NeuriTES. Monitoring neurite changes through transfer entropy and semantic segmentation in bright-field time-lapse microscopy
| Autor: | Hervé Isambert, Eugenio Martinelli, Gianni Antonelli, Davide Di Giuseppe, Arianna Mencattini, Maria Colomba Comes, Alida Spalloni, Joanna Filippi, Patrizia Longone, Francesca Corsi, Corrado Di Natale, Paola Casti, Michele D'Orazio |
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| Přispěvatelé: | Laboratoire Physico-Chimie Curie [Institut Curie] (PCC), Institut Curie [Paris]-Institut de Chimie du CNRS (INC)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
cancer cell treatment
Neurite Computer science [PHYS.PHYS.PHYS-BIO-PH]Physics [physics]/Physics [physics]/Biological Physics [physics.bio-ph] General Decision Sciences Settore ING-INF/07 Article Field (computer science) Time-lapse microscopy [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] 03 medical and health sciences QA76.75-76.765 0302 clinical medicine [STAT.ML]Statistics [stat]/Machine Learning [stat.ML] DSML 2: Proof-of-Concept: Data science output has been formulated implemented and tested for one domain/problem video analysis Segmentation Computer software bright-field time-lapse microscopy ComputingMilieux_MISCELLANEOUS 030304 developmental biology 0303 health sciences business.industry transfer entropy Pattern recognition [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation semantic segmentation ALS disease Chemotherapy Drugs [INFO.INFO-IT]Computer Science [cs]/Information Theory [cs.IT] Evolving systems Transfer entropy Artificial intelligence [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM] business 030217 neurology & neurosurgery |
| Zdroj: | Patterns Patterns, Cell Press Elsevier, 2021, 2 (6), pp.100261. ⟨10.1016/j.patter.2021.100261⟩ Patterns, Vol 2, Iss 6, Pp 100261-(2021) |
| ISSN: | 2666-3899 |
| DOI: | 10.1016/j.patter.2021.100261⟩ |
| Popis: | Summary One of the most challenging frontiers in biological systems understanding is fluorescent label-free imaging. We present here the NeuriTES platform that revisits the standard paradigms of video analysis to detect unlabeled objects and adapt to the dynamic evolution of the phenomenon under observation. Object segmentation is reformulated using robust algorithms to assure regular cell detection and transfer entropy measures are used to study the inter-relationship among the parameters related to the evolving system. We applied the NeuriTES platform to the automatic analysis of neurites degeneration in presence of amyotrophic lateral sclerosis (ALS) and to the study of the effects of a chemotherapy drug on living prostate cancer cells (PC3) cultures. Control cells have been considered in both the two cases study. Accuracy values of 93% and of 92% are achieved, respectively. NeuriTES not only represents a tool for investigation in fluorescent label-free images but demonstrates to be adaptable to individual needs. Highlights • Monitoring of cell phenotype changes by fluorescence label-free time-lapse microscopy • Adaptive semantic segmentation for the robust detection of cell shape • TE to correlate morphological and textural soma descriptors along time • Directed TE graph for the representation of mutual relationship among descriptors The bigger picture One of the most challenging frontiers for the automatic understanding of biological systems is fluorescent label-free imaging in which the behavior changes of living being are characterized without cell staining. To this aim, we present here the NeuriTES platform that revisits standard paradigms of video analysis to detect unlabeled objects and correlate the analysis to phenotype evolution of the mechanisms under observation. Through the exploitation of adaptive algorithms and of transfer entropy measures, the platform assures regular cell detection and the possibility to extract reliable parameters related to the evolving cell system. As a proof-of-concept, NeuriTES is applied to two fascinating phenotype investigation scenarios, amyotrophic lateral sclerosis (ALS) disease mechanism and the study of the effects of a chemotherapy drug on living prostate cancer cells (PC3) cultures. Directed graphs assist the biologists with a visual understanding of the mechanisms identified. Nowadays, observational traits (phenotype) of biological entities complement with genomics offering the possibility to monitor phenomenon evolutions and underlying mechanism (neurodegeneration, cancer cell replication, etc.). By complementing video, data analysis, and machine learning approaches with label-free time-lapse microscopy, NeuriTES tool allows automatically analyzing such traits and extracting as outcomes proof of concepts and visual graph-based representation of the phenotypical rationale behind. |
| Databáze: | OpenAIRE |
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