A new study published in the journal Cell Systems on November 20, 2019, reports the use of machine learning to help form complex cell architectures from pluripotent stem cells, a sophisticated technology that could solve multiple issues that currently hampers the production of artificial tissues and organs.
Medical scientists faced with irreparably damaged organs have long wanted to know how to stimulate their regeneration or to replace them with new ones, to prolong survival and to provide improved quality of life.
Another equally important area of research involves creating artificial tissues which are identical to those in the body, in order to help understand how disease processes evolve and which drugs can be used to treat such disorders. This means that scientists must know how to direct the development of stem cells in the desired pattern to form multiple tissues in the right way.
Pluripotent ('capable of multiple tasks') stem cells are cells that can divide indefinitely or can develop into any of the three germ layers found in the early embryo. Germ layers are the fundamental tissues that give rise to all the different types of cells and tissues found in the adult organism. Given this property, stem cells are potentially able to recreate any tissue or organ found in the body. However, they need to be directed to form the right patterns as they mature. Such signals and organizers are found at the right times in the natural environment, allowing the development of a mature organism.
However, replicating this spatial organization of natural tissues in the laboratory environment is a challenging task, and among the biggest obstacles to crossing the bridge between stem cells and mature functional organs. One current approach is 3D printing, which creates a biocompatible matrix over which stem cells can divide to create the desired shape and complexity. However, recently, it has been found that stem cells can migrate from the spot where they were laid down, leading to tissue aberrations and loss of function.
Gladstone Institutes researchers have made use of a computational approach to help them in this all-important task of directing the development of stem cells into the right spatial arrangement.
The stem cells used in this study are pluripotent stem cells (iPSCs), which are derived from mature adult somatic cells engineered to regain stem cell characteristics such as self-renewal (the ability to keep dividing into its clones indefinitely), and pluripotency. These have already been used to recreate models of almost all kinds of cell types, such as brain cells, heart cells, and kidney cells. Such cells are now being used in cultures in order to find out how diseases occur and to use as transplants in patients. But the fact remains that both structurally, functionally and biologically, these loose disordered cell clusters that comprise a mix of several types of cells at most, aren't the same as a fully developed, complex, properly arranged organ.
Earlier research showed that knocking out (inhibiting) two genes called ROCK1 and CDH1 caused stem cells in a culture medium to grow in a different arrangement. This gave rise to the idea that they could find out how exactly different cell arrangements could be brought about by allowing each of these genes to be expressed at different levels at different time points.
