Scientists in a lab analyzing a 3D brain model with digital neural data overlays, representing the BrainSTEM atlas for Parkinson's research.
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Duke-NUS unveils BrainSTEM atlas to guide Parkinson’s research

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Duke-NUS Medical School researchers, working with the University of Sydney, have developed BrainSTEM—a two-tier, single-cell atlas of the developing human brain that profiles nearly 680,000 cells. Published online in Science Advances on October 31, 2025, the resource focuses on midbrain dopaminergic neurons, flags off‑target cell types in lab-grown models, and will be released openly for the research community.

Scientists at Duke-NUS Medical School and collaborators have created BrainSTEM (Brain Single-cell Two tiEr Mapping), a comprehensive single-cell atlas of the fetal human brain designed to benchmark and improve models for Parkinson’s disease.

  • Scope and focus: The atlas profiles nearly 680,000 cells from the developing human brain and includes a higher-resolution midbrain sub-atlas that pinpoints dopaminergic neurons—cells impaired in Parkinson’s disease.
  • Why it matters: Parkinson’s disease is Singapore’s second most common neurodegenerative condition, affecting about three in every 1,000 people aged 50 and above. More faithful midbrain models could strengthen research and support future cell-based therapies.

The study, published in Science Advances (Vol. 11, Issue 44; DOI: 10.1126/sciadv.adu7944) on October 31, 2025, reports that leading laboratory differentiation protocols can yield unwanted cells from other brain regions, underscoring the need to refine both experimental methods and data-analysis pipelines.

Lead authors and senior investigators highlighted the tool’s practical value. “Our data-driven blueprint helps scientists produce high-yield midbrain dopaminergic neurons that faithfully reflect human biology. Grafts of this quality are pivotal to increasing cell therapy efficacy and minimizing side effects, paving the way to offer alternative therapies to people living with Parkinson’s disease,” said Dr. Hilary Toh, an MD–PhD candidate at Duke‑NUS.

“By mapping the brain at single-cell resolution, BrainSTEM gives us the precision to distinguish even subtle off-target cell populations. This rich cellular detail provides a critical foundation for AI-driven models that will transform how we group patients and design targeted therapies for neurodegenerative diseases,” said Dr. John Ouyang, a senior author from Duke‑NUS’ Centre for Computational Biology. Assistant Professor Alfred Sun added that the rigorous, data-driven approach “will speed the development of reliable cell therapies for Parkinson’s disease,” while Professor Patrick Tan, Senior Vice‑Dean for Research at Duke‑NUS, called the work a new benchmark for capturing cellular detail in complex systems.

The team will release the atlases as open resources alongside an out‑of‑the‑box BrainSTEM package, enabling researchers to apply the multi‑tier mapping approach to any brain cell type. The project involved collaborators at the University of Sydney and received support including the USyd–NUS Ignition Grant and the Duke‑NUS Parkinson’s Research Fund.

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Microscopic image of alpha-synuclein oligomers in Parkinson's brain tissue, visualized with advanced imaging techniques.
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Researchers directly visualize alpha‑synuclein oligomers in human Parkinson’s brain tissue

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Scientists in the U.K. and Canada report the first direct visualization and measurement of alpha‑synuclein oligomers—the small protein clusters long suspected of triggering Parkinson’s—in human brain tissue. Using an ultra‑sensitive imaging method, the team found these clusters were larger and more numerous in Parkinson’s than in age‑matched controls, a result published in Nature Biomedical Engineering that may help guide earlier diagnosis and targeted therapies.

Researchers at Nagoya University in Japan have developed miniature brain models using stem cells to study interactions between the thalamus and cortex. Their work reveals the thalamus's key role in maturing cortical neural networks. The findings could advance research into neurological disorders like autism.

በAI የተዘገበ

A new genetic study has identified 331 genes essential for transforming stem cells into brain cells, including a novel gene linked to neurodevelopmental disorders. Led by scientists at the Hebrew University of Jerusalem, the research highlights how early genetic disruptions can lead to conditions like autism and developmental delay. The findings, published in Nature Neuroscience, also reveal patterns in how these disorders are inherited.

Using 7‑Tesla fMRI and a placebo paradigm, University of Sydney researchers mapped how the human brainstem modulates pain by body region. The study, published in Science on August 28, 2025, outlines a somatotopic system centered on the periaqueductal gray and rostral ventromedial medulla and suggests avenues for localized, non‑opioid treatments.

በAI የተዘገበ እውነት ተፈትሸ

Researchers have shown how mutations in key actin genes can lead to abnormally small brains in children with Baraitser–Winter syndrome. Using lab-grown human brain organoids, the team found that these mutations alter the orientation of early brain progenitor cell divisions and deplete crucial stem cell populations, providing a cellular mechanism for the syndrome‑associated microcephaly.

Researchers at Rutgers Health have identified how the brain integrates fast and slow processing through white matter connections, influencing cognitive abilities. Published in Nature Communications, the study analyzed data from nearly 1,000 people to map these neural timescales. Variations in this system may explain differences in thinking efficiency and hold promise for mental health research.

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Scientists are on the verge of simulating a human brain using the world's most powerful supercomputers, aiming to unlock secrets of brain function. Led by researchers at Germany's Jülich Research Centre, the project leverages the JUPITER supercomputer to model 20 billion neurons. This breakthrough could enable testing of theories on memory and drug effects that smaller models cannot achieve.

 

 

 

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