Cell-based screening: extracting meaning from complex data

Abstract

Unbiased discovery approaches have the potential to uncover neurobiological insights into CNS disease and lead to the development of therapies. Here, we review lessons learned from imaging-based screening approaches and recent advances in these areas, including powerful new computational tools to synthesize complex data into more useful knowledge that can reliably guide future research and development.

Figure 1. Generic flow scheme for cell-based high throughput screening

The basic stages of target discovery and selection are outlined

Figure 2. The power of high-throughput longitudinal single-cell analysis

Conventionally, cell-based screening has compared images of two unrelated cell populations (top row). Typically, these populations differ due to the passage of time or to a genetic or pharmacological manipulation of the cells. Assays often involve a measure of cell number (each circle) and a specific biological change/response (shown here as a conversion from a black circle to a red one). With this approach, it is impossible to determine the precise relationship between the observed biological change/response and cell number, and variations in the number of cells present or that exhibit a change or response under nominally identical conditions necessarily must be treated as technical variation, which reduces the overall sensitivity of the system. Longitudinal single-cell analysis overcomes these limitations because each cell is followed over time, effectively allowing it to serve as its own control (bottom row). Whether a biological change or response is associated with a change in cell number can be measured precisely. Indeed, for assays in which the change or response is graded, cell-to-cell variation can be harnessed to uncover exquisitely resolved dose-response relationships. The biological significance of transient or rare changes or responses, which might be highly significant but dismissed with conventional approaches because of their nature, can be elucidated because they can be captured and quantitatively linked to some future fate. For these reasons, the single-cell analysis is 100–1000-fold more sensitive than conventional approaches for measuring effects of perturbations on neuronal survival (Arrasate and Finkbeiner, 2005; Sharma et al., 2012), is well suited to resolve complex pathogenic and coping responses, and is well-suited to enable high-throughput screening of rare, precious or heterogeneous cell populations (e.g., disease models based on patient-derived differentiated human iPSCs).

Figure 3. Analysis of longitudinal single-cell data from screens in primary neurons

A. Shows a typical montage of microscope fields at the first-time scan after image segmentation. Unique cell-track numbers are shown in red next to each cell. Two regions within this montage have been zoomed in to show cell lifetime detection from the entire time-lapse imaging data set to illustrate the ability to do longitudinal single-cell analysis. B. A typical delimited text file showing the arrangement of data output and feature extraction by the analysis program. C. A representative heat map of a control plate in which a known modifier, the trophic factor brain-derived neurotrophic factor (blue outlined wells) was spotted at different doses throughout the plate in the midst of positive control wells (green outlined wells) and analyzed in a blinded fashion. White shaded wells indicate longer survival and dark red-shaded wells indicate reduced survival. The numbers represent the cumulative death in each well at the end of the experiment. This survival assay had a positive predictive value of 91% and a negative predictive value of 97%, underscoring the highly sensitive and specific nature of robotic microscopy. D. A survival curve from a modifier well showing decreased hazard (increased survival), compared to the negative control wells. Adapted with permission from (Sharma et al., 2012).

Figure 4. Future applications of high-throughput cell-based screening

The increasing ability to collect complex cell-based and clinical data and to analyze these data with machine learning approaches is opening up new possibilities for high-throughput cell-based screening. The figure illustrates an initiative being pursued by the authors (S.F. and M.F.) to develop a preclinical pipeline that is more predictive of clinical results. One element of the overall approach, supported by the NIH Common Fund and NINDS, involves the creation of an array of hundreds of biosensors suitable for longitudinal live single-cell imaging and capable of generating a deep phenotypic understanding of a variety of cell structures and functions in brain cells from patient-derived induced pluripotent stem cells (iPSCs). The array of biosensors is referred to as the “Physical Exam of the Cell” and is designed to broadly survey cell physiology, performing a role at a cellular level similar to the role that the physical exam of patients plays in medicine. These large and complex datasets will then be related to similarly deep and complex clinical datasets collected from the patients from whom the iPSCs were generated. Then, with unsupervised machine learning, the two datasets can be examined with the goal of finding features that can be measured from iPSCs that predict one or more clinical features, such as disease progression, of the patients from whom the cells came. New or additional clinical features may be predicted as the iPSC models of disease increase in physiological relevance with improved differentiation protocols or by combining different cell types that lead to emergent properties. A preclinical pipeline that predicts clinical findings could be invaluable for lowering the risk of developing neurotherapeutics by improving the sensitivity of clinical trials through patient stratification and for identifying therapeutic targets and therapies that have better chance of working when tested in patients.