Machine learning approach for recognition and morphological analysis of isolated astrocytes in phase contrast microscopy.

Autor: Yakovlev EV; Scientific-Educational Centre 'Soft matter and physics of fluids', Bauman Moscow State Technical University, 2nd Baumanskaya Street 5, Moscow, 105005, Russia. yakov.egor@gmail.com., Simkin IV; Scientific-Educational Centre 'Soft matter and physics of fluids', Bauman Moscow State Technical University, 2nd Baumanskaya Street 5, Moscow, 105005, Russia., Shirokova AA; Scientific-Educational Centre 'Soft matter and physics of fluids', Bauman Moscow State Technical University, 2nd Baumanskaya Street 5, Moscow, 105005, Russia., Kolotieva NA; Scientific-Educational Centre 'Soft matter and physics of fluids', Bauman Moscow State Technical University, 2nd Baumanskaya Street 5, Moscow, 105005, Russia.; Research Center of Neurology, 80 Volokolamskoye Shosse, Moscow, 125367, Russia., Novikova SV; Scientific-Educational Centre 'Soft matter and physics of fluids', Bauman Moscow State Technical University, 2nd Baumanskaya Street 5, Moscow, 105005, Russia.; Research Center of Neurology, 80 Volokolamskoye Shosse, Moscow, 125367, Russia., Nasyrov AD; Scientific-Educational Centre 'Soft matter and physics of fluids', Bauman Moscow State Technical University, 2nd Baumanskaya Street 5, Moscow, 105005, Russia., Denisenko IR; Scientific-Educational Centre 'Soft matter and physics of fluids', Bauman Moscow State Technical University, 2nd Baumanskaya Street 5, Moscow, 105005, Russia., Gursky KD; Scientific-Educational Centre 'Soft matter and physics of fluids', Bauman Moscow State Technical University, 2nd Baumanskaya Street 5, Moscow, 105005, Russia., Shishkov IN; Scientific-Educational Centre 'Soft matter and physics of fluids', Bauman Moscow State Technical University, 2nd Baumanskaya Street 5, Moscow, 105005, Russia., Narzaeva DE; Scientific-Educational Centre 'Soft matter and physics of fluids', Bauman Moscow State Technical University, 2nd Baumanskaya Street 5, Moscow, 105005, Russia.; Research Center of Neurology, 80 Volokolamskoye Shosse, Moscow, 125367, Russia., Salmina AB; Scientific-Educational Centre 'Soft matter and physics of fluids', Bauman Moscow State Technical University, 2nd Baumanskaya Street 5, Moscow, 105005, Russia.; Research Center of Neurology, 80 Volokolamskoye Shosse, Moscow, 125367, Russia., Yurchenko SO; Scientific-Educational Centre 'Soft matter and physics of fluids', Bauman Moscow State Technical University, 2nd Baumanskaya Street 5, Moscow, 105005, Russia., Kryuchkov NP; Scientific-Educational Centre 'Soft matter and physics of fluids', Bauman Moscow State Technical University, 2nd Baumanskaya Street 5, Moscow, 105005, Russia. kruchkov_nkt@mail.ru.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2024 Apr 29; Vol. 14 (1), pp. 9846. Date of Electronic Publication: 2024 Apr 29.
DOI: 10.1038/s41598-024-59773-2
Abstrakt: Astrocytes are glycolytically active cells in the central nervous system playing a crucial role in various brain processes from homeostasis to neurotransmission. Astrocytes possess a complex branched morphology, frequently examined by fluorescent microscopy. However, staining and fixation may impact the properties of astrocytes, thereby affecting the accuracy of the experimental data of astrocytes dynamics and morphology. On the other hand, phase contrast microscopy can be used to study astrocytes morphology without affecting them, but the post-processing of the resulting low-contrast images is challenging. The main result of this work is a novel approach for recognition and morphological analysis of unstained astrocytes based on machine-learning recognition of microscopic images. We conducted a series of experiments involving the cultivation of isolated astrocytes from the rat brain cortex followed by microscopy. Using the proposed approach, we tracked the temporal evolution of the average total length of branches, branching, and area per astrocyte in our experiments. We believe that the proposed approach and the obtained experimental data will be of interest and benefit to the scientific communities in cell biology, biophysics, and machine learning.
(© 2024. The Author(s).)
Databáze: MEDLINE
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