Large-scale multiparameter screening has become increasingly feasible and straightforward to perform thanks to developments in technologies such as high-content microscopy and high-throughput flow cytometry. The automated toolkits for analyzing similarities and differences between large numbers of tested conditions have not kept pace with these technological developments. Thus, effective analysis of multiparameter screening datasets becomes a bottleneck and a limiting factor in unbiased interpretation of results. Here we introduce compaRe, a toolkit for large-scale multiparameter data analysis, which integrates quality control, data bias correction, and data visualization methods with a mass-aware gridding algorithm-based similarity analysis providing a much faster and more robust analyses than existing methods. Using mass and flow cytometry data from acute myeloid leukemia and myelodysplastic syndrome patients, we show that compaRe can reveal interpatient heterogeneity and recognizable phenotypic profiles. By applying compaRe to high-throughput flow cytometry drug response data in AML models, we robustly identified multiple types of both deep and subtle phenotypic response patterns, highlighting how this analysis could be used for therapeutic discoveries. In conclusion, compaRe is a toolkit that uniquely allows for automated, rapid, and precise comparisons of large-scale multiparameter datasets, including high-throughput screens.
Keywords: AML; computational biology; drug discovery; drug dose response analysis; high-content screening; high-throughput screening; human; mouse; sample clustering; single cell; systems biology.
Biology has seen huge advances in technology in recent years. This has led to state-of-the-art techniques which can test hundreds of conditions simultaneously, such as how cancer cells respond to different drugs. In addition to this, each of the tens of thousands of cells studied can be screened for multiple variables, such as certain proteins or genes. This generates massive datasets with large numbers of parameters, which researchers can use to find similarities and differences between the tested conditions. Analyzing these ‘high-throughput’ experiments, however, is no easy task, as the data is often contaminated with meaningless information, or ‘background noise’, as well as sources of bias, such as non-biological variations between experiments. As a result, most analysis methods can only probe one parameter at a time, or are unautomated and require manual interpretation of the data. Here, Chalabi Hajkarim et al. have developed a new toolkit that can analyze multiparameter datasets faster and more robustly than current methods. The kit, which was named ‘compaRe’, combines a range of computational tools that automatically ‘clean’ the data of background noise or bias: the different conditions are then compared and any similarities are visually displayed using a graphical interface that is easy to explore. Chalabi Hajkarim et al. used their new method to study data from patients with acute myeloid leukemia (AML) and myelodysplastic syndrome, two forms of cancer that disrupt the production of functional immune cells. The toolkit was able to identify subtle differences between the patients and categorize them into groups based on the proteins present on immune cells. Chalabi Hajkarim et al. also applied compaRe to high-throughput data on cells from patients and mouse models with AML that had been treated with large numbers of specific drugs. This revealed that different cell types in the samples responded to the treatments in distinct ways. These findings suggest that the toolkit created by Chalabi Hajkarim et al. can automatically, rapidly and precisely compare large multiparameter datasets collected using high-throughput screens. In the future, compaRe could be used to identify drugs that illicit a specific response, or to predict how newly developed treatments impact different cell types in the body.
© 2022, Chalabi Hajkarim et al.