Although it is well understood that genetics influence the development of autism spectrum disorder, no one could yet pinpoint the precise cause and mechanism. To study the biological background of diseases, researchers use models: Cell models allow us to study how changes in the genes affect the shape and function of the cell, while animal models show how the change in its cellular components affects health and behavior. Despite significant differences between mice and humans, many disease-causing genes are very similar and cause similar conditions across these species. “One of the problems, however, is the lack of a standardized biological model to study the effects of the different mutations associated with autism spectrum disorder. This makes it difficult to find out, for example, whether they have common effects or what is specific to certain cell types,” explains Kobe University neuroscientist TAKUMI Toru.

Thus, twelve years ago, Takumi and his team embarked on a journey to change that. Being experts in studying mouse models of the disorder, they combined a conventional manipulation technique for mouse embryonic stem cells — cells that can be made to develop into almost any kind of cell in the body — with the then-newly discovered, highly specific and easy-to-handle CRISPR gene editing system. This new method proved highly efficient in making genetic variants of these cells and allowed the Kobe University team to produce a bank of 63 mouse embryonic stem cell lines of the genetic variants most strongly associated with autism spectrum disorder.

In the journal Cell Genomics, Takumi and his team now published that they were able to develop their cells into a broad range of cell types and tissues, and even generate adult mice with their genetic variations. The analysis of these alone proved that their cell lines were adequate models for studying autism spectrum disorder. However, the cell lines also allowed them to conduct large-scale data analyses to clearly identify genes that are abnormally active, and in which cell types this is the case. 

One of the things the data analysis brought to light is that autism-causing mutations often result in neurons being unable to eliminate misshapen proteins. “This is particularly interesting since the local production of proteins is a unique feature in neurons, and a lack of quality control of these proteins may be a causal factor of neuronal defects,” explains Takumi.

The Kobe University neuroscientist expects that his team’s achievement, which has been made available to other researchers and can be flexibly integrated with other lab techniques and adjusted to other targets, will be an invaluable resource for the scientific community studying autism and trying to find drug targets. He adds: “Interestingly, the genetic variants we studied are also implicated in other neuropsychiatric disorders such as schizophrenia and bipolar disorder. So, this library may be useful for studying other conditions as well.”

This research was funded by the Japan Society for the Promotion of Science (grants 16H06316, 16F16110, 21H00202, 21H04813, 23KK0132, 23H04233, 24H00620, 24H01241, 24K22036, 17K07119 and 21K07820), the Japan Agency for Medical Research and Development (grant JP21wm0425011), the Japan Science and Technology Agency (grants JPMJPF2018, JPMJMS2299 and JPMJMS229B), the National Center of Neurology and Psychiatry (grant 6-9), the Takeda Science Foundation, the Smoking Research Foundation, the Tokyo Biochemical Research Foundation, the Kawano Masanori Memorial Public Interest Incorporated Foundation for Promotion of Pediatrics, the Taiju Life Social Welfare Foundation, the Tokumori Yasumoto Memorial Trust for Researches on Tuberous Sclerosis Complex and Related Rare Neurological Diseases, and Takeda Pharmaceutical Company Ltd. It was conducted in collaboration with researchers from the RIKEN Center for Brain Science, Radboud University, the RIKEN Center for Integrative Medical Sciences, the Agency for Science, Technology and Research, the RIKEN Center for Biosystems Dynamics Research, and Hiroshima University.

Kobe University is a national university with roots dating back to the Kobe Higher Commercial School founded in 1902. It is now one of Japan’s leading comprehensive research universities with nearly 16,000 students and nearly 1,700 faculty in 11 faculties and schools and 15 graduate schools. Combining the social and natural sciences to cultivate leaders with an interdisciplinary perspective, Kobe University creates knowledge and fosters innovation to address society’s challenges.

Understanding this carbon cycle is key to climate change research, according to Hamdi Zurqani, an assistant professor of geospatial science for the Arkansas Forest Resources Center and the College of Forestry, Agriculture and Natural Resources at the University of Arkansas at Monticello. The center is headquartered at UAM and conducts research and extension activities through the Arkansas Agricultural Experiment Station and the Cooperative Extension Service, the University of Arkansas System Division of Agriculture's research and outreach arms.

"Forests are often called the lungs of our planet, and for good reason," Zurqani said. "They store roughly 80 percent of the world's terrestrial carbon and play a critical role in regulating Earth's climate."

To measure a forest's carbon cycle, a calculation of forest aboveground biomass is needed. Though effective, traditional ground-based methods for estimating forest aboveground biomass are labor-intensive, time-consuming and limited in spatial coverage abilities, Zurqani said.

In a study recently published in Ecological Informatics, Zurqani shows how information from open-access satellites can be integrated on Google Earth Engine with artificial intelligence algorithms to quickly and accurately map large-scale forest aboveground biomass, even in remote areas where accessibility is often an issue.

Zurqani's novel approach uses data from NASA's Global Ecosystem Dynamics Investigation LiDAR, also known as GEDI LiDAR, which includes three lasers installed on the International Space Station. The system can precisely measure three-dimensional forest canopy height, canopy vertical structure and surface elevation. LiDAR stands for "light detection and ranging" and uses light pulses to measure distance and create 3D models.

Zurqani also used imagery data from the European Space Agency's collection of Earth observation Copernicus Sentinel satellites -- Sentinel-1 and Sentinel-2. Combining the 3D imagery from GEDI and the optical imagery from the Sentinels, Zurqani improved the accuracy of biomass estimations.

The study tested four machine learning algorithms to analyze the data: Gradient tree boosting, random forest, classification and regression trees, or CART, and support vector machine. Gradient tree boosting achieved the highest accuracy score and the lowest error rates. Random forest came in second, proving reliable but slightly less precise. CART provided reasonable estimates but tended to focus on a smaller subset. The support vector machine algorithm struggled, Zurqani said, highlighting that not all AI models are equally suited for estimating aboveground forest biomass in this study.

The most accurate predictions, Zurqani said, came from combining Sentinel-2 optical data, vegetation indices, topographic features, and canopy height with the GEDI LiDAR dataset serving as the reference input for both training and testing the machine learning models, showing that multi-source data integration is critical for reliable biomass mapping.

Why it matters

Zurqani said that accurate forest biomass mapping has real-world implications for better accounting of carbon and improved forest management on a global scale. With more accurate assessments, governments and organizations can more precisely track carbon sequestration and emissions from deforestation to inform policy decisions.

The road ahead

While the study marks a leap forward in measuring aboveground forest biomass, Zurqani said the challenges remaining include the impact weather can have on satellite data. Some regions still lack high-resolution LiDAR coverage. He added that future research may explore deeper AI models, such as neural networks, to refine predictions further.

"One thing is clear," Zurqani said. "As climate change intensifies, technology like this will be indispensable in safeguarding our forests and the planet."