One day in the future when you need medical care, someone will examine you, diagnose the problem, remove some of your body's healthy cells, and then use them to grow a cure for your ailment. The therapy will be personalized and especially attuned to you and your body, your genes, and the microbes that live in your gut. This is the dream of modern medical science in the field of "regenerative medicine." There are many obstacles standing between this dream and its implementation in real life, however. Cells often differ so much from one another and differ in so many ways that scientists have a hard time predicting what the cells will do in any given therapeutic scenario.
Increases in the number of cell therapies in the preclinical and clinical phases have prompted the need for reliable and non-invasive assays to validate transplant function in clinical biomanufacturing. We developed a robust characterization methodology composed of quantitative bright-field absorbance microscopy (QBAM) and deep neural networks (DNNs) to non-invasively predict tissue function and cellular donor identity. The methodology was validated using clinical-grade induced pluripotent stem cell derived retinal pigment epithelial cells (iPSC-RPE). QBAM images of iPSC-RPE were used to train DNNs that predicted iPSC-RPE monolayer transepithelial resistance, predicted polarized vascular endothelial growth factor (VEGF) secretion, and matched iPSC-RPE monolayers to the stem cell donors. DNN predictions were supplemented with traditional machine learning algorithms that identified shape and texture features of single cells that were used to predict tissue function and iPSC donor identity.
A fully automated artificial intelligence (AI)-based multispectral absorbance imaging system effectively classified function and potency of induced pluripotent stem cell derived retinal pigment epithelial cells (iPSC-RPE) from patients with age-related macular degeneration (AMD). The finding from the system could be applied to assessing future cellular therapies, according to research presented at the 2018 ARVO annual meeting. The software, which uses convolutional neural network (CNN) deep learning algorithms, effectively evaluated release criterion for the iPSC-RPE cell-based therapy in a standard, reproducible, and cost-effective fashion. The AI-based analysis was as specific and sensitive as traditional molecular and physiological assays, without the need for human intervention. "Cells can be classified with high accuracy using nothing but absorbance images," wrote lead investigator Nathan Hotaling and colleagues from the National Institutes of Health in their poster.
When she entered medicine in the mid-1980s, Masayo Takahashi chose ophthalmology as her specialty, she said, because she wanted to have a family and thought the discipline would spare her from sudden work calls in the middle of the night, helping her best balance work and life. Three decades later, the 56-year-old mother of two grown-up daughters is at the forefront of the nation's -- even the world's -- research into regenerative medicine. In September 2014, she offered a ray of hope to scores of patients with a severe eye condition when her team at the Riken institute's Center for Developmental Biology in Kobe succeeded in a world-first transplanting of cells made from induced pluripotent stem (iPS) cells into a human body. The operation, conducted as a clinical study, involved creating a retinal sheet from iPS cells, which were developed by Shinya Yamanaka, a researcher at Kyoto University. His 2006 discovery of iPS cells, which can grow into any kind of tissue in the body, won him a Nobel Prize in 2012.
Getting older is supposed to give you perspective. But for one out of five people over the age of 65, it does the opposite. Macular degeneration is a common progressive eye condition, one that thins and breaks down a tissue behind the center of the retina. Without that tissue, the light-sensing cells it supports atrophy and die, making it impossible to get a clear picture of anything straight ahead of you--like, say, the faces of your loved ones or anything past your steering wheel. Treatments can slow the loss of vision, but there's no way to reverse it.