The complex heterogeneity of cells, and their interconnectedness with each other, are major challenges to identifying clinically relevant measurements that reflect the state and capability of the immune system. Highly multiplexed, single-cell technologies may be critical for identifying correlates of disease or immunological interventions as well as for elucidating the underlying mechanisms of immunity. Here we review limitations of bulk measurements and explore advances in single-cell technologies that overcome these problems by expanding the depth and breadth of functional and phenotypic analysis in space and time. The geometric increases in complexity of data make formidable hurdles for exploring, analyzing and presenting results. We summarize recent approaches to making such computations tractable and discuss challenges for integrating heterogeneous data obtained using these single-cell technologies.
The process of conducting cell-based phenotypic screens can result in data sets from small libraries or portions of large libraries, making accurate hit picking from multiple data sets important for efficient drug discovery. Here, we describe a screen design and data analysis approach that allow for normalization not only between quadrants and plates but also between screens or batches in a robust, quantitative fashion, enabling hit selection from multiple data sets. We independently screened the MicroSource Spectrum and NCI Diversity Set II libraries using a cell-based phenotypic high-throughput screening (HTS) assay that uses an interferon-stimulated response element (ISRE)–driven luciferase-reporter assay to identify interferon (IFN) signal enhancers. Inclusion of a per-plate, per-quadrant IFN dose-response standard curve enabled conversion of ISRE activity to effective IFN concentrations. We identified 45 hits based on a combined z score ≥2.5 from the two libraries, and 25 of 35 available hits were validated in a compound concentration-response assay when tested using fresh compound. The results provide a basis for further analysis of chemical structure in relation to biological function. Together, the results establish an HTS method that can be extended to screening for any class of compounds that influence a quantifiable biological response for which a standard is available.
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High-content screening (HCS) allows the exploration of complex cellular phenotypes by automated microscopy and is increasingly being adopted for small interfering RNA genomic screening and phenotypic drug discovery. We introduce a series of cell-based evaluation metrics that have been implemented and validated in a mono-parametric HCS for regulators of the membrane trafficking protein caveolin 1 (CAV1) and have also proved useful for the development of a multiparametric phenotypic HCS for regulators of cytoskeletal reorganization. Imaging metrics evaluate imaging quality such as staining and focus, whereas cell biology metrics are fuzzy logic–based evaluators describing complex biological parameters such as sparseness, confluency, and spreading. The evaluation metrics were implemented in a data-mining pipeline, which first filters out cells that do not pass a quality criterion based on imaging metrics and then uses cell biology metrics to stratify cell samples to allow further analysis of homogeneous cell populations. Use of these metrics significantly improved the robustness of the monoparametric assay tested, as revealed by an increase in Z′ factor, Kolmogorov-Smirnov distance, and strict standard mean difference. Cell biology evaluation metrics were also implemented in a novel supervised learning classification method that combines them with phenotypic features in a statistical model that exceeded conventional classification methods, thus improving multiparametric phenotypic assay sensitivity.
Determining the activity of a compound and the potential impact on a diseased state is frequently undertaken using phenotypic or target-based approaches. Phenotypic screens have the advantage of the whole organism being exposed to the compound and thus all the targets and biological pathways associated with it. Cell penetration and access to targets in their “natural” environment are taken into account. Unless utilizing a genetically modified organism with an additional target associated indicator, elucidation of specific target(s) of active compounds is necessary. Target discovery is desirable to allow development of chemical entities based upon knowledge of the target structure. Phenotypic drug discovery has successfully identified new molecular entities for neglected protozoan disease research. In this perspective, the phenotypic approaches used to identify chemical entities for drug discovery and for use as tools against the parasites Plasmodium falciparum, Trypanosoma brucei brucei, and Trypanosoma cruzi will be outlined.
Melissa L. Sykes and Vicky M. Avery*Discovery Biology, Eskitis Institute for Drug Discovery, Griffith University, Nathan, Queensland, Australia
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