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Journal ArticleDOI

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

TL;DR: The expanded CMap is reported, made possible by a new, low-cost, high-throughput reduced representation expression profiling method that is shown to be highly reproducible, comparable to RNA sequencing, and suitable for computational inference of the expression levels of 81% of non-measured transcripts.
About: This article is published in Cell.The article was published on 2017-11-30 and is currently open access. It has received 1943 citations till now.
Citations
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Journal ArticleDOI
TL;DR: This work uses CRISPR-Cas9-based intron tagging to generate cell pools expressing hundreds of GFP-fusion proteins from their endogenous promoters and monitors localization changes by time-lapse microscopy followed by clone identification using in situ sequencing to identify nonclassical effects such as modulation of protein-protein interactions, condensate formation, and chemical degradation.
Abstract: The levels and subcellular localizations of proteins regulate critical aspects of many cellular processes and can become targets of therapeutic intervention. However, high-throughput methods for the discovery of proteins that change localization either by shuttling between compartments, by binding larger complexes, or by localizing to distinct membraneless organelles are not available. Here we describe a scalable strategy to characterize effects on protein localizations and levels in response to different perturbations. We use CRISPR-Cas9-based intron tagging to generate cell pools expressing hundreds of GFP-fusion proteins from their endogenous promoters and monitor localization changes by time-lapse microscopy followed by clone identification using in situ sequencing. We show that this strategy can characterize cellular responses to drug treatment and thus identify nonclassical effects such as modulation of protein-protein interactions, condensate formation, and chemical degradation.

9 citations

Journal ArticleDOI
11 Sep 2020-Genes
TL;DR: The enhanced COXEN method is enhanced to select genes that are predictive of the efficacies of multiple drugs for building general drug response prediction models that are not specific to a particular drug.
Abstract: The co-expression extrapolation (COXEN) method has been successfully used in multiple studies to select genes for predicting the response of tumor cells to a specific drug treatment. Here, we enhance the COXEN method to select genes that are predictive of the efficacies of multiple drugs for building general drug response prediction models that are not specific to a particular drug. The enhanced COXEN method first ranks the genes according to their prediction power for each individual drug and then takes a union of top predictive genes of all the drugs, among which the algorithm further selects genes whose co-expression patterns are well preserved between cancer cases for building prediction models. We apply the proposed method on benchmark in vitro drug screening datasets and compare the performance of prediction models built based on the genes selected by the enhanced COXEN method to that of models built on genes selected by the original COXEN method and randomly picked genes. Models built with the enhanced COXEN method always present a statistically significantly improved prediction performance (adjusted p-value ≤ 0.05). Our results demonstrate the enhanced COXEN method can dramatically increase the power of gene expression data for predicting drug response.

9 citations


Cites methods from "A Next Generation Connectivity Map:..."

  • ...In the comparison between the enhanced COXEN gene selection and random genes picked from the LINCS gene set [22], again, models built using the enhanced COXEN method always statistically significantly outperformed models built using randomly picked genes (adjusted p-values ≤ 0....

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  • ...We compared the prediction performance of models built based on genes selected by the enhanced COXEN method to those of prediction models built based on three baseline gene selection methods, including genes randomly selected from all available genes, genes randomly selected from the LINCS gene set that includes 942 “landmark” genes that well represent cellular transcriptomic changes identified in the Library of Integrated Network-Based Cellular Signatures (LINCS) project [22], and genes selected by the original COXEN method that was applied ignoring the drug difference....

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Journal ArticleDOI
TL;DR: In this article, the authors identify ZNF768 as a phosphoprotein destabilized upon RAS activation, which impairs proliferation and induces senescence by modulating the expression of key cell cycle effectors and established p53 targets.
Abstract: RAS proteins are GTPases that lie upstream of a signaling network impacting cell fate determination. How cells integrate RAS activity to balance proliferation and cellular senescence is still incompletely characterized. Here, we identify ZNF768 as a phosphoprotein destabilized upon RAS activation. We report that ZNF768 depletion impairs proliferation and induces senescence by modulating the expression of key cell cycle effectors and established p53 targets. ZNF768 levels decrease in response to replicative-, stress- and oncogene-induced senescence. Interestingly, ZNF768 overexpression contributes to bypass RAS-induced senescence by repressing the p53 pathway. Furthermore, we show that ZNF768 interacts with and represses p53 phosphorylation and activity. Cancer genomics and immunohistochemical analyses reveal that ZNF768 is often amplified and/or overexpressed in tumors, suggesting that cells could use ZNF768 to bypass senescence, sustain proliferation and promote malignant transformation. Thus, we identify ZNF768 as a protein linking oncogenic signaling to the control of cell fate decision and proliferation.

9 citations

Posted ContentDOI
11 Jul 2019-bioRxiv
TL;DR: The combination of population-scale gene expression information with HD patient genomic data identified novel modifier genes for the disorder, including genes that showed evidence for colocalization and replication.
Abstract: Huntington disease (HD) is a neurodegenerative disorder that is caused by a CAG repeat expansion in the HTT gene. In an attempt to identify genomic modifiers that contribute towards the age of onset of HD, we performed a transcriptome wide association study assessing heritable differences in genetically determined expression in diverse tissues, employing genome wide data from over 4,000 patients. This identified genes that showed evidence for colocalization and replication, with downstream functional validation being performed in isogenic HD stem cells and patient brains. Enrichment analyses detected associations with various biologically-relevant gene sets and striatal coexpression modules that are mediated by CAG length. Further, cortical coexpression modules that are relevant for HD onset were also associated with cognitive decline and HD-related traits in a longitudinal cohort. In summary, the combination of population-scale gene expression information with HD patient genomic data identified novel modifier genes for the disorder.

9 citations

Posted ContentDOI
15 May 2020-bioRxiv
TL;DR: The phenotypic response of human bone marrow-derived mesenchymal stem cells is defined by probing basic functions such as migration, proliferation, protein synthesis, apoptosis, and differentiation using quantitative image analysis.
Abstract: Learning rules by which cell shape impacts cell function would enable control of cell physiology and fate in medical applications, particularly, on the interface of cells and material of the implants. We defined the phenotypic response of human bone marrow-derived mesenchymal stem cells (hMSCs) to 2176 randomly generated surface topographies by probing basic functions such as migration, proliferation, protein synthesis, apoptosis, and differentiation using quantitative image analysis. Clustering the surfaces into 28 archetypical cell shapes, we found a very strict correlation between cell shape and physiological response and selected seven cell shapes to describe the molecular mechanism leading to phenotypic diversity. Transcriptomics analysis revealed a tight link between cell shape, molecular signatures, and phenotype. For instance, proliferation is strongly reduced in cells with limited spreading, resulting in down-regulation of genes involved in the G2/M cycle and subsequent quiescence, whereas cells with large filopodia are related to activation of early response genes and inhibition of the osteogenic process. Thus, we have started to unravel the open question of how cell function follows cell shape. This will allow designing implants that can actively regulate cellular, molecular signalling through cell shape. Here we are proposing an approach to tackle this question.

9 citations


Cites background from "A Next Generation Connectivity Map:..."

  • ...Lists of Differentially Expressed Genes (DEGs) per topography were queried in the Connectivity Map (CMap) (Subramanian et al., 2017), a library of gene expression signatures induced by chemical compounds or genetic interference (perturbants), and connectivity scores between the topography-induced DEGs and Connectivity Map DEGs were retrieved....

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  • ...Lists of Differentially Expressed Genes (DEGs) per topography were queried in the Connectivity Map (CMap) (Subramanian et al., 2017), a library of gene expression signatures induced by chemical compounds or genetic interference (perturbants), and connectivity scores between the topography-induced…...

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  • ...The gene signatures related to cell shape and their underlying topographies can be used to browse the Connectivity Map database for genetic signatures that resemble the effect of small molecule or RNAi-induced gene signatures (Subramanian et al., 2017)....

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References
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Journal ArticleDOI
TL;DR: The Gene Set Enrichment Analysis (GSEA) method as discussed by the authors focuses on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation.
Abstract: Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.

34,830 citations

Journal Article
TL;DR: A new technique called t-SNE that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map, a variation of Stochastic Neighbor Embedding that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map.
Abstract: We present a new technique called “t-SNE” that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map. The technique is a variation of Stochastic Neighbor Embedding (Hinton and Roweis, 2002) that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map. t-SNE is better than existing techniques at creating a single map that reveals structure at many different scales. This is particularly important for high-dimensional data that lie on several different, but related, low-dimensional manifolds, such as images of objects from multiple classes seen from multiple viewpoints. For visualizing the structure of very large datasets, we show how t-SNE can use random walks on neighborhood graphs to allow the implicit structure of all of the data to influence the way in which a subset of the data is displayed. We illustrate the performance of t-SNE on a wide variety of datasets and compare it with many other non-parametric visualization techniques, including Sammon mapping, Isomap, and Locally Linear Embedding. The visualizations produced by t-SNE are significantly better than those produced by the other techniques on almost all of the datasets.

30,124 citations

Journal ArticleDOI
TL;DR: The Gene Expression Omnibus (GEO) project was initiated in response to the growing demand for a public repository for high-throughput gene expression data and provides a flexible and open design that facilitates submission, storage and retrieval of heterogeneous data sets from high-power gene expression and genomic hybridization experiments.
Abstract: The Gene Expression Omnibus (GEO) project was initiated in response to the growing demand for a public repository for high-throughput gene expression data. GEO provides a flexible and open design that facilitates submission, storage and retrieval of heterogeneous data sets from high-throughput gene expression and genomic hybridization experiments. GEO is not intended to replace in house gene expression databases that benefit from coherent data sets, and which are constructed to facilitate a particular analytic method, but rather complement these by acting as a tertiary, central data distribution hub. The three central data entities of GEO are platforms, samples and series, and were designed with gene expression and genomic hybridization experiments in mind. A platform is, essentially, a list of probes that define what set of molecules may be detected. A sample describes the set of molecules that are being probed and references a single platform used to generate its molecular abundance data. A series organizes samples into the meaningful data sets which make up an experiment. The GEO repository is publicly accessible through the World Wide Web at http://www.ncbi.nlm.nih.gov/geo.

10,968 citations

Journal ArticleDOI
TL;DR: How BLAT was optimized is described, which is more accurate and 500 times faster than popular existing tools for mRNA/DNA alignments and 50 times faster for protein alignments at sensitivity settings typically used when comparing vertebrate sequences.
Abstract: Analyzing vertebrate genomes requires rapid mRNA/DNA and cross-species protein alignments A new tool, BLAT, is more accurate and 500 times faster than popular existing tools for mRNA/DNA alignments and 50 times faster for protein alignments at sensitivity settings typically used when comparing vertebrate sequences BLAT's speed stems from an index of all nonoverlapping K-mers in the genome This index fits inside the RAM of inexpensive computers, and need only be computed once for each genome assembly BLAT has several major stages It uses the index to find regions in the genome likely to be homologous to the query sequence It performs an alignment between homologous regions It stitches together these aligned regions (often exons) into larger alignments (typically genes) Finally, BLAT revisits small internal exons possibly missed at the first stage and adjusts large gap boundaries that have canonical splice sites where feasible This paper describes how BLAT was optimized Effects on speed and sensitivity are explored for various K-mer sizes, mismatch schemes, and number of required index matches BLAT is compared with other alignment programs on various test sets and then used in several genome-wide applications http://genomeucscedu hosts a web-based BLAT server for the human genome

8,326 citations

Journal ArticleDOI
TL;DR: This paper proposed parametric and non-parametric empirical Bayes frameworks for adjusting data for batch effects that is robust to outliers in small sample sizes and performs comparable to existing methods for large samples.
Abstract: SUMMARY Non-biological experimental variation or “batch effects” are commonly observed across multiple batches of microarray experiments, often rendering the task of combining data from these batches difficult. The ability to combine microarray data sets is advantageous to researchers to increase statistical power to detect biological phenomena from studies where logistical considerations restrict sample size or in studies that require the sequential hybridization of arrays. In general, it is inappropriate to combine data sets without adjusting for batch effects. Methods have been proposed to filter batch effects from data, but these are often complicated and require large batch sizes (>25) to implement. Because the majority of microarray studies are conducted using much smaller sample sizes, existing methods are not sufficient. We propose parametric and non-parametric empirical Bayes frameworks for adjusting data for batch effects that is robust to outliers in small sample sizes and performs comparable to existing methods for large samples. We illustrate our methods using two example data sets and show that our methods are justifiable, easy to apply, and useful in practice. Software for our method is freely available at: http://biosun1.harvard.edu/complab/batch/.

6,319 citations

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