ArticlePloS one2014
Identifying and quantifying heterogeneity in high content analysis: application of heterogeneity indices to drug discovery.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 29 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
29 citing papers in PubMed.
- A multilevel hierarchical framework for quantification of experimental heterogeneity in population snapshot data.PLoS computational biology · 2026Article
- Microfluidics-enabled proteomic profiling reveal iron-driven immune evasion by an antimicrobial-resistant pathogen.Cell reports methods · 2026Article
- SPACe: an open-source, single-cell analysis of Cell Painting data.Nature communications · 2024Article
- Optimization of Application-Driven Development ofTissue engineering. Part B, Reviews · 2022Review
- Application of Hexanoyl Glycol Chitosan as a Non-cell Adhesive Polymer in Three-Dimensional Cell Culture.ACS omega · 2022Article
- Quality Control for Single Cell Imaging Analytics Using Endocrine Disruptor-Induced Changes in Estrogen Receptor Expression.Environmental health perspectives · 2022Article
- Nuisance compounds in cellular assays.Cell chemical biology · 2021Review
- Development and Optimization of a High-Content Analysis Platform to Identify Suppressors of Lamin B1 Overexpression as a Therapeutic Strategy for Autosomal Dominant Leukodystrophy.SLAS discovery : advancing life sciences R & D · 2020Article
- Single-Cell Distribution Analysis of AR Levels by High-Throughput Microscopy in Cell Models: Application for Testing Endocrine-Disrupting Chemicals.SLAS discovery : advancing life sciences R & D · 2020Article
- Metabolic Heterogeneity in Patient Tumor-Derived Organoids by Primary Site and Drug Treatment.Frontiers in oncology · 2020Article
- High-Throughput Automated Single-Cell Imaging Analysis Reveals Dynamics of Glioblastoma Stem Cell Population During State Transition.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2019Article
- Eliciting the impacts of cellular noise on metabolic trade-offs by quantitative mass imaging.Nature communications · 2019Article
- Characterizing Glioblastoma Heterogeneity via Single-Cell Receptor Quantification.Frontiers in bioengineering and biotechnology · 2018Article
- Connecting Neuronal Cell Protective Pathways and Drug Combinations in a Huntington's Disease Model through the Application of Quantitative Systems Pharmacology.Scientific reports · 2017Article
- Platform for Quantitative Evaluation of Spatial Intratumoral Heterogeneity in Multiplexed Fluorescence Images.Cancer research · 2017Article
- A method for characterizing phenotypic changes in highly variable cell populations and its application to high content screening of Arabidopsis thaliana protoplasts.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2017Article
- Biologically Relevant Heterogeneity: Metrics and Practical Insights.SLAS discovery : advancing life sciences R & D · 2017Review
- Article
- Morphological single cell profiling of the epithelial-mesenchymal transition.Integrative biology : quantitative biosciences from nano to macro · 2016Article
- Stiffness of the microenvironment upregulates ERBB2 expression in 3D cultures of MCF10A within the range of mammographic density.Scientific reports · 2016Article
Corrections and comments
- Erratum issued
Authors and funding
13 authors.
Funding
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.
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Registered trials
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