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Parallel RNAi screens across different cell lines identify generic and cell type-specific regulators of actin organization and cell morphology.

Liu T, Sims D, Baum B - Genome Biol. (2009)

Bottom Line: This cell type-specific requirement was not due to the peculiarities in the morphology of CNS-derived cells and could not be attributed to differences in mnb expression.Instead, it likely reflects differences in gene expression that constitute the cell type-specific functional context in which mnb/DYRK1A acts.This analysis reveals the importance of using different cell types to gain a thorough understanding of gene function across the genome and, in the case of kinases, the difficulties of using the differential gene expression to predict function.

View Article: PubMed Central - HTML - PubMed

Affiliation: MRC Laboratory of Molecular Cell Biology, UCL, London, UK. tao.liu@ucl.ac.uk

ABSTRACT

Background: In recent years RNAi screening has proven a powerful tool for dissecting gene functions in animal cells in culture. However, to date, most RNAi screens have been performed in a single cell line, and results then extrapolated across cell types and systems.

Results: Here, to dissect generic and cell type-specific mechanisms underlying cell morphology, we have performed identical kinome RNAi screens in six different Drosophila cell lines, derived from two distinct tissues of origin. This analysis identified a core set of kinases required for normal cell morphology in all lines tested, together with a number of kinases with cell type-specific functions. Most significantly, the screen identified a role for minibrain (mnb/DYRK1A), a kinase associated with Down's syndrome, in the regulation of actin-based protrusions in CNS-derived cell lines. This cell type-specific requirement was not due to the peculiarities in the morphology of CNS-derived cells and could not be attributed to differences in mnb expression. Instead, it likely reflects differences in gene expression that constitute the cell type-specific functional context in which mnb/DYRK1A acts.

Conclusions: Using parallel RNAi screens and gene expression analyses across cell types we have identified generic and cell type-specific regulators of cell morphology, which include mnb/DYRK1A in the regulation of protrusion morphology in CNS-derived cell lines. This analysis reveals the importance of using different cell types to gain a thorough understanding of gene function across the genome and, in the case of kinases, the difficulties of using the differential gene expression to predict function.

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Parallel RNAi screens reveal cell line-specific phenotypes. (a) Different cell lines exhibited different hit rates in RNAi screens (Additional file 3). (b) Venn diagrams depict the segregation of screen hits between related cell lines. (c) A Venn diagram depicts the classification of hits into three distinct classes: those that are hits in both CNS and hemocyte cell lines; those that are hits in neuronal cell lines only; and those that are hits in hemocyte cell lines only. (d) Hierarchical clustering of hits across cell lines (depicted in the form of a tree) was used to give a more detailed picture of the three hit classes. Two hits of particular interest, CG7236 and minibrain (mnb), are highlighted. Note that the relationships defined by the functional analysis (depicted in the form of a tree at the top of figure) mirror the relationships defined by the microarray analysis (see Table 1 for the Pearson correlation coefficients in each case).
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Figure 2: Parallel RNAi screens reveal cell line-specific phenotypes. (a) Different cell lines exhibited different hit rates in RNAi screens (Additional file 3). (b) Venn diagrams depict the segregation of screen hits between related cell lines. (c) A Venn diagram depicts the classification of hits into three distinct classes: those that are hits in both CNS and hemocyte cell lines; those that are hits in neuronal cell lines only; and those that are hits in hemocyte cell lines only. (d) Hierarchical clustering of hits across cell lines (depicted in the form of a tree) was used to give a more detailed picture of the three hit classes. Two hits of particular interest, CG7236 and minibrain (mnb), are highlighted. Note that the relationships defined by the functional analysis (depicted in the form of a tree at the top of figure) mirror the relationships defined by the microarray analysis (see Table 1 for the Pearson correlation coefficients in each case).

Mentions: After elimination of false positives, 17.3% (46 out of 265) of the kinases screened yielded a visible phenotype in at least one of six cell lines (Additional data file 3). This hit rate was similar to that determined in a related screen [14], but varied considerably across lines (Figure 2a; Additional data file 3). Much of the variation in hit rates across cell lines is likely to reflect variation in the ease of identifying defects in cell morphology in each line, since all the phenotypes identified in the BG3-c1 cell line, which is prone to grow in clumps, were also seen in at least one of the better spread central nervous system (CNS) lines (Figure 2b). Similarly, there were only two genes that yielded an RNAi phenotype in S2 or Kc167 cells that did not show up as a hit in the screen in large, well-spread S2R+ cells (Figure 2b). By contrast, there were significant differences in the kinase requirements of hemocyte and CNS-derived lines (Figure 2c,d) as expected based on the differences in the form and gene expression profiles that separate these two sets of lines (Figure 1a). This indicates that both gene expression and function can be used as indicators of a common origin. Using these data, it was possible to identify a set of cell type-specific hits (Figure 2d). However, there was no detectable bias in the number of hits in each kinase class between the two different tissues of origin (Table 2).


Parallel RNAi screens across different cell lines identify generic and cell type-specific regulators of actin organization and cell morphology.

Liu T, Sims D, Baum B - Genome Biol. (2009)

Parallel RNAi screens reveal cell line-specific phenotypes. (a) Different cell lines exhibited different hit rates in RNAi screens (Additional file 3). (b) Venn diagrams depict the segregation of screen hits between related cell lines. (c) A Venn diagram depicts the classification of hits into three distinct classes: those that are hits in both CNS and hemocyte cell lines; those that are hits in neuronal cell lines only; and those that are hits in hemocyte cell lines only. (d) Hierarchical clustering of hits across cell lines (depicted in the form of a tree) was used to give a more detailed picture of the three hit classes. Two hits of particular interest, CG7236 and minibrain (mnb), are highlighted. Note that the relationships defined by the functional analysis (depicted in the form of a tree at the top of figure) mirror the relationships defined by the microarray analysis (see Table 1 for the Pearson correlation coefficients in each case).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2690997&req=5

Figure 2: Parallel RNAi screens reveal cell line-specific phenotypes. (a) Different cell lines exhibited different hit rates in RNAi screens (Additional file 3). (b) Venn diagrams depict the segregation of screen hits between related cell lines. (c) A Venn diagram depicts the classification of hits into three distinct classes: those that are hits in both CNS and hemocyte cell lines; those that are hits in neuronal cell lines only; and those that are hits in hemocyte cell lines only. (d) Hierarchical clustering of hits across cell lines (depicted in the form of a tree) was used to give a more detailed picture of the three hit classes. Two hits of particular interest, CG7236 and minibrain (mnb), are highlighted. Note that the relationships defined by the functional analysis (depicted in the form of a tree at the top of figure) mirror the relationships defined by the microarray analysis (see Table 1 for the Pearson correlation coefficients in each case).
Mentions: After elimination of false positives, 17.3% (46 out of 265) of the kinases screened yielded a visible phenotype in at least one of six cell lines (Additional data file 3). This hit rate was similar to that determined in a related screen [14], but varied considerably across lines (Figure 2a; Additional data file 3). Much of the variation in hit rates across cell lines is likely to reflect variation in the ease of identifying defects in cell morphology in each line, since all the phenotypes identified in the BG3-c1 cell line, which is prone to grow in clumps, were also seen in at least one of the better spread central nervous system (CNS) lines (Figure 2b). Similarly, there were only two genes that yielded an RNAi phenotype in S2 or Kc167 cells that did not show up as a hit in the screen in large, well-spread S2R+ cells (Figure 2b). By contrast, there were significant differences in the kinase requirements of hemocyte and CNS-derived lines (Figure 2c,d) as expected based on the differences in the form and gene expression profiles that separate these two sets of lines (Figure 1a). This indicates that both gene expression and function can be used as indicators of a common origin. Using these data, it was possible to identify a set of cell type-specific hits (Figure 2d). However, there was no detectable bias in the number of hits in each kinase class between the two different tissues of origin (Table 2).

Bottom Line: This cell type-specific requirement was not due to the peculiarities in the morphology of CNS-derived cells and could not be attributed to differences in mnb expression.Instead, it likely reflects differences in gene expression that constitute the cell type-specific functional context in which mnb/DYRK1A acts.This analysis reveals the importance of using different cell types to gain a thorough understanding of gene function across the genome and, in the case of kinases, the difficulties of using the differential gene expression to predict function.

View Article: PubMed Central - HTML - PubMed

Affiliation: MRC Laboratory of Molecular Cell Biology, UCL, London, UK. tao.liu@ucl.ac.uk

ABSTRACT

Background: In recent years RNAi screening has proven a powerful tool for dissecting gene functions in animal cells in culture. However, to date, most RNAi screens have been performed in a single cell line, and results then extrapolated across cell types and systems.

Results: Here, to dissect generic and cell type-specific mechanisms underlying cell morphology, we have performed identical kinome RNAi screens in six different Drosophila cell lines, derived from two distinct tissues of origin. This analysis identified a core set of kinases required for normal cell morphology in all lines tested, together with a number of kinases with cell type-specific functions. Most significantly, the screen identified a role for minibrain (mnb/DYRK1A), a kinase associated with Down's syndrome, in the regulation of actin-based protrusions in CNS-derived cell lines. This cell type-specific requirement was not due to the peculiarities in the morphology of CNS-derived cells and could not be attributed to differences in mnb expression. Instead, it likely reflects differences in gene expression that constitute the cell type-specific functional context in which mnb/DYRK1A acts.

Conclusions: Using parallel RNAi screens and gene expression analyses across cell types we have identified generic and cell type-specific regulators of cell morphology, which include mnb/DYRK1A in the regulation of protrusion morphology in CNS-derived cell lines. This analysis reveals the importance of using different cell types to gain a thorough understanding of gene function across the genome and, in the case of kinases, the difficulties of using the differential gene expression to predict function.

Show MeSH
Related in: MedlinePlus