Literature DB >> 28231035

Biologically Relevant Heterogeneity: Metrics and Practical Insights.

Albert Gough1,2, Andrew M Stern1,2, John Maier3, Timothy Lezon1,2, Tong-Ying Shun2, Chakra Chennubhotla1,2, Mark E Schurdak1,2,4, Steven A Haney5, D Lansing Taylor1,2,4.   

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.

Entities:  

Keywords:  cellular models; computational pathology; drug discovery; flow cytometry; heterogeneity; high-content screening; organs-on-chips; precision medicine; quantitative systems pharmacology; systems biology

Mesh:

Year:  2017        PMID: 28231035      PMCID: PMC5464733          DOI: 10.1177/2472555216682725

Source DB:  PubMed          Journal:  SLAS Discov        ISSN: 2472-5552            Impact factor:   3.341


  209 in total

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4.  A Perspective on Implementing a Quantitative Systems Pharmacology Platform for Drug Discovery and the Advancement of Personalized Medicine.

Authors:  Andrew M Stern; Mark E Schurdak; Ivet Bahar; Jeremy M Berg; D Lansing Taylor
Journal:  J Biomol Screen       Date:  2016-03-08

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Authors:  Mario Niepel; Sabrina L Spencer; Peter K Sorger
Journal:  Curr Opin Chem Biol       Date:  2009-10-14       Impact factor: 8.822

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8.  High-content screening with siRNA optimizes a cell biological approach to drug discovery: defining the role of P53 activation in the cellular response to anticancer drugs.

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Journal:  Genome Biol       Date:  2014-12-03       Impact factor: 13.583

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  18 in total

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3.  Single-cell measurements of two-dimensional binding affinity across cell contacts.

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Review 4.  Mass Spectrometry Imaging: A Review of Emerging Advancements and Future Insights.

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5.  Building with intent: technologies and principles for engineering mammalian cell-based therapies to sense and respond.

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Journal:  Curr Opin Biomed Eng       Date:  2017-10-18

6.  Single-Cell Distribution Analysis of AR Levels by High-Throughput Microscopy in Cell Models: Application for Testing Endocrine-Disrupting Chemicals.

Authors:  Fabio Stossi; Ragini M Mistry; Pankaj K Singh; Hannah L Johnson; Maureen G Mancini; Adam T Szafran; Michael A Mancini
Journal:  SLAS Discov       Date:  2020-06-18       Impact factor: 3.341

7.  Connecting Neuronal Cell Protective Pathways and Drug Combinations in a Huntington's Disease Model through the Application of Quantitative Systems Pharmacology.

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8.  Quantitation and Comparison of Phenotypic Heterogeneity Among Single Cells of Monoclonal Microbial Populations.

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9.  A massively multi-scale approach to characterizing tissue architecture by synchrotron micro-CT applied to the human placenta.

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Journal:  J R Soc Interface       Date:  2021-06-02       Impact factor: 4.118

10.  Utilizing the Heterogeneity of Clinical Data for Model Refinement and Rule Discovery Through the Application of Genetic Algorithms to Calibrate a High-Dimensional Agent-Based Model of Systemic Inflammation.

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Journal:  Front Physiol       Date:  2021-05-19       Impact factor: 4.566

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