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Zakian, S. M.

Publications and source records attributed to Zakian, S. M..

2 recordsLinked to original sources

A cell-based platform for oxidative stress monitoring in motor neurons using genetically encoded biosensors of H2O2

BackgroundOxidative stress plays an important role in the development of neurodegenerative diseases: it either can be the initiator or part of a pathological cascade leading to the neurons death. Although a lot of methods are known for oxidative stress study, most of them operate on non-native cellular substrates or interfere with the cell functioning. Genetically encoded (GE) biosensors of oxidative stress demonstrated their general functionality and overall safety in various live systems. However, there is still insufficient data regarding their use for research of disease-related phenotypes in relevant model systems, such as human cells. MethodsWe applied CRISPR/Cas9 genome editing to introduce mutations (c.272A>C and c.382G>C) in the associated with amyotrophic lateral sclerosis SOD1 gene of induced pluripotent stem cells (iPSC) obtained from a healthy individual. Using CRISPR/Cas9, we modified these mutant iPSC lines, as well as the parental iPSC line, and a patient-specific SOD1D91A/D91A iPSC line with ratiometric GE biosensors of cytoplasmic (Cyto-roGFP2-Orp1) and mitochondrial (Mito-roGFP2-Orp1) H2O2. The biosensors sequences along with a specific transactivator for doxycycline-controllable expression were inserted in the "safe harbor" AAVS1 (adeno-associated virus site 1) locus. We differentiated these transgenic iPSCs into motor neurons and investigated the functionality of the biosensors in such a system. We measured relative oxidation in the cultured motor neurons and its dependence on culture conditions, age, and genotype, as well as kinetics of H2O2 elimination in real-time. ResultsWe developed a cell-based platform consisting of isogenic iPSC lines with different genotypes associated with amyotrophic lateral sclerosis. The iPSC lines were modified with GE biosensors of cytoplasmic and mitochondrial H2O2. We provide proof-of-principle data showing that this approach may be suitable for monitoring oxidative stress in cell models of various neurodegenerative diseases as the biosensors reflect the redox state of neurons. ConclusionWe found that the GE biosensors inserted in the AAVS1 locus remain functional in motor neurons and reflect pathological features of mutant motor neurons, although the readout largely depends on the severity of the mutation.

cell biology↗

CellCountCV - a web-application for accurate cell counting and automated batch processing of microscopy images using fully-convolutional neural networks

The in vitro cellular models are promising tools for studying normal and pathological conditions. One of their important applications is the development of genetically engineered biosensor systems to investigate the processes occurring in living cells in real time. Today, there are fluorescence protein based sensory systems for detecting various substances in living cells (for example, hydrogen peroxide, ATP, Ca2+ etc.) or for detecting processes such as endoplasmic reticulum stress. Such systems help to study mechanisms underlying the pathogenic processes and diseases and for screening potential therapeutic compounds. It is also necessary to develop new tools for processing and analysis of obtained microimages. Here we present our web-application CellCountCV for automation of microscopy cell images analysis which is based on fully-convolutional deep neural networks. This approach can efficiently deal with non-convex overlapping objects, that are virtually inseparable with conventional image processing methods. The cell counts predicted with CellCountCV were very close to expert estimates (the average error rate was < 4%). CellCountCV was used to analyse large series of microscopy images obtained in experimental studies and it was able to demonstrate the endoplasmic reticulum stress development and to catch the dose-dependent effect of tunicamycin.

cell biology↗