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Keywords:

  • cell viability;
  • live/dead test;
  • monocytes;
  • flow cytometry;
  • microscopy

Abstract

  1. Top of page
  2. Abstract
  3. Materials and Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. Literature Cited
  8. Supporting Information

We compare flow cytometric and microscopic determination of cell viability by fluorescence labeling using calcein acetoxy-methyl-ester and ethidium homodimer-1 as live and dead stain, respectively. Peripheral blood monocytes served as model system and were accumulated applying density gradients. Subsequently, monocytes were further enriched by magnetic-activated or fluorescence-activated cell sorting (MACS, FACS) targeting the antigen CD14. Identical samples were used for flow cytometric and microscopic analysis to allow direct comparison of both analysis methods. More than 1,000 cells were measured for each sample to minimize the measurement uncertainty caused by counting statistics. We observed good agreement of flow cytometric and microscopic viability measurements. On average, the difference in viability measured by flow cytometry and microscopy amounted to (2.7 ± 1.4)% for live staining and (1.7 ± 1.2)% for dead staining. These deviations were similar to the uncertainty of measurement for cell viability, thus demonstrating that both methods delivered equal results. Besides monocytes, comparison of flow cytometric and microscopy viability for MACS enriched CD34-positive cells also showed consistent results. © 2012 International Society for Advancement of Cytometry

Viability testing is of high importance in many areas of cell research including cytotoxicity tests based on cell and tissue cultures (1–3), the selection of proper tissue scaffolds for regenerative medicine (4), quality assurance of products for transplantation (5, 6), research for cancer treatment (7, 8), and also for the inspection of hybridoma cells (9–11). For the evaluation of cell viability, flow cytometry and microscopy are used besides the detection of cellular secretion products (11).

Flow cytometry offers high-speed quantitative multiparameter analysis of cells in suspension (12, 13) and is superior to microscopy in that respect. On the other hand, microscopy is a more versatile technique to examine the viability of individual cells, particularly in the case of adherently growing cells (14–16). Previous comparisons of flow cytometry and microscopy showed fair agreement between the two methods in dye exclusion tests (17, 18). Lange et al. investigated yeast cells using different staining methods and different detection modalities (17). Kim et al. studied mixtures of viable and dead white blood cells employing dye exclusion tests for dead cell staining (18). Besides the trypan blue exclusion test, flow cytometry and microchip based microscopy were compared using the same staining procedure for fluorescence labeling of DNA to avoid dye specific differences (18). However, trypan blue is known to produce unreliable results in dye exclusion tests on a time scale of a few minutes (19) and comparisons between flow cytometry and microscopy would be awkward, as both methods require different acquisition times. To eliminate these difficulties, an established live/dead double staining protocol based on calcein acetoxy-methyl-ester (calcein AM) and ethidium homodimer-1 (EHD) was selected here for validation of cell viability. Calcein AM probes esterase activity (1, 6, 20–23) thus identifying living cells. For dead cells, positive staining of DNA is characteristic, which indicates loss of membrane integrity. To analyze eukaryotic cells, EHD and calcein AM are frequently used in combination (2, 3, 16, 19, 24, 25). These reagents, applied simultaneously to detect live and dead cells by respective positive staining, have negligible influence on normal cell function (26, 27).

The aim of this contribution is to quantitatively compare flow cytometric and wide-field microscopic cell viability analysis following two-step cell enrichment. Special care was taken to use identical samples and a defined subpopulation of peripheral blood cells, that is, CD14-positive monocytes, for flow cytometric and microscopic evaluation. Selection of a defined cell population is required to exclude cell-specific influences like differing degradation times during transport, storage, and preparation on the comparison of both measurement methods (28, 29). We chose test samples containing CD14-positive monocytes, enriched from peripheral blood samples, as model system. Besides the investigation of monocytes, we compared microscopic and flow cytometric determination of viability of CD34-positive cells, also accumulated from peripheral blood.

Materials and Methods

  1. Top of page
  2. Abstract
  3. Materials and Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. Literature Cited
  8. Supporting Information

Enrichment of Peripheral Blood Mononuclear Cells

Venous blood was drawn from six healthy volunteers, their gender ratio being 1:1 and aged between 30 and 61 years, into 7.5 mL tubes containing K3-EDTA as anticoagulant. The healthy volunteers have given their informed consent in written form that the blood samples are used for research purposes. In total, 25 blood samples were analyzed. Density gradient accumulation of peripheral blood mononuclear cells (PBMCs) was performed according to the recommendations of the manufacturer (Miltenyi Biotec, Bergisch-Gladbach, Germany, protocol P0001.04, P140-000-067.06) of the device for magnetic-activated cell sorting (MACS). Monocytes or stem cells were accumulated from 25 to 40 mL blood using the Ficoll-Paque™ solution (density of 1.077 g/mL; GE Healthcare Bio-Sciences AB, Uppsala, Sweden) (30). Diluted blood was layered over the Ficoll-Paque™ solution in a Falcon tube (50 mL, Greiner Bio-One GmbH, Frickenhausen, Germany). Peripheral blood mononuclear cells (PBMCs) were harvested after centrifugation at 400g for 30 min, washed using phosphate buffered saline (PBS) solution and again centrifuged at 300g for 10 min. The last washing and centrifugation step (100g, 10 min) served to improve the purity of the PBMC suspension by removing residual platelets. The remaining pellet was resuspended in a 0.5 to 1 mL PBS solution and the concentration of PBMCs was measured with an ABX Micros 60 Hematology Analyzer (Horiba Medical, Montpellier, France) to determine the amount of reagents required for antibody labeling. For the investigation of monocytes, the concentration of PBMCs ranged from 12 to 50 nL-1 (15–25% of the cells being CD14-positive). When investigating stem cells, suspensions of PBMCs with concentrations between 80 and 100 nL-1 were used for subsequent magnetic- or fluorescence-activated sorting. For this purpose, the sample suspension was precooled and incubated with an appropriate amount of anti-CD14 or anti-CD34 labeled super-paramagnetic microbeads (Miltenyi Biotec) at 4°C for 15 min. Afterwards, monocytes were stained with anti-CD14-APC (APC, allophycocyanin) for 5 min at the same temperature while anti-CD34-APC was used for stem cells staining. In the latter case, FcR blocking reagent (Miltenyi Biotec) served to reduce unspecific labeling of residual granulocytes.

Magnetic-Activated Cell Sorting

Magnetic sorting was applied to all blood samples. The target cells were either CD14 or CD34-positive cells. Positive magnetic sorting was performed using a MiniMACS™ separator similarly as described by Miltenyi et al. (31), except that the steel wool is replaced by nearly spherical particles (measured size (250 ± 40) μm) in the presently used MS type columns (Miltenyi Biotec). The cells were eluted into polypropylene culture tubes (T405-2A, Simport, Beloeil Qc, Canada) resulting in a sample volume of 1 mL. The purity of the MACS-enriched suspension was determined by flow cytometry to be about 90% for monocytes, whereas only 20% was reached when sorting CD34-positive cells. Viability, however, was measured for all cells in the sample in the comparison of microscopic and flow cytometric measurement.

Flow Cytometric Sorting of CD14-Positive Monocytes

Flow cytometric sorting of CD14-positive cells was performed with a high-speed cell sorter. Flow cytometric sorting of CD34-positive cells was not considered because of the low concentration of these target cells in the blood of healthy volunteers. The MoFlo cell sorter was equipped with a 70 μm nozzle (purchased from Cytomation, Fort Collins, CO; electronics upgrade from Beckman Coulter, Brea, CA) (12, 32). Different measurement quantities or parameters were determined for each cell while passing three optical interaction zones. In the first interaction zone, particles are detected using superimposed laser beams from an Ar+-laser (488 nm, 290 mW) and a Kr++-Laser (413 nm, 50 mW) (both from Coherent, Santa Clara, CA). The second and third interaction zone was defined by focusing a diode laser emitting at 640 nm (90 mW, cube 640-100c, Coherent) and a frequency doubled ND:YAG laser with 532 nm (11.7 mW, from Millennia XS, Spectra-Physics, Santa Clara, CA), respectively, in the liquid jet. Forward light scatter at 488 nm (FSC 488) was measured by a photo diode, whereas for all other parameters photomultiplier tubes (Hamamatsu H957-15) were applied. For cell sorting as well as for the flow cytometric reanalysis of sorted cells, side scatter at 488 nm (SSC 488) was used as a trigger. Light scatter at 413.1 nm served to evaluate the purity of the samples with respect to residual erythrocytes (33). Further details are given in Supporting Information 2.

The sort decision was based on choosing an adequate gate in the APC versus SSC 488 plot. Sorting was performed at an analysis rate of typically 3 kHz and a sorting rate of about 500 Hz. The FACS-enriched samples contained about 0.4 × 106 cells accumulated in typically 1,000 s. The total volume ranged from 100 to 400 μL. In two experiments, the sample volume was increased to 900 μL and half of the sorted sample was kept for a prolonged time to obtain lower cell viability.

Live/Dead Staining

The advantage of the live/dead staining procedure applied in this work is that the respective red and green fluorescence of EHD and calcein are easily discernible by fluorescence microscopy (1, 19, 25). The solution of calcein-AM (1 μM) and EHD (6 μM) (protocol MP 03224, Molecular Probes, Eugene, OR) was mixed with cell suspensions for staining of typically 107 target cells. Removing unused EHD by washing was not necessary, since the contrast of stained nuclei to background fluorescence is sufficiently high in flow cytometric as well as microscopic measurements (cf. Fig. 2b and 3, discussed below).

For microscopy, an amount of 40 μL aliquot was pipetted in a Petri dish (35 mm diameter μ-dish 81156, ibidi, Martinsried, Germany) and cells were allowed to sediment at 36°C for about 10 min before inspection. To avoid vignetting by different heights of the liquid and to reduce evaporation, a cover slip was placed on top of the fluid resulting in a constant thickness of the liquid layer.

Flow Cytometric Measurement

The MoFlo cell sorter was also used for measurement of cell viability of FACS and MACS sorted samples. The parameter SSC 488 served as a trigger. Calcein is conveniently excited by 488 nm laser light. To prevent overload of the detectors due to the bright fluorescence of live monocytes, an attenuator had to be added in the optical detection path. The manufacturer of the life/dead staining kit recommends excitation of EHD fluorescence at 488 nm, the corresponding parameter is designated as EHD (blue). However, the spectral wings of the calcein fluorescence around 630 nm lead to considerable crosstalk for the parameter EHD (blue) and spectral compensation is necessary. Therefore, in addition to the recommended fluorescence excitation wavelength, excitation at 532 nm was used in the third interaction zone to observe EHD fluorescence. The latter parameter is designated as EHD (green). Calcein is not excited at this specific wavelength and no further data processing is required in this case.

Microscopic Measurement

For microscopy, an inverted microscope (Axiovert 200M) equipped with a 40× objective (LD Plan-Neofluar 40×/0.6 Korr Ph 2) was used. A triple-band filter set (exciter XF1058 (390/486/577 nm), dichroic XF2054 (485/555/650 nm), emitter XF3058 (457/528/633 nm), Omega Optical, Brattleboro, VT) allowed simultaneous fluorescence excitation of EHD and calcein by a mercury-arc lamp and image recording by a color CCD camera (AxioCam HRc Rev. 3, 14 bit resolution), mounted on a 0.63× video adapter (all from Carl Zeiss MicroImaging Göttingen, Germany). Besides fluorescence images, concurrent measurements of bright field images were accomplished in the blue spectral range using light from a halogen lamp filtered by a bandpass (BP425/50) and matching the transmission characteristics of the triple band filter. In contrast to the flow cytometric setup, the configuration of the microscope did not allow detection of APC fluorescence, so that nontarget cells could not be differentiated. The comparison of microscopic and flow cytometric viability measurement was, therefore, carried out for all cells present in the respective samples.

The advantage of color CCD detection is that all images are captured simultaneously. Hence, artifacts which result from cell movement when successively recording different fluorescence channels are avoided. In the experiments discussed here, successive image capture would be a major problem, since not all blood cells analyzed adhered to the surface. A single microscopic image covers only an area of 344 μm by 258 μm containing about 100 cells. To allow a reliable analysis, 100 tile images were recorded for each sample. The tile images were fused into a single image with 12 × 106 pixels for dataanalysis (pixel resolution 0.8 μm). During microscopic measurement, the sample was incubated at 37°C and high humidity.

Results

  1. Top of page
  2. Abstract
  3. Materials and Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. Literature Cited
  8. Supporting Information

Evaluation of Cell Viability by Flow Cytometry

Reliable determination of the relative abundance of viable cells from flow cytometric data requires proper selection of parameters, that is, excitation wavelengths and gating procedures. In Figure 1, scatterplots are shown for live/dead staining of a sample of FACS sorted monocytes. Fluorescence intensity of EHD (blue) and EHD (green) are plotted versus the calcein signal in Figures 1a and 1b, respectively. Original data are shown in each of these scatterplots, that is, no spectral compensation was applied. Besides the three clusters of monocytes (M), the major remainders in the samples were platelets (Plt), which can be stained by calcein but not by EHD because of the lack of DNA. Microscopic inspection showed that no adherence of platelets to monocytes occurred. When comparing Figures 1a and 1b, the advantage of the excitation of the EHD fluorescence at 532 nm is clearly discernible. The excitation wavelength of 488 nm results in an extensive crosstalk of calcein signal in the EHD (blue) fluorescence detection channel, and hence Plt and (viable) monocytes are arranged close to a straight line of upward slope (Fig. 1a). In contrast, when exciting at 532 nm, no influences between both detection channels occur (Fig. 1b) and discrimination of different subpopulations is straightforward by quadrant analysis. This is demonstrated by selection of color gates for dead monocytes (R2, red), live monocytes (R3, green) and platelets (R1, blue) in Figure 1b. These gates cover about 98.5% of all events observed. The corresponding analysis when exciting at 488 nm would not be based on Figure 1a to avoid problematic selection of delineation lines. Instead spectral compensation as demonstrated in Supporting Information 1 would be used to derive the relative cell numbers. This is no longer necessary, since 532 nm excitation results in a significant improvement for the cell differentiation.

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Figure 1. Flow cytometric differentiation of calcein and ethidium homodimer-1 (EHD) stained cells (example for a FACS sorted sample); M, monocytes; Plt, platelets. EHD fluorescence of stained cells was excited by 488 nm (a) or 532 nm (b) laser light. Color labeling of regions R1, R2, R3 was applied to compare corresponding cell populations in both scatterplots (gate statistics: total count 200040, R1: 5.31%, R2: 16.94%, R3: 76.21%). [Color figure can be viewed in the online issue which is available at wileyonlinelibrary.com.]

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Besides the parameters EHD (green), EHD (blue), and calcein, light scatter at 488 and 640 nm and anti-CD14-APC fluorescence was measured. It should be noted that the fluorescence signal of anti-CD14 labeled monocytes (M) is less intense than usual, since magnetic labeling of the CD14 epitopes was carried out first and consequently the number of free reaction sites decreased. To evaluate the stability of our data analysis, we examined different gating strategies for the identification of monocytes based on the SSC 640 versus FSC 640 scatterplot, the anti-CD14-APC fluorescence versus SSC 488 diagram, and a lower threshold for the SSC 488 light scatter intensity in the FSC 488 versus SSC 488 scatterplot. All these approaches yielded good agreement with respect to cell viability and we chose one parameter gating of light scattering intensity to discriminate of the CD14 target cells against debris and platelets. This is illustrated in Figure 2a, where we included the threshold UG for the SSC 488 in the FSC 488 versus SSC 488 scatterplot. In the context of this work, selection of the light scattering parameter (SSC 488) for flow cytometric analysis is essential to allow a well-founded comparison with microscopic measurement, since an equivalent or corresponding morphological parameter is accessible in bright field microscopy, as discussed below.

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Figure 2. Gating for flow cytometric determination of cell viability from scatterplots (example for a FACS sorted sample). (a) Threshold setting for separating target cells (M: monocytes) and background (Plt:platelets) based on light scatter. The solid line indicates the threshold for delineation of target cells and dotted lines mark the thresholds used for uncertainty analysis. (b) Scatterplot for live/dead staining using data from excitation by 532 nm laser light. The result was obtained applying gating by threshold UG. Four regions were used to count calcein and ethidium positive and negative cells, respectively. The color scale is the same for (a) and (b) and gives the number of events on a logarithmic scale (gate statistics: total count above UG: 188794, NI: 153053, NII: 32961, NIII: 86, NIV: 2694). [Color figure can be viewed in the online issue which is available at wileyonlinelibrary.com.]

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In Figure 2a, platelets and other particles exhibit small SSC 488 signals below the threshold UG. As result of the gating procedure, which excludes all these events, we obtain the scatter plot in Figure 2b. It should be noted that Figure 2b, depicting EHD (green) versus calcein fluorescence intensities, represents the same measurement as Figure 1b. Further analysis was based on the identification of live and dead cells by quadrant analysis as shown in Figure 2b. In region I, viable cells were located as measured by positive calcein staining, their number being NI. The corresponding relative abundance with respect to all events (NI + NII + NIII + NIV) in Figure 2b is designated as RI = NI / (NI + NII + NIII + NIV). In region II, NII EHD-positive cells were enumerated as dead cells yielding the fraction RII. Region III covers a relatively small fraction of cells, which were positive for both EHD and calcein. By microscopy, this interpretation was supported and coincidences were excluded since these cells simultaneously show a cytoplasm stained with calcein and a nucleus stained by EHD. However, RIII rarely exceeded 0.1% when immediately measured after live/dead staining. Region IV covers cells that neither react with calcein nor with EHD. Typically, frequencies were below 2%, that is, RIV < 2%.

The measurement procedure applied here allows classification of each event according to both, live stain and dead stain and hence allows the exclusion of double positive (region III) and double negative (region IV) events. However, to be consistent with usually applied procedures, where live or dead cells are identified by staining with a single dye, and to facilitate reliable comparison with microscopy, we followed the approach of Lange et al. (17) and derived cell counts corresponding to single staining procedures. The fraction of cells stained with calcein AM determine the relative abundance of viable cells with respect to all cells as Vca = RI + RIII while positive EHD staining yielded the complement to viable cells as DEHD = RII + RIII. These relative abundances were measured with respect to all events in the scatterplot in Figure 2b. Both, that is live and dead staining methods provide complementary information and their sum can be used to prove consistency of results. For FACS- and MACS-treated samples we derived Vca + DEHD = (98.8 ± 0.5)% and (99.2 ± 1.1)%, respectively. The close proximity to the expected value of 100% indicates adequate data analysis. Concerning the procedure for cell accumulation, our measurements revealed that the viability of monocytes is slightly higher when sorted by MACS compared to FACS.

Uncertainty determination is essential to judge comparison of flow cytometric and microscopic viability determination. Besides statistical contributions derived from the number of counted events, for flow cytometric measurements a significant contribution to the uncertainty originates from the delineation of different populations in the scatterplots. For instance, the threshold UG can be shifted leading to different values for Vca and DEHD. To estimate this contribution to uncertainty, data analysis was also carried out using additional gating thresholds Umath image and Umath image (Fig. 2a), symmetrically chosen with respect to UG according to Umath image / UG = UG / Umath image = 0.75. It should be noted that this selection also covers extreme values observed in all measurements discussed here. Besides selection of UG, two additional thresholds were set to define the quadrants in Figure 2b and the contribution to the overall error can be determined using a variation of those thresholds in just the same way as described for the scattering signal. This approach was tested for a subset of nine measurements, showing that the uncertainty contribution caused by setting the fluorescence gate was on average approximately twice as large (median 2.2) as the contribution from varying the threshold for light scattering. It was further observed that the light scattering and fluorescence signal are correlated in some cases whereas in some cases they were not. Therefore, we decided not to use the standard uncertainty propagation, that is, taking the square root of the sum of squared errors. Instead the maximal error (linear sum of individual contributions) was used to estimate the overall uncertainty. Since this maximal uncertainty was typically three times larger than the one derived from light scattering alone, the combined uncertainties arising from setting gates for fluorescence and scattering were determined by multiplying the uncertainty estimated by the variation of UG by a factor of 3. It should be noted that the contribution from counting statistics was found to be of minor importance for flow cytometric measurements.

Evaluation of Cell Viability Using Wide-Field Microscopy

Automated cell counting in microscopic images is typically performed by binary masks for object definition (34, 35). An RGB image was recorded (Fig. 3), where the channels R and G represent the EHD and calcein fluorescence, respectively, and B gives the bright field image. Two binary masks were generated, one of which using the image obtained by calculating the average of R, G, and the inverted bright field image B*. The second mask was derived from B* only (Fig. 3). Cells appear mostly as disks in the averaged image and in G, but occasionally as rings in B* and R. In both cases, rings were obtained for all cells by Sobel operation using manual thresholding to generate binary images. Noise emerging from the Sobel operation was reduced by excluding objects with less or equal 10 pixels. Rings were closed by single octagon (radius 2) expansion and dilatation to obtain the binary masks for automated total cell number determination.

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Figure 3. Image analysis based on mask derived from the inverted bright field image. For illustration, only a small fraction of the original image is shown (monocytes obtained by FACS). Grey scale and RGB images are scaled for better visibility. [Color figure can be viewed in the online issue which is available at wileyonlinelibrary.com.]

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Cell counting and the determination of different cell parameters were performed using the AutoMeasure Plus (Carl Zeiss MicroImaging) software. Generated data spreadsheets were converted to FCS listmode format. An example for FACS-separated monocytes is shown in Figure 4. As microscopic analog to flow cytometric data, analyzed on the basis of forward and side scatter, we selected the cell area in the histogram. Monocytes are discriminated from smaller objects like platelets and debris by the threshold AG. Again, uncertainties caused by the choice of AG as lower threshold for the selection of monocytes were estimated by applying the Amath image and Amath image thresholds, defined according to Amath image / AG = AG / Amath image = 0.75.

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Figure 4. Gating for microscopic determination of cell viability (same sample as Fig. 1). The histogram of the cell area measured by microscopy shows the major peak for single cells and smaller peaks for clusters of two or more cells, which were not separated by image analysis.

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The procedure applied to determine cell viability was consistent with the analysis of flow cytometric data and allows an adequate comparison of both methods. The fluorescence intensities for calcein and EHD were obtained by summing intensities of all pixels attributed to a specific cell. Compared to flow cytometry, the relative number of events in region III (Fig. 2b) was higher and exceeded 1% in some measurements due to cell clusters interpreted as single objects. Cell clustering limits the applicability of microscopy, in particular at high density of cells. Hence, microscopic images showing excessive clustering of cells were excluded from the analysis. Combined measurement uncertainties were derived in the same manner as described for flow cytometric data analysis.

Comparison of Cell Counting Methods

The fractions of live (Vca = RI + RIII) and dead (DEHD = RII + RIII) monocytes (blue symbols) as well as CD34-positive cells (red symbols) are plotted in Figure 5. The straight line indicates equal results for flow cytometry and microscopy. It follows from Figures 5a and 5b that the results agree well for calcein-positive and for EHD-positive cells. The uncertainties included are composed of statistical and systematic uncertainties due to gating of the target cells, as described above. However, several cases are observed where the deviation of the measured values from the straight line is larger than the estimated uncertainties. This could be attributed to additional influence quantities, like cell loss due to adhesion, which would predominantly take place in the tubing system of the flow cytometer, and the clustering of cells in microscopic measurements. Microscopic investigation indicated that live monocytes exhibit pronounced adhesion in contrast to dead monocytes. Such correlation could cause higher losses of live monocytes and hence the measured percentage of viable monocytes could be reduced. Selection of gating strategies based on various parameters also contributes to uncertainty of measurement. For example, the number of events misidentified as target cells is different when gating is based on size-correlated measuring quantities like FSC, SSC, and area in microscopy. Typically, we found differences in the range of 1–2% for flow cytometric data when analyzing fluorescence of CD14-positive target cells compared to size-correlated parameters.

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Figure 5. Comparison of relative cell abundance measured by flow cytometry and microscopy for CD14-positive cells (monocytes)—blue, and CD34-positive cells—red: (a) live staining of cells with calcein; (b) dead staining of cells with EHD. The dashed line indicates equal viability. [Color figure can be viewed in the online issue which is available at wileyonlinelibrary.com.]

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The CD34-positive target cells were enriched by two steps, that is, by applying density gradient followed by MACS. Although the error bars plotted in Figure 5 were estimated in the same way as for the measurements on CD14-positive cells, their size seems to be overestimated. This can be explained as follows. The delineation of target cells by cell size in microscopy and light scattering in flow cytometry is difficult, since circulating stem cells are not particularly large compared to other blood cells. This leads to a substantially higher contribution to the uncertainty even if the delineation of live and dead cells poses no particular difficulty.

In order to extend the range covered by our analysis to low viability, two sets of samples with artificially reduced viability were prepared by storage for 24 and 48 h (23°C, in the dark), respectively, before live/dead staining and subsequent measurement by flow cytometry and microscopy. Figure 5 reveals that viability of CD34 cells measured by microscope and flow cytometer differs by up to 13%. Whereas for CD14 cell viabilities above 95% were reached, for CD34 the maximum percentage of living cells is 75% (Fig. 5), possibly due to contamination by other dead cells.

Discussion

  1. Top of page
  2. Abstract
  3. Materials and Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. Literature Cited
  8. Supporting Information

When evaluating different measurement techniques, a small biological diversity of the cells studied is advantageous for an accurate comparison. Therefore, a purified cell population was prepared for viability measurements using microscopy and flow cytometry. Density gradients followed by FACS or MACS were applied in our work to select target cells from peripheral blood. Monocytes were considered as a model system because of their high concentration (100–900 μL-1) and their straightforward selection by the highly expressed antigen CD14. Furthermore, the variation of light scattering intensity in this subpopulation is smaller than that of granulocytes (for example), and the analysis is easier in particular for flow cytometry. Monocytes have pleomorphic nuclei, which however are not segmented and nucleoli are rarely present (36). This simplifies the counting of EHD-stained nuclei in microscopy, where these objects would otherwise be misinterpreted as multiple objects when generating the binary mask for image analysis, including fluorescence images. Another advantage of using monocytes for microscopic measurement is that live monocytes tend to adhere to the substrate surface, which reduces imaging artifacts caused by moving cells during acquisition of tile image series. On the other hand, for flow cytometry adhesion could be a disadvantage, since the corresponding loss in the tubing might be selectively increased for living cells relative to dead cells. In our experiments, reasonable sample purity was achieved.

We observed good agreement between microscopic and flow cytometric viability measurements. On average, the fraction of calcein-positive cells counted by microscopy is higher by (2.7 ± 1.4)% compared to flow cytometry. Also, the fraction of cells positively stained by EHD is a bit higher when measured microscopically, in this case (1.7 ± 1.2)%. Such small deviations were observed by Kim et al. comparing microscopic and flow cytometric measurements of peripheral blood mononuclear cells. They used dye exclusion tests (18) and applied different staining methods for microscopy and flow cytometry. It is apparent that the uncertainties estimated for the individual measurements cannot fully explain the discrepancies between microscopic and flow cytometric measurements (Fig. 5). A potential explanation for the observed deviations is that microscopy has difficulties to detect unstained cells, which could lead to overestimation of the fractions of live as well as dead cells.

When dealing with CD34-positive cells, accumulated from peripheral blood, we observed fair agreement between microscopy and flow cytometry. The high uncertainties in these results are probably due to the variety of cell types in that sample. Compared to CD14 target cells, uncertainties for viability tests of CD34 cells were significantly higher. This is to be expected since CD14-positive cells occur at a relatively high concentration in peripheral blood when compared to rare cell populations like CD34-positive stem cells, the concentration of which was typically 3 μL–1 in the original blood sample. Apart from the statistical uncertainties, a major contribution to the combined uncertainties is due to the delicate discrimination of CD34 cells against other cells with potentially different viability. Therefore, changing the threshold for light scattering leads to the selection of different cell sub-populations, which results in large changes in viability.

In conclusion, we have shown that on average microscopy and flow cytometry yield equal results within about 2% when measuring the viability of monocytes in cell suspensions by live or dead staining. The correlation between these methods was studied in the range of 10–95% viability. It follows that microscopic methods are equally suited to measure cell viability with the same accuracy as flow cytometry, provided that sufficiently high numbers of cells are counted (i.e., more than 2,500 cells for 2% statistical uncertainty). When analyzing the viability of high abundant cells, flow cytometry is to be preferred because of easier handling, better counting statistics and a smaller turnaround time. On the other hand, microscopy is the only choice for adherently growing cells, for example, in culture flasks or when growing tissue. Image based analyzers for concentration and viability measurement are offered by a number of manufacturers (37). This study proves that such image based tests have the potential to determine viability with similar accuracy as the more elaborate flow cytometry approach. Both methods complement each other and need to be combined, in particular when evaluating the seeding efficiency and growing tissue in regenerative medicine.

Acknowledgements

  1. Top of page
  2. Abstract
  3. Materials and Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. Literature Cited
  8. Supporting Information

The authors would like to thank Denny Ragusch for his support when developing data acquisition software and Peter Pawlak for providing technical assistance.

Literature Cited

  1. Top of page
  2. Abstract
  3. Materials and Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. Literature Cited
  8. Supporting Information
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Supporting Information

  1. Top of page
  2. Abstract
  3. Materials and Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. Literature Cited
  8. Supporting Information

Additional Supporting Information may be found in the online version of this article.

FilenameFormatSizeDescription
CYTO_22213_sm_SuppFgi1.tif1340KSupporting Information Figure 1
CYTO_22213_sm_SuppFgi2.tif420KSupporting Information Figure 2

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