ArticleMethods in enzymology2009
Trait variability of cancer cells quantified by high-content automated microscopy of single cells.
Article in Methods in enzymology, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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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.
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Who cites it
17 citing papers in PubMed, 24 citations in OpenAlex.
- A community-based approach to image analysis of cells, tissues and tumors.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2022Review
- Thunor: visualization and analysis of high-throughput dose-response datasets.Nucleic acids research · 2021Article
- Extracellular matrix density regulates the formation of tumour spheroids through cell migration.PLoS computational biology · 2021Article
- Leveraging Mathematical Modeling to Quantify Pharmacokinetic and Pharmacodynamic Pathways: Equivalent Dose Metric.Frontiers in physiology · 2019Article
- A Nonquiescent "Idling" Population State in Drug-Treated, BRAF-Mutated Melanoma.Biophysical journal · 2018Article
- A drift-diffusion checkpoint model predicts a highly variable and growth-factor-sensitive portion of the cell cycle G1 phase.PloS one · 2018Article
- A Predictive Mathematical Modeling Approach for the Study of Doxorubicin Treatment in Triple Negative Breast Cancer.Scientific reports · 2017Article
- An unbiased metric of antiproliferative drug effect in vitro.Nature methods · 2016Article
- Evolution of intratumoral phenotypic heterogeneity: the role of trait inheritance.Interface focus · 2013Article
- Beyond genetics in personalized cancer treatment: assessing dynamics and heterogeneity of tumor responses.Personalized medicine · 2013Article
- Cancer as a moving target: understanding the composition and rebound growth kinetics of recurrent tumors.Evolutionary applications · 2013Article
- Measurement and modeling of signaling at the single-cell level.Biochemistry · 2012Review
- Fractional proliferation: a method to deconvolve cell population dynamics from single-cell data.Nature methods · 2012Article
- CellAnimation: an open source MATLAB framework for microscopy assays.Bioinformatics (Oxford, England) · 2012Article
- Development of high-throughput quantitative assays for glucose uptake in cancer cell lines.Molecular imaging and biology · 2011Article
- Targeting GLI1 expression in human inflammatory breast cancer cells enhances apoptosis and attenuates migration.British journal of cancer · 2011Article
- Not all noise is waste.Nature methods · 2010Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
Mapping quantitative cell traits (QCT) to underlying molecular defects is a central challenge in cancer research because heterogeneity at all biological scales, from genes to cells to populations, is recognized as the main driver of cancer progression and treatment resistance. A major roadblock to a multiscale framework linking cell to signaling to genetic cancer heterogeneity is the dearth of large-scale, single-cell data on QCT-such as proliferation, death sensitivity, motility, metabolism, and other hallmarks of cancer. High-volume single-cell data can be used to represent cell-to-cell genetic and nongenetic QCT variability in cancer cell populations as averages, distributions, and statistical subpopulations. By matching the abundance of available data on cancer genetic and molecular variability, QCT data should enable quantitative mapping of phenotype to genotype in cancer. This challenge is being met by high-content automated microscopy (HCAM), based on the convergence of several technologies including computerized microscopy, image processing, computation, and heterogeneity science. In this chapter, we describe an HCAM workflow that can be set up in a medium size interdisciplinary laboratory, and its application to produce high-throughput QCT data for cancer cell motility and proliferation. This type of data is ideally suited to populate cell-scale computational and mathematical models of cancer progression for quantitatively and predictively evaluating cancer drug discovery and treatment.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.