Abstract
Phase contrast time-lapse microscopy is a non-destructive technique that generates large volumes of image-based information to quantify the behaviour of individual cells or cell populations. To guide the development of algorithms for computer-aided cell tracking and analysis, 48 time-lapse image sequences, each spanning approximately 3.5 days, were generated with accompanying ground truths for C2C12 myoblast cells cultured under 4 different media conditions, including with fibroblast growth factor 2 (FGF2), bone morphogenetic protein 2 (BMP2), FGF2 + BMP2, and control (no growth factor). The ground truths generated contain information for tracking at least 3 parent cells and their descendants within these datasets and were validated using a two-tier system of manual curation. This comprehensive, validated dataset will be useful in advancing the development of computer-aided cell tracking algorithms and function as a benchmark, providing an invaluable opportunity to deepen our understanding of individual and population-based cell dynamics for biomedical research.
Highlights
Background & SummaryStudying the dynamic behaviour of cells and their interactions with the local microenvironment requires large datasets and accurate ground truths to develop sophisticated quantification tools
While advances in microscopy automation and computational hardware have simplified the acquisition of live cell imaging data, many biological insights remain undiscovered and ‘buried’ by the sheer data volume and intractable nature of analysis
Accurate computer algorithms for precise cell tracking are crucial towards uncovering new biological phenomenon
Summary
Studying the dynamic behaviour of cells and their interactions with the local microenvironment requires large datasets and accurate ground truths to develop sophisticated quantification tools. Muscle stem cells cultured on rigid hydrogels did not exhibit any changes in overall cell numbers due to similar rates of cell division and cell death[1] Such success has led to the availability of commercial cell tracking software[2,3]. Both biology and the biomedical sciences stand to benefit from the development of accurate cell tracking algorithms Despite their reported success, a crucial bottleneck still remains in the universal applicability of cell tracking algorithms, especially under drastically different experimental conditions. A feedback loop between tracking and detection modules was necessary to reject more than 97% of false positives in terms of cell identification Where such customized solutions are not possible, as with the case of commercially available software, accuracy becomes a crucial outcome that must be properly monitored and taken into account. Phase contrast microscopy was chosen because this imaging modality is highly prevalent and
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