Flow cytometric methods to investigate culture heterogeneities for plant metabolic engineering

Abstract

Plant cell cultures provide an important method for production and supply of a variety of natural products, where conditions can be easily controlled, manipulated, and optimized. Development and optimization of plant cell culture processes require both bioprocess engineering and metabolic engineering approaches. Cultures are generally highly heterogeneous, with significant variability amongst cells in terms of growth, metabolism, and productivity of key metabolites. Taxus cultures produce the important anti-cancer agent Taxol((R)) (i.e., paclitaxel) and have demonstrated significant variability amongst cell populations in culture with regard to paclitaxel accumulation, cell cycle participation, and protein synthesis. To fully understand the link between cellular metabolism and culture behavior and to enable targeted metabolic engineering approaches, cultures need to be studied at a single cell level. This chapter describes the application of plant cell flow cytometric techniques to investigate culture heterogeneity at the single cell level, in order to optimize culture performance through targeted metabolic engineering. Flow cytometric analytical methods are described to study Taxus single cells, protoplasts, and nuclei suspensions with respect to secondary metabolite accumulation, DNA content, cell size, and complexity. Reproducible methods to isolate these single particle suspensions from aggregated Taxus cultures are discussed. Methods to stain both fixed and live cells for a variety of biological markers are provided to enable characterization of cell phenotypes. Fluorescence-activated cell sorting (FACS) methods are also presented to facilitate isolation of certain plant cell culture populations for both analysis and propagation of superior cell lines for use in bioprocesses.

Fig 3.1

(a) Flow cytometric scatter dot plot (FSC vs. SSC) of Taxus cuspidata P991 single cells, stained for paclitaxel (see Section 3.3). The figure shows a method for gating the population of interest while recording the fluorescent data. A manual polygon-shaped gate, excluding the debris and aggregates, is drawn in the dot plot before recording the data. Debris, which is characterized by low FSC and low SSC values in the dot plot, is largely comprised of cellular fragments and undesirable sub-cellular particles. While these are often counted as individual events by the cytometer, they are not intact single cells and do not accurately represent the correct metabolic information pertaining to the stained cells. Aggregates and larger particles, which are formed by fusion of two or more cells, are also not included in the analysis, owing to their incorrect optical measurements. (b and c) Flow cytometric histograms of control (secondary antibody only) and stained (primary and secondary antibody) samples of Taxus cuspidata P991 single cells: (b) cells were not elicited with methyl jasmonate (−MJ), i.e., “low/nonpaclitaxel-accumulating” cells; (c) cells were elicited with methyl jasmonate (+MJ), i.e., “paclitaxel-accumulating cells”. Both elicited and nonelicited cells were stained under identical conditions (see Section 3.3), and can thus be compared on the basis of their fluorescent histograms. Percentage positive is defined as the percentage of stained cells above the threshold set by 99.5 % of control cells (8). In this experiment, nonelicited cells were 2 % positive, while elicited cells were 89 % positive, indicating that significantly more elicited cells accumulate paclitaxel. The relative fluorescent intensity, defined as the difference between the mean fluorescence of stained and unstained cells, can also be used to compare samples. In this experiment, the relative fluorescent intensities of nonelicited and elicited cells were 882 and 2895, respectively, also indicating that elicited cells accumulate more paclitaxel than nonelicited cells. These data indicate cell-cell heterogeneity with regards to secondary metabolite accumulation, as demonstrated by the distribution in paclitaxel-related fluorescence in the samples. This type of analysis can be used to understand secondary metabolite heterogeneity in cell culture populations to ultimately suggest strategies for optimizing accumulation and stability.

Fig 3.2

Flow cytometric scatter dot plots of sorting of single cells (a and b) and protoplasts (c and d) by size: (a) single cells before sorting, 52 % (P1) of the entire parent events were selected for sorting based on high FSC and SSC values; (b) single cells after sorting, 91 % of the selected events fall within P1 in the sorted cells plot; (c) protoplasts before sorting, 62 % (P1) of the entire parent events were selected for sorting based on high FSC and SSC values; and (d) protoplasts after sorting, 93 % of the selected events fall within P1 in the sorted protoplasts plot. Single cells and protoplasts were isolated from Taxus cuspidata cell line P991 (see Section 3.2) and were run through a BD FACSVantage (see Section 3.4.2). High sort purities (91 % for single cells and 93 % for protoplasts) were obtained for sorting based on size. These results demonstrate the feasibility of sorting both intact Taxus cells and protoplasts. These distinct populations differing in size and complexity can be isolated and further analyzed to investigate culture heterogeneity.

Fig 3.3

(a) Preparation of Taxus cuspidata P991 nuclei suspensions: cells were placed in a Petri dish with the isolation buffer and chopped with a mini glass scrapper (see Section 3.5); and (b) histogram of PI fluorescence intensity as a measurement of relative 2C nuclear DNA content obtained after PI staining and subsequent flow cytometric analysis of the isolated nuclei suspension (see Sections 3.6 and 3.7). Note that there are two peaks in the histogram. A nucleus in the first peak has two copies of the unreplicated genome and has a relative DNA content of 2C. Similarly, a nucleus in the second peak has 4C relative DNA content. The two peaks can be gated and their mean fluorescence intensities determined which can be used to estimate the absolute DNA content using known standards. This method can be used to explore culture variability by determining the DNA content/genome size variation across different cells lines and under different culture conditions.