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. 2017 Jul 24;8(1):105.
doi: 10.1038/s41467-017-00136-z.

PLATE-Seq for Genome-Wide Regulatory Network Analysis of High-Throughput Screens

Free PMC article

PLATE-Seq for Genome-Wide Regulatory Network Analysis of High-Throughput Screens

Erin C Bush et al. Nat Commun. .
Free PMC article


Pharmacological and functional genomic screens play an essential role in the discovery and characterization of therapeutic targets and associated pharmacological inhibitors. Although these screens affect thousands of gene products, the typical readout is based on low complexity rather than genome-wide assays. To address this limitation, we introduce pooled library amplification for transcriptome expression (PLATE-Seq), a low-cost, genome-wide mRNA profiling methodology specifically designed to complement high-throughput screening assays. Introduction of sample-specific barcodes during reverse transcription supports pooled library construction and low-depth sequencing that is 10- to 20-fold less expensive than conventional RNA-Seq. The use of network-based algorithms to infer protein activity from PLATE-Seq data results in comparable reproducibility to 30 M read sequencing. Indeed, PLATE-Seq reproducibility compares favorably to other large-scale perturbational profiling studies such as the connectivity map and library of integrated network-based cellular signatures.Despite the importance of pharmacological and functional genomic screens the readouts are of low complexity. Here the authors introduce PLATE-Seq, a low-cost genome-wide mRNA profiling method to complement high-throughput screening.

Conflict of interest statement

M.J.A. is chief scientific officer of DarwinHealth Inc. A.C. is a founder of DarwinHealth Inc. The remaining authors declare no competing financial interests.


Fig. 1
Fig. 1
Schematic illustration of PLATE-Seq workflow. a After conducting a screen in multi-well plates, we lyse the cells and capture mRNA from the cell lysate using an oligo(dT)-coated capture plate. The purified mRNA is then reverse transcribed with barcoded, adapter-linked olig(dT) primers and the resulting cDNA is pooled. All of these steps are automated. The remaining steps, which take place on a single pooled sample, are conducted manually and include cDNA purification, second-strand synthesis, and PCR enrichment. b Molecular-level schematic for constructing 3′-end PLATE-Seq libraries. After reverse transcription with oligo(dT), second-strand synthesis of the pooled cDNA is accomplished using random hexamer primers prior to PCR enrichment of the barcoded pool
Fig. 2
Fig. 2
PLATE-Seq performance. a Histogram of genes symbols detected per sample for a 96-well PLATE-Seq experiment in BT20 cells. b Histogram of uniquely mapped reads per sample for the experiment in a. c We pooled half of the sample from every six wells for conventional RNA-Seq with 30 M raw reads (Illumina TruSeq). Here we show a histogram of gene symbols detected per sample for each six-well TruSeq pool and for the sum of the corresponding six PLATE-Seq samples. d Same as c for uniquely mapped reads per sample. e Gene detection saturation curve for PLATE-Seq samples based on random subsampling. The points represent the average over all 96 wells and the error bars are deviations s.e.m. f Same as e but for each six-well TruSeq pool and for the sum of the corresponding six PLATE-Seq samples. g MDS clustering of PLATE-Seq and TruSeq samples based on differentially expressed genes identifying using the PLATE-Seq replicates for each drug compared to vehicle control samples. The PLATE-Seq replicates for each drug cluster together and also with the corresponding TruSeq samples. h Heat map showing the top 40 most differentially expressed genes based on PLATE-Seq of mitoxantrone- and idarubicin-treated BT20 cells measured with both PLATE-Seq and TruSeq. The two drugs are both topoisomerase II inhibitors and have similar gene expression signatures. i Same as h but with differentially active proteins as inferred using VIPER. Note that TOP2A, the gene that encodes the target of the two drugs, is strongly deactivated. j Gene expression of differentially active proteins inferred using VIPER. Most of these genes are not differentially expressed and some are difficult to detect with PLATE-Seq, yet VIPER can still infer their activities
Fig. 3
Fig. 3
Comparison of PLATE-Seq to CMap and LINCS. a Mean CV vs. relative mean expression for PLATE-Seq U87 184-compound screen and CMap GPL3921 microarray platform duplicates. b Same as a for PLATE-Seq U87 screen and CMap GPL96 microarray platform. c Same as a for PLATE-Seq U87 screen and LINCS/L1000 screen. d CV histogram across genes for duplicates in the PLATE-Seq U87 screen and CMap GPL3921 microarray platform. e Same as d for PLATE-Seq U87 screen and CMap GPL96 microarray platform. f Same as d for PLATE-Seq U87 screen and LINCS/L1000 screen

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