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Human Brain Organoids The team of Paola Arlotta published a study in Nature on August 19, detailing the five-year growth of human cortical organoids, which are three-dimensional models of the cortex derived from stem cells. At 16 time points, the cells in these models underwent consecutive changes in maturation programs similar to those of the human cortex. Human cortex development takes years, while typical organoid experiments often end after months, with the previous longest culture lasting 694 days. Arlotta's team grew organoids for five years to test whether the cells would continue to progress through the maturation program. The series consisted of 110 organoids and 424,720 individual cells. Researchers compared gene expression with human cortex data: from nine months to five years, the organoid cells increasingly resembled late prenatal and early postnatal development. DNA methylation, which affects gene expression, provided an independent scale: two out of three methylation clocks showed increasing tissue age as the culture period increased. The most direct test was conducted in mixed organoids, where researchers combined precursor cells from 9-12 month organoids with cells from 15-day organoids. After 15 days, the younger portion gave rise to early types of cortex cells, while the older portion gave rise to later nerve and support cells, even though both grew in the same environment. Young cells partially shifted the programs of old precursors to earlier types, but later cell fates were preserved. In the shared environment, cells still carried the legacy of their already passed developmental stage. To maintain exciting neurons that transmit signals to other cells in long-term culture, from the 70th day onwards, part of the organoids were grown in a medium that supported their spontaneous electrical activity. After a year, all nine such organoids exhibited network bursts; in eight control organoids, there were no bursts. In this medium, active networks were preserved for at least two years. After five years of observation, Arlotta identified the next challenge: "Now we need to understand how to make this process faster," she said. 🔗 Read original →