A robotic lab assistant will help scientists grow neurons

Stem cells are one of the most popular topics in medicine and physiology. But for tissues to be grown from these cells, researchers all over the world need to learn to optimize this process. Researchers Anton Nikolaev, a neurobiologist at the University of Sheffield (the UK), and Pavel Katunin, a Ph.D. student at ITMO University, have created a robot that will make it possible to control the growth of neurons from stem cells.

Today, there is already a method for converting stem cells into other cells using various extracellular signaling molecules, for example retinoic acid or Wnt signaling pathways. Though widely used, this method has a range of disadvantages, one being that only a small percentage of cells is converted as is necessary: "The problem is that this takes a long time to happen," explains Anton Nikolaev. "To, roughly speaking, convert a plate of stem cells into neurons, you'll need a month or even more than that. The second problem is that you don't have any control over which neurons the cells on the plate will turn into. We, however, want to learn how to grow certain types of neurons and achieve a better understanding of how neural circuits work."

The key feature of the project is that it draws on machine learning and computer vision to find and maintain optimal conditions for converting (differentiating) cells into neurons. But machine learning requires an enormous number of cases: experiments on stem cell conversion would have to be conducted thousands of times, which is extremely difficult even for a large research group. To that end, the scientists created a robotic lab assistant, which was printed on a regular 3-D printer and will help automate this process.

"In itself, selecting specific differentiation factors and the protocol for their use is an optimization problem," says Pavel Katunin. "In other words, for example, there are various parameters of signaling molecules—their concentrations, application dynamics, etc. – and we're trying to find the best combination of these parameters so that the maximum amount of cells is converted into ones we need. Computer vision, which uses microscope data to automatically determine whether the process is proceeding in the way we need it to, can help us optimize this process and estimate the percentage of cells differentiated to the needed ones."

As a result, the researchers developed a robot able to automatically carry out a large number of experiments and generate big data. It is on this robot that the optimization algorithm was tested.

Presently, work on the robot is nearly complete, with an article preprint published as its result. From now on, other researchers will also be able to use this invention in their experiments. In the future, this model will allow scientists to monitor the process of the conversion of cells in early stages and select the ones needed for a specific experiment.

In the first stage, experiments are carried out not on embryonic stem cells but on much cheaper cell lines of the NTERA-2 cancer stem cells. This replacement is very useful for training the model and debugging the robot as it significantly cuts the cost of each experiment. Besides, working with these cells can in itself yield scientific results and help find a potential application in the field of medicine, for example in oncology. But for now, the scientists will focus on working out a method for obtaining a large number of neurons from stem cells and, in the future, using them to create logical circuits.

More information:
www.biorxiv.org/content/10.110 … /2020.07.02.185454v1

Provided by ITMO University