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SUMMARY:BMI Distinguished Seminar // Anne Brunet: Modeling and targeting a
 ging
DTSTART:20250430T121500
DTEND:20250430T131500
DTSTAMP:20260510T225858Z
UID:21db8bea8f33600d018185ff9c02de5b8eeaeeb5ad136a65ee8ec585
CATEGORIES:Conferences - Seminars
DESCRIPTION:Anne Brunet\, Stanford University\, USA\nOld age is associated
  with a decline in cognitive function and an increase in neurodegenerative
  disease risk. However\, the mechanisms of We generated spatiotemporal dat
 a at single-cell resolution for the mouse brain across lifespan and we dev
 eloped machine learning models based on spatial transcriptomics (‘spatia
 l aging clocks’) to reveal cell proximity effects during brain aging and
  rejuvenation. We identified spatial and cell type-specific transcriptomic
  fingerprints of aging\, rejuvenation\, and disease\, including for rare c
 ell types. Interestingly\, we identify that specific cells have. These res
 ults suggest that rare cells can have a drastic influence on their neighbo
 rs and could be targeted to counter tissue aging. We anticipate that these
  spatial aging clocks will not only allow scalable assessment of the effic
 acy of interventions for aging and disease but also represent a new tool f
 or studying cell-cell interactions in many spatial contexts.\n 
LOCATION:SV 1717 https://plan.epfl.ch/?room==SV%201717 https://epfl.zoom.u
 s/j/64813563657
STATUS:CONFIRMED
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