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Using fluorescence flow cytometry data for single-cell gene expression analysis in bacteria.

ABSTRACT: Fluorescence flow cytometry is increasingly being used to quantify single-cell expression distributions in bacteria in high-throughput. However, there has been no systematic investigation into the best practices for quantitative analysis of such data, what systematic biases exist, and what accuracy and sensitivity can be obtained. We investigate these issues by measuring the same E. coli strains carrying fluorescent reporters using both flow cytometry and microscopic setups and systematically comparing the resulting single-cell expression distributions. Using these results, we develop methods for rigorous quantitative inference of single-cell expression distributions from fluorescence flow cytometry data. First, we present a Bayesian mixture model to separate debris from viable cells using all scattering signals. Second, we show that cytometry measurements of fluorescence are substantially affected by autofluorescence and shot noise, which can be mistaken for intrinsic noise in gene expression, and present methods to correct for these using calibration measurements. Finally, we show that because forward- and side-scatter signals scale non-linearly with cell size, and are also affected by a substantial shot noise component that cannot be easily calibrated unless independent measurements of cell size are available, it is not possible to accurately estimate the variability in the sizes of individual cells using flow cytometry measurements alone. To aid other researchers with quantitative analysis of flow cytometry expression data in bacteria, we distribute E-Flow, an open-source R package that implements our methods for filtering debris and for estimating true biological expression means and variances from the fluorescence signal. The package is available at https://github.com/vanNimwegenLab/E-Flow.

SUBMITTER: Galbusera L

PROVIDER: S-EPMC7549788 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Using fluorescence flow cytometry data for single-cell gene expression analysis in bacteria.

Galbusera Luca L Bellement-Theroue Gwendoline G Urchueguia Arantxa A Julou Thomas T van Nimwegen Erik E

PloS one 20201012 10

Fluorescence flow cytometry is increasingly being used to quantify single-cell expression distributions in bacteria in high-throughput. However, there has been no systematic investigation into the best practices for quantitative analysis of such data, what systematic biases exist, and what accuracy and sensitivity can be obtained. We investigate these issues by measuring the same E. coli strains carrying fluorescent reporters using both flow cytometry and microscopic setups and systematically co ...[more]

PMID: 33045012

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Project description:BackgroundPresently available flow cytometric methods of bromodeoxyuridine (BrdUrd) labelling do not provide information on the cell cycle time (TC) and the growth fraction (GF). In this paper, we describe a novel and simple method to estimate TC and GF from flow cytometric analysis of a single tumour sample after BrdUrd labelling.MethodsThe proposed method is based on two assumptions: (1) the number of labelled cells traversing the cell cycle per unit time is constant and (2) the total number of labelled cells is constant throughout the cycle, provided that cells produced after division are excluded. The total numbers of labelled divided G1 cells, labelled divided S cells, labelled undivided S cells, and labelled undivided G2 cells were obtained for DNA histograms of BrdUrd-positive cells in a collected sample. These cell numbers were used to write equations to determine the durations of cell cycle phases, TC and GF. To illustrate the application of the proposed formulae, cell cycle kinetic parameters were analysed in solid SL2 tumours growing in DBA/2 mice and in human T-leukaemia Jurkat cells in culture.ResultsThe suitability of the proposed method for estimating durations of the cell cycle phases, TC and GF was demonstrated. TC in SL2 tumours was found to be relatively constant at 4 and 10 days after tumour implantation (20.3 +/- 1.1 h and 21.6 +/- 0.9 h, respectively). GF in tumours at day 10 was lower than GF at day 4 (54.2 +/- 7.7% vs. 79.2 +/- 5.9%, p = 0.0003). Approximate values of TC and GF of cultured Jurkat cells were 23.9 h and 79.3%, respectively.ConclusionThe proposed method is relatively simple and permits estimation of the cell cycle parameters, including TC and GF, from a single tumour sample after labelling with BrdUrd. We have shown that this method may be useful in preclinical studies, allowing estimation of changes in GF during growth of murine tumours. Experiments with human Jurkat cells suggest that the proposed method might also prove suitable for measurement of cell kinetics in human tumours. Development of suitable software enabling more objective interpretation of the DNA profile in this method would be desirable.

Dataset Information

Using fluorescence flow cytometry data for single-cell gene expression analysis in bacteria.

Publications

Using fluorescence flow cytometry data for single-cell gene expression analysis in bacteria.

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