Back

Discovery of Abundant Nano-scale Lymphatic-like Vessels in Brains

Gu, S.; Dong, H.; Chen, H.; Yu, J.; Liu, L.; Yan, J.; Wang, H.; Jiang, Z.; Huang, W.; Wang, W.; Liang, S. H.; Zhang, C.; Tanzi, R. E.; Shen, S.; Ran, C.

2025-12-31 neuroscience
10.64898/2025.12.30.697095 bioRxiv
Show abstract

As one of the most metabolically active organs, the brain requires an exceptionally efficient system for waste clearance to sustain its high metabolic demands. However, whether such a system exists--and, if so, what structural features enable its efficiency--remains incompletely understood. More than a decade ago, the "glymphatic system" was proposed to describe neurofluid transport through cerebrospinal fluid (CSF) and perivascular spaces (PVS), in conjunction with dural and meningeal lymphatic pathways. Nevertheless, it remains unresolved whether neurofluid transport is organized as a structured, vessel-like flow network. Moreover, in stark contrast to the dense network of blood capillaries in the brain, only a sparse population of lymphatic vessels has been identified, raising doubts as to whether the currently recognized lymphatic architecture alone can support efficient metabolic waste clearance. By combining expansion microscopy with CRANAD-3, a pan-{beta}-amyloid fluorescent probe, we discovered abundant nanoscale lymphatic-like vessels (NLVs) within the brain parenchyma of both mice and humans. The majority of these structures have diameters below 1,000 nm and exhibit moderate positivity for multiple lymphatic markers, including LYVE-1, Prox-1, PDPN, and VEGFR3. NLVs frequently coil around blood vessels, and putative connections between vascular structures were observed. Notably, some NLVs traverse multiple cortical layers and display distinct orientation patterns that vary across cortical laminae. This discovery reveals a previously "hidden" vascular network in the brain parenchyma and raises the possibility that an abundant, highly organized system of nanoscale tubular structures may provide an efficient conduit for metabolic waste clearance. Such a system could represent a critical, previously unrecognized component supporting the brains extraordinary metabolic demands.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.