Background: Video recording of cells offers a straightforward way to gainvaluable information from their response to treatments. An indispensable stepin obtaining such information involves tracking individual cells fr...Background: Video recording of cells offers a straightforward way to gainvaluable information from their response to treatments. An indispensable stepin obtaining such information involves tracking individual cells from therecorded data. A subsequent step is reducing such data to represent essentialbiological information. This can help to compare various single‐cell trackingdata yielding a novel source of information. The vast array of potential datasources highlights the significance of methodologies prioritizing simplicity,robustness, transparency, affordability, sensor independence, and freedomfrom reliance on specific software or online services.Methods: The provided data presents single‐cell tracking of clonal (A549)cells as they grow in two‐dimensional (2D) monolayers over 94 hours,spanning several cell cycles. The cells are exposed to three differentconcentrations of yessotoxin (YTX). The data treatments showcase theparametrization of population growth curves, as well as other statisticaldescriptions. These include the temporal development of cell speed in familytrees with and without cell death, correlations between sister cells, single‐cellaverage displacements, and the study of clustering tendencies.Results: Various statistics obtained from single‐cell tracking reveal patternssuitable for data compression and parametrization. These statistics encompassessential aspects such as cell division, movements, and mutual informationbetween sister cells.Conclusion: This work presents practical examples that highlight theabundant potential information within large sets of single‐cell tracking data.Data reduction is crucial in the process of acquiring such information whichcan be relevant for phenotypic drug discovery and therapeutics, extendingbeyond standardized procedures. Conducting meaningful big data analysistypically necessitates a substantial amount of data, which can stem fromstandalone case studies as an initial foundation.展开更多
文摘Background: Video recording of cells offers a straightforward way to gainvaluable information from their response to treatments. An indispensable stepin obtaining such information involves tracking individual cells from therecorded data. A subsequent step is reducing such data to represent essentialbiological information. This can help to compare various single‐cell trackingdata yielding a novel source of information. The vast array of potential datasources highlights the significance of methodologies prioritizing simplicity,robustness, transparency, affordability, sensor independence, and freedomfrom reliance on specific software or online services.Methods: The provided data presents single‐cell tracking of clonal (A549)cells as they grow in two‐dimensional (2D) monolayers over 94 hours,spanning several cell cycles. The cells are exposed to three differentconcentrations of yessotoxin (YTX). The data treatments showcase theparametrization of population growth curves, as well as other statisticaldescriptions. These include the temporal development of cell speed in familytrees with and without cell death, correlations between sister cells, single‐cellaverage displacements, and the study of clustering tendencies.Results: Various statistics obtained from single‐cell tracking reveal patternssuitable for data compression and parametrization. These statistics encompassessential aspects such as cell division, movements, and mutual informationbetween sister cells.Conclusion: This work presents practical examples that highlight theabundant potential information within large sets of single‐cell tracking data.Data reduction is crucial in the process of acquiring such information whichcan be relevant for phenotypic drug discovery and therapeutics, extendingbeyond standardized procedures. Conducting meaningful big data analysistypically necessitates a substantial amount of data, which can stem fromstandalone case studies as an initial foundation.
