Department of Molecular Genetics, Erasmus University Medical Center, 3015 GD Rotterdam, the Netherlands; Erasmus MC Cancer Institute, 3015 GD Rotterdam, the Netherlands; Oncode Institute, 3521 AL Utrecht, the Netherlands.
Department of Molecular Genetics, Erasmus University Medical Center, 3015 GD Rotterdam, the Netherlands; Erasmus MC Cancer Institute, 3015 GD Rotterdam, the Netherlands; Oncode Institute, 3521 AL Utrecht, the Netherlands.
Cell Rep Methods. 2023 Nov 20;3(11):100636. doi: 10.1016/j.crmeth.2023.100636. Epub 2023 Nov 13.
Quantifying cellular characteristics from a large heterogeneous population is essential to identify rare, disease-driving cells. A recent development in the combination of high-throughput screening microscopy with single-cell profiling provides an unprecedented opportunity to decipher disease-driving phenotypes. Accurately and instantly processing large amounts of image data, however, remains a technical challenge when an analysis output is required minutes after data acquisition. Here, we present fast and accurate real-time cell tracking (FACT). FACT can segment ∼20,000 cells in an average of 2.5 s (1.9-93.5 times faster than the state of the art). It can export quantifiable features minutes after data acquisition (independent of the number of acquired image frames) with an average of 90%-96% precision. We apply FACT to identify directionally migrating glioblastoma cells with 96% precision and irregular cell lineages from a 24 h movie with an average F1 score of 0.91.
从大量异质群体中量化细胞特征对于鉴定稀有、疾病驱动的细胞至关重要。高通量筛选显微镜与单细胞分析相结合的最新进展为破译疾病驱动表型提供了前所未有的机会。然而，当需要在数据采集后几分钟内输出分析结果时，准确且即时地处理大量图像数据仍然是一个技术挑战。在这里，我们提出了快速准确的实时细胞跟踪（FACT）。FACT 可以在平均 2.5 秒内分割约 20000 个细胞（比现有技术快 1.9-93.5 倍）。它可以在数据采集后几分钟内导出可量化的特征（与采集的图像帧数无关），平均精度为 90%-96%。我们应用 FACT 以 96%的精度识别出定向迁移的神经胶质瘤细胞，并从平均 F1 分数为 0.91 的 24 小时电影中识别出不规则的细胞谱系。
