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Single cell cytometry of protein function in RNAi treated cells and in native populations.

LaPan P, Zhang J, Pan J, Hill A, Haney SA - BMC Cell Biol. (2008)

Bottom Line: In a third example, reduction of STAT3 levels by siRNA causes an accumulation of cells in the G1 phase of the cell cycle, but does not induce apoptosis or necrosis when compared to control cells that express the same levels of STAT3.In a final example, the effect of reduced p53 levels on increased adriamycin sensitivity for colon carcinoma cells was demonstrated at the whole-well level using siRNA knockdown and in control and untreated cells at the single cell level.Biological differences that result from changes in protein level or pathway activation state can be modulated directly by RNAi treatment or extracted from the natural variability intrinsic to cells grown under normal culture conditions.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biological Technologies, Oncology Research, Wyeth Research, 87 Cambridge Park Drive, Cambridge, MA 02140, USA. plapan@wyeth.cpm

ABSTRACT

Background: High Content Screening has been shown to improve results of RNAi and other perturbations, however significant intra-sample heterogeneity is common and can complicate some analyses. Single cell cytometry can extract important information from subpopulations within these samples. Such approaches are important for immune cells analyzed by flow cytometry, but have not been broadly available for adherent cells that are critical to the study of solid-tumor cancers and other disease models.

Results: We have directly quantitated the effect of resolving RNAi treatments at the single cell level in experimental systems for both exogenous and endogenous targets. Analyzing the effect of an siRNA that targets GFP at the single cell level permits a stronger measure of the absolute function of the siRNA by gating to eliminate background levels of GFP intensities. Extending these methods to endogenous proteins, we have shown that well-level results of the knockdown of PTEN results in an increase in phospho-S6 levels, but at the single cell level, the correlation reveals the role of other inputs into the pathway. In a third example, reduction of STAT3 levels by siRNA causes an accumulation of cells in the G1 phase of the cell cycle, but does not induce apoptosis or necrosis when compared to control cells that express the same levels of STAT3. In a final example, the effect of reduced p53 levels on increased adriamycin sensitivity for colon carcinoma cells was demonstrated at the whole-well level using siRNA knockdown and in control and untreated cells at the single cell level.

Conclusion: We find that single cell analysis methods are generally applicable to a wide range of experiments in adherent cells using technology that is becoming increasingly available to most laboratories. It is well-suited to emerging models of signaling dysfunction, such as oncogene addition and oncogenic shock. Single cell cytometry can demonstrate effects on cell function for protein levels that differ by as little as 20%. Biological differences that result from changes in protein level or pathway activation state can be modulated directly by RNAi treatment or extracted from the natural variability intrinsic to cells grown under normal culture conditions.

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siRNA mediated knockdown of STAT3. A. Histogram of STAT3 levels in SW480 colon carcinoma cells treated with STAT3 and NTC (non-targeting) siRNAs. Red bars denote STAT3 siRNA treated cells and blue bars represent NTC treated cells. Data presents ~22,000 cells for samples treated with STAT3 and NTC siRNAs each. A region of low-STAT3 expressing cells examined in panels (C) and (E) is indicated in the panel (top left corner). B. DNA histogram of cells treated with the STAT3 siRNA. C. DNA histogram of low-STAT3 expressing cells (cells are highlighted in panel A). D. Nuclear size as a function of DNA content for the entire dataset. E. Nuclear size as a function of DNA content for the low-STAT3 cells highlighted in part A, for both STAT3 and NTC treated cells. The measure of DNA content for panels B-E are identical, and therefore the comparison of nuclear size as a function of DNA content may be made directly to the fraction of cells in each phase of the cell cycle (panels C and D, respectively). Color schemes for panels D and E are as in A.
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Figure 4: siRNA mediated knockdown of STAT3. A. Histogram of STAT3 levels in SW480 colon carcinoma cells treated with STAT3 and NTC (non-targeting) siRNAs. Red bars denote STAT3 siRNA treated cells and blue bars represent NTC treated cells. Data presents ~22,000 cells for samples treated with STAT3 and NTC siRNAs each. A region of low-STAT3 expressing cells examined in panels (C) and (E) is indicated in the panel (top left corner). B. DNA histogram of cells treated with the STAT3 siRNA. C. DNA histogram of low-STAT3 expressing cells (cells are highlighted in panel A). D. Nuclear size as a function of DNA content for the entire dataset. E. Nuclear size as a function of DNA content for the low-STAT3 cells highlighted in part A, for both STAT3 and NTC treated cells. The measure of DNA content for panels B-E are identical, and therefore the comparison of nuclear size as a function of DNA content may be made directly to the fraction of cells in each phase of the cell cycle (panels C and D, respectively). Color schemes for panels D and E are as in A.

Mentions: To further investigate the contribution of single cell analysis to cellular signaling studies, we turned to a less complex signaling pathway, the role of STAT3 in cancer cell proliferation and apoptosis suppression. Two examples are shown in Figure 4. In Figure 4A, knockdown of STAT3 in SW480 colon carcinoma cells are shown at the single cell level. Knockdown of STAT3 at the protein level is about 30%, based on average values for replicate wells (3 for each condition, data not shown). Although weakly separated when analyzed at the whole well level, the single cell distributions show a clear effect of treating with the STAT3 siRNA; a K-S test (the Kolmogorov-Smirnov statistic, [29,59]) shows a difference of 0.349 (p < 2.2e-16). Such reductions are typically too small to produce robust phenotypic differences in most whole-well assay formats. There are likely to be many cases where this is correct, but Figure 4A provides a different perspective that more accurately states the situation. It is clear that distribution of STAT3 levels in SW480 cells is too wide for an average reduction of 30% to effectively demonstrate a phenotype associated with STAT3 levels at the whole well level. The overall reduction can be observed in the shift of the distributions, but residual overlap is greater than 50%. If a 30% reduction in STAT3 level does in fact have an effect on these cells, an average change of 30% of STAT3 levels in these samples may not show such an effect because of the wide range in each sample.


Single cell cytometry of protein function in RNAi treated cells and in native populations.

LaPan P, Zhang J, Pan J, Hill A, Haney SA - BMC Cell Biol. (2008)

siRNA mediated knockdown of STAT3. A. Histogram of STAT3 levels in SW480 colon carcinoma cells treated with STAT3 and NTC (non-targeting) siRNAs. Red bars denote STAT3 siRNA treated cells and blue bars represent NTC treated cells. Data presents ~22,000 cells for samples treated with STAT3 and NTC siRNAs each. A region of low-STAT3 expressing cells examined in panels (C) and (E) is indicated in the panel (top left corner). B. DNA histogram of cells treated with the STAT3 siRNA. C. DNA histogram of low-STAT3 expressing cells (cells are highlighted in panel A). D. Nuclear size as a function of DNA content for the entire dataset. E. Nuclear size as a function of DNA content for the low-STAT3 cells highlighted in part A, for both STAT3 and NTC treated cells. The measure of DNA content for panels B-E are identical, and therefore the comparison of nuclear size as a function of DNA content may be made directly to the fraction of cells in each phase of the cell cycle (panels C and D, respectively). Color schemes for panels D and E are as in A.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2529295&req=5

Figure 4: siRNA mediated knockdown of STAT3. A. Histogram of STAT3 levels in SW480 colon carcinoma cells treated with STAT3 and NTC (non-targeting) siRNAs. Red bars denote STAT3 siRNA treated cells and blue bars represent NTC treated cells. Data presents ~22,000 cells for samples treated with STAT3 and NTC siRNAs each. A region of low-STAT3 expressing cells examined in panels (C) and (E) is indicated in the panel (top left corner). B. DNA histogram of cells treated with the STAT3 siRNA. C. DNA histogram of low-STAT3 expressing cells (cells are highlighted in panel A). D. Nuclear size as a function of DNA content for the entire dataset. E. Nuclear size as a function of DNA content for the low-STAT3 cells highlighted in part A, for both STAT3 and NTC treated cells. The measure of DNA content for panels B-E are identical, and therefore the comparison of nuclear size as a function of DNA content may be made directly to the fraction of cells in each phase of the cell cycle (panels C and D, respectively). Color schemes for panels D and E are as in A.
Mentions: To further investigate the contribution of single cell analysis to cellular signaling studies, we turned to a less complex signaling pathway, the role of STAT3 in cancer cell proliferation and apoptosis suppression. Two examples are shown in Figure 4. In Figure 4A, knockdown of STAT3 in SW480 colon carcinoma cells are shown at the single cell level. Knockdown of STAT3 at the protein level is about 30%, based on average values for replicate wells (3 for each condition, data not shown). Although weakly separated when analyzed at the whole well level, the single cell distributions show a clear effect of treating with the STAT3 siRNA; a K-S test (the Kolmogorov-Smirnov statistic, [29,59]) shows a difference of 0.349 (p < 2.2e-16). Such reductions are typically too small to produce robust phenotypic differences in most whole-well assay formats. There are likely to be many cases where this is correct, but Figure 4A provides a different perspective that more accurately states the situation. It is clear that distribution of STAT3 levels in SW480 cells is too wide for an average reduction of 30% to effectively demonstrate a phenotype associated with STAT3 levels at the whole well level. The overall reduction can be observed in the shift of the distributions, but residual overlap is greater than 50%. If a 30% reduction in STAT3 level does in fact have an effect on these cells, an average change of 30% of STAT3 levels in these samples may not show such an effect because of the wide range in each sample.

Bottom Line: In a third example, reduction of STAT3 levels by siRNA causes an accumulation of cells in the G1 phase of the cell cycle, but does not induce apoptosis or necrosis when compared to control cells that express the same levels of STAT3.In a final example, the effect of reduced p53 levels on increased adriamycin sensitivity for colon carcinoma cells was demonstrated at the whole-well level using siRNA knockdown and in control and untreated cells at the single cell level.Biological differences that result from changes in protein level or pathway activation state can be modulated directly by RNAi treatment or extracted from the natural variability intrinsic to cells grown under normal culture conditions.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biological Technologies, Oncology Research, Wyeth Research, 87 Cambridge Park Drive, Cambridge, MA 02140, USA. plapan@wyeth.cpm

ABSTRACT

Background: High Content Screening has been shown to improve results of RNAi and other perturbations, however significant intra-sample heterogeneity is common and can complicate some analyses. Single cell cytometry can extract important information from subpopulations within these samples. Such approaches are important for immune cells analyzed by flow cytometry, but have not been broadly available for adherent cells that are critical to the study of solid-tumor cancers and other disease models.

Results: We have directly quantitated the effect of resolving RNAi treatments at the single cell level in experimental systems for both exogenous and endogenous targets. Analyzing the effect of an siRNA that targets GFP at the single cell level permits a stronger measure of the absolute function of the siRNA by gating to eliminate background levels of GFP intensities. Extending these methods to endogenous proteins, we have shown that well-level results of the knockdown of PTEN results in an increase in phospho-S6 levels, but at the single cell level, the correlation reveals the role of other inputs into the pathway. In a third example, reduction of STAT3 levels by siRNA causes an accumulation of cells in the G1 phase of the cell cycle, but does not induce apoptosis or necrosis when compared to control cells that express the same levels of STAT3. In a final example, the effect of reduced p53 levels on increased adriamycin sensitivity for colon carcinoma cells was demonstrated at the whole-well level using siRNA knockdown and in control and untreated cells at the single cell level.

Conclusion: We find that single cell analysis methods are generally applicable to a wide range of experiments in adherent cells using technology that is becoming increasingly available to most laboratories. It is well-suited to emerging models of signaling dysfunction, such as oncogene addition and oncogenic shock. Single cell cytometry can demonstrate effects on cell function for protein levels that differ by as little as 20%. Biological differences that result from changes in protein level or pathway activation state can be modulated directly by RNAi treatment or extracted from the natural variability intrinsic to cells grown under normal culture conditions.

Show MeSH
Related in: MedlinePlus