., Riordan, D. P., Srinivas, N., et al. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0470-y · Cited by 465 · see citing works
Massively multiplex chemical transcriptomics at single-cell resolution
Srivatsan, S. R., McFaline-Figueroa, J. L., Ramani, V., Saunders, L., Cao, J., Packer, J., Pliner, H. A., Jackson, D. L., Daza, R. M., Christiansen, L., et al. (2020) · Science · doi:10.1126/science.aax6234 · Cited by 401 · see citing works
Artificial intelligence in cancer target identification and drug discovery
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Self-citationMéndez‐Lucio, O., Baillif, B., Clevert, D.-A., Rouquié, D., & Wichard, J. (2020) · Nature Communications · doi:10.1038/s41467-019-13807-w · Cited by 422 · see citing works
Cai, Y., Song, W., Li, J., Jing, Y., Liang, C., Zhang, L., Zhang, X., Zhang, W., Liu, B., An, Y., et al. (2022) · Science China Life Sciences · doi:10.1007/s11427-022-2161-3 · Cited by 436 · see citing works
Self-citationWhy Hypothesis Testers Should Spend Less Time Testing Hypotheses
Scheel, A. M., Tiokhin, L., Isager, P. M., & Lakens, D. (2021) · Perspectives on Psychological Science · doi:10.1177/1745691620966795 · Cited by 382 · see citing works
Image-based profiling for drug discovery: due for a machine-learning upgrade?
Chandrasekaran, S. N., Ceulemans, H., Boyd, J. D., & Carpenter, A. E. (2020) · Nature Reviews Drug Discovery · doi:10.1038/s41573-020-00117-w · Cited by 387 · see citing works
Deep learning in drug discovery: an integrative review and future challenges
Askr, H., Elgeldawi, E., Aboul-Ella, H., Elshaier, Y., Gomaa, M. M., & Hassanien, A. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10306-1 · Cited by 328 · see citing works
A proteogenomic portrait of lung squamous cell carcinoma
Satpathy, S., Krug, K., Beltran, P. M. J., Savage, S. R., Petralia, F., Kumar‐Sinha, C., Dou, Y., Reva, B. A., Kane, M. H., Avanessian, S. C., et al. (2021) · Cell · doi:10.1016/j.cell.2021.07.016 · Cited by 375 · see citing works
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Dictionary of immune responses to cytokines at single-cell resolution
Cui, A., Huang, T., Li, S., Ma, A., Pérez, J. L., Sander, C., Keskin, D. B., Wu, C. J., Fraenkel, E., & Hacohen, N. (2023) · Nature · doi:10.1038/s41586-023-06816-9 · Cited by 364 · see citing works
Phenotypic drug discovery: recent successes, lessons learned and new directions
Vincent, F., Nueda, A., Lee, J. L. C., Schenone, M., Prunotto, M., & Mercola, M. A. (2022) · Nature Reviews Drug Discovery · doi:10.1038/s41573-022-00472-w · Cited by 372 · see citing works
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Jarada, T. N., Rokne, J. G., & Alhajj, R. (2020) · Journal of Cheminformatics · doi:10.1186/s13321-020-00450-7 · Cited by 384 · see citing works
Independent Drug Action in Combination Therapy: Implications for Precision Oncology
Plana, D., Palmer, A. C., & Sorger, P. K. (2022) · Cancer Discovery · doi:10.1158/2159-8290.cd-21-0212 · Cited by 322 · see citing works
A new era in functional genomics screens
Przybyla, L., & Gilbert, L. A. (2022) · Nature Reviews Genetics · doi:10.1038/s41576-021-00409-w · Cited by 281 · see citing works
Integrated Proteogenomic Characterization across Major Histological Types of Pediatric Brain Cancer
Petralia, F., Tignor, N., Reva, B., Koptyra, M., Chowdhury, S., Rykunov, D., Krek, A., Ma, W., Zhu, Y., Ji, J., et al. (2020) · Cell · doi:10.1016/j.cell.2020.10.044 · Cited by 317 · see citing works
Empowering biomedical discovery with AI agents
Gao, S., Fang, A., Huang, Y., Giunchiglia, V., Noori, A., Schwarz, J. R., Ektefaie, Y., Kondic, J., & Zitnik, M. (2024) · Cell · doi:10.1016/j.cell.2024.09.022 · Cited by 315 · see citing works
Cas9 activates the p53 pathway and selects for p53-inactivating mutations
Enache, O. M., Rendo, V., Abdusamad, M., Lam, D., Davison, D., Pal, S., Currimjee, N., Hess, J., Pantel, S., Nag, A., et al. (2020) · Nature Genetics · doi:10.1038/s41588-020-0623-4 · Cited by 285 · see citing works
Self-citationMolecular classification and therapeutic targets in extrahepatic cholangiocarcinoma
Montal, R., Sia, D., Montironi, C., Leow, W. Q., Esteban-Fabró, R., Pinyol, R., Torres‐Martín, M., Bassaganyas, L., Moeini, A., Peix, J., et al. (2020) · Journal of Hepatology · doi:10.1016/j.jhep.2020.03.008 · Cited by 304 · see citing works
Ge, Y., Tian, T., Huang, S., Wan, F., Li, J., Li, S., Wang, X., Yang, H., Hong, L., Wu, N., et al. (2021) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-021-00568-6 · Cited by 300 · see citing works
Assessment of network module identification across complex diseases
Choobdar, S., Ahsen, M. E., Crawford, J., Tomasoni, M., Fang, T., Lamparter, D., Lin, J., Hescott, B. J., Hu, X., Mercer, J., et al. (2019) · Nature Methods · doi:10.1038/s41592-019-0509-5 · Cited by 298 · see citing works
Self-citationAneuploidy renders cancer cells vulnerable to mitotic checkpoint inhibition
Cohen‐Sharir, Y., McFarland, J. M., Abdusamad, M., Marquis, C., Bernhard, S. V., Kazachkova, M., Tang, H., Ippolito, M. R., Laue, K., Zerbib, J., et al. (2021) · Nature · doi:10.1038/s41586-020-03114-6 · Cited by 293 · see citing works
Self-citationTranscription factors: Bridge between cell signaling and gene regulation
Weidemüller, P., Kholmatov, M. M., Petsalaki, E., & Zaugg, J. B. (2021) · PROTEOMICS · doi:10.1002/pmic.202000034 · Cited by 282 · see citing works
Disease severity-specific neutrophil signatures in blood transcriptomes stratify COVID-19 patients
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Advancing the use of genome-wide association studies for drug repurposing
Reay, W. R., & Cairns, M. J. (2021) · Nature Reviews Genetics · doi:10.1038/s41576-021-00387-z · Cited by 277 · see citing works
Targeted Perturb-seq enables genome-scale genetic screens in single cells
Schraivogel, D., Gschwind, A. R., Milbank, J. H., Leonce, D. R., Jakob, P., Mathur, L., Korbel, J. O., Merten, C. A., Velten, L., & Steinmetz, L. M. (2020) · Nature Methods · doi:10.1038/s41592-020-0837-5 · Cited by 245 · see citing works
Machine and deep learning approaches for cancer drug repurposing
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The evolving role of investigative toxicology in the pharmaceutical industry
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The ferroptosis inducing compounds RSL3 and ML162 are not direct inhibitors of GPX4 but of TXNRD1
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Big data in basic and translational cancer research
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Genome-wide association study of medication-use and associated disease in the UK Biobank
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GSEA-InContext: identifying novel and common patterns in expression experiments
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Deep learning for drug response prediction in cancer
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Network-Based Methods for Prediction of Drug-Target Interactions
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Diverse compounds from pleuromutilin lead to a thioredoxin inhibitor and inducer of ferroptosis
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Chemical probes and drug leads from advances in synthetic planning and methodology
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Self-citationMachine learning applications in drug development
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Artificial intelligence, machine learning, and drug repurposing in cancer
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DRUG-seq for miniaturized high-throughput transcriptome profiling in drug discovery
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Genetics of Atrial Fibrillation in 2020
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Murine single-cell RNA-seq reveals cell-identity- and tissue-specific trajectories of aging
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An omics perspective on drug target discovery platforms
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Drug repositioning and repurposing for Alzheimer disease
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Omics Advances in Ecotoxicology
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GREIN: An Interactive Web Platform for Re-analyzing GEO RNA-seq Data
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Quantifying drug combination synergy along potency and efficacy axes
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Integrated multi-omics approaches to improve classification of chronic kidney disease
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Noncanonical open reading frames encode functional proteins essential for cancer cell survival
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Self-citationMachine Learning and Artificial Intelligence in Toxicological Sciences
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Self-citationL1000FWD: fireworks visualization of drug-induced transcriptomic signatures
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Prediction of drug efficacy from transcriptional profiles with deep learning
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Self-citationTumour evolution and microenvironment interactions in 2D and 3D space
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An Overview of Machine Learning and Big Data for Drug Toxicity Evaluation
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MatchMaker: A Deep Learning Framework for Drug Synergy Prediction
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Deep learning for drug repurposing: Methods, databases, and applications
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Advancing Computational Toxicology by Interpretable Machine Learning
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High-Throughput Transcriptomics Platform for Screening Environmental Chemicals
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Wang, M., Li, A., Sekiya, M., Beckmann, N. D., Quan, X., Schrode, N., Fernando, M. B., Yu, A., Zhu, L., Cao, J., et al. (2021) · Neuron · doi:10.1016/j.neuron.2020.11.002 · Cited by 185 · see citing works
High-Throughput Methods in the Discovery and Study of Biomaterials and Materiobiology
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Multi-ancestry study of the genetics of problematic alcohol use in over 1 million individuals
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- 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 · 2016: 1
- 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
- Physical Sciences › Computer Science › Computational Theory and Mathematics › 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
About these checks
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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