The authors would like to acknowledge PhD student Dave Hartnell and&#160;&#160;Bournemouth University for support and funding for the research and facilities.

1. Preparation of the Molecular Probe and Flow Cytometer
<ol>
	<li>Dilute stock solutions of the nucleic acid probes, which are supplied as a 5 mM solution in dimethylsulfoxide (DMSO) to aliquots of required concentrations in ultrapure filtered H2O.</li>
	<li>Store the nucleic acid probes in dark conditions between -5 oC and - 25 oC until use.</li>
	<li>Turn on the flow cytometer and load software package (see table of Materials/Equipment for FCM specifications).</li>
	<li>Place an empty haemolysis tube (12 x 75 mm) on the sample injection probe (SIP), click unclog and then back flush to start the FCM cleaning process. 
	NOTE: Some sample stages for the SIP can accommodate several types of tubes including microcentrifuge tubes.&#160; The molecular probe diluent and sheath fluid used in the FCM apparatus is from an analytical grade &#8220;type 1&#8221; 0.22 &#181;m membrane filtered source.</li>
	<li>Place a fresh haemolysis tube with 2 ml&#160;of ultrapure filtered H2O on the SIP, set a time limit for 10 - 15 min and a fluidic speed to fast (or a flow rate of &#62;66 &#181;l/min).</li>
	<li>Select a new data cell, put on relevant thresholds for fluorescence and light scatter channels to reduce background noise and click &#39;run&#39;.</li>
	<li>If the total events per second are not below the manufacturers&#39; recommendation, then run a 2 ml sample of decontamination solution for 2 min&#160;on fast and then repeat steps 1.4 &#38; 1.5. 
	NOTE: Please check manufacturers&#8217; recommendations for FCM start-up cleaning protocols, as they can vary between models.&#160; Testing for specific thresholds also needs to be in line with the FCM model as some equipment allows the user to apply voltages gains to the photomultiplier tubes (PMTs) enhancing or decreasing the electrical signal recorded from the optical detectors.&#160; The FCM model used in this experiment has fixed voltage PMTs and used a threshold of 80,000 forward light scatter (FSC-H) to exclude particles smaller than 2.0&#160;&#181;m and electronic noise.</li>
</ol>
2. Preparation of Cultures and Initial Cell Counts
<ol>
	<li>Autoclave 98 ml of ultrapure filtered H2O and 2 ml of algal media (x50 concentration) in a 250 ml beaker for 20 min&#160;at 120 oC.</li>
	<li>From an initial monoculture of M.aeruginosa (PCC 7806) in a high steady state density place 2 ml of the sample into a tube under the SIP. Disaggregate any colony formation by vortexing or sonication20 and confirm evenly dispersed cells through light microscopy. 
	NOTE: Excessive exposure to ultrasonic waves can lead to cell lysis and should be used with caution.&#160; Since sonication can collapse gas vesicles (as found in species like M.aeruginosa) outputs such as side light scatter (SSC-H) intensity can become more sensitive.</li>
	<li>Within the FCM software, select a histogram plot to record data from forward light scatter (FSC-H) and configure the plot specifications by clicking &#34;log&#34; to view in a log scale on the axis.</li>
	<li>In a separate output, select another histogram (log axis) to record natural fluorescence such as the accessory photosynthetic pigment phycocyanin (FL4-H, 675 &#177;&#160;12.5 nm), found in M.aeruginosa.</li>
	<li>Use a light source that can excite phycocyanin and a detector which can filter the emissions from the resulting fluorescence. 
	NOTE: Photosynthetic pigment such as chlorophyll can be excited with the commonly used 488 nm blue laser, whereas phycocyanin is excited over 600 nm and will only be detected with a red light source21.&#160; Check with the flow cytometers manufacturer for excitation light sources and the spectra of emission detectors, here both a 488 nm and a 640 nm&#160;laser were used along with&#160;a 675 &#177;&#160;12.5 nm optical filter.</li>
	<li>For recording of the highest resolution select settings closest to the core size of the target organism (10 &#181;m) and a relatively slow flow rate (14 &#181;l/min). 
	NOTE: For the best resolution samples should be run according to manufactures recommendation of events per second.</li>
	<li>Before acquiring data use a threshold to gate out light scatter and / or fluorescence signals that are caused by electronic background noise or cell sample debris.</li>
	<li>Select a new data cell, create a density plot with FSC-H and SSC-H parameters on a log scale and click run.</li>
	<li>If sample density leads to an excessive event rate, dilution steps can be taken to increase accuracy and precision.</li>
	<li>On the density plot apply software gates from the previous histogram to exclude low level scatter signals, produced by background noise or debris (FSC &#60;320,000). Conversely gate the higher relative fluorescence phycocyanin signals from cells and only include these events (FL4, 56,000 - 1,950,000).</li>
	<li>Use the number of cells recorded in the gated areas and divide it by the total volume of sample that has passed through the FCM, to work out how many cells per ml. 
	NOTE: The FCM model used here has a microprocessor controlled peristaltic pump system allowing sample volume to be determined.&#160; Other FCM equipment may require a calibrated bead suspension or a calculation of H2O&#160;weight/volume differences to verify total sample volume.</li>
	<li>Add the required volume of cells to the freshly prepared media in order to start a batch growth cycle (250,000 cells/ml) of M.aeruginosa. 
	NOTE: Species will differ in growth rates depending on their in-situ environment parameters and nutrient availability, so a batch cycle should be pre-recorded to determine life cycle phases.</li>
</ol>
3. Optimization of Molecular Probe Cell Uptake
<ol>
	<li>Harvest half of the M.aeruginosa culture prepared in step 2 from an exponential phase and use as a &#39;live&#39; control. 
	NOTE: Samples diluted from a high density culture straight to an exponential phase may affect optimization results through dead cell turnover, compared to that of cultures inoculated from an initial lag / induction phase.</li>
	<li>Prepare the other half as a &#39;dead&#39; control by using methods such as 70% ethanol, heating samples at 60 oC for 1 hr, paraformaldehyde or 4% formaldehyde for 30 min6,11,22. Check variations in the samples microenvironment (e.g., pH). 
	NOTE: The positive, heat-killed, &#8216;dead&#8217; control in M.aeruginosa is distinguished from a &#8216;live&#8217; sample through its decrease in phycocyanin signals.&#160; Inducing mortality by other methods may not cause the same output and will vary in species.</li>
	<li>Set up mixed samples using different ratios of &#39;live&#39; and &#39;dead&#39; samples (e.g., 0%, 25%, 50%, 100%).</li>
	<li>Disaggregate colony formation by vortexing or sonication and check pH.</li>
	<li>Select a 488 nm laser alongside detectors which can record fluorescence from the green (FL1, 530 &#177;&#160;15 nm) and orange (FL2, 585 &#177;&#160;20 nm) nucleic acid probes and the 640 nm laser to record phycocyanin signals through its respective detector. 
	NOTE: When bound to DNA, the green nucleic acid probe has an approximate fluorescence excitation wavelength of 504 nm and emission maxima of 523 nm, whilst the orange nucleic acid probe has an excitation wavelength of 547 nm and emissions maxima of 570 nm.&#160; A 488 nm argon ion solid state laser can be employed to excite both molecular probes, however, a green laser (up to 547 nm) will produce a higher orange fluorescence.</li>
	<li>As a starting point, introduce the molecular probe with manufacturers recommended concentration to the 50% &#39;live&#39; and 50% &#39;dead&#39; culture and incubate in the dark.</li>
	<li>Select a new data cell, place the sample under the SIP with thresholds and triggers that will reduce background noise (FSC-H 80,000).</li>
	<li>Create a density plot with FSC-H and SSC-H parameters, and three histograms. One histogram using the respective molecular probe optical detector channel (FL1 or 2), one to detect phycocyanin emissions (FL4-H) and the other FSC-H, all on a log scale.</li>
	<li>Incubate the samples of M.aeruginosa with the nucleic acid probes in darkness for up to 60 min, recording in separate data cells, at a number of time points (1, 5, 10, 15, 30 and 60 min). 
	NOTE: When adjusting parameters such as pH check with manufactures for potential reactions from certain chemicals (for the tested nucleic acid probes a buffer cannot contain phosphates or high levels of monovalent or divalent cations, as the binding with DNA will be reduced).</li>
	<li>Apply a software gate to only include the FSC-H histogram (320,000 - 1,500,000) for the target organisms cell size, into the respective fluorescence probe channel histogram. 
	NOTE: The concentrations used in this protocol were 0.05, 0.1, 0.5, 1, 5, 10, 50 and 100 &#181;M, which can be altered by either increasing or decreasing the volume of M.aeruginosa sample or initial stock solution of nucleic probes.</li>
	<li>In the fluorescence probe channel, apply another inclusive software gate to the highest peak in the histogram (green FL1-H, 240,000 - 1,650,000, orange FL2-H, 30,000 - 165,000) and subsequently gate that positive probe fluorescence into the density plot.</li>
	<li>Run steps 3.1 - 3.11 with 100% &#34;live&#34;, 100% &#39;dead&#39; and all mixed culture samples, adjusting the molecular probe concentrations (e.g x 0.1 to x 10) and / or temperature and pH levels if necessary.</li>
	<li>Compare the number of positive molecular probe fluorescence signals to the original cell density of &#39;dead&#39; cells (taken from half total FSC-H or a 100% &#39;dead&#39; culture) to find the total percentage of cells stained with the nucleic acid probes.</li>
	<li>Do this for each time period within each concentration to find the optimal protocol for the highest percentage of cell nucleic probe uptake without producing non-specific staining (use the means in a One-Way ANOVA or Kruskal-Wallis one-way analysis of variance, if the data is non-parametric).</li>
</ol>
4. Molecular Probe Fluorescence Discrimination
<ol>
	<li>For testing of fluorescence interference / overlap from intrinsic or non-specific cell staining select the 50% &#39;live&#39; and 50% &#39;dead&#39; mixed culture data and take off all gates.</li>
	<li>Apply a software gate to only include the FSC-H histogram (320,000 - 1,500,000) for the target organisms cell size, into the phycocyanin channel histogram.</li>
	<li>Gate the highest phycocyanin peak and label as &#39;live&#39;, with the addition of the lowest peak labelled as &#39;dead&#39;.</li>
	<li>Perform a further gate step into the respective molecular probe fluorescence histogram channel, using one at a time, the &#39;live&#39; and then &#39;dead&#39; phycocyanin (FL4) signals, recording both mean wavelengths. 
	NOTE:&#160; The mode for inducing mortality in this experiment was done by heat treatment, which under this protocol clearly decreases phycocyanin signals.&#160; Other methods of producing populations of &#8216;dead&#8217; cells may yield different outputs in the acquired runs.</li>
	<li>Ratio the mean wavelengths of the positive molecular probe fluorescence (&#39;dead&#39;) and the intrinsic / non-specific signal (&#39;live&#39;) to determine protocol sensitivity. 
	NOTE: To improve fluorescence discrimination of &#8216;live&#8217; and &#8216;dead&#8217; populations, increase the carbon source to active stain uptake in nutrient depleted cells or ethylenediaminetetraacetic acid (EDTA) to improve cell wall permeability and resulting fluorescence signals6.&#160; Limited compensation in some FCM models for spectral overlap can be performed by user-manipulated pairwise correction during sample acquisition (PMT voltage changes) or from post-analytical specialised software.</li>
	<li>Select the optimized protocol where the highest amount of &#39;dead&#39; cells has been stained without the occurrence non-specific staining. If a number of test have similar results accept the protocol with the lowest concentration and incubation time, along with good fluorescence signal discrimination. Follow manufactures instructions for the cytometer shutdown procedure.</li>
</ol>

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The authors declare that they have no competing financial interests.

The increased numbers of publications using molecular probes indicates that reliable and informative data can be obtained5,6,8-15,19,22,23. As of yet there is no perfect stain for cell viability that can be effective across all species with the same concentration and incubation time6,10. Even the same type of probe with altered fluorescence emissions shows a need to establish the correct concentration and incubation time (Tables 1 &#38; 3). As seen when using this protocol, the optimal concentration and incubation time for orange nucleic acid probe is 10 min&#160;at 1 &#181;M, whereas the green nucleic acid probe would only need half the concentration but takes three times as long (Figures 5 &#38; 6). Both of these cell-impermeant cyanine monomers with concentrations above 1.0 &#181;M started to produce an overlap in emissions spectra with non-membrane injured cells. This overlap of signals provides evidence that non-specific staining occurs where &#39;live&#39; cells are taking up the molecular probe. The resulting over fluorescence from non-specific staining generated false positives, underlining the need for protocol development to be carefully established for each molecular probe and target organism. The most critical steps in optimizing a molecular probe would be: 1) understanding how the molecular probe interacts with the FCM instrument; 2) adopting suitable live and dead controls; 3) developing knowledge of gating parameters from FCM outputs and 4) modifying the protocol through understanding of in-situ environments.
The light source, optical filters, ability to change PMT voltage and detectors within flow cytometers vary from manufacturer to manufacturer, so an awareness of what excitation wavelength from the laser and where the emission spectra lies in relation to the fluorescence channels are crucial. With acquisitions of data, thresholds can be imposed increasing resolution that would otherwise be lost to perhaps background noise, debris or dead cell aggregates. Molecular probes may offer insight into the viability of species without cultivation, through single parameters such as, membrane potential, DNA content and enzyme activity. However, within environmental samples there can be many organisms of a similar cell size and fluorescence. In addition within uniagal cultures, populations of cells will inevitably have a degree of physiological states3, which may affect molecular probe uptake. One of the main advantages of FCM is its ability to distinguish and characterise physiological states at the individual cell level in real time, which is essential when studying microorganisms viability in-situ, as they are often subjected to rapid and dynamic conditions. Despite its frequent use, the concept of cell viability still remains difficult to define. Usually applied to cellular proliferation, viability is very much scored on a growth based approach. This underpinning assumption may fall short, as conditions may not suit individual cells to reproduce but they may still be tolerable. Cells in a nonproliferating physiological state which have entered a quiescence or a G(0) cell cycle phase27 may not interact with a molecular probe giving false negatives in DNA cycle or metabolic based probes.
Using laboratory grown or freshly isolated cells to optimize molecular probes will aid the refining of data used in ecological studies. Cells used in optimization from batch cultures are commonly taken at exponential or early stationary phases and assumed to have the highest viability. However, extra caution must be taken when using high cell densities, those in a late stationary phase or microbes producing extracellular matrices. In this protocol the control for the dead population of cells were exposed to 60 oC heat for 1 hr, with confirmation of membrane damage and pigment degradation seen through microscopy (Figure 4) and FCM fluorescence outputs (Figures 2 &#38; 3). Simulating the causes of natural cell death is extremely difficult as; ingestion by grazers, viral lysis, programmed cell death or the possibility of asymmetric division24 are not always feasible or be observed under laboratory conditions. Experimental modes of morality are often questioned as they may not be equivalent to stresses induced from the environment (e.g., heat killing) and there may be a heterogeneous response, by which certain individuals are killed and other cells show no observable cell degradation or death3,25. To avoid such a paradox the operational definition by which the experiments measured must be clearly established so as not to lead to confusion6,23, easing normalisation between studies.
Various alternate methods have been employed to analysis cell viability in cultures in conjunction with molecular probes. Epifluorescence microscopy is a traditional technique used, although photobleaching or the introduction of artefact signals from nearby cells may give an under or over estimate of a populations physiological state if not recorded quickly enough. This makes optimization through imaging very difficult, with potentially low accuracy. Genomic assessment of cellular viability using both DNA and RNA amplification methods such as; polymerase chain reaction (PCR), reverse transcriptase (RT-PCR) and nucleic acid sequence-based amplification (NASBA) have also been used. However, genomic assessment serves only as an indirect method of viability validation as the persistence of nucleic acid heavily depends on environmental conditions, with the correlation of actual cell viability potentially varying immensely28. By using a combination of light scatter and / or fluorescence through natural photosynthetic pigments (Figure 1) gating of populations can:&#160;increase resolution of data, identify rare population responses and reduce false positive results. FCM along molecular probes stands out from the previous mentioned techniques in testing single cell physiological for its speed, accuracy and recording of multiple parameters.
FCM is a powerful analytical tool in aquatic microbiology and with the addition of molecular probes has a wide potential in a number of areas including, single cell and community analysis to industrial sectors (e.g., monitoring drinking water supplies). Future advances could see FCM incorporate fluorescence in situ hybridization (FISH), which can detect and localise specific genetic sequences in individual cells within dense heterogeneous samples. The development of FCM and molecular probe portability for in-situ recordings could provide vital data on water sources to implement early strategies for resource management. Following the optimization protocol developed here, FCM and molecular probes can potentially play a critical role in providing valuable data about microbial physiology in aquatic environments.

The cell is a complex system, which constantly responds to the environment by modifying physiological parameters and altering its function. The population dynamics of isogenic microbial populations both in nature and the laboratory are affected by the development of subpopulations, occurring even under relatively constant environmental conditions1-3. The variability of natural microbial communities arises due to the highly variable nature of environmental conditions. These sometimes stochastic processes subsequently produce subpopulations that are very different to the population average. Recent evidence has revealed that these physiological subpopulations respond differently to environmental conditions and can produce signal compounds or inhibitors that dramatically affect and influence the overall population3,4.
Establishing a method to define heterogeneity within a population is key to understanding the ecology of microbes in various environments and is essential when building knowledge of nuisance cyanobacteria, such as the toxic Microcystis, which impacts heavily on human&#160;water security. Species such as Anabaena display morphological heterogeneity in response to environmental fluctuations, developing specialised cells like heterocysts and akinetes2. In contrast, Microcystis cells do not display obvious morphological heterogeneity during a stress response. The discrimination between viable and non-viable cells is the most important aspect of physiological differentiation and allows a better understanding of microbial population dynamics. However, the conceptual problem of bacterial viability itself remains difficult and poorly characterised1,5,6.
Flow cytometry (FCM) is a reliable and rapid method of analysing individual cells. To increase the understanding of single cell physiology through FCM, molecular probes have been used to distinguish a number of metabolic and biochemical processes7. This has led to increased knowledge of species on a cellular and population level and in turn helped water resource management8,9. However, organisms differ in terms of molecular probe uptake and efflux due to the pores and pumps in cellular walls and membranes, which have led to a number of molecular probe design and protocol implementation6,10,11. Molecular probes available for commercial and research purposes are often supplied with a generic protocol which may be applicable to a very different cell type. One must be very cautious in transferring protocols developed for one cell type to another6, it is therefore an essential task to optimize molecular probes effectively before use.
The green and orange nucleic acid probes bind to both double and single stranded nucleic acids with minimal base selectivity and are used to assess the plasma membrane integrity of cells. The green nucleic acid probe has a markedly improved cell labelling fluorescence signal compared to other molecular probes, such as propidium iodide-based compounds12, which can also act as an indicator of cell viability. The term &#39;cell viability&#39; here assumes that DNA degradation occurs after the loss of plasma membrane integrity. The nucleic acid probes are unsymmetrical cyanine dyes with three positive charges and cannot enter cells with intact membranes under characterised concentrations, in both eukaryotic11,13 and prokaryotic14,15 organisms. The binding of a nucleic acid probe to nucleic acids can result in up to a &#62;500 fold increase of fluorescence emissions from endogenous signals in cells that have their membrane integrity compromised. Although molecular probes such as the green nucleic acid probe can be a good indicator of single cell physiology, there is a need to optimize each probe with the intended target organism, as incubation times have varied from 7 min&#160;- 30 min&#160;and concentration ranges from 0.1 &#181;M - 0.5 &#181;M in Microcystis experiments alone15-19.
Here we present a protocol to optimize the cytometric assays of green and the relatively new orange nucleic acid probes (which to date not been tested on the cyanobacterial species M.aeruginosa). The following developed methodology can then be transferred to other species and used as a platform for optimizing protocols in other molecular probes, thereby increasing the understanding of microbes and their ecological behavior.

<table><tbody><tr><td>Cyanobacteria Media</td><td>Sigma-Aldrich</td><td>C3061-500ML</td><td>BG-11 Freshwater concentrated solution (x50 dilution)</td></tr><tr><td>Decontamination Fluid</td><td>BD Biosciences</td><td>653155</td><td>Run for 2 min when outputs are more than 12 events per second on fast or a flow rate of 66 &amp;micro;l/min.&amp;nbsp; Followed by 2 min of sheath H&lt;sub&gt;2&lt;/sub&gt;O.</td></tr><tr><td>Flow Cytometer</td><td>BD Biosciences</td><td>by request</td><td>BD Accuri C6</td></tr><tr><td>SYTOX Green</td><td>Life Technologies</td><td>S7020</td><td>Nucleic acid stain &amp;ndash; 5 mM solution in DMSO</td></tr><tr><td>SYTOX Orange</td><td>Life Technologies</td><td>S11368</td><td>Nucleic acid stain &amp;ndash; 5 mM solution in DMSO</td></tr></tbody></table>

molecular probe optimization to determine cell mortality in a photosynthetic organism (microcystis aeruginosa) using flow cytometry

Microbial subpopulations in field and laboratory studies have been shown to display high heterogeneity in morphological and physiological parameters. Determining the real time state of a microbial cell goes beyond live or dead categories, as microbes can exist in a dormant state, whereby cell division and metabolic activities are reduced. Given the need for detection and quantification of microbes, flow cytometry (FCM) with molecular probes provides a rapid and accurate method to help determine overall population viability. By using SYTOX Green and SYTOX Orange in the model cyanobacteria Microcystis aeruginosa to detect membrane integrity, we develop a transferable method for rapid indication of single cell mortality. The molecular probes used within this journal will be referred to as green or orange nucleic acid probes respectively (although there are other products with similar excitation and emission wavelengths that have a comparable modes of action, we specifically refer to the fore mentioned probes). Protocols using molecular probes vary between species, differing principally in concentration and incubation times. Following this protocol set out on M.aeruginosa the green nucleic acid probe was optimized at concentrations of 0.5 &#181;M after 30 min of incubation and the orange nucleic acid probe at 1 &#181;M after 10 min. In both probes concentrations less than the stated optimal led to an under reporting of cells with membrane damage. Conversely, 5 &#181;M concentrations and higher in both probes exhibited a type of non-specific staining, whereby &#39;live&#39; cells produced a target fluorescence, leading to an over representation of &#39;non-viable&#39; cell numbers. The positive controls (heat-killed) provided testable dead biomass, although the appropriateness of control generation remains subject to debate. By demonstrating a logical sequence of steps for optimizing the green and orange nucleic acid probes we demonstrate how to create a protocol that can be used to analyse cyanobacterial physiological state effectively.

Watch this Scientific Journal Video about Molecular Probe Optimization to Determine Cell Mortality in a Photosynthetic Organism Microcystis aeruginosa Using Flow Cytometry at JoVE.com

Molecular Probe Optimization to Determine Cell Mortality in a Photosynthetic Organism Microcystis aeruginosa Using Flow Cytometry

Microbial populations contain substantial cell heterogeneity, which can dictate overall behavior. Molecular probe analysis through flow cytometry can determine physiological states of cells, however its application varies between species. This study provides a protocol to accurately determine cell mortality within a cyanobacterium population, without underestimating or recording false positive results.

Molecular Probe Optimization to Determine Cell Mortality in a Photosynthetic Organism (Microcystis aeruginosa) Using Flow Cytometry

Microbial subpopulations in field and laboratory studies have been shown to display high heterogeneity in morphological and physiological parameters. ...

molecular-probe-optimization-to-determine-cell-mortality

Nanyang Technological University

Research

JoVE Journal

Medicine

8.9K Views.  Bournemouth University.  Microbiële populaties bevatten aanzienlijke cel heterogeniteit, die de algehele gedrag kan dicteren. Moleculaire probe analyse door middel van flowcytometrie kan fysiologische staten van cellen te bepalen, maar de toepassing ervan varieert tussen soorten. Deze studie geeft een protocol naar cel sterfte binnen een cyanobacterie bevolking nauwkeurig bepalen, zonder onderschatten of opname vals positieve resultaten. 

Video: Molecular Probe Optimization to Determine Cell Mortality in a Photosynthetic Organism Microcystis aeruginosa Using Flow Cytometry

Single-cell Analysis of Bacillus subtilis Biofilms Using Fluorescence Microscopy and Flow Cytometry

Biofilm formation is a general attribute to almost all bacteria 1-6. When bacteria form biofilms, cells are encased in extracellular matrix that is mostly constituted by proteins and exopolysaccharides, among other factors 7-10. The microbial community encased within the biofilm often shows the differentiation of distinct subpopulation of specialized cells 11-17. These subpopulations coexist and often show spatial and temporal organization within the biofilm 18-21.
Biofilm formation in the model organism Bacillus subtilis requires the differentiation of distinct subpopulations of specialized cells. Among them, the subpopulation of matrix producers, responsible to produce and secrete the extracellular matrix of the biofilm is essential for biofilm formation 11,19. Hence, differentiation of matrix producers is a hallmark of biofilm formation in B. subtilis.
We have used fluorescent reporters to visualize and quantify the subpopulation of matrix producers in biofilms of B. subtilis 15,19,22-24. Concretely, we have observed that the subpopulation of matrix producers differentiates in response to the presence of self-produced extracellular signal surfactin 25. Interestingly, surfactin is produced by a subpopulation of specialized cells different from the subpopulation of matrix producers 15.
We have detailed in this report the technical approach necessary to visualize and quantify the subpopulation of matrix producers and surfactin producers within the biofilms of B. subtilis. To do this, fluorescent reporters of genes required for matrix production and surfactin production are inserted into the chromosome of B. subtilis. Reporters are expressed only in a subpopulation of specialized cells. Then, the subpopulations can be monitored using fluorescence microscopy and flow cytometry (See Fig 1).
The fact that different subpopulations of specialized cells coexist within multicellular communities of bacteria gives us a different perspective about the regulation of gene expression in prokaryotes. This protocol addresses this phenomenon experimentally and it can be easily adapted to any other working model, to elucidate the molecular mechanisms underlying phenotypic heterogeneity within a microbial community.

Single-cell Analysis of Bacillus subtilis Biofilms Using Fluorescence Microscopy and Flow Cytometry

Microbial biofilms are generally constituted by distinct subpopulations of specialized cells. Single-cell analysis of these subpopulations requires the use of fluorescent reporters. Here we describe a protocol to visualize and monitor several subpopulationswithin B. subtilis biofilms using fluorescence microscopy and flow cytometry.

Biofilm formation is a general attribute to almost all bacteria 1-6. When bacteria form biofilms, cells are encased in extracellular matrix that is mostly ...

Immunology and Infection

Characterization of Aquatic Biofilms with Flow Cytometry

Biofilms are dynamic consortia of microorganism that play a key role in freshwater ecosystems. By changing their community structure, biofilms respond quickly to environmental changes and can be thus used as indicators of water quality. Currently, biofilm assessment is mostly based on integrative and functional endpoints, such as photosynthetic or respiratory activity, which do not provide information on the biofilm community structure. Flow cytometry and computational visualization offer an alternative, sensitive, and easy-to-use method for assessment of the community composition, particularly of the photoautotrophic part of freshwater biofilms. It requires only basic sample preparation, after which the entire sample is run through the flow cytometer. The single-cell optical and fluorescent information is used for computational visualization and biological interpretation. Its main advantages over other methods are the speed of analysis and the high-information-content nature. Flow cytometry provides information on several cellular and biofilm traits in a single measurement: particle size, density, pigment content, abiotic content in the biofilm, and coarse taxonomic information. However, it does not provide information on biofilm composition on the species level. We see high potential in the use of the method for environmental monitoring of aquatic ecosystems and as an initial biofilm evaluation step that informs downstream detailed investigations by complementary and more detailed methods.

Flow cytometry in combination with visual clustering offers an easy-to-use and fast method for studying aquatic biofilms. It can be used for biofilm characterization, detection of changes in biofilm community structure, and detection of abiotic particles embedded in the biofilm.

Biofilms are dynamic consortia of microorganism that play a key role in freshwater ecosystems. By changing their community structure, biofilms respond ...

Environment

Characterizing Microbiome Dynamics &#8211; Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

The investigation of pure cultures and monitoring of microbial community dynamics is vital to understand and control natural ecosystems and technical applications driven by microorganisms. Next generation sequencing methods are widely utilized to resolve microbiomes, but they are generally resource and time intensive and deliver mostly qualitative information. Flow cytometric microbiome analysis does not suffer from those disadvantages and can provide relative subcommunity abundances and absolute cell numbers at-line. Although it does not deliver direct phylogenetic information, it can enhance the analysis depth and resolution of sequencing approaches. In sharp contrast to medical applications in both research and routine settings, flow cytometry is still not widely used for microbiome analysis. Missing information on sample preparation and data analysis pipelines may create an entry barrier for the researchers facing microbiome analysis challenges that would often be textbook flow cytometry applications. Here, we present three comprehensive workflows for pure cultures, complex communities in clear medium and complex communities in challenging matrices, respectively. We describe individual sampling and fixation procedures and optimized staining protocols for the respective sample sets. We elaborate the cytometric analysis with a complex research centered and an application focused bench top device, describe the cell sorting procedure and suggest data analysis packages. We furthermore propose important experimental controls and apply the presented workflows to the respective sample sets.

Flow cytometric analysis has proven valuable for investigating pure cultures and monitoring microbial community dynamics. We present three comprehensive workflows, from sampling to data analysis, for pure cultures and complex communities in clear medium as well as in challenging matrices, respectively.

The investigation of pure cultures and monitoring of microbial community dynamics is vital to understand and control natural ecosystems and technical ...

Cell-based Flow Cytometry Assay to Measure Cytotoxic Activity

Cytolytic activity of CD8+ T cells is rarely evaluated. We describe here a new cell-based assay to measure the capacity of antigen-specific CD8+ T cells to kill CD4+ T cells loaded with their cognate peptide. Target CD4+ T cells are divided into two populations, labeled with two different concentrations of CFSE. One population is pulsed with the peptide of interest (CFSE-low) while the other remains un-pulsed (CFSE-high). Pulsed and un-pulsed CD4+ T cells are mixed at an equal ratio and incubated with an increasing number of purified CD8+ T cells. The specific killing of autologous target CD4+ T cells is analyzed by flow cytometry after coculture with CD8+ T cells containing the antigen-specific effector CD8+ T cells detected by peptide/MHCI tetramer staining. The specific lysis of target CD4+ T cells measured at different effector versus target ratios, allows for the calculation of lytic units, LU30/106 cells. This simple and straightforward assay allows for the accurate measurement of the intrinsic capacity of CD8+ T cells to kill target CD4+ T cells.

This protocol describes a sensitive, cell-based cytotoxicity assay. By enumerating the decrease in frequency of live target CD4+ T cells in the presence of an increasing number of effector CD8+ T cells, this assay allows for the direct assessment of cytolytic activity of antigen-specific CD8+ T cells.

Cytolytic activity of CD8+ T cells is rarely evaluated. We describe here a new cell-based assay to measure the capacity of antigen-specific CD8+ T cells ...

Far-Red Fluorescent Senescence-Associated &#946;-Galactosidase Probe for Identification and Enrichment of Senescent Tumor Cells by Flow Cytometry

Cellular senescence is a state of proliferative arrest induced by biological damage that normally accrues over years in aging cells but may also emerge rapidly in tumor cells as a response to damage induced by various cancer treatments. Tumor cell senescence is generally considered undesirable, as senescent cells become resistant to death and block tumor remission while exacerbating tumor malignancy and treatment resistance. Therefore, the identification of senescent tumor cells is of ongoing interest to the cancer research community. Various senescence assays exist, many based on the activity of the well-known senescence marker, senescence-associated beta-galactosidase (SA-&#946;-Gal). 
Typically, the SA-&#946;-Gal assay is performed using a chromogenic substrate (X-Gal) on fixed cells, with the slow and subjective enumeration of "blue" senescent cells by light microscopy. Improved assays using cell-permeant, fluorescent SA-&#946;-Gal substrates, including C12-FDG (green) and DDAO-Galactoside (DDAOG; far-red), have enabled the analysis of living cells and allowed the use of high-throughput fluorescent analysis platforms, including flow cytometers. C12-FDG is a well-documented probe for SA-&#946;-Gal, but its green fluorescent emission overlaps with intrinsic cellular autofluorescence (AF) that arises during senescence due to the accumulation of lipofuscin aggregates. By utilizing the far-red SA-&#946;-Gal probe DDAOG, green cellular autofluorescence can be used as a secondary parameter to confirm senescence, adding reliability to the assay. The remaining fluorescence channels can be used for cell viability staining or optional fluorescent immunolabeling.
Using flow cytometry, we demonstrate the use of DDAOG and lipofuscin autofluorescence as a dual-parameter assay for the identification of senescent tumor cells. Quantitation of the percentage of viable senescent cells is performed. If desired, an optional immunolabeling step may be included to evaluate cell surface antigens of interest. Identified senescent cells can also be flow cytometrically sorted and collected for downstream analysis. Collected senescent cells can be immediately lysed (e.g., for immunoassays or 'omics analysis) or further cultured.

A protocol for fluorescent, flow cytometric quantification of senescent cancer cells induced by chemotherapy drugs in cell culture or in murine tumor models is presented. Optional procedures include co-immunostaining, sample fixation to facilitate large batch or time point analysis, and the enrichment of viable senescent cells by flow cytometric sorting.

Cellular senescence is a state of proliferative arrest induced by biological damage that normally accrues over years in aging cells but may also emerge ...

Cancer Research

Improvement of Bacillus subtilis Spore Enumeration and Label Analysis in Flow Cytometry

The spores of Bacillus subtilis have already been proposed for different biotechnological and immunological applications; however, there is an increasing need for the development of methodologies that improve the detection of antigens immobilized on the surface of spores together with their quantification. Flow cytometry-based analyses have been previously proposed as fast, reliable, and specific approaches for detecting labeled cells of B. subtilis. Herein, we propose the use of flow cytometry to evaluate the display efficiency of a fluorescent antibody (FA) on the surface of the spore and quantify the number of spores using counting beads. 
For this, we used ethidium bromide as a DNA marker and an allophycocyanin (APC)-labeled antibody, which was coupled to the spores, as a surface marker. The quantification of spores was performed using counting beads since this technique demonstrates high accuracy in the detection of cells. The labeled spores were analyzed using a flow cytometer, which confirmed the coupling. As a result, it was demonstrated that DNA labeling improved the accuracy of quantification by flow cytometry, for the detection of germinated spores. It was observed that ethidium bromide was not able to label dormant spores; however, this technique provides a more precise determination of the number of spores with fluorescent protein coupled to their surface, thus helping in the development of studies that focus on the use of spores as a biotechnological platform in different applications.

Improvement of Bacillus subtilis Spore Enumeration and Label Analysis in Flow Cytometry

This protocol focuses on the use of flow cytometry and counting beads to quantify bacterial spores labeled with ethidium bromide. The method is also efficient for analyzing the covalent coupling of proteins on the surface of intact spores.

The spores of Bacillus subtilis have already been proposed for different biotechnological and immunological applications; however, there is an increasing ...

Biochemistry

Measuring Cell Cycle Progression Kinetics with Metabolic Labeling and Flow Cytometry

Precise control of the initiation and subsequent progression through the various phases of the cell cycle are of paramount importance in proliferating cells. Cell cycle division is an integral part of growth and reproduction and deregulation of key cell cycle components have been implicated in the precipitating events of carcinogenesis 1,2. Molecular agents in anti-cancer therapies frequently target biological pathways responsible for the regulation and coordination of cell cycle division 3. Although cell cycle kinetics tend to vary according to cell type, the distribution of cells amongst the four stages of the cell cycle is rather consistent within a particular cell line due to the consistent pattern of mitogen and growth factor expression. Genotoxic events and other cellular stressors can result in a temporary block of cell cycle progression, resulting in arrest or a temporary pause in a particular cell cycle phase to allow for instigation of the appropriate response mechanism. 
The ability to experimentally observe the behavior of a cell population with reference to their cell cycle progression stage is an important advance in cell biology. Common procedures such as mitotic shake off, differential centrifugation or flow cytometry-based sorting are used to isolate cells at specific stages of the cell cycle 4-6. These fractionated, cell cycle phase-enriched populations are then subjected to experimental treatments. Yield, purity and viability of the separated fractions can often be compromised using these physical separation methods. As well, the time lapse between separation of the cell populations and the start of experimental treatment, whereby the fractionated cells can progress from the selected cell cycle stage, can pose significant challenges in the successful implementation and interpretation of these experiments.
Other approaches to study cell cycle stages include the use of chemicals to synchronize cells. Treatment of cells with chemical inhibitors of key metabolic processes for each cell cycle stage are useful in blocking the progression of the cell cycle to the next stage. For example, the ribonucleotide reductase inhibitor hydroxyurea halts cells at the G1/S juncture by limiting the supply of deoxynucleotides, the building blocks of DNA. Other notable chemicals include treatment with aphidicolin, a polymerase alpha inhibitor for G1 arrest, treatment with colchicine and nocodazole, both of which interfere with mitotic spindle formation to halt cells in M phase and finally, treatment with the DNA chain terminator 5-fluorodeoxyridine to initiate S phase arrest 7-9. Treatment with these chemicals is an effective means of synchronizing an entire population of cells at a particular phase. With removal of the chemical, cells rejoin the cell cycle in unison. Treatment of the test agent following release from the cell cycle blocking chemical ensures that the drug response elicited is from a uniform, cell cycle stage-specific population. However, since many of the chemical synchronizers are known genotoxic compounds, teasing apart the participation of various response pathways (to the synchronizers vs. the test agents) is challenging. 
Here we describe a metabolic labeling method for following a subpopulation of actively cycling cells through their progression from the DNA replication phase, through to the division and separation of their daughter cells. Coupled with flow cytometry quantification, this protocol enables for measurement of kinetic progression of the cell cycle in the absence of either mechanically- or chemically- induced cellular stresses commonly associated with other cell cycle synchronization methodologies 10. In the following sections we will discuss the methodology, as well as some of its applications in biomedical research.

Tracking subtle changes in the progression and kinetics of cell cycle stages can be accomplished by use of a combination of metabolic labeling of nucleic acids with BrdU and total genomic DNA staining via Propidium Iodide. This method avoids the need of chemical synchronization of cycling cells, thereby preventing the introduction of non-specific DNA damage, which in turn affects cell cycle progression.

Precise control of the initiation and subsequent progression through the various phases of the cell cycle are of paramount importance in proliferating ...

Biology

Visualization of Bacterial Resistance using Fluorescent Antibiotic Probes

Fluorescent antibiotics are multipurpose research tools that are readily used for the study of antimicrobial resistance, due to their significant advantage over other methods. To prepare these probes, azide derivatives of antibiotics are synthesized, then coupled with alkyne-fluorophores using azide-alkyne dipolar cycloaddition by click chemistry. Following purification, the antibiotic activity of the fluorescent antibiotic is tested by minimum inhibitory concentration assessment. In order to study bacterial accumulation, either spectrophotometry or flow cytometry may be used, allowing for much simpler analysis than methods relying on radioactive antibiotic derivatives. Furthermore, confocal microscopy can be used to examine localization within the bacteria, affording valuable information about mode of action and changes that occur in resistant species. The use of fluorescent antibiotic probes in the study of antimicrobial resistance is a powerful method with much potential for future expansion.

Fluorescently tagged antibiotics are powerful tools that can be used to study multiple aspects of antimicrobial resistance. This article describes the preparation of fluorescently tagged antibiotics and their application to studying antibiotic resistance in bacteria. Probes can be used to study mechanisms of bacterial resistance (e.g., efflux) by spectrophotometry, flow cytometry, and microscopy.

Fluorescent antibiotics are multipurpose research tools that are readily used for the study of antimicrobial resistance, due to their significant ...

Quantification of Reactive Oxygen Species Using 2&#8242;,7&#8242;-Dichlorofluorescein Diacetate Probe and Flow-Cytometry in M&#252;ller Glial Cells

The redox balance has an important role in maintaining cellular homeostasis. The increased generation of reactive oxygen species (ROS) promotes the modification of proteins, lipids, and DNA, which finally may lead to alteration in cellular function and cell death. Therefore, it is beneficial for cells to increase their antioxidant defense in response to detrimental insults, either by activating an antioxidant pathway like Keap1/Nrf2 or by improving redox scavengers (vitamins A, C, and E, &#946;-carotene, and polyphenols, among others). Inflammation and oxidative stress are involved in the pathogenesis and progression of retinopathies, such as diabetic retinopathy (DR) and retinopathy of prematurity (ROP). Since M&#252;ller glial cells (MGCs) play a key role in the homeostasis of neural retinal tissue, they are considered an ideal model to study these cellular protective mechanisms. In this sense, quantifying ROS levels with a reproducible and simple method is essential to assess the contribution of pathways or molecules that participate in the antioxidant cell defense mechanism. In this article, we provide a complete description of the procedures required for the measurement of ROS with DCFH-DA probe and flow cytometry in MGCs. Key steps for flow cytometry data processing with the software are provided here, so the readers will be able to measure ROS levels (geometric means of FITC) and analyze fluorescence histograms. These tools are highly helpful to evaluate not only the increase in ROS after a cellular insult but also to study the antioxidant effect of certain molecules that can provide a protective effect on the cells.

Here, we propose a systematized, accessible, and reproducible protocol to detect cellular reactive oxygen species (ROS) using 2&#8242;,7&#8242;-dichlorofluorescein diacetate probe (DCFH-DA) in M&#252;ller glial cells (MGCs). This method quantifies total cellular ROS levels with a flow cytometer. This protocol is very easy to use, suitable, and reproducible.

The redox balance has an important role in maintaining cellular homeostasis. The increased generation of reactive oxygen species (ROS) promotes the ...

Neuroscience

Natural Killer Cell-Mediated Cytotoxicity Assay Sample Preparation for Flow Cytometric Analysis

Source: McBride, J. E., et al. <a href="https://app-jove-com.remotexs.ntu.edu.sg/v/54779">Flow Cytometric Analysis of Natural Killer Cell Lytic Activity in Human Whole Blood.</a> J. Vis. Exp. (2017).
This video demonstrates the lytic activity of natural killer cells against fluorescently-stained target cells. The method enables NK cell-induced cytotoxicity measurement by staining dead cells and using a flow cytometer to quantify live and dead target cells.

Molecular Probe Optimization to Determine Cell Mortality in a Photosynthetic Organism (Microcystis aeruginosa) Using Flow Cytometry

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