Signatures of the Consolidated Response of Astrocytes to Ischemic Factors In Vitro.
Elena V MitroshinaMikhail I KrivonosovDmitriy E BurmistrovMaria O SavyukTatiana A MishchenkoMikhail V IvanchenkoMaria V VedunovaPublished in: International journal of molecular sciences (2020)
Whether and under what conditions astrocytes can mount a collective network response has recently become one of the central questions in neurobiology. Here, we address this problem, investigating astrocytic reactions to different biochemical stimuli and ischemic-like conditions in vitro. Identifying an emergent astrocytic network is based on a novel mathematical approach that extracts calcium activity from time-lapse fluorescence imaging and estimates the connectivity of astrocytes. The developed algorithm represents the astrocytic network as an oriented graph in which the nodes correspond to separate astrocytes, and the edges indicate high dynamical correlations between astrocytic events. We demonstrate that ischemic-like conditions decrease network connectivity in primary cultures in vitro, although calcium events persist. Importantly, we found that stimulation under normal conditions with 10 µM ATP increases the number of long-range connections and the degree of corresponding correlations in calcium activity, apart from the frequency of calcium events. This result indicates that astrocytes can form a large functional network in response to certain stimuli. In the post-ischemic interval, the response to ATP stimulation is not manifested, which suggests a deep lesion in functional astrocytic networks. The blockade of Connexin 43 during ischemic modeling preserves the connectivity of astrocytes in the post-hypoxic period.
Keyphrases
- fluorescence imaging
- ischemia reperfusion injury
- resting state
- cerebral ischemia
- functional connectivity
- machine learning
- genome wide
- gene expression
- photodynamic therapy
- oxidative stress
- dna methylation
- subarachnoid hemorrhage
- brain injury
- density functional theory
- sentinel lymph node
- neoadjuvant chemotherapy
- rectal cancer
- convolutional neural network