A Platform for Studying Neurodegeneration Mechanisms Using Genetically Encoded Biosensors

Oxidative stress monitoring in iPSC-derived motor neurons using genetically encoded biosensors of H2O2

Oxidative stress plays an important role in the development of neurodegenerative diseases, being either the initiator or part of a pathological cascade that leads to the neuron’s death. Genetically encoded biosensors of oxidative stress demonstrated their general functionality and overall safety in various systems. However, there is still insufficient data regarding their use in the research of disease-related phenotypes in relevant model systems, such as human cells. Here, we establish an approach for monitoring the redox state of live motor neurons with SOD1 mutations associated with amyotrophic lateral sclerosis. Using CRISPR/Cas9, we insert genetically encoded biosensors of cytoplasmic and mitochondrial H₂O₂ in the genome of induced pluripotent stem cell (iPSC) lines. We demonstrate that the biosensors remain functional in motor neurons derived from these iPSCs and reflect the differences in the stationary redox state of the neurons with different genotypes. Moreover, we show that the biosensors respond to alterations in motor neuron oxidation caused by either environmental changes or cellular stress. Thus, the obtained platform is suitable for cell-based research of neurodegenerative mechanisms.

Blood-Brain Barrier and Neurovascular Unit In Vitro Models for Studying Mitochondria-Driven Molecular Mechanisms of Neurodegeneration

Pathophysiology of chronic neurodegeneration is mainly based on complex mechanisms related to aberrant signal transduction, excitation/inhibition imbalance, excitotoxicity, synaptic dysfunction, oxidative stress, proteotoxicity and protein misfolding, local insulin resistance and metabolic dysfunction, excessive cell death, development of glia-supported neuroinflammation, and failure of neurogenesis. These mechanisms tightly associate with dramatic alterations in the structure and activity of the neurovascular unit (NVU) and the blood-brain barrier (BBB). NVU is an ensemble of brain cells (brain microvessel endothelial cells (BMECs), astrocytes, pericytes, neurons, and microglia) serving for the adjustment of cell-to-cell interactions, metabolic coupling, local microcirculation, and neuronal excitability to the actual needs of the brain. The part of the NVU known as a BBB controls selective access of endogenous and exogenous molecules to the brain tissue and efflux of metabolites to the blood, thereby providing maintenance of brain chemical homeostasis critical for efficient signal transduction and brain plasticity. In Alzheimer's disease, mitochondria are the target organelles for amyloid-induced neurodegeneration and alterations in NVU metabolic coupling or BBB breakdown. In this review we discuss understandings on mitochondria-driven NVU and BBB dysfunction, and how it might be studied in current and prospective NVU/BBB in vitro models for finding new approaches for the efficient pharmacotherapy of Alzheimer's disease.

Generation of induced pluripotent stem cell lines ICGi021-A and ICGi022-A from peripheral blood mononuclear cells of two healthy individuals from Siberian population

ICGi021-A and ICGi022-A iPSC lines were obtained by reprogramming PBMCs of two healthy women of the Siberian population using episomal non-integrating vectors expressing Yamanaka factors. iPSC lines expressed pluripotency markers, had a normal karyotype and demonstrated the ability to differentiate into derivatives of the three germ layers. Clinical exome sequencing data of the original biosamples of the donors are available in the NCBI SRA database. The generated cell lines are useful as "healthy" control in biomedical studies.

CellCountCV-A Web-Application for Accurate Cell Counting and Automated Batch Processing of Microscopic Images Using Fully Convolutional Neural Networks

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, in real time, the processes occurring in living cells. At present, 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 the mechanisms underlying the pathogenic processes and diseases and to screen for potential therapeutic compounds. It is also necessary to develop new tools for the processing and analysis of obtained microimages. Here, we present our web-application CellCountCV for automation of microscopic 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 analyze large series of microscopic images obtained in experimental studies and it was able to demonstrate endoplasmic reticulum stress development and to catch the dose-dependent effect of tunicamycin.