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.
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TL;DR: In this paper, the authors demonstrate feasibility of applying scRNAseq to endovascular biopsy samples and identify a subpopulation of endothelial cells associated with cerebral aneurysm in polyarteritis nodosa.
Abstract: Background and Purpose: Management of unruptured intracranial aneurysms remains controversial. Decisions to treat are heavily informed by estimated risk of bleeding. However, these estimates are imprecise, and better methods for stratifying risk or tailoring treatment strategy are badly needed. Here, we demonstrate an initial proof-of-principle concept for endovascular biopsy to identify key molecular pathways and gene expression changes associated with aneurysm formation. We couple this technique with single cell RNA sequencing (scRNAseq) to develop a roadmap of the pathogenic changes of a dolichoectatic vertebrobasilar aneurysm in a polyarteritis nodosa patient. Methods: Endovascular biopsy and fluorescence activated cell sorting was used to isolate viable endothelial cells (ECs) using established techniques. scRNAseq was then performed on 24 aneurysmal ECs and 23 patient-matched non-aneurysmal ECs. An integrated panel of bioinformatic tools was applied to determine differential gene expression, enriched signaling pathways, and cell subpopulations hypothesized to drive disease pathogenesis. Results: We identify a subset of 7 (29%) aneurysm-specific ECs with a distinct gene expression signature not found in patient-matched control ECs. Gene set enrichment analysis identified these ECs to have increased expression of genes regulating leukocyte-endothelial cell adhesion, major histocompatibility complex (MHC) class I, T cell receptor recycling, tumor necrosis factor alpha (TNFα) response, and interferon gamma signaling. Histopathologic analysis of a different intracranial aneurysm that was later resected yielded a diagnosis of polyarteritis nodosa and positive staining for TNFα. Conclusions: We demonstrate feasibility of applying scRNAseq to endovascular biopsy samples and identify a subpopulation of endothelial cells associated with cerebral aneurysm in polyarteritis nodosa. Endovascular biopsy may be a safe method for deriving insight into disease pathogenesis and tailoring personalized treatment approaches to intracranial aneurysms.
5 citations
TL;DR: An exhaustive, systematic assessment of the breadth and diversity of biological pathways within a corpus of 206,324 dementia publication abstracts suggests that not only is this disorder incredibly complex, but that its functional reach is also nearly global.
Abstract: Alzheimer’s disease (AD) is a complex neurodegenerative disorder. The relative contribution of the numerous underlying functional mechanisms is poorly understood. To comprehensively understand the context and distribution of pathways that contribute to AD, we performed text-mining to generate an exhaustive, systematic assessment of the breadth and diversity of biological pathways within a corpus of 206,324 dementia publication abstracts. A total of 91% (325/335) of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways have publications containing an association via at least 5 studies, while 63% of pathway terms have at least 50 studies providing a clear association with AD. Despite major technological advances, the same set of top-ranked pathways have been consistently related to AD for 30 years, including AD, immune system, metabolic pathways, cholinergic synapse, long-term depression, proteasome, diabetes, cancer, and chemokine signaling. AD pathways studied appear biased: animal model and human subject studies prioritize different AD pathways. Surprisingly, human genetic discoveries and drug targeting are not enriched in the most frequently studied pathways. Our findings suggest that not only is this disorder incredibly complex, but that its functional reach is also nearly global. As a consequence of our study, research results can now be assessed in the context of the wider AD literature, supporting the design of drug therapies that target a broader range of mechanisms. The results of this study can be explored at www.adpathways.org.
5 citations
TL;DR: Timmons et al. as discussed by the authors developed a clinical-based human tissue insulin signature by combining lifestyle-mediated treatment responses (>500 human adipose and muscle biopsies) with biomarkers of disease status.
Abstract: Insulin resistance (IR) contributes to the pathophysiology of diabetes, dementia, viral infection, and cardiovascular disease. Drug repurposing (DR) may identify treatments for IR; however, barriers include uncertainty whether in vitro transcriptomic assays yield quantitative pharmacological data, or how to optimise assay design to best reflect in vivo human disease. We developed a clinical-based human tissue IR signature by combining lifestyle-mediated treatment responses (>500 human adipose and muscle biopsies) with biomarkers of disease status (fasting IR from >1200 biopsies). The assay identified a chemically diverse set of >130 positively acting compounds, highly enriched in true positives, that targeted 73 proteins regulating IR pathways. Our multi-gene RNA assay score reflected the quantitative pharmacological properties of a set of epidermal growth factor receptor-related tyrosine kinase inhibitors, providing insight into drug target specificity; an observation supported by deep learning-based genome-wide predicted pharmacology. Several drugs identified are suitable for evaluation in patients, particularly those with either acute or severe chronic IR.Developing a new drug that is both safe and effective is a complex and expensive endeavor. An alternative approach is to ‘repurpose’ existing, safe compounds – that is, to establish if they could treat conditions others than the ones they were initially designed for. To achieve this, methods that can predict the activity of thousands of established drugs are necessary. These approaches are particularly important for conditions for which it is hard to find promising treatment. This includes, for instance, heart failure, dementia and other diseases that are linked to the activity of the hormone insulin becoming modified throughout the body, a defect called insulin resistance. Unfortunately, it is difficult to model the complex actions of insulin using cells in the lab, because they involve intricate networks of proteins, tissues and metabolites. Timmons et al. set out to develop a way to better assess whether a drug could be repurposed to treat insulin resistance. The aim was to build a biological signature of the disease in multiple human tissues, as this would help to make the findings more relevant to the clinic. This involved examining which genes were switched on or off in thousands of tissue samples from patients with different degrees of insulin resistance. Importantly, some of the patients had their condition reversed through lifestyle changes, while others did not respond well to treatment. These ‘non-responders’ provided crucial new clues to screen for active drugs. Carefully piecing the data together revealed the molecules and pathways most related to the severity of insulin resistance. Cross-referencing these results with the way existing drugs act on gene activity, highlighted 138 compounds that directly bind 73 proteins responsible for regulating insulin resistance pathways. Some of the drugs identified are suitable for short-term clinical studies, and it may even be possible to rank similar compounds based on their chemical activity. Beyond giving a glimpse into the complex molecular mechanisms of insulin resistance in humans, Timmons et al. provide a fresh approach to how drugs could be repurposed, which could be adapted to other conditions.
5 citations
TL;DR: In this article , a large-scale transcriptomic study using 508 wild-type mouse striatal tissue samples dissected exclusively in the afternoons was conducted to define 38 highly reproducible gene coexpression modules, including 13 and 11 modules enriched in cell-type and molecular complex markers, respectively.
5 citations
TL;DR: A method for the quantification of the dose-response potency of a gene-signature as EC50 and IC50 values is introduced and gene-Signature potencies are proposed as a novel valid alternative for the quantitative prioritization, optimization and development of novel drugs.
Abstract: Multiplexed gene-signature-based phenotypic assays are increasingly used for the identification and profiling of small molecule-tool compounds and drugs. Here we introduce a method (provided as R-package) for the quantification of the dose-response potency of a gene-signature as EC50 and IC50 values. Two signaling pathways were used as models to validate our methods: beta-adrenergic agonistic activity on cAMP generation (dedicated dataset generated for this study) and EGFR inhibitory effect on cancer cell viability. In both cases, potencies derived from multi-gene expression data were highly correlated with orthogonal potencies derived from cAMP and cell growth readouts, and superior to potencies derived from single individual genes. Based on our results we propose gene-signature potencies as a novel valid alternative for the quantitative prioritization, optimization and development of novel drugs.
5 citations
References
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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
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
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
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