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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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Expression profiles from multi-parametric data.PHH3 (green) and cyclin A2 (red) profiles, extracted from single cyclin sample data, are co-plotted with PHH3 (blue) and cyclin A2, extracted from multi-cyclin sample data, in three views that emphasize the entire cell cycle (top), G2 and M (lower left) and mitosis (lower right).
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pone-0030870-g012: Expression profiles from multi-parametric data.PHH3 (green) and cyclin A2 (red) profiles, extracted from single cyclin sample data, are co-plotted with PHH3 (blue) and cyclin A2, extracted from multi-cyclin sample data, in three views that emphasize the entire cell cycle (top), G2 and M (lower left) and mitosis (lower right).

Mentions: Improvements in instrumentation, antibody specificity and affinity, and fluorescence labeling probes have colluded in recent years to push the possibility of multiparameter cytometric analyses to 17 or more biomarkers [19]. However, fluorescence spectral overlap and the general non-availability of labeled antibodies often reduce the practical number of simultaneous biomarkers to much less than this. In analyses like we have presented here, where expression does not equal well-separated clusters of “negative” and “positive” cells but rather forms a continuum from low to high expression, the spectral overlap problems are even more inhibitory. This is something that we have suspected or known, but is difficult to demonstrate. We performed the measurements and analyses presented in Figures 11, 12, 13 to (1) further validate that the composite analyses in Figure 10 was correct, and (2) to evaluate the impact of spectral compensation for the overlap of Alexa Fluor 488 (on the PHH3 antibody) and phycoerythrin (on the cyclin A2 antibody). Three samples from the same population of cells used to generate the “single color” cyclins A2 and B1 data were stained for cyclin B1 (Alexa Fluor 647), cyclin A2 (phycoerythrin), PHH3 (Alexa Fluor 488), and DNA content (DAPI). Bivariate plots of the resulting data for one sample, and the segmentation scheme are shown in Figure 11. We used a different segmentation scheme that takes advantage of the correlation between cyclins A2 and B1 and the early rise of cyclin B1 to produce a smaller base cluster in G1 (orange dots, Figure 11B) and to ask whether a different segmentation scheme affected the data. Additionally, since cyclins A2 and B1 were correlated, we could dissect mitosis in more detail, examining the periods of time when first cyclin A2 and then cyclin B1 is degraded. The segmentation starts in Figure 11A with the region labeled “1”, then jumps to Figure 11B to region “2”. The consecutive regions move unidirectionally from “2” to “21” following the bivariate synthesis pattern of the two cyclins in interphase. The next region, “22”, appears in Figure 11A with consecutive, unlabeled regions ending at “34”. This region set is very similar to the mitotic regions in Figure 4B. The next region, “35”, starts in Figure 11C and ends just before the terminal cluster in this bivariate plot. Regions “39” and “40” finish the sequence and are shown in the three dimension view of PHH3, cyclin B1, and cyclin A2 expression in mitosis (Figure 11D). The expression profile data for each of the three measured samples are plotted for cyclin A2 (black circles) and PHH3 (blue circles) with the “single color” data of Figure 5 (red and green data) in Figure 12. It is obvious the PHH3 data are not different and for interphase and early mitosis, the cyclin A2 data match as well. However, the cyclin A2 data appear to be lower in values than expected (from “single color” data) when cyclin A2 was actively degraded in mitosis. This is also the data that are most sensitive to spectral compensation problems and where event numbers are small. Co-plotting of the multi-color (MC) cyclin B1 data with “single color” (SC) data in Figure 13B show that cyclin B1 was not subject to the same errors. The cyclin B1 probe, labeled with Alexa Fluor 647 was not subject to spectral overlap problems and compensation was not employed. Figure 13A shows the overlay of multi-color PHH3 with single color PHH3 from the cyclin B1 series of samples and shows identity. The payoff from multiparametric data is enhanced clarity of information as shown from the cyclin B1 values (Figure 13B, arrow) that were obtained indirectly from the regions set on the cyclin A2 versus PHH3 plot on the region that defines the period of cyclin A2 degradation. These measurements are not possible in “single color” cyclin B1 data.


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

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

Expression profiles from multi-parametric data.PHH3 (green) and cyclin A2 (red) profiles, extracted from single cyclin sample data, are co-plotted with PHH3 (blue) and cyclin A2, extracted from multi-cyclin sample data, in three views that emphasize the entire cell cycle (top), G2 and M (lower left) and mitosis (lower right).
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3275612&req=5

pone-0030870-g012: Expression profiles from multi-parametric data.PHH3 (green) and cyclin A2 (red) profiles, extracted from single cyclin sample data, are co-plotted with PHH3 (blue) and cyclin A2, extracted from multi-cyclin sample data, in three views that emphasize the entire cell cycle (top), G2 and M (lower left) and mitosis (lower right).
Mentions: Improvements in instrumentation, antibody specificity and affinity, and fluorescence labeling probes have colluded in recent years to push the possibility of multiparameter cytometric analyses to 17 or more biomarkers [19]. However, fluorescence spectral overlap and the general non-availability of labeled antibodies often reduce the practical number of simultaneous biomarkers to much less than this. In analyses like we have presented here, where expression does not equal well-separated clusters of “negative” and “positive” cells but rather forms a continuum from low to high expression, the spectral overlap problems are even more inhibitory. This is something that we have suspected or known, but is difficult to demonstrate. We performed the measurements and analyses presented in Figures 11, 12, 13 to (1) further validate that the composite analyses in Figure 10 was correct, and (2) to evaluate the impact of spectral compensation for the overlap of Alexa Fluor 488 (on the PHH3 antibody) and phycoerythrin (on the cyclin A2 antibody). Three samples from the same population of cells used to generate the “single color” cyclins A2 and B1 data were stained for cyclin B1 (Alexa Fluor 647), cyclin A2 (phycoerythrin), PHH3 (Alexa Fluor 488), and DNA content (DAPI). Bivariate plots of the resulting data for one sample, and the segmentation scheme are shown in Figure 11. We used a different segmentation scheme that takes advantage of the correlation between cyclins A2 and B1 and the early rise of cyclin B1 to produce a smaller base cluster in G1 (orange dots, Figure 11B) and to ask whether a different segmentation scheme affected the data. Additionally, since cyclins A2 and B1 were correlated, we could dissect mitosis in more detail, examining the periods of time when first cyclin A2 and then cyclin B1 is degraded. The segmentation starts in Figure 11A with the region labeled “1”, then jumps to Figure 11B to region “2”. The consecutive regions move unidirectionally from “2” to “21” following the bivariate synthesis pattern of the two cyclins in interphase. The next region, “22”, appears in Figure 11A with consecutive, unlabeled regions ending at “34”. This region set is very similar to the mitotic regions in Figure 4B. The next region, “35”, starts in Figure 11C and ends just before the terminal cluster in this bivariate plot. Regions “39” and “40” finish the sequence and are shown in the three dimension view of PHH3, cyclin B1, and cyclin A2 expression in mitosis (Figure 11D). The expression profile data for each of the three measured samples are plotted for cyclin A2 (black circles) and PHH3 (blue circles) with the “single color” data of Figure 5 (red and green data) in Figure 12. It is obvious the PHH3 data are not different and for interphase and early mitosis, the cyclin A2 data match as well. However, the cyclin A2 data appear to be lower in values than expected (from “single color” data) when cyclin A2 was actively degraded in mitosis. This is also the data that are most sensitive to spectral compensation problems and where event numbers are small. Co-plotting of the multi-color (MC) cyclin B1 data with “single color” (SC) data in Figure 13B show that cyclin B1 was not subject to the same errors. The cyclin B1 probe, labeled with Alexa Fluor 647 was not subject to spectral overlap problems and compensation was not employed. Figure 13A shows the overlay of multi-color PHH3 with single color PHH3 from the cyclin B1 series of samples and shows identity. The payoff from multiparametric data is enhanced clarity of information as shown from the cyclin B1 values (Figure 13B, arrow) that were obtained indirectly from the regions set on the cyclin A2 versus PHH3 plot on the region that defines the period of cyclin A2 degradation. These measurements are not possible in “single color” cyclin B1 data.

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