Speaker
Description
Sub-cellular structures that are visualized as spots with fluorescence microscopy are ubiquitous in microscopy data. However, automated and accurate detection of such spots is often a challenging task. Additionally, many microscopy datasets contain multiple channels, where in addition to the spots and the cells also a second structure is visualized, such as the nucleus in single-molecule FISH experiments. However, this information is often not exploited in the context of spot detection.
Here we present spotMAX, a fully automated image analysis pipeline that takes advantage of multi-dimensional information such as time-lapse and multi-color imaging. Fully integrated into our previously published software Cell-ACDC, spotMAX combines state-of-the-art segmentation, tracking, and cell-pedigree analysis of single-cells with detection and quantification of fluorescent globular structures over time. SpotMAX can also automatically segment a reference structure enabling further filtering and quantification of valid spots.
We extensively benchmarked spotMAX and we show that consistently outperforms current SOTA models. Beyond spot detection, spotMAX provides a feature-rich space that can be used for downstream machine-learning tasks.
To make spotMAX as generalist as possible we applied it to a variety of settings, with different imaging modalities, different microscopes, and multiple model organisms. For example, we used it to quantify the number of synaptonemal complexes in C. elegans. We analysed the dynamics of mitochondria homeostasis in yeast during nutrient change. Finally, we performed mRNA quantification from single-molecule FISH datasets and telomers/centromeres analysis in stem cells with DNA FISH.
| Authors | Francesco Padovani*, Benedikt Mairhörmann, Ivana Čavka, Simone Köhler, Jette Lengefeld, Nada Al-Refaie, Daphne Cabianca, and Kurt M. Schmoller |
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| Keywords | Machine learning, Live-cell imaging, Fluorescence Spot Detection, smFISH, AI-driven Bioimage Analysis |
