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Recent Advances on Multi-Parameter Flow Cytometry to Characterize Antimicrobial Treatments

The investigation on antimicrobial mechanisms is a challenging and crucial issue in the fields of food or clinical microbiology, as it constitutes a prerequisite to the development of new antimicrobial processes or compounds, as well as to anticipate phenomenon of microbial resistance. Nowadays it is accepted that a cells population exposed to a stress can cause the appearance of different cell populations and in particular sub-lethally compromised cells which could be defined as viable but non-culturable (VBNC). Recent advances on flow cytometry (FCM) and especially on multi-parameter flow cytometry (MP-FCM) provide the opportunity to obtain high-speed information at real time on damage at single-cell level. This review gathers MP-FCM methodologies based on individual and simultaneous staining of microbial cells employed to investigate their physiological state following different physical and chemical antimicrobial treatments. Special attention will be paid to recent studies exploiting the possibility to corroborate MP-FCM results with additional techniques (plate counting, microscopy, spectroscopy, molecular biology techniques, membrane modeling) in order to elucidate the antimicrobial mechanism of action of a given antimicrobial treatment or compound. The combination of MP-FCM methodologies with these additional methods is namely a promising and increasingly used approach to give further insight in differences in microbial sub-population evolutions in response to antimicrobial treatments.

INTRODUCTION
Inactivation of microorganisms by physical treatments [heat, Ultra-Violet (UV) light irradiation, supercritical carbon dioxide, high hydrostatic pressure,. . . ] or by the action of antimicrobial compounds (biocides, organic acids, peptides, essential oils,. . . ) can result from several mechanisms: inhibition of cell wall synthesis, disruption of the cytoplasmic membrane, binding to DNA, inhibition of protein synthesis, or anti-metabolite activity (Lee et al., 2015). To develop new antimicrobial processes or compounds in food or medical microbiology, an understanding of their mechanisms of action is vital in order to apply them best and to anticipate microbial resistance phenomenon.
Over the years, several methods have been developed to measure viability and vitality of microbes under various stresses: plating, slide culture, vital stains, metabolic activity monitoring, cell components monitoring, fermentation capacity, acidification potential, or oxygen uptake ability (Hayouni et al., 2008). Such methods are time-consuming and labor intensive (Wilkinson, 2015). Besides, when classical plate count method is used to assess survival of a bacterial population after exposure to an environmental stress, the viability is determined by counting live cells which are those that managed to replicate under the particular experimental conditions, while all the others will be presumed dead (Hayouni et al., 2008). Nowadays, it is well-documented that, under stress conditions a population will exhibit cell subpopulations with phenotypes that most likely escape this logic. Environmental stresses can trigger the occurrence of certain cell populations, called viable but nonculturable cells (VBNC), which were stressed and lost their ability to grow on agar medium, but still showed metabolic activity (Ananta et al., 2004). Quantification of injured cells is a great concern for microbiologists, as this subpopulation might be critical if cells can recover and revert to their physiologically active condition (Ayari et al., 2013). For these reasons, fluorescence techniques combined with direct optical detection methods for the rapid assessment of bacterial viability have been increasingly favored for about 10 years.
Among these techniques, multi-parameter flow cytometry (MP-FCM) has been shown to be a powerful tool for rapidly analyzing populations on a cell-by-cell basis and can be applied in many areas of food or medical microbiology (detection of pathogenic bacteria, monitoring lactic acid bacteria fermentation, rapid microbiological analysis of drinking water; Schenk et al., 2011). A flow cytometer can be described as an "automatic microscope" with the advantages of objectivity, high analysis rate, precision and sensitivity (Díaz et al., 2010). The principle is that particles in suspension are pumped into a narrow flow stream intersected by one or more laser beams. Single particles, such as microbial cells, are illuminated individually with the resulting light scatter and fluorescence emission detected at appropriate wavelengths (Bridier et al., 2015). A very large number of particles can be measured, 5000 cells per second in common and even up to 100,000 in specialized instruments, measuring multiple cellular parameters on each cell simultaneously (Díaz et al., 2010). Each individual cell can be characterized based on its fluorescence color, the intensity of the fluorescence signal, as well as the size, shape and granularity of the particles (Bridier et al., 2015). This method is highly compatible with a broad range of fluorescent stains and cell labeling methods. Díaz et al. (2010) detailed principles and instrumentation through schematic descriptions in their review about application of flow cytometry (FCM) to monitor industrial microbial bioprocess.
Firstly FCM developments occurred in human clinical applications especially for immunological analysis (Wilkinson, 2015). For less than 20 years, FCM has become an indispensable tool to the complex area of microbiology. In 2000, a set of publications in the Journal of Microbiological Methods presented cytometry for bacteria (Nebe-von-Caron et al., 2000;Shapiro, 2000;Steen, 2000). More recently, these first data were supplemented by a review of Tracy et al. (2010). The ability to use FCM to visualize, enumerate and analyze a population of cells into subpopulations of varying physiological status is a valuable aid to understanding this intricate area for the microbiologists. Bridier et al. (2015) reviewed the applications in food microbiology such as study of food bacteria function, detection of food microbial communities or detection and persistence of food pathogens. Moreover FCM can be used to elucidate antimicrobial mechanism in food or health domain. Recently, Mathur et al. (2016) published a review about FCM as a tool to study the effects of bacteriocins on prokaryotic and eukaryotic cells. The advancement of FCM and the introduction of novel fluorochromes allow to study the viability of cells, the membrane structure and its integrity, and the membrane potential at a single-cell level. In the perspective of elucidation of the antimicrobial mechanism, FCM should be a very interesting tool. In this review, we describe some recent studies that use FCM as a tool to evaluate the effect of antimicrobial treatment on microbial cells. More specifically, we focus on the use of MP-FCM with individual and simultaneous staining describing the advantages and the limitations. FCM is by definition a multiparametric technique: cells are gated on at least size or complexity parameters and one parameter of fluorescence. Nevertheless, MP-FCM was defined here, as described in the literature, as FCM using several fluorochromes in the same study (Tracy et al., 2010). Based on recently published results, the complementarity with other methods and particularly plate counts methods is discussed.

INFORMATIONS RESULTING FROM DIRECT ANALYSIS OF MICROBIAL CELLS BY FLOW CYTOMETRY
Although staining of microbial cells with dyes prior to flow cytometry analysis is dominating (and will be presented thereafter), direct analysis of cells without staining cells already gives information regarding the morphology of analyzed cells.
Even without staining the sample, a cell immersed in the injected solution already produces signals through the orthogonal-to-flow laser focused beam. The fraction of light scattered collected in the same direction as the incident light is known as Forward Angle Light Scatter (FALS) or Forward Scatter (FS or FSC). This fraction allows an estimation of the cell size: indeed, the quantity of the scattered light increases with the cell size (Díaz et al., 2010;Tracy et al., 2010). The fraction of light scattered laterally and fluorescence are collected and divided by a lens at 90 • from the incidence axis of the laser. The fraction of light scattered in right angle is known as Right Angle Light Scatter (RALS) or Side Scatter (SS or SSC). This signal is related to cell complexity described by morphological characteristics such as cell surface roughness, cell membrane, nucleus and internal granular material, number of organelles (Díaz et al., 2010). For example, this first information concerning SSC vs. FSC allowed Kramer and Thielmann (2016) detecting that bacterial cells aggregated during heat exposure by change of the scatter signals. The strong increase of SSC and FSC indicated that cells formed large agglomerates, exhibiting up to 100-times higher scatter signals than single cells. Booyens and Thantsha (2014) also detected differences in shape and density of the populations' scatter patterns after exposure to garlic clove extract compared to control population for all the tested Bifidobacterium strains. Their hypothesis was that this change in size and external morphology was a change from rod to coccoid shape. Schenk et al. (2011) highlighted the same modification for Escherichia coli cells after UV-C light treatment.

ANALYSIS OF MICROBIAL CELLS BY FLOW CYTOMETRY AFTER THEIR STAINING WITH DYES
Additional information can be obtained provided that samples are stained using fluorochromes. Scattering and fluorescence signals provide information about intrinsic and extrinsic cell parameters, respectively.

Multi-Parameter Flow Cytometric Analysis: Individual Use of Dyes
A way to use the flow cytometry to characterize antimicrobial treatments is to perform a multi-parameter analysis with different stains in combination. This approach will be presented thereafter when these stains are used simultaneously (in 3.2. part). However, several researchers have chosen to use dyes separately ( Table 1).
The following paragraphs briefly discuss microbial FCM dyes commonly employed in recent works to characterize antimicrobial treatments.

Membrane Integrity
To interrogate membrane integrity, the nucleic acids (NA) content of individual cells is analyzed and dye exclusion methods are favored. NA dyes can stain DNA, RNA, or both (Tracy et al., 2010). Cells showing intact membranes are impermeable to multiple charges dyes such as dyes of the Sytox TM family or to cyanines such as TO-PRO R 3. If cells lose membrane integrity, these dyes enter into the cells emitting fluorescence upon NA binding. Propidium iodide (PI) is the most commonly used dye (Díaz et al., 2010). This dye is usually employed for dead cells detection and it is suitable for multi-parametric analysis along with green fluorochromes such as SYTO9 R . It contains two positive charges and is normally excluded from cells due to its divalence (Kim et al., 2009). Therefore, PI can only enter permeabilized cytoplasmic membranes. For instance, the commercial available LIVE/DEAD R BacLight TM kit from Molecular Probes is the most used (Possemiers et al., 2005;Kim et al., 2009;Muñoz et al., 2009;Martínez-Abad et al., 2012;Choi et al., 2013;Booyens and Thantsha, 2014;Fernandes et al., 2014;Manoil et al., 2014;Pal and Srivastava, 2014;Boda et al., 2015;Freire et al., 2015;Li H. et al., 2015;Li W. et al., 2015).

Pump Activity
Ethidium bromide (EB) is a positively-charged monovalent compound that is used to evaluate the efflux pump system of bacteria. It is a membrane-permeant and it enters into intact cell membranes, but it is actively pumped out of the cell via a non-specific proton anti-port transport system (Kim et al., 2009;Díaz et al., 2010). When the membrane is damaged and the efflux pump also malfunctions, EB can stain the intracellular DNA of the cell (Kim et al., 2009).

Membrane Potential
Membrane potential is generated due to the different ions content inside and outside the cell and it varies from 100 to 200 mV. Only living cells are able to maintain membrane potential: therefore, it is one of the most used parameters to assess cell viability (Díaz et al., 2010). When this difference decreases to zero, the membrane is structurally damaged, and ions go across the membrane freely, but if it means a decrease in cell activity, it does not necessarily imply death. Measurements are carried out by using lipophilic dyes which go through the cell membrane and accumulate according to their charge. The fluorescence signal can be directly related to cell energy levels and to test the reliability of staining, it is recommended to observe if the dye uptake is sensitive to ionophores such as carbonyl cyanide mchlorophenylhydrazone (CCCP; Pianetti et al., 2008;Díaz et al., 2010;Hammer and Heel, 2012;Li W. et al., 2015).
Cationic dyes, such as carbocyanins, DiOC n (3), or Rhodamine 123, accumulate inside polarized cells because viable cells are permeable to those dyes (Díaz et al., 2010;Tracy et al., 2010). Nevertheless, Gram negative bacteria outer membranes can present a barrier to lipophilic dyes uptake. However, a mild treatment with a chelating agent such as Tris-EDTA (ethylenediamine tetraacetic acid) can overcome this limitation (Tracy et al., 2010;Boda et al., 2015).

Metabolic Activity
Metabolic activity detection suggests the absence of cell death, but giving the conclusion of alive cell or dead cell is difficult in the case of cell damage, dormancy, and starvation (Nebe-von-Caron et al., 2000;Díaz et al., 2010). In general, a non-fluorescent permeant substrate is taken up by the cell by diffusion and converted inside the cell by intracellular enzymes to a fluorescent substance which is ideally retained in cells with intact membranes (Tracy et al., 2010).

Respiratory activity
Bacterial cells with electron transport system activity or respiratory activity are able to reduce 5-cyano-2, 3-ditolyl tetrazolium chloride (CTC) to an insoluble fluorescent CTCformazan product that accumulates inside the cells (Caldeira et al., 2013;Ferreira et al., 2014;Duarte et al., 2015). For ∼60 and ∼70% reduction was recorded in the survival of staphylococcal species and Escherichia coli, respectively at pulsed magnetic field as evaluated by colony forming unit (CFU) analysis and flow cytometry. A 2-5 fold increase in intracellular ROS (reactive oxygen species) levels suggests oxidative stress as the key mediator in PMF induced bacterial death/injury. Caldeira et al., 2013 Assessment of antibacterial properties of L-cysteine and mechanism of action against Staphylococcus aureus and Klebsiella pneumoniae • PI (membrane permeability) • DiBAC 4 (membrane potential) • CTC (respiratory activity) The main mechanism of action of L-Cys on both bacteria, K. pneumonia, and S. aureus, was the reduction in metabolic activity, consistent with the results that confirmed the bacteriostatic effect of L-Cys on both these bacteria. Kramer and Muranyi, 2014 Investigate the effects of a pulsed light treatment on the physiological properties of Listeria innocua and Escherichia coli • PI (membrane permeability) • DiBAC 4 (membrane potential) • EB (efflux pump activity) • cFDA (esterase activity) Oxidative stress with concomitant damage to the DNA molecule was shown to be directly responsible for the loss of cultivability due to pulsed light rather than a direct rupture of cell membranes or inactivation of intracellular enzymes. Kramer and Thielmann, 2016 Monitoring the live to dead transition of bacteria during thermal stress • PI (membrane permeability) • DiBAC 4 (membrane potential) • cFDA (esterase activity) • 2-NBGD (glucose uptake) Exposure to moderate heat first of all compromised the function of the respiration chain and other heat sensitive proteins of the cell membrane such as efflux pumps. Membrane rupture and intracellular esterase activity were less affected and strong differences depending on the type of bacteria regarding their Gram-staining behavior were observed. CTC-formazan fluorescence analysis, two regions of CTCformazan relative fluorescence were analyzed, depending on the fluorescence intensity of positive and negative controls (Ferreira et al., 2014;Morishige et al., 2015). Ferreira et al. (2014) and Díaz et al. (2010) expressed the limit of this stain: CTC staining allows the detection of the most metabolically active bacteria, cells with low respiratory activity may not be detected as CTCpositive, probably due to the relative toxicity of cellular CTC accumulation.

Enzymatic activity
Esterase activity is the most common way to evaluate enzymatic activity (Díaz et al., 2010), particularly in studies about damages after antimicrobial treatment (Ananta et al., 2004;Hayouni et al., 2008;Ananta and Knorr, 2009;Schenk et al., 2011;Ayari et al., 2013;Thabet et al., 2013;Antolinos et al., 2014;Kramer and Muranyi, 2014;Surowsky et al., 2014;Hong et al., 2015;Combarros et al., 2016;Kramer and Thielmann, 2016;Meng et al., 2016). Fluorescein and fluorescein derivatives have been used for a wide range of microorganisms as probes for enzymatic activity measurement (Díaz et al., 2010). Among these, carboxyfluorescein diacetate (cFDA) is used primarily for the evaluation of esterase cellular activity (Ananta et al., 2004). It is a lipophilic non-fluorescent precursor that rapidly diffuses across the cell membranes. In the intracellular compartment, diacetate groups of cFDA are hydrolyzed by unspecific esterases into carboxyfluorescein (cF) which is a polar membrane-permeant fluorescent compound. The cells only remain fluorescent if their membranes are intact, thus for cells to be associated as viable, this probe requires both active intracellular enzymes and intact membranes (Hoefel et al., 2003). Moreover, efflux of cF upon glucose addition could also be used as an additional indicator of metabolic performance of the cell (Ananta et al., 2004). Schenk et al. (2011) showed how carefully this staining must be used. Untreated E. coli, Listeria innocua, and Saccharomyces cerevisiae stained with cFDA showed a heterogeneous behavior in their fluorescence labeling properties. Not all cells yielded high green fluorescence and appeared in expected quadrant in the cytogram. They formulated two hypotheses: (i) the presence of the outer membrane with lipopolysaccharides in Gram negative bacteria or the thick wall of peptidoglycan in Gram positive bacteria which does not allow cFDA freely diffusing across cytoplasmic membrane; (ii) the active expulsion of cF outside the cell by bacteria pumps and consequently the lack of green fluorescence despite the existence of metabolic activity. Anyway, concentrations of probes, incubation periods, cytometric setting, and control should be assessed for each studied bacteria individually (Kramer and Thielmann, 2016), as performed by Nexmann Jacobsen et al. (1997) for Listeria monocytogenes. They compared the use of LIVE/DEAD R BacLight TM kit, Rhodamine 123, 2 ′ ,7 ′ -bis(2-carboxyethyl)-5(6)-carboxyfluorescein acetoxymethylester (BCECF-AM), Chemchrome B and cFDA. In conclusion, they determined that only cFDA and Chemchrome B were suitable for rapid, almost real time counting of pure cultures of L. monocytogenes by flow cytometry, after 6−24 h incubation in selective enrichment media.
FCM with fluorescent dyes can thus be considered as a suitable tool for the assessment of structural and/or functional microbial cell properties such as metabolic activity, membrane potential, and integrity. The combination of dyes is a pertinent multi-method approach to reveal the presence of intermediate physiological states between life and cell death, showing the heterogeneities of microbial populations. Nebe-von-Caron et al. (2000) classified cells according to some active functions or the integrity of cell structures. These authors distinguished among reproductively viable, metabolically active, intact, and permeabilized cells.
A multi-method approach is a suitable tool to monitor the impact of inactivation treatments on bacteria, providing information about the mode of action, the heterogeneity of populations, species-specific differences to stressors and valuable insight in vital functions beyond pure culturability (Kramer and Thielmann, 2016).
The results obtained by FCM are in the form of graphical visualization of scattering and fluorescence cell parameters, which are being analyzed and stored for further analysis (Díaz et al., 2010). Acquired data are identified as events, as the number of cells showing a desired physical property or probe. In the case of single-staining cells, two types of graphical results could be obtained (e.g., Figure 1). A monoparametric histogram presents the number of cells (y-axis) vs. the scattering or fluorescence intensity (x-axis; e.g., Figure 1-I). Lee et al. (2015) detected membrane permeabilization of Candida albicans by Sytox Green fluorescence. Cells treated by scolopendin 2 and melittin indicated a significant increase in fluorescence intensity. These results show that the fungal cell membranes were permeabilized by the peptides. A biparametric histogram represents the intensity of the signals corresponding to different parameters in each axis (e.g., Figure 1-II: scattering intensity (y-axis) vs. CTC-fluorescence activity). Each dot represents a single cell and different regions can be defined into the cytogram to describe cells physiological state.
The publication of Silva et al. (2011) showed well the value of this approach in determining the mechanism of coriander essential oil against Candida spp. with three dyes: PI (membrane permeability), DiBAC 4 (membrane potential), DRAQ5 (DNA staining). Firstly, the percentage of PI-positive cells seemed to depend on essential oil concentration: higher essential oil concentration caused higher membrane permeability. The structure of the cell membrane was disrupted by the essential oil. Permeation to PI, particularly following short incubation period, such as 30 min, indicated that the mode of action of the essential oil involved a lesion of the cell membrane that resulted from direct damage to the cell membrane instead of a metabolic impairment leading to secondary membrane damage. Secondly, the percentage of depolarized cells was also essential oil concentration dependent. Thirdly, DNA distribution histograms were very similar in the control and in essential oiltreated cells, which could indicate that coriander essential oil did not interfere with DNA synthesis. However, at ½Minimal Inhibitory Concentration (MIC), fluorescence intensity values were slightly different: essential oil-treated cells showed higher fluorescence intensity values than control cells. This could indicate that in response to cell damage by coriander essential oil, cells were synthesizing more DNA in order to repair damage functions. Moreover, at 1 MIC, lowest fluorescence intensity values were observed, probably indicating DNA leakage from the cells. In conclusion, these three staining procedures permitted Silva et al. (2011) to propose an antimicrobial mechanism: coriander essential oil killed Candida spp. by damaging the cytoplasmic membrane, leading to an impairment of all cellular functions. In the same way, Teng et al. (2014) used two dyes (PI and FITC-labeled AvBD103b) to demonstrate that defensin AvBD103b destroyed the membrane integrity. The antimicrobial target of the defensin was the Salmonella enteritidis CVCC3377 cell membrane. A strong permeation occurred in just 5 min treatment. Nevertheless, they made Transmission Electron Microscopy (TEM) observations and followed cellular DNA by spectroscopy to elucidate further the antimicrobial mechanism. Membrane injury was not the only mechanism of microbial killing. In the same way, by using PI-DiBAC4-EB-cFDA individual staining, Kramer and Muranyi (2014) demonstrated that pulsed light exposition induced oxidative stress with concomitant damage to the DNA molecule which were directly responsible for the loss of culturability of L. innocua and E. coli cells rather than a direct rupture of cell membranes or inactivation of intracellular enzymes.
Multi-parameter FCM analysis allowed highlighting VBNC cells populations such as in the study of Morishige et al. (2015). They treated an environmental isolate of Salmonella enteritidis clone (SE C1#15-1) with different concentrations of H 2 O 2 and analyzed by MP-FCM the respiratory activity, the glucose-uptake activity and DNA synthesis activity individually. Compared with plate count results, they defined VBNC populations as "metabolically active but non-culturable." H 2 O 2treated S. enteritidis cells lost their respiratory activity in a dose-dependent manner. Besides, they showed that H 2 O 2 treatment did not severely decrease the glucose-uptake activity. These results suggested that metabolic mechanism of glucoseuptake might have been more tolerant to H 2 O 2 treatment than aerobic respiration. Finally, the subpopulation of cells with DNA synthesis disappeared almost entirely after treatment with more than 3 mM H 2 O 2 . Therefore, the higher the concentration of H 2 O 2 was, the lower the population of cells showing activities of viable cells. Nevertheless, the size of the population varied considerably among the biological activities: glucoseuptake being the largest, CTC-reduction and DNA synthesis, the smallest. In this manner, Morishige et al. (2015) showed that VBNC state might be divided differently according to the metabolic activities by which they were estimated. This illustrated the value of MP-FCM analysis to screen several physiological responses in order to describe thoroughly the cellular state. Moreover, Kramer and Thielmann (2016) revealed the difficulty to discuss with only one staining and the importance of the complementary results of the multi-staining approach. They developed this idea especially for the use of cFDA. Indeed cF, a fluorescent molecule released by esterase activity on cFDA, is usually retained in intact bacteria but leaks from damaged cells, leading to a lower fluorescence intensity. cFDA, esterase substrate may not only serve as an indicator for enzymatic activity but also for membrane integrity. The discrimination of cells with compromised membrane from cells with inactive esterase is difficult. Kramer and Thielmann (2016) recommended to check for a time dependent change in the fluorescence properties of cF-stained cells and also to include a membrane impermeable dye like PI in the characterization of bacterial cells to check membrane integrity. This limitation encouraged many authors to develop a different MP-FCM approach, namely the use of two dyes simultaneously.

Multi-Parameter Flow Cytometric Analysis: Simultaneous Use of Dyes
Simultaneous double-staining could allow characterizing an intermediate state where cells show fluorescence with both probes (Figure 2). The analysis with several dyes depends on the compatibility of the dyes namely spectral emission and staining conditions. If the two dyes are discriminating, four quadrants can be delimited corresponding to four different physiological states (Figure 2-I). If only one dye is discriminating, such as Syto9 R which is able to enter all cells, two states are described: an initial and a final state (Figure 2-II).
Different dye couples can be used depending on the targeted cell functions ( Table 2).
During an antimicrobial treatment, an increase of unstained population could appear as reported by Booyens and Thantsha (2014). They formulated two hypotheses: (i) a correspondence to the cells that have undergone severe lysis and thus lost their nucleic acids, thereby rendering them unstainable called "ghost cells"; (ii) cells that clumped together or formed interlaced chains which may decrease staining accuracy, correlated to a change in light scatter signals.
Thiazole orange (TO) is another dye for membrane integrity, which is very interesting because it is able to bind both to DNA and RNA. Since TO-DNA complex has a higher fluorescent intensity than TO-RNA complex, this allows a differentiation. Based on this approach, Surowsky et al. (2014) defined thus the bactericidal action of cold plasma against Citrobacter freundii as a mechanism based on cell permeabilization and RNA damage.

Membrane Integrity/Physiological State
Enzymatic activity is principally studied by using cFDA dye ( Table 2). After treatment, four kinds of subpopulations were observed with different staining characteristics: (i) lower left quadrant, unstained debris; (ii) upper left quadrant, PI-labeled cells considered as dead; (iii) lower right quadrant, cFlabeled cells considered as viable; (iv) upper right quadrant, double stained population considered as injured/compromised (Hayouni et al., 2008;Ayari et al., 2013). In Hayouni et al. (2008) study, the percentage of the unstained subpopulation after treatment with essential oils did not exceed 8%. Lactobacilli are well-known to exhibit a planar division with cells remaining attached to each other, thus producing chains characteristic for the genus and misleading FCM results. This phenomenon was avoided in many works by using sonication treatment of the cells or gentle agitation (Hayouni et al., 2008). With a PI/cFDA staining, Hong et al. (2015) explored the antimicrobial mechanism of an antimicrobial peptide, Tachyplesin I, against E. coli and Staphylococcus aureus cells.
The fraction of E. coli cells with PI fluorescence increased proportionally with increase in the concentration of Tachyplesin I, as well as the fraction with cF fluorescence decreased. Nevertheless, intracellular esterase inactivation and membrane damage did not occur simultaneously after exposure to a low concentration of Tachyplesin I (5 µg.mL −1 ), whereas bacteria died instantaneously at concentrations exceeding 5 µg.mL −1 . They detected injured bacteria which cannot form visible colonies on agar plates, but which exhibited detectable metabolic activity. Schenk et al. (2011) highlighted difference of strains behavior after exposure to thermal treatment. For E. coli and S. cerevisiae, inactivation coincided with compromise of membrane, whereas for L. innocua, four subpopulations appeared. For this latter strain, thermal treatment induced cell death and was achieved in presence or absence of membrane degradation and in cells with or without enzymatic activity depending on the intensity of the treatment.
The dual staining with a cell-permeant dye and a dye detecting loss in membrane potential is another interesting possibility to distinguish between different degrees of damage. Coronel-León et al. (2016) used PI-DiBAC 4 double-staining to study the antimicrobial mechanism of N α -lauroyl arginate ethylester (LAE R ). The reduction in viability of Yersinia enterocolitica was evidenced for sub-MIC values of LAE R , the bactericidal action of LAE R increased with its concentration. Even if a similar effect was observed on Lactobacillus plantarum, a high proportion of unstained population was found despite the high reduction in viability. This suggested that the effect of LAE R on L. plantarum not only caused membrane depolarization and permeability but also some other non-specific effects inside the cell, such as a collapse of the cytoplasmic material, could be observed. Novo et al. (2000) reported a discrepancy between the drop in membrane potential of the cytoplasmic material and cell viability, suggesting a recovery in the bacterial population after the initial antimicrobial impact. Nucleic acid dyes should be used with caution as an indicator of cell death (Novo et al., 2000). This was the case of LAE R -treated L. plantarum: 72% of the population remained unstained, while the reduction in viability was 98.6%. This difficulty could be due to the multitarget sites of biocides, or to a reversion to an impermeable state (Novo et al., 2000). This illustrated well the need to analyze several parameters to have a comprehensive understanding of the mechanism (Coronel-León et al., 2016). Novo et al. (2000) compared two couples of dyes, PI/DiBAC 4 and TO-PRO R 3/DiOC 2 , to analyze antibiotic effects on membrane potential, membrane permeability, and bacterial counts of S. aureus and Micrococcus luteus. The results for membrane permeability were comparable with PI and TO-PRO R 3 staining, whereas discrepancies were measured between the results obtained with DiBAC 4 and DiOC 2 for measurement of membrane permeability. Novo et al. (2000) expressed reservation about DiBAC 4 staining which was strongly influenced by cell size.
Another element to take into account is the nature of microorganisms: the behaviors of yeast and bacterial cells may differ. Thabet et al. (2013) treated S. cerevisiae cells by UV-A photocatalysis. Damaged cells, PI-and cF-fluorescent cells, were culturable. This was probably due to the greater  complexity of cell organization compared to bacteria. Damages to the membrane do not imply an immediate enzyme activity break and cell death. For example, contrary to bacteria, respiration functions are not supported by plasma membrane, but in yeast and other eukaryotic organisms, they are compartmented to mitochondria. Thus, considering exclusively the membrane status to characterize photocatalysis effects on yeast cell viability appeared to be insufficient (Thabet et al., 2013). Following analysis by combining dyes, a single staining may also be done in addition to not excessively complicate the analysis. This is often the case for the screening of depolarization by DiBAC 4 or DiOC 2 . In several works, these dyes were used to study the polarization of cell membrane in parallel of a double-staining experimentation (Pianetti et al., 2008;Surowsky et al., 2014;Tamburini et al., 2014;Li W. et al., 2015). (Li W. et al., 2015) performed a Syto9/PI doublestaining and a single DiOC 2 staining of E. coli cells to study the antimicrobial mechanisms of proline-rich peptides (monomers, dimers, and tetramers). The shift in the green fluorescent population as peptide concentration increased, indicated a shift from a mixed hyperpolarized and depolarized cell population to a more depolarized membrane population. With all MP-FCM results, they concluded that multimerization of the Chex-Arg20 monomer to dimer and tetramer altered the mode of action from non-lytic to a membrane disruptive capacity. Huang et al. (2016) reported the development of a high sensitive FCM method to probe minority population of antibioticresistant bacteria. Nucleic acid dye Syto62 was used to stain all the bacteria red. Then, monoclonal antibody against TEM-1 βlactamase and Alexa Fluor 488-conjugated secondary antibody were used to selectively label resistant bacteria which retained β-lactamase activity green. This immunofluorescent-staining method remains marginal as it requires specific antibodies to be available. Fernandes et al. (2014) expressed reservations about the use of MP-FCM. Intermediate states are generally misclassified or simply identified as "unknown" and poorly characterized population. According to them, a way to circumvent this limitation is through a well-defined gating strategy that is highly dependent on how accurate the positive and negative controls are in order to define cell population (viable, compromised, and dead cells). Moreover, that is also why other methods could be applied in parallel for a better description of the cell populations identified by MP-FCM (Table 3).

CORRELATION WITH PLATE COUNTS AND OTHER TECHNIQUES USED SIMULTANEOUSLY Viability vs. Culturability
Nowadays it is accepted that a cell population exposed to a stress can cause the appearance of different cell populations and, in particular sub-lethally stressed/injured cells which could be defined as "active but non-culturable" or "viable but nonculturable." Nevertheless, the definition of these states is still controversial (Antolinos et al., 2014). The presence of such injured bacteria in food or in clinical applications might be critical in terms of their potential activity on excreting toxic or food spoiling metabolites, on transferring of genes (Ananta et al., 2004;Schenk et al., 2011;Ayari et al., 2013;Hong et al., 2015). Even if FCM analysis provides high-speed information at real time on damage at single cell level, plate count method also gives an indication of cells able to grow at a certain time.
First of all, Kramer and Thielmann (2016) described bacterial cell aggregation after a heat treatment. They discussed that this phenomenon could lead to an erroneous result and an overestimation of the inactivation. Indeed a single colony on the plate could contain a cell aggregate, namely more than one culturable cell (Hayouni et al., 2008). Agglomeration effect remains undetected with classical culture-based methods, then FCM acquisition was in this case a very useful method (Manoil et al., 2014).
Most studies included fluorescence staining FCM analysis and plate counting to monitor antimicrobial effects. Nevertheless, the consistency of results obtained by these two methods varied greatly across studies. On one hand, some revealed a good correlation; while on the other hand, others showed clearly a significant discrepancy between conventional plate counts and different viability staining parameters (Table 4). If the comparison between Colonies Forming Units (CFU) and FCM results shows a significant difference, this suggests the presence of sub-lethally stressed subpopulations, not able to form colonies on agar plates (Ayari et al., 2013). Such observations depended on the nature of antimicrobial treatments applied, and thus likely on underlying mechanisms involved. These studies also revealed differences of behavior between strains for the same treatment. The question whether injured cells are able or not to retain their  Chain reaction (PMA-qPCR) Tamburini et al., 2013 proliferation capacity when returned to a favorable environment is still under debate (Manoil et al., 2014).

Viability-Culturability Correlation
On one hand, some researchers showed a strong correlation between MP-FCM and plate counting method results (Possemiers et al., 2005;Hayouni et al., 2008;Muñoz et al., 2009;Ayari et al., 2013;Thabet et al., 2013;Boda et al., 2015;Combarros et al., 2016;Grau-Campistany et al., 2016). For example, Muñoz et al. (2009) used MP-FCM and plate counting to evaluate the viability of L. monocytogenes exposed to several essential oils with antibacterial properties. By comparing MP-FCM results and plate counts, they assumed that compromised cells may recover and grow in appropriate media. As well, Live/Dead analysis proved to be a good alternative to plate counts to monitor the antimicrobial effect of D-erythro-sphingosine in saline toward three intestinal pathogens (Possemiers et al., 2005). According to Grau-Campistany et al. (2016), the viability reduction obtained by plate count is generally in good correlation with membrane damage caused by antimicrobial peptides.

Absence of Correlation between Viability and Culturability
One the other hand, for example, carbon dioxide treatment seemed to be a treatment causing the appearance of VBNC cells (Kim et al., 2009;Spilimbergo et al., 2010;Tamburini et al., 2013;Ferrentino et al., 2015;Li H. et al., 2015). After 5 min Super-Critical carbon dioxide (SC-CO 2 ) pasteurization treatment at 45 and 50 • C of Listeria cells, about 4 and 6 log reductions were measured by plate counts, respectively, while the reductions measured by MP-FCM were 2 log at 45 • C and 4 log at 50 • C (Ferrentino et al., 2015). These results indicated that a substantial amount of Listeria cells of about 10 2 cells.g −1 remained intact but non-culturable after treatment. Spilimbergo et al. (2010) performed a study on S. cerevisiaie cells in saline solutions treated by SC-CO 2 at 36 • C and 10 mPa and they also indicated that conventional cultivation-based methods did not allow an accurate quantification of intact cells. For a treatment at 5000 rpm-100 bar-36 • C during 10 min, the difference was particularly evident with a difference in the percentage of cell survival exceeding 55%. These data indicated a high presence of non-culturable but still intact yeast cells in the treated samples. Nevertheless, after a treatment under the same operative conditions but during 20 min, the difference decreased down to 10% between the two analytical methods. This experimental evidence was probably due to the mechanical irreversible stresses to the cells membranes induced by a longer time. Similar findings were reported on FCM and plate counts analysis to evaluate the effect of SC-CO 2 (Tamburini et al., 2013) and high-pressure carbon dioxide on food borne bacteria L. monocytogenes, Salmonella enterica, and E. coli (Garcia-Gonzalez et al., 2010). Conversely, the efflux pump was completely damaged or malfunctioning before the effect of SC-CO 2 on the culturability of S. enterica serotype Typhimurium cells became apparent (Kim et al., 2009). Kramer and Muranyi (2014) also observed a significant discrepancy between conventional plate counts and different viability staining parameters, which showed that a pulsed light treatment against L. innocua DSM 20649 and E. coli DSM 498 did not cause an immediate shutdown of vitality functions even when the number of colony-forming units already decreased for more than 6 log 10 sample −1 . Even if L. innocua DSM 20649 and S. aureus DSM 346 showed a same trend of concomitant depolarization and loss of respiration activity after a thermal treatment, colony counts were decreased only by less than 0.5 log for L. innocua whereas by more than 3 log for S. aureus (Kramer and Thielmann, 2016). A part of the heat induced de-energized L. innocua population was still able to recover and multiply, and an opposite trend was observed for S. aureus. These results highlighted residual activity of non-culturable bacteria and the strain-dependence of the response to an antimicrobial treatment. In the same study, these authors also observed a strong difference for the uptake of PI after thermal treatment for Gram negative bacteria, which were much more susceptible than Gram positive ones. However, this difference was not reflected by the plate count results: this suggested thus that membrane disruption is not the primary mechanism of mild thermal inactivation of bacterial cells (Kramer and Thielmann, 2016). Even if MP-FCM is a very valuable rapid microbiological method for the assessment of functional properties of bacteria exposed to external stressors, classical plate count method can bring crucial additional information. In fact, reproductive growth requires stringent conditions including both metabolic activity and membrane integrity (Spilimbergo et al., 2010). Manoil et al. (2014) isolated by fluorescent-activated cell sorting an unknown injured population of Streptococcus mutans cells which were exposed to blue light-activated curcumin. Then they cultured this subpopulation on agar plates. They indicated that only 0.6% of this unknown injured population was able to grow, thus supporting the idea of injured cells losing their proliferation capacity in their case. Ananta et al. (2004) also showed that the occurrence of esterase activity did not correlate with viability according to plate enumeration. High hydrostratic pressure treated Lactobacillus rhamnosus GG cells still possessed residual esterase activity. This metabolic activity was not critical for the maintenance of viability.
The existence of differences between culturability and viability with silver as antimicrobial has already been reported (Martínez-Abad et al., 2012). But, conversely, with silver as antimicrobial, it was found that after a period where viable counts were not detected, bacterial populations recovered, and were able to proliferate in most cases. The resuscitation of the cultures was explained by both the existence of a resilient fraction of bacteria in a compromised state and the parallel inactivation of the silver species (Martínez-Abad et al., 2012). Ayari et al. (2013) described the two cases (correlation and non-correlation) in the same study. For an intensive treatment, such as Bacillus cereus cells pre-treated with carvacrol combined with nisin and then exposed to sub-lethal radiation treatment (1 kGy), CFU results were in accordance with FCM analysis, whereas for single treatment with carvacrol and nisin, good agreement was not found. These results showed that the culturability of subpopulations with identical fluorescence characteristics depends on the treatments imposed to the cells. Therefore, a rapid loss of culturability is not necessarily correlated to the complete cell death (Ayari et al., 2013). In the same way, Hayouni et al. (2008) showed a high correlation between CFU and FCM results, except for Lactobacillus pentosus and Lactobacillus rhamnosus. This could be due to the distribution of dead cells in chains.
The presence of VBNC subpopulations was often as a function of the intensity of the treatment. For example, thermal treatments at different temperatures against L. rhamnosus resulted in different responses of the cell to PI/cFDA labeling (Ananta and Knorr, 2009). Exposure to 60 • C reduced considerably the cFaccumulation activity, however, there was no significant loss of membrane integrity. In contrast, when cells were subjected to 75 • C, PI uptake already occurred in the first 90 s. From this staining behavior, they concluded that at higher temperatures the primary target of lethal effect of heat was the bacterial cytoplasmic membrane.
Moreover the correlation seemed to depend on the physiological mechanism which is analyzed. Duarte et al. (2015) showed a lack of direct correlation between the percentage of depolarized cells and the time-kill curves results for Campylobacter spp. and Arcobacter butzleri after exposure to resveratrol inclusion complexes. The cell membrane depolarization could be only a transition state which would occur before membrane permeabilization (Hammer and Heel, 2012) and might be caused by several factors or antimicrobial agents. The cells in this state would have the capacity to regain culturability. Metabolic activity reduction may not be directly related to a decrease in the number of culturable cells (Ferreira et al., 2014).

Microscopy
Transmission Electron Microscopy (TEM; Wu et al., 2010a;Ayari et al., 2013;Choi et al., 2013;Teng et al., 2014;Hong et al., 2015;Li H. et al., 2015;Coronel-León et al., 2016), Scanning Electron Microscopy (SEM; Spilimbergo et al., 2010;Ferreira et al., 2014;Surowsky et al., 2014;Hong et al., 2015;Li H. et al., 2015;Muriel-Galet et al., 2015) and fluorescence microscopy (Tamburini et al., 2013;Thabet et al., 2013;Fernandes et al., 2014;Hong et al., 2015;Li W. et al., 2015) could be used to observe cell interior structure, cell surface morphology and localize fluorescence compounds or components, respectively. Boda et al. (2015) confirmed Syto9/PI double-staining FCM results with TEM micrographs showing the ruptures of E. coli cell walls following their exposure to a 4 Tesla pulsed magnetic field. Spilimbergo et al. (2010) used scanning electron microscopy (SEM) to confirm a clear modification of S. cerevisiae cell structure due to CO 2 treatment. Untreated sample showed cells having a round shape with a smooth surface and no spot, while almost the totality of the cells of the treated sample presented an irregular shape with some dark points/zones, indicating a modification of the cell wall. As well, Muriel-Galet et al. (2015) observed L. monocytogenes and E. coli cell membranes by SEM: they concluded that the membrane of these bacteria is the main target of LAE R . Microscopic observations allow thus to gain further insights into the inactivation mechanism.
If the studied antimicrobial molecule can be labeled, microscopy methods can also be applied to visualize these molecules on bacterial cells as recently performed by (Li W. et al., 2015). Together with MP-FCM results, these authors could conclude that multimerization of the Chex-Arg20 antimicrobial monomer peptide to dimer and tetramer altered its mode of action against E. coli cells from non-lytic to a membrane disruptive capacity. To localize peptides in E. coli cells, Alexa-Fluor 647-labeled peptides were assembled and E. coli membranes were labeled with FM lipophilic styryl dye (FM,. With high-resolution fluorescence microscopy, they observed that peptides localized primarily in the cytosol, while at higher peptide concentrations, peptides associated with both the cytosol, and membrane. It was clear that membrane interaction of the dimer and the tetramer induced membrane lysis, whereas the monomer did not.

Other Techniques
Spectroscopy Fourier transform infrared spectroscopy (FTIR) permits to study the entire molecular composition of microbial cells, to reveal the biochemical composition of cellular constituents such as cell wall, membrane (phospholipid bilayer, peptidoglycan, lipopolysaccharides), and cytoplasm (fatty acids, water, nucleic acids, proteins, polysaccharides). Booyens and Thantsha (2014) observed a change in size and granularity of Bifidobacterium populations exposed to garlic clove extract by MP-FCM and completed this approach by using FTIR spectroscopy. In the presence of garlic, there was a decrease in lipid content of the membrane, which was an additional element that could best describe the state of the cells and the antimicrobial mechanism. Meng et al. (2016) revealed a change of the membrane phospholipid molecules of B. subtilis after an ultrahigh hydrostatic pressure and a mild heat (HPMH) treatment by FTIR spectroscopy. They passed from a liquid crystalline state to a gel state with a decrease in membrane fluidity. HPMH treatment decreased the α-helix content, while it increased the random coil content of the cellular proteins, which resulted in protein denaturation. Tamburini et al. (2014) used the Nuclear Magnetic Resonance spectroscopy (NMR) to observe the changes in cell membrane lipid composition. Firstly, they established the phospholipid profile of E. coli K12 membranes and after SC-CO 2 treatment, they revealed that there were strong perturbations of membrane architecture namely on the two dominant phospholipid species (phosphatidyglycerol and phosphatidylethanolamine). This treatment had no detectable effect on cell density or granularity, whereas the cellular volume changed.
Circular Dichroism spectroscopy (CD) is used extensively to study chiral molecules particularly secondary structure or conformation of proteins sensitive to the environment, temperature, pH modifications. CD can be used to observe how secondary structure of proteins changes with environmental conditions or on interaction with other molecules. Teng et al. (2014) investigated change of the secondary structure of S. enteritidis genomic DNA after exposure to the avian defensin AvBD103b. S. enteritidis genomic DNA showed a typical negative peak and a positive peak around 240 and 270 nm, respectively. When the cells were treated with AvBD103b, the intensity of the DNA ellipticity became weaker. These results suggested that AvBD103b interacted with the S. enteritidis genomic DNA by changing the DNA conformation. With a gel retardation assay, they showed the insertion of the base pairs. Vesicles as Membrane Models Lee et al. (2015) elucidated the scolopendin 2 antimicrobial mechanism as a membrane-active mechanism leading to the formation of pores in microbial plasma membrane. Using giant unilamellar vesicles encapsulating calcein and large unilamellar vesicles containing fluorescein isothiocyanatedextran, which were similar in composition to typical E. coli O157:H7 and Candida albicans membranes, they demonstrated that scolopendin 2 disrupted membranes, resulting in a pore size between 4.8 and 5.0 nm. Membrane modeling appeared thus as a useful method to corroborate and complete the results obtained by flow cytometry. Grau-Campistany et al. (2016) and Wu et al. (2010a) also used lipid vesicles models of POPG (1-palmitoyl-2-oleoyl-glycero-sn-glycero-3-phospho-(1 ′ -racglycerol)) or POPE/POPG (1-palmitoyl-2-oleoyl-sn-glycero-3phosphoethanolamine/POPG) to mimic the Gram positive and Gram negative bacterial membrane. Grau-Campistany et al. (2016) correlated MP-FCM data with biophysical experiments in model membranes, with more permeabilization for E. coli than for S. aureus, corresponding to more leakage from POPE/POPG vesicles than pure anionic POPG vesicles at the same antimicrobial lipopeptide antibiotics concentration. Wu et al. (2010a) described S-thanantin antimicrobial mechanism against E. coli and Bacillus subtilis: first, it disrupted the membrane permeability, and then depolarized the cell membrane.

Propidium MonoAzide Quantitative-Polymerase Chain Reaction (PMA-qPCR)
Propidium monoazide quantitative-Polymerase Chain Reaction (PMA-qPCR) is another viability test method that Ferrentino et al. (2015) used in parallel of a SYBR-PI double-staining. The apparent minor inactivation efficiency evaluated by PMA-qPCR (about 2 log reduction) compared to MP-FCM could be due to incomplete exclusion of dead cells signals leading to false-positive signals, a known drawback of this technique (Ferrentino et al., 2015). The suppression of dead cells signals depends on the complexity of the sample matrix and on the length of DNA amplicon. Besides, PMA-qPCR is also the most sensitive quantification technique compared to MP-FCM and plate counts (Ferrentino et al., 2015). Tamburini et al. (2013) also used PMA-qPCR to assess the effect of SC-CO 2 treatment on L. monocytogenes, E. coli, and S. enterica cells, and their PMA-qPCR and FCM results strongly correlated, even if they detected VBNC population. Indeed, a vast majority of cells remained in the partially permeabilized state.

CONCLUSION
To investigate the efficacy of antimicrobial treatments against microbial cells and to elucidate their mechanism of action, it is interesting to conduct MP-FCM which provides a rapid acquisition of information related to the physiological state of cells and to interpret precisely their survival modes. Nonetheless it has to be kept in mind, that the cellular vital properties are not measured directly, but through the distribution of fluorescent probes or the conversion of substrates. Therefore, the interpretation of data could be different between Gram negative and Gram positive bacteria as well as between bacteria and yeast. Controls must be performed with caution and conclusions should always be drawn carefully.
Additional methods such as microscopy, spectroscopy, membrane modeling, or molecular biology techniques could complete and corroborate MP-FCM results. Viability and culturability potential of microbial cells could be determined by MP-FCM and plate counts, respectively. Depending on the antimicrobial treatment, its intensity, the strains, and the target cell physiological state, viability and culturability can correlate or not. If there was no correlation, this revealed the presence of a compromised VBNC subpopulation which appeared as viable with MP-FCM results but unable to grow on agar plates. The characterization by MP-FCM of such injured bacteria in food or in clinical applications might be critical in terms of their potential capacity to excrete toxic or food spoiling metabolites, to transfer genes (Ananta et al., 2004;Schenk et al., 2011;Ayari et al., 2013;Hong et al., 2015). In food associated microbial communities, gene transfer can have direct implications for human health via the acquisition of new metabolic traits: substrate utilization, bacteriocin, exopolysaccharide and biogenic amine production, immunity to bacteriophages and antibiotic resistance (Kelly et al., 2009;Rossi et al., 2014). This phenomenon could contribute to the distribution of acquired traits to intestinal bacteria (Rossi et al., 2014).
This review of MP-FCM methodologies to assess antimicrobial mechanism gives access to a set of protocols and antimicrobial mechanisms of actions following different treatments descriptions. This could allow the development of such methodology and thus give further information about impact of antimicrobial compounds or physical treatments on microbial cells.

AUTHOR CONTRIBUTIONS
All authors listed, have made substantial, direct and intellectual contribution to the work and approved it for publication.


extfieldsofstudy: Biology Medicine

s2fieldsofstudy: Biology Engineering

text:
Comprehensive genome and epigenome characterization of CHO cells in response to evolutionary pressures and over time

ABSTRACT The most striking characteristic of CHO cells is their adaptability, which enables efficient production of proteins as well as growth under a variety of culture conditions, but also results in genomic and phenotypic instability. To investigate the relative contribution of genomic and epigenetic modifications towards phenotype evolution, comprehensive genome and epigenome data are presented for six related CHO cell lines, both in response to perturbations (different culture conditions and media as well as selection of a specific phenotype with increased transient productivity) and in steady state (prolonged time in culture under constant conditions). Clear transitions were observed in DNA‐methylation patterns upon each perturbation, while few changes occurred over time under constant conditions. Only minor DNA‐methylation changes were observed between exponential and stationary growth phase; however, throughout a batch culture the histone modification pattern underwent continuous adaptation. Variation in genome sequence between the six cell lines on the level of SNPs, InDels, and structural variants is high, both upon perturbation and under constant conditions over time. The here presented comprehensive resource may open the door to improved control and manipulation of gene expression during industrial bioprocesses based on epigenetic mechanisms. Biotechnol. Bioeng. 2016;113: 2241–2253. © 2016 The Authors. Biotechnology and Bioengineering Published by Wiley Periodicals, Inc.

Introduction
The epigenetic regulation and genomic variation which define the behavior of cells, for instance during development of cancer, embryogenesis, or the reprogramming of stem cells, were the focus of numerous recent studies. However, while rapid changes occur in epigenome and transcriptome upon adaptation of primary cells to in-vitro culture, little data is available on the contribution of the above towards adaptation of continuous cell lines that may be maintained in culture under a variety of conditions or towards specific selected phenotypes.
Epigenetic mechanisms may influence gene expression both on a short term (as within a batch culture during changing environmental conditions) (Hernandez-Bort et al., 2012;Le et al., 2013;Wippermann et al., 2014) and on a long-term basis (as during prolonged culture periods over months or during permanent adaptation to different media/culture conditions). The later could explain the phenomenon of phenotypic drift that has been observed, for instance, in long-term cultures (Bailey et al., 2012). Epigenetic control is conveyed via two primary, interacting mechanisms, namely DNA-methylation, and modifications of histones. While the former tends to be more long term, histone modifications, which can have both repressing and enhancing effects on transcription, can change faster in response to environmental stimuli. The effect of these modifications is a change in chromatin structure, influencing the activity of the transcriptional machinery at the respective locus (Cedar and Bergman, 2009;Rose and Klose, 2014). This can be further modified by additional mechanisms, such as the interaction with long non-coding RNAs or by structural DNA sequences such as matrix associated regions or ubiquitous chromatin opening elements that lead to chromatin remodeling (Brinkman et al., 2012;Sarkies and Sale, 2012). So far, these mechanisms were mostly investigated in the context of cancer and developmental biology, so that very little information is currently available on changes in epigenetic regulation in cells maintained in culture (Nestor et al., 2015;Wippermann et al., 2015). The concept of changing the epigenome globally, however, has been used to advantage, both for cell line optimisation (Seth et al., 2006) and for short-term transcriptome modification to increase recombinant productivity by histone deacetylase inhibitors such as sodium butyrate (Kantardjieff et al., 2010;Lee et al., 2014;Liu et al., 2014). The few available studies of epigenetics in Chinese Hamster Ovary (CHO) focused on biotechnologically important issues, such as the silencing of the product gene (Osterlehner et al., 2011;Spencer et al., 2015;Yang et al., 2010), but did not investigate the global dynamics of epigenetics. Several reports indicate changes of the transcriptome during the changing nutrient and metabolite concentrations encountered by cells during batch or fedbatch culture (Hernandez-Bort et al., 2012;Le et al., 2013), while most of the available literature compares gene expression patterns in different CHO clones that produce recombinant protein(s), trying to capture the differences that define their performance in industrial processes, with a focus on high productivity and growth (Charaniya et al., 2009;Clarke et al., 2011;Dinnis et al., 2006;Doolan et al., 2008;Nissom et al., 2006;Vishwanathan et al., 2014). Although epigenetic regulation of gene expression was proposed as one possible contributor to the diversity observed in phenotypes (Dahodwala and Sharfstein, 2014), the issue was marred by the fact that a large number of genomic variants are frequently found in continuous cell lines, due to the high number of divisions they incur (Lin et al., 2014;Weissbein et al., 2014). Similarly, continuous cell lines exhibit variation in the number of chromosomes per cell even within a clonal population, a phenomenon that occurs frequently in cancerous cell lines (Jefford and Irminger-Finger, 2006) and is well documented for CHO cells (Derouazi et al., 2006). In addition to chromosome number aberrations, frequent translocations within and between chromosomes occur resulting in interesting and varying karyotypes (Cao et al., 2012). In addition, there are indications that this phenomenon is not entirely due to specific properties of immortal or cancer cell lines, but may be a result of in vitro culture conditions and the frequent number of divisions that cells undergo in culture, as similar observations were made in stem cells after prolonged in vitro culture periods (Weissbein et al., 2014).
We here present a study of genomic and epigenetic variations in related cell lines that underwent different evolutionary pressures, including adaptation to different media formulations, adaptation to growth in suspension, prolonged culture times and selection of phenotypic variants by cell sorting and subcloning. We use CHO cells because (i) extensive data on their genome (Brinkrolf et al., 2013;Hammond et al., 2012;Lewis et al., 2013;Schroeder Kaas et al., 2015;Xu et al., 2011), transcriptome (Hernandez-Bort et al., 2012Kildegaard et al., 2013;Le et al., 2013), their stability or rather instability (Barnes et al., 2003;Kim et al., 2011) as well as the diversity of phenotypes (Pilbrough et al., 2009) for this cell line is available; (ii) they are a frequently used model system for many applications, so that understanding the processes underlying adaptation and environmental response may have implications for interpretation of research results and finally; and (iii) because their widespread use as a mammalian cell factory for production of biotherapeutic proteins such as human antibodies has large economical and medical consequences (Walsh, 2014).
Both full genome sequences and DNA-methylation patterns were obtained. In addition, short-term response to environmental changes was analysed during a batch culture on the level of DNA-methylation and histone modifications. All data were collated into an online resource.

Cell Lines and Culture Conditions
All cell lines analyzed were derived from serum-dependent CHO-K1 cells (ECACC CCL-61) which were grown in 1:1 DMEM/Ham's F12 (Biochrom) containing 2% fetal calf serum (PAA) and 4 mM L-Gln (Sigma-Aldrich), in T-flasks at 37 C in an atmosphere containing 7% CO 2 (FCS sample). . Cells were adapted to protein-free medium and suspension growth by passaging at 1:3 dilution into CD-CHO (Gibco/Life Technologies) with 8 mM L-Gln and Anti-Clumping Agent (Gibco/Life Technologies). Cells recovered within 3 weeks and were expanded into a master cell bank (MCB). All suspensionadapted cells were grown in shaker flasks at constant shaking at 140 rpm. Cells were thawed from the MCB and allowed to recover for 2 weeks (PF-MCB sample). Cells were then maintained in culture by passaging twice weekly for 6 months (PF-6 months). PF-MCB cells previously adapted to grow in the same medium without glutamine (Hernandez-Bort et al., 2010) (no-Gln) were sampled likewise 2 weeks after thawing as were cells from a subclone obtained by cell sorting for increased transient productivity (Pichler et al., 2011) (K1/1D9-MCB). These high-qP cells were maintained in culture, their transient productivity tested every month until it dropped from initially 13 pg/cell/day to 4 pg/cell/day after 3 months (K1/1D9-3 months). Testing for transient productivity was performed by nucleofection using 20 ng plasmid DNA per 10 7 cells as described previously (Pichler et al., 2011). All samples were taken on day 3 of an exponentially growing culture. For PF-MCB an additional sample was taken during stationary phase (day 8) for bisulfite sequencing (see Fig. S1 for batch sampling points).

Isolation of Genomic DNA
Genomic DNA was isolated from 5 Â 10 6 cells of a mid-exponential culture (day 3 after seeding) following the protocol of the DNeasy Blood & Tissue Kit (Qiagen Cat. No 69504). The sample was diluted to 40 ng/mL in a total volume of 100 mL into 1.5 mLTPX microtubes (Diagenode) and sonicated in a Bioruptor (Diagenode) at 4 C using nine cycles at high setting [30 s "ON"/30 s "OFF"]. The average size obtained was verified to be 400 bp by Bioanalyzer (Agilent DNA 7500 kit).

Library Preparation and Sequencing
Library preparation for genome sequencing was performed using KAPA Library Preparation Kit Illumina series (KK8201) following the manufacturer's guidelines. The first sample sequenced (PF-MCB) was analyzed using an Illumina HiSeq 2000 (pairedend, 100 bp read length), all other samples were analyzed using the upgraded Illumina HiSeq 2500 (paired-end, 150 bp read length). Library preparation and sequencing for bisulfite-treated samples was performed as previously described (Tomazou et al., 2015).

Genomic Mapping and Normalization
Preprocessed reads were aligned in paired-end mode to the Chinese Hamster reference sequence published by Brinkrolf et al. (2013) with BWA mem  using the default parameters with -M and -R flags as recommended for SNP/InDel calling in the corresponding variant calling documentation. Aligned reads were coordinate sorted with Picard SortSam v1.112 (http://broadinstitute.github.io/picard/) and indexed with SAMtools index v0.1.19 . Duplicates were removed with Picard MarkDuplicates v1.112. The quality of the mappings was assessed with QualiMap v2.0 (Garc ıa-Alcalde et al., 2012). The mappings of all cell lines were normalized to match the coverage of the cell line with the lowest average coverage using SAMtools view v0.1.19. The average coverage was computed with QualiMap v2.0 and BEDtools genomeCoverageBed v2.17.0 (Quinlan and Hall, 2010). To normalize the PF-MCB data with a read length of 100 bp (all other cell line data have a read length of 150 bp), the data was subsampled to the lowest average coverage for SNP/InDel analysis and to the lowest read count for SVanalysis, as this analysis is based on broken read pairs.

SNP/InDel Calling
Local realignment, SNP/InDel detection and filtering were performed using the Genome Analysis Toolkit (GATK) v2. 7-4 (McKenna et al., 2010) as recommended by the GATK documentation, with default settings unless stated otherwise. The pipeline was applied on every normalized and full-coverage dataset for each cell line separately. The GATK HaplotypeCaller was run with a minimum phred-scaled confidence threshold at which variants should be emitted of 10 and at which variants should be called of 30 as well as with a minimum pruning of 10. Only SNP/ InDels with a quality score equal or higher than 30 were included in further analyses. Summary statistics were computed using VCFtools vcf-stats v0.1.11 (Danecek et al., 2011). SNP/InDel effects on coding sequences were evaluated using CooVar v0.07 (Vergara et al., 2012).

Structural Variant (SV) Calling
Delly v0.5.9 (Rausch et al., 2012) was applied to call SVs with an insert size cut-off of three (for deletions only) and a minimum paired-end mapping quality of 20. All variants with a minimum of five broken read pairs supporting the variant as well as with a minimum length of 300 bp (for deletions, inversions, and duplications) were included in further analyses as recommended by the Delly documentation.

Bisulfite Mapping and Methylation Calling
Preprocessed reads were aligned in paired-end mode to the reference sequence (Brinkrolf et al., 2013) using Bowtie 2 v2.2.2 (Langmead and Salzberg, 2012) under the control of the Bismark v0.13.0 (Krueger and Andrews, 2011) Bismark was also used to remove duplicate reads. The quality of the mappings was assessed with QualiMap v2.0.
Methylation profiles were generated for all C contexts using Bismark v0.14.3 with default settings. Based on M-bias plots, the first 15 bp from the 5 0 end and 3 bp from the 3 0 end were ignored in both reads of a pair for all samples to avoid biases in methylation calling.

Annotation
Annotation files were downloaded from NCBI for the reference genome. SNPs/InDels were annotated by means of VCFtools vcf-annotate v0.1.11, whereas SVs were annotated by means of Delly io-ver v0.5.9. Methylation profiles were annotated using BEDtools window v.2.17.

Comparative Analyses and Evaluations
The GATK SelectVariants v2.7-4 was used to compute concordance and discordance SNP/InDel lists between all cell line datasets. Comparative summary statistics were computed using VCFtools vcf-compare v0.1.11. To compare SV variant lists between all cell line datasets, BEDtools intersect v2.17.0 with a reciprocal fraction overlap of 0.8, and a breakpoint deviation of 500 bp (for translocation only) was used, generating concordance and discordance SV lists. Finally, the supporting read counts for SNP/InDel and SV calls as well as the homozygosity (0/0) or heterozygosity (0/1) of these calls were extracted from the variant calling results (VCF files).
The differential analysis of methylation profiles was conducted using the R package methylKit v0.9.2 (Akalin et al., 2012). After coverage normalization, methylation profiles were tiled with a window size of 1 kb (step size 1 kb) and filtered to keep only regions covered in all samples. DMRs were called with a minimum difference in methylation of 25%, a minimal coverage threshold of three and a Q-value < 0.1 (based on SLIM ) using Fisher's exact test. R (R Core Team, 2013) and the Bioconductor package GenomicRanges (Lawrence et al., 2013) were used for assignment of called DMRs to genomic features and preparation of summary statistics and plots.

Impact of Coverage on Identification of Variants
To estimate the effect of coverage on the reliability with which variants are called unique at a coverage of 13.5, the reads of K1/1D9-MCB, available at 21.5Â coverage, were subsampled to three sets of 13.5Â each, which were compared against each other both for SMs and SVs. The reads of PF-MCB, available at 37.5Â, were likewise subsampled to 20Â and compared. In addition, K1/1D9-MCB and K1/1D9-3 months, both available at 21Â coverage, were compared at this coverage in addition to the standard 13.5xÂ (Suppl. File-WS13).

Batch Culture for ChIP-Seq-Cell Fixation and Immunoprecipitation
PF-MCB cells were seeded into eight parallel 500 mL shaker flasks at 2 Â 10 5 cells/mL, with working volumes of 250 mL. The cultures were analyzed twice daily for total and viable cell count using a ViCell analyzer (Beckmann Coulter). Samples were taken as indicated in Suppl. File-WS14 from each flask and pooled for ChIP-seq analysis.
Cell fixation and subsequent lysis of 10 7 cells were performed following the protocol of the Shearing ChIP kit by Diagenode (kchredmod-400). Sonication was performed by Bioruptor (Diagenode) using 1 run of 10 cycles [30 s "ON", 30 s "OFF"] at high power setting, 10 min on ice, vortex and spin down, 1 run of 10 cycles [30 s "ON", 30 s "OFF"] at high power setting, 10 min on ice, vortex and spin down, 1 run of 15 cycles [30 s "ON", 30 s "OFF"] at high power setting, 10 min on ice, vortex and spin down, and 1 run of 5 cycles [30 s "ON", 30 s "OFF"] at high power setting. At this point sheared chromatin was stored at À80 C after snap freezing in liquid Nitrogen. Magnetic immunoprecipitation was performed following the protocol from the iDeal ChIP-seq kit by Diagenode (AB-001-0024) using 10 6 cells per 2 mg antibody (H3K4me1, H3K4me3, H3K9me3, H3K27ac, H3K27me3, H3K36me3, and IgG as negative control). DNA concentration was quantified using Quant-IT Picogreen dsDNA (N P7589, Life Technologies). DNA quality control and recovery were assessed by Real-time PCR using TSS-GAPDH (Forward-primer: CCCTTGAGCTGTGACTGGAT, Reverse-primer: CACTCTGCGGTTTT-CACCTG) as positive control target and target myoglobin exon 2 as provided by the kit as negative control. Final control of size and quantity was performed on a Bioanalyzer (Agilent DNA 7500 and DNA 12000 Kit-C5067-1506).

Data Preprocessing and Chip-Seq Mapping
Raw reads were preprocessed with Flexbar-v2.4 (Dodt et al., 2012) using a minimum overlap of adapter and read sequence of 6, 2 allowed mismatches, and gaps per 10 bases overlap and 18 as a minimum read length to remain after removal. The quality was assessed with FastQC v0.10.1 (http://www.bioinformatics. babraham.ac.uk/projects/fastqc/). Although the required depth depends on the nature of the mark and the state of the cells in each experiment, ChIPseq experiments in mouse (Jung et al., 2014) suggest that a depth of 25 million reads are sufficient. Therefore only those samples with more than 25 million reads that passed the filter (50 bp read length) were used. Preprocessed reads were aligned in single end mode to the Chinese Hamster reference sequence published by Brinkrolf et al. (2013) with BWA mem v0.7.7 with default parameters. Aligned SAM files were converted to BAM files with SAMtools view v0.0.18. Aligned BAM files were coordinate sorted with Picard SortSam v1.95 and duplicates removed with Picard MarkDuplicates v1.95 (http://broadinstitute.github.io/ picard/).

Chip-Seq Peak Calling and Genome Annotation
The fragment size was modeled using the PhantomPeakQualTools (http://code.google.com/p/phantompeakqualtools) R script. MACSv2.0.10 (Zhang et al., 2008) peak caller was used to compare ChIP-seq mapped reads to a corresponding input sample sequenced control with the fragment size predicted by PhantomPeakQualTools. Both the standard method for H3K4me3 and H3K27ac and thebroad flag method for H3K4me1, H3K27me3, H3K36me3, H3K4me9 with default parameters were used. All samples were normalized by sequencing depth in million reads with the flag-SPMR. ChIPseeker package (Yu et al., 2015) and the Genomic Features package for R (v.3.02) (Lawrence et al., 2013) were used to annotate the location of normalized peaks relative to genomic features.

Chromatin State Learning Across Time Points
Chromatin states were learned by applying the ChromHMM hidden Markov Model v1.10 (Ernst and Kellis, 2012) at 200 bp resolution to the six histone marks (H3K4me3, H3K4me1, H3K9me3, H3K27ac, H3K27me3, H3K36me3) for each time point. ChIP-seq mapped reads were converted to BED files with BEDTools v2.16.2. Data was binarized in 200 bp using the BinarizeBed function of ChromHMMv1.10, using the input sample sequenced as a control. After testing a range of possible states, an 11 state model was trained with the full data set, to identify biologically meaningful patterns similar to previously published states from human cell types (Roadmap Epigenomics Consortium, 2015) using the ChromHMM LearnModel function. Each time point was segmented using the 11 state model by the MakeSegmentation function, resulting in a segmentation covering 99.8% of the genome. The enrichment of each state of the segmentation was computed for a set of external coordinates (CpG islands, mRNA, exon, TSS, transcription end site (TES) and regions within 2 kb of the TSS) in BED format. Finally, the enrichment of each state at fixed positions relative to anchor positions was computed with the NeighborhoodEnrichment function. Chromatin state coverage was plotted for all reads with ggplot package for R (v.3.02).

Relationship Between Histone Marks and Methylation
An intersection between chromatin states and CpG-methylation coordinates with a minimum read Coverage of three was calculated with BEDTools v2.16.2 and a boxplot generated with ggplot2 package for R (v.3.02).

Differential Binding Analysis ChIP Seq Data
Peaks were subjected to DiffBind (http://bioconductor.org/ packages/release/bio/DiffBind/inst/doc/DiffBind.pdf) analysis to perform differential analysis of histone modifications. The binding matrix was calculated with affinity scores based on TMM normalization (EdgeR), using read counts minus control read counts and Effective Library size. PCA plots were generating using affinity data for all sites. Then, a contrast between the three culture phases was established to run edgeR analysis to identify differentially bound sites using the default threshold of FDR > ¼ 0.1.

Results
For our study, six related cell lines (Fig. 1) were sequenced both for their genome sequence and DNA-methylation pattern. The parental cell line CHO-K1 grown adherent in FCS supplemented medium (FCS) was adapted to growth in suspension in a proteinfree medium, containing 8 mM glutamine, and a master cell bank established (Pf-MCB). This cell line was also used for analysis of histone modifications and changes in DNA-methylation patterns during batch culture. After the initial sampling, cells were passaged for 6 months and reanalyzed (Pf-6 months). Pf-MCB cells were adapted to grow at similar growth rate and to high cell density in the same medium lacking glutamine, the main energy source in vitro, which required metabolic modifications to ensure sufficient energy supply (no-Gln) (Hernandez-Bort et al., 2010). In addition, a subclone isolated for threefold higher transient productivity (Pichler et al., 2011) was analyzed at two time points, 2 weeks after thawing (K1/1D9-MCB) and 3 months later, after the phenotype of high productivity was lost (K1/1D9-3 months). Importantly, except for K1/1D9, cells were adapted to the new culture conditions without subcloning, so represent pools. To enable a valid comparison, all genomes were subsampled to the lowest coverage (13.5-fold) for analysis of SNPs and InDels <5 bp (from now on summarized as small mutations or SM) (Suppl. File WS2). For structural variant (SV) comparison, subsampling was done to the matching number of reads rather than coverage, as analysis for SVs is based on the number of broken read pairs. All reads were mapped against the chromosome assigned Chinese Hamster draft genome (Brinkrolf et al., 2013). subsequently cultivated for 6 months (PF-6 months). PF-MCB cells were adapted to growth in glutamine free medium (no-Gln). A subclone isolated for increased transient productivity was isolated by repeated cell sorting and subcloning (K1/1D9-MCB). This subclone was also passaged for 3 months and reanalyzed (K1/1D9-3 months). Colors shown are used in all figures.

Genomic Variants Occurring During Cell Line Evolution
The first question investigated was the occurrence of genomic modifications during adaptation and selection and over time in culture. In Figure 2a and b, the shared and cell line specific calls of SMs and SVs are presented for comparisons of each cell line relative to the next stage of its evolution (absolute numbers and percentages are listed in Suppl. File WS5 and WS9). Comparative analysis of SMs indicates that 51-57% of these are shared between the two cell lines compared in each case and the rest are uniquely found in one or the other cell type, with SMs both disappearing and new ones appearing at each transition. We observe a larger percentage of SMs lost than gained relative to the total number of SMs within that comparison during the transition from adherent culture to protein-free suspension culture (À29% vs. þ19% for FCS vs. Pf-MCB), which suggests the selection of a suspension compatible genotype. Subsequently, during selection of specific phenotypes such as growth in glutamine-free medium or enhanced transient productivity, more SMs are gained than lost (À18% vs. þ28% for Pf-MCB vs. no-Gln and À19% vs. þ30% for Pf-MCB vs. K1/ 1D9-MCB, respectively). Overall, only $1.0-1.2% of SMs have a direct effect on protein sequence, while 98.5% are intergenic or intronic (Suppl. File WS4).
The variation in SVs, consisting of duplications, deletions, inversions, and translocations, is much higher, with only approx. 30% shared in each comparison. In all cell lines, the majority of SVs consist of translocations (80% or more). Similar to the SMs, the total number of SVs found in each population is comparable, with the highest absolute number found in PF-6 months. The higher overlap in SVs between K1/1D9-MCB and K1/1D9-3 months (37.9% shared SVs vs. 28.4% in the comparison of PF-MCB against PF-6 months) may be explained by the shorter period that passed between the two sampling points. A simulation using subsampled datasets derived from a single sample available at higher coverage also results in unique calls for each subsample (Suppl. File WS13), an effect that decreases with higher coverage. Nevertheless, even at 13.5Â coverage, the percentage of unique calls in the two-sample comparison (1D9-MCB vs. 1D9-3 months) is distinctly higher than in the subsample comparison from the same population (1D9-MCB vs. MCB subsamples), thus supporting the theory of continuously occurring rearrangements in the CHO genome.
An important question in this context is whether, on the genome level, clones are more homogenous than pools. Variant calling in populations comprising several subpopulations with more widely varying genotypes (as expected in a pool) would lead to (i) a higher number of variants with lower alternative allele frequencies and confidence levels ( Fig. 3a and b); (ii) a generally higher number of variant calls with a larger proportion of heterozygous variants (Fig.  3c); and/or (iii) with confidence levels below the threshold considered significant for a given coverage (Fig. 3d). The distribution of homozygosity and heterozygosity for SVs is more diverse than for SMs, with a larger number of heterozygous SVs (0/1) and a significant fraction of variants with a small alternative allele frequency (0/0). In all cases, however, there is no significant difference in distribution between the pools and the clonal cell lines. In addition, the percentage of translocations identified below the quality threshold ranges from 73-82%, with the highest percentage actually found in subclone K1/ 1D9-MCB. Thus, on the level of genome homogeneity, the diversity of a pool of cells is comparable to that of this clone derived from a continuous, immortalized cell line.

DNA-Methylation Changes During Cell Line Evolution
Bisulfite-treated full genome DNA-sequences were mapped against the reference genome (Brinkrolf et al., 2013) and differentially methylated regions of 1 kb (DMR) assigned to promoter regions, transcription start sites (TSS), introns, exons, and intergenic regions ( Fig. 4a and b, Suppl. File WS11). Noticeably, time in culture has the least effect on differential methylation (total number of DMRs: 2,947 for PF over 6 months and 2,480 for K1/1D9 over 3 months), indicating that DNA-methylation patterns are largely passed on to daughter cells under constant culture conditions. In contrast, adaptation to different media/growth conditions causes intermediate changes in DMRs (13,674 DMRs for transition from FCS-containing to protein-free medium and 21,788 DMRs for adaptation to glutamine-free medium). The highest number of DMRs (69,040) is found in the comparison of the K1/1D9-MCB subclone with the parental PF-MCB, where its establishment involved several rounds of sorting for this specific phenotype (top 1% productivity) and a final round of subcloning (Pichler et al., 2011). Overall, 55-65% of all CpGs are fully methylated, and 15-20% fully demethylated (Fig. 4c). Hypermethylation of CpGs is highest in K1/1D9-MCB and lowest in no-Gln, and it decreases over time in culture both for K1/1D9 and PF. Summarized, these results indicate that transcription patterns to fit a given set of culture conditions are laid down by corresponding DNA-methylation patterns which are inherited by daughter cells, but may be altered upon a change in culture conditions.

Epigenetic Response During Batch Culture
In the course of a batch culture, with its declining nutrient availability and increasing waste metabolite concentrations, cells  need to respond by rapidly changing gene transcription. PF-MCB cells were seeded into shaker flasks and sampled every 12 h for ChIP-seq analysis of six histone modifications as recommended by the International Human Epigenetics Consortium (http://ihecepigenomes.org/research/reference-epigenome-standards/) (Suppl. File WS14). Using ChromHMM (Ernst and Kellis, 2012), a model consisting of 11 defined chromatin states corresponding to putative promoters, enhancers, transcribed or heterochromatic regions was generated and these states assigned to in total 11,642,738 segments of 200 bp of the genome (Fig. 5). The majority of the genome ($89%) is either quiescent (very low or no signal) or repressed (Suppl. File WS13). Overall, $22,3% of the 200 bp segments change state during the course of the batch culture. Comparing DNAmethylation patterns in exponential (day 3) and stationary (day 8) phase, 143 regions of 1 kb are differentially methylated, of which only 19 are in annotated promoter regions (Suppl. File WS11).
Intersection of CpG-methylation with the ChromHMM model reveals high methylation in areas of high transcriptional activity, while highly active promoters (state 9) are fully de-methylated (Fig. 5c). Methylation of promoters with weaker activity (state 10) range from fully de-methylated to semi-methylated, which might indicate activity in one allele and inactivity in the other. While there are few DMRs between these two culture stages, the percentage of methylation in the active chromatin states 4-11 do shift to higher levels of methylation, indicating that those changes in DNA-methylation that occur are predominantly found in active regulatory regions. PCA analysis of histone modifications reveals that highly active, transcription related modifications undergo a continuous adaptation during the batch culture, while repressed regions undergo fewer changes ( Fig. 6; Table I). However, for all marks there is a clear separation between these three culture phases: exponential growth (tp 0-8 ¼ 0-101 h), stationary growth (tp 9-12 ¼ 114-149 h) and decline phase (tp 13-17 ¼ 162-210 h). Together, these results indicate that short-term regulation of transcription is a continuous, adaptive process that is primarily controlled by alterations in histone modifications.

Discussion
Comparison of the different cell lines reveals that changes occur at all levels, including SMs, SVs, and DNA-methylation, but are not necessarily linked to the process of adaptation or selection. New SMs and SVs occur also simply during time in culture. The highest degree of variation occurs with SVs, most prominently with translocations, confirming previous reports of chromosomal rearrangements and irregularities in numbers (Cao et al., 2012;Derouazi et al., 2006). Such genomic inconsistencies have also been reported in another immortalized cell line, HEK293 (Lin et al., 2014). Strikingly, variants disappear to a similar extent as new ones appear which can only in part be explained by the sequencing coverage used. Overall, the genome of immortalized cells is seemingly undergoing a continuous and random rearrangement, where certain variants may be prohibitive and therefore are immediately lost (if they impact a vital gene or functionality), while others are carried along or may even be expanded if they provide a slight growth advantage. As only a small percentage of SMs have a direct effect on protein sequence, there presumably is, for most SMs, no evolutionary pressure that promotes or removes them from the population. We note that the number of variants, their distribution and frequency within a population is not higher in a pool of cells compared to the subclone. Given the observed large number of variants that are continuously generated and lost, a "clone" would accumulate a comparable number of variants during the 50-70 population doublings required to expand it into a cell bank, thus essentially eliminating the initial genomic homogeneity of a single cell.
While genomic diversification appears to be a continuous process, changes in DNA-methylation patterns are more significantly linked to changes in phenotype or culture conditions. Both adaptation to different media (as with most cell pools analyzed) and selection/subcloning for a specific phenotype (high transient productivity, as with subclone K1/1D9) generates prominent changes in DNA-methylation patterns. In contrast, long-term maintenance under constant culture conditions results only in minor changes in DNA-methylation. Interestingly, subclone K1/1D9 has the highest overall degree of CpG-methylation and the largest number of differentially methylated promoters relative to its parent. This confirms a recent transcriptome analysis of this cell line where two thirds of differentially expressed genes were downregulated (Harreither et al., 2015), indicating that a high productivity phenotype is only in part achieved by enhanced expression of genes needed for production, but just as importantly requires focusing on this specific task by reducing expression of unrelated functions and pathways. Finally, the lowest overall level of CpG-methylation was observed in no-Gln cells, which were harshly treated during their adaptation and may need to express more genes to enable survival and successful growth under such minimal conditions.
At the current state of the Chinese Hamster and CHO reference genomes, the chromatin state model will be a valuable resource to help with the annotation of promoters, TSS, regulatory regions and both coding, and non-coding genes. Although active transcription states in many cases overlap with annotated genes, frequently transcription can be observed also in unannotated regions, while in other instances promoter regions are found in the middle of an annotated gene, possibly incorrectly assembled, although a translocation in the CHO genome cannot be excluded. The correctness of active promoter prediction is confirmed by the overlap of predicted active promoters with highly demethylated regions. While DNA-methylation in annotated promoter regions is not notably altered during the batch culture, approx. 22.3% of the chromatin state segments of the genome undergo changes, indicating that short term changes in transcription in CHO cells are controlled by histone modifications and that these occur in a continuous adaptation to the altering conditions. Exponential growth, stationary growth, and decline phase can be clearly distinguished by PCA. The continuous adaptation during exponential growth phase is most prominent in histone marks that define regulatory units or promoters (H3K27ac, H3K4me1, and H3K4me3), thus demonstrating the efficiency of cells in achieving homeostasis in the form of constant output (¼ growth rate) during this phase in spite of decreasing nutrient availability, but also indicating the need for continuous adaptation.
In view of common procedures during cell line development, which typically include the requirement for subcloning, it is striking that the genome homogeneity of the analyzed subclone is not significantly different from that of the pools. Thus, the common expectation that a clone is more homogeneous should be questioned when working with fast growing, immortalized cell lines, as the divisions required to expand cells to sufficient numbers Table I. Percentage of chromatin states in the genome during batch culture and differential histone modification binding sites. for a master cell bank or a bioreactor allows for accumulation of high numbers of new variants. At the epigenetic level, we observe that DNA-methylation plays a major role in defining and differentiating between phenotypes and is inherited by daughter cells. Besides established approaches of changing the epigenome, such as addition of sodium butyrate or valproic acid, which both have a global impact on epigenetic regulation, other, so far unexplored mechanisms of targeted control of gene expression could be put to good use, including long-non-coding RNAs (Holoch and Moazed, 2015), or the application of site specific (de-) methylases or histone modifying enzymes (Hilton et al., 2015;Lang et al., 2015). Such methods open up new options for control of cell behavior that, compared to classical overexpression or knock-out engineering, may be easier to achieve on the one hand, but also more difficult to stabilize and maintain on the other. In either case, they would significantly enlarge the toolbox for metabolic engineering of cell behavior. Summarized, our results suggest that culture conditions support and enforce a given phenotype as defined by the DNAmethylome which in turn defines the gene expression pattern required for this set of conditions. Short-term adaptation to rapidly changing nutrient availability as observed during a batch culture on the other hand is primarily controlled via a continuous modulation of histone modifications. Insights gained from these data will improve our understanding of the dynamics of the genome and epigenome in cells that are used as model systems for many basic research questions and that are maintained in culture under a variety of conditions and over prolonged periods of time. In view of the industrial use of CHO as a cell factory, they may also guide future developments towards improved epigenetic control and manipulation of cell behavior in the context of industrial bioprocesses.

Accession Codes And Jbrowse Access
DNA-seq and BS-seq raw data: PRJEB9185; ChIP-seq results: PRJEB9291. All data are available for download and visualized in a JBrowse server available at cho-epigenome.boku.ac.at/ with a link provided from www.CHOgenome.org.