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Dynamic epitope expression from static cytometry data: principles and reproducibility.

Jacobberger JW, Avva J, Sreenath SN, Weis MC, Stefan T - PLoS ONE (2012)

Bottom Line: The resulting 5 dimensional data were analyzed as a series of bivariate plots to isolate the data as segments of an N-dimensional "worm" through the data space.Very precise, correlated expression profiles for important cell cycle regulating and regulated proteins and their modifications can be produced, limited only by the number of available high-quality antibodies.These profiles can be assembled into large information libraries for calibration and validation of mathematical models.

View Article: PubMed Central - PubMed

Affiliation: Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio, United States of America. jwj@case.edu

ABSTRACT

Background: An imprecise quantitative sense for the oscillating levels of proteins and their modifications, interactions, and translocations as a function of the cell cycle is fundamentally important for a cartoon/narrative understanding for how the cell cycle works. Mathematical modeling of the same cartoon/narrative models would be greatly enhanced by an open-ended methodology providing precise quantification of many proteins and their modifications, etc. Here we present methodology that fulfills these features.

Methodology: Multiparametric flow cytometry was performed on Molt4 cells to measure cyclins A2 and B1, phospho-S10-histone H3, DNA content, and light scatter (cell size). The resulting 5 dimensional data were analyzed as a series of bivariate plots to isolate the data as segments of an N-dimensional "worm" through the data space. Sequential, unidirectional regions of the data were used to assemble expression profiles for each parameter as a function of cell frequency.

Results: Analysis of synthesized data in which the true values where known validated the approach. Triplicate experiments demonstrated exceptional reproducibility. Comparison of three triplicate experiments stained by two methods (single cyclin or dual cyclin measurements with common DNA and phospho-histone H3 measurements) supported the feasibility of combining an unlimited number of epitopes through this methodology. The sequential degradations of cyclin A2 followed by cyclin B1 followed by de-phosphorylation of histone H3 were precisely mapped. Finally, a two phase expression rate during interphase for each cyclin was robustly identified.

Conclusions: Very precise, correlated expression profiles for important cell cycle regulating and regulated proteins and their modifications can be produced, limited only by the number of available high-quality antibodies. These profiles can be assembled into large information libraries for calibration and validation of mathematical models.

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Overlayed expression profiles of cyclins A2 and B1.Overlays of expression profiles for PHH3 (A, B) from the data set Molt4 cells stained for cyclin A2, PHH3, and DNA content (labeled PHH3 from A2) presented in Figure 5, and from cells stained for cyclin B1, PHH3, and DNA content (labeled PHH3 from B1). C, D: overlays of expression profiles for cyclin A2 and cyclin B1 obtained independently on the same samples. Inset in D: zoomed view of initial decrease in both cyclins as cells enter mitosis. In A, B, and C, means (of median specific fluorescence) are shown. In D, error bars are SEM.
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pone-0030870-g009: Overlayed expression profiles of cyclins A2 and B1.Overlays of expression profiles for PHH3 (A, B) from the data set Molt4 cells stained for cyclin A2, PHH3, and DNA content (labeled PHH3 from A2) presented in Figure 5, and from cells stained for cyclin B1, PHH3, and DNA content (labeled PHH3 from B1). C, D: overlays of expression profiles for cyclin A2 and cyclin B1 obtained independently on the same samples. Inset in D: zoomed view of initial decrease in both cyclins as cells enter mitosis. In A, B, and C, means (of median specific fluorescence) are shown. In D, error bars are SEM.

Mentions: In this series of stained samples, the same population of Molt4 cells used for cyclin A2 and PHH3 (above) were stained for cyclin B1, PHH3, and DNA content. The purpose was to obtain the cleanest measure of cyclin B1 (i.e., without fluorescence compensation) and to test the ability to measure cyclins A2 and B1 independently and put them back together through the frequency domain. Figure 8 shows segmentation of flow cytometry data for cyclin B1 versus PHH3 for a sample from the same population of Molt4 cells for which the cyclin A2 analyses were made. Essentially, the cytometric data patterns look similar from this view, reflecting the similar shapes of the expression profiles for these two cell cycle regulated proteins. We can measure cyclin B1 in G1, earlier than cyclin A2, and the subsequent rise cyclin B1, peaking at the G2/M transition, can be fit by a compound equation with two exponential components (Figure S2). Apparently, both cyclins are expressed at one rate through S and a second, increased rate in G2, with cyclin B1 affected more dramatically. This comparison is shown in Figure 9C. In Figure 9, the data sets for cyclin A2 and cyclin B1 (3 independently analyzed samples of each) are co-plotted. In Figure 9A/9B, the PHH3 profiles from the two data sets are co-plotted, demonstrating remarkable accuracy for two independently stained and measured samples, and independent analyses of the two data sets. The agreement in the two PHH3 profiles validates co-plotting of the cyclins with the features of each cyclin, relative to the other, valid as well. As independent confirmation, the analysis shows that cyclin A2 expression decreases to background levels prior to a similar decrease in cyclin B1 (Figure 9D), which is known to be the case [18]. Figure 9D (inset) shows that both sets of data indicate a decrease in both cyclins at the same time in the beginning of M when PHH3 is rising. This adds weight the idea that this is a real event, at least for this cell line under the culture conditions at the time of the experiment, and if more generally observed, should eventually be accounted in mathematical models of the biochemistry of mitotic entry. The data presented in Figure 9 show that by the expression profile extraction approach, the relative expression behavior of an essentially unlimited number of biomarkers can be assessed as a function of the cell cycle by independent sampling, measurement, and analysis, and quantitatively put together as a whole analysis. This then becomes a powerful approach to validating mathematical models of the cell cycle. Figure 10 shows a close-up of mitosis from the view of both analyses. In this figure, the means for the two sets of PHH3 expression data are plotted as one set, demonstrating an increase in information by (1) more points defining PHH3 during the periods of cyclin degradation (arrow) and (2) the temporal offsets of cyclins A2 and B1 degradation and PHH3 dephosphorylation.


Dynamic epitope expression from static cytometry data: principles and reproducibility.

Jacobberger JW, Avva J, Sreenath SN, Weis MC, Stefan T - PLoS ONE (2012)

Overlayed expression profiles of cyclins A2 and B1.Overlays of expression profiles for PHH3 (A, B) from the data set Molt4 cells stained for cyclin A2, PHH3, and DNA content (labeled PHH3 from A2) presented in Figure 5, and from cells stained for cyclin B1, PHH3, and DNA content (labeled PHH3 from B1). C, D: overlays of expression profiles for cyclin A2 and cyclin B1 obtained independently on the same samples. Inset in D: zoomed view of initial decrease in both cyclins as cells enter mitosis. In A, B, and C, means (of median specific fluorescence) are shown. In D, error bars are SEM.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3275612&req=5

pone-0030870-g009: Overlayed expression profiles of cyclins A2 and B1.Overlays of expression profiles for PHH3 (A, B) from the data set Molt4 cells stained for cyclin A2, PHH3, and DNA content (labeled PHH3 from A2) presented in Figure 5, and from cells stained for cyclin B1, PHH3, and DNA content (labeled PHH3 from B1). C, D: overlays of expression profiles for cyclin A2 and cyclin B1 obtained independently on the same samples. Inset in D: zoomed view of initial decrease in both cyclins as cells enter mitosis. In A, B, and C, means (of median specific fluorescence) are shown. In D, error bars are SEM.
Mentions: In this series of stained samples, the same population of Molt4 cells used for cyclin A2 and PHH3 (above) were stained for cyclin B1, PHH3, and DNA content. The purpose was to obtain the cleanest measure of cyclin B1 (i.e., without fluorescence compensation) and to test the ability to measure cyclins A2 and B1 independently and put them back together through the frequency domain. Figure 8 shows segmentation of flow cytometry data for cyclin B1 versus PHH3 for a sample from the same population of Molt4 cells for which the cyclin A2 analyses were made. Essentially, the cytometric data patterns look similar from this view, reflecting the similar shapes of the expression profiles for these two cell cycle regulated proteins. We can measure cyclin B1 in G1, earlier than cyclin A2, and the subsequent rise cyclin B1, peaking at the G2/M transition, can be fit by a compound equation with two exponential components (Figure S2). Apparently, both cyclins are expressed at one rate through S and a second, increased rate in G2, with cyclin B1 affected more dramatically. This comparison is shown in Figure 9C. In Figure 9, the data sets for cyclin A2 and cyclin B1 (3 independently analyzed samples of each) are co-plotted. In Figure 9A/9B, the PHH3 profiles from the two data sets are co-plotted, demonstrating remarkable accuracy for two independently stained and measured samples, and independent analyses of the two data sets. The agreement in the two PHH3 profiles validates co-plotting of the cyclins with the features of each cyclin, relative to the other, valid as well. As independent confirmation, the analysis shows that cyclin A2 expression decreases to background levels prior to a similar decrease in cyclin B1 (Figure 9D), which is known to be the case [18]. Figure 9D (inset) shows that both sets of data indicate a decrease in both cyclins at the same time in the beginning of M when PHH3 is rising. This adds weight the idea that this is a real event, at least for this cell line under the culture conditions at the time of the experiment, and if more generally observed, should eventually be accounted in mathematical models of the biochemistry of mitotic entry. The data presented in Figure 9 show that by the expression profile extraction approach, the relative expression behavior of an essentially unlimited number of biomarkers can be assessed as a function of the cell cycle by independent sampling, measurement, and analysis, and quantitatively put together as a whole analysis. This then becomes a powerful approach to validating mathematical models of the cell cycle. Figure 10 shows a close-up of mitosis from the view of both analyses. In this figure, the means for the two sets of PHH3 expression data are plotted as one set, demonstrating an increase in information by (1) more points defining PHH3 during the periods of cyclin degradation (arrow) and (2) the temporal offsets of cyclins A2 and B1 degradation and PHH3 dephosphorylation.

Bottom Line: The resulting 5 dimensional data were analyzed as a series of bivariate plots to isolate the data as segments of an N-dimensional "worm" through the data space.Very precise, correlated expression profiles for important cell cycle regulating and regulated proteins and their modifications can be produced, limited only by the number of available high-quality antibodies.These profiles can be assembled into large information libraries for calibration and validation of mathematical models.

View Article: PubMed Central - PubMed

Affiliation: Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio, United States of America. jwj@case.edu

ABSTRACT

Background: An imprecise quantitative sense for the oscillating levels of proteins and their modifications, interactions, and translocations as a function of the cell cycle is fundamentally important for a cartoon/narrative understanding for how the cell cycle works. Mathematical modeling of the same cartoon/narrative models would be greatly enhanced by an open-ended methodology providing precise quantification of many proteins and their modifications, etc. Here we present methodology that fulfills these features.

Methodology: Multiparametric flow cytometry was performed on Molt4 cells to measure cyclins A2 and B1, phospho-S10-histone H3, DNA content, and light scatter (cell size). The resulting 5 dimensional data were analyzed as a series of bivariate plots to isolate the data as segments of an N-dimensional "worm" through the data space. Sequential, unidirectional regions of the data were used to assemble expression profiles for each parameter as a function of cell frequency.

Results: Analysis of synthesized data in which the true values where known validated the approach. Triplicate experiments demonstrated exceptional reproducibility. Comparison of three triplicate experiments stained by two methods (single cyclin or dual cyclin measurements with common DNA and phospho-histone H3 measurements) supported the feasibility of combining an unlimited number of epitopes through this methodology. The sequential degradations of cyclin A2 followed by cyclin B1 followed by de-phosphorylation of histone H3 were precisely mapped. Finally, a two phase expression rate during interphase for each cyclin was robustly identified.

Conclusions: Very precise, correlated expression profiles for important cell cycle regulating and regulated proteins and their modifications can be produced, limited only by the number of available high-quality antibodies. These profiles can be assembled into large information libraries for calibration and validation of mathematical models.

Show MeSH