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

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.

Reduced-representation Phosphosignatures Measured by Quantitative Targeted MS Capture Cellular States and Enable Large-scale Comparison of Drug-induced Phenotypes.

Profiling post-translational modifications represents an alternative dimension to gene expression data in characterizing cellular processes. Many cellular responses to drugs are mediated by changes in cellular phosphosignaling. We sought to develop a common platform on which phosphosignaling responses could be profiled across thousands of samples, and created a targeted MS assay that profiles a reduced-representation set of phosphopeptides that we show to be strong indicators of responses to chemical perturbagens.To develop the assay, we investigated the coordinate regulation of phosphosites in samples derived from three cell lines treated with 26 different bioactive small molecules. Phosphopeptide analytes were selected from these discovery studies by clustering and picking 1 to 2 proxy members from each cluster. A quantitative, targeted parallel reaction monitoring assay was developed to directly measure 96 reduced-representation probes. Sample processing for proteolytic digestion, protein quantification, peptide desalting, and phosphopeptide enrichment have been fully automated, making possible the simultaneous processing of 96 samples in only 3 days, with a plate phosphopeptide enrichment variance of 12%. This highly reproducible process allowed ∼95% of the reduced-representation phosphopeptide probes to be detected in ∼200 samples.The performance of the assay was evaluated by measuring the probes in new samples generated under treatment conditions from discovery experiments, recapitulating the observations of deeper experiments using a fraction of the analytical effort. We measured these probes in new experiments varying the treatments, cell types, and timepoints to demonstrate generalizability. We demonstrated that the assay is sensitive to disruptions in common signaling pathways (e.g. MAPK, PI3K/mTOR, and CDK). The high-throughput, reduced-representation phosphoproteomics assay provides a platform for the comparison of perturbations across a range of biological conditions, suitable for profiling thousands of samples. We believe the assay will prove highly useful for classification of known and novel drug and genetic mechanisms through comparison of phosphoproteomic signatures.

Proteomic profiling dataset of chemical perturbations in multiple biological backgrounds.

While gene expression profiling has traditionally been the method of choice for large-scale perturbational profiling studies, proteomics has emerged as an effective tool in this context for directly monitoring cellular responses to perturbations. We previously reported a pilot library containing 3400 profiles of multiple perturbations across diverse cellular backgrounds in the reduced-representation phosphoproteome (P100) and chromatin space (Global Chromatin Profiling, GCP). Here, we expand our original dataset to include profiles from a new set of cardiotoxic compounds and from astrocytes, an additional neural cell model, totaling 5300 proteomic signatures. We describe filtering criteria and quality control metrics used to assess and validate the technical quality and reproducibility of our data. To demonstrate the power of the library, we present two case studies where data is queried using the concept of "connectivity" to obtain biological insight. All data presented in this study have been deposited to the ProteomeXchange Consortium with identifiers PXD017458 (P100) and PXD017459 (GCP) and can be queried at https://clue.io/proteomics .

Cas9 activates the p53 pathway and selects for p53-inactivating mutations.

Cas9 is commonly introduced into cell lines to enable CRISPR-Cas9-mediated genome editing. Here, we studied the genetic and transcriptional consequences of Cas9 expression itself. Gene expression profiling of 165 pairs of human cancer cell lines and their Cas9-expressing derivatives revealed upregulation of the p53 pathway upon introduction of Cas9, specifically in wild-type TP53 (TP53-WT) cell lines. This was confirmed at the messenger RNA and protein levels. Moreover, elevated levels of DNA repair were observed in Cas9-expressing cell lines. Genetic characterization of 42 cell line pairs showed that introduction of Cas9 can lead to the emergence and expansion of p53-inactivating mutations. This was confirmed by competition experiments in isogenic TP53-WT and TP53-null (TP53-/-) cell lines. Lastly, Cas9 was less active in TP53-WT than in TP53-mutant cell lines, and Cas9-induced p53 pathway activation affected cellular sensitivity to both genetic and chemical perturbations. These findings may have broad implications for the proper use of CRISPR-Cas9-mediated genome editing.

Author Correction: Cas9 activates the p53 pathway and selects for p53-inactivating mutations.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

A Library of Phosphoproteomic and Chromatin Signatures for Characterizing Cellular Responses to Drug Perturbations.

Although the value of proteomics has been demonstrated, cost and scale are typically prohibitive, and gene expression profiling remains dominant for characterizing cellular responses to perturbations. However, high-throughput sentinel assays provide an opportunity for proteomics to contribute at a meaningful scale. We present a systematic library resource (90 drugs × 6 cell lines) of proteomic signatures that measure changes in the reduced-representation phosphoproteome (P100) and changes in epigenetic marks on histones (GCP). A majority of these drugs elicited reproducible signatures, but notable cell line- and assay-specific differences were observed. Using the "connectivity" framework, we compared signatures across cell types and integrated data across assays, including a transcriptional assay (L1000). Consistent connectivity among cell types revealed cellular responses that transcended lineage, and consistent connectivity among assays revealed unexpected associations between drugs. We further leveraged the resource against public data to formulate hypotheses for treatment of multiple myeloma and acute lymphocytic leukemia. This resource is publicly available at https://clue.io/proteomics.