Neural network fast-classifies biological images through features selecting to power automated microscopy.

Artificial intelligence is nowadays used for cell detection and classification in optical microscopy during post-acquisition analysis. The microscopes are now fully automated and next expected to be smart by making acquisition decisions based on the images. It calls for analysing them on the fly. Biology further imposes training on a reduced data set due to cost and time to prepare the samples and have the data sets annotated by experts. We propose a real-time image processing compliant with these specifications by balancing accurate detection and execution performance. We characterised the images using a generic, high-dimensional feature extractor. We then classified the images using machine learning to understand the contribution of each feature in decision and execution time. We found that the non-linear-classifier random forests outperformed Fisher's linear discriminant. More importantly, the most discriminant and time-consuming features could be excluded without significant accuracy loss, offering a substantial gain in execution time. It suggests a feature-group redundancy likely related to the biology of the observed cells. We offer a method to select fast and discriminant features. In our assay, a 79.6 ± 2.4% accurate classification of a cell took 68.7 ± 3.5 ms (mean ± SD, 5-fold cross-validation nested in 10 bootstrap repeats), corresponding to 14 cells per second, dispatched into eight phases of the cell cycle, using 12 feature groups and operating a consumer market ARM-based embedded system. A simple neural network offered similar performances paving the way to faster training and classification, using parallel execution on a general-purpose graphic processing unit. Finally, this strategy is also usable for deep neural networks paving the way to optimizing these algorithms for smart microscopy.

Automated screening of AURKA activity based on a genetically encoded FRET biosensor using fluorescence lifetime imaging microscopy.

Fluorescence Lifetime Imaging Microscopy (FLIM) is a robust tool to measure Förster Resonance Energy Transfer (FRET) between two fluorescent proteins, mainly when using genetically-encoded FRET biosensors. It is then possible to monitor biological processes such as kinase activity with a good spatiotemporal resolution and accuracy. Therefore, it is of interest to improve this methodology for future high content screening purposes. We here implement a time-gated FLIM microscope that can image and quantify fluorescence lifetime with a higher speed than conventional techniques such as Time-Correlated Single Photon Counting (TCSPC). We then improve our system to perform automatic screen analysis in a 96-well plate format. Moreover, we use a FRET biosensor of AURKA activity, a mitotic kinase involved in several epithelial cancers. Our results show that our system is suitable to measure FRET within our biosensor paving the way to the screening of novel compounds, potentially allowing to find new inhibitors of AURKA activity.