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Shapira Lots I, Alroy I. Virtual plates: Getting the best out of high content screens. SLAS DISCOVERY : ADVANCING LIFE SCIENCES R & D 2024; 29:77-85. [PMID: 38036292 DOI: 10.1016/j.slasd.2023.11.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 11/08/2023] [Accepted: 11/27/2023] [Indexed: 12/02/2023]
Abstract
High content screening (HCS) is becoming widely adopted as a high throughput screening modality, using hundred-of-thousands compounds library. The use of machine learning and artificial intelligence in image analysis is amplifying this trend. Another factor is the recognition that diverse cell phenotypes can be associated with changes in biological pathways relevant to disease processes. There are numerous challenges in HCS campaigns. These include limited ability to support replicates, low availability of precious and unique cells or reagents, high number of experimental batches, lengthy preparation of cells for imaging, image acquisition time (45-60 min per plate) and image processing time, deterioration of image quality with time post cell fixation and variability within wells and batches. To take advantage of the data in HCS, cell population based rather than well-based analyses are required. Historically, statistical analysis and hypothesis testing played only a limited role in non-high content high throughput campaigns. Thus, only a limited number of standard statistical criteria for hit selection in HCS have been developed so far. In addition to complex biological content in HCS campaigns, additional variability is impacted by cell and reagent handling and by instruments which may malfunction or perform unevenly. Together these can cause a significant number of wells or plates to fail. Here we describe an automated approach for hit analysis and detection in HCS. Our approach automates HCS hit detection using a methodology that is based on a documented statistical framework. We introduce the Virtual Plate concept in which selected wells from different plates are collated into a new, virtual plate. This allows the rescue and analysis of compound wells which have failed due to technical issues as well as to collect hit wells into one plate, allowing the user easier access to the hit data.
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Affiliation(s)
| | - Iris Alroy
- Anima Biotech Ltd., 2 Shoham St., Ramat Gan 5251003, Israel
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2
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Ajetunmobi OH, Wall G, Bonifacio BV, Montelongo-Jauregui D, Lopez-Ribot JL. A 384-Well Microtiter Plate Model for Candida Biofilm Formation and Its Application to High-Throughput Screening. Methods Mol Biol 2023; 2658:53-64. [PMID: 37024695 DOI: 10.1007/978-1-0716-3155-3_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/08/2023]
Abstract
Candidiasis, infections caused by Candida spp., represents one of the most common nosocomial infections afflicting an expanding number of compromised patients. Antifungal therapeutic options are few and show limited efficacy. Moreover, biofilm formation is frequently associated with different manifestations of candidiasis and further complicates therapy. Thus, there is an urgent need for new effective therapeutic agents, particularly those with anti-biofilm activity. Here we describe the development of a novel, simple, fast, economical, and highly reproducible 384-well microtiter plate model for the formation of both Candida albicans and Candida auris biofilms and its application in high-throughput screening (HTS) techniques.
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Affiliation(s)
- Olabayo H Ajetunmobi
- Department of Molecular Microbiology and Immunology, and South Texas Center for Emerging Infectious Diseases, The University of Texas at San Antonio, San Antonio, TX, USA
| | - Gina Wall
- Department of Molecular Microbiology and Immunology, and South Texas Center for Emerging Infectious Diseases, The University of Texas at San Antonio, San Antonio, TX, USA
| | - Bruna V Bonifacio
- Department of Molecular Microbiology and Immunology, and South Texas Center for Emerging Infectious Diseases, The University of Texas at San Antonio, San Antonio, TX, USA
| | - Daniel Montelongo-Jauregui
- Department of Molecular Microbiology and Immunology, and South Texas Center for Emerging Infectious Diseases, The University of Texas at San Antonio, San Antonio, TX, USA
| | - Jose L Lopez-Ribot
- Department of Molecular Microbiology and Immunology, and South Texas Center for Emerging Infectious Diseases, The University of Texas at San Antonio, San Antonio, TX, USA.
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3
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Tansey W, Tosh C, Blei DM. A Bayesian model of dose-response for cancer drug studies. Ann Appl Stat 2022. [DOI: 10.1214/21-aoas1485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Wesley Tansey
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center
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4
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Mazoure B, Mazoure A, Bédard J, Makarenkov V. DUNEScan: a web server for uncertainty estimation in skin cancer detection with deep neural networks. Sci Rep 2022; 12:179. [PMID: 34996997 PMCID: PMC8741961 DOI: 10.1038/s41598-021-03889-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 12/10/2021] [Indexed: 11/16/2022] Open
Abstract
Recent years have seen a steep rise in the number of skin cancer detection applications. While modern advances in deep learning made possible reaching new heights in terms of classification accuracy, no publicly available skin cancer detection software provide confidence estimates for these predictions. We present DUNEScan (Deep Uncertainty Estimation for Skin Cancer), a web server that performs an intuitive in-depth analysis of uncertainty in commonly used skin cancer classification models based on convolutional neural networks (CNNs). DUNEScan allows users to upload a skin lesion image, and quickly compares the mean and the variance estimates provided by a number of new and traditional CNN models. Moreover, our web server uses the Grad-CAM and UMAP algorithms to visualize the classification manifold for the user’s input, hence providing crucial information about its closeness to skin lesion images from the popular ISIC database. DUNEScan is freely available at: https://www.dunescan.org.
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Affiliation(s)
- Bogdan Mazoure
- School of Computer Science, McGill University and Quebec AI Institute (MILA), Montreal, Canada.
| | | | - Jocelyn Bédard
- Département d'Informatique, Université du Québec à Montréal, Montreal, Canada
| | - Vladimir Makarenkov
- Département d'Informatique, Université du Québec à Montréal, Montreal, Canada
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5
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Organoids in image-based phenotypic chemical screens. Exp Mol Med 2021; 53:1495-1502. [PMID: 34663938 PMCID: PMC8569209 DOI: 10.1038/s12276-021-00641-8] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 04/08/2021] [Accepted: 05/04/2021] [Indexed: 12/18/2022] Open
Abstract
Image-based phenotypic screening relies on the extraction of multivariate information from cells cultured under a large variety of conditions. Technical advances in high-throughput microscopy enable screening in increasingly complex and biologically relevant model systems. To this end, organoids hold great potential for high-content screening because they recapitulate many aspects of parent tissues and can be derived from patient material. However, screening is substantially more difficult in organoids than in classical cell lines from both technical and analytical standpoints. In this review, we present an overview of studies employing organoids for screening applications. We discuss the promises and challenges of small-molecule treatments in organoids and give practical advice on designing, running, and analyzing high-content organoid-based phenotypic screens.
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6
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Normandeau F, Ng A, Beaugrand M, Juncker D. Spatial Bias in Antibody Microarrays May Be an Underappreciated Source of Variability. ACS Sens 2021; 6:1796-1806. [PMID: 33973474 DOI: 10.1021/acssensors.0c02613] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Antibody microarrays enable multiplexed protein detection with minimal reagent consumption, but they continue to be plagued by lack of reproducibility. Chemically functionalized glass slides are used as substrates, yet antibody binding spatial inhomogeneity across the slide has not been analyzed in antibody microarrays. Here, we characterize spatial bias across five commercial slides patterned with nine overlapping dense arrays (by combining three buffers and three different antibodies), and we measure signal variation for both antibody immobilization and the assay signal, generating 270 heatmaps. Spatial bias varied across models, and the coefficient of variation ranged from 4.6 to 50%, which was unexpectedly large. Next, we evaluated three layouts of spot replicates-local, random, and structured random-for their capacity to predict assay variation. Local replicates are widely used but systematically underestimate the whole-slide variation by up to seven times; structured random replicates gave the most accurate estimation. Our results highlight the risk and consequences of using local replicates: the underappreciation of spatial bias as a source of variability, poor assay reproducibility, and possible overconfidence in assay results. We recommend the detailed characterization of spatial bias for antibody microarrays and the description and use of distributed positive replicates for research and clinical applications.
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Affiliation(s)
- Frédéric Normandeau
- McGill Genome Centre, McGill University, Montreal, Quebec H3A 0G1, Canada
- Department of Biomedical Engineering, McGill University, Montreal, Quebec H3A 2B4, Canada
| | - Andy Ng
- McGill Genome Centre, McGill University, Montreal, Quebec H3A 0G1, Canada
- Department of Biomedical Engineering, McGill University, Montreal, Quebec H3A 2B4, Canada
| | - Maiwenn Beaugrand
- McGill Genome Centre, McGill University, Montreal, Quebec H3A 0G1, Canada
- Department of Biomedical Engineering, McGill University, Montreal, Quebec H3A 2B4, Canada
| | - David Juncker
- McGill Genome Centre, McGill University, Montreal, Quebec H3A 0G1, Canada
- Department of Biomedical Engineering, McGill University, Montreal, Quebec H3A 2B4, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec H3A 2B4, Canada
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7
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Tansey W, Li K, Zhang H, Linderman SW, Rabadan R, Blei DM, Wiggins CH. Dose-response modeling in high-throughput cancer drug screenings: an end-to-end approach. Biostatistics 2021; 23:643-665. [PMID: 33417699 DOI: 10.1093/biostatistics/kxaa047] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2019] [Revised: 09/25/2020] [Accepted: 09/29/2020] [Indexed: 12/18/2022] Open
Abstract
Personalized cancer treatments based on the molecular profile of a patient's tumor are an emerging and exciting class of treatments in oncology. As genomic tumor profiling is becoming more common, targeted treatments for specific molecular alterations are gaining traction. To discover new potential therapeutics that may apply to broad classes of tumors matching some molecular pattern, experimentalists and pharmacologists rely on high-throughput, in vitro screens of many compounds against many different cell lines. We propose a hierarchical Bayesian model of how cancer cell lines respond to drugs in these experiments and develop a method for fitting the model to real-world high-throughput screening data. Through a case study, the model is shown to capture nontrivial associations between molecular features and drug response, such as requiring both wild type TP53 and overexpression of MDM2 to be sensitive to Nutlin-3(a). In quantitative benchmarks, the model outperforms a standard approach in biology, with $\approx20\%$ lower predictive error on held out data. When combined with a conditional randomization testing procedure, the model discovers markers of therapeutic response that recapitulate known biology and suggest new avenues for investigation. All code for the article is publicly available at https://github.com/tansey/deep-dose-response.
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Affiliation(s)
- Wesley Tansey
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, NewYork, NY, USA
| | - Kathy Li
- Data Science Institute, Columbia University and Columbia University Medical Center, New York, NY, USA and Applied Physics and Applied Mathematics, Columbia University and Columbia University Medical Center, New York, NY, USA
| | - Haoran Zhang
- Applied Physics and Applied Mathematics, Columbia University and Columbia University Medical Center, New York, NY, USA
| | - Scott W Linderman
- Data Science Institute, Columbia University and Columbia University Medical Center, New York, NY, USA and Department of Statistics, Columbia University and Columbia University Medical Center, New York, NY, USA
| | - Raul Rabadan
- Department of Systems Biology, Columbia University and Columbia University Medical Center, New York, NY, USA
| | - David M Blei
- Data Science Institute, Columbia University and Columbia University Medical Center, New York, NY, USA, Department of Statistics, Columbia University and Columbia University Medical Center, New York, NY, USA and Department of Statistics, Columbia University and Columbia University Medical Center, New York, NY, USA
| | - Chris H Wiggins
- Data Science Institute, Columbia University and Columbia University Medical Center, New York, NY, USA, Department of Applied Physics and Applied Mathematics, Columbia University and Columbia University Medical Center, New York, NY, USA and Department of Systems Biology, Columbia University and Columbia University Medical Center, New York, NY, USA
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8
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Goodwin S, Shahtahmassebi G, Hanley QS. Statistical models for identifying frequent hitters in high throughput screening. Sci Rep 2020; 10:17200. [PMID: 33057035 PMCID: PMC7560657 DOI: 10.1038/s41598-020-74139-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Accepted: 09/28/2020] [Indexed: 12/12/2022] Open
Abstract
High throughput screening (HTS) interrogates compound libraries to find those that are “active” in an assay. To better understand compound behavior in HTS, we assessed an existing binomial survivor function (BSF) model of “frequent hitters” using 872 publicly available HTS data sets. We found large numbers of “infrequent hitters” using this model leading us to reject the BSF for identifying “frequent hitters.” As alternatives, we investigated generalized logistic, gamma, and negative binomial distributions as models for compound behavior. The gamma model reduced the proportion of both frequent and infrequent hitters relative to the BSF. Within this data set, conclusions about individual compound behavior were limited by the number of times individual compounds were tested (1–1613 times) and disproportionate testing of some compounds. Specifically, most tests (78%) were on a 309,847-compound subset (17.6% of compounds) each tested ≥ 300 times. We concluded that the disproportionate retesting of some compounds represents compound repurposing at scale rather than drug discovery. The approach to drug discovery represented by these 872 data sets characterizes the assays well by challenging them with many compounds while each compound is characterized poorly with a single assay. Aggregating the testing information from each compound across the multiple screens yielded a continuum with no clear boundary between normal and frequent hitting compounds.
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Affiliation(s)
- Samuel Goodwin
- School of Science and Technology, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, UK
| | - Golnaz Shahtahmassebi
- School of Science and Technology, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, UK
| | - Quentin S Hanley
- School of Science and Technology, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, UK.
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9
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Geldert A, Huang H, Herr AE. Probe-target hybridization depends on spatial uniformity of initial concentration condition across large-format chips. Sci Rep 2020; 10:8768. [PMID: 32472029 PMCID: PMC7260366 DOI: 10.1038/s41598-020-65563-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Accepted: 04/23/2020] [Indexed: 12/30/2022] Open
Abstract
Diverse assays spanning from immunohistochemistry (IHC), to microarrays (protein, DNA), to high-throughput screens rely on probe-target hybridization to detect analytes. These large-format 'chips' array numerous hybridization sites across centimeter-scale areas. However, the reactions are prone to intra-assay spatial variation in hybridization efficiency. The mechanism of spatial bias in hybridization efficiency is poorly understood, particularly in IHC and in-gel immunoassays, where immobilized targets are heterogeneously distributed throughout a tissue or hydrogel network. In these systems, antibody probe hybridization to a target protein antigen depends on the interplay of dilution, thermodynamic partitioning, diffusion, and reaction. Here, we investigate parameters governing antibody probe transport and reaction (i.e., immunoprobing) in a large-format hydrogel immunoassay. Using transport and bimolecular binding theory, we identify a regime in which immunoprobing efficiency (η) is sensitive to the local concentration of applied antibody probe solution, despite the antibody probe being in excess compared to antigen. Sandwiching antibody probe solution against the hydrogel surface yields spatially nonuniform dilution. Using photopatterned fluorescent protein targets and a single-cell immunoassay, we identify regimes in which nonuniformly distributed antibody probe solution causes intra-assay variation in background and η. Understanding the physicochemical factors affecting probe-target hybridization reduces technical variation in large-format chips, improving measurement precision.
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Affiliation(s)
- Alisha Geldert
- UC Berkeley - UCSF Graduate Program in Bioengineering, Berkeley, United States
| | - Haiyan Huang
- Department of Statistics, University of California Berkeley, Berkeley, California, 94720, United States
- Center for Computational Biology, University of California Berkeley, Berkeley, California, 94720, United States
| | - Amy E Herr
- UC Berkeley - UCSF Graduate Program in Bioengineering, Berkeley, United States.
- Department of Bioengineering, University of California Berkeley, Berkeley, California, 94720, United States.
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10
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Niepel M, Hafner M, Mills CE, Subramanian K, Williams EH, Chung M, Gaudio B, Barrette AM, Stern AD, Hu B, Korkola JE, Gray JW, Birtwistle MR, Heiser LM, Sorger PK. A Multi-center Study on the Reproducibility of Drug-Response Assays in Mammalian Cell Lines. Cell Syst 2019; 9:35-48.e5. [PMID: 31302153 PMCID: PMC6700527 DOI: 10.1016/j.cels.2019.06.005] [Citation(s) in RCA: 66] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2018] [Revised: 02/01/2019] [Accepted: 06/12/2019] [Indexed: 12/18/2022]
Abstract
Evidence that some high-impact biomedical results cannot be repeated has stimulated interest in practices that generate findable, accessible, interoperable, and reusable (FAIR) data. Multiple papers have identified specific examples of irreproducibility, but practical ways to make data more reproducible have not been widely studied. Here, five research centers in the NIH LINCS Program Consortium investigate the reproducibility of a prototypical perturbational assay: quantifying the responsiveness of cultured cells to anti-cancer drugs. Such assays are important for drug development, studying cellular networks, and patient stratification. While many experimental and computational factors impact intra- and inter-center reproducibility, the factors most difficult to identify and control are those with a strong dependency on biological context. These factors often vary in magnitude with the drug being analyzed and with growth conditions. We provide ways to identify such context-sensitive factors, thereby improving both the theory and practice of reproducible cell-based assays. Factors that impact the reproducibility of experimental data are poorly understood. Five NIH-LINCS centers performed the same set of drug-response measurements and compared results. Technical and biological variables that impact precision and reproducibility and are also sensitive to biological context were the most problematic.
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Affiliation(s)
- Mario Niepel
- Laboratory of Systems Pharmacology, HMS LINCS Center, Harvard Medical School, Boston, MA 02115, USA
| | - Marc Hafner
- Laboratory of Systems Pharmacology, HMS LINCS Center, Harvard Medical School, Boston, MA 02115, USA
| | - Caitlin E Mills
- Laboratory of Systems Pharmacology, HMS LINCS Center, Harvard Medical School, Boston, MA 02115, USA
| | - Kartik Subramanian
- Laboratory of Systems Pharmacology, HMS LINCS Center, Harvard Medical School, Boston, MA 02115, USA
| | - Elizabeth H Williams
- Laboratory of Systems Pharmacology, HMS LINCS Center, Harvard Medical School, Boston, MA 02115, USA
| | - Mirra Chung
- Laboratory of Systems Pharmacology, HMS LINCS Center, Harvard Medical School, Boston, MA 02115, USA
| | - Benjamin Gaudio
- Laboratory of Systems Pharmacology, HMS LINCS Center, Harvard Medical School, Boston, MA 02115, USA
| | - Anne Marie Barrette
- Department of Pharmacological Sciences, Drug Toxicity Signature Generation (DToxS) LINCS Center, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Alan D Stern
- Department of Pharmacological Sciences, Drug Toxicity Signature Generation (DToxS) LINCS Center, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Bin Hu
- Department of Pharmacological Sciences, Drug Toxicity Signature Generation (DToxS) LINCS Center, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - James E Korkola
- Microenvironment Perturbagen (MEP) LINCS Center, OHSU Center for Spatial Systems Biomedicine, Oregon Health & Sciences University, Portland, OR 97201, USA
| | - Joe W Gray
- Microenvironment Perturbagen (MEP) LINCS Center, OHSU Center for Spatial Systems Biomedicine, Oregon Health & Sciences University, Portland, OR 97201, USA
| | - Marc R Birtwistle
- Department of Pharmacological Sciences, Drug Toxicity Signature Generation (DToxS) LINCS Center, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA.
| | - Laura M Heiser
- Microenvironment Perturbagen (MEP) LINCS Center, OHSU Center for Spatial Systems Biomedicine, Oregon Health & Sciences University, Portland, OR 97201, USA.
| | - Peter K Sorger
- Laboratory of Systems Pharmacology, HMS LINCS Center, Harvard Medical School, Boston, MA 02115, USA.
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11
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The Distribution of Standard Deviations Applied to High Throughput Screening. Sci Rep 2019; 9:1268. [PMID: 30718587 PMCID: PMC6361996 DOI: 10.1038/s41598-018-36722-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2018] [Accepted: 11/24/2018] [Indexed: 12/14/2022] Open
Abstract
High throughput screening (HTS) assesses compound libraries for “activity” using target assays. A subset of HTS data contains a large number of sample measurements replicated a small number of times providing an opportunity to introduce the distribution of standard deviations (DSD). Applying the DSD to some HTS data sets revealed signs of bias in some of the data and discovered a sub-population of compounds exhibiting high variability which may be difficult to screen. In the data examined, 21% of 1189 such compounds were pan-assay interference compounds. This proportion reached 57% for the most closely related compounds within the sub-population. Using the DSD, large HTS data sets can be modelled in many cases as two distributions: a large group of nearly normally distributed “inactive” compounds and a residual distribution of “active” compounds. The latter were not normally distributed, overlapped inactive distributions – on both sides –, and were larger than typically assumed. As such, a large number of compounds are being misclassified as “inactive” or are invisible to current methods which could become the next generation of drugs. Although applied here to HTS, it is applicable to data sets with a large number of samples measured a small number of times.
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12
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Mazoure B, Caraus I, Nadon R, Makarenkov V. Identification and Correction of Additive and Multiplicative Spatial Biases in Experimental High-Throughput Screening. SLAS DISCOVERY 2018; 23:448-458. [PMID: 29346010 DOI: 10.1177/2472555217750377] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Data generated by high-throughput screening (HTS) technologies are prone to spatial bias. Traditionally, bias correction methods used in HTS assume either a simple additive or, more recently, a simple multiplicative spatial bias model. These models do not, however, always provide an accurate correction of measurements in wells located at the intersection of rows and columns affected by spatial bias. The measurements in these wells depend on the nature of interaction between the involved biases. Here, we propose two novel additive and two novel multiplicative spatial bias models accounting for different types of bias interactions. We describe a statistical procedure that allows for detecting and removing different types of additive and multiplicative spatial biases from multiwell plates. We show how this procedure can be applied by analyzing data generated by the four HTS technologies (homogeneous, microorganism, cell-based, and gene expression HTS), the three high-content screening (HCS) technologies (area, intensity, and cell-count HCS), and the only small-molecule microarray technology available in the ChemBank small-molecule screening database. The proposed methods are included in the AssayCorrector program, implemented in R, and available on CRAN.
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Affiliation(s)
- Bogdan Mazoure
- 1 Département d'Informatique, Université du Québec à Montréal, Montréal, QC, Canada.,2 McGill University and Genome Quebec Innovation Centre, Montréal, QC, Canada
| | - Iurie Caraus
- 1 Département d'Informatique, Université du Québec à Montréal, Montréal, QC, Canada.,2 McGill University and Genome Quebec Innovation Centre, Montréal, QC, Canada
| | - Robert Nadon
- 2 McGill University and Genome Quebec Innovation Centre, Montréal, QC, Canada.,3 Department of Human Genetics, McGill University, Montréal, QC, Canada
| | - Vladimir Makarenkov
- 1 Département d'Informatique, Université du Québec à Montréal, Montréal, QC, Canada
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