Automated flow cytometry for acquisition of time‐dependent population data

Abu‐Absi, Nicholas R.; Zamamiri, Abdelqader M.; Kacmar, James; Balogh, Steven J.; Srienc, Friedrich

doi:10.1002/cyto.a.10016

Cited by 58 publications

(65 citation statements)

References 15 publications

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“…However, if many techniques are available for the determination of cell physiology at a given moment of culture, dynamic evolution of microbial resistance to stress and adaptation is still poorly described. For this purpose, an automated version of FC has been proposed for the on-line monitoring of cell population heterogeneity under process-related conditions [73,74]. Specific interfaces comprising a mixing chamber where microbial cells are diluted prior to analysis and stained (if required) have been developed and commercialized.…”

Section: Flow Cytometry For On-line Bioprocess Monitoringmentioning

confidence: 99%

Microbial heterogeneity affects bioprocess robustness: Dynamic single‐cell analysis contributes to understanding of microbial populations

Delvigne

Goffin

2013

Biotechnology Journal

130

102

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Heterogeneity or segregation of microbial populations has been the subject of much research, but the real impact of this phenomenon on bioprocesses remains poorly understood. The main reason for this lack of knowledge is the difficulty in monitoring microbial population heterogeneity under dynamic process conditions. The main concepts resulting in microbial population heterogeneity in the context of bioprocesses have been summarized by two distinct hypotheses. The first involves the individual history of microbial cells or the "path" followed during their residence time inside the process equipment. The second hypothesis involves a coordinated response by the microbial population as a bet-hedging strategy, in order to cope with process-related stresses. The respective contribution of each hypothesis to microbial heterogeneity in bioprocesses is still unclear. This illustrates the fact that, although microbial phenotypic heterogeneity has been thoroughly investigated at a fundamental level, the implications of this phenomenon in the context of microbial bioprocesses are still subject to debate. At this time, automated flow cytometry is the best technique for investigating microbial heterogeneity under process conditions. However, dedicated software and relevant biomarkers are needed for the proper integration of flow cytometry as a bioprocess control tool.

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Section: Flow Cytometry For On-line Bioprocess Monitoringmentioning

confidence: 99%

Microbial heterogeneity affects bioprocess robustness: Dynamic single‐cell analysis contributes to understanding of microbial populations

Delvigne

Goffin

2013

Biotechnology Journal

130

102

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“…Samples were automatically withdrawn from the bioreactor every 21 min and processed by the previously described automated cell preparation system (Abu-Absi et al 2003;Zhao et al 1999). A commercial version of such sample preparation system has been recently developed by MSP Corp, Shoreview, MN (http://www.mspcorp.com).…”

Section: Sampling and Analysismentioning

confidence: 99%

“…Therefore, it is of considerable interest to detect this heterogeneity, to monitor it online, and ultimately to control it with appropriate process conditions. To accomplish this, we developed an automated flow cytometry system (Abu-Absi et al 2003) that has already been shown to be useful for a variety of applications including monitoring of microbial (Kacmar et al 2004) and mammalian cell cultures (Kacmar and Srienc 2005). Automated flow cytometry can provide online cell number and viability, with the ability to discriminate subpopulations even if they are present as a small fraction of the total cell population.…”

Section: Introductionmentioning

confidence: 99%

Transient gene expression in CHO cells monitored with automated flow cytometry

2006

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“…The earlier approaches to the CFSE data analysis were based upon assumption that there is no death in the proliferating lymphocytes. The histogram data were interpreted using a simple discrete doubling model [11,19] or the exponential growth model [1]. These models allowed to estimate the frequency of responding cells and the specific exponential growth rates, respectively.…”

Section: Introductionmentioning

confidence: 99%

Computational analysis of CFSE proliferation assay

et al. 2006

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CFSE based tracking of the lymphocyte proliferation using flow cytometry is a powerful experimental technique in immunology allowing for the tracing of labelled cell populations over time in terms of the number of divisions cells undergone. Interpretation and understanding of such population data can be greatly improved through the use of mathematical modelling. We apply a heterogeneous linear compartmental model, described by a system of ordinary differential equations similar to those proposed by Kendall. This model allows division number-dependent rates of cell proliferation and death and describes the rate of changes in the numbers of cells having undergone j divisions. The experimental data set that we specifically analyze specifies the following characteristics of the kinetics of PHA-induced human T lymphocyte proliferation assay in vitro: (i) the total number of live cells, (ii) the total number of dead but not disintegrated cells and (iii) the number of cells divided j times. Following the maximum likelihood approach for data fitting, we estimate the model parameters which, in particular, present the CTL birth-and death rate "functions". It is the first study of CFSE labelling data which convincingly shows that the lymphocyte proliferation and death both in vitro and in vivo are division number dependent. For the first time, the confidence in the estimated parameter values is analyzed by comparing three major methods: the technique based on the variance-covariance matrix, the profile-likelihood-based approach and the bootstrap technique. We compare results and performance of these methods with respect to their robustness and computational cost. We show that for evaluating mathematical models of differing complexity the information-theoretic approach, based upon indicators measuring the information loss for a particular model (Kullback-Leibler information), provides a consistent basis. We specifically discuss methodological and computational difficulties in parameter identification with CFSE data, e.g., the loss of confidence in the parameter estimates starting around the 6-th division. Overall, our study suggests that the heterogeneity inherent in cell kinetics should be explicitly incorporated into the structure of mathematical models.

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Automated flow cytometry for acquisition of time‐dependent population data

Cited by 58 publications

References 15 publications

Microbial heterogeneity affects bioprocess robustness: Dynamic single‐cell analysis contributes to understanding of microbial populations

Microbial heterogeneity affects bioprocess robustness: Dynamic single‐cell analysis contributes to understanding of microbial populations

Transient gene expression in CHO cells monitored with automated flow cytometry

Computational analysis of CFSE proliferation assay

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