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.

Scalable, compressed phenotypic screening using pooled perturbations.

High-throughput phenotypic screens using biochemical perturbations and high-content readouts are constrained by limitations of scale. To address this, we establish a method of pooling exogenous perturbations followed by computational deconvolution to reduce required sample size, labor and cost. We demonstrate the increased efficiency of compressed experimental designs compared to conventional approaches through benchmarking with a bioactive small-molecule library and a high-content imaging readout. We then apply compressed screening in two biological discovery campaigns. In the first, we use early-passage pancreatic cancer organoids to map transcriptional responses to a library of recombinant tumor microenvironment protein ligands, uncovering reproducible phenotypic shifts induced by specific ligands distinct from canonical reference signatures and correlated with clinical outcome. In the second, we identify the pleotropic modulatory effects of a chemical compound library with known mechanisms of action on primary human peripheral blood mononuclear cell immune responses. In sum, our approach empowers phenotypic screens with information-rich readouts to advance drug discovery efforts and basic biological inquiry.

Optimizing the Cell Painting assay for image-based profiling.

In image-based profiling, software extracts thousands of morphological features of cells from multi-channel fluorescence microscopy images, yielding single-cell profiles that can be used for basic research and drug discovery. Powerful applications have been proven, including clustering chemical and genetic perturbations on the basis of their similar morphological impact, identifying disease phenotypes by observing differences in profiles between healthy and diseased cells and predicting assay outcomes by using machine learning, among many others. Here, we provide an updated protocol for the most popular assay for image-based profiling, Cell Painting. Introduced in 2013, it uses six stains imaged in five channels and labels eight diverse components of the cell: DNA, cytoplasmic RNA, nucleoli, actin, Golgi apparatus, plasma membrane, endoplasmic reticulum and mitochondria. The original protocol was updated in 2016 on the basis of several years' experience running it at two sites, after optimizing it by visual stain quality. Here, we describe the work of the Joint Undertaking for Morphological Profiling Cell Painting Consortium, to improve upon the assay via quantitative optimization by measuring the assay's ability to detect morphological phenotypes and group similar perturbations together. The assay gives very robust outputs despite various changes to the protocol, and two vendors' dyes work equivalently well. We present Cell Painting version 3, in which some steps are simplified and several stain concentrations can be reduced, saving costs. Cell culture and image acquisition take 1-2 weeks for typically sized batches of ≤20 plates; feature extraction and data analysis take an additional 1-2 weeks.This protocol is an update to Nat. Protoc. 11, 1757-1774 (2016): https://doi.org/10.1038/nprot.2016.105.

Build/couple/pair strategy for the synthesis of stereochemically diverse macrolactams via head-to-tail cyclization.

A build/couple/pair (B/C/P) strategy was employed to generate a library of 7936 stereochemically diverse 12-membered macrolactams. All 8 stereoisomers of a common linear amine precursor were elaborated to form the corresponding 8 stereoisomers of two regioisomeric macrocyclic scaffolds via head-to-tail cyclization. Subsequently, these 16 scaffolds were further diversified via capping of two amine functionalities on SynPhase Lanterns. Reagents used for solid-phase diversification were selected using a sparse matrix design strategy with the aim of maximizing coverage of chemical space while adhering to a preset range of physicochemical properties.

Small-Molecule Library Synthesis on Silicon-Functionalized SynPhase Lanterns.

Silicon-functionalized SynPhase Lanterns are useful for the combinatorial synthesis of small-molecule libraries. Lanterns bearing an alkyl tethered diisopropylarylsilane are first activated with triflic acid to afford the corresponding diisopropylsilyl triflate, which is then reacted with a library scaffold bearing a free alcohol. Once the scaffold has been loaded onto the solid phase, a variety of transformations can be run, including amine cappings, cross-coupling reactions and amide bond formation. These reactions can yield a variety of products when run sequentially using split-pool synthesis strategies. Upon completion of the solid-phase transformations, the small-molecules are released from the Lanterns using HF/pyridine. Using the techniques described within, libraries can be made ranging from a few compounds to >10,000 members in a highly efficient manner.