Giusti, A. | bioRxiv Science

bioRxiv2025

Data-driven inference of digital twins for high-throughput phenotyping of motile and light-responsive microorganisms

Light can affect the movement of microorganisms. These responses can drive collective behaviours like photoaccumulation and photodispersion, which play a key role in broader biological functions like photosynthesis. Our understanding of these emergent phenomena is severely limited by difficulties in obtaining data needed to establish accurate models that can serve as a basis for multi-scale analyses. Here, we address this issue by developing an integrated experimental and computational platform to collect large temporal imaging datasets that allow for the inference of digital twins -- mathematically precise computational models that accurately mirror the behavior of individual microorganisms -- and show that they can replicate the light response of diverse microorganisms in silico. We show that a generalised phenomenological model of movement can be effectively parameterised from experimental data to capture key behavioural traits of two commonly studied photo-responsive microorganisms (Euglena gracilis and Volvox aureus) and demonstrate our models ability to accurate reproduce patterns of movement for individuals and populations in response to dynamic and spatially varying light patterns. This work takes steps towards the automated phenotyping of multi-scale behaviours in biology and complements high-throughput genome sequencing efforts by allowing for more comprehensive and quantitative genotype-to-phenotype mappings. It also unlocks new opportunities for the design of spatial control algorithms to guide collective microorganism behaviour.

systems biology↗

bioRxiv2022

ADeS: a deep learning based Apoptosis Detection System for live cell imaging.

Intravital microscopy has revolutionized live cell imaging by allowing the study of spatial-temporal cell dynamics in living animals. However, the complexity of the data generated by this technology has limited the development of effective computational tools to identify and quantify cell processes. Amongst them, apoptosis is a crucial form of regulated cell death involved in tissue homeostasis and host defense. Live-cell imaging enabled the study of apoptosis at the cellular level, enhancing our understanding of its spatial-temporal regulation. However, at present, no computational method can deliver robust detection of apoptosis in microscopy time-lapses. To overcome this limitation, we developed ADeS, a deep learning-based apoptosis detection system that employs the principle of activity recognition. We trained ADeS on extensive datasets containing more than 10,000 apoptotic instances collected both in vitro and in vivo, achieving a classification accuracy above 98% and outperforming state-of-the-art solutions. ADeS is the first method capable of detecting the location and duration of multiple apoptotic events in full microscopy time-lapses, surpassing human performance in the same task. We demonstrated the effectiveness and robustness of ADeS across various imaging modalities, cell types, and staining techniques. Finally, we employed ADeS to quantify cell survival in vitro and tissue damage in vivo, demonstrating its potential application in toxicity assays, treatment evaluation, and inflammatory dynamics. Our findings suggest that ADeS is a valuable tool for the accurate detection and quantification of apoptosis in live-cell imaging and, in particular, intravital microscopy data, providing insights into the complex spatial-temporal regulation of this process.

bioinformatics↗