Machine learning helps grow artificial organs

Researchers from the Moscow Institute of Physics and Technology, Ivannikov Institute for System Programming, and the Harvard Medical School-affiliated Schepens Eye Research Institute have developed a neural network capable of recognizing retinal tissues during the process of their differentiation in a dish. Unlike humans, the algorithm achieves this without the need to modify cells, making the method suitable for growing retinal tissue for developing cell replacement therapies to treat blindness and conducting research into new drugs. The study was published in Frontiers in Cellular Neuroscience.

This would allow to expand the applications of the technology for multiple fields including the drug discovery and development of cell replacement therapies to treat blindness

In multicellular organisms, the cells making up different organs and tissues are not the same. They have distinct functions and properties, acquired in the course of development. They start out the same, as so-called stem cells, which have the potential to become any kind of cell the mature organism incorporates. They then undergo differentiation by producing proteins specific to certain tissues and organs.

The most advanced technique for replicating tissue differentiation in vitro relies on 3D cell aggregates called organoids. The method has already proved effective for studying the development of the retina, the brain, the inner ear, the intestine, the pancreas, and many other tissue types. Since organoid-based differentiation closely mimics natural processes, the resulting tissue is very similar to the one in an actual biological organ.

Some of the stages in cell differentiation toward retina have a stochastic (random) nature, leading to considerable variations in the number of cells with a particular function even between artificial organs in the same batch. The discrepancy is even greater when different cell lines are involved. As a result, it is necessary to have a means of determining which cells have already differentiated at a given point in time. Otherwise, experiments will not be truly replicable, making clinical applications less reliable, too.

To spot differentiated cells, tissue engineers use fluorescent proteins. By inserting the gene responsible for the production of such a protein into the DNA of cells, researchers ensure that it is synthesized and produces a signal once a certain stage in cell development has been reached. While this technique is highly sensitive, specific, and convenient for quantitative assessments, it is not suitable for cells intended for transplantation or hereditary disease modeling.

To address that pitfall, the authors of the recent study in Frontiers in Cellular Neuroscience have proposed an alternative approach based on tissue structure. No reliable and objective criteria for predicting the quality of differentiated cells have been formulated so far. The researchers proposed that the best retinal tissues — those most suitable for transplantation, drug screening, or disease modeling — should be selected using neural networks and artificial intelligence.

“One of the main focuses of our lab is applying the methods of bioinformatics, machine learning, and AI to practical tasks in genetics and molecular biology. And this solution, too, is at the interface between sciences. In it, neural networks, which are among the things MIPT traditionally excels at, address a problem important for biomedicine: predicting stem cell differentiation into retina,” said study co-author Pavel Volchkov, who heads the Genome Engineering Lab at MIPT.

“The human retina has a very limited capacity for regeneration,” the geneticist went on. “This means that any progressive loss of neurons — for example, in glaucoma — inevitably leads to complete loss of vision. And there is nothing a physician can recommend, short of getting a head start on learning Braille. Our research takes biomedicine a step closer to creating a cellular therapy for retinal diseases that would not only halt the progression but reverse vision loss.”

The team trained a neural network — that is, a computer algorithm that mimics the way neurons work in the human brain — to identify the tissues in a developing retina based on photographs made by a conventional light microscope. The researchers first had a number of experts identify the differentiated cells in 1,200 images via an accurate technique that involves the use of a fluorescent reporter. The neural network was trained on 750 images, with another 150 used for validation and 250 for testing predictions. At this last stage, the machine was able to spot differentiated cells with an 84% accuracy, compared with 67% achieved by humans.

“Our findings indicate that the current criteria used for early-stage retinal tissue selection may be subjective. They depend on the expert making the decision. However, we hypothesized that the tissue morphology, its structure, contains clues that enable predicting retinal differentiation, even at very early stages. And unlike a human, the computer program can extract that information!” commented Evgenii Kegeles of the MIPT Laboratory for Orphan Disease Therapy and Schepens Eye Research Institute, U.S.

“This approach does not require images of a very high quality, fluorescent reporters, or dyes, making it relatively easy to implement,” the scientist added. “It takes us one step closer to developing cellular therapies for the retinal diseases such as glaucoma and macular degeneration, which today invariably lead to blindness. Besides that, the approach can be transferred not just to other cell lines, but also to other human artificial organs.”


The Moscow Institute of Physics and Technology is a leading Russian technical university featured in the top international university rankings. It offers degrees in fundamental and applied physics, mathematics, informatics and computer science, chemistry, biology, and other natural and engineering sciences. MIPT is an advanced scientific center that conducts research into aging and aging-related diseases, applied and fundamental physics, 2D materials, quantum technology, artificial intelligence, genome engineering, Arctic and space exploration.



Original post:

33 comentários em “Machine learning helps grow artificial organs

  1. I’m the business owner of JustCBD company ( and I am currently trying to grow my wholesale side of business. I really hope that anybody at targetdomain share some guidance ! I considered that the most effective way to do this would be to connect to vape shops and cbd retail stores. I was hoping if anybody could recommend a qualified site where I can purchase CBD Shops B2B Email Marketing List I am presently examining, and Not sure which one would be the best solution and would appreciate any assistance on this. Or would it be much simpler for me to scrape my own leads? Suggestions?

  2. I’m the proprietor of JustCBD Store brand ( and I am currently aiming to expand my wholesale side of company. I really hope that someone at targetdomain is able to provide some guidance . I thought that the most ideal way to accomplish this would be to talk to vape shops and cbd retail stores. I was hoping if anyone could recommend a reliable website where I can purchase CBD Shops International Sales Leads I am currently looking at, and Not exactly sure which one would be the very best selection and would appreciate any support on this. Or would it be much simpler for me to scrape my own leads? Suggestions?

  3. Hi there! This blog post could not be written much better! Looking through this post reminds me of my previous roommate! He continually kept talking about this. I’ll send this post to him. Fairly certain he will have a good read. Thank you for sharing!

  4. You’ve made some decent points there. I checked on the web to find out more about the issue and found most individuals will go along with your views on this web site.

  5. I would like to thank you for the efforts you have put in penning this website. I am hoping to see the same high-grade blog posts by you later on as well. In truth, your creative writing abilities has motivated me to get my own blog now 😉

  6. Nice post. I learn something totally new and challenging on websites I stumbleupon every day. It’s always interesting to read through content from other writers and use something from other sites.

  7. Aw, this was an extremely good post. Taking the time and actual effort to generate a great article… but what can I say… I hesitate a whole lot and never manage to get anything done.

  8. This is a great tip especially to those new to the blogosphere. Brief but very precise information… Appreciate your sharing this one. A must read post!

  9. Achieving your fitness goals does not need a certified personal trainer or an expensive gym memberships, it’s not hard to exercise at home. It’s easy to go down a training and fitness rabbit hole, however, when you’re looking for the best home exercise equipment to outfit your personal home gym.

  10. I’m impressed, I must say. Seldom do I encounter a blog that’s both educative and entertaining, and let me tell you, you’ve hit the nail on the head. The problem is something which too few men and women are speaking intelligently about. I am very happy that I stumbled across this during my search for something concerning this.

  11. Having read this I thought it was really informative. I appreciate you taking the time and effort to put this short article together. I once again find myself spending a significant amount of time both reading and leaving comments. But so what, it was still worthwhile!

  12. After looking into a handful of the blog articles on your blog, I honestly appreciate your way of writing a blog. I book-marked it to my bookmark site list and will be checking back soon. Please check out my website as well and let me know your opinion.

  13. You’re so interesting! I do not believe I’ve truly read through anything like this before. So great to find someone with genuine thoughts on this issue. Really.. many thanks for starting this up. This web site is something that is required on the internet, someone with a little originality!

  14. An intriguing discussion is definitely worth comment. I do believe that you need to publish more on this topic, it may not be a taboo matter but usually people don’t talk about such subjects. To the next! All the best!!

  15. You’ve made some decent points there. I checked on the internet to learn more about the issue and found most people will go along with your views on this web site.

  16. The next time I read a blog, Hopefully it does not disappoint me just as much as this particular one. I mean, I know it was my choice to read, but I actually believed you would have something helpful to say. All I hear is a bunch of whining about something you could possibly fix if you weren’t too busy looking for attention.

Leave a Reply

Your email address will not be published. Required fields are marked *