Four times faster
Robots helped to speed up the preparation of stem cells 4 times in order to grow a retina in a test tube
Scientists from the Moscow Institute of Physics and Technology and Harvard have almost four times accelerated the process of producing stem cells for growing tissues in vitro. The new algorithm also helps in the study of factors affecting cell specialization. The results are published in the journal Translational Vision Science and Technology (Kegeles et al., Semi-Automated Approach for Retinal Tissue Differentiation).
The retina of the eye is a set of organized layers of neurons connected together and forming a neural circuit. It perceives light and processes incoming visual information before sending it to the brain. Due to the limited regenerative ability, the loss of retinal neurons leads to irreversible blindness. In 2015, more than 2.5 million people in Russia suffered from various retinal diseases. Various approaches to the treatment of these diseases are being developed: neuroprotection, gene therapy, cell replacement, and others. Differing in the mechanism of action, target disease and methodology, they all require a huge number of retinal cells for research.
With the help of stem cells, it is possible to reproduce the development of the retina in a test tube. First, stem cell clusters are placed in a special environment where spontaneous formation of undeveloped neurons is induced. This is followed by the formation and maturation of the retina. This approach leads to the production of real retinal neurons, organized into a complex tissue, without external stimulation of the development pathways in the process of specialization. However, the method has its limitations: the random nature of the initial stimulation of neuronal growth. Also, the time required for the proper development of the artificial retina is 30 days for the mouse retina and up to a year for human organoids. The authors of the article tried to solve these problems by increasing the number of cells produced and improving their quality.
To compare the quality of tissue cultivation by a robot and a human, scientists have grown several thousand samples of retinal tissue for further automatic processing and the same number of samples for manual removal. The authors scanned the wells with tissue from the first group, and the resulting images were analyzed using a specially developed Python script. The program calculates the areas of the photo in which the fluorescent protein glows most intensely. Since this protein is produced only in developing retinal cells, high intensity shows areas of the sample with the desired tissue. Thus, the program is able to determine the amount of developing retina in each organoid.
It turned out that the use of automatic algorithms does not reduce the quality of the grown tissues and helps to optimize the protocol of cell production due to the large number of simultaneously tested systems. The use of a semi-automatic algorithm of operation allowed scientists to reduce the time spent on processing cells from 2 hours to 34 minutes.
"We implemented a robotic fluid change in the course of retinal differentiation and showed that this does not adversely affect the result of cell specialization. In addition, we have developed a tool for automatic retinal detection and organ classification and demonstrated its application to optimize the conditions of specialization and quality control. One of the tasks that we sought to solve in our work is the ability to scale differentiation to produce a large amount of tissue for drug trials and cell transplantation experiments. Automatic sample processing reduces the necessary efforts on the part of the staff and increases the number of cells produced at times. After minor modifications, this algorithm can be used to grow other organs, not just the retina," comments Evgeny Kegeles, an employee of the MIPT Laboratory of Genomic Engineering.
"This is exactly the case when quantity matters: thanks to automation, we can get trillions of retinal neurons for transplantation," adds Peter Baranov, head of the laboratory at The Schepens Eye Research Institute of Mass Eye and Ear.
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