The Library of Integrated Network-Based Cellular Signatures (LINCS) consortium from the National Institutes of Health (NIH) is a collective effort to catalog changes in gene expression and other cellular processes that occur when cells undergo genetic or pharmacologic perturbations.
It uses this systematic functional perturbation approach to discover the cellular signatures associated with and the connections between the genes that cause disease and the drugs that might treat them (Subramanian et al., 2017; Keenan et al., 2018).
Six LINCS Data and Signature Generation Centers (DSGCs) have produced a wide range of perturbation data from different disease cell types using different genomic and proteomic assays to open exciting new avenues for learning about the mechanisms of diseases and the search for effective therapeutics (Keenan et al., 2018).
But what is the main focus of the LINCS consortium, and what role does each of the six LINCS DSGCs play in producing high-throughput functional data at scale?
Let’s find out.
The LINCS between our genes, diseases, and drugs
When seeking answers to specific biological questions where certain genetic players or compounds are already known, small-scale genetic or chemical disruptions can provide targeted and highly specific results.
But they lack the breadth and variety of cell types and perturbations required to address broader research questions or to identify connections between biological targets and new disease therapies.
So, a key goal of the LINCS consortium is to provide a large-scale functional catalog of the cellular signatures associated with genetic and small molecule perturbations for hundreds of healthy and disease cell lines.
A suite of complementary experimental approaches was used to achieve this. A reduced representation transcriptomic profiling technology called L1000 was often used alongside proteomic, epigenomic, and imaging technologies to provide rich information on gene expression, protein levels, their localization, their states of modification, and the networks they contribute to.
But it was only made possible thanks to the collaboration of six DSGCs to produce holistic cellular signatures to address some of the major challenges in our functional understanding of diverse biological systems.
The role of LINCS DSGCs
Each DSGC is tasked with specific goals and challenges to address (Keenan et al., 2018):
1. Drug Toxicity Signature (DToxS) Generation LINCS Center
The goal of the DToxS center is to produce cellular signatures that can predict potential negative reactions resulting from FDA-approved medications, and to find alternative drugs that can mitigate these adverse effects.
It does this using genomic and proteomic approaches combined with the FDA’s Adverse Event Reporting System database with an emphasis on neuropathy, hepatotoxicity, and cardiotoxicity.
Among other things, DToxS has discovered that some anti-cancer protein kinase inhibitors are toxic to heart cells and might therefore be a potential risk for patients (van Hasselt et al., 2020).
2. HMS LINCS Center
This center is focused on generating multi-dimensional perturbation signatures with complementary experimental technologies such as live- and fixed-cell imaging, proteomics, and bulk and single-cell mRNA profiling after compound treatment.
It has led to the discovery of novel roles of an anti-breast cancer drug in disrupting the cell cycle to cause cell death among many other findings (Chopra et al., 2020).
3. LINCS Proteomic Characterization Center for Signaling and Epigenetics
Using proteomics, this center investigates how cell-to-cell communication is affected by early genetic or chemical perturbations and how the epigenetic landscape of cells is altered as a result.
The center integrated high-throughput proteomics data with reduced representation L1000 transcriptomic profiling to reveal that different types of cancer cells responded in similar ways to some drugs suggesting their broad therapeutic potential (Litichevskiy et al., 2018).
4. Microenvironment Perturbagen (MEP) LINCS Center
This center investigates how malignant and non-malignant cancer cells are controlled by the microenvironments in which they live.
They use proteomics, transcriptome profiling, and imaging to discover functionally related molecular features linked to specific cellular phenotypes (Gross et al., 2022).
5. NeuroLINCS Center
This center aims to understand the complex causes of neurodegenerative disease by directly perturbing different types of brain cells and recording the resulting cellular signatures by imaging, proteomics, and transcriptome profiling.
This dataset has been used to identify key features of amyotrophic lateral sclerosis which could be targeted with therapeutics (Ziff et al., 2023).
6. LINCS Center for Transcriptomics
This center is focused on expression profiling of cells using the L1000 gene expression profiling technology after the treatment of cell lines with small molecules or genetic perturbations.
They have generated a comprehensive Connectivity Map to discover the connections between gene function, their role in disease, and the drugs that could treat them (Subramanian et al., 2017).
The future of transcriptomic perturbation screening?
While transcriptome profiling with L1000 from the LINCS consortium has informed on many areas of biology, less than 1000 genes are directly measured and used to computationally infer only half the genes in the human genome.
In contrast, novel bulk 3’ mRNA-seq methods provide reliable direct expression data for over 20,000 genes in thousands of samples in the same sequencing run (Alpern et al., 2019).
Technologies such as MERCURIUS™ Bulk RNA-Barcoding and Sequencing (BRB-seq) and RNA-extraction free MERCURIUS™ DRUG-seq now allow ultra-high-throughput compound screenings with more samples, cell types, compounds, and experimental conditions at a reduced cost compared to standard bulk RNA-seq approaches (Alpern et al., 2019).
Please contact us to learn more about the LINCS consortium or how MERCURIUS™ BRB-seq and MERCURIUS™ DRUG-seq can help your next compound screen.
References
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Alpern, D. et al. (2019) ‘BRB-seq: ultra-affordable high-throughput transcriptomics enabled by bulk RNA barcoding and sequencing’. Genome biology, 20(1), pp.1-15. Available at: https://doi.org/10.1186/s13059-019-1671-x.
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Chopra, S.S. et al. (2020) ‘Torin2 exploits replication and checkpoint vulnerabilities to cause death of PI3K-activated triple-negative breast cancer cells’. Cell systems, 10(1), pp.66-81. Available at: https://doi.org/10.1016/j.cels.2019.11.001.
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Gross, S.M. et al. (2022) ‘A multi-omic analysis of MCF10A cells provides a resource for integrative assessment of ligand-mediated molecular and phenotypic responses’. Communications Biology, 5(1), p.1066. Available at: https://doi.org/10.1038/s42003-022-03975-9.
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Keenan, A.B. et al. (2018) ‘The library of integrated network-based cellular signatures NIH program: system-level cataloging of human cells response to perturbations’. Cell systems, 6(1), pp.13-24. Available at: https://doi.org/10.1016/j.cels.2017.11.001.
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Litichevskiy, L. et al. (2018). ‘A library of phosphoproteomic and chromatin signatures for characterizing cellular responses to drug perturbations’. Cell systems, 6(4), pp.424-443. Available at: https://doi.org/10.1016/j.cels.2018.03.012.
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Subramanian, A. et al. (2017) ‘A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles’, Cell, 171(6), pp. 1437-1452.e17. Available at: https://doi.org/10.1016/j.cell.2017.10.049.
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van Hasselt, J.C. et al. (2020). ‘Transcriptomic profiling of human cardiac cells predicts protein kinase inhibitor-associated cardiotoxicity’. Nature Communications, 11(1), p.4809. Available at: https://doi.org/10.1038/s41467-020-18396-7.
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Ziff, O.J. et al. (2023) ‘Integrated transcriptome landscape of ALS identifies genome instability linked to TDP-43 pathology’. Nature Communications, 14(1), p.2176. Available at: https://doi.org/10.1038/s41467-023-37630-6.
