We have applied the general concept of multiparametric single cell analysis to the use of RNAi, and to the relationship between protein levels and chemotherapeutic response. High Content Screening is becoming an important and general approach to biological and therapeutic studies. In addition to increasing the options available for cell-based assays in general, it is opening up new approaches to biological processes and drug development, such as cytological profiling [28
]. Inherent in the latter approaches is the use of single cell cytometry to analyze complex patterns in cellular responses [27
]. We have generalized the use of single cell cytometry in several experimental systems and have found that it generally improves experimental analysis, and in some cases, enables challenging questions to be addressed directly. We have used single cell cytometry to address four biological problems: identifying the relevant cells in a knockdown of GFP, correlating the knockdown of PTEN with the increase in activity of pS6 kinase, the effect of knockdown of STAT3 on proliferation and death of colon carcinoma cells and the relationship between p53 levels and responsiveness to DNA damage (both as manipulated by RNAi and as occur intrinsically through standard cell culture conditions).
For RNAi screening in general, there are two applications of single cell cytometry that are potentially valuable. First is a general analysis of knockdown phenotypes by number of cells showing an altered phenotype, rather than average phenotypic change for the two samples. This approach is more in line with other distribution-based methods such as sectoring samples in flow cytometry, and can present data in more biologically-relevant way than reporting as percent-of-control (discussed below). Rigorous analysis of RNAi screening data is currently challenging [15
], and would benefit from clearer definitions of what constitutes a hit [9
]. The second benefit of single cell cytometry is the capacity to score cells as a function of the amount of siRNA effectively introduced in cells, as evidenced by the accumulation of the (non-functional) sense strand in P-bodies following efficient transfection. Transfection of siRNAs are frequently associated with off-target effects [76
], particularly at concentrations typically used for library-based screening (>20 nM) [79
]. Off-target effects result in many false positive hits in RNAi screens, and impose a significant burden on the post-screening confirmation phase of a project [81
]. Transfection at low concentrations (< 10 nM) has been shown to reduce such artifacts, however library screening is performed with many siRNAs that have not been well-validated, particularly for off-target effects. Library screening typically involves higher concentrations because a productive screen requires that cells be reliably transfected, and some balance between the efficiency of transfection and a lack of specificity can be tolerated in the initial screen [15
], as long as an effective strategy exists for demonstrating authentic gene-phenotype connections [81
]. Therefore, off-target effects resulting from high concentrations of siRNA transfections are a common and perhaps unavoidable complication of running siRNA screens. Reduced off-target effects have been associated with pooling or multiplexing siRNAs, particularly in highly complex pools such as are generated by enzymatic preparation of gene-specific siRNA pools (esiRNAs, [83
]), at least in part because the concentration of any single siRNA is low.
Reverse-transfection, including the live cell array [7
], is frequently used in functional screens. This format spots the siRNA (or dsRNA for screens in Drosophila
cells) onto a surface prior to use with cultured cells, and therefore cells are not transfected at a specific concentration, strictly speaking. Single cell analysis can be readily performed on assays following reverse transfection, since these explicitly require image-based readouts. Selecting a subpopulation with consistent siRNA uptake for each siRNA is computationally intensive, and therefore would be difficult to use directly in the primary screen endpoint, but could be used to analyze data from a primary screen that uses a high content (image-based) assay. The siRNAs need to be labeled directly or co-transfected with a labeled siRNA, in order for siRNA levels to be quantitated. However, the benefit of this is that knockdown phenotypes can be scored for cells within specific thresholds of siRNA accumulation, and these thresholds can be adjusted as the data is reviewed, rather than during image analysis.
Scoring perturbations by fraction of responding cells (in the case of GFP knockdown at the single cell level) and by response magnitude as a function of target level (such as in the example of DNA damage response as a function of p53 levels) highlight important characteristics of biological samples, particularly in the development of human diseases such as cancer. Clinically important roles are played by minor populations within cell types, such as the growth of solid tumors through tumor-initiating cells (cancer stem cells) and the importance of regions within tumors that control angiogenesis and chemoresistance (the hypoxic core of cells within solid tumors). These properties can be observed in cell culture models, but this differentiation is lost in whole-well methods. Tracking effects of candidate therapeutics among rare cells or cells that have reduced proliferation rates can focus decisions on how well promising a strategy may be by limiting analysis to the cells that play the biggest role in disease progression.
A similar situation occurs with pathway analyses. An assay that measures a change in a complex pathway, such as the PI3K/AKT/mTor pathway, cannot help but exclude important factors that contribute to a diverse set of outputs. This heterogeneity may be as much a part of the discordance between target inhibition and clinical response as widely cited factors, such as tumor heterogeneity as a result of genetic instability. In both cases, variability in the cells that constitute a tumor enable a significant number of cells to escape death. The difference between these two scenarios is that genetic instability suggests a somatic evolutionary process, whereas signaling heterogeneity suggests that insufficient control of the pathway results in escape from a therapeutic. In such cases, single cell analysis could improve the search for combination therapeutic strategies. mTor activity is subject to multiple levels of feedback regulation [86
] and to cross-talk with other pathways, particularly the influence of amino acid and cellular energy levels on mTor activity [55
]. As such these influences would need to be measured in a multiparametric assay system, to track changes between two points in such a complex pathway. Taken together, the results presented here suggest that pathways that are quiescent (such p53 during periods of low DNA damage) or truly linear (such as activation of STAT signaling by JAK kinases) should show correlations between two points at the single cell level. This correlation could be used to validate results from RNAi experiments by providing a separate method of linking protein levels to pathway function.
Studies that integrate complex signaling interactions, as opposed to linear events within single pathways, are at the root of systems biology [31
], and are better able to characterize pathway states in their biological contexts. Such approaches are being shown to be of direct relevance to signaling in disease biology [25
]. HCS is a strong complement to flow cytometry as a method of single cell analysis because signaling pathway responses can be integrated with cytological dynamics, and as such will extend systems biology into areas such as cancer cell motility and invasion [27
]. These approaches will lead to more innovative approaches to treating disease [90
], including complex molecular studies which can be integrated with genetic and epidemiological studies that show subtle but important interactions between common disease loci.