Automated live cell imaging systems reveal dynamic cell behavior

Chirieleison, Steven M.; Bissell, Taylor A.; Scelfo, Christopher C.; Anderson, J. Edgar; Li, Yong; Koebler, Doug; Deasy, Bridget M.

doi:10.1002/btpr.629

Cited by 12 publications

(11 citation statements)

References 51 publications

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“…In particular, as interest in multiparametric imaging assays grows, the need for efficient workflows that encompass data acquisition, processing and analysis has become more evident (Chirieleison et al, 2011;Singh et al, 2014). Here, we developed a selectable lentiviral reagent that enables rapid expression of the two FUCCI reporter transgenes in cells.…”

Section: Discussionmentioning

confidence: 99%

“…In particular, advances in automated imaging hardware have not been matched by the ease and accessibility of multiparametric imagebased processing workflows. As a result, many live-cell assays are not designed with good throughput capability and often depend on manual handling of a limited number of datasets (Chirieleison et al, 2011;Boutros et al, 2015;Singh et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

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A quantitative FastFUCCI assay defines cell cycle dynamics at a single-cell level

Koh

Mascalchi

Rodríguez

et al. 2017

Journal of Cell Science

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The fluorescence ubiquitination-based cell cycle indicator (FUCCI) is a powerful tool for use in live cells but current FUCCI-based assays have limited throughput in terms of image processing and quantification. Here, we developed a lentiviral system that rapidly introduced FUCCI transgenes into cells by using an all-in-one expression cassette, FastFUCCI. The approach alleviated the need for sequential transduction and characterisation, improving labelling efficiency. We coupled the system to an automated imaging workflow capable of handling large datasets. The integrated assay enabled analyses of single-cell readouts at high spatiotemporal resolution. With the assay, we captured in detail the cell cycle alterations induced by antimitotic agents. We found that treated cells accumulated at G2 or M phase but eventually advanced through mitosis into the next interphase, where the majority of cell death occurred, irrespective of the preceding mitotic phenotype. Some cells appeared viable after mitotic slippage, and a fraction of them subsequently re-entered S phase. Accordingly, we found evidence that targeting the DNA replication origin activity sensitised cells to paclitaxel. In summary, we demonstrate the utility of the FastFUCCI assay for quantifying spatiotemporal dynamics and identify its potential in preclinical drug development.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A quantitative FastFUCCI assay defines cell cycle dynamics at a single-cell level

Koh

Mascalchi

Rodríguez

et al. 2017

Journal of Cell Science

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“…Using time-lapsed imaging6061, we recorded the cell growth and examined the structure and cell division dynamics for both muscle stem cells6263 from mouse and rat, and human mesenchymal stem cells6465. Time-lapsed images were acquired at 10-minute intervals over the course of 5 days.…”

Section: Resultsmentioning

confidence: 99%

Heterogeneous Structure of Stem Cells Dynamics: Statistical Models and Quantitative Predictions

Bogdan

Deasy

Gharaibeh

et al. 2014

Sci Rep

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Understanding stem cell (SC) population dynamics is essential for developing models that can be used in basic science and medicine, to aid in predicting cells fate. These models can be used as tools e.g. in studying patho-physiological events at the cellular and tissue level, predicting (mal)functions along the developmental course, and personalized regenerative medicine. Using time-lapsed imaging and statistical tools, we show that the dynamics of SC populations involve a heterogeneous structure consisting of multiple sub-population behaviors. Using non-Gaussian statistical approaches, we identify the co-existence of fast and slow dividing subpopulations, and quiescent cells, in stem cells from three species. The mathematical analysis also shows that, instead of developing independently, SCs exhibit a time-dependent fractal behavior as they interact with each other through molecular and tactile signals. These findings suggest that more sophisticated models of SC dynamics should view SC populations as a collective and avoid the simplifying homogeneity assumption by accounting for the presence of more than one dividing sub-population, and their multi-fractal characteristics.

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“…Computational image processing (e.g. segmentation algorithms Chirieleison et al, 2011; Fero and Pogliano, 2010) increases the throughput by reducing the time for analysis and eliminating user bias. However, other methods may be more straightforward technically but at the expense of providing only static rather than dynamic information.…”

Section: Experimental Methods For the Analysis Of Stem Cell Populamentioning

confidence: 99%

Deconstructing stem cell population heterogeneity: Single-cell analysis and modeling approaches

Tzanakakis

2013

Biotechnology Advances

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Isogenic stem cell populations display cell-to-cell variations in a multitude of attributes including gene or protein expression, epigenetic state, morphology, proliferation and proclivity for differentiation. The origins of the observed heterogeneity and its roles in the maintenance of pluripotency and the lineage specification of stem cells remain unclear. Addressing pertinent questions will require the employment of single-cell analysis methods as traditional cell biochemical and biomolecular assays yield mostly population-average data. In addition to time-lapse microscopy and flow cytometry, recent advances in single-cell genomic, transcriptomic and proteomic profiling are reviewed. The application of multiple displacement amplification, next generation sequencing, mass cytometry and spectrometry to stem cell systems is expected to provide a wealth of information affording unprecedented levels of multiparametric characterization of cell ensembles under defined conditions promoting pluripotency or commitment. Establishing connections between single-cell analysis information and the observed phenotypes will also require suitable mathematical models. Stem cell self-renewal and differentiation are orchestrated by the coordinated regulation of subcellular, intercellular and niche-wide processes spanning multiple time scales. Here, we discuss different modeling approaches and challenges arising from their application to stem cell populations. Integrating single-cell analysis with computational methods will fill gaps in our knowledge about the functions of heterogeneity in stem cell physiology. This combination will also aid the rational design of efficient differentiation and reprogramming strategies as well as bioprocesses for the production of clinically valuable stem cell derivatives.

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Automated live cell imaging systems reveal dynamic cell behavior

Cited by 12 publications

References 51 publications

A quantitative FastFUCCI assay defines cell cycle dynamics at a single-cell level

A quantitative FastFUCCI assay defines cell cycle dynamics at a single-cell level

Heterogeneous Structure of Stem Cells Dynamics: Statistical Models and Quantitative Predictions

Deconstructing stem cell population heterogeneity: Single-cell analysis and modeling approaches

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