Implications of Cellular Heterogeneity on Plant Cell Culture Performance



Plant cell culture is an attractive platform technology for production and supply of several important plant-derived medicinal products. A unique characteristic of these dedifferentiated cells is the ability to grow as multicellular aggregates in suspension culture. The presence of these nonuniform aggregates results in creation of distinct microenvironments, which induce variations in cellular metabolism that are dependent on spatial position. This heterogeneity can lead to unpredictable and suboptimal performance in large-scale bioreactors. This review focuses on the role of cellular aggregation on the observed heterogeneity associated with plant cell cultures. Techniques used to study aggregation at the culture level as well as flow cytometric-based techniques to investigate and characterize heterogeneity at the single cell level are discussed. We focus on the commercially relevant production of the anticancer agent paclitaxel in Taxus suspension culture. An understanding of culture heterogeneity can lead to the development of targeted strategies to optimize culture performance for supply of medicinal products.

9.1 Introduction

Traditionally, valuable plant-derived phytochemicals, also referred to as secondary metabolites, have been obtained either by natural harvestation or chemical synthesis. Natural harvestation necessitates destroying the whole plant or selectively harvesting specialized organs; this makes the process expensive, time-consuming, and environmentally unfriendly (McCoy and O’Connor 2008). These issues are compounded further with slow growing, rare or endangered plants, as well as medicinal plants growing in remote areas (Hawkins 2008). Total or partial chemical synthesis is economically viable only for production of relatively simple structures such as aspirin and ephedrine (Li et al. 2010), but impractical for secondary metabolites with complex structures, such as multiple rings and chiral centers (Chemler and Koffas 2008). Over the past decade, genetic engineering approaches to transfer plant pathways into microbial hosts have provided a competitive alternative for production of certain plant natural products [e.g., artemisinin synthesis in E. coli (Martin et al. 2003) and Saccharomyces cerevisiae (Ro et al. 2006)]. Microbial fermentation processes are well established and offer the advantages of rapid doubling times, shorter production times, easier extraction of the final product, and inexpensive feed stocks for growth. While a number of plant proteins have been heterologously expressed in microbial hosts (Yesilirmak and Sayers 2009), only a handful of plant natural products have been completely produced in microbes (Alper et al. 2005; Ro et al. 2006). Production of a nonnative compound in microbes requires identification and successful transfer of all the relevant plant biosynthetic pathway genes. For a number of plant-derived natural products (e.g., paclitaxel), the metabolic pathways leading to product formation in planta are very complex, and are either partially or completely unknown (Croteau et al. 2006). Even after a complete pathway for a particular natural product has been identified, production in prokaryotic hosts is still complicated, as they lack cellular compartmentalization that may be necessary for spatial and temporal partitioning of intermediates en route to final product (Vongpaseuth and Roberts 2007; Wu et al. 2006). Technical drawbacks associated with functional expression of native plant enzymes such as cytochrome P450s (Chemler and Koffas 2008) further impede efficient transfer of a complete biosynthetic pathway for production of a desired compound. Thus, despite the attractiveness of synthesis in microbial hosts, significant engineering challenges remain for more complex secondary metabolites.

The use of plant-based in vitro systems for production of specific secondary metabolites provides an attractive alternative to natural harvestation, chemistry-based routes, and microbial engineering (Kolewe et al. 2008; Rao and Ravishankar 2002; Wilson and Roberts 2012). In vitro culture of plant cells is a mature technology with several decades of success, and can be applied to almost any plant species (Wink et al. 2005). As well as offering a realistic option for large-scale production of secondary metabolites, in vitro plant cultures offer a controlled and regulated environment for studies of growth, metabolism, cell-environment interactions, and for establishment of superior plant species through genetic manipulation.

9.2 In vitro Plant Culture

In vitro culture of plants or plant cells can involve various degrees of differentiation. Whole plants or seedlings, organ cultures, or dedifferentiated suspension cultures propagated from callus can be grown aseptically in a defined culture media. These different types of in vitro cultures (see Fig. 9.1 for Taxus species) can be interconverted using established techniques, most of which rely on specific phytohormone concentrations. Differentiated plant cell cultures, specifically organ cultures such as roots and shoots, have been shown to accumulate significant levels of secondary metabolites, often comparable with levels quantified in the whole plant (Matkowski 2008; Roberts and Wink 1998). In particular, hairy root cultures, obtained by transforming root cultures with Agrobacterium rhizogenes, have been promising for increased capacity of secondary metabolite production (Srivastava and Srivastava 2007). Several medicinal compounds belonging to the alkaloid, terpenoid, and phenolic families have been produced successfully in hairy root cultures. Examples include resveratrol in Arachis hypogaea, artemisinin in Artemisia annua, indole alkaloids in Catharanthus roseus, and camptothecin in Camptotheca acuminate and Ophiorrhiza pumila (Ono and Tian 2011). In recent years, a number of valuable reviews on differentiated cultures for secondary metabolite production have been published (Guillon et al. 2006; Karuppusamy 2009; Kim et al. 2002; Loyola-Vargas and Miranda-Ham 1995; Pistelli et al. 2010; Verpoorte and Memelink 2002), and therefore, we will not discuss them at length in this chapter. Although many promising reactor designs exist at the small scale, it has been difficult to scale up differentiated cultures to large-sized bioreactors primarily due to issues with nutrient delivery, limiting the widespread commercial application of this technology (Georgiev et al. 2007; Mishra and Ranjan 2008).
Fig. 9.1

Three different types of in vitro culture for Taxus. a callus culture, b hairy root culture (Syklowska-Baranek et al. 2009), and c suspension culture

On the other hand, dedifferentiated suspension cultures can be maintained in batch, semi-continuous or continuous environments, and are more amenable to scale up than hairy root cultures or other differentiated organ cultures (Kieran et al. 1997). Plant cell suspension cultures under strictly controlled conditions provide a rapid and flexible means for production of desired compounds and a number of processes have been commercialized for the production of secondary metabolites [reviewed in Eibl and Eibl (2002) and Kolewe et al. (2008)] including ginseng, shikonin, and berberine. The bioprocessing principles applied to the culture of microbial and mammalian cells also apply to dedifferentiated plant suspension cells (Hellwig et al. 2004); although plant cell culture technology has lagged behind equivalent fermentation/culture systems for microbes, yeast, and animal cells (Evans et al. 2003; Hellwig et al. 2004), as described below.

9.3 Characteristics of Plant Cell Suspension Cultures

Table 9.1 shows comparison of some of the characteristics of microbial, mammalian, and plant cells of relevance to bioprocessing. Unlike other cell culture systems, dedifferentiated plant cells grow slower and are more easily damaged by traditional mechanisms for aeration and agitation that are required for culture maintenance and processing. Typically in microbial and mammalian systems, the growth phase and production phase are uncoupled, and hence optimal conditions for growth and production can be established and applied separately to maximize synthesis of the desired compound. For plant cell cultures, the product accumulation is sometimes associated with the growth phase, and hence two-phase cultures are not always feasible (Roberts 2007). Plant cell cultures also tend to have poor genetic stability, which has been associated with aneuploidy and polyploidy, intrachromosomal rearrangements, and single gene mutations (Cassells and Curry 2001; Phillips et al. 1994), hence affecting culture performance. In addition, many secondary metabolic pathways are only active in differentiated organs, leading to no or very low accumulation in dedifferentiated culture. Even in cultures that successfully produce the compound of interest, yields are often low and variable (Ketchum and Gibson 1996; Roberts 2007). A number of strategies including strain improvement, selection of high-producing lines, medium optimization, elicitation with biotic or abiotic compounds, precursor addition, permeabilization, immobilization, in situ extraction, and genetic engineering have been used with mixed success to increase metabolite yields to suitable levels for commercial production [reviewed in Bourgaud et al. (2001), Dornenburg and Knorr (1995), Kolewe et al. (2008), Shuler (1999), Smetanska (2008), Verpoorte et al. (1999)]. Long-term variability in product yield over successive subcultures has often been observed (Deusneumann and Zenk 1984; Kim et al. 2004; Ogino et al. 1978; Qu et al. 2005). Relatively, little research has been done to understand and ultimately control this variability in secondary metabolite production, which can have a considerable impact on the success of a commercial plant cell culture process. Plant cell culture bioprocessing and metabolism are complicated by the natural tendency of plant cells to form aggregates (Kieran et al. 1997; Roberts 2007). This chapter reviews the causes and effects of cellular aggregation in plant cell suspension cultures, along with approaches used to characterize cellular heterogeneity at both the whole culture and single cell levels. Understanding heterogeneity can lead to successful strategies to optimize and stabilize production, making plant cell culture a more attractive commercial technology for the supply of valuable plant-derived medicinal compounds.
Table 9.1

A comparison of key characteristics of microbial, mammalian, and plant cells relevant to bioprocessing


Bacterial cells

Mammalian cells

Plant cells

Size (μm)




Doubling time

<1 h

~1 day

Several days

Shear sensitivity




Oxygen demand




Product accumulation

Typically extracellular

Typically extracellular

Often cell-associated

Production phase

Uncoupled with growth

Uncoupled with growth

Often growth associated

Variability in accumulation




Contamination risk




Cell line stability




Product yields




Post translational processing








Cryopreservation techniques




9.4 Aggregation and Heterogeneity of Plant Cell Suspension Cultures

Plant cell cultures are initiated by transferring pieces of dedifferentiated callus tissue from solid media to a liquid suspension operating under suitable conditions for aeration and agitation. Depending on the friability of the callus tissue, either single cells or small aggregates dissociate and begin growing in a medium containing specific types and concentrations of nutrients and growth hormones (Muir et al. 1954). During cell division, the dividing cells often remain connected to each other via their cell walls, and as a result, aggregates ranging from two to a few hundred cells exist in culture; see Fig. 9.2 for typical aggregate morphology in plant cell culture.
Fig. 9.2

Typical plant cell culture aggregates in suspension culture. aBrightfield image showing cellular aggregation in Papaversomniferum suspension cultures (bar represents 100 μm) (Siah and Doran 1991). b fluorescent image stained with fluorescein diacetate to indicate viability, showing a large aggregate in T. cuspidata cell cultures (bar represents 500 μm)

The presence of aggregates during large-scale culture affects mixing, as they tend to sediment and/or stick to the reactor surface, sometimes resulting in extensive wall adhesion/growth. Moreover, large aggregates cause rheological problems by creating dead zones in the culture vessel, blocking reactor ports and gas lines, and affecting the operation of probes to monitor culture conditions (e.g., pH and dissolved oxygen) during growth and product formation. Cellular aggregation has also been found to be the primary reason for the high viscosities observed in a number of plant cell suspension systems (Doran 1999; Kato et al. 1978). These problems are further compounded during the later stages of culture where high cell densities prevail and plant cells become more adherent due to secretion of cell wall extracellular polysaccharides.

Cells within aggregates are subject to different microenvironments with respect to light, oxygen and nutrient availability, cell-to-cell signaling, and applied surface shear forces. These microenvironments often lead to biochemical and morphological heterogeneity amongst cells, with some aggregate populations exhibiting vastly different characteristics. For example, in cell cultures of Arachis hypogoea, differences in peroxidase and catalase activity were found amongst cell aggregates of different sizes (Verma and Van Huystee 1970b). Differences in activities of several enzymes including dehydrogenases, oxidases, and hydrolases have been correlated to cellular position in aggregates of tobacco suspension cultures during early stages of growth (De Jong et al. 1967). Similarly, rates of protein synthesis and concentrations of free amino acids have also been shown to vary with aggregate size (Verma and Van Huystee 1970a). Cellular aggregation can additionally promote differentiation, where cells in the core of large aggregates can differentiate and form specialized structures such as tracheary elements (Kuboi and Yamada 1978a).

9.4.1 Factors Affecting the Degree of Cellular Aggregation

The degree of cellular aggregation in suspension cultures varies with the type of plant species. For example, in liquid suspension cultures of Antirrhinum (Melchers and Bergmann 1959) and Saussurea medusa (Zhao et al. 2003), large aggregates up to 4 mm in diameter have been observed, whereas cultures of Rosa sp. Paul’s Scarlet (Tulecke 1966), Anchusa officinalis and Nicotianatabacum (Su 2006) form highly dispersed suspension cultures with very few large aggregates. Moreover, variation in aggregate size has been observed across different species of the same plant family. For example, in Tagetes (marigolds), suspension cultures of Tagetes patula exhibited large cell aggregates (diameters up to 20 mm) in contrast to Tagetes erecta and Tagetes minuta with significantly smaller aggregates (diameters up to 2 mm) (Hulst et al. 1989).

The composition of the basal medium has shown to have a significant influence on the degree of cell separation in suspension cultures. A better separation of cells can be obtained in a purely synthetic medium, where all constituents are precisely known, as opposed to a medium containing undefined components, such as yeast extract, or coconut milk (King and Street 1977). Cellular aggregation is particularly influenced by the concentration of growth regulators in the medium (Lai Keng et al. 2008; Wallner and Nevins 1973). Higher auxin levels generally lead to more dispersed cultures with better cell separation, whereas lower auxin levels increase cell aggregation (Liau and Boll 1971; Machackova et al. 2007; Torrey et al. 1962). Conversely, higher cytokinin levels often induce more aggregation (Kinnersley and Dougall 1980; Kuboi and Yamada 1978b; Zhao et al. 2001; Halperin and Minocha 1973). In some cases, the pH of the medium (Steiner and Dougall 1995) and the frequency of subculture (Meyer et al. 2002) can influence the extent of aggregation. Frequent transfer to fresh medium to maintain cells in an actively dividing state has shown to increase cell aggregation (Henshaw et al. 1966). In addition, suspension cultures need constant agitation for adequate aeration, which can significantly affect aggregate size distributions (Kieran et al. 2000; Rajasekhar et al. 1971).

9.4.2 Effect of Aggregation on Typical Culture Parameters Aggregation and Growth

Plant cells grown in batch culture increase in biomass by cell division until depletion of an essential nutrient shifts cells into stationary phase. Multiple analyses of changes in aggregate size distributions in a batch culture indicate that aggregate size increases during the culture exponential phase, and decreases during the culture stationary phase (Capataz-Tafur et al. 2011; Kolewe et al. 2010; Mavituna and Park 1987; Ranch and Giles 1980; Scragg et al. 1987). Figure 9.3 illustrates changes in biomass and mean aggregate diameter during the culture period of Taxus cuspidata suspension cells. Formation of aggregates occurs as a result of cell division, and hence aggregation increases during the period of maximal cell division (i.e., exponential phase). Conversely, during stationary phase, cells are released from aggregates and there is reduced cell division, which leads to a decrease in mean aggregate size.
Fig. 9.3

Increase in cell biomass and mean aggregate size during exponential growth in Taxus cell cultures (Kolewe et al. 2010)

There is no clear trend in the relationship between culture mean aggregate size and growth rate, and it is very much species dependent. In wheat suspension cultures, finer (less aggregated) suspensions had a higher growth rate than their large aggregate counterparts (Yang et al. 1994). A similar observation was seen with cultures of Coffea arabica, where the growth of cultures inoculated with smaller aggregates was significantly superior to those inoculated with larger aggregates (Dubuis et al. 1995). In contrast, studies in celery (Watts et al. 1984) and safflower (Hanagata et al. 1993) suspension cultures show that some degree of aggregation was necessary for rapid growth and cell division, and finer suspensions had slower growth rates compared to aggregated suspensions. Highly dispersed sycamore cultures obtained by incubation with low concentrations of cell wall degrading enzymes show a growth pattern similar to aggregated suspensions (King et al. 1973), indicating that cell aggregation is not essential for high rates of growth and division in this system. Aggregation and Oxygen Consumption

Oxygen requirements in plant cells (typically 1–4 mmol L−1 h−1) are comparatively lower than microorganisms (typically 5–90 mmol L−1 h−1), due to their lower growth rates (Hellwig et al. 2004; Kobayashi et al. 1989; Taticek et al. 1990). Oxygen supply is known to affect both growth and production of metabolites in plant cell cultures (Huang and Chou 2000; Linden et al. 2001; Thanh et al. 2005). A number of bioreactors have been designed to study the effects of aeration in plant cell cultures, with a primary focus on the influence of mass transfer at the gas–liquid boundary (kLa) (Kieran et al. 1997). However, it has been suggested that the solid–liquid boundary between an aggregate and the culture medium is far more constraining for the delivery of oxygen to the cell than the gas–liquid boundary (Curtis and Tuerk 2006). Oxygen enrichment of the gas phase increases the driving force through bulk liquid phase and can be used to minimize mass transfer limitations of oxygen at the solid–liquid interface. In addition, suspension cultured plant cells must have significant oxygen transport within aggregates to maintain aerobic respiration and desired growth rates. Due to these various constraints, there often exists a critical aggregate size, above which oxygen limitations to the centermost cells of an aggregate may result. Relatively, few studies have been performed to study oxygen transport within aggregates, primarily because experimental measurements within the shear-sensitive aggregates are difficult. The majority of research has been based on development of mathematical models to calculate the critical size of an aggregate, using simple oxygen mass balances (Hulst et al. 1989; Pepin et al. 1999). These models typically assume a zero order reaction with known oxygen uptake rate, diffusion coefficient, and dissolved oxygen concentration to calculate the oxygen concentration profile within an aggregate. These studies suggest that diffusion of oxygen is moderately restricted in the interior of aggregates of 1 mm in diameter, and severe oxygen deficiencies are observed when the aggregates reach approximately 3 mm in size (Hulst et al. 1989; Pepin et al. 1999). A diffusion–reaction model was used to analyze experimentally measured oxygen uptake rates in immobilized cultures of Solanum aviculare (Ananta et al. 1995). Results from direct experimental measurement of the aggregate properties revealed that the critical aggregate size at which oxygen limitations occur was significantly larger than that predicted by theory. It was hypothesized that the plasmodesmata, which interconnect cells within aggregates, and the negative pressures created by the gas-filled cavities within the porous aggregates, promote oxygen transfer, factors which were not accounted for in the theoretical analysis (Ananta et al. 1995). The effects of oxygen limitations on culture performance are species dependent, with both increased (Schlatmann et al. 1995) and decreased (Hulst et al. 1989) secondary metabolite synthesis reported in large aggregates. Aggregation and Secondary Metabolite Accumulation

For recombinant protein production, cellular aggregation in plant cell cultures is usually considered undesirable, as it complicates bioreactor operation (Hellwig et al. 2004). However, certain degrees of cellular aggregation and differentiation promote secondary metabolite production in plant cell cultures (Becker 1970; Zhao et al. 2001). Aggregation causes changes in environmental conditions experienced by each cell in an aggregate, altering cellular metabolism, and inducing differential biochemical responses (Verma and Van Huystee 1970b). In some suspension cultures, cells do not just aggregate, but form sophisticated differentiated structures (Ellis et al. 1996; Hoekstra et al. 1990; Kuboi and Yamada 1978a; Xu et al. 1998; Zhao et al. 2001), which can lead to increased secondary metabolite accumulation. Studies in tobacco suspension cultures indicate that all cells in culture have a uniform ability to form aggregates (Kuboi and Yamada 1978a); however, the potential for tracheid differentiation in these studies was higher when cells were located in the center of the aggregate, suggesting that differentiation is a result of environmental circumstances, and not an inherited trait by particular cells.

To date, a number of studies have been performed to understand the effect of cellular aggregation on secondary metabolite accumulation in cell cultures. However, there is no consensus across plant species, and results vary depending on the particular species and secondary metabolite (Table 9.2). Larger aggregates have been shown to have a positive effect on secondary metabolite production, a positive effect up to a critical size, or a negative effect. This lack of trend in metabolite accumulation with aggregate size is not unexpected, as fundamental differences exist amongst distinct plant species, and their metabolic pathways may be differentially regulated.
Table 9.2

Relationship between aggregate size and secondary metabolite accumulation in various plant cell culture systems


2° Metabolite

Effect of increasing aggregate size on metabolite accumulation


Fragaria ananassa (strawberry)



Edahiro and Seki (2006)

Apium graveolens L. (celery)

Phthalides and Terpenoids


Watts et al. (1984)

Catharanthus roseus (periwinkle)


No clear trend

Kessler et al. (1999)

Salvia officinalis (sage)

Ursolic acid


Bolta et al. (2003)

Daucus carota (carrot)



Kinnersley and Dougall (1980)

Vaccinium pahalae (ohelo)



Pepin et al. (1999)

Carthamus tinctorius L. (safflower)



Hanagata et al. (1993)

Taxuscuspidata (yew)



Kolewe et al. (2011)

Daucus carota (carrot)


Increased up to critical diameter, then decreased

Madhusudhan and Ravishankar (1996)

Saussurea medusa (snow lotus)


Increased up to critical diameter, then decreased

Zhao et al. (2003)

Tagetes patula (marigold)


Increased up to critical diameter, then decreased

Hulst et al. (1989)

The majority of these studies aimed at investigating the relationship between aggregate size and secondary metabolite accumulation rely on separation of aggregates based on size using a series of sieves, followed by measurement of biomass associated secondary metabolites. In most cases, cultures were allowed to accumulate secondary metabolites (either by elicitation or through alternate stress-mediated responses) and then fractionated to measure cell-associated metabolite levels. Though straightforward, this method of fractionation neglects metabolites secreted to the extracellular medium, which can be quite significant for some culture systems. In contrast, some studies were performed by altering the aggregate size distribution at the time of culture initiation (Hanagata et al. 1993; Kinnersley and Dougall 1980; Kolewe et al. 2011). In these studies, cultures are fractionated on the day of inoculation, transferred to fresh media, and maintained independently as small and large aggregate cultures. Over time measurements of total metabolite accumulation (e.g., both cell-associated and extracellular) as a function of aggregate size were made. Results from these studies suggest a possible process optimization strategy, in which rational manipulation of the aggregate size at culture initiation can lead to high yielding cell cultures for use in bioprocesses.

9.4.3 Methods to Study Aggregation and Determine the Aggregated State of a Culture

Quantification of cell growth is essential for monitoring success of both batch and continuous cell culture processes. A number of methods have been established to evaluate growth kinetics in plant cell suspension cultures (Mustafa et al. 2011; Ryu et al. 1990) that include: dry cell weight, fresh cell weight, packed cell volume, cell number (Kolewe et al. 2010; Kubek and Shuler 1978b), culture optical density or turbidity (Eriksson 1965; James and Lee 2000; Sung 1976), electrical conductivity (Hahlbrock and Kuhlen Hahlbrock and Kuhlen 1972), osmolarity (Madhusudhan et al. 1995; Tanaka et al. 1993), nutrient and/or metabolite concentrations (Albiol et al. 1993; Thom et al. 1981), protein (David et al. 1989; Dougall 1964), and nucleic acid content (Lamboursain and Jolicoeur 2005; Nash and Davies 1972), and pH measurements (Nesius and Fletcher 1973). Aggregates of various sizes co-exist in suspension culture and because cells within an aggregate are subject to different local environments, aggregate size can significantly affect cell physiology, resulting in different aggregate growth rates (Mavituna and Park 1987). Information regarding aggregate dynamics can be incorporated into kinetic models to more accurately predict culture growth. Recently, a population balance equation framework to describe plant cell aggregates as particulate system was developed and utilized to quantitatively predict changes in total biomass, mean aggregate size, and aggregate size distributions in a Taxus cell suspension batch culture (Kolewe et al. 2012). Such models can be further improved by including information regarding cellular metabolism and product formation. These structured and segregated models can be used to predict operating conditions that optimize culture performance. In order to formulate these models, it is important to quantify aggregation dynamics during batch culture. This section describes the techniques currently used for measuring aggregate size profiles during the cell culture period, along with their advantages and limitations. Wet Sieving and Dry Weight Measurements

The most common technique to measure aggregate size distributions in plant cell cultures is wet sieving, using a series of standard filters with different pore sizes. Fractions are collected and dry weight can be determined for each aggregate size range (Ge et al. 2006; Hulst et al. 1989; Madhusudhan and Ravishankar 1996; Mavituna and Park 1987; Tanaka et al. 1992; Wongsamuth and Doran 1997; Zhao et al. 2003). The dry weight measurement is considered to be a simple, reliable, and inexpensive means of estimating plant cell growth (McDonald et al. 2001). However, measuring dry weight is a time-consuming process, as sample drying times range from 1 to 3 days, minimizing applicability for online measurements and control (Nunez-Palenius et al. 2005). The distribution resolution is limited by the number of filters used and the amount of total biomass needed for analysis. Additionally, cell cakes can be formed during mechanical sieving, which can interfere with cell separation, leading to potentially erroneous results (Trejo et al. 2003). With highly nonspherical and elongated aggregates, sieving analysis has shown to overestimate particle size (McDonald et al. 2001). For these reasons, filtering and measurement of resulting aggregate fraction dry weight is not feasible for routine monitoring of the aggregate profiles throughout a culture period. Image Analysis

Ex situ image analysis using microscopy to quantify plant cell aggregate size distributions has been employed. Image analysis techniques consist of sample plating, collecting a statistically relevant number of microscopy images, and performing size analysis using established software programs (Ibaraki and Kenji 2001). As plant cell aggregates are not always spherical, the imaging programs need to calculate elliptical form factor (Trejo et al. 2003) or equivalent spherical diameter (Kolewe et al. 2010) in order to calculate the size. The use of image analysis was found to be more precise than mechanical sieving to describe aggregate size distribution changes during growth in Solanum chrysotrichum suspension cultures, primarily because mechanical sieving was limited by inefficient separation of aggregates (Trejo et al. 2003). As with sieving, the processing steps increase the likelihood of altering the aggregate size distribution due to potential breakage. Additionally, due to the low-throughput nature of this technique, long and labor intensive procedures are necessary to obtain statistically significant results (Edahiro and Seki 2006), which become impractical for multiple measurements. To minimize culture sampling, imaging systems connected to a bioreactor (i.e., in situ image analysis) have been designed to measure product accumulation (Grand d’Esnon et al. 1989; Smith et al. 1995), growth rate, and cell aggregate distribution (Harrell et al. 1992; McDonald et al. 2001). From a product accumulation point of view, in situ image analysis becomes particularly beneficial when the product formed is a pigment or associated with a pigment, as it allows visualization of changes in cell status over the culture period. Such information can be used for online bioprocess feedback control (Smith and Reid 1996). However, a distinct disadvantage of using an online imaging system for aggregate distribution measurements in bioreactors is that it requires pumping cell aggregates to the imaging cell, which can disturb culture conditions, and alter aggregate size distributions. Image analysis, thus, provides an effective means for studying morphological characteristics and product accumulation in suspension cultures (Ceoldo et al. 2005; Kieran et al. 2000; Takeda et al. 1994; Trejo et al. 2003), but its utility in determining aggregate size distributions is limited (Kolewe et al. 2010). Focused Beam Reflectance Method

Another in situ technique to monitor biomass concentration and aggregate size distribution is the focused beam reflectance method (FBRM). FBRM is an optical technique developed by Lasentec Inc. (Redmond, WA) (schematic shown in Fig. 9.4) which operates by projecting a highly focused laser beam, through a set of rotating optics and a sapphire window, into a suspension sample. When the beam intersects a particle, light is backscattered and measured. The duration of particle’s backscatter correlates directly to the particle size and is expressed in terms of chord length. The number of such chords measured for a given amount of sample and in a specific time period, is related to particle concentration. A detailed description on the working of the FBRM system is presented elsewhere (Barrett and Glennon 1999). This technique has been used for particle size characterization in several chemical processing applications in hydrometallurgy (Richmond et al. 1998), crystallization (Kougoulos et al. 2005), liquid–liquid dispersions, and emulsions (O’Rourke and MacLoughlin 2005; Simmons and Azzopardi 2001). The feasibility of this technique was first demonstrated for the plant cell cultures of Oryza sativa, Nicotiana benthamiana and Trichosanthes kirilowii (McDonald et al. 2001). The chord length distribution data obtained from FBRM depends on aggregate morphology, density, and reflective properties. The average aggregate diameter of the suspension culture, determined either by image analysis or by sieving measurements, can be related to the average chord length obtained from FBRM measurements. A challenge in applying the FBRM technique to plant cell cultures is to determine the best way to utilize this chord length distribution data to obtain biomass and aggregate size. FBRM has been shown to provide a good representation of the biomass concentration and aggregate size in cell cultures with varying morphological characteristics including cultures with roughly spherical aggregates, irregular aggregates with bulging cells, and cylindrical unbranched aggregates (Jeffers et al. 2003; McDonald et al. 2001). For its use in bioreactors, a probe can be installed for the in situ characterization of particle size and concentration using the FBRM technique; however, issues regarding sterility and optimal probe location/orientation have to be considered (McDonald et al. 2001). A disadvantage as opposed to other biomass and aggregate size measurement techniques is the relatively large sample size required in FBRM technique. For example, both the reports which used FBRM to measure aggregation characteristics of plant cell cultures used approximately 100 mL of sample volume (Jeffers et al., 2003, McDonald et al. 2001). Also this technique is culture type and culture condition-specific and has to be validated for the suspension culture of interest. However, once validated the FBRM technique is well suited for rapid and sensitive monitoring of aggregate distribution and biomass of plant cell cultures and has the potential for in situ application.
Fig. 9.4

Schematic diagram of the focused beam reflectance method (FBRM) probe Electrical Sensing Zone or Coulter Counter Technique

An alternate method for determining particle size distributions is the electrical resistance pulse sizing technique, commonly known as the Coulter counter (Fig. 9.5) technique. Particles are suspended in an electrolyte solution and pass between electrodes across which a constant current is applied. The measured change in resistivity across two electrodes as particles pass through can be related to the volume of the particles (Graham 2003). This technique has been routinely used for cell counting and particle size characterization in numerous biological systems such as bacteria, yeast, and mammalian cells (Kubitschek 1969). The first application of this technology to plant cell cultures was shown in enzymatically treated cultures of Paul’s Scarlet Rose (Rosa sp.) and soybean (Glycine. max L.) (Kubek and Shuler 1978a). Enzymatic or chemical digestion of the middle lamella of plant cell walls forms either single cells or finely dispersed cultures, allowing them to be analyzed with the Coulter counter (Davis et al. 1984). As this technique was primarily developed for single particles, its application to highly aggregated plant cell cultures has been limited. Recently, using a larger size aperture, this technique was applied to measure both biomass and aggregate size distribution for cell cultures of T. cuspidata, which consisted of relatively spherical aggregates up to 2,000 μm in diameter (Kolewe et al. 2010). In this study, a comparison of the aggregate size measured using Coulter counter, filtration, and microscopic image analysis was made. The measurements using the Coulter counter were shown to underestimate the aggregate size compared to the other two methods; however, the relative sizes were found to be consistent. A linear correlation was obtained amongst all three measurements, which allowed aggregate size measured using the Coulter counter to match either filtration or microscopy measurements. The Coulter counter method was found to provide higher resolution than filtration measurements and image analysis, as well as being faster and more reliable. In this case, a sample size of 2 × 2 mL analyzed for 1 min was sufficient to accurately represent the aggregation distribution for Taxus cell cultures (Kolewe et al. 2010). However, the applicability of this technique to cultures with different morphological characteristics such as chain-like aggregates and irregular-shaped aggregates still needs to be investigated.
Fig. 9.5

Schematic diagram illustrating the working principle of the Coulter counter

9.4.4 Methods to Control or Manipulate Aggregate Size Distributions in Plant Cell Cultures Chemical and Physical Treatments

Controlling aggregate size in plant cell cultures is important for optimizing both growth and secondary metabolite production. Minimizing the extent of aggregation during plant cell growth has been subject to extensive investigation, primarily due to the negative effects of large aggregates on rheology and mixing in bioreactors. In an effort to maintain cultures as single cell suspensions or in a desired aggregated state, a variety of techniques have been explored, including: the use of chemical treatments such as cell wall degrading enzymes (King et al. 1973; Kubek and Shuler 1978b; Naill and Roberts 2004), addition of hormones (Diwan and Malpathak 2010; Simpkins et al. 1970), application of compounds such as colchicine (Hayashi and Yoshida 1988; Umetsu et al. 1975) or casein hydrolysate (Wallner and Nevins 1973), inclusion of metabolic inhibitors such as L-α-aminooxy-β-phenylpropionic acid (AOPP) (Edahiro and Seki 2006), and lowering of Ca2+ ion concentration (Takayama et al. 1977); as well as physical treatments such as filtration using a series of sieves (Dixon 1995; Henshaw et al. 1966), pulses of pressurized air (Kurz 1971), operation in different bioreactor configurations (Tanaka et al. 1988; Yuan et al. 2004), shearing using homogenization (Rodriguez-Monroy et al. 2004; Williams et al. 1988), selective removal of large aggregates (Prenosil and Hegglin 1990), and cell immobilization (Brodelius 1985; Morris and Fowler 1981). Chemical and enzymatic treatments can create cellular metabolic changes by altering gene expression; whereas size distribution cannot be precisely controlled with mechanical methods such as sedimentation and shearing (Morris and Fowler 1981). Some of these treatments can negatively affect cell viability (Doran 1999; Dunlop et al. 1994; Ishii 1988; Kolewe et al. 2011). Use of a particular technique to obtain the desired aggregated state is dependent on both cell type and culture conditions, and generally has to be evaluated individually for each system of interest. In most cases, once fine suspension cultures are established, reversion back to aggregated conditions upon repeated subculture is observed (Morris and Fowler 1981; Naill and Roberts 2005b). Use of Undifferentiated Suspension Cultures

Instead of culturing heterogeneous mixtures of dedifferentiated plant cells, cultures consisting of innately undifferentiated cells derived from the vascular cambium of a plant tissue can be used for production of important metabolites (Lee et al. 2010). Isolation and culture of these undifferentiated cells, referred to as cambial merismatic cells (CMSs) have been shown for a variety of plant species including T. cuspidata, Panax ginseng and Gingko biloba (Lee et al. 2010). CMCs bypass the dedifferentiation step and grow in suspension primarily as singletons or small aggregates comprised of 2–3 cells. Besides reduced aggregate size, CMCs demonstrated improved performance as compared to dedifferentiated cultures with respect to growth rate, secondary metabolite accumulation, and shear tolerance, at both the laboratory and pilot scales. Furthermore, the ability of CMC cultures to grow in a finely dispersed state has the potential to minimize the variability in growth and secondary metabolite accumulation, which is typically observed in suspension cultures (Roberts and Kolewe 2010). Therefore, CMCs offer distinct advantages over dedifferentiated cultures for production of valuable plant-derived secondary metabolites. However, some secondary metabolites accumulate to much higher extents in differentiated organ cultures such as root or shoot, for example, vinca alkaloids in C. roseus hairy root cultures (Leonard et al. 2009), and camptothecin in root cultures (Lorence and Nessler 2004). The benefits of using CMC cultures for the synthesis of such products need to be evaluated.

9.5 Study of Cellular Heterogeneity at the Single Cell Level

Although heterogeneity in plant cell cultures is well accepted, the underlying mechanisms are unclear. Most research relies solely on examination of typical culture parameters, such as biomass accumulation, substrate utilization, and product levels, which are averaged across all cells in a culture. As discussed above, the presence of different local environments can create cell-to-cell differences within an aggregate. Macroscopic analyses under these circumstances give unclear and incomplete results, as they fail to provide information on how local environments affect individual cell metabolism and behavior. Distinct subpopulations of cells with regards to cell cycle participation (Naill and Roberts 2005a; Yanpaisan et al. 1998), protein content (Naill and Roberts 2005c), and product accumulation (Hall and Yeoman 1987; Naill and Roberts 2005d) have been observed within a single plant cell culture. The occurrence of such subpopulations, along with additional genetic and epigenetic factors relevant in dedifferentiated cell cultures, is considered to be a primary influence on variability in culture metabolite production. Analyzing the properties of individual cells enables identification of distinct subpopulations and differentiation of different cell types existing simultaneously in a culture. Such studies are essential in characterizing the morphological and metabolic heterogeneities observed in plant cell cultures.

9.5.1 Flow Cytometry

The most direct and powerful technique to study cell populations is flow cytometry (FCM). Single particle suspensions are fed through the flow cytometer and interrogated with focused light sources of specific wavelengths. Each individual particle produces light scatter and fluorescence signals, which are detected through a series of filters or photomultiplier tubes, and ultimately displayed in the form of histograms for data analysis. FCM technology and its practice have been reviewed in Shapiro (1994) and Ormerod (1990). The first application of FCM to plants was done using suspensions of field bean nuclei for measurement of DNA content (Heller 1973). Unlike microbial, animal and mammalian cells, plant cells have several unique features that make analyzes using FCM challenging. Cellular aggregates can block the orifice of the FCM, necessitating the preparation of single particle suspensions (Galbraith 1990). Typical plant-derived particles analyzed by FCM include intact single cells, protoplasts—achieved by digesting the plant cell wall under hyperosmotic conditions, and subcellular components such as nuclei, mitochondria, chloroplasts, and chromosomes (Fig. 9.6). Applications include cell counting using cells with compromised membranes (Nicoloso et al. 1994), DNA and RNA analysis using nuclei isolated from protoplasts (Bergounioux et al. 1988b; Dolezel et al. 2007a), secondary metabolite accumulation using protoplasts (Hara et al. 1989) and intact single cells (Naill and Roberts 2005d), measurement of ploidy and genome size (Bennett and Leitch 2005; Sharma et al. 1983), karyotyping (Conia et al. 1987), cell-cycle stage analyses (Naill and Roberts 2005a; Yanpaisan et al. 1998), subcellular organelle (e.g., chloroplasts and mitochondria) investigations (Ashcroft et al. 1986; Petit et al. 1986; Schroder and Petit 1992), cell membrane and cell wall regeneration (Petit 1992), and measurement of cell-specific gene expression (Birnbaum et al. 2003). FCM also allows simultaneous multi-parametric analysis of the physical and/or chemical characteristics of biological particles present in a heterogeneous sample. For example, a sample consisting of viable and dead particles (e.g., plant cells, protoplasts or pollen) can be distinguished by staining with either fluorescein diacetate, which stains only viable cells, or propidium iodide, which stains dead cells (Huang et al. 1986; Schwab and Hulskamp 2008). Simultaneous biparametric analysis of DNA and RNA is possible using Hoescht 33342 dye and pyronin Y, respectively (Darzynkiewicz et al. 2001; Shapiro 1981). When a flow cytometer is equipped with a sorting functionality, it allows isolation of distinct subpopulations of cells with desired characteristics. A detailed review on the use of plant cells in FCM and sorting has been presented recently (Dolezel et al. 2007b). In this chapter, we will limit the discussion to FCM as applied to plant cell cultures in the context of cellular heterogeneity.
Fig. 9.6

Typical plant suspension culture-derived particles that can be analyzed using flow cytometry. a nuclei (indicated by arrows), b protoplasts, and c intact single cells (Naill and Roberts 2004) isolated from Taxus cell cultures. Scale bar indicates 50 μm

9.5.2 Limitations of Flow Cytometry and Cell Sorting in Plant Suspension Cultures

The use of FCM to study cellular heterogeneity requires a liquid suspension of intact single particles. Isolation of intact single cells from plant cell cultures is not trivial, as it requires digestion of the complex extracellular matrix without alteration of cellular physiology or morphology (Kubek and Shuler 1978b; Naill and Roberts 2004). In addition, the presence of the plant cell wall may restrict penetration of various dyes and reagents for labeling, as well as elicit nonspecific binding to fluorescent probes. For these reasons, the use of protoplasts is more common in plant cell FCM. However, hydrophobic metabolites are often stored in the cell wall, for example, paclitaxel in Taxus cultures (Aoyagi et al. 2002; Roberts et al. 2003; Russin et al. 1995); hence, removal of the cell wall can delete critical information about the metabolic state of the cell. In addition, the characteristically large size of some plant protoplasts and intact cells creates clogging in conventional flow cytometer chambers, which are typically developed for much smaller microbial and mammalian cells (Dolezel et al. 2007a). To ensure a clog-free run, flow cytometers with special nozzles (ca. 150–200 μm in diameter) are often required for larger plant cells (Galbraith 2004; Gaurav et al. 2010; Harkins and Galbraith 1987). Moreover, the irregular shape of plant cells disturbs the laminar flow pattern and creates flow instabilities that lead to variations in signal detection and incorrect optical measurements (Dolezel et al. 2007a). Flow cytometers with larger nozzle sizes require flow rates and sheath pressures to be maintained at low values to maintain a stable flow stream, which increases the run times for analysis and sorting. As mentioned above, subcellular components (e.g., nuclei, mitochondria, chloroplasts, and chromosomes) can be used for FCM analysis and sorting (Dolezel et al. 2001; Safar et al. 2004). The release of subcellular components of interest is often accompanied by release of cytosol-localized secondary metabolites, which can interfere with analysis, for example, the staining of nuclear DNA (Bennett et al. 2008; Loureiro et al. 2006). Overcoming these limitations can increase utility of FCM for both analysis and sorting of plant cell cultures.

9.5.3 Flow Cytometry to Study Heterogeneity in Plant Suspension Cultures

The ability of FCM to simultaneously measure multiple parameters and analyze a multitude of plant particles has improved our understanding about plants at cellular, subcellular, and molecular levels. Most of this work has been accomplished in the field of plant physiology and molecular biology, with limited applications to plant cell cultures. However, it is important to realize that most of the techniques developed for whole plant FCM can be applied to plant cell cultures. Cell–cell heterogeneity in aggregates of plant cell cultures can lead to unpredictable changes in product accumulation over time (Ketchum and Gibson 1996; Kim et al. 2004). FCM analysis of plant cell cultures can identify populations of cells with common characteristics from potentially different regions of an aggregate. Identification and characterization of such subpopulations can lead both to the design of superior strategies for enhancing secondary metabolite yields in culture and development of a fundamental understanding of aggregation behavior. In the following section, we will discuss the use of FCM and cell sorting to characterize heterogeneity in plant cell cultures. Variable Ploidy Levels

The use of FCM to rapidly determine ploidy levels is by far the most widespread application of plant FCM, with extensive uses in plant breeding (Ochatt et al. 2011), plant ecology, and population biology (Kron et al. 2007). In the context of in vitro cultures, variation in ploidy level is common, and often leads to phenotypic heterogeneity, which affects culture performance. The variable degrees of polyploidy and aneuploidy in plant cell cultures depend on the type and concentration of growth regulators (Mishiba et al. 2001; Weber et al. 2008), the degree of endopolyploidy of the explants (Damato 1985), oxidative stress damage caused to the plant tissues during explant preparation (Cassells and Curry 2001), and the duration of the in vitro cultivation (Evans and Gamborg 1982; Ramulu and Dijkhuis 1986). Using FCM, ploidy levels of cultured plant cells have been correlated to variations in cell growth, cellular differentiation (Schween et al. 2005), and regeneration capacity (Shiba and Mii 2005). Co-existence of subpopulations of different ploidy levels in cultures of T. x media has been observed (Baebler et al. 2005). In some instances, changes in ploidy levels have been correlated to secondary metabolite accumulation. For instance, tetraploid cultures of A. annua had higher levels of artemisinin than diploids, whereas Hypericum perforatum cultures ith higher ploidy levels accumulated less hypericin. Consequently, variation in ploidy levels can have a significant impact on cellular metabolic activity, and careful monitoring of the structure and stability of DNA in large-scale suspension cultures is recommended. Strategic manipulation of ploidy levels using either mitotic inhibitors or growth regulators can be used to increase secondary metabolite levels in suspension cultures (Dhawan and Lavania 1996). Variability in Cell Cycle Analysis and Presence of Noncycling Cells

FCM methods to monitor cell cycle progression in suspension cultures enable a more detailed understanding of culture growth and differentiation. For homogenous populations of cells, where all cells cycle at equal rates, univariate DNA content measurements using FCM are sufficient to understand the cell cycle kinetics (Paau et al. 1977). However, dedifferentiated plant cell cultures are heterogeneous, and often contain quiescent or noncycling cells that rest in the G0 phase of the cell cycle (Naill and Roberts 2005a; Yanpaisan et al. 1999). In such cases, univariate analysis for DNA content does not resolve every phase of the cell cycle, as cells in G0 and G1 both have 2c DNA content, while cells in G2 and M both have 4c DNA content. Multi-parametric FCM that uses an additional cellular marker such as RNA (Bergounioux et al. 1988a) or protein content (Citterio et al. 1992; Darzynkiewicz et al. 1980), has been used to distinguish between cycling and noncycling cells. Another widely used strategy to distinguish between G0 and G1 cells is based on incorporation of the thymidine analog 5-bromo-2-deoxyuridine (BrdU) into DNA during replication and its subsequent detection using a monoclonal antibody. Alternatively, incorporation of another thymidine analog 5-ethynyl-2’-deoxyuridine (EdU), whose detection is based on click chemistry with an azide-conjugated fluorochrome (such as Alexa Fluor 488) and copper (I) as the catalyst, can be used. EdU detection does not rely on bulky antibodies and DNA denaturation steps, which are necessary when using BrdU labeling, providing a more rapid analysis and better preservation of cellular structures (Ayaydin et al. 2010) (Fig. 9.7). Subpopulations of noncycling cells were observed in cell cultures of Taxus (Naill and Roberts 2005a) and S. aviculare (Yanpaisan et al. 1998) using the BrdU method. The presence of such noncycling cells affects overall culture growth rate and activity of secondary metabolic pathways (Mak and Doran 1993; Yanpaisan et al. 1999). For instance, in S. aviculare suspension cultures, the dry weight doubling time was approximately 2 days, whereas the duration of the cell cycle of actively dividing cells was only 1 day, and the proportion of actively dividing cells never exceeded 52 % of the total population (Yanpaisan et al. 1998). Similar results were found in Taxus where approximately 65 % of the suspension cells were found to be noncycling during the exponential growth phase of culture (Naill and Roberts 2005a). Analysis of such noncycling cells can provide information as to why particular groups of cells in aggregates behave differently, and yield important basic information on cell cycle control in suspension cultures.
Fig. 9.7

EdU and DNA staining to differentiate between cycling and noncycing cells in Taxus suspension cultures. Two parameter dot plot for a culture not incubated with EdU, and b EdU-treated culture. DNA content using 7-aminoactinomycin D (7-AAD) staining is represented on the X-axis and EdU incorporation detected via Alexa Fluor 488 azide is plotted on the Y-axis. EdU incorporation in S-phase cells is clearly evident in b. EdU incorporation is determined by overlaying and comparing the two parameter dot plots of treated and untreated samples Cell Sorting for Isolation and Analysis of Distinct Populations

While, FCM allows, recognition of distinct cells in a heterogeneous sample based on their optical properties, cell sorting can be employed to allow the selective isolation of subsets of cells for propagation of new cell lines or for further analysis. For instance, FCM analysis of protoplasts derived from embryogenic carrot cell cultures revealed two different subpopulations based on their light scattering properties–one with a higher cytoplasm/vacuole ratio and one with more vacuolated cells. After sorting and reculture, a differential embryogenic potential was observed; the former developed numerous proembryogenic masses and the latter were unable to develop any proembryogenic masses (Guzzo et al. 2002). Given that, plant cell cultures in general exhibit metabolic heterogeneity amongst cells, applying sorting can provide a potential means to enhance yield, and stabilize production of secondary metabolites. FCM sorting has been successfully applied to isolate protoplasts and ultimately generate highly productive cell lines for anthocyanin synthesis in Aralia cordata (Sakamoto et al. 1994) and berberine synthesis in Coptis japonica suspension cell cultures (Hara et al. 1989). Heterogeneity in paclitaxel accumulation (Naill and Roberts 2005d) and protein content (Naill and Roberts 2005c) has been observed in intact single cells isolated from Taxus cultures. The feasibility of reculturing intact single Taxus cells has also been demonstrated (Naill and Roberts 2005b). Current work aimed at sorting and reculture of Taxus cells based on paclitaxel accumulation to produce high-paclitaxel producing cultures is in progress.

The combination of FCM sorting with the currently available and emerging methods for analysis of global gene expression can provide valuable insights into the regulatory mechanisms leading to a particular cellular state. For example, differential gene expression analysis of isolated subpopulations in cell cultures can reveal the molecular fingerprint for particular cellular phenotypes. FCM and sorting have been used to characterize cell-type specific gene expression in protoplasts isolated from root (Brady et al. 2007; Petersson et al. 2009) and shoot tissues (Yadav et al. 2009) of Arabidopsis. Once information on gene expression and operational metabolic pathways within a single cell is known, targeted metabolic engineering strategies using both overexpression and silencing approaches can be defined and applied using suitable gene transfer methods.

9.6 Conclusions and Future Directions

The complexity and cellular heterogeneity associated with plant cell cultures have limited the systematic study of culture behavior. As a result, successful strategies for manipulating culture conditions for increased growth and productivity are still largely empirical. Apart from the engineering and technological issues associated with plant cell aggregation in bioprocesses, structural organization of cells within aggregates results in different local microenvironments, contributing to differences in metabolic activity amongst cells in culture that ultimately influence overall culture success. Morphological heterogeneity due to aggregate formation has been shown to affect growth, oxygen consumption, and secondary metabolite accumulation in a number of plant cell culture systems. However, the diversity of plant species and different conditions under which suspension cultures are maintained makes generalizations difficult; no clear trends have emerged to relate aggregate size directly to important culture parameters. The majority of studies to date have relied on cell-associated biomass metabolite measurements to relate secondary product formation to aggregate size. However, this approach neglects secretion of products to the extracellular medium, which is quite substantial for many systems. Careful consideration regarding experimental design must be taken to ensure accurate interpretation of data and information needs to be collected for each system relevant to the plant species and metabolite of interest. Rational approaches to manipulate and engineer aggregate sizes in cell culture should be developed to provide high-metabolite accumulating cell lines for use in bioprocesses.

Biomass accumulation must be augmented with measurements of the aggregate size distributions for an accurate depiction of culture growth. New techniques for measuring aggregate size distributions rapidly and reliably using a simple Coulter counter allow for studies of aggregation dynamics in batch cultures and analysis of long-term changes in aggregate distributions over multiple subcultures. Population balance equation models to describe heterogeneous cell growth and/or product formation have been developed for some cellular systems which aggregate, like filamentous fungi and hairy root cultures. However, models focusing on plant cell aggregation and culture heterogeneity in suspension cultures are just being developed and need future attention. Information about aggregation dynamics during batch culture along with relevant bioprocess data can be used to develop predictive models to optimize plant cell bioprocess design.

While process conditions can be easily controlled in the bulk culture environment, there are significant differences in the local environment of individual cells dependent on spatial position within an aggregate, about which very little is known. Modern analytical techniques such as flow cytometry allow rapid analysis of an individual cell and/or its components, which can be related to crucial culture parameters. Multi-parametric flow cytometric analysis aimed at correlating distinct subpopulations such as cell cycle participation, size, cellular protein and DNA content, and secondary metabolite accumulation can be used to gain an in-depth understanding of the heterogeneity observed amongst cells in plant cell cultures. Integrating these flow cytometry-based methods for analyzing culture heterogeneity with sophisticated multi-scale modeling can provide a new approach for the optimization of plant cell culture processes. The combination of flow cytometry and cell sorting to monitor and isolate distinct populations present in suspension cultures can be combined with modern methods for metabolic analysis to increase our knowledge of the regulatory networks leading to a particular subpopulation phenotype. Ultimately, if sufficient information is collected from multiple possible cellular states, a complete description of culture metabolic heterogeneity can be obtained. Using information from such system-wide analyses, targeted metabolic engineering strategies can be better designed, enabling high, stable yields of secondary metabolites, and promoting plant cell culture biotechnology for commercial use.


  1. Albiol J, Robuste J, Casas C, Poch M (1993) Biomass estimation in plant cell cultures using an extended Kalman filter. Biotechnol Prog 9(2):174–178Google Scholar
  2. Alper H, Miyaoku K, Stephanopoulos G (2005) Construction of lycopene-overproducing E. coli strains by combining systematic and combinatorial gene knockout targets. Nat Biotechnol 23(5):612–616PubMedGoogle Scholar
  3. Ananta I, Subroto MA, Doran PM (1995) Oxygen-transfer and culture characteristics of self-immobilized Solanum Aviculare aggregates. Biotechnol Bioengineering 47(5):541–549Google Scholar
  4. Aoyagi H, DiCosmo F, Tanaka H (2002) Efficient paclitaxel production using protoplasts isolated from cultured cells of Taxus cuspidata. Planta Med 68(5):420–424PubMedGoogle Scholar
  5. Ashcroft R, Preston C, Cleland R, Critchley C (1986) Flow-cytometry of isolated-chloroplasts and thylakoids. Photobiochem Photobiophys 13(1–2):1–14Google Scholar
  6. Ayaydin F, Kotogany E, Dudits D, Horvath GV (2010) A rapid and robust assay for detection of S-phase cell cycle progression in plant cells and tissues by using ethynyl deoxyuridine. Plant Methods 6:5PubMedGoogle Scholar
  7. Baebler S, Hren M, Camloh M, Ravnikar M, Bohanec B, Plaper I, Ucman R, Zel J (2005) Establishment of cell suspension cultures of Yew (Taxus x Media Rehd.) and assessment of their genomic stability. In Vitro Cell Dev-Pl 41(3):338–343Google Scholar
  8. Barrett P, Glennon B (1999) In-line FBRM monitoring of particle size in dilute agitated suspensions. Part Part Syst Char 16(5):207–211Google Scholar
  9. Becker H (1970) Studies on the formation of volatile substances in plant tissue cultures. Biochem Physiol Pflanzen 161:425–441Google Scholar
  10. Bennett MD, Leitch IJ (2005) Nuclear DNA amounts in angiosperms: progress, problems and prospects. Ann Botany 95(1):45–90Google Scholar
  11. Bennett MD, Price HJ, Johnston JS (2008) Anthocyanin inhibits propidium iodide DNA fluorescence in Euphorbia pulcherrima: implications for genome size variation and flow cytometry. Ann Botany 101(6):777–790Google Scholar
  12. Bergounioux C, Perennes C, Brown SC, Gadal P (1988a) Cytometric analysis of growth-regulator-dependent transcription and cell-cycle progression in petunia protoplast cultures. Planta 175(4):500–505Google Scholar
  13. Bergounioux C, Perennes C, Brown SC, Gadal P (1988b) Nuclear-Rna quantification in protoplast cell-cycle phases. Cytometry 9(1):84–87PubMedGoogle Scholar
  14. Birnbaum K, Shasha DE, Wang JY, Jung JW, Lambert GM, Galbraith DW, Benfey PN (2003) A gene expression map of the Arabidopsis root. Science 302(5652):1956–1960PubMedGoogle Scholar
  15. Bolta Z, Baricevic D, Raspor P (2003) Biomass segregation in sage cell suspension culture. Biotechnol Lett 25(1):61–65PubMedGoogle Scholar
  16. Bourgaud F, Gravot A, Milesi S, Gontier E (2001) Production of plant secondary metabolites: a historical perspective. Plant Sci 161(5):839–851Google Scholar
  17. Brady SM, Orlando DA, Lee JY, Wang JY, Koch J, Dinneny JR, Mace D, Ohler U, Benfey PN (2007) A high-resolution root spatiotemporal map reveals dominant expression patterns. Science 318(5851):801–806PubMedGoogle Scholar
  18. Brodelius P (1985) The potential role of immobilization in plant cell biotechnology. Trends Biotechnol 3(11):280–285Google Scholar
  19. Capataz-Tafur J, Trejo-Tapia G, Rodríguez-Monroy M, Sepúlveda-Jiménez G (2011) Arabinogalactan proteins are involved in cell aggregation of cell suspension cultures of Beta vulgaris L. Plant Cell Tissue Organ Cult 106(1):169–177Google Scholar
  20. Cassells A, Curry R (2001) Oxidative stress and physiological, epigenetic and genetic variability in plant tissue culture: implications for micropropagators and genetic engineers. Plant Cell Tiss Organ Cult 64(2):145–157Google Scholar
  21. Ceoldo S, Levi M, Marconi AM, Baldan G, Giarola M, Guzzo F (2005) Image analysis and in vivo imaging as tools for investigation of productivity dynamics in anthocyanin-producing cell cultures of Daucus carota. New Phytol 166(1):339–352PubMedGoogle Scholar
  22. Chemler JA, Koffas MAG (2008) Metabolic engineering for plant natural product biosynthesis in microbes. Curr Opin Biotechnol 19(6):597–605PubMedGoogle Scholar
  23. Citterio S, Sgorbati S, Levi M, Colombo BM, Sparvoli E (1992) Pcna and total nuclear-protein content as markers of cell-proliferation in Pea tissue. J Cell Sci 102:71–78Google Scholar
  24. Conia J, Bergounioux C, Perennes C, Muller P, Brown S, Gadal P (1987) Flow cytometric analysis and sorting of plant chromosomes from Petunia-Hybrida protoplasts. Cytometry 8(5):500–508PubMedGoogle Scholar
  25. Croteau R, Ketchum R, Long R, Kaspera R, Wildung M (2006) Taxol biosynthesis and molecular genetics. Phytochem Rev 5(1):75–97PubMedGoogle Scholar
  26. Curtis WR, Tuerk AL (2006) Oxygen transport in plant tissue culture systems. In: Gupta SD, Ibaraki Y (eds) Plant tissue culture engineering. Springer, Dordrecht, pp 173–186Google Scholar
  27. Damato F (1985) Cytogenetics of plant-cell and tissue-cultures and their regenerates. CRC Crit Rev Plant Sci 3(1):73–112Google Scholar
  28. Darzynkiewicz Z, Juan G, Srour EF (2001) Differential staining of DNA and RNA. Curr Protoc Cytom 7(7):3Google Scholar
  29. Darzynkiewicz Z, Sharpless T, Staianocoico L, Melamed MR (1980) Subcompartments of the G1 phase of cell-cycle detected by flow cytometry. Proc Nat Acad Sci U S A 77(11):6696–6699Google Scholar
  30. David H, Laigneau C, David A (1989) Growth and soluble proteins of cell cultures derived from explants and protoplasts of Pinus pinaster cotyledons. Tree Physiol 5(4):497–506PubMedGoogle Scholar
  31. Davis DG, Stolzenberg RL, Dusky JA (1984) A comparison of various growth-parameters of cell-suspension cultures to determine phytotoxicity of Xenobiotics. Weed Sci 32(2):235–242Google Scholar
  32. De Jong DW, Jansen EF, Olson AC (1967) Oxidoreductive and hydrolytic enzyme patterns in plant suspension culture cells : local and time relationships. Exp Cell Res 47(1–2):139–156PubMedGoogle Scholar
  33. Deusneumann B, Zenk MH (1984) Instability of indole alkaloid production in Catharanthus-roseus cell-suspension cultures. Planta Med 50(5):427–431Google Scholar
  34. Dhawan OP, Lavania UC (1996) Enhancing the productivity of secondary metabolites via induced polyploidy: a review. Euphytica 87(2):81–89Google Scholar
  35. Diwan R, Malpathak N (2010) Histochemical localization in Ruta graveolens cell cultures: elucidating the relationship between cellular differentiation and furanocoumarin production. In Vitro Cell Dev Plant 46(1):108–116Google Scholar
  36. Dixon RA (1995) Isolation and maintenance of callus and cell suspension cultures. In: Dixon RA (ed) Plant cell culture. IRL Press, Oxford, pp 1–20Google Scholar
  37. Dolezel J, Greilhuber J, Suda J (2007a) Estimation of nuclear DNA content in plants using flow cytometry. Nat Protoc 2(9):2233–2244PubMedGoogle Scholar
  38. Dolezel J, Greilhuber J, Suda J (2007b) Flow cytometry with plant cells: analysis of genes, chromosomes and genomes. Wiley, WeinheimGoogle Scholar
  39. Dolezel J, Macas J, Lucretti S (2001) Flow analysis and sorting of plant chromosomes. Curr Protoc Cytom 5(5):3PubMedGoogle Scholar
  40. Doran PM (1999) Design of mixing systems for plant cell suspensions in stirred reactors. Biotechnol Prog 15(3):319–335PubMedGoogle Scholar
  41. Dornenburg H, Knorr D (1995) Strategies for the improvement of secondary metabolite production in plant-cell cultures. Enzyme Microb Technol 17(8):674–684Google Scholar
  42. Dougall DK (1964) A method of plant tissue culture giving high growth rates. Exp Cell Res 33(3):438–444PubMedGoogle Scholar
  43. Dubuis B, Kut OM, Prenosil JE (1995) Pilot-scale culture of Coffea arabica in a novel loop fluidised bed reactor. Plant Cell Tissue Organ Cult 43(2):171–183Google Scholar
  44. Dunlop EH, Namdev PK, Rosenberg MZ (1994) Effect of fluid shear forces on plant-cell suspensions. Chem Eng Sci 49(14):2263–2276Google Scholar
  45. Edahiro J, Seki M (2006) Phenylpropanoid metabolite supports cell aggregate formation in strawberry cell suspension culture. J Biosci Bioeng 102(1):8–13PubMedGoogle Scholar
  46. Eibl R, Eibl D (2002) Bioreactors for plant cell and tissue cultures. In: Oksman-Caldentey KM (ed) Plant biotechnology and transgenic plants. Marcel Dekker, New York, pp 163–199Google Scholar
  47. Ellis DD, Zeldin EL, Brodhagen M, Russin WA, McCown BH (1996) Taxol production in nodule cultures of Taxus. J Nat Prod 59(3):246–250PubMedGoogle Scholar
  48. Eriksson T (1965) Studies on growth requirements and growth measurements of cell cultures of Haplopappus gracilis. Physiol Plantarum 18(4):976–993Google Scholar
  49. Evans DA, Gamborg OL (1982) Chromosome stability of cell suspension cultures of Nicotiana spp. Plant Cell Rep 1:104–107Google Scholar
  50. Evans DE, Coleman JOD, Kearns A (2003) Plant cell culture. BIOS Scientific, LondonGoogle Scholar
  51. Galbraith DW (1990) Isolation and flow cytometric characterization of plant protoplasts. Methods Cell Biol 33:527–547PubMedGoogle Scholar
  52. Galbraith DW (2004) Cytometry and plant sciences: a personal retrospective. Cytom Part A 58A(1):37–44Google Scholar
  53. Gaurav V, Kolewe ME, Roberts SC (2010) Flow cytometric methods to investigate culture heterogeneities for plant metabolic engineering. Methods Mol Biol 643:243–262PubMedGoogle Scholar
  54. Ge F, Yuan XF, Wang XD, Zhao B, Wang YC (2006) Cell growth and shikonin production of Arnebia euchroma in a periodically submerged airlift bioreactor. Biotechnol Lett 28(8):525–529PubMedGoogle Scholar
  55. Georgiev MI, Pavlov AI, Bley T (2007) Hairy root type plant in vitro systems as sources of bioactive substances. Appl Microbiol Biotechnol 74(6):1175–1185PubMedGoogle Scholar
  56. Graham MD (2003) The Coulter principle: foundation of an industry. J Assoc Lab Autom 8:72–81Google Scholar
  57. Grand d’Esnon A, Chee R, Harrell RC, Cantliffe DJ (1989) Qualitative and quantitative evaluation of liquid tissue cultures by artificial vision. Biofutur 76:S3Google Scholar
  58. Guillon S, Tremouillaux-Guiller J, Pati PK, Rideau M, Gantet P (2006) Harnessing the potential of hairy roots: dawn of a new era. Trends Biotechnol 24(9):403–409PubMedGoogle Scholar
  59. Guzzo F, Cantamessa K, Portaluppi P, Levi M (2002) Flow cytometry and sorting of protoplasts from carrot cell cultures reveal two cell subpopulations with different morphogenetic potential. Plant Cell Rep 21(3):214–219Google Scholar
  60. Hahlbrock K, Kuhlen E (1972) Relationship between growth of parsley and soybean cells in suspension cultures and changes in conductivity of culture medium. Planta 108(3):271–278Google Scholar
  61. Hall RD, Yeoman MM (1987) Intercellular and intercultural heterogeneity in secondary metabolite accumulation in cultures of Catharanthus roseus following cell line selection. J Exp Bot 38(193):1391–1398Google Scholar
  62. Halperin W, Minocha S (1973) Benzyladenine effects on cell separation and wall metabolism. Can J Botany 51(7):1347–1354Google Scholar
  63. Hanagata N, Ito A, Uehara H, Asari F, Takeuchi T, Karube I (1993) Behavior of cell aggregate of Carthamus tinctorius L. cultured cells and correlation with red pigment formation. J Biotechnol 30(3):259–269Google Scholar
  64. Hara Y, Yamagata H, Morimoto T, Hiratsuka J, Yoshioka T, Fujita Y, Yamada Y (1989) Flow cytometric analysis of cellular berberine contents in high-producing and low-producing cell-lines of Coptis japonica obtained by repeated selection. Planta Medica 2:151–154Google Scholar
  65. Harkins KR, Galbraith DW (1987) Factors governing the flow cytometric analysis and sorting of large biological particles. Cytometry 8(1):60–70PubMedGoogle Scholar
  66. Harrell RC, Bieniek M, Cantliffe DJ (1992) Noninvasive evaluation of somatic embryogenesis. Biotechnol Bioeng 39(4):378–383PubMedGoogle Scholar
  67. Hawkins B (2008) Plants for life: medicinal plant conservation and botanic gardens. Botanic Gardens Conservation International, RichmondGoogle Scholar
  68. Hayashi T, Yoshida K (1988) Cell expansion and single-cell separation induced by Colchicine in suspension-cultured soybean cells. Proc Natl Acad Sci U S A 85(8):2618–2622PubMedGoogle Scholar
  69. Heller FO (1973) DNS-Bestimmung an Keimwurzeln von Vicia faba L. mit Hilfe der Impulscytophotometrie. Ber Deutsch Bot Ges 86:437–441Google Scholar
  70. Hellwig S, Drossard J, Twyman RM, Fischer R (2004) Plant cell cultures for the production of recombinant proteins. Nat Biotechnol 22(11):1415–1422PubMedGoogle Scholar
  71. Henshaw GG, Jha KK, Mehta AR, Shakesha Dj, Street HE (1966) Studies on growth in culture of plant cells.1. Growth patterns in batch propagated suspension cultures. J Exp Bot 17(51):362–377Google Scholar
  72. Hoekstra SS, Harkes PAA, Verpoorte R, Libbenga KR (1990) Effect of auxin on cytodifferentiation and production of quinoline alkaloids in compact globular structures of Cinchona ledgeriana. Plant Cell Rep 8(10):571–574Google Scholar
  73. Huang CN, Cornejo MJ, Bush DS, Jones RL (1986) Estimating viability of plant protoplasts using double and single staining. Protoplasma 135(2):80–87Google Scholar
  74. Huang SY, Chou CJ (2000) Effect of gaseous composition on cell growth and secondary metabolite production in suspension culture of Stizolobium hassjoo cells. Bioproc Biosyst Eng 23(6):585–593Google Scholar
  75. Hulst AC, Meyer MMT, Breteler H, Tramper J (1989) Effect of aggregate size in cell cultures of Tagetes patula on thiophene production and cell growth. Appl Microbiol Biotechnol 30(1):18–25Google Scholar
  76. Ibaraki Y, Kenji K (2001) Application of image analysis to plant cell suspension cultures. Comput Electron Agric 30(1–3):193–203Google Scholar
  77. Ishii S (1988) Factors influencing protoplast viability of suspension-cultured rice cells during isolation process. Plant Physiol 88(1):26–29PubMedGoogle Scholar
  78. James E, Lee JM (2000) An improved optical technique for monitoring plant cell concentration. Plant Cell Rep 19(3):283–285Google Scholar
  79. Jeffers P, Glennon B, Kieran P (2003) Focussed beam reflectance measurement (FBRM) monitoring of particle size and morphology in suspension cultures of Morinda citrifolia and Centaurea calcitrapa. Biotechnol Lett 25:2023–2028PubMedGoogle Scholar
  80. Karuppusamy S (2009) A review on trends in production of secondary metabolites from higher plants by in vitro tissue, organ and cell cultures. J Med Plants Res 3(13):1222–1239Google Scholar
  81. Kato A, Kawazoe S, Soh Y (1978) Viscosity of broth of tobacco cells in suspension culture. J Ferment Technol 56(3):224–228Google Scholar
  82. Kessler M, ten Hoopen HJG, Furusaki S (1999) The effect of the aggregate size on the production of ajmalicine and tryptamine in Catharanthus roseus suspension culture. Enzyme Microb Technol 24(5–6):308–315Google Scholar
  83. Ketchum REB, Gibson DM (1996) Paclitaxel production in suspension cell cultures of Taxus. Plant Cell Tissue Organ Cult 46(1):9–16Google Scholar
  84. Kieran P, Malone D, ML P (2000) Effects of hydrodynamic and interfacial forces on plant cell suspension systems. Adv Biochem Eng Biotechnol 67:139–185PubMedGoogle Scholar
  85. Kieran PM, MacLoughlin PF, Malone DM (1997) Plant cell suspension cultures: some engineering considerations. J Biotechnol 59(1–2):39–52PubMedGoogle Scholar
  86. Kim BJ, Gibson DM, Shuler ML (2004) Effect of subculture and elicitation on instability of Taxol production in Taxus sp suspension cultures. Biotechnol Prog 20(6):1666–1673PubMedGoogle Scholar
  87. Kim Y, Wyslouzil BE, Weathers PJ (2002) Invited review: secondary metabolism of hairy root cultures in bioreactors. In Vitro Cell Dev Plants 38(1):1–10Google Scholar
  88. King PJ, Mansfiel Kj, Street HE (1973) Control of growth and cell-division in plant-cell suspension cultures. Can J Botany 51(10):1807–1823Google Scholar
  89. King PJ, Street HE (1977) Growth patterns in cell cultures. In: Street HE (ed) Plant tissue and cell culture, 2nd edn. Blackwell, Oxford, pp 307–387Google Scholar
  90. Kinnersley AM, Dougall DK (1980) Increase in anthocyanin yield from wild-carrot cell-cultures by a selection system based on cell-aggregate size. Planta 149(2):200–204Google Scholar
  91. Kobayashi Y, Fukui H, Tabata M (1989) Effect of oxygen-supply on berberine production in cell-suspension cultures and immobilized cells of Thalictrum minus. Plant Cell Rep 8(4):255–258Google Scholar
  92. Kolewe ME, Gaurav V, Roberts SC (2008) Pharmaceutically active natural product synthesis and supply via plant cell culture technology. Mol Pharm 5(2):243–256PubMedGoogle Scholar
  93. Kolewe ME, Henson MA, Roberts SC (2010) Characterization of aggregate size in Taxus suspension cell culture. Plant Cell Rep 29(5):485–494PubMedGoogle Scholar
  94. Kolewe ME, Henson MA, Roberts SC (2011) Analysis of aggregate size as a process variable affecting paclitaxel accumulation in Taxus suspension cultures. Biotechnol Prog 27(5):1365–1372PubMedGoogle Scholar
  95. Kolewe ME, Roberts SC, Henson MA (2012) A population balance equation model of aggregation dynamics in Taxus suspension cell cultures. Biotechnol Bioeng 109(2):472–482PubMedGoogle Scholar
  96. Kougoulos E, Jones AG, Jennings KH, Wood-Kaczmar MW (2005) Use of focused beam reflectance measurement (FBRM) and process video imaging (PVI) in a modified mixed suspension mixed product removal (MSMPR) cooling crystallizer. J Cryst Growth 273(3–4):529–534Google Scholar
  97. Kron P, Suda J, Husband BC (2007) Applications of flow cytometry to evolutionary and population biology. Annu Rev Ecol Evol Syst 38:847–876Google Scholar
  98. Kubek DJ, Shuler ML (1978a) Electronic measurement of plant-cell number and size in suspension culture. J Exp Bot 29(109):511–523Google Scholar
  99. Kubek DJ, Shuler ML (1978b) Generality of methods to obtain single-cell plant suspension cultures. Can J Botany 56(20):2521–2527Google Scholar
  100. Kubitschek HE (1969) Counting and sizing micro-organisms with the Coulter counter. In: Norris R, Ribbons DW (eds) Methods in microbiology. Academic Press, New York, pp 593–610Google Scholar
  101. Kuboi T, Yamada Y (1978a) Changing cell aggregations and lignification in tobacco suspension cultures. Plant Cell Physiol 19(3):437–443Google Scholar
  102. Kuboi T, Yamada Y (1978b) Regulation of enzyme-activities related to lignin synthesis in cell aggregates of tobacco cell-culture. Biochim Biophys Acta 542(2):181–190PubMedGoogle Scholar
  103. Kurz WGW (1971) A chemostat for growing higher plant cells in single cell suspension cultures. Exp Cell Res 64(2):476–479PubMedGoogle Scholar
  104. Lai Keng C, Koay Suan S, Low Poay H, Boey Peng L (2008) Effect of plant growth regulators and subculture frequency on callus culture and the establishment of Melastoma malabathricum cell suspension cultures for the production of pigments. Biotechnology 7:678–685Google Scholar
  105. Lamboursain L, Jolicoeur M (2005) Determination of cell concentration in a plant cell suspension using a fluorescence microplate reader. Plant Cell Rep 23(10):665–672PubMedGoogle Scholar
  106. Lee EK, Jin YW, Park JH, Yoo YM et al (2010) Cultured cambial meristematic cells as a source of plant natural products. Nat Biotech 28(11):1213–1217Google Scholar
  107. Leonard E, Runguphan W, O’Connor S, Prather KJ (2009) Opportunities in metabolic engineering to facilitate scalable alkaloid production. Nat Chem Biol 5(5):292–300PubMedGoogle Scholar
  108. Li SY, Yuan W, Yang PY, Antoun MD, Balick MJ, Cragg GM (2010) Pharmaceutical crops: an overview. Pharm Crops 1:1–17Google Scholar
  109. Liau DF, Boll WG (1971) Growth, and patterns of growth and division, in cell suspension cultures of bush bean (Phaseolus vulgaris Cv Contender). Can J Botany 49(7):1131–1139Google Scholar
  110. Linden JC, Haigh JR, Mirjalili N, Phisaphalong M (2001) Gas concentration effects on secondary metabolite production by plant cell cultures. Adv Biochem Eng Biotechnol 72:27–62PubMedGoogle Scholar
  111. Lorence A, Nessler CL (2004) Camptothecin, over four decades of surprising findings. Phytochemistry 65(20):2735–2749PubMedGoogle Scholar
  112. Loureiro J, Rodriguez E, Dolezel J, Santos C (2006) Flow cytometric and microscopic analysis of the effect of tannic acid on plant nuclei and estimation of DNA content. Ann Bot 98(3):515–527PubMedGoogle Scholar
  113. Loyola-Vargas VM, Miranda-Ham ML (1995) Root culture as a source of secondary metabolites of economic importance. In: Arnason JT, Mata R, Romeo JT (eds) Phytochemistry of medicinal plants. Plenum Press, New York, pp 217–248Google Scholar
  114. Machackova I, Zazimalova E, George EF (2007) Plant growth regulators I. In: George EF, Hall M, Klerk G (eds) Plant propagation by tissue culture, 3rd edn. Springer Dordrecht, pp 175–204Google Scholar
  115. Madhusudhan R, Rao SR, Ravishankar GA (1995) osmolarity as a measure of growth of plant-cells in suspension-cultures. Enzyme Microb Technol 17(11):989–991Google Scholar
  116. Madhusudhan R, Ravishankar GA (1996) Gradient of anthocyanin in cell aggregates of Daucus carota in suspension cultures. Biotechnol Lett 18(11):1253–1256Google Scholar
  117. Mak YX, Doran PM (1993) Effect of cell-cycle inhibition on synthesis of steroidal alkaloids by Solanum aviculare Plant Cells. Biotechnol Lett 15(10):1031–1034Google Scholar
  118. Martin VJJ, Pitera DJ, Withers ST, Newman JD, Keasling JD (2003) Engineering a mevalonate pathway in Escherichia coli for production of terpenoids. Nat Biotechnol 21(7):796–802PubMedGoogle Scholar
  119. Matkowski A (2008) Plant in vitro culture for the production of antioxidants—a review. Biotechnol Adv 26(6):548–560PubMedGoogle Scholar
  120. Mavituna F, Park JM (1987) Size distribution of plant-cell aggregates in batch culture. Chem Eng J 35(1):B9–B14Google Scholar
  121. McCoy E, O’Connor SE (2008) Natural products from plant cell cultures. Prog Drug Res 65(329):331–370Google Scholar
  122. McDonald KA, Jackman AP, Hurst S (2001) Characterization of plant suspension cultures using the focused beam reflectance technique. Biotechnol Lett 23(4):317–324Google Scholar
  123. Melchers G, Bergmann L (1959) Untersuchungen an Kulturen von haploiden Geweben von Antirrhinum majus. Ber Dtsch Bot Ges 78:21–29Google Scholar
  124. Meyer JE, Pepin MF, Smith MAL (2002) Anthocyanin production from Vaccinium pahalae: limitations of the physical micro environment. J Biotechnol 93(1):45–57PubMedGoogle Scholar
  125. Mishiba KI, Okamoto T, Mii M (2001) Increasing ploidy level in cell suspension cultures of Doritaenopsis by exogenous application of 2,4-dichlorophenoxyacetic acid. Physiol Plantarum 112(1):142–148Google Scholar
  126. Mishra BN, Ranjan R (2008) Growth of hairy-root cultures in various bioreactors for the production of secondary metabolites. Biotechnol App Biochem 49:1–10Google Scholar
  127. Morris P, Fowler M (1981) A new method for the production of fine plant cell suspension cultures. Plant Cell Tiss Organ Cult 1(1):15–24Google Scholar
  128. Muir WH, Hildebrandt AC, Riker AJ (1954) Plant tissue cultures produced from single isolated cells. Science 119(3103):877–878Google Scholar
  129. Mustafa NR, de Winter W, van Iren F, Verpoorte R (2011) Initiation, growth and cryopreservation of plant cell suspension cultures. Nat Protoc 6(6):715–742PubMedGoogle Scholar
  130. Naill MC, Roberts SC (2004) Preparation of single cells from aggregated Taxus suspension cultures for population analysis. Biotechnol Bioeng 86(7):817–826PubMedGoogle Scholar
  131. Naill MC, Roberts SC (2005a) Cell cycle analysis of Taxus suspension cultures at the single cell level as an indicator of culture heterogeneity. Biotechnol Bioeng 90(4):491–500PubMedGoogle Scholar
  132. Naill MC, Roberts SC (2005b) Culture of isolated single cells from Taxus suspensions for the propagation of superior cell populations. Biotechnol Lett 27(21):1725–1730PubMedGoogle Scholar
  133. Naill MC, Roberts SC (2005c) Flow cytometric analysis of protein content in Taxus protoplasts and single cells as compared to aggregated suspension cultures. Plant Cell Rep 23(8):528–533PubMedGoogle Scholar
  134. Naill MC, Roberts SC (2005d) Flow cytometric identification of paclitaxel-accumulating subpopulations. Biotechnol Prog 21(3):978–983PubMedGoogle Scholar
  135. Nash DT, Davies ME (1972) Some aspects of growth and metabolism of Pauls Scarlet rose cell suspensions. J Exp Bot 23(74):75–91Google Scholar
  136. Nesius KK, Fletcher JS (1973) Carbon-dioxide and ph requirements of non-photosynthetic tissue-culture cells. Physiol Plant 28(2):259–263Google Scholar
  137. Nicoloso FT, Val J, Vanderkeur M, Vaniren F, Kijne JW (1994) Flow-cytometric cell counting and DNA estimation for the study of plant-cell population-dynamics. Plant Cell Tiss Organ Cult 39(3):251–259Google Scholar
  138. Nunez-Palenius H, Cantliffe D, Klee H, Ochoa-Alejo N, Ramirez-Malagon R, Perez-Molphe E (2005) Methods in plant tissue culture. In: Shetty K, Paliyath G, Pometto A, Levin R (eds) Food biotechnology, 2nd edn. CRC Press, Boca Raton, pp 553–603Google Scholar
  139. O’Rourke AM, MacLoughlin PF (2005) A comparison of measurement techniques used in the analysis of evolving liquid–liquid dispersions. Chem Eng Process 44(8):885–894Google Scholar
  140. Ochatt SJ, Patat-Ochatt EM, Moessner A (2011) Ploidy level determination within the context of in vitro breeding. Plant Cell Tiss Organ Cult 104(3):329–341Google Scholar
  141. Ogino T, Hiraoka N, Tabata M (1978) Selection of high nicotine-producing cell lines of tobacco callus by single-cell cloning. Phytochemistry 17(11):1907–1910Google Scholar
  142. Ono NN, Tian L (2011) The multiplicity of hairy root cultures: prolific possibilities. Plant Sci 180(3):439–446PubMedGoogle Scholar
  143. Ormerod MG (1990) Flow cytometry: a practical approach. Oxford University Press, OxfordGoogle Scholar
  144. Paau AS, Cowles JR, Oro J (1977) Flow-microfluorometric analysis of Escherichia coli, Rhizobium meliloti, and Rhizobium japonicum at different stages of the growth cycle. Can J Microbiol 23(9):1165–1169PubMedGoogle Scholar
  145. Pepin MF, Smith MAL, Reid JF (1999) Application of imaging tools to plant cell culture: relationship between plant cell aggregation and flavonoid production. In Vitro Cell Dev Plants 35(4):290–295Google Scholar
  146. Petersson SV, Johansson AI, Kowalczyk M, Makoveychuk A, Wang JY, Moritz T, Grebe M, Benfey PN, Sandberg G, Ljung K (2009) An auxin gradient and maximum in the arabidopsis root apex shown by high-resolution cell-specific analysis of IAA distribution and synthesis. Plant Cell 21(6):1659–1668PubMedGoogle Scholar
  147. Petit P, Diolez P, Muller P, Brown SC (1986) Binding of concanavalin-A to the outer-membrane of potato-tuber mitochondria detected by flow-cytometry. FEBS Lett 196(1):65–70Google Scholar
  148. Petit PX (1992) Flow cytometric analysis of rhodamine-123 fluorescence during modulation of the membrane-potential in plant-mitochondria. Plant Physiol 98(1):279–286PubMedGoogle Scholar
  149. Phillips RL, Kaeppler SM, O P (1994) Genetic instability of plant tissue cultures: breakdown of normal controls. Proc Nati Acad Sci U S A 91:5222–5226Google Scholar
  150. Pistelli L, Giovannini A, Ruffoni B, Bertoli A, Pistelli L (2010) Hairy root cultures for secondary metabolites production. In: Giardi M, Ria G, Berra B (eds) Bio-farms for nutraceuticals: functional food and safety control by biosensors, 1st edn. Springer, New York, pp 167–184Google Scholar
  151. Prenosil JE, Hegglin M (1990) Self-immobilized plant cell aggregates in a bioreactor system with low shear stress. Ann N Y Acad Sci 613(1):234–247Google Scholar
  152. Qu JG, Zhang W, Yu XJ, Jin MF (2005) Instability of anthocyanin accumulation in Vitis vinifera L. var. Gamay Freaux suspension cultures. Biotechnol Bioprocess Eng 10(2):155–161Google Scholar
  153. Rajasekhar EW, Edwards M, Wilson SB, Street HE (1971) Studies on growth in culture of plant cells.11. Influence of shaking rate on growth of suspension cultures. J Exp Bot 22(70):107–117Google Scholar
  154. Ramulu KS, Dijkhuis P (1986) Flow cytometric analysis of polysomaty and in vitro genetic instability in potato. Plant Cell Rep 5(3):234–237Google Scholar
  155. Ranch JP, Giles KL (1980) Factors affecting growth and aggregate dissociation in batch suspension-cultures of Datura innoxia (Miller). Ann Bot 46(6):667–683Google Scholar
  156. Rao SR, Ravishankar GA (2002) Plant cell cultures: chemical factories of secondary metabolites. Biotechnol Adv 20(2):101–153PubMedGoogle Scholar
  157. Richmond WR, Jones RL, Fawell PD (1998) The relationship between particle aggregation and rheology in mixed silica-titania suspensions. Chem Eng J 71(1):67–75Google Scholar
  158. Ro DK, Paradise EM, Ouellet M, Fisher KJ et al (2006) Production of the antimalarial drug precursor artemisinic acid in engineered yeast. Nature 440(7086):940–943PubMedGoogle Scholar
  159. Roberts MF, Wink M (1998) Alkaloids: biochemistry, ecology, and medicinal applications. Plenum Press, New YorkGoogle Scholar
  160. Roberts SC, Naill M, Gibson DM, Shuler ML (2003) A simple method for enhancing paclitaxel release from Taxus canadensis cell suspension cultures utilizing cell wall digesting enzymes. Plant Cell Rep 21(12):1217–1220PubMedGoogle Scholar
  161. Roberts SC (2007) Production and engineering of terpenoids in plant cell culture. Nat Chem Biol 3(7):387–395PubMedGoogle Scholar
  162. Roberts SC, Kolewe M (2010) Plant natural products from cultured multipotent cells. Nat Biotech 28(11):1175–1176Google Scholar
  163. Rodriguez-Monroy M, Trejo-Espino JL, Jimenez-Aparicio A, Morante MDL, Villarreal ML, Trejo-Tapia G (2004) Evaluation of morphological properties of Solanum chrysotrichum cell cultures in a shake flask and fermentor and rheological properties of broths. Food Technol Biotech 42(3):153–158Google Scholar
  164. Russin WA, Ellis DD, Gottwald JR, Zeldin EL, Brodhagen M, Evert RF (1995) Immunocytochemical localization of Taxol in Taxus cuspidata. Int J Plant Sci 156(5):668–678Google Scholar
  165. Ryu DDY, Lee SO, Romani RJ (1990) Determination of growth-rate for plant-cell cultures—comparative studies. Biotechnol Bioeng 35(3):305–311PubMedGoogle Scholar
  166. Safar J, Noa-Carrazana JC, Vrana J, Bartos J et al (2004) Creation of a BAC resource to study the structure and evolution of the banana (Musa balbisiana) genome. Genome 47(6):1182–1191PubMedGoogle Scholar
  167. Sakamoto K, Iida K, Koyano T, Asada Y, Furuya T (1994) Studies on plant-tissue cultures.91. Method for selecting anthocyanin-producing cells by a cell sorter. Planta Med 60(3):253–259PubMedGoogle Scholar
  168. Schlatmann JE, Vinke JL, Tenhoopen HJG, Heijnen JJ (1995) Relation between dissolved-oxygen concentration and Ajmalicine production-rate in high-density cultures of Catharanthus roseus. Biotechnol Bioeng 45(5):435–439PubMedGoogle Scholar
  169. Schroder WP, Petit PX (1992) Flow-cytometry of spinach-chloroplasts—determination of intactness and lectin-binding properties of the envelope and the thylakoid membranes. Plant Physiol 100(3):1092–1102PubMedGoogle Scholar
  170. Schwab B, Hulskamp M (2008) Vital stain for plant cytoplasm. CSH Protoc 2008: pdb prot4936Google Scholar
  171. Schween G, Schulte J, Reski R (2005) Effect of ploidy level on growth, differentiation, and morphology in Physcomitrella patens. Bryologist 108(1):27–35Google Scholar
  172. Scragg AH, Bond P, Leckie F, Cresswell R, Fowler MW, Allan EJ (1987) Growth and product formation by plant cell suspensions cultivated in bioreactors. In: Moody JW, Baker PB (eds) Bioreactors and biotransformations. Elsevier Applied Science Publications, New York, pp 12–25Google Scholar
  173. Shapiro H (1994) Practical flow cytometry. Wiley, New YorkGoogle Scholar
  174. Shapiro HM (1981) Flow cytometric estimation of DNA and RNA content in intact cells stained with Hoechst 33342 and pyronin Y. Cytometry 2(3):143–150PubMedGoogle Scholar
  175. Sharma DP, Firoozabady E, Ayres NM, Galbraith DW (1983) Improvement of anther culture in Nicotiana—media, cultural conditions and flow cytometric determination of ploidy levels. Z Pflanzenphysiol 111(5):441–451Google Scholar
  176. Shiba T, Mii M (2005) Visual selection and maintenance of the cell lines with high plant regeneration ability and low ploidy level in Dianthus acicularis by monitoring with flow cytometry analysis. Plant Cell Rep 24(10):572–580PubMedGoogle Scholar
  177. Shuler ML (1999) Overview of yield improvement strategies for secondary metabolite production in plant cell culture. In: Fu TJ, Sing G, Curtis WR (eds) Proceedings of the symposium on plant cell and tissue culture for the production of food ingredients. Kluwer Academic, New York, pp 75–83Google Scholar
  178. Siah CL, Doran PM (1991) Enhanced codeine and morphine production in suspended Papaver somniferum cultures after removal of exogenous hormones. Plant Cell Rep 10(6):349–353Google Scholar
  179. Simmons MJH, Azzopardi BJ (2001) Drop size distributions in dispersed liquid–liquid pipe flow. Int J Multiphas Flow 27(5):843–859Google Scholar
  180. Simpkins I, Collin HA, Street HE (1970) The growth of Acer pseudoplantanus cells in a synthetic liquid medium. Am J Bot 49:420–425Google Scholar
  181. Smetanska I (2008) Production of secondary metabolites using plant cell cultures. Adv Biochem Eng Biotechnol 111:187–228PubMedGoogle Scholar
  182. Smith MAL, Reid JF (1996) Machine vision and automation in secondary metabolite bioprocess control. In: Misawa M, DiCosmo F (eds) Plant cell culture secondary metabolismtoward industrial application. CRC Press, Boca Raton, pp 53–77Google Scholar
  183. Smith MAL, Reid JF, Hansen AC, Li Z, Madhavi DL (1995) Non-destructive machine vision analysis of pigment-producing cell cultures. J Biotechnol 40:1–11Google Scholar
  184. Srivastava S, Srivastava AK (2007) Hairy root culture for mass-production of high-value secondary metabolites. CRC Critic Rev Biotechnol 27(1):29–43Google Scholar
  185. Steiner HY, Dougall DK (1995) Ammonium uptake in carrot cell structures is influenced by pH-dependent cell aggregation. Physiol Plantarum 95(3):415–422Google Scholar
  186. Su WW (2006) Bioreactor engineering for recombinant protein production using plant cell suspension culture. In: Gupta DS, Ibaraki Y (eds) Plant tissue culture engineering. Springer, Berlin, pp 135–159Google Scholar
  187. Sung ZR (1976) Turbidimetric measurement of plant-cell culture growth. Plant Physiol 57(3):460–462PubMedGoogle Scholar
  188. Syklowska-Baranek K, Pietrosiuk A, Kokoszka A, Furmanowa M (2009) Enhancement of taxane production in hairy root culture of Taxus x media var. Hicksii. J Plant Physiol 166(17):1950–1954PubMedGoogle Scholar
  189. Takayama S, Misawa M, Ko K, Misato T (1977) Effect of cultural conditions on growth of Agrostemma githago cells in suspension culture and concomitant production of an anti-plant virus substance. Physiol Plantarum 41(4):313–320Google Scholar
  190. Takeda T, Seki M, Furusaki S (1994) Hydrodynamic damage of cultured-cells of Carthamus tinctorius in a stirred-tank reactor. J Chem Eng Jpn 27(4):466–471Google Scholar
  191. Tanaka H, Aoyagi H, Jitsufuchi T (1992) Turbidimetric measurement of cell biomass of plant-cell suspensions. J Ferment Bioeng 73(2):130–134Google Scholar
  192. Tanaka H, Semba H, Jitsufuchi T, Harada H (1988) The effect of physical stress on plant cells in suspension cultures. Biotechnol Lett 10(7):485–490Google Scholar
  193. Tanaka H, Uemura M, Kaneko Y, Aoyagi H (1993) Estimation of cell biomass in plant-cell suspensions by the osmotic-pressure measurement of culture broth. J Ferment Bioeng 76(6):501–504Google Scholar
  194. Taticek RA, Mooyoung M, Legge RL (1990) Effect of bioreactor configuration on substrate uptake by cell-suspension cultures of the plant Eschscholtzia californica. App Microbiol Biot 33(3):280–286Google Scholar
  195. Thanh NT, Murthy HN, Yu KW, Hahn EJ, Paek KY (2005) Methyl jasmonate elicitation enhanced synthesis of ginsenoside by cell suspension cultures of Panax ginseng in 5-l balloon type bubble bioreactors. Appl Microbiol Biot 67(2):197–201Google Scholar
  196. Thom M, Maretzki A, Komor E, Sakai W (1981) Nutrient uptake and accumulation by sugarcane cell cultures in relation to the growth cycle. Plant Cell Tiss Organ Cult 1(1):3–14Google Scholar
  197. Torrey JG, Merkel N, Reinert J (1962) Mitosis in suspension cultures of higher plant cells in a synthetic medium. Am J Cardiol 10(4):420–425Google Scholar
  198. Trejo TG, Hernandez TR, Trejo EJL, Jimenez AA, Rodriguez MM (2003) Analysis of morphological characteristics of Solanum chrysotrichum cell suspension cultures. World J Microbiol Biotechnol 19(9):929–932Google Scholar
  199. Tulecke W (1966) Continuous cultures of higher plant cells in liquid media—advantages and potential use of a phytostat. Ann N Y Acad Sci 139(A1):162–175Google Scholar
  200. Umetsu N, Ojima K, Matsuda K (1975) Enhancement of cell separation by colchicine in cell-suspension cultures of soybean. Planta 125(2):197–200Google Scholar
  201. Verma D, Van Huystee R (1970a) Cellular differentiation and peroxidase isozymes in cell cultures of peanut cotyledons. Can J Botany 48:429–431Google Scholar
  202. Verma D, Van Huystee R (1970b) Relationship between peroxidase, catalase and protein synthesis during cellular development in cell cultures of peanut. Can J Biochem 48:444–449PubMedGoogle Scholar
  203. Verpoorte R, Memelink J (2002) Engineering secondary metabolite production in plants. Curr Opin Biotechnol 13(2):181–187PubMedGoogle Scholar
  204. Verpoorte R, van der Heijden R, ten Hoopen HJG, Memelink J (1999) Metabolic engineering of plant secondary metabolite pathways for the production of fine chemicals. Biotechnol Lett 21(6):467–479Google Scholar
  205. Vongpaseuth K, Roberts SC (2007) Advancements in the understanding of Paclitaxel metabolism in tissue culture. Curr Pharm Biotechnol 8(4):219–236PubMedGoogle Scholar
  206. Wallner SJ, Nevins DJ (1973) Formation and dissociation of cell aggregates in suspension cultures of Pauls Scarlet Rose. Am J Botany 60(3):255–261Google Scholar
  207. Watts MJ, Galpin IJ, Collin HA (1984) The effect of growth-regulators, light and temperature on flavor production in celery tissue-cultures. New Phytol 98(4):583–591Google Scholar
  208. Weber J, Georgiev V, Pavlov A, Bley T (2008) Flow cytometric investigations of diploid and tetraploid plants and in vitro cultures of Datura stramonium and Hyoscyamus niger. Cytom Part A 73A(10):931–939Google Scholar
  209. Williams PD, Wilkinson AK, Lewis JA, Black GM, Mavituna F (1988) A method for the rapid production of fine plant-cell suspension-cultures. Plant Cell Rep 7(6):459–462Google Scholar
  210. Wilson SA, Roberts SC (2012) Recent advances towards development and commercialization of plant cell culture processes for the synthesis of biomolecules. Plant Biotechnol J 10(3):249–268PubMedGoogle Scholar
  211. Wink M, Alfermann AW, Franke R, Wetterauer B et al (2005) Sustainable bioproduction of phytochemicals by plant in vitro cultures: anticancer agents. Plant Genetic Res 3(02):90–100Google Scholar
  212. Wongsamuth R, Doran PM (1997) The filtration properties of Atropa belladonna plant cell suspensions; effects of hydrodynamic shear and elevated carbon dioxide levels on culture and filtration parameters. J Chem Technol Biotechnol 69(1):15–26Google Scholar
  213. Wu SQ, Schalk M, Clark A, Miles RB, Coates R, Chappell J (2006) Redirection of cytosolic or plastidic isoprenoid precursors elevates terpene production in plants. Nat Biotechnol 24(11):1441–1447PubMedGoogle Scholar
  214. Xu JF, Xie J, Han AM, Feng PS, Su ZG (1998) Kinetic and technical studies on large-scale culture of Rhodiola sachalinensis compact callus aggregates with air-lift reactors. J Chem Technol Biotechnol 72(3):227–234Google Scholar
  215. Yadav RK, Girke T, Pasala S, Xie MT, Reddy V (2009) Gene expression map of the Arabidopsis shoot apical meristem stem cell niche. Proc Natl Acad Sci U S A 106(12):4941–4946PubMedGoogle Scholar
  216. Yang YM, He DG, Scott KJJ (1994) Cell aggregates in wheat suspension cultures and their effects on isolation and culture of protoplasts. Plant Cell Rep 13(3):176–179Google Scholar
  217. Yanpaisan W, King NJC, Doran PM (1998) Analysis of cell cycle activity and population dynamics in heterogeneous plant cell suspensions using flow cytometry. Biotechnol Bioeng 58(5):515–528PubMedGoogle Scholar
  218. Yanpaisan W, King NJC, Doran PM (1999) Flow cytometry of plant cells with applications in large-scale bioprocessing. Biotechnol Adv 17(1):3–27PubMedGoogle Scholar
  219. Yesilirmak F, Sayers Z (2009) Heterelogous expression of plant genes. Int J Plant Genomics 2009:296482PubMedGoogle Scholar
  220. Yuan X, Zhao B, Wang Y (2004) Cell culture of Saussurea medusa in a periodically submerged air-lift bioreactor. Biochem Eng J 21(3):235–239Google Scholar
  221. Zhao D, Huang Y, Jin Z, Qu W, Lu D (2003) Effect of aggregate size in cell cultures of Saussurea medusa on cell growth and jaceosidin production. Plant Cell Rep 21(11):1129–1133PubMedGoogle Scholar
  222. Zhao J, Zhu WH, Hu Q, Guo YQ (2001) Compact callus cluster suspension cultures of Catharanthus roseus with enhanced indole alkaloid biosynthesis. In Vitro Cell Dev Plants 37(1):68–72Google Scholar

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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Chemical EngineeringUniversity of MassachusettsAmherstUSA

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