Bacterial Flow Cytometry and Imaging As Potential Process Monitoring Tools for Industrial Biotechnology

Article Bacterial Flow Cytometry and Imaging as Potential Process Monitoring Tools for Industrial Biotechnology

Sumana Kadamalakunte Narayana 1,* , Sanjaya Mallick 2, Henrik Siegumfeldt 1 and Frans van den Berg 1

1 Department of Food Science, Faculty of Science, University of Copenhagen, Rolighedsvej 26, DK-1958 Frederiksberg C, Denmark; [email protected] (H.S.); [email protected] (F.v.d.B.) 2 Cytometry Solutions Pvt. Ltd., Balia Main Rd, Garia, Kolkata, West Bengal 700084, India; [email protected] * Correspondence: [email protected]

Received: 24 November 2019; Accepted: 8 January 2020; Published: 17 January 2020

Abstract: Minimizing process variations by early identification of deviations is one approach to make industrial production processes robust. Cell morphology is a direct representation of the physiological state and an important factor for the cell’s survival in harsh environments as encountered during industrial processing. The adverse effects of fluctuating process parameters on cells were studied using flow cytometry and imaging. Results showed that altered pH caused a shift in cell size distribution from a heterogeneous mix of elongated and short cells to a homogenous population of short cells. Staining based on membrane integrity revealed a dynamics in the pattern of cluster formation during fermentation. Contradictory findings from forward scatter and imaging highlight the need for use of complementary techniques that provide visual confirmation to interpret changes. An atline flow cytometry or imaging capable of identifying subtle population deviations serves as a powerful monitoring tool for industrial biotechnology.

Keywords: ﬂow cytometry; industrial biotechnology; microscopy; imaging; viability; cell size; forward scatter; Lactobacillus acidophilus

1. Introduction Lactic acid bacteria are one of the most important bacterial species employed in the food industry. They are used as starter cultures in making essential food products like yoghurt, cheese, kefir, wines, etc. [1]. Some lactic acid bacteria like Lactobacillus acidophilus are also directly consumed as probiotics due to the health benefits offered by them [2]. The global probiotic market is rapidly growing and was predicted to be valued at USD 77 billion by 2025 [3]. Given the increasing competition among manufacturers, making an industrial probiotic production process efficient and robust is crucial. Reducing the batch-to-batch variations and early identification of unwanted process deviations could assist in this regard [4]. Presently, industries producing probiotics monitor the fermentation process through parameters like pH and amount of base consumed. These are easy-to-get parameters, but often fail to explain the quality or performance variations in the final yield. There are many upstream and downstream possible explanations as to why the probiotic end-product might vary in performance and/or quantity. The formation of zones/gradients in large tanks due to non-ideal mixing is one known issue [5,6]. Lactic acid bacteria are self-acidifiers (they produce lactic acid as a product during growth and thus acidify the medium). In processes involving exponentially growing lactic acid bacteria, zones of low pH can be present locally in the reactor if the rate of external base addition is not sufficient to compensate the

Fermentation 2020, 6, 10; doi:10.3390/fermentation6010010 www.mdpi.com/journal/fermentation Fermentation 2020, 6, 10 2 of 15 lactic acid formed. As a result, the cells could experience an exposure to changing pH-values as they travel through the different zones in the bioreactor [7]. Cell shape and size (i.e., morphology) are fundamental biological properties. They play an important role in the cell’s survival in stressed environments. Primarily, genetic factors determine the morphology of a cell. However, studies have shown that environmental factors such as pH (permanent level changes or shocks), mechanical stress, nutrient starvation during the fermentation process, etc. also have a strong influence on the cell’s morphology [8,9]. Many bacteria show morphological changes as a consequence of adapting to stressful conditions [9]. Understanding the cell size distribution and its dynamics under normal process conditions is an important part of process optimization in the biotech industry. E.g., a historical database of cell morphology could be constructed to assist in (long-term) process performance-monitoring and optimization. Unexpected changes occurring due to changing process parameters or gradual cell line changes can be identified using this knowledge. In this study, two analytical methods–flow cytometry and imaging (both manual and automated)-were used to understand the effect of changing process pH on Lactobacillus acidophilus cell morphology. Flow cytometry is a powerful tool, mainly found in research and development laboratories, capable of analyzing multiple physical properties of cells and particles. It records morphological features through light scatter and physiological features through fluorescence signals from the cells. This technique furthermore enables data collection from a large number of isolated events in a very short time span, and thus facilitates the understanding of population characteristics with statistical robustness. This capability is very useful in detecting the occurrence of rare, unwanted events such as cell line deviations. The scatter parameters obtained with a flow cytometer are forward angle scatter (FSC) which varies primarily in accordance with cell size-but factors like refractive index also influence it-and orthogonal/side scatter (SSC) which varies according to the cell’s internal complexity and composition. The multiple fluorescence parameters are attributed to the pigments, fluorescent proteins or staining with fluorescent probes, of which the latter can be selected towards a wide array of biochemical properties of the cells [10]. Morphological investigations through imaging techniques like traditional microscopy have been in use in the industry for many years, mostly as a confirmatory quality control method. It often involves analyzing only few samples per batch, collected at certain, specific process steps. Just like flow cytometry, image analysis is usually performed offline i.e., samples collected from the process are transported to a lab facility, and requires skilled technicians for analysis. There are inevitable delays in obtaining the results and manual errors involved. While microscopes have the advantage of revealing intracellular details, the information is only qualitative. It is relatively cumbersome for quantifying the cell counts and cellular characteristics and instruments with higher magnification are needed to obtain high-quality images for such quantitative analysis. In-situ microscopic probes are now available for noninvasive online morphology monitoring by direct incorporation into the fermenter or process stream [11]. Results are acquired continuously and reported to the control system. However, the high cost and maintenance required make it suitable only for low-volume-high-value biopharmaceutical productions [11]. For high-volume-low-value processes such as probiotics, an atline morphology monitoring (imaging or flow cytometry) is more suitable. An atline method is here defined as one where, the measurements can be performed near the fermenter, by operators. Automated imaging/microscopy has the advantage of recording a higher number of cells per sample (200–5000). It enables determining the cell size distribution, as opposed to traditional microscopy where cell counts are typically limited to around 50–100 cells. Delays due to transport (as for offline modes) and the excessive costs (online modes) are eliminated in the atline analysis. As an intermediate to the two, atline uses simpler equipment compared to the online, and provides quicker results than lab-based offline analysis. An automated imaging system or flow cytometry set-up is a feasible and easy to incorporate solution for industries. Fermentation 2020, 6, 10 3 of 15

Fermentation 2020, 6, x FOR PEER REVIEW 3 of 15 2. Materials and Methods 2. Materials and Methods 2.1. Fermentation Process Details 2.1. Fermentation Process Details The process under study is a Lactobacillus acidophilus fermentation, where the cells are the final The process under study is a Lactobacillus acidophilus fermentation, where the cells are the final product. Commercially available freeze-dried LA-5 cells from Chr. Hansen A/S (Denmark) were product. Commercially available freeze-dried LA-5 cells from Chr. Hansen A/S (Denmark) were used used to prepare an overnight culture, and was used as the inoculum. All batch fermentations were to prepare an overnight culture, and was used as the inoculum. All batch fermentations were performed anaerobically with N2 purging, at 37 ◦C in a 2 L bioreactor (working volume 1250 mL). performed anaerobically with N2 purging, at 37 °C in a 2 L bioreactor (working volume 1250 mL). Autoclaved MRS [12] was the growth media and a starting inoculum of 1% cell inoculum (12.5 mL Autoclaved MRS [12] was the growth media and a starting inoculum of 1% cell inoculum (12.5 of overnight culture) was used for all batches. Each fermentation batch was run for a duration of 10 h. mL of overnight culture) was used for all batches. Each fermentation batch was run for a duration of 10 Threeh. different pH regimes were used in this study (Figure1a). The initial pH of the medium was 6.5 0.2, which gradually drops due to lactic acid production by the growing cells. For a normal Three± different pH regimes were used in this study (Figure 1a). The initial pH of the medium batchwas run, 6.5 ± pH 0.2, control which gradually using 1M drops NH3 and due 1Mto lactic HCl ac solutionsid production (Sigma-Aldrich, by the growing St. Louis, cells. MO,For a USA)normal was startedbatch oncerun, pH the control pH reached using 5.51M toNH thereafter3 and 1M maintainHCl solutions at this (Sigma-Aldrich, set-point, until St.the Louis, end MO, of fermentation USA) was (pH-A).started Data once from the pH seven reached batches 5.5 areto thereafter investigated maintain in this at manuscript, this set-point, three until batches–called the end of fermentation normal from here(pH-A). on-were Data run from inpH-A. seven Twobatches abnormal are investigated batches are in includedthis manuscript, which werethree runbatches–called at a sub-optimal normal pH conditionfrom here of on-were 4.5, from run the in time pH-A. pH Two dropped abnormal down batches to 5.5 are until included the end which of fermentation were run at a (pH-B). sub-optimal A third trialpH trajectory condition (pH-C) of 4.5, from was followedthe time pH for dropped one test batch,down whereto 5.5 until the batchthe end pH of was fermentation controlled (pH-B). at 5.5 fromA timethird zero, trial and trajectory was gradually (pH-C) was lowered followed (manually) for one fromtest batch, 5.5 at where the start the of batch exponential pH was growthcontrolled phase at 5.5 (4th hour)from to time pH 4.5zero, and and maintained was gradually there lowered for one hour,(manually) afterwhich from 5.5 the at pH the was start bought of exponential back to the growth normal setphase point (4th of 5.5 hour) at the to endpH of4.5 the and exponential maintained phase there (8thfor one hour). hour, The after pH which value the of 4.5pH was was chosen bought with back the aimto ofthe mimicking normal set thepoint pH of stress 5.5 at thatthe end could of the potentially exponential be phase encountered (8th hour). by theThe bacteriapH value in of large-scale 4.5 was fermenterschosen with or inthe large aim of tanks mimicking without the proper pH stress mixing that could during potentially holding be between encountered different by the downstream bacteria processin large-scale steps (frequently fermenters seen or in in large the industry,tanks withou causedt proper by e.g., mixing scheduling during optimization).holding between different downstream process steps (frequently seen in the industry, caused by e.g., scheduling optimization). 2.2. Biomass Monitoring 2.2. Biomass Monitoring Optical density (OD), recorded online during the fermentations using a DenCyte (Hamilton Optical density (OD), recorded online during the fermentations using a DenCyte (Hamilton A/S) A/S) probe measuring at 880 nm, was used as the measure of total cell counts (un-calibrated probe measuring at 880 nm, was used as the measure of total cell counts (un-calibrated for specific for specific cell morphologies, giving relative units/growth profiles, Figure1b). The probe was cell morphologies, giving relative units/growth profiles, Figure 1b). The probe was calibrated/zeroed calibrated/zeroed against sterile medium, at the start of each batch, before inoculation, in order to against sterile medium, at the start of each batch, before inoculation, in order to negate the negate the background interference. background interference.

Figure 1. (a) Representative pH proﬁles and (b) OD proﬁles for three pH-A batches and two pH-B Figure 1. (a) Representative pH profiles and (b) OD profiles for three pH-A batches and two pH-B batches run in 2 L bioreactor. batches run in 2 L bioreactor.

Fermentation 2020, 6, 10 4 of 15

2.3. Flow Cytometry

2.3.1. Flow Cytometer Settings All flow cytometry analysis were performed using a BD FACSJazzTM cell sorter, which uses a 488nm argon ion laser as the light source. Rainbow Calibration Particles (8 peaks, 3.0 to 3.4 µm) from BD Biosciences were used for laser alignment each day before analysis. The forward scatter (FSC) signal was collected in the direction of the laser and side scatter (SSC) at a 90◦ angle to the laser. FSC versus SSC plots were used as reference to ensure recoding of the right bacterial population. Fluorescence was collected in two channels–green fluorescence from SYTO9 stained cells (called FL1 from here on) with a band-pass filter of 530 20 nm and red fluorescence from PI stained cells (FL ) with a band-pass ± 2 filter of 692 20 nm. Sheath fluid from BD FACSFlow™ was used at a flow rate of 0.5 L h 1 and the ± · − event count for each recording was set to 100,000. Milli-Q water was run in-between samples until the event count was below 100 per second to ensure no carry over from one sample to the next.

2.3.2. Sample Preparation, Cell Staining, and Data Analysis 5 mL of fermentation broth was withdrawn aseptically from the fermenter every hour, 1 mL of which was centrifuged using a bench-top micro centrifuge (LLG labware, Meckenheim, Germany) to separate the spent media from the cells. Centrifuged supernatant was discarded and the cell pellets were stored at 4 ◦C until analysis. 1 mL of 0.85% NaCl solution was used to re-suspend the cells prior to flow cytometric analysis. A LIVE/DEADTM bacterial viability kit from ThermoFisher Scientific (Waltham, MA, United States), consisting of Propidium iodide (PI) and SYTO9, which stain the cells selectively based on membrane integrity, was used. All sample preparation and staining protocols were followed as per the kits instructions [13]. Viability analysis by cell staining was performed for most samples in pH-A and pH-B, while in pH-C staining was only done for selected samples. All ‘.fcs’ data files from the flow cytometry instrument were read and analyzed using MATLAB scripts. In-house MATLAB code and the image processing toolbox were used to make density plots of FL1 against FL2 signals and to manually pick the number of clusters, and initial cluster center. Once this initiation was done, all events belonging to one cluster were included by manually adjusting the (hexagonal) boundary of the cluster marked, after which the center was recalculated. Data processing with the above method was used for all recordings to extract the percentage of the total counts for each cluster.

2.4. Imaging Techniques for Morphology Analysis

2.4.1. Microscopic Analysis To appreciate the correlation between cell size and flow cytometry FSC, microscopic analysis was performed using a bright field oil-immersion light microscope with 60 magnification. 1 mL of × the hourly sample was centrifuged to remove the supernatant, and cells were re-suspended in 0.85% NaCl buffer for dilution. Approximately 10 µL of sample was pipetted onto a microscopic glass slide, covered with gel pads (made from 5% agarose) and digital images were recorded for evaluation.

2.4.2. Automated Cell Imaging Analysis The oCelloScopeTM (BioSense Solutions ApS, Farum, Denmark) was used for the morphology analysis in atline mode. Samples were presented to the instrument on a 96-well plate, where 100 µL of diluted sample (in 0.85% NaCl buffer) was pipetted onto each well. Dilution level was determined based on the cell concentration in the batch. For the first hours, undiluted samples worked well; while in the later hours (higher biomass), the samples were diluted to make sure that individual cells could be accurately quantified by the morphological descriptors (in the digital images). For image acquisition, default settings on the oCelloScopeTM with auto-illumination plus 2 ms illumination time Fermentation 2020, 6, x FOR PEER REVIEW 5 of 15 Fermentation 2020, 6, 10 5 of 15 were used. Number of images used was 10 and recording was done at a distance of 10 µm above the plate bottom. Built-in segmentation analysis from the instrument software (BioSense Solutions ApS, µ Farum,were used. Denmark) Number was of used images to usedextract was few 10 features and recording to describe was donethe cell at amorphology-circularity, distance of 10 m above and the cellplate skeleton bottom. length Built-in [14]. segmentation analysis from the instrument software (BioSense Solutions ApS, Farum, Denmark) was used to extract few features to describe the cell morphology-circularity, and cell 3.skeleton Results length [14].

3. Results 3.1. pH Effect on Biomass Formation and Growth 3.1. pHThe E ffprimaryect on Biomass focus Formationin this study and Growth was to investigate the effects of pH alterations that can potentiallyThe primary occur focusduring in thisan studyindustrial-scale was to investigate fermentation the eff ectson bacterial of pH alterations cell size/morphology, that can potentially its distributionoccur during and an industrial-scalecell viability. A fermentation significantly on lower bacterial final cell cell size density–based/morphology, on its OD distribution measurements– and cell wasviability. observed A significantly when cells lower were final grown cell in density–based experimental on conditions OD measurements–was pH-B, as opposed observed to the when normal cells pH-A,were grown as shown in experimental in Figure 1b. conditionsThe difference pH-B, in bioma as opposedss is a toreflection the normal of the pH-A, stress as encountered shown in Figure during1b. theThe process. difference in biomass is a reflection of the stress encountered during the process.

3.2.3.2. Cell Cell Size Size Dynamics Dynamics UnderstandingUnderstanding the the cell cell size size distribution distribution and and its its dynamics dynamics under under normal normal process process conditions conditions is is an an importantimportant part part of of process process optimization optimization in in the the biotec biotechh industry. industry. For For example, example, a a historical historical database database of of cellcell morphology morphology could could be be constructed constructed to to assist assist in (long-term) process performance-monitoring and and optimization.optimization. Unexpected Unexpected changes changes occurring occurring due due to th toe the changing changing process process parameters parameters or gradual or gradual cell linecell changes line changes can be can identified be identiﬁed using this using knowledge. this knowledge. In our study, In our change study, in change the light in scatter the light collected scatter atcollected the FSC at channel the FSC was channel used wasto investigate used to investigate the effect the of changed eﬀect of changedpH on cell pH si onze celldistribution. size distribution. Figure 2Figure shows2 theshows normalized the normalized FSC histograms FSC histograms for the pH-A for the and pH-A pH-B and batches. pH-B For batches. pH-A, Fora clear, pH-A, systematic a clear, trendsystematic in the trend development in the development of two (or ofpossible two (or mo possiblere) subpopulations more) subpopulations from one fromcommon one commonstarting populationstarting population is observed, is observed, i.e., a broad i.e., range a broad of cell range sizes of towards cell sizes the towards end of thefermentation end of fermentation is seen. The is twoseen. distinct The two but distinctoverlapping but overlapping subpopulations, subpopulations, can be interpreted can be as interpreted small and elongated as small and cells elongated existing incells an existingactively in growing an actively and growing dividing and cell dividing population. cell population. In contrast, In pH-B contrast, batches pH-B showed batches ashowed distinct, a narrower,distinct, narrower, and more and homogenous more homogenous cell population cell population towards towardsthe end of the the end fermentation. of the fermentation.

Figure 2. Forward scatter signal (normalized) for samples analyzed every hour during the fermentation. Figure 2. Forward scatter signal (normalized) for samples analyzed every hour during the fermentation.For a better understanding of the diﬀerences in FSC signals and correlate them to actual cell size, microscopic imaging of cells was performed. Figure3 shows the recordings for samples at the last hourFor for a pH-A better (Figure understanding3a) and pH-B of the (Figure differences3b); the in FSC images signals are representativeand correlate them for the to generalactual cell trends size, microscopicobserved. The imaging statistical of cells moments was pe forrformed. cell area Figure extracted 3 shows from the microscopic recordings imagesfor samples are reported at the last in hour for pH-A (Figure 3a) and pH-B (Figure 3b); the images are representative for the general trends

Fermentation 2020, 6, x FOR PEER REVIEW 6 of 15 Fermentation 2020, 6, 10 6 of 15 observed. The statistical moments for cell area extracted from microscopic images are reported in Table1 1.. TheThe imagesimages inin FigureFigure3 3 reveal reveal that that the the cells cells in in pH-B pH-B are are considerably considerably smaller smaller than than those those in in pH-A, and that the population in pH-B is rather homogenous in comparison to pH-A.

Figure 3. Microscopic images (false color) for 10th hour sample taken from (a) pH-A and (b) pH-B. Figure 3. Microscopic images (false color) for 10th hour sample taken from (a) pH-A and (b) pH-B. Table 1. Statistical moments for cell area (in pixels) extracted from microscopic and automated images Tableof final-hour 1. Statistical fermentation moments samples for cell fromarea (in pH-A pixels) and ex pH-B.tracted from microscopic and automated images of final-hour fermentation samples from pH-A and pH-B. Microscope Automated Imaging Microscope Automated Imaging pH-A pH-B pH-A pH-B pH-A pH-B pH-A pH-B Object countsObject 129 counts 129 160 390 390845 845 Mean 518 328 41 16 MedianMean 440 518 326328 41 3516 12 Std Dev a Median439 440 187326 35 3112 14 IQR b Std758 Dev a 439 458187 31 5314 21 IQRa —Standardb 758 deviation, 458b—Inter-quartile 53 range. 21 a—Standard deviation, b—Inter-quartile range. The results from automated imaging were similar to that from microscopy. Two representative The results from automated imaging were similar to that from microscopy. Two representative images obtained from the automated imaging system are shown in Figure 4. These images also images obtained from the automated imaging system are shown in Figure4. These images also showed showed a heterogeneous population in pH-A (Figure 4a) in contrast to a homogenous population a heterogeneous population in pH-A (Figure4a) in contrast to a homogenous population with smaller with smaller cells for pH-B (Figure 4b). The areas extracted from all images taken during this cells for pH-B (Figure4b). The areas extracted from all images taken during this investigation are investigation are reported in Table 1 and give the confirming interpretation that pH-B results in reported in Table1 and give the confirming interpretation that pH-B results in smaller cells. Figure5 smaller cells. Figure 5 shows normalized histograms for two morphological features–circularity and shows normalized histograms for two morphological features–circularity and cell length-extracted cell length-extracted from automated images for samples at the 5th and 10th hour of fermentation. It from automated images for samples at the 5th and 10th hour of fermentation. It can be seen that can be seen that at the 5th hour of fermentation (Figure 5a,c), the cell populations in pH-A and pH-B at the 5th hour of fermentation (Figure5a,c), the cell populations in pH-A and pH-B are almost are almost similarly distributed. Towards the end of fermentation (Figure 5b,d), the two populations similarly distributed. Towards the end of fermentation (Figure5b,d), the two populations are clearly are clearly different-pH-B has many shorter, more circular cells while pH-A has longer, elongated different-pH-B has many shorter, more circular cells while pH-A has longer, elongated cells with cells with a wider range of cell lengths. Although both the features distinguish the pH-A and pH-B a wider range of cell lengths. Although both the features distinguish the pH-A and pH-B cells, cells, distinction between the populations is clearest via circularity (Figure 5b) in comparison to the distinction between the populations is clearest via circularity (Figure5b) in comparison to the cell cell length (Figure 5d). length (Figure5d).

FermentationFermentation 20202020,, 66,, 10x FOR PEER REVIEW 77 of of 15 Fermentation 2020, 6, x FOR PEER REVIEW 7 of 15

Figure 4. Automated (oCelloScopeTM) images for 10th hour samples taken from (a) pH-A and (b) pH-B. FigureFigure 4. 4. AutomatedAutomated (oCelloScope(oCelloScopeTM) )images images for for 10th 10th hour hour samples samples taken taken from from (a ()a )pH-A pH-A and and (b (b) pH-B.) pH-B.

Figure 5. Normalized histograms for (a) circularity, 5th hour samples; (b) circularity, 10th hour samples; (Figurec) cell length5. Normalized 5th hour samples;histograms and for (d )( cella) circularity, length 10th 5th hour hour samples samples; from (b one) circularity, representative 10th pH-Ahour Figure(blue)samples; and5. Normalized( pH-Bc) cell (red) length batch.histograms 5th hour for samples; (a) circularity, and (d 5th) cell hour length samples; 10th (hourb) circularity, samples 10thfrom hour one samples;representative (c) cell pH-A length (blue) 5th and hour pH-B samples; (red) batch. and (d) cell length 10th hour samples from one representativeThe most striking pH-A observation(blue) and pH-B when (red) comparing batch. results from ﬂow cytometry and imaging was that theThe actual most sizestriking of cells observation in pH-B are when much comparing smaller and results shorter from (Figures flow cytometry3b and4b) and than imaging the ones was in pH-Athat theThe (Figures actual most 3strikingsizea and of 4cells a)observation while in pH-B the are FSCwhen much signals comparing smaller give theand results opposite shorter from (Figures impressionflow cytometry 3b and i.e., 4b) that and than pH-Bimaging the results ones was in thatinpH-A larger the (Figures actual cells (Figuresize 3a andof cells2 ).4a) To inwhile understandpH-B the are FSC much this signals smaller anomaly give and the in shorter theopposite scatter (Figures impression results, 3b theand i.e., trigger 4b) that than pulsepH-B the ones widthresults in pH-Adistributionsin larger (Figures cells were (Figure3a and plotted 2).4a) To inwhile Figureunderstand the6 a.FSC The signalsthis contrast anomaly give between the in oppositethe trigger scatter impression pulse results, width the i.e., (Figuretrigger that 6pH-B pulsea) and results width FSC indistributions larger cells were (Figure plotted 2). To in understandFigure 6a. The this contra anomalyst between in the triggerscatter pulseresults, width the trigger(Figure pulse 6a) and width FSC distributions were plotted in Figure 6a. The contrast between trigger pulse width (Figure 6a) and FSC

Fermentation 2020, 6, 10 8 of 15 Fermentation 2020, 6, x FOR PEER REVIEW 8 of 15

(Figure6 6b)b)can can bebe seenseen fromfrom thethe statisticalstatistical moment-valuesmoment-values inin TableTable2 .2. From From these these trigger trigger width width values, values, it is clear that pH-B cells areare indeedindeed smallersmaller comparedcompared toto pH-ApH-A cells.cells.

Figure 6. Normalized histograms of (a) flow cytometry trigger pulse width (b) forward scatter (FSC) Figure 6. Normalized histograms of (a) flow cytometry trigger pulse width (b) forward scatter (FSC) signal for 10th hour samples taken from a representative pH-A and pH-B batch. signal for 10th hour samples taken from a representative pH-A and pH-B batch. Table 2. Statistical moments for flow cytometry (100,000 events) forward angle scatter/FSC (arbitrary units)Table and2. Statistical trigger pulse moments width for (arbitrary flow cytometry units) of final-hour(100,000 events) fermentation forward samples angle scatter/FSC from pH-A and(arbitrary pH-B. units) and trigger pulse width (arbitrary units) of final-hour fermentation samples from pH-A and pH-B. FSC Trigger-Pulse Width pH-A pH-B pH-A pH-B FSC Trigger-Pulse Width Mean 9237 13809 4925 3608 pH-A pH-B pH-A pH-B Median 8180 13620 4934 3620 Std Dev a Mean5068 9237 13809 2384 4925 3608 1641 1442 IQR b Median12418 8180 13620 14677 4934 3620 5860 4611 a Std Deva—Standard 5068 deviation, 2384b —Inter-quartile 1641 range. 1442 IQR b 12418 14677 5860 4611 a—Standard deviation, b—Inter-quartile range. 3.3. Viability Analysis 3.3. Viability Analysis The multi-parametric nature of flow cytometry allows for investigation of cell viability next to size,The through multi-parametric fluorescent staining. nature of In flow this study,cytometry the common allows for interpretation investigation was of cell used viability that cells next with to ansize, intact, through tight fluorescent cell membrane staining. are In alive, this study, and cells the withcommon porous interpretation/damaged cell was membrane used that cells are dead.with Thean intact, recorded tight eventscell membrane were divided are alive, into and three cell distincts with porous/damaged clusters based on cell the membrane difference are in PIdead./SYTO9 The stainingrecorded intensity events were (no divided compensation into three was distinct used). clus Theters clusters based were on the quantified difference using in PI/SYTO9 in-house staining scripts, twointensity representative (no compensation examples arewas shown used). in The Figure clusters7. Cluster were C quantified1 is composed using of cellsin-house with higherscripts, green two thanrepresentative red fluorescence, examples stained are shown mainly in by Figure SYTO9. 7. TheCluster cells C that1 is makecomposed up cluster of cells C2 hadwith approximately higher green equalthan red amounts fluorescence, of red and stained green mainly fluorescence by SYTO9. and The are identifiedcells that make as damaged up cluster cells C (or2 had cells approximately with a more fluidequal cellamounts membrane), of red and which green allow fluorescence both PI and and SYTO9 are identified to stain as them. damaged Finally, cells the (or cells cells in with cluster a more C3 havefluid highercell membrane), red than green which fluorescence allow both andPI and are SYTO9 identified to stain as dead them. or membraneFinally, the compromised cells in cluster cells. C3 Fromhave higher here onwards, red than C green1 is therefore fluorescence considered and are as id theentified live cluster, as dead C2 oras themembrane intermediate compromised cluster and cells. C3 asFrom the here dead onwards, cluster. MicrosphereC1 is therefore beads considered with pre-defined as the live greencluster, and C2 redas the fluorescence intermediate can cluster be seen and as theC3 as fixed the populationdead cluster. in Microsphere cluster C4 (in beads Figure with7a). Percentagepre-defined of green live, damagedand red fluorescence and dead cells can for be three seen pH-Aas the batches,fixed population one pH-B in batch, cluster and C trail4 (in runFigure pH-C 7a). are Percentage reported inof Tablelive, 3damaged. and dead cells for three pH-A batches, one pH-B batch, and trail run pH-C are reported in Table 3.

Fermentation 2020, 6, 10 9 of 15 Fermentation 2020, 6, x FOR PEER REVIEW 9 of 15

Fermentation 2020, 6, x FOR PEER REVIEW 9 of 15

Figure 7. PlotFigure of FL7. Plot1 against of FL FL1 against2 fluorescence FL2 fluorescence intensities intensities showing showing four clustersfour clusters (C1-live, (C1-live, C2-intermediate, C2- C3-dead andintermediate, C4-beads) C3-dead identified and C4-beads) using MATLABidentified using scripts MATLAB for (a scripts) 10th for hour (a) 10th sample hour sample from pH-A,from (b) 10th pH-A, (b) 10th hour sample from pH-B and (c) 7th hour sample from pH-C. hour sampleFigure from 7. Plot pH-B of andFL1 (againstc) 7th hourFL2 fluorescence sample from intensities pH-C. showing four clusters (C1-live, C2- intermediate, C3-dead and C4-beads) identified using MATLAB scripts for (a) 10th hour sample from The staining revealed a dynamics in the cluster formation and the group-shapes changed over The stainingpH-A, revealed(b) 10th hour a sample dynamics from pH-B in theand (c) cluster 7th hour formation sample from pH-C. and the group-shapes changed over batch time, an example of which is shown in Figure 8. We did not observe noticeable differences in batch time, an example of which is shown in Figure8. We did not observe noticeable di fferences in live live cellThe percentagesstaining revealed due to a stressing dynamics the in cellsthe cluste in anr acidic formation pH of and 4.5 the(pH-B). group-shapes Special test changed batch pH-C over cell percentagesbatchon the time, other due an hand, to example stressing caused of whichthe the live cellsis cellshown inpercentage anin Figure acidic to 8.drop pH We ofdrasticallydid 4.5 not (pH-B). observe to 29% noticeable Special at the 7th test differences hour batch (Figure pH-Cin on the other hand,live7b) caused andcell percentagesa slow the (but live gradual) due cell to percentage stressing improvement the tocells was drop in obse an drastically acidicrved whenpH of the to4.5 29%pH(pH-B). was at shifted theSpecial 7th backtest hour batchto 5.5 (Figure pH-Cat the 7b) and a 8th hour (Table 3). Plating results also showed fewer cells at the end of fermentation in pH-B as slow (buton gradual) the other improvementhand, caused the waslive cell observed percentage when to drop the drastically pH was to shifted 29% at the back 7th tohour 5.5 (Figure at the 8th hour compared to pH-A (Table 4). (Table3). Plating7b) and a resultsslow (but also gradual) showed improvement fewer cellswas obse at therved endwhen of the fermentation pH was shifted inback pH-B to 5.5 as at comparedthe to 8th hour (Table 3). Plating results also showed fewer cells at the end of fermentation in pH-B as pH-A (Tablecompared4). to pH-A (Table 4).

Figure 8. Plot of FL1 against FL2 fluorescence intensities of hourly samples showing dynamics of

clusters observed during fermentation, shown for one representative pH-A batch (Plots for other Figure 8. batchesPlot of shown FL1 inagainst Appendix FL A).2 ﬂuorescence intensities of hourly samples showing dynamics of Figure 8. Plot of FL1 against FL2 fluorescence intensities of hourly samples showing dynamics of clusters observedclusters observed during during fermentation, fermentation, shown shown for for one one representative representative pH-A pH-Abatch (Plots batch for (Plotsother for other Table 3. Percentage event counts from clusters C1 (live)/C2 (intermediate)/C3 (dead). batches shownbatchesin shown Appendix in AppendixA). A). pH-A pH-B pH-C Table 3. Time (h) TablePercentage 3. Percentage event event(1) counts counts from from(2) clustersclusters (3)C C1 (live)/C1 (live)2 (intermediate)/C/(1)C2 (intermediate)(1) 3 (dead)./C 3 (dead). a 0 99/00/01 86/14/00pH-A 100/00/00 96/04/00pH-B pH-C - Time (h) pH-A pH-B pH-C Time (h) 1 76/17/06(1) 27/35/38(2) 76/20/05(3) 62/20/08(1) 80/12/08(1) (1)02 99/00/01 28/57/14 (2)86/14/0020/62/18 100/00/0043/45/11 (3) 96/04/0047/41/12 - -a (1) (1) 3 35/55/09 26/64/09 60/36/04 26/48/26 - 0 99/00/011 76/17/06 86 /1427/35/38/00 76/20/05 100/ 0062/20/08/00 80/12/08 96/04 /00 - a 4 91/06/04 71/23/06 82/12/06 36/56/08 88/09/03 1 76/17/062 28/57/14 27 /3520/62/18/38 43/45/11 76/ 20/47/41/1205 -62 /20/08 80/12/08 5 69/23/08 75/20/05 64/07/29 64/34/02 77/14/09 2 28/57/143 35/55/09 20 /6226/64/09/18 60/36/04 43/ 45/26/48/2611 -47 /41/12 - 4 91/06/04 71/23/06 82/12/06 36/56/08 88/09/03 3 35/55/09 26/64/09 60/36/04 26/48/26 - 4 91/06/045 69/23/08 71 /2375/20/05/06 64/07/29 82/ 12/64/34/0206 77/14/09 36/56 /08 88/09/03 5 69/23/08 75/20/05 64/07/29 64/34/02 77/14/09

6 71/18/10 72/24/03 61/23/16 74/22/04 77/20/03 7 88/07/05 61/31/08 73/18/09 69/26/05 29/44/27 8 90/07/03 73/19/08 64/22/13 78/14/07 35/45/20 9 98/01/01 95/05/01 88/09/03 94/04/01 55/30/14 10 94/05/01 92/07/01 80/14/06 93/05/02 78/13/09 a—Flow cytometry staining analysis not performed for these hours and second pH-B experiment. Fermentation 2020, 6, 10 10 of 15

Table 4. Average colony forming unit (CFU) counts for zero and ﬁnal-hour fermentation samples from pH-A and pH-B.

Time (h) pH-A pH-B h 00 2.23 108 1.04 109 × × h 10 2.41 1012 4.00 1010 × ×

4. Discussion Industrial bioprocessing includes several downstream steps following the fermentation step. During processing, due to practical reasons such as low capacity of the equipment or (unpredictable) time lags between the operating steps could result in hold up of the cells in large tanks, (typically) without proper mixing or pH control. High density lactic acid cultures can rapidly lower the pH and cause an adverse effect on the live cell percentage (as seen in pH-C). This would cause a loss of product and potentially noticeable economic consequences. Use of better monitoring tools on the work floor can thus, not only detect deviating cell size distributions, but also identify changes in viability in the case of flow cytometry and help improve process productivity. Lactic acid bacteria are known to be resistant to pH stresses [15]. The behavior observed in pH-B, where the cells adapt to the new environment and maintain good viability until the end of fermentation, is therefore maybe not very surprising. Although L. acidophilus are known to survive under lower pH conditions, a drop in pH from the set point would induce an unwanted stress to the cells. Studies have revealed that the cells strive to maintain a neutral pH inside, by pumping excessive protons (introduced by the low pH of the medium) out of the cell, thereby diverting the energy available towards maintenance rather than forming new cells [16]. The lower biomass obtained in pH-B (Figure1b, Table4) was interpreted as a manifestation of the cell’s survival in altered pH conditions. Bacterial stress response includes a wide variety of metabolic and physiological changes, including formation of inactive spores, shift into dormant states like viable-but-non-culturable (VBNC) states [8,17] and pleomorphism i.e., change of cell shape and size. The shift in cell size distribution as observed in the case of pH-B is a known response/survival strategy. Phenotypic heterogeneity of a population is a well-known strategy that allows for survival and functionality of the microorganism under stressful environmental conditions [18,19]. The importance of cell shape and size for the survival of bacteria in case of threats like nutrient depletion or pH stresses and the resulting morphological changes has been discussed in literature [9,20]. Several examples of cells changing size in response to the encountered stress are available. This includes studies such as Bifidobacterium changing their shape due to stress [21], and L. bulgaricus increasing chain length and shifting from rod shaped to filamentous form by inhibiting cell division upon encounter with alkaline pH [22] etc. The unexpected, contrasting results obtained from FSC and imaging techniques while analyzing the changes in cell size distribution, highlight the significance of using complementary analytical techniques in process understanding. According to convention, forward scattered light in the small angle region (θ 2 , where θ = 0 corresponds to the direction of incident light) is primarily dependent ≤ ◦ ◦ on the cell’s size and refractive index [23]. Accordingly, the changed FSC observed in pH-B could be naively interpreted as increase in cell size. However, other parameters such as cell shape, refractive index and density are important factors contributing to the forward scatter generated by the cell [24]. Therefore, a linear relation cannot always be expected between FSC signals and cell size [25,26]. The pulse width is a parameter recorded by the flow cytometer that reflects the electric pulse created at the detector when a cell passes through the laser. Rod-shaped cells such as L. acidophilus have a tendency to be aligned (by the hydrodynamic focusing of the cytometer) along their long-axis when traveling through the laser [27,28]. Therefore, the pulse width parameter is an indication of travel time required and this value increases with cell length (for the cells used here). Many studies suggest that light scattered from biological particles (cells) depends on other cellular properties such as refractive index, density, and orientation in addition to the size [24,29]. Kerker and Fermentation 2020, 6, 10 11 of 15 co-authors [24] describe the dependence of light scatter by cells on refractive index in detail. Another recent study by Liu and co-authors [30] suggests that the average refractive index of a cell suspension depends on the volume fraction of cells. Using these investigations as guidelines, it was inferred here that for pH-B, the reduced size of the cells reduces the volume fraction and in turn, results in an increased refractive index of the cell suspension [30]. Further, the increased refractive index (from the decreased cell size) is reflected as increased FSC signal from the pH-B cells. Combining this with the results from imaging and trigger pulse width, we confirm that pH-B caused shrinking of the cells. However, it is important to note that the effects of the shifted cell size distribution on cell resistance to stress caused during further (downstream) processing steps have to be evaluated separately. While microscopic images show the true state of cells, automated imaging systems are comparatively inexpensive, and practically provide the same type of information (cell shape and size). In addition to acting as a confirmatory technique to FSC, the results from automated imaging depict here that analyzing multiple cellular features such as circularity, elongation etc., that are not readily detected in traditional microscopy provides a means for easier identification of small changes in the cell populations.

5. Conclusions The focus of this study was to understand the impact of altered pH conditions, which could potentially occur in an industrial probiotic production process, on cell morphology and product quality. The two analytical techniques used in this study to assess morphology-flow cytometry and imaging (manual and automated)-complemented each other and helped understand the manifestation of pH effect on the cells. Contrary to the conventional interpretation that cells with bigger size give higher FSC, we observed that smaller, denser cells resulting from a shift in pH to 4.5, showed higher FSC numbers. The results emphasize the need to understand the principle that FSC does not depend on cell size only, but other factors as well like refractive index, angle of scatter collection, etc. Therefore, using only FSC to analyze cell size could be misleading. The distinct shift in cell size distribution towards a homogenous population seen in both the FSC signals from flow cytometry and images reflect the impact of changed pH. Manifestation of the changed population heterogeneity on the cell’s survival in downstream process steps could be significant. In addition, OD and CFU analysis confirmed that the reduced values of OD and CFU counts in pH-B indicate that acidic conditions occurring within fermenters could be harmful to the cells and result in reduced final cell density at the end of fermentation. While neither OD nor flow cytometry could be used individually to describe the effect of pH on the cells, combining OD and percentage alive from flow cytometry gives a better/reliable count of the amount of live cells in a fermentation harvest. The viability counts also showed that a constant exposure to a lower than optimal pH (here represented by the somewhat extreme value 4.5, simulating the industrial scale pH gradients) is far less lethal in comparison to a continuously changing/dropping pH (that could result from holding of the product between process steps). The clear shift in cell size distribution from heterogeneous to homogenous under the changed pH is a stress response. The cells produced from the processes operated at suboptimal pH could have lower resistance to future stress or may have longer lag periods. Investigations such as the one performed here are already included in process design and up-scaling experiments in the biotech industry. However, for daily performance monitoring they are too costly, or more generally, too involved. Identifying such shifts in cell size distributions occurring during fermentation, over many batches, could prove to be crucial in reducing loss of viable cells in the freeze-drying step. Flow cytometry gives a deeper insight into the process by describing not only the morphology of the culture at any time point, but also quantifying the viable cell counts. The results from our study indicate that an atline implementation of morphology analysis with flow cytometry or automated imaging could prove to be valuable in detecting such process deviations that could otherwise go unnoticed. Fermentation 2020, 6, x FOR PEER REVIEW 12 of 15 cytometry gives a deeper insight into the process by describing not only the morphology of the culture at any time point, but also quantifying the viable cell counts. The results from our study indicate that an atline implementation of morphology analysis with flow cytometry or automated imaging could prove to be valuable in detecting such process deviations that could otherwise go Fermentationunnoticed.2020 , 6, 10 12 of 15 Image analysis is possibly the more attractive and competitive process analytical tool for industrial biotechnology mainly due to its atline applicability, real-time results and simple nature as Image analysis is possibly the more attractive and competitive process analytical tool for industrial opposed to the currently available traditional, post-quality-control methods like plating. In our biotechnology mainly due to its atline applicability, real-time results and simple nature as opposed results, both microscopic and automated images showed similar findings. This indicates that newer, to the currently available traditional, post-quality-control methods like plating. In our results, both automated imaging systems, which are less expensive and easier to handle, compared to microscopes microscopic and automated images showed similar findings. This indicates that newer, automated can be implemented for routine, quick checking of samples on the work floor. At the cost of some imaging systems, which are less expensive and easier to handle, compared to microscopes can be operator time, they are easy-to-use and can be performed by nearly everybody, without the need of implemented for routine, quick checking of samples on the work floor. At the cost of some operator highly trained personnel as in the case of microscopy. Imaging techniques provide visual time, they are easy-to-use and can be performed by nearly everybody, without the need of highly confirmation of the cellular characteristics. Although imaging does not provide the same process trained personnel as in the case of microscopy. Imaging techniques provide visual confirmation of the insight as flow cytometry, the morphological distribution information might be sufficient, especially cellular characteristics. Although imaging does not provide the same process insight as flow cytometry, after a (laboratory-based) comparison within a bacterial strain is available. Atline imaging is a the morphological distribution information might be sufficient, especially after a (laboratory-based) powerful, affordable, user-friendly process monitoring tool for industrial biotechnology. comparison within a bacterial strain is available. Atline imaging is a powerful, affordable, user-friendly process monitoring tool for industrial biotechnology. Author Contributions: All authors contributed to the writing of this manuscript. Conducting of experiments- lab fermentations, flow cytometry and image analysis was done by S.K.N., expert opinion on results from Author Contributions: All authors contributed to the writing of this manuscript. Conducting of experiments- lab fermentations,forward scatter flow and cytometry trigger pulse and imagewidth analysisof flow wascytome donetry by by S.K.N., S.M., expertreview opinion of data on and results results from from forward flow scattercytometer and and trigger microscopy pulse width by H.S., of flow assistance cytometry with by MATLAB S.M., review, data ofanalysis data and and results overall from planning flow cytometerof the work and by microscopyF.v.d.B. All authors by H.S., have assistance read and with agreed MATLAB, to the datapublished analysis version and overallof the manuscript. planning of the work by F.v.d.B. All authors have read and agreed to the published version of the manuscript. Funding: This work is a part of the BIOPRO2 strategic research initiative. The Innovation Fund Denmark (grant Funding: This work is a part of the BIOPRO2 strategic research initiative. The Innovation Fund Denmark (grant number 4105-00020B), Region Zealand, the European Regional Development Fund (ERDF) and its partners number 4105-00020B), Region Zealand, the European Regional Development Fund (ERDF) and its partners sponsorsponsor BIOPRO2BIOPRO2 (More(More infoinfo atat www.biopro.nuwww.biopro.nu).). ConflictsConflicts ofof Interest:Interest: TheThe authorsauthors declaredeclare nono conflictconflict ofof interest.interest.

Appendix A Appendix A

Figure A1. Plot of FL1 against FL2 ﬂuorescence intensities of hourly samples showing dynamics of clustersFigure A1. observed Plot of during FL1 against fermentation FL2 fluorescence for pH-A (2)intensities batch. of hourly samples showing dynamics of clusters observed during fermentation for pH-A (2) batch.

Fermentation 2020, 6, x FOR PEER REVIEW 13 of 15

FermentationFermentation 20202020,, 66,, 10x FOR PEER REVIEW 1313 of of 15

Figure A2. Plot of FL1 against FL2 fluorescence intensities of hourly samples showing dynamics of Figure A2. Plot of FL against FL ﬂuorescence intensities of hourly samples showing dynamics of clusters observed during1 fermentation2 for pH-A (3) batch. clustersFigure A2. observed Plot of during FL1 against fermentation FL2 fluorescence for pH-A (3)intensities batch. of hourly samples showing dynamics of clusters observed during fermentation for pH-A (3) batch.

Figure A3. Plot of FL1 against FL2 ﬂuorescence intensities of hourly samples showing dynamics of Figure A3. Plot of FL1 against FL2 fluorescence intensities of hourly samples showing dynamics of clusters observed during fermentation for pH-B (1) batch. clusters observed during fermentation for pH-B (1) batch. Figure A3. Plot of FL1 against FL2 fluorescence intensities of hourly samples showing dynamics of clusters observed during fermentation for pH-B (1) batch.

Fermentation 2020, 6, 10 14 of 15 Fermentation 2020, 6, x FOR PEER REVIEW 14 of 15

Figure A4. Plot of FL1 against FL2 ﬂuorescence intensities of hourly samples showing dynamics of Figure A4. Plot of FL1 against FL2 fluorescence intensities of hourly samples showing dynamics of clusters observed during fermentation for pH-C (1) batch. clusters observed during fermentation for pH-C (1) batch. References References 1. Leroy, F.; De Vuyst, L. Lactic acid bacteria as functional starter cultures for the food fermentation industry.

1. TrendsLeroy, Food F.; De Sci. Vuyst, Technol. L. 2004Lactic, 15 acid, 67–78. bacteria [CrossRef as functional] starter cultures for the food fermentation industry. 2. Bigret,Trends M.; Food Mäyrä-Mäkinen, Sci. Technol. 2004 A., Industrial15, 67–78. Use and Production of Lactic Acid Bacteria. Lact. Acid Bact. 2004.

2. [CrossRefBigret, M.;] Mäyrä-Mäkinen, A. Industrial Use and Production of Lactic Acid Bacteria. Lact. Acid Bact. 2004. 3. Research,doi:10.1201/9780824752033.ch5. G. View Probiotics Market Worth $77.09 Billion By 2025. Available online: https://www.

3. grandviewresearch.comResearch, G. View /press-releaseProbiotics /global-probiotics-marketMarket Worth $77.09 (accessed Billion onBy 14 September2025. Available 2019). online: 4. Narayana,https://www.grandviewresearch.com/pr S.; Christensen, L.; Skov, T.; van deness-release/global-probiotics-market Berg, F. Mid-Infrared Spectroscopy (accessed and Multivariate on 14 September Analysis to2019). Characterize Lactobacillus acidophilus Fermentation Processes. Appl. Spectrosc. 2019, 73, 1087–1098.

4. [CrossRefNarayana,] S.; Christensen, L.; Skov, T.; van den Berg, F. Mid-Infrared Spectroscopy and Multivariate 5. Spann,Analysis R.; Glibstrup,to Characterize J.; Pellicer-Alborch, Lactobacillus acidophilus K.; Junne, S.; Fermentation Neubauer, P.; Processes. Roca, C.; Appl. Kold, Spectrosc. D.; Lantz, 2019 A.E.;, 73 Sin,, 1087– G.; Gernaey,1098. K.V.; et al. CFD predicted pH gradients in lactic acid bacteria cultivations. Biotechnol. Bioeng. 2018, 5. 116Spann,.[CrossRef R.; Glibstrup,] J.; Pellicer-Alborch, K.; Junne, S.; Neubauer, P.; Roca, C.; Kold, D.; Lantz, A.E.; Sin, G.; 6. Carlquist,Gernaey, M.;K.V.; Fernandes, et al. CFD R.;predicted Helmark, pH S.; gradients Heins, A.-L.; in lactic Lundin, acid bacteria L.; Sørensen, cultivations. S.J.; Gernaey, Biotechnol. K.V.; Bioeng. Lantz, 2018 A. , Physiological116, doi:10.1002/bit.26868 heterogeneities in microbial populations and implications for physical stress tolerance. Microb. 6. CellCarlquist, Fact. 2012 M.;, 11 Fernandes,, 94. [CrossRef R.; Helmark,] S.; Heins, A.-L.; Lundin, L.; Sørensen, S.J.; Gernaey, K.V.; Lantz, A. 7. Enfors,Physiological S.O.; Jahic, heterogeneities M.; Rozkov, in A.; microbial Xu, B.; Hecker, populations M.; Jürgen, and implications B.; Krüger, E.;for Schweder,physical stress T.; Hamer, tolerance. G.; O’Beirne,Microb. Cell D.; etFact. al. Physiological2012, 11, 94. responses to mixing in large scale bioreactors. J. Biotechnol. 2001, 85, 175–185. 7. [CrossRefEnfors, S.O.;] Jahic, M.; Rozkov, A.; Xu, B.; Hecker, M.; Jürgen, B.; Krüger, E.; Schweder, T.; Hamer, G.; 8. vanO’Beirne, Teeseling, D.; M.C.F.;et al. Physiological de Pedro, M.A.; responses Cava, F.to Determinantsmixing in large of scale bacterial bioreactors. morphology: J. Biotechnol. From fundamentals 2001, 85, 175– to185. possibilities for antimicrobial targeting. Front. Microbiol. 2017, 8, 1–18. [CrossRef] 9.8. Yang,van Teeseling, D.C.; Blair, M.C.F.; K.M.; Salama,de Pedro, N.R. M.A.; Staying Cava, inF. Shape:Determinants The Impact of bacterial of Cell morphology: Shape on Bacterial From fundamentals Survival in Diverseto possibilities Environments. for antimicrobialMicrobiol. targeting. Mol. Biol. Rev.Front.2000 Microbiol., 80, 187–203. 2017, 8 [, CrossRef1–18. ] 10.9. Adan,Yang, A.; D.C.; Alizada, Blair, G.;K.M.; Kiraz, Salama, Y.; Baran, N.R. Staying Y.; Nalbant, in Shape: A. Flow The cytometry: Impact of Cell Basic Shape principles on Bacterial and applications. Survival in Crit.Diverse Rev. Environments. Biotechnol. 2017 Microbiol., 37, 163–176. Mol. [ CrossRefBiol. Rev.] 2000, 80, 187–203. 11.10. Joeris,Adan, K.; A.; Frerichs, Alizada, J.G.;G.; Kiraz, Konstantinov, Y.; Baran, K.; Y.; Scheper, Nalbant, T. A. In-situ Flow microscopy:cytometry: Basic Online principles process and monitoring applications. of mammalianCrit. Rev. Biotechnol. cell cultures. 2017Cytotechnology, 37, 163–176. 2002, 38, 129–134. [CrossRef] 12.11. DeJoeris, Man, K.; J.C.; Frerichs, Rogosa, J.G.; M.; Konstantin Sharpe, M.E.ov, AK.; Medium Scheper, For T. TheIn-situ Cultivation microscopy: Of Lactobacilli. Online processJ. Appl. monitoring Bacteriol. of 1960mammalian, 23, 130–135. cell cultures. [CrossRef Cytotechnology] 2002, 38, 129–134. 13.12. ProtocolsDe Man, LIVE J.C.;/ DEADRogosa,® M.;Bac Sharpe, LightTM M.E.Bacterial A Medium Viability For and The Counting Cultivation Kit Of (L34856) Lactobacilli. (Flow J. Cytometry Appl. Bacteriol. Kit); ThermoFisher:1960, 23, 130–135. Waltham, MA, USA, 2004. 13. Protocols LIVE/DEAD® Bac LightTM Bacterial Viability and Counting Kit (L34856) (Flow Cytometry Kit); ThermoFisher: Waltham, MA, USA, 2004.

Fermentation 2020, 6, 10 15 of 15

14. BioSense Solutions. oCelloScope Technology; BioSense Solutions: Farum, Denmark, 2017. 15. Hussain, M.A.; Hosseini Nezhad, M.; Sheng, Y.; Amoafo, O. Proteomics and the stressful life of lactobacilli. FEMS Microbiol. Lett. 2013, 349, 1–8. [CrossRef][PubMed] 16. Šušković,J.; Kos, B.; Beganović,J.; Pavunc, A.L.; Habjaniˇc,K.; Matoć,S. Surviving the Acid Test: Responses of Gram-Positive Bacteria to Low pH. Food Technol. Biotechnol. 2010, 48, 296–307. 17. Davis, K.M.; Isberg, R.R. Defining heterogeneity within bacterial populations via single cell approaches. BioEssays 2016, 38, 782–790. [CrossRef][PubMed] 18. De Jong, I.G.; Haccou, P.; Kuipers, O.P. Bet hedging or not? A guide to proper classification of microbial survival strategies. BioEssays 2011, 33, 215–223. [CrossRef] 19. Van Boxtel, C.; Van Heerden, J.H.; Nordholt, N.; Schmidt, P.; Bruggeman, F.J. Taking chances and making mistakes: Non-genetic phenotypic heterogeneity and its consequences for surviving in dynamic environments. J. R. Soc. Interface 2017, 14.[CrossRef] 20. Cesar, S.; Huang, K.C. Thinking big: The tunability of bacterial cell size. FEMS Microbiol. Rev. 2017, 41, 672–678. [CrossRef] 21. Macfarlane, G.; Cummings, J. Probiotics and prebiotics: Can regulating the activities of intestinal bacteria benefit health? BMJ 1999, 318, 999–1003. [CrossRef] 22. Rhee, S.K.; Pack, M.Y. Effect of environmental pH on fermentation balance of Lactobacillus bulgaricus. J. Bacteriol. 1980, 144, 217–221. [CrossRef] 23. Mullaney, P.F.; Van Dilla, M.A.; Coulter, J.R.; Dean, P.N. Cell sizing: A light scattering photometer for rapid volume determination. Rev. Sci. Instrum. 1969, 40, 1029–1032. [CrossRef] 24. Kerker, M.; Chew, H.; McNulty, P.J.; Kratohvil, J.P.; Cooke, D.D.; Sculley, M.; Lee, M.P. Light scattering and fluorescence by small particles having internal structure. J. Histochem. Cytochem. 1979, 27, 250–263. [CrossRef][PubMed] 25. Lopez-Amoros, R.; Comas, J.; Carulla, C.; Vives-Rego, J. Variations in flow cytometric forward scatter signals and cell size in batch cultures of Escherichia coli. FEMS Microbiol. Lett. 1994, 117, 225–229. [CrossRef] 26. Müller, S.; Nebe-Von-Caron, G. Functional single-cell analyses: Flow cytometry and cell sorting of microbial populations and communities. FEMS Microbiol. Rev. 2010, 34, 554–587. [CrossRef][PubMed] 27. Pinder, A.C.; Purdy, P.W.; Poulter, S.A.G.; Clark, D.C. Validation of flow cytometry for rapid enumeration of bacterial concentrations in pure cultures. J. Appl. Bacteriol. 1990, 69, 92–100. [CrossRef] 28. Kachel, V.; Fellner-Feldegg, H.; Menke, E. Hydrodynamic properties of flow cytometry instruments. In Flow Cytometry and Sorting; Melamed, M.R., Lindmo, T., Mendelsohn, M.L., Eds.; John Wiley & Sons Ltd.: New York, NY, USA, 1990; pp. 27–44. 29. Watson, D.; Hagen, N.; Diver, J.; Marchand, P.; Chachisvilis, M. Elastic light scattering from single cells: Orientational dynamics in optical trap. Biophys. J. 2004, 87, 1298–1306. [CrossRef] 30. Liu, P.Y.; Chin, L.K.; Ser, W.; Chen, H.F.; Hsieh, C.M.; Lee, C.H.; Sung, K.B.; Ayi, T.C.; Yap, P.H.; Liedberg, B.; et al. Cell refractive index for cell biology and disease diagnosis: Past, present and future. Lab Chip 2016, 16, 634–644. [CrossRef]

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).