A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles

Abstract

Summary 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.

Efficient Generation of Transcriptomic Profiles by Random Composite Measurements

“…A simple alternative to performing random composite measurements is using the training phase to select a set of individual “signature genes” for measurements (Biswas et al, 2016; Donner et al, 2012; Peck et al, 2006; Subramanian et al, 2017), along with learning a model to predict the remaining genes from this measured set.…”

“…In some cases, a limited subset (“signature”) of genes can be identified, which, when measured in new samples, can be used together with the earlier profiling data to estimate the abundance of the remaining unmeasured (unobserved) genes in these new samples (Biswas et al, 2016; Donner et al, 2012; Peck et al, 2006; Subramanian et al, 2017). With the recent advent of massively parallel single-cell RNA-Seq, shallow RNA-Seq data is often used for each cell to draw inferences about the full expression profile and to recover meaningful biological distinctions on cell type (Jaitin et al, 2014; Shekhar et al, 2016) and state (Paul et al, 2015; Shalek et al, 2013, 2014).…”

From gene networks to drugs: systems pharmacology approaches for AUD

“…Fortunately, many of these challenges have been overcome largely by the pioneering work by a collaborative effort of the Broad Institute and funding from a National Institutes of Health Common Fund (https://commonfund.nih.gov/lincs). They have compiled the Library of Integrated Network-based Cellular Signatures (LINCS-L1000) database which contains gene expression responses to genetic and pharmacologic manipulation across a diverse set of human cell lines (Subramanian et al 2017). They also maintain a repurposing hub that contains over 5,000 manually-curated drugs that are either FDA approved or in clinical trials (Corsello et al 2017).…”

“…However, for larger datasets, such as LINCS-L1000, implementation of permutations tests becomes computationally expensive and less straightforward. Currently, the web app for querying the LINCS-L1000 data (https://clue.io/l1000-query) uses the “sig_gutc” tool (Subramanian et al 2017) to summarize the connectivity scores and provide a measure of reliability. Each compound has been profiled under multiple experimental conditions (different cell lines, drug doses, and exposure time points).…”

“…To compare the disease and drug signatures the KS-like statistic (as described by (Lamb et al 2006; Subramanian et al 2017) is the most frequently used similarity metric, although several studies also use Spearman or Pearson correlation coefficients (Azim et al 2017; Siavelis et al 2016; So et al 2017) or Fischer’s Exact Test (Delahaye-Duriez et al 2016). Most studies operate under the transcriptional “reversal hypothesis”, which assumes that drugs with negative connectivity scores (i.e., with gene expression signatures that revert the disease’s effects on gene expression to the control state) would ameliorate disease phenotype.…”