# NIH grant: A Label-Free Imaging Platform to Resolve Age- and Lineage-Specific Lipid–Mitochondrial Interactions in the Enteric Nervous System (1R01DK147407-01) — retained record

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NIH grant 1R01DK147407-01: A Label-Free Imaging Platform to Resolve Age- and Lineage-Specific Lipid–Mitochondrial Interactions in the Enteric Nervous System
Institute: {'code': 'DK', 'abbreviation': 'NIDDK', 'name': 'National Institute of Diabetes and Digestive and Kidney Diseases', 'admin_org_id': '12500020', 'admin_funding_url': ''}. PI: Sixian  You. Award: $837398.
Start: 2026-07-10T00:00:00.
Summary
Aging is characterized by two phases—maturation, when function is optimized, and senility, when function
declines—and these phases are driven in the enteric nervous system (ENS) by the expansion of mesoderm‐
derived enteric neurons (MENs). Parallel to these lineage shifts, cellular metabolism also changes with age, but
the organelle‐level metabolic phenotypes that underlie ENS maturation, aging, and disease remain unknown.
Lipid droplets (LDs), which store fatty acids, and mitochondria (MTs), which oxidize metabolic substrates, work
in concert to regulate energy balance. A subset of LDs located distal from MTs (LD‐D) serve as neutral lipid
reservoirs, whereas LDs in close proximity to MTs (LD‐P) support fatty‐acid catabolism. Accurate, three‐
dimensional (3D) mapping of LD–MT colocalization and redox dynamics in live tissues is therefore essential to
understand how ENS metabolism changes across the lifespan and in disease, but existing methods rely on
fluorescent dyes or are restricted to two‐dimensional (2D) samples.
We will first integrate the deep metabolic imaging microscope with fast, time‐resolved detection and tunable laser
pulses so that each pixel can tell apart droplets that store fat from those that feed mitochondria, as well as
pinpoint mitochondria themselves. We will check the accuracy of our label‐free images by comparing them to
standard fluorescent dyes that mark droplets (BODIPY) and mitochondria (MitoTracker, TMRM) in 3D liver
organoid models and freshly removed gut tissue. Using minimally supervised machine‐learning tools, we will
develop robust algorithms to identify lipid and mitochondrial regions with over 90% accuracy. Finally, we will test
LMI on liver and breast cancer cells treated with increasing amounts of oleic acid (to load them with fat) or
metabolic drugs to see how droplets and mitochondria reorganize under stress. Next, we will apply LMI to mouse
models where specific enteric neurons are genetically labeled to track how lipid droplets and mitochondria
change as the gut matures after birth and as it ages. We will also study four mouse models of metabolic
disease—using a mitochondrial blocker, a high‐fat diet, a genetic mutation that accelerates aging, and a defect
in mitochondrial complex I—to see how these conditions alter droplet–mitochondria interactions in the gut. By
enabling, for the first time, three‐dimensional, live‐tissue mapping of lipid droplets, mitochondria, and
mitochondrial activity, LMI will reveal how gut neurons manage their energy stores during development, aging,
and disease. These insights will point to new biomarkers and guide future treatments for gastrointestinal
disorders that affect children and older adults.
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