Here, we make a case for multivariate measurements in cell biology with minimal perturbation. We discuss how correlative data can identify cause-effect relationships in cellular pathways with potentially greater accuracy than conventional perturbation studies.
Marco Vilela and Gaudenz Danuser
How often do reviews for a paper contain “unfortunately, the link between these data is correlative”? As authors, we fear this critique; it is either the death sentence for a manuscript or, with a more forgiving editor, it is the beginning of a long series of new experiments. So, why is a correlative link considered weak? One common answer is that correlated observations can equally represent a cause-effect relation between two interacting components (A causes B causes C) or a common-cause relation between two independent components (B and C are both caused by A). Although this is a valid concern, the root of the problem lies in the unclear definition of what causation actually means. Thus, it becomes a subjective measure. Even mathematicians, whose job is to bring formalism to science, are still engaged in a vigorous debate of how to define causation.
Cell biologists usually use perturbations of pathways to establish cause-effect relationships. We break the system and then conclude that the perturbed pathway component is responsible for the difference we observe relative to the behaviour of the unperturbed system; for example, tens of thousands of studies have derived the function of a protein from the phenotype produced by its knockdown. Although this approach has led to immensely valuable models of cellular pathways, it has its limitations: first, the approach is again correlative, at best. All it does is correlate the intervention with the shifts in system behaviour. Second, this correlation is relatively easy to interpret in a linear relation between the perturbed component and the measured system parameter. However, nonlinearities in the system complicate the analysis of the outcome. Third, system adaptations and side effects in response to interventions are concerns. If there is a correlation between an intervention and the behaviour of an altered system, how can we be sure that it is indeed related to the perturbed component? Strictly, we can only conclude that we observe the system behaviour in the absence of the intact component. However, it is exceedingly difficult to infer how the targeted component contributed to the unperturbed system behaviour. Of course, we do controls to address this issue. For example, we pair knockdown of a protein with its overexpression, or carry out rescue experiments of mutants. But how often are conclusions drawn with imperfect controls? On the bright side, many powerful tools are emerging with the capability to perturb pathways specifically, acutely and locally. Although this will not remedy the ambiguities of interpreting results from interventions in nonlinear systems, it will greatly reduce the risk of system adaptation.
Read more: Nature Cell Biology Volume: 13, Page: 1011 (2011)