1
|
Pearson YE, Kremb S, Butterfoss GL, Xie X, Fahs H, Gunsalus KC. A statistical framework for high-content phenotypic profiling using cellular feature distributions. Commun Biol 2022; 5:1409. [PMID: 36550289 PMCID: PMC9780213 DOI: 10.1038/s42003-022-04343-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 12/05/2022] [Indexed: 12/24/2022] Open
Abstract
High-content screening (HCS) uses microscopy images to generate phenotypic profiles of cell morphological data in high-dimensional feature space. While HCS provides detailed cytological information at single-cell resolution, these complex datasets are usually aggregated into summary statistics that do not leverage patterns of biological variability within cell populations. Here we present a broad-spectrum HCS analysis system that measures image-based cell features from 10 cellular compartments across multiple assay panels. We introduce quality control measures and statistical strategies to streamline and harmonize the data analysis workflow, including positional and plate effect detection, biological replicates analysis and feature reduction. We also demonstrate that the Wasserstein distance metric is superior over other measures to detect differences between cell feature distributions. With this workflow, we define per-dose phenotypic fingerprints for 65 mechanistically diverse compounds, provide phenotypic path visualizations for each compound and classify compounds into different activity groups.
Collapse
Affiliation(s)
- Yanthe E. Pearson
- grid.440573.10000 0004 1755 5934Center for Genomics and Systems Biology, New York University Abu Dhabi, P. O. Box 129188, Abu Dhabi, UAE
| | - Stephan Kremb
- grid.440573.10000 0004 1755 5934Center for Genomics and Systems Biology, New York University Abu Dhabi, P. O. Box 129188, Abu Dhabi, UAE
| | - Glenn L. Butterfoss
- grid.440573.10000 0004 1755 5934Center for Genomics and Systems Biology, New York University Abu Dhabi, P. O. Box 129188, Abu Dhabi, UAE
| | - Xin Xie
- grid.440573.10000 0004 1755 5934Center for Genomics and Systems Biology, New York University Abu Dhabi, P. O. Box 129188, Abu Dhabi, UAE
| | - Hala Fahs
- grid.440573.10000 0004 1755 5934Center for Genomics and Systems Biology, New York University Abu Dhabi, P. O. Box 129188, Abu Dhabi, UAE
| | - Kristin C. Gunsalus
- grid.440573.10000 0004 1755 5934Center for Genomics and Systems Biology, New York University Abu Dhabi, P. O. Box 129188, Abu Dhabi, UAE ,grid.137628.90000 0004 1936 8753Department of Biology and Center for Genomics and Systems Biology, New York University, New York, NY 10003 USA
| |
Collapse
|
2
|
GRAN3SAT: Creating Flexible Higher-Order Logic Satisfiability in the Discrete Hopfield Neural Network. MATHEMATICS 2022. [DOI: 10.3390/math10111899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
One of the main problems in representing information in the form of nonsystematic logic is the lack of flexibility, which leads to potential overfitting. Although nonsystematic logic improves the representation of the conventional k Satisfiability, the formulations of the first, second, and third-order logical structures are very predictable. This paper proposed a novel higher-order logical structure, named G-Type Random k Satisfiability, by capitalizing the new random feature of the first, second, and third-order clauses. The proposed logic was implemented into the Discrete Hopfield Neural Network as a symbolic logical rule. The proposed logic in Discrete Hopfield Neural Networks was evaluated using different parameter settings, such as different orders of clauses, different proportions between positive and negative literals, relaxation, and differing numbers of learning trials. Each evaluation utilized various performance metrics, such as learning error, testing error, weight error, energy analysis, and similarity analysis. In addition, the flexibility of the proposed logic was compared with current state-of-the-art logic rules. Based on the simulation, the proposed logic was reported to be more flexible, and produced higher solution diversity.
Collapse
|
3
|
Rezvani A, Bigverdi M, Rohban MH. Image-based cell profiling enhancement via data cleaning methods. PLoS One 2022; 17:e0267280. [PMID: 35507559 PMCID: PMC9067647 DOI: 10.1371/journal.pone.0267280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 04/06/2022] [Indexed: 11/18/2022] Open
Abstract
With the advent of high-throughput assays, a large number of biological experiments can be carried out. Image-based assays are among the most accessible and inexpensive technologies for this purpose. Indeed, these assays have proved to be effective in characterizing unknown functions of genes and small molecules. Image analysis pipelines have a pivotal role in translating raw images that are captured in such assays into useful and compact representation, also known as measurements. CellProfiler is a popular and commonly used tool for this purpose through providing readily available modules for the cell/nuclei segmentation, and making various measurements, or features, for each cell/nuclei. Single cell features are then aggregated for each treatment replica to form treatment “profiles”. However, there may be several sources of error in the CellProfiler quantification pipeline that affects the downstream analysis that is performed on the profiles. In this work, we examined various preprocessing approaches to improve the profiles. We consider the identification of drug mechanisms of action as the downstream task to evaluate such preprocessing approaches. Our enhancement steps mainly consist of data cleaning, cell level outlier detection, toxic drug detection, and regressing out the cell area from all other features, as many of them are widely affected by the cell area. Our experiments indicate that by performing these time-efficient preprocessing steps, image-based profiles can preserve more meaningful information compared to raw profiles. In the end, we also suggest possible avenues for future research.
Collapse
Affiliation(s)
- Arghavan Rezvani
- Department of Computer Engineering, Sharif University of Technology, Tehran, Tehran, Iran
| | - Mahtab Bigverdi
- Department of Computer Engineering, Sharif University of Technology, Tehran, Tehran, Iran
| | - Mohammad Hossein Rohban
- Department of Computer Engineering, Sharif University of Technology, Tehran, Tehran, Iran
- * E-mail:
| |
Collapse
|
4
|
Hagemann C, Tyzack GE, Taha DM, Devine H, Greensmith L, Newcombe J, Patani R, Serio A, Luisier R. Automated and unbiased discrimination of ALS from control tissue at single cell resolution. Brain Pathol 2021; 31:e12937. [PMID: 33576079 PMCID: PMC8412073 DOI: 10.1111/bpa.12937] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Revised: 12/21/2020] [Accepted: 01/07/2021] [Indexed: 12/27/2022] Open
Abstract
Histopathological analysis of tissue sections is invaluable in neurodegeneration research. However, cell-to-cell variation in both the presence and severity of a given phenotype is a key limitation of this approach, reducing the signal to noise ratio and leaving unresolved the potential of single-cell scoring for a given disease attribute. Here, we tested different machine learning methods to analyse high-content microscopy measurements of hundreds of motor neurons (MNs) from amyotrophic lateral sclerosis (ALS) post-mortem tissue sections. Furthermore, we automated the identification of phenotypically distinct MN subpopulations in VCP- and SOD1-mutant transgenic mice, revealing common morphological cellular phenotypes. Additionally we established scoring metrics to rank cells and tissue samples for both disease probability and severity. By adapting this paradigm to human post-mortem tissue, we validated our core finding that morphological descriptors robustly discriminate ALS from control healthy tissue at single cell resolution. Determining disease presence, severity and unbiased phenotypes at single cell resolution might prove transformational in our understanding of ALS and neurodegeneration more broadly.
Collapse
Affiliation(s)
- Cathleen Hagemann
- The Francis Crick InstituteLondonUK
- Centre for Craniofacial & Regenerative BiologyKing's College LondonLondonUK
| | - Giulia E. Tyzack
- The Francis Crick InstituteLondonUK
- Department of Neuromuscular DiseasesUCL Queen Square Institute of NeurologyLondonUK
| | - Doaa M. Taha
- The Francis Crick InstituteLondonUK
- Department of Neuromuscular DiseasesUCL Queen Square Institute of NeurologyLondonUK
| | - Helen Devine
- Department of Neuromuscular DiseasesUCL Queen Square Institute of NeurologyLondonUK
| | - Linda Greensmith
- Department of Neuromuscular DiseasesUCL Queen Square Institute of NeurologyLondonUK
| | - Jia Newcombe
- NeuroResourceDepartment of NeuroinflammationUCL Queen Square Institute of NeurologyLondonUK
| | - Rickie Patani
- The Francis Crick InstituteLondonUK
- Department of Neuromuscular DiseasesUCL Queen Square Institute of NeurologyLondonUK
| | - Andrea Serio
- The Francis Crick InstituteLondonUK
- Centre for Craniofacial & Regenerative BiologyKing's College LondonLondonUK
| | | |
Collapse
|
5
|
Li Z, Cheng S, Lin Q, Cao W, Yang J, Zhang M, Shen A, Zhang W, Xia Y, Ma X, Ouyang Z. Single-cell lipidomics with high structural specificity by mass spectrometry. Nat Commun 2021; 12:2869. [PMID: 34001877 PMCID: PMC8129106 DOI: 10.1038/s41467-021-23161-5] [Citation(s) in RCA: 62] [Impact Index Per Article: 20.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 04/15/2021] [Indexed: 12/12/2022] Open
Abstract
Single-cell analysis is critical to revealing cell-to-cell heterogeneity that would otherwise be lost in ensemble analysis. Detailed lipidome characterization for single cells is still far from mature, especially when considering the highly complex structural diversity of lipids and the limited sample amounts available from a single cell. We report the development of a general strategy enabling single-cell lipidomic analysis with high structural specificity. Cell fixation is applied to retain lipids in the cell during batch treatments prior to single-cell analysis. In addition to tandem mass spectrometry analysis revealing the class and fatty acyl-chain for lipids, batch photochemical derivatization and single-cell droplet treatment are performed to identify the C=C locations and sn-positions of lipids, respectively. Electro-migration combined with droplet-assisted electrospray ionization enables single-cell mass spectrometry analysis with easy operation but high efficiency in sample usage. Four subtypes of human breast cancer cells are correctly classified through quantitative analysis of lipid C=C location or sn-position isomers in ~160 cells. Most importantly, the single-cell deep lipidomics strategy successfully discriminates gefitinib-resistant cells from a population of wild-type human lung cancer cells (HCC827), highlighting its unique capability to promote precision medicine.
Collapse
Affiliation(s)
- Zishuai Li
- State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing, China
| | - Simin Cheng
- State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing, China
| | - Qiaohong Lin
- MOE Key Laboratory of Bioorganic Phosphorus Chemistry & Chemical Biology, Department of Chemistry, Tsinghua University, Beijing, China
| | - Wenbo Cao
- State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing, China
| | - Jing Yang
- State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing, China
| | - Minmin Zhang
- Division of Anti-tumor Pharmacology, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
| | - Aijun Shen
- Division of Anti-tumor Pharmacology, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
| | - Wenpeng Zhang
- State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing, China
| | - Yu Xia
- MOE Key Laboratory of Bioorganic Phosphorus Chemistry & Chemical Biology, Department of Chemistry, Tsinghua University, Beijing, China
| | - Xiaoxiao Ma
- State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing, China.
| | - Zheng Ouyang
- State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing, China.
- Department of Chemistry, Purdue University, West Lafayette, IN, USA.
| |
Collapse
|
6
|
Malandraki-Miller S, Riley PR. Use of artificial intelligence to enhance phenotypic drug discovery. Drug Discov Today 2021; 26:887-901. [PMID: 33484947 DOI: 10.1016/j.drudis.2021.01.013] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 12/28/2020] [Accepted: 01/15/2021] [Indexed: 01/17/2023]
Abstract
Research and development (R&D) productivity across the pharmaceutical industry has received close scrutiny over the past two decades, especially taking into consideration reports of attrition rates and the colossal cost for drug development. The respective merits of the two main drug discovery approaches, phenotypic and target based, have divided opinion across the research community, because each hold different advantages for identifying novel molecular entities with a successful path to the market. Nevertheless, both have low translatability in the clinic. Artificial intelligence (AI) and adoption of machine learning (ML) tools offer the promise of revolutionising drug development, and overcoming obstacles in the drug discovery pipeline. Here, we assess the potential of target-driven and phenotypic-based approaches and offer a holistic description of the current state of the field, from both a scientific and industry perspective. With the emerging partnerships between AI/ML and pharma still in their relative infancy, we investigate the potential and current limitations with a particular focus on phenotypic drug discovery. Finally, we emphasise the value of public-private partnerships (PPPs) and cross-disciplinary collaborations to foster innovation and facilitate efficient drug discovery programmes.
Collapse
Affiliation(s)
| | - Paul R Riley
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK.
| |
Collapse
|
7
|
Optimum concentration-response curve metrics for supervised selection of discriminative cellular phenotypic endpoints for chemical hazard assessment. Arch Toxicol 2020; 94:2951-2964. [PMID: 32601827 DOI: 10.1007/s00204-020-02813-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2020] [Accepted: 06/15/2020] [Indexed: 10/24/2022]
Abstract
High-content imaging (HCI) provides quantitative and information-rich measurements of chemical effects on human in vitro cell models. Identification of discriminative phenotypic endpoints from cellular features obtained from HCI is required for accurate assessments of potential chemical hazards. However, the use of suboptimal metrics to quantify the concentration-response curves (CRC) of chemicals based on these features may obscure discriminative features, and lead to non-predictive endpoints and poor chemical classifications or hazard assessments. Here, we present a systematic and data-driven study on the performances of different CRC metrics in identifying image-based phenotypic features that can accurately classify the effects of reference chemicals with known in vivo toxicities. We studied four previous HCI in vitro nephro- or pulmono-toxicity datasets, which contain phenotypic feature measurements from different cell and feature types. Within a feature type, we found that efficacy metrics at higher chemical concentrations tend to give higher classification accuracy, whereas potency metrics do not have obvious trends across different response levels. Across different cell and feature types, efficacy metrics generally gave higher classification accuracy than potency metrics and area under the curve (AUC). Our results suggest that efficacy metrics, especially at higher concentrations, are more likely to help us to identify discriminative phenotypic endpoints. Therefore, HCI experiments for toxicological applications should include measurements at sufficiently high chemical concentrations, and efficacy metrics should always be analyzed. The identified features may be used as specific toxicity endpoints for further chemical hazard assessment.
Collapse
|
8
|
Boyd J, Fennell M, Carpenter A. Harnessing the power of microscopy images to accelerate drug discovery: what are the possibilities? Expert Opin Drug Discov 2020; 15:639-642. [PMID: 32200648 DOI: 10.1080/17460441.2020.1743675] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Affiliation(s)
- Justin Boyd
- Internal Medicines Research Unit, Pfizer Inc ., Cambridge, MA, USA
| | - Myles Fennell
- Neuroscience and Platform Biology, Arvinas , New Haven, CT, USA
| | - Anne Carpenter
- Imaging Platform, Broad Institute of MIT and Harvard , Cambridge, MA, USA
| |
Collapse
|
9
|
Farhy C, Hariharan S, Ylanko J, Orozco L, Zeng FY, Pass I, Ugarte F, Forsberg EC, Huang CT, Andrews DW, Terskikh AV. Improving drug discovery using image-based multiparametric analysis of the epigenetic landscape. eLife 2019; 8:e49683. [PMID: 31637999 PMCID: PMC6908434 DOI: 10.7554/elife.49683] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Accepted: 10/05/2019] [Indexed: 12/16/2022] Open
Abstract
High-content phenotypic screening has become the approach of choice for drug discovery due to its ability to extract drug-specific multi-layered data. In the field of epigenetics, such screening methods have suffered from a lack of tools sensitive to selective epigenetic perturbations. Here we describe a novel approach, Microscopic Imaging of Epigenetic Landscapes (MIEL), which captures the nuclear staining patterns of epigenetic marks and employs machine learning to accurately distinguish between such patterns. We validated the MIEL platform across multiple cells lines and using dose-response curves, to insure the fidelity and robustness of this approach for high content high throughput drug discovery. Focusing on noncytotoxic glioblastoma treatments, we demonstrated that MIEL can identify and classify epigenetically active drugs. Furthermore, we show MIEL was able to accurately rank candidate drugs by their ability to produce desired epigenetic alterations consistent with increased sensitivity to chemotherapeutic agents or with induction of glioblastoma differentiation.
Collapse
Affiliation(s)
- Chen Farhy
- Sanford Burnham Prebys Medical Discovery InstituteLa JollaUnited States
| | - Santosh Hariharan
- Biological Sciences Platform, Sunnybrook Research InstituteUniversity of TorontoOntarioCanada
- Department of Medical BiophysicsUniversity of TorontoOntarioCanada
| | - Jarkko Ylanko
- Biological Sciences Platform, Sunnybrook Research InstituteUniversity of TorontoOntarioCanada
- Department of Medical BiophysicsUniversity of TorontoOntarioCanada
| | - Luis Orozco
- Sanford Burnham Prebys Medical Discovery InstituteLa JollaUnited States
| | - Fu-Yue Zeng
- Sanford Burnham Prebys Medical Discovery InstituteLa JollaUnited States
| | - Ian Pass
- Sanford Burnham Prebys Medical Discovery InstituteLa JollaUnited States
| | - Fernando Ugarte
- Department of Biomolecular EngineeringUniversity of California, Santa CruzSanta CruzUnited States
- Institute for the Biology of Stem CellsUniversity of California, Santa CruzSanta CruzUnited States
| | - E Camilla Forsberg
- Department of Biomolecular EngineeringUniversity of California, Santa CruzSanta CruzUnited States
- Institute for the Biology of Stem CellsUniversity of California, Santa CruzSanta CruzUnited States
| | - Chun-Teng Huang
- Sanford Burnham Prebys Medical Discovery InstituteLa JollaUnited States
| | - David W Andrews
- Biological Sciences Platform, Sunnybrook Research InstituteUniversity of TorontoOntarioCanada
- Department of Medical BiophysicsUniversity of TorontoOntarioCanada
- Department of BiochemistryUniversity of TorontoOntarioCanada
| | - Alexey V Terskikh
- Sanford Burnham Prebys Medical Discovery InstituteLa JollaUnited States
| |
Collapse
|
10
|
Capturing single-cell heterogeneity via data fusion improves image-based profiling. Nat Commun 2019; 10:2082. [PMID: 31064985 PMCID: PMC6504923 DOI: 10.1038/s41467-019-10154-8] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Accepted: 04/24/2019] [Indexed: 12/13/2022] Open
Abstract
Single-cell resolution technologies warrant computational methods that capture cell heterogeneity while allowing efficient comparisons of populations. Here, we summarize cell populations by adding features’ dispersion and covariances to population averages, in the context of image-based profiling. We find that data fusion is critical for these metrics to improve results over the prior alternatives, providing at least ~20% better performance in predicting a compound’s mechanism of action (MoA) and a gene’s pathway. A challenge with single-cell resolution methods is that cell heterogeneity should be captured while allowing for comparisons between populations. Here the authors fuse information from the dispersion profiles with the average profiles at the level of profiles’ similarity matrices for single cell imaging data.
Collapse
|
11
|
Bryce NS, Hardeman EC, Gunning PW, Lock JG. Chemical biology approaches targeting the actin cytoskeleton through phenotypic screening. Curr Opin Chem Biol 2019; 51:40-47. [PMID: 30901618 DOI: 10.1016/j.cbpa.2019.02.013] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2018] [Revised: 02/05/2019] [Accepted: 02/12/2019] [Indexed: 12/29/2022]
Abstract
The actin cytoskeleton is dysregulated in cancer, yet this critical cellular machinery has not translated as a druggable clinical target due to cardio-toxic side-effects. Many actin regulators are also considered undruggable, being structural proteins lacking clear functional sites suitable for targeted drug design. In this review, we discuss opportunities and challenges associated with drugging the actin cytoskeleton through its structural regulators, taking tropomyosins as a target example. In particular, we highlight emerging data acquisition and analysis trends driving phenotypic, imaging-based compound screening. Finally, we consider how the confluence of these trends is now bringing functionally integral machineries such as the actin cytoskeleton, and associated structural regulatory proteins, into an expanded repertoire of druggable targets with previously unexploited clinical potential.
Collapse
Affiliation(s)
- Nicole S Bryce
- School of Medical Sciences, UNSW Sydney, NSW 2052, Australia
| | - Edna C Hardeman
- School of Medical Sciences, UNSW Sydney, NSW 2052, Australia
| | - Peter W Gunning
- School of Medical Sciences, UNSW Sydney, NSW 2052, Australia.
| | - John G Lock
- School of Medical Sciences, UNSW Sydney, NSW 2052, Australia
| |
Collapse
|
12
|
Integrating Analysis of Cellular Heterogeneity in High-Content Dose-Response Studies. Methods Mol Biol 2018. [PMID: 29476461 DOI: 10.1007/978-1-4939-7680-5_2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
Abstract
Heterogeneity is a complex property of cellular systems and therefore presents challenges to the reliable identification and characterization. Large-scale biology projects may span many months, requiring a systematic approach to quality control to track reproducibility and correct for instrumental variation and assay drift that could mask biological heterogeneity and preclude comparisons of heterogeneity between runs or even between plates. However, presently there is no standard approach to the tracking and analysis of heterogeneity. Previously, we demonstrated the use of the Kolmogorov-Smirnov statistic as a metric for monitoring the reproducibility of heterogeneity in a screen and described the use of three heterogeneity indices as a means to characterize, filter, and browse cellular heterogeneity in big data sets (Gough et al., Methods 96:12-26, 2016). In this chapter, we present a detailed method for integrating the analysis of cellular heterogeneity in assay development, validation, screening, and post screen. Importantly, we provide a detailed method for quality control, to normalize cellular data, track heterogeneity over time, and analyze heterogeneity in big data sets, along with software tools to assist in that process. The example screen for this method is from an HCS project, but the approach applies equally to other experimental methods that measure populations of cells.
Collapse
|
13
|
Rose F, Basu S, Rexhepaj E, Chauchereau A, Del Nery E, Genovesio A. Compound Functional Prediction Using Multiple Unrelated Morphological Profiling Assays. SLAS Technol 2017; 23:243-251. [PMID: 29100480 DOI: 10.1177/2472630317740831] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Phenotypic cell-based assays have proven to be efficient at discovering first-in-class therapeutic drugs mainly because they allow for scanning a wide spectrum of possible targets at once. However, despite compelling methodological advances, posterior identification of a compound's mechanism of action (MOA) has remained difficult and highly refractory to automated analyses. Methods such as the cell painting assay and multiplexing fluorescent dyes to reveal broadly relevant cellular components were recently suggested for MOA prediction. We demonstrated that adding fluorescent dyes to a single assay has limited impact on MOA prediction accuracy, as monitoring only the nuclei stain could reach compelling levels of accuracy. This observation suggested that multiplexed measurements are highly correlated and nuclei stain could possibly reflect the general state of the cell. We then hypothesized that combining unrelated and possibly simple cell-based assays could bring a solution that would be biologically and technically more relevant to predict a drug target than using a single assay multiplexing dyes. We show that such a combination of past screen data could rationally be reused in screening facilities to train an ensemble classifier to predict drug targets and prioritize a possibly large list of unknown compound hits at once.
Collapse
Affiliation(s)
- France Rose
- 1 Computational Bioimaging and Bioinformatics, Institut de biologie de l'Ecole normale supérieure (IBENS), Ecole normale supérieure, CNRS, INSERM, PSL Research University, Paris, France
| | - Sreetama Basu
- 1 Computational Bioimaging and Bioinformatics, Institut de biologie de l'Ecole normale supérieure (IBENS), Ecole normale supérieure, CNRS, INSERM, PSL Research University, Paris, France
| | - Elton Rexhepaj
- 1 Computational Bioimaging and Bioinformatics, Institut de biologie de l'Ecole normale supérieure (IBENS), Ecole normale supérieure, CNRS, INSERM, PSL Research University, Paris, France.,2 Biophenics High-Content Screening Laboratory, Institut Curie, PSL Research University, Paris, France
| | - Anne Chauchereau
- 3 Prostate Cancer Group, Institut Gustave Roussy, Univ Paris-Sud, Inserm UMR981, Villejuif, France
| | - Elaine Del Nery
- 2 Biophenics High-Content Screening Laboratory, Institut Curie, PSL Research University, Paris, France
| | - Auguste Genovesio
- 1 Computational Bioimaging and Bioinformatics, Institut de biologie de l'Ecole normale supérieure (IBENS), Ecole normale supérieure, CNRS, INSERM, PSL Research University, Paris, France
| |
Collapse
|
14
|
Gough A, Stern AM, Maier J, Lezon T, Shun TY, Chennubhotla C, Schurdak ME, Haney SA, Taylor DL. Biologically Relevant Heterogeneity: Metrics and Practical Insights. SLAS DISCOVERY 2017; 22:213-237. [PMID: 28231035 DOI: 10.1177/2472555216682725] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Heterogeneity is a fundamental property of biological systems at all scales that must be addressed in a wide range of biomedical applications, including basic biomedical research, drug discovery, diagnostics, and the implementation of precision medicine. There are a number of published approaches to characterizing heterogeneity in cells in vitro and in tissue sections. However, there are no generally accepted approaches for the detection and quantitation of heterogeneity that can be applied in a relatively high-throughput workflow. This review and perspective emphasizes the experimental methods that capture multiplexed cell-level data, as well as the need for standard metrics of the spatial, temporal, and population components of heterogeneity. A recommendation is made for the adoption of a set of three heterogeneity indices that can be implemented in any high-throughput workflow to optimize the decision-making process. In addition, a pairwise mutual information method is suggested as an approach to characterizing the spatial features of heterogeneity, especially in tissue-based imaging. Furthermore, metrics for temporal heterogeneity are in the early stages of development. Example studies indicate that the analysis of functional phenotypic heterogeneity can be exploited to guide decisions in the interpretation of biomedical experiments, drug discovery, diagnostics, and the design of optimal therapeutic strategies for individual patients.
Collapse
Affiliation(s)
- Albert Gough
- 1 Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.,2 University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA
| | - Andrew M Stern
- 1 Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.,2 University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA
| | - John Maier
- 3 Department of Family Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Timothy Lezon
- 1 Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.,2 University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA
| | - Tong-Ying Shun
- 2 University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA
| | - Chakra Chennubhotla
- 1 Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.,2 University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA
| | - Mark E Schurdak
- 1 Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.,2 University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA.,4 University of Pittsburgh Cancer Institute, Pittsburgh, PA, USA
| | - Steven A Haney
- 5 Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN, USA
| | - D Lansing Taylor
- 1 Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.,2 University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA.,4 University of Pittsburgh Cancer Institute, Pittsburgh, PA, USA
| |
Collapse
|
15
|
Zhang ER, Liu S, Wu LF, Altschuler SJ, Cobb MH. Chemoattractant concentration-dependent tuning of ERK signaling dynamics in migrating neutrophils. Sci Signal 2016; 9:ra122. [PMID: 27965424 DOI: 10.1126/scisignal.aag0486] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
The directed migration (chemotaxis) of neutrophils toward the bacterial peptide N-formyl-Met-Leu-Phe (fMLP) is a crucial process in immune defense against invading bacteria. While navigating through a gradient of increasing concentrations of fMLP, neutrophils and neutrophil-like HL-60 cells switch from exhibiting directional migration at low fMLP concentrations to exhibiting circuitous migration at high fMLP concentrations. The extracellular signal-regulated kinase (ERK) pathway is implicated in balancing this fMLP concentration-dependent switch in migration modes. We investigated the role and regulation of ERK signaling through single-cell analysis of neutrophil migration in response to different fMLP concentrations over time. We found that ERK exhibited gradated, rather than all-or-none, responses to fMLP concentration. Maximal ERK activation occurred in response to about 100 nM fMLP, and ERK inactivation was promoted by p38. Furthermore, we found that directional migration of neutrophils reached a maximal extent at about 100 nM fMLP and that ERK, but not p38, was required for neutrophil migration. Thus, our data suggest that, in chemotactic neutrophils responding to fMLP, ERK displays gradated activation and p38-dependent inhibition and that these ERK dynamics promote neutrophil migration.
Collapse
Affiliation(s)
- Elizabeth R Zhang
- Department of Pharmacology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.,Green Center for Systems Biology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Shanshan Liu
- Department of Pharmacology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.,Green Center for Systems Biology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Lani F Wu
- Green Center for Systems Biology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.,Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Steven J Altschuler
- Green Center for Systems Biology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.,Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Melanie H Cobb
- Department of Pharmacology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
| |
Collapse
|
16
|
Bray MA, Singh S, Han H, Davis CT, Borgeson B, Hartland C, Kost-Alimova M, Gustafsdottir SM, Gibson CC, Carpenter AE. Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes. Nat Protoc 2016; 11:1757-74. [PMID: 27560178 PMCID: PMC5223290 DOI: 10.1038/nprot.2016.105] [Citation(s) in RCA: 448] [Impact Index Per Article: 56.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
In morphological profiling, quantitative data are extracted from microscopy images of cells to identify biologically relevant similarities and differences among samples based on these profiles. This protocol describes the design and execution of experiments using Cell Painting, which is a morphological profiling assay that multiplexes six fluorescent dyes, imaged in five channels, to reveal eight broadly relevant cellular components or organelles. Cells are plated in multiwell plates, perturbed with the treatments to be tested, stained, fixed, and imaged on a high-throughput microscope. Next, an automated image analysis software identifies individual cells and measures ∼1,500 morphological features (various measures of size, shape, texture, intensity, and so on) to produce a rich profile that is suitable for the detection of subtle phenotypes. Profiles of cell populations treated with different experimental perturbations can be compared to suit many goals, such as identifying the phenotypic impact of chemical or genetic perturbations, grouping compounds and/or genes into functional pathways, and identifying signatures of disease. Cell culture and image acquisition takes 2 weeks; feature extraction and data analysis take an additional 1-2 weeks.
Collapse
Affiliation(s)
- Mark-Anthony Bray
- Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| | - Shantanu Singh
- Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| | - Han Han
- Recursion Pharmaceuticals, Salt Lake City, Utah, USA
| | | | | | - Cathy Hartland
- Center for the Science of Therapeutics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| | - Maria Kost-Alimova
- Center for the Science of Therapeutics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| | - Sigrun M Gustafsdottir
- Center for the Science of Therapeutics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| | | | - Anne E Carpenter
- Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| |
Collapse
|
17
|
Caicedo JC, Singh S, Carpenter AE. Applications in image-based profiling of perturbations. Curr Opin Biotechnol 2016; 39:134-142. [PMID: 27089218 DOI: 10.1016/j.copbio.2016.04.003] [Citation(s) in RCA: 94] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2015] [Revised: 03/29/2016] [Accepted: 04/01/2016] [Indexed: 12/19/2022]
Abstract
A dramatic shift has occurred in how biologists use microscopy images. Whether experiments are small-scale or high-throughput, automatically quantifying biological properties in images is now widespread. We see yet another revolution under way: a transition towards using automated image analysis to not only identify phenotypes a biologist specifically seeks to measure ('screening') but also as an unbiased and sensitive tool to capture a wide variety of subtle features of cell (or organism) state ('profiling'). Mapping similarities among samples using image-based (morphological) profiling has tremendous potential to transform drug discovery, functional genomics, and basic biological research. Applications include target identification, lead hopping, library enrichment, functionally annotating genes/alleles, and identifying small molecule modulators of gene activity and disease-specific phenotypes.
Collapse
Affiliation(s)
- Juan C Caicedo
- Imaging Platform of the Broad Institute of Harvard and Massachusetts Institute of Technology, 415 Main Street, Cambridge, MA, USA; Fundación Universitaria Konrad Lorenz, Bogotá, Colombia
| | - Shantanu Singh
- Imaging Platform of the Broad Institute of Harvard and Massachusetts Institute of Technology, 415 Main Street, Cambridge, MA, USA
| | - Anne E Carpenter
- Imaging Platform of the Broad Institute of Harvard and Massachusetts Institute of Technology, 415 Main Street, Cambridge, MA, USA.
| |
Collapse
|
18
|
Gough A, Shun TY, Lansing Taylor D, Schurdak M. A metric and workflow for quality control in the analysis of heterogeneity in phenotypic profiles and screens. Methods 2015; 96:12-26. [PMID: 26476369 DOI: 10.1016/j.ymeth.2015.10.007] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2015] [Revised: 10/12/2015] [Accepted: 10/13/2015] [Indexed: 12/14/2022] Open
Abstract
Heterogeneity is well recognized as a common property of cellular systems that impacts biomedical research and the development of therapeutics and diagnostics. Several studies have shown that analysis of heterogeneity: gives insight into mechanisms of action of perturbagens; can be used to predict optimal combination therapies; and can be applied to tumors where heterogeneity is believed to be associated with adaptation and resistance. Cytometry methods including high content screening (HCS), high throughput microscopy, flow cytometry, mass spec imaging and digital pathology capture cell level data for populations of cells. However it is often assumed that the population response is normally distributed and therefore that the average adequately describes the results. A deeper understanding of the results of the measurements and more effective comparison of perturbagen effects requires analysis that takes into account the distribution of the measurements, i.e. the heterogeneity. However, the reproducibility of heterogeneous data collected on different days, and in different plates/slides has not previously been evaluated. Here we show that conventional assay quality metrics alone are not adequate for quality control of the heterogeneity in the data. To address this need, we demonstrate the use of the Kolmogorov-Smirnov statistic as a metric for monitoring the reproducibility of heterogeneity in an SAR screen, describe a workflow for quality control in heterogeneity analysis. One major challenge in high throughput biology is the evaluation and interpretation of heterogeneity in thousands of samples, such as compounds in a cell-based screen. In this study we also demonstrate that three heterogeneity indices previously reported, capture the shapes of the distributions and provide a means to filter and browse big data sets of cellular distributions in order to compare and identify distributions of interest. These metrics and methods are presented as a workflow for analysis of heterogeneity in large scale biology projects.
Collapse
Affiliation(s)
- Albert Gough
- University of Pittsburgh Drug Discovery Institute, 3501 Fifth Avenue, Pittsburgh, PA, USA; Dept. of Computational and Systems Biology, University of Pittsburgh, 3501 Fifth Avenue, Pittsburgh, PA, USA.
| | - Tong Ying Shun
- University of Pittsburgh Drug Discovery Institute, 3501 Fifth Avenue, Pittsburgh, PA, USA
| | - D Lansing Taylor
- University of Pittsburgh Drug Discovery Institute, 3501 Fifth Avenue, Pittsburgh, PA, USA; Dept. of Computational and Systems Biology, University of Pittsburgh, 3501 Fifth Avenue, Pittsburgh, PA, USA
| | - Mark Schurdak
- University of Pittsburgh Drug Discovery Institute, 3501 Fifth Avenue, Pittsburgh, PA, USA; Dept. of Computational and Systems Biology, University of Pittsburgh, 3501 Fifth Avenue, Pittsburgh, PA, USA
| |
Collapse
|
19
|
Collins TJ, Ylanko J, Geng F, Andrews DW. A Versatile Cell Death Screening Assay Using Dye-Stained Cells and Multivariate Image Analysis. Assay Drug Dev Technol 2015; 13:547-57. [PMID: 26422066 PMCID: PMC4652219 DOI: 10.1089/adt.2015.661] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
A novel dye-based method for measuring cell death in image-based screens is presented. Unlike conventional high- and medium-throughput cell death assays that measure only one form of cell death accurately, using multivariate analysis of micrographs of cells stained with the inexpensive mix, red dye nonyl acridine orange, and a nuclear stain, it was possible to quantify cell death induced by a variety of different agonists even without a positive control. Surprisingly, using a single known cytotoxic agent as a positive control for training a multivariate classifier allowed accurate quantification of cytotoxicity for mechanistically unrelated compounds enabling generation of dose–response curves. Comparison with low throughput biochemical methods suggested that cell death was accurately distinguished from cell stress induced by low concentrations of the bioactive compounds Tunicamycin and Brefeldin A. High-throughput image-based format analyses of more than 300 kinase inhibitors correctly identified 11 as cytotoxic with only 1 false positive. The simplicity and robustness of this dye-based assay makes it particularly suited to live cell screening for toxic compounds.
Collapse
Affiliation(s)
- Tony J Collins
- 1 David Braley Human Stem Cell Screening Facility, Stem Cell and Cancer Research Institute, McMaster University , Hamilton, Ontario, Canada
| | - Jarkko Ylanko
- 2 Department of Biological Sciences, Sunnybrook Research Institute , Toronto, Ontario, Canada
| | - Fei Geng
- 3 School of Engineering Technology, McMaster University , Hamilton, Ontario, Canada
| | - David W Andrews
- 2 Department of Biological Sciences, Sunnybrook Research Institute , Toronto, Ontario, Canada
| |
Collapse
|
20
|
Singh S, Wu X, Ljosa V, Bray MA, Piccioni F, Root DE, Doench JG, Boehm JS, Carpenter AE. Morphological Profiles of RNAi-Induced Gene Knockdown Are Highly Reproducible but Dominated by Seed Effects. PLoS One 2015. [PMID: 26197079 PMCID: PMC4511418 DOI: 10.1371/journal.pone.0131370] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
RNA interference and morphological profiling-the measurement of thousands of phenotypes from individual cells by microscopy and image analysis-are a potentially powerful combination. We show that morphological profiles of RNAi-induced knockdown using the Cell Painting assay are in fact highly sensitive and reproducible. However, we find that the magnitude and prevalence of off-target effects via the RNAi seed-based mechanism make morphological profiles of RNAi reagents targeting the same gene look no more similar than reagents targeting different genes. Pairs of RNAi reagents that share the same seed sequence produce image-based profiles that are much more similar to each other than profiles from pairs designed to target the same gene, a phenomenon previously observed in small-scale gene-expression profiling experiments. Various strategies have been used to enrich on-target versus off-target effects in the context of RNAi screening where a narrow set of phenotypes are measured, mostly based on comparing multiple sequences targeting the same gene; however, new approaches will be needed to make RNAi morphological profiling (that is, comparing multi-dimensional phenotypes) viable. We have shared our raw data and computational pipelines to facilitate research.
Collapse
Affiliation(s)
- Shantanu Singh
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America
| | - Xiaoyun Wu
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America
| | - Vebjorn Ljosa
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America
| | - Mark-Anthony Bray
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America
| | - Federica Piccioni
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America
| | - David E. Root
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America
| | - John G. Doench
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America
| | - Jesse S. Boehm
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America
| | - Anne E. Carpenter
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America
- * E-mail:
| |
Collapse
|
21
|
Brodin P, DelNery E, Soleilhac E. [High content screening in chemical biology: overview and main challenges]. Med Sci (Paris) 2015; 31:187-96. [PMID: 25744266 DOI: 10.1051/medsci/20153102016] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
The last two decades have seen the development of high content screening (HCS) methodology and its adaptation for the evaluation of small molecules as drug candidates or their use as chemical tools for research purpose. HCS was initially set-up for the understanding of the mechanism of action of compounds by testing them on cell based-assays for pharmacological and toxicological studies. Since the last decade, the use of HCS has been extended to academic research laboratories and this technology has become the starting point for numerous projects aiming at the identification of molecular targets and cellular pathways for a given disease on which novel type of drugs could act. This screening approach relies on image capture of fluorescently labeled cells therefore generating a large amount of data that must be handled by appropriate automated image analysis methods and storage instrumentation. These latter in addition to the integration and data sharing are current challenges that HCS must still tackle.
Collapse
Affiliation(s)
- Priscille Brodin
- Inserm U1019, CNRS UMR8204, université de Lille-Nord de France, institut Pasteur de Lille, centre pour l'infection et l'immunité, 1, rue du professeur Calmette, 59000 Lille, France
| | - Elaine DelNery
- Institut Curie, centre de recherche, département de recherche translationnelle, 26, rue d'Ulm, 75005 Paris, France
| | - Emmanuelle Soleilhac
- Université Grenoble Alpes, institut de recherches en technologies et sciences pour le vivant (iRTSV) -biologie à grande échelle (BGE), 38000 Grenoble, France - CEA, iRTSV (Institut de recherches en technologies et sciences pour le vivant) - BGE (biologie à grande échelle) - criblages de molécules bioactives (CMBA), 38000 Grenoble, France - Inserm, BGE, 38000 Grenoble, France
| |
Collapse
|
22
|
Aftab O, Fryknäs M, Hammerling U, Larsson R, Gustafsson MG. Detection of cell aggregation and altered cell viability by automated label-free video microscopy: a promising alternative to endpoint viability assays in high-throughput screening. ACTA ACUST UNITED AC 2014; 20:372-81. [PMID: 25520371 DOI: 10.1177/1087057114562158] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Automated phase-contrast video microscopy now makes it feasible to monitor a high-throughput (HT) screening experiment in a 384-well microtiter plate format by collecting one time-lapse video per well. Being a very cost-effective and label-free monitoring method, its potential as an alternative to cell viability assays was evaluated. Three simple morphology feature extraction and comparison algorithms were developed and implemented for analysis of differentially time-evolving morphologies (DTEMs) monitored in phase-contrast microscopy videos. The most promising layout, pixel histogram hierarchy comparison (PHHC), was able to detect several compounds that did not induce any significant change in cell viability, but made the cell population appear as spheroidal cell aggregates. According to recent reports, all these compounds seem to be involved in inhibition of platelet-derived growth factor receptor (PDGFR) signaling. Thus, automated quantification of DTEM (AQDTEM) holds strong promise as an alternative or complement to viability assays in HT in vitro screening of chemical compounds.
Collapse
Affiliation(s)
- Obaid Aftab
- Department of Medical Sciences, Cancer Pharmacology and Computational Medicine, Uppsala University, Uppsala Academic Hospital, Uppsala, Sweden
| | - Mårten Fryknäs
- Department of Medical Sciences, Cancer Pharmacology and Computational Medicine, Uppsala University, Uppsala Academic Hospital, Uppsala, Sweden
| | - Ulf Hammerling
- Department of Medical Sciences, Cancer Pharmacology and Computational Medicine, Uppsala University, Uppsala Academic Hospital, Uppsala, Sweden
| | - Rolf Larsson
- Department of Medical Sciences, Cancer Pharmacology and Computational Medicine, Uppsala University, Uppsala Academic Hospital, Uppsala, Sweden
| | - Mats G Gustafsson
- Department of Medical Sciences, Cancer Pharmacology and Computational Medicine, Uppsala University, Uppsala Academic Hospital, Uppsala, Sweden
| |
Collapse
|
23
|
Abstract
Large-scale genetic perturbation screens are a classical approach in biology and have been crucial for many discoveries. New technologies can now provide unbiased quantification of multiple molecular and phenotypic changes across tens of thousands of individual cells from large numbers of perturbed cell populations simultaneously. In this Review, we describe how these developments have enabled the discovery of new principles of intracellular and intercellular organization, novel interpretations of genetic perturbation effects and the inference of novel functional genetic interactions. These advances now allow more accurate and comprehensive analyses of gene function in cells using genetic perturbation screens.
Collapse
|
24
|
Steininger RJ, Rajaram S, Girard L, Minna JD, Wu LF, Altschuler SJ. On comparing heterogeneity across biomarkers. Cytometry A 2014; 87:558-67. [PMID: 25425168 DOI: 10.1002/cyto.a.22599] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2014] [Revised: 10/30/2014] [Accepted: 11/06/2014] [Indexed: 01/28/2023]
Abstract
Microscopy reveals complex patterns of cellular heterogeneity that can be biologically informative. However, a limitation of microscopy is that only a small number of biomarkers can typically be monitored simultaneously. Thus, a natural question is whether additional biomarkers provide a deeper characterization of the distribution of cellular states in a population. How much information about a cell's phenotypic state in one biomarker is gained by knowing its state in another biomarker? Here, we describe a framework for comparing phenotypic states across biomarkers. Our approach overcomes the current limitation of microscopy by not requiring costaining biomarkers on the same cells; instead, we require staining of biomarkers (possibly separately) on a common collection of phenotypically diverse cell lines. We evaluate our approach on two image datasets: 33 oncogenically diverse lung cancer cell lines stained with 7 biomarkers, and 49 less diverse subclones of one lung cancer cell line stained with 12 biomarkers. We first validate our method by comparing it to the "gold standard" of costaining. We then apply our approach to all pairs of biomarkers and use it to identify biomarkers that yield similar patterns of heterogeneity. The results presented in this work suggest that many biomarkers provide redundant information about heterogeneity. Thus, our approach provides a practical guide for selecting independently informative biomarkers and, more generally, will yield insights into both the connectivity of biological networks and the complexity of the state space of biological systems.
Collapse
Affiliation(s)
- Robert J Steininger
- Green Center for Systems Biology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Satwik Rajaram
- Department of Pharmaceutical Chemistry, University of California at San Francisco, San Francisco, California
| | - Luc Girard
- Hamon Center for Therapeutic Oncology Research and Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, Texas
| | - John D Minna
- Hamon Center for Therapeutic Oncology Research and Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, Texas.,Departments of Pharmacology and Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Lani F Wu
- Green Center for Systems Biology, University of Texas Southwestern Medical Center, Dallas, Texas.,Department of Pharmaceutical Chemistry, University of California at San Francisco, San Francisco, California
| | - Steven J Altschuler
- Green Center for Systems Biology, University of Texas Southwestern Medical Center, Dallas, Texas.,Department of Pharmaceutical Chemistry, University of California at San Francisco, San Francisco, California
| |
Collapse
|
25
|
Gough AH, Chen N, Shun TY, Lezon TR, Boltz RC, Reese CE, Wagner J, Vernetti LA, Grandis JR, Lee AV, Stern AM, Schurdak ME, Taylor DL. Identifying and quantifying heterogeneity in high content analysis: application of heterogeneity indices to drug discovery. PLoS One 2014; 9:e102678. [PMID: 25036749 PMCID: PMC4103836 DOI: 10.1371/journal.pone.0102678] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2014] [Accepted: 06/22/2014] [Indexed: 12/04/2022] Open
Abstract
One of the greatest challenges in biomedical research, drug discovery and diagnostics is understanding how seemingly identical cells can respond differently to perturbagens including drugs for disease treatment. Although heterogeneity has become an accepted characteristic of a population of cells, in drug discovery it is not routinely evaluated or reported. The standard practice for cell-based, high content assays has been to assume a normal distribution and to report a well-to-well average value with a standard deviation. To address this important issue we sought to define a method that could be readily implemented to identify, quantify and characterize heterogeneity in cellular and small organism assays to guide decisions during drug discovery and experimental cell/tissue profiling. Our study revealed that heterogeneity can be effectively identified and quantified with three indices that indicate diversity, non-normality and percent outliers. The indices were evaluated using the induction and inhibition of STAT3 activation in five cell lines where the systems response including sample preparation and instrument performance were well characterized and controlled. These heterogeneity indices provide a standardized method that can easily be integrated into small and large scale screening or profiling projects to guide interpretation of the biology, as well as the development of therapeutics and diagnostics. Understanding the heterogeneity in the response to perturbagens will become a critical factor in designing strategies for the development of therapeutics including targeted polypharmacology.
Collapse
Affiliation(s)
- Albert H. Gough
- Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Department of Computational & Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- * E-mail:
| | - Ning Chen
- Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Tong Ying Shun
- Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Timothy R. Lezon
- Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Department of Computational & Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Robert C. Boltz
- Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Department of Computational & Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Celeste E. Reese
- Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Jacob Wagner
- Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Lawrence A. Vernetti
- Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Department of Computational & Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Jennifer R. Grandis
- University of Pittsburgh Cancer Institute, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Department of Otolaryngology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Department of Pharmacology and Chemical Biology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Adrian V. Lee
- University of Pittsburgh Cancer Institute, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Department of Pharmacology and Chemical Biology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Andrew M. Stern
- Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Department of Computational & Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Mark E. Schurdak
- Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Department of Computational & Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- University of Pittsburgh Cancer Institute, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - D. Lansing Taylor
- Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Department of Computational & Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- University of Pittsburgh Cancer Institute, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| |
Collapse
|
26
|
Chai J, Song Q. Multiple-protein detections of single-cells reveal cell-cell heterogeneity in human cells. IEEE Trans Biomed Eng 2014; 62:30-8. [PMID: 24710818 DOI: 10.1109/tbme.2014.2315437] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Cell population represents an intrinsically heterogeneous and stochastic system, in which individual cells often behave very differently in molecular contents, functions and even genotypes from the population average in response to uniform physiological stimuli. The traditional bulk cellular analysis often overlooks cellular heterogeneity and does not provide information on cell-cell variations. Single-cell measurements can reveal information obscured in population averages, and enable us to determine distributions rather than averaged properties within a cell population. The level of complexity, with numerous variables acting at the same time, requires multiparametric and dynamic investigation of a large number of single cells. Multiplexed study can provide quantitative correlations or inter-relationships among multiple cellular components and molecular markers within a protein network or family in biological processes. In this paper, we applied multiple fluorophore-conjugated primary antibodies to detect multiple proteins expressed on the same singe cells from a clonal population. To reveal cell-cell heterogeneity, we quantified the histograms of six proteins within a cell population as functions of TNF-α stimulation time. Then, we quantified noise and noise strength of these protein histograms as functions of TNF-α stimulation time. Thirdly, we quantified correlation coefficients of multiple proteins expressed on same single-cells as functions of TNF-α stimulation time. Above parameters demonstrated nonlinear relationships with TNF-α stimulation. Quantification of above parameters on independent cell subpopulations further reveals the cell-cell heterogeneity when exposed to identical environmental conditions. Such cellular heterogeneity will be useful to characterize the disease progression and disease diagnoses.
Collapse
|
27
|
Loo LH, Laksameethanasan D, Tung YL. Quantitative protein localization signatures reveal an association between spatial and functional divergences of proteins. PLoS Comput Biol 2014; 10:e1003504. [PMID: 24603469 PMCID: PMC3945119 DOI: 10.1371/journal.pcbi.1003504] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2013] [Accepted: 01/22/2014] [Indexed: 12/17/2022] Open
Abstract
Protein subcellular localization is a major determinant of protein function. However, this important protein feature is often described in terms of discrete and qualitative categories of subcellular compartments, and therefore it has limited applications in quantitative protein function analyses. Here, we present Protein Localization Analysis and Search Tools (PLAST), an automated analysis framework for constructing and comparing quantitative signatures of protein subcellular localization patterns based on microscopy images. PLAST produces human-interpretable protein localization maps that quantitatively describe the similarities in the localization patterns of proteins and major subcellular compartments, without requiring manual assignment or supervised learning of these compartments. Using the budding yeast Saccharomyces cerevisiae as a model system, we show that PLAST is more accurate than existing, qualitative protein localization annotations in identifying known co-localized proteins. Furthermore, we demonstrate that PLAST can reveal protein localization-function relationships that are not obvious from these annotations. First, we identified proteins that have similar localization patterns and participate in closely-related biological processes, but do not necessarily form stable complexes with each other or localize at the same organelles. Second, we found an association between spatial and functional divergences of proteins during evolution. Surprisingly, as proteins with common ancestors evolve, they tend to develop more diverged subcellular localization patterns, but still occupy similar numbers of compartments. This suggests that divergence of protein localization might be more frequently due to the development of more specific localization patterns over ancestral compartments than the occupation of new compartments. PLAST enables systematic and quantitative analyses of protein localization-function relationships, and will be useful to elucidate protein functions and how these functions were acquired in cells from different organisms or species. A public web interface of PLAST is available at http://plast.bii.a-star.edu.sg. Proteins are fundamental building blocks of cells. They perform a variety of biological functions, many of which are essential to the vitality and normal functioning of cells. Proteins have to be located at the appropriate regions inside a cell to perform their functions. Therefore, when proteins change their locations, they may acquire new or different functions. However, the relationships between the locations and functions of proteins are difficult to analyze because protein locations are often represented in distinct and manually-defined categories of subcellular regions. Many proteins have complex or subtle differences in their localization patterns that can be hardly represented by these categories. Here, we present an automated analysis tool for generating quantitative signatures of protein localization patterns without requiring manual or automated assignments of proteins into distinct categories. We show that our tool can identify proteins located at the same subcellular regions more accurately than existing categorization-based methods. Our tool allows comprehensive and more accurate studies of the relationships between protein localizations and functions. By knowing where proteins are located and how their locations were changed, we may discover their functions and better understand how they acquire these functions.
Collapse
Affiliation(s)
- Lit-Hsin Loo
- Bioinformatics Institute, Agency for Science, Technology and Research, Singapore, Singapore
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- * E-mail:
| | - Danai Laksameethanasan
- Bioinformatics Institute, Agency for Science, Technology and Research, Singapore, Singapore
| | - Yi-Ling Tung
- Bioinformatics Institute, Agency for Science, Technology and Research, Singapore, Singapore
| |
Collapse
|
28
|
Parameterizing cell-to-cell regulatory heterogeneities via stochastic transcriptional profiles. Proc Natl Acad Sci U S A 2014; 111:E626-35. [PMID: 24449900 DOI: 10.1073/pnas.1311647111] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Regulated changes in gene expression underlie many biological processes, but globally profiling cell-to-cell variations in transcriptional regulation is problematic when measuring single cells. Transcriptome-wide identification of regulatory heterogeneities can be robustly achieved by randomly collecting small numbers of cells followed by statistical analysis. However, this stochastic-profiling approach blurs out the expression states of the individual cells in each pooled sample. Here, we show that the underlying distribution of single-cell regulatory states can be deconvolved from stochastic-profiling data through maximum-likelihood inference. Guided by the mechanisms of transcriptional regulation, we formulated plausible mixture models for cell-to-cell regulatory heterogeneity and maximized the resulting likelihood functions to infer model parameters. Inferences were validated both computationally and experimentally for different mixture models, which included regulatory states for multicellular function that were occupied by as few as 1 in 40 cells of the population. Importantly, when the method was extended to programs of heterogeneously coexpressed transcripts, we found that population-level inferences were much more accurate with pooled samples than with one-cell samples when the extent of sampling was limited. Our deconvolution method provides a means to quantify the heterogeneous regulation of molecular states efficiently and gain a deeper understanding of the heterogeneous execution of cell decisions.
Collapse
|
29
|
Zhang X, Boutros M. A novel phenotypic dissimilarity method for image-based high-throughput screens. BMC Bioinformatics 2013; 14:336. [PMID: 24256072 PMCID: PMC4225524 DOI: 10.1186/1471-2105-14-336] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2013] [Accepted: 11/13/2013] [Indexed: 02/02/2023] Open
Abstract
Background Discovering functional relationships of genes through cell-based phenotyping has become an important approach in functional genomics. High-throughput imaging offers the ability to quantitatively assess complex phenotypes after perturbation by RNA interference (RNAi). Such image-based high-throughput RNAi screening studies have facilitated the discovery of novel components of gene networks and their interactions. Images generated by automated microscopy are typically analyzed by extracting quantitative features of individual cells, resulting in large multidimensional data sets. Robust and sensitive methods to interpret these data sets and to derive biologically relevant information in a high-throughput and unbiased manner remain to be developed. Results Here we propose a new analysis method, PhenoDissim, which computes the phenotypic dissimilarity between cell populations via Support Vector Machine classification and cross validation. Applying this method to a kinome RNAi screening data set, we demonstrate that the proposed method shows a good replicate reproducibility, separation of controls and clustering quality, and we are able to identify siRNA phenotypes and discover potential functional links between genes. Conclusions PhenoDissim is a novel analysis method for image-based high-throughput screen, relying on two parameters which can be automatically optimized without a priori knowledge. PhenoDissim is freely available as an R package.
Collapse
Affiliation(s)
- Xian Zhang
- German Cancer Research Center (DKFZ), Div, Signaling and Functional Genomics and Department of Cell and Molecular Biology, Medical Faculty Mannheim, Im Neuenheimer Feld 580, D-69120 Heidelberg, Germany.
| | | |
Collapse
|
30
|
Laksameethanasan D, Tan R, Toh G, Loo LH. cellXpress: a fast and user-friendly software platform for profiling cellular phenotypes. BMC Bioinformatics 2013; 14 Suppl 16:S4. [PMID: 24564609 PMCID: PMC3853218 DOI: 10.1186/1471-2105-14-s16-s4] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND High-throughput, image-based screens of cellular responses to genetic or chemical perturbations generate huge numbers of cell images. Automated analysis is required to quantify and compare the effects of these perturbations. However, few of the current freely-available bioimage analysis software tools are optimized for efficient handling of these images. Even fewer of them are designed to transform the phenotypic features measured from these images into discriminative profiles that can reveal biologically meaningful associations among the tested perturbations. RESULTS We present a fast and user-friendly software platform called "cellXpress" to segment cells, measure quantitative features of cellular phenotypes, construct discriminative profiles, and visualize the resulting cell masks and feature values. We have also developed a suite of library functions to load the extracted features for further customizable analysis and visualization under the R computing environment. We systematically compared the processing speed, cell segmentation accuracy, and phenotypic-profile clustering performance of cellXpress to other existing bioimage analysis software packages or algorithms. We found that cellXpress outperforms these existing tools on three different bioimage datasets. We estimate that cellXpress could finish processing a genome-wide gene knockdown image dataset in less than a day on a modern personal desktop computer. CONCLUSIONS The cellXpress platform is designed to make fast and efficient high-throughput phenotypic profiling more accessible to the wider biological research community. The cellXpress installation packages for 64-bit Windows and Linux, user manual, installation guide, and datasets used in this analysis can be downloaded freely from http://www.cellXpress.org.
Collapse
|
31
|
Reisen F, Zhang X, Gabriel D, Selzer P. Benchmarking of Multivariate Similarity Measures for High-Content Screening Fingerprints in Phenotypic Drug Discovery. ACTA ACUST UNITED AC 2013; 18:1284-97. [DOI: 10.1177/1087057113501390] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
High-content screening (HCS) is a powerful tool for drug discovery being capable of measuring cellular responses to chemical disturbance in a high-throughput manner. HCS provides an image-based readout of cellular phenotypes, including features such as shape, intensity, or texture in a highly multiplexed and quantitative manner. The corresponding feature vectors can be used to characterize phenotypes and are thus defined as HCS fingerprints. Systematic analyses of HCS fingerprints allow for objective computational comparisons of cellular responses. Such comparisons therefore facilitate the detection of different compounds with different phenotypic outcomes from high-throughput HCS campaigns. Feature selection methods and similarity measures, as a basis for phenotype identification and clustering, are critical for the quality of such computational analyses. We systematically evaluated 16 different similarity measures in combination with linear and nonlinear feature selection methods for their potential to capture biologically relevant image features. Nonlinear correlation-based similarity measures such as Kendall’s τ and Spearman’s ρ perform well in most evaluation scenarios, outperforming other frequently used metrics (such as the Euclidian distance). We also present four novel modifications of the connectivity map similarity that surpass the original version, in our experiments. This study provides a basis for generic phenotypic analysis in future HCS campaigns.
Collapse
Affiliation(s)
- Felix Reisen
- Novartis Institutes for Biomedical Research, Center for Proteomic Chemistry, Basel, Switzerland
| | - Xian Zhang
- Novartis Institutes for Biomedical Research, Center for Proteomic Chemistry, Basel, Switzerland
| | - Daniela Gabriel
- Novartis Institutes for Biomedical Research, Center for Proteomic Chemistry, Basel, Switzerland
| | - Paul Selzer
- Novartis Institutes for Biomedical Research, Center for Proteomic Chemistry, Basel, Switzerland
| |
Collapse
|
32
|
Schauer K, Grossier JP, Duong T, Chapuis V, Degot S, Lescure A, Del Nery E, Goud B. A Novel Organelle Map Framework for High-Content Cell Morphology Analysis in High Throughput. ACTA ACUST UNITED AC 2013; 19:317-24. [DOI: 10.1177/1087057113497399] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
A screening procedure was developed that takes advantage of the cellular normalization by micropatterning and a novel quantitative organelle mapping approach that allows unbiased and automated cell morphology comparison using black-box statistical testing. Micropatterns of extracellular matrix proteins force cells to adopt a reproducible shape and distribution of intracellular compartments avoiding strong cell-to-cell variation that is a major limitation of classical culture conditions. To detect changes in cell morphology induced by compound treatment, fluorescently labeled intracellular structures from several tens of micropatterned cells were transformed into probabilistic density maps. Then, the similarity or difference between two given density maps was quantified using statistical testing that evaluates differences directly from the data without additional analysis or any subjective decision. The versatility of this organelle mapping approach for different magnifications and its performance for different cell shapes has been assessed. Density-based analysis detected changes in cell morphology due to compound treatment in a small-scale proof-of-principle screen demonstrating its compatibility with high-throughput screening. This novel tool for high-content and high-throughput cellular phenotyping can potentially be used for a wide range of applications from drug screening to careful characterization of cellular processes.
Collapse
Affiliation(s)
- Kristine Schauer
- Molecular Mechanisms of Intracellular Transport, Unité Mixte de Recherche144 Centre National de la Recherche Scientifique/Institut Curie, Paris, France
| | - Jean-Philippe Grossier
- Molecular Mechanisms of Intracellular Transport, Unité Mixte de Recherche144 Centre National de la Recherche Scientifique/Institut Curie, Paris, France
| | - Tarn Duong
- Molecular Mechanisms of Intracellular Transport, Unité Mixte de Recherche144 Centre National de la Recherche Scientifique/Institut Curie, Paris, France
- Current address: Theoretical and Applied Statistics Laboratory (LSTA), University of Paris, Paris, France
| | | | | | - Aurianne Lescure
- BioPhenics Platform, Institut Curie–Translational Research Department, Hôpital Saint Louis, Paris, France
| | - Elaine Del Nery
- BioPhenics Platform, Institut Curie–Translational Research Department, Hôpital Saint Louis, Paris, France
| | - Bruno Goud
- Molecular Mechanisms of Intracellular Transport, Unité Mixte de Recherche144 Centre National de la Recherche Scientifique/Institut Curie, Paris, France
| |
Collapse
|
33
|
A simple high-content cell cycle assay reveals frequent discrepancies between cell number and ATP and MTS proliferation assays. PLoS One 2013; 8:e63583. [PMID: 23691072 PMCID: PMC3656927 DOI: 10.1371/journal.pone.0063583] [Citation(s) in RCA: 105] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2012] [Accepted: 04/08/2013] [Indexed: 11/19/2022] Open
Abstract
In order to efficiently characterize both antiproliferative potency and mechanism of action of small molecules targeting the cell cycle, we developed a high-throughput image-based assay to determine cell number and cell cycle phase distribution. Using this we profiled the effects of experimental and approved anti-cancer agents with a range mechanisms of action on a set of cell lines, comparing direct cell counting versus two metabolism-based cell viability/proliferation assay formats, ATP-dependent bioluminescence, MTS (3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium) reduction, and a whole-well DNA-binding dye fluorescence assay. We show that, depending on compound mechanisms of action, the metabolism-based proxy assays are frequently prone to 1) significant underestimation of compound potency and efficacy, and 2) non-monotonic dose-response curves due to concentration-dependent phenotypic ‘switching’. In particular, potency and efficacy of DNA synthesis-targeting agents such as gemcitabine and etoposide could be profoundly underestimated by ATP and MTS-reduction assays. In the same image-based assay we showed that drug-induced increases in ATP content were associated with increased cell size and proportionate increases in mitochondrial content and respiratory flux concomitant with cell cycle arrest. Therefore, differences in compound mechanism of action and cell line-specific responses can yield significantly misleading results when using ATP or tetrazolium-reduction assays as a proxy for cell number when screening compounds for antiproliferative activity or profiling panels of cell lines for drug sensitivity.
Collapse
|
34
|
Furia L, Pelicci PG, Faretta M. A computational platform for robotized fluorescence microscopy (I): high-content image-based cell-cycle analysis. Cytometry A 2013; 83:333-43. [PMID: 23463605 DOI: 10.1002/cyto.a.22266] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2012] [Revised: 01/11/2013] [Accepted: 01/23/2013] [Indexed: 12/28/2022]
Abstract
Hardware automation and software development have allowed a dramatic increase of throughput in both acquisition and analysis of images by associating an optimized statistical significance with fluorescence microscopy. Despite the numerous common points between fluorescence microscopy and flow cytometry (FCM), the enormous amount of applications developed for the latter have found relatively low space among the modern high-resolution imaging techniques. With the aim to fulfill this gap, we developed a novel computational platform named A.M.I.CO. (Automated Microscopy for Image-Cytometry) for the quantitative analysis of images from widefield and confocal robotized microscopes. Thanks to the setting up of both staining protocols and analysis procedures, we were able to recapitulate many FCM assays. In particular, we focused on the measurement of DNA content and the reconstruction of cell-cycle profiles with optimal parameters. Standard automated microscopes were employed at the highest optical resolution (200 nm), and white-light sources made it possible to perform an efficient multiparameter analysis. DNA- and protein-content measurements were complemented with image-derived information on their intracellular spatial distribution. Notably, the developed tools create a direct link between image-analysis and acquisition. It is therefore possible to isolate target populations according to a definite quantitative profile, and to relocate physically them for diffraction-limited data acquisition. Thanks to its flexibility and analysis-driven acquisition, A.M.I.CO. can integrate flow, image-stream and laser-scanning cytometry analysis, providing high-resolution intracellular analysis with a previously unreached statistical relevance.
Collapse
Affiliation(s)
- Laura Furia
- Department of Experimental Oncology, European Institute of Oncology, IFOM-IEO Campus for Oncogenomics, Milano 20139, Italy
| | | | | |
Collapse
|
35
|
Xia X, Wong ST. Concise review: a high-content screening approach to stem cell research and drug discovery. Stem Cells 2013; 30:1800-7. [PMID: 22821636 DOI: 10.1002/stem.1168] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
High-throughput screening (HTS) is a technology widely used for early stages of drug discovery in pharmaceutical and biotechnology industries. Recent hardware and software improvements have enabled HTS to be used in combination with subcellular resolution microscopy, resulting in cell image-based HTS, called high-content screening (HCS). HCS allows the acquisition of deeper knowledge at a single-cell level such that more complex biological systems can be studied in a high-throughput manner. The technique is particularly well-suited for stem cell research and drug discovery, which almost inevitably require single-cell resolutions for the detection of rare phenotypes in heterogeneous cultures. With growing availability of facilities, instruments, and reagent libraries, small-to-moderate scale HCS can now be carried out in regular academic labs. We envision that the HCS technique will play an increasing role in both basic mechanism study and early-stage drug discovery on stem cells. Here, we review the development of HCS technique and its past application on stem cells and discuss possible future developments.
Collapse
Affiliation(s)
- Xiaofeng Xia
- Department of Systems Medicine and Bioengineering, The Methodist Hospital Research Institute, Houston, TX 77030, USA.
| | | |
Collapse
|
36
|
Lo E, Soleilhac E, Martinez A, Lafanechère L, Nadon R. Intensity quantile estimation and mapping--a novel algorithm for the correction of image non-uniformity bias in HCS data. Bioinformatics 2012; 28:2632-9. [PMID: 22914219 DOI: 10.1093/bioinformatics/bts491] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Image non-uniformity (NU) refers to systematic, slowly varying spatial gradients in images that result in a bias that can affect all downstream image processing, quantification and statistical analysis steps. Image NU is poorly modeled in the field of high-content screening (HCS), however, such that current conventional correction algorithms may be either inappropriate for HCS or fail to take advantage of the information available in HCS image data. RESULTS A novel image NU bias correction algorithm, termed intensity quantile estimation and mapping (IQEM), is described. The algorithm estimates the full non-linear form of the image NU bias by mapping pixel intensities to a reference intensity quantile function. IQEM accounts for the variation in NU bias over broad cell intensity ranges and data acquisition times, both of which are characteristic of HCS image datasets. Validation of the method, using simulated and HCS microtubule polymerization screen images, is presented. Two requirements of IQEM are that the dataset consists of large numbers of images acquired under identical conditions and that cells are distributed with no within-image spatial preference. AVAILABILITY AND IMPLEMENTATION MATLAB function files are available at http://nadon-mugqic.mcgill.ca/.
Collapse
Affiliation(s)
- Ernest Lo
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada
| | | | | | | | | |
Collapse
|
37
|
Shedding light on filovirus infection with high-content imaging. Viruses 2012; 4:1354-71. [PMID: 23012631 PMCID: PMC3446768 DOI: 10.3390/v4081354] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2012] [Revised: 08/09/2012] [Accepted: 08/09/2012] [Indexed: 12/14/2022] Open
Abstract
Microscopy has been instrumental in the discovery and characterization of microorganisms. Major advances in high-throughput fluorescence microscopy and automated, high-content image analysis tools are paving the way to the systematic and quantitative study of the molecular properties of cellular systems, both at the population and at the single-cell level. High-Content Imaging (HCI) has been used to characterize host-virus interactions in genome-wide reverse genetic screens and to identify novel cellular factors implicated in the binding, entry, replication and egress of several pathogenic viruses. Here we present an overview of the most significant applications of HCI in the context of the cell biology of filovirus infection. HCI assays have been recently implemented to quantitatively study filoviruses in cell culture, employing either infectious viruses in a BSL-4 environment or surrogate genetic systems in a BSL-2 environment. These assays are becoming instrumental for small molecule and siRNA screens aimed at the discovery of both cellular therapeutic targets and of compounds with anti-viral properties. We discuss the current practical constraints limiting the implementation of high-throughput biology in a BSL-4 environment, and propose possible solutions to safely perform high-content, high-throughput filovirus infection assays. Finally, we discuss possible novel applications of HCI in the context of filovirus research with particular emphasis on the identification of possible cellular biomarkers of virus infection.
Collapse
|
38
|
Closed-form density-based framework for automatic detection of cellular morphology changes. Proc Natl Acad Sci U S A 2012; 109:8382-7. [PMID: 22586080 DOI: 10.1073/pnas.1117796109] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
A primary method for studying cellular function is to examine cell morphology after a given manipulation. Fluorescent markers attached to proteins/intracellular structures of interest in conjunction with 3D fluorescent microscopy are frequently exploited for functional analysis. Despite the central role of morphology comparisons in cell biological approaches, few statistical tools are available that allow biological scientists without a high level of statistical training to quantify the similarity or difference of fluorescent images containing multifactorial information. We transform intracellular structures into kernels and develop a multivariate two-sample test that is nonparametric and asymptotically normal to directly and quantitatively compare cellular morphologies. The asymptotic normality bypasses the computationally intensive calculations used by the usual resampling techniques to compute the P-value. Because all parameters required for the statistical test are estimated directly from the data, it does not require any subjective decisions. Thus, we provide a black-box method for unbiased, automated comparison of cell morphology. We validate the performance of our test statistic for finite synthetic samples and experimental data. Employing our test for the comparison of the morphology of intracellular multivesicular bodies, we detect changes in their distribution after disruption of the cellular microtubule cytoskeleton with high statistical significance in fixed samples and live cell analysis. These results demonstrate that density-based comparison of multivariate image information is a powerful tool for automated detection of cell morphology changes. Moreover, the underlying mathematics of our test statistic is a general technique, which can be applied in situations where two data samples are compared.
Collapse
|
39
|
Berlinicke CA, Ackermann CF, Chen SH, Schulze C, Shafranovich Y, Myneni S, Patel VL, Wang J, Zack DJ, Lindvall M, Bova GS. High-content screening data management for drug discovery in a small- to medium-size laboratory: results of a collaborative pilot study focused on user expectations as indicators of effectiveness. JOURNAL OF LABORATORY AUTOMATION 2012; 17:255-65. [PMID: 22357564 DOI: 10.1177/2211068211431207] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
High-content screening (HCS) technology provides a powerful vantage point to approach biological problems; it allows analysis of cell parameters, including changes in cell or protein movement, shape, or texture. As part of a collaborative pilot research project to improve bioscience research data integration, we identified HCS data management as an area ripe for advancement. A primary goal was to develop an integrated data management and analysis system suitable for small- to medium-size HCS programs that would improve research productivity and increase work satisfaction. A system was developed that uses Labmatrix, a Web-based research data management platform, to integrate and query data derived from a Cellomics STORE database. Focusing on user expectations, several barriers to HCS productivity were identified and reduced or eliminated. The impact of the project on HCS research productivity was tested through a series of 18 lab-requested integrated data queries, 7 of which were fully enabled, 7 partially enabled, and 4 enabled through data export to standalone data analysis tools. The results are limited to one laboratory, but this pilot suggests that through an "implementation research" approach, a network of small- to medium-size laboratories involved in HCS projects could achieve greater productivity and satisfaction in drug discovery research.
Collapse
|
40
|
Downey MJ, Jeziorska DM, Ott S, Tamai TK, Koentges G, Vance KW, Bretschneider T. Extracting fluorescent reporter time courses of cell lineages from high-throughput microscopy at low temporal resolution. PLoS One 2011; 6:e27886. [PMID: 22194797 PMCID: PMC3240619 DOI: 10.1371/journal.pone.0027886] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2011] [Accepted: 10/27/2011] [Indexed: 11/29/2022] Open
Abstract
The extraction of fluorescence time course data is a major bottleneck in high-throughput live-cell microscopy. Here we present an extendible framework based on the open-source image analysis software ImageJ, which aims in particular at analyzing the expression of fluorescent reporters through cell divisions. The ability to track individual cell lineages is essential for the analysis of gene regulatory factors involved in the control of cell fate and identity decisions. In our approach, cell nuclei are identified using Hoechst, and a characteristic drop in Hoechst fluorescence helps to detect dividing cells. We first compare the efficiency and accuracy of different segmentation methods and then present a statistical scoring algorithm for cell tracking, which draws on the combination of various features, such as nuclear intensity, area or shape, and importantly, dynamic changes thereof. Principal component analysis is used to determine the most significant features, and a global parameter search is performed to determine the weighting of individual features. Our algorithm has been optimized to cope with large cell movements, and we were able to semi-automatically extract cell trajectories across three cell generations. Based on the MTrackJ plugin for ImageJ, we have developed tools to efficiently validate tracks and manually correct them by connecting broken trajectories and reassigning falsely connected cell positions. A gold standard consisting of two time-series with 15,000 validated positions will be released as a valuable resource for benchmarking. We demonstrate how our method can be applied to analyze fluorescence distributions generated from mouse stem cells transfected with reporter constructs containing transcriptional control elements of the Msx1 gene, a regulator of pluripotency, in mother and daughter cells. Furthermore, we show by tracking zebrafish PAC2 cells expressing FUCCI cell cycle markers, our framework can be easily adapted to different cell types and fluorescent markers.
Collapse
Affiliation(s)
- Mike J. Downey
- Molecular Organisation and Assembly in Cells, University of Warwick, Coventry, United Kingdom
| | | | - Sascha Ott
- Warwick Systems Biology Centre, University of Warwick, Coventry, United Kingdom
| | - T. Katherine Tamai
- Department of Cell and Developmental Biology, University College London, London, United Kingdom
| | - Georgy Koentges
- School of Life Sciences, University of Warwick, Coventry, United Kingdom
| | - Keith W. Vance
- Warwick Systems Biology Centre, University of Warwick, Coventry, United Kingdom
| | - Till Bretschneider
- Warwick Systems Biology Centre, University of Warwick, Coventry, United Kingdom
| |
Collapse
|
41
|
Sutherland JJ, Low J, Blosser W, Dowless M, Engler TA, Stancato LF. A Robust High-Content Imaging Approach for Probing the Mechanism of Action and Phenotypic Outcomes of Cell-Cycle Modulators. Mol Cancer Ther 2011; 10:242-54. [DOI: 10.1158/1535-7163.mct-10-0720] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
|
42
|
Abstract
Systems-level approaches have emerged that rely on analytical, microscopy-based technology for the discovery of novel drug targets and the mechanisms driving AR signaling, transcriptional activity, and ligand independence. Single cell behavior can be quantified by high-throughput microscopy methods through analysis of endogenous protein levels and localization or creation of biosensor cell lines that can simultaneously detect both acute and latent responses to known and unknown androgenic stimuli. The cell imaging and analytical protocols can be automated to discover agonist/antagonist response windows for nuclear translocation, reporter gene activity, nuclear export, and subnuclear transcription events, facilitating access to a multiplex model system that is inherently unavailable through classic biochemical approaches. In this chapter, we highlight the key steps needed for developing, conducting, and analyzing high-throughput screens to identify effectors of AR signaling.
Collapse
|
43
|
Sequential array cytometry: multi-parameter imaging with a single fluorescent channel. Ann Biomed Eng 2010; 39:1328-34. [PMID: 21136165 PMCID: PMC3069325 DOI: 10.1007/s10439-010-0199-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2010] [Accepted: 10/19/2010] [Indexed: 11/25/2022]
Abstract
Heterogeneity within the human population and within diseased tissues necessitates a personalized medicine approach to diagnostics and the treatment of diseases. Functional assays at the single-cell level can contribute to uncovering heterogeneity and ultimately assist in improved treatment decisions based on the presence of outlier cells. We aim to develop a platform for high-throughput, single-cell-based assays using well-characterized hydrodynamic cell isolation arrays which allow for precise cell and fluid handling. Here, we demonstrate the ability to extract spatial and temporal information about several intracellular components using a single fluorescent channel, eliminating the problem of overlapping fluorescence emission spectra. Integrated with imaging technologies such as wide field-of-view lens-free fluorescent imaging, fiber-optic array scanning technology, and microlens arrays, use of a single fluorescent channel will reduce the cost of reagents and optical components. Specifically, we sequentially stain hydrodynamically trapped cells with three biochemical labels all sharing the same fluorescence excitation and emission spectrum. These markers allow us to analyze the amount of DNA, and compare nucleus-to-cytoplasm ratio, as well as glycosylation of surface proteins. By imaging cells in real-time we enable measurements of temporal localization of cellular components and intracellular reaction kinetics, the latter is used as a measurement of multi-drug resistance. Demonstrating the efficacy of this single-cell analysis platform is the first step in designing and implementing more complete assays, aimed toward improving diagnosis and personalized treatments to complex diseases.
Collapse
|
44
|
Singh DK, Ku CJ, Wichaidit C, Steininger RJ, Wu LF, Altschuler SJ. Patterns of basal signaling heterogeneity can distinguish cellular populations with different drug sensitivities. Mol Syst Biol 2010; 6:369. [PMID: 20461076 PMCID: PMC2890326 DOI: 10.1038/msb.2010.22] [Citation(s) in RCA: 97] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2009] [Accepted: 03/18/2010] [Indexed: 12/31/2022] Open
Abstract
Phenotypic heterogeneity has been widely observed in cellular populations. However, the extent to which heterogeneity contains biologically or clinically important information is not well understood. Here, we investigated whether patterns of basal signaling heterogeneity, in untreated cancer cell populations, could distinguish cellular populations with different drug sensitivities. We modeled cellular heterogeneity as a mixture of stereotyped signaling states, identified based on colocalization patterns of activated signaling molecules from microscopy images. We found that patterns of heterogeneity could be used to separate the most sensitive and resistant populations to paclitaxel within a set of H460 lung cancer clones and within the NCI-60 panel of cancer cell lines, but not for a set of less heterogeneous, immortalized noncancer human bronchial epithelial cell (HBEC) clones. Our results suggest that patterns of signaling heterogeneity, characterized as ensembles of a small number of distinct phenotypic states, can reveal functional differences among cellular populations.
Collapse
Affiliation(s)
- Dinesh Kumar Singh
- Department of Pharmacology, Green Center for Systems Biology, Simmons Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX 75390-9041, USA
| | | | | | | | | | | |
Collapse
|
45
|
Abstract
High-content screening (HCS) was introduced in 1997 based on light microscope imaging technologies to address the need for an automated platform that could analyze large numbers of individual cells with subcellular resolution using standard microplates. Molecular specificity based on fluorescence was a central element of the platform taking advantage of the growing list of reagent classes and the ability to multiplex. In addition, image analysis coupled to data management, data mining, and data visualization created a tool that focused on biological information and knowledge to begin exploring the functions of genes identified in the genomics revolution. This overview looks at the development of HCS, the evolution of the technologies, and the market up to the present day. In addition, the options for adopting uniform definitions is suggested along with a perspective on what advances are needed to continue building the value of HCS in biomedical research, drug discovery, and development and diagnostics.
Collapse
|
46
|
Caie PD, Walls RE, Ingleston-Orme A, Daya S, Houslay T, Eagle R, Roberts ME, Carragher NO. High-Content Phenotypic Profiling of Drug Response Signatures across Distinct Cancer Cells. Mol Cancer Ther 2010; 9:1913-26. [DOI: 10.1158/1535-7163.mct-09-1148] [Citation(s) in RCA: 121] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
|
47
|
Abstract
A central challenge of biology is to understand how individual cells process information and respond to perturbations. Much of our knowledge is based on ensemble measurements. However, cell-to-cell differences are always present to some degree in any cell population, and the ensemble behaviors of a population may not represent the behaviors of any individual cell. Here, we discuss examples of when heterogeneity cannot be ignored and describe practical strategies for analyzing and interpreting cellular heterogeneity.
Collapse
Affiliation(s)
- Steven J Altschuler
- Department of Pharmacology, Green Center for Systems Biology, Simmons Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
| | | |
Collapse
|
48
|
Soleilhac E, Nadon R, Lafanechere L. High-content screening for the discovery of pharmacological compounds: advantages, challenges and potential benefits of recent technological developments. Expert Opin Drug Discov 2010; 5:135-44. [DOI: 10.1517/17460440903544456] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
|