Systematic Quality Control Analysis of LINCS Data

Cheng, Luis; Li, L

doi:10.1002/psp4.12107

Cited by 23 publications

(19 citation statements)

References 35 publications

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“…Importantly, continued quality control of data and use of orthogonal assays is important when applying transcriptomic technologies; L1000 and RNA-seq median platform self-correlation across CCLs and >3000 tissues was reported to be 0.84 33 , 63 . Here, our RNA-seq experiments do not specifically address the fidelity of the L1000 platform, as they were conducted on independently cultured and drug-treated hiPSC NPCs (with the addition of a second independent NPC line differentiated from an independent clonal hiPSC line), using different batches of all critical reagents, reflecting both biological as well as technical effects.…”

Section: Discussionmentioning

confidence: 99%

Expression-based drug screening of neural progenitor cells from individuals with schizophrenia

et al. 2018

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A lack of biologically relevant screening models hinders the discovery of better treatments for schizophrenia (SZ) and other neuropsychiatric disorders. Here we compare the transcriptional responses of 8 commonly used cancer cell lines (CCLs) directly with that of human induced pluripotent stem cell (hiPSC)-derived neural progenitor cells (NPCs) from 12 individuals with SZ and 12 controls across 135 drugs, generating 4320 unique drug-response transcriptional signatures. We identify those drugs that reverse post-mortem SZ-associated transcriptomic signatures, several of which also differentially regulate neuropsychiatric disease-associated genes in a cell type (hiPSC NPC vs. CCL) and/or a diagnosis (SZ vs. control)-dependent manner. Overall, we describe a proof-of-concept application of transcriptomic drug screening to hiPSC-based models, demonstrating that the drug-induced gene expression differences observed with patient-derived hiPSC NPCs are enriched for SZ biology, thereby revealing a major advantage of incorporating cell type and patient-specific platforms in drug discovery.

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Section: Discussionmentioning

confidence: 99%

Expression-based drug screening of neural progenitor cells from individuals with schizophrenia

et al. 2018

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“…The experiments were performed by using the L1000 platform, which aims to capture the greatest amount of variation while measuring only a subset of 978 genes, named "landmark genes" [124]. This subset of genes was chosen since they are able to capture the greatest proportion of the variance in expression [124,135,136].…”

Section: Publicly Available Datasets For Toxicogenomicsmentioning

confidence: 99%

Transcriptomics in Toxicogenomics, Part I: Experimental Design, Technologies, Publicly Available Data, and Regulatory Aspects

et al. 2020

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The starting point of successful hazard assessment is the generation of unbiased and trustworthy data. Conventional toxicity testing deals with extensive observations of phenotypic endpoints in vivo and complementing in vitro models. The increasing development of novel materials and chemical compounds dictates the need for a better understanding of the molecular changes occurring in exposed biological systems. Transcriptomics enables the exploration of organisms' responses to environmental, chemical, and physical agents by observing the molecular alterations in more detail. Toxicogenomics integrates classical toxicology with omics assays, thus allowing the characterization of the mechanism of action (MOA) of chemical compounds, novel small molecules, and engineered nanomaterials (ENMs). Lack of standardization in data generation and analysis currently hampers the full exploitation of toxicogenomics-based evidence in risk assessment. To fill this gap, TGx methods need to take into account appropriate experimental design and possible pitfalls in the transcriptomic analyses as well as data generation and sharing that adhere to the FAIR Nanomaterials 2020, 10, 750 2 of 23 recent advancements in the design and analysis of DNA microarray, RNA sequencing (RNA-Seq), and single-cell RNA-Seq (scRNA-Seq) data. We provide guidelines on exposure time, dose and complex endpoint selection, sample quality considerations and sample randomization. Furthermore, we summarize publicly available data resources and highlight applications of TGx data to understand and predict chemical toxicity potential. Additionally, we discuss the efforts to implement TGx into regulatory decision making to promote alternative methods for risk assessment and to support the 3R (reduction, refinement, and replacement) concept. This review is the first part of a three-article series on Transcriptomics in Toxicogenomics. These initial considerations on Experimental Design, Technologies, Publicly Available Data, Regulatory Aspects, are the starting point for further rigorous and reliable data preprocessing and modeling, described in the second and third part of the review series.

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“…Advanced bioinformatics analyses. We also used the Library of Integrated Networkbased Cellular Signatures (LINCS) (http://ilincs.org) to analyze the connectivity of region specific PSD-95 protein interactome patterns [41][42][43][44][45][46][47]. With the goal of identifying connected gene knockdown and small drug-like molecule signatures, we used iLINCS to first retrieve transcriptional signatures of gene knockdowns for genes contributing to HPC versus ACC specific patterns.…”

Section: Clustering Analyses and Generationmentioning

confidence: 99%

Region-Specific PSD-95 Interactomes Contribute to Functional Diversity of Excitatory Synapses in Human Brain

Funk

Labilloy

Reigle

et al. 2020

Preprint

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Running title -PSD-95 protein-protein interactions in human brainThe overarching goal of this exploratory study is to link subcellular microdomain specific protein-protein interactomes with big data analytics. We isolated postsynaptic density-95 (PSD-95) complexes from four human brain regions and compared their protein interactomes using multiple bioinformatics techniques. We demonstrate that human brain regions have unique postsynaptic protein signatures that may be used to interrogate perturbagen databases. Assessment of our hippocampal signature using the iLINCS database yielded several compounds with recently characterized "off target" effects on protein-protein interactions in the posynaptic density compartment. Acknowledgments:We would like to thank all those that have supported this research: the L.I.F.E. Foundation, UCNI pilot award, Lindsay Brinkmeyer Schizophrenia Research Fund, Center for Clinical and Translational Science Training (CCTST), and NIH grant MH107916. The MS data was acquired in the University of Cincinnati Proteomics Laboratory on a mass spectrometer funded in part through the NIH S10 shared instrumentation grant RR027015.Altering the expression of PSD-95 protein significantly changes synaptic structure and function, including molecular correlates of learning and memory, long-term potentiation (LTP) and long-term depression (LTD). For example, PSD-95 overexpression increases the amplitude of excitatory postsynaptic currents (EPSCs), to the extent that additional stimulus is unable to further strengthen LTP [13][14][15][16][17]. In contrast, PSD-95 knockdown disrupts postsynaptic density structure and suppresses EPSCs, consistent with a role for PSD-95 in synaptic fidelity [18][19][20]. Further highlighting the importance of PSD-95 in the synapse, knockout of PSD-95 in mice and genomic variants within postsynaptic density hub proteins are particularly implicated in neuropsychiatric disorders such as schizophrenia and autism [21,22].To investigate excitatory postsynaptic protein hubs, we targeted PSD-95 due to its high level of localization to the postsynaptic density (~95%), its well-characterized role as a hub protein, and the large number of constituents in its protein interactome (~1000) [23][24][25]. We designed a series of experiments to examine brain region-specific excitatory postsynaptic proteomes to answer the following questions. 1) Do postsynaptic protein-protein interactions differ by brain region? 2) Can we accurately detect and separate samples based on these differences? and 3) Can region-specific protein interactomes inform functional differences? For these experiments we used four human brain regions (anterior cingulate cortex (ACC), dorsolateral prefrontal cortex (DLPFC), hippocampus (HPC), and superior temporal gyrus (STG)) from 3 well-matched control male subjects from the Alabama Brain Collection. A total of 289 proteins were identified as part of a PSD-95 protein interactome that were present across all four brain regions.We hypothesize that there is region-specific ...

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Systematic Quality Control Analysis of LINCS Data

Cited by 23 publications

References 35 publications

Expression-based drug screening of neural progenitor cells from individuals with schizophrenia

Expression-based drug screening of neural progenitor cells from individuals with schizophrenia

Transcriptomics in Toxicogenomics, Part I: Experimental Design, Technologies, Publicly Available Data, and Regulatory Aspects

Region-Specific PSD-95 Interactomes Contribute to Functional Diversity of Excitatory Synapses in Human Brain

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