Regeneration-on-a-chip: a planarian microfluidic device enabling automated cultivation, individual tracking and in vivo imaging for regeneration study

Abstract

Freshwater planarian, a model organism renowned for its remarkable regenerative capabilities, is widely used in studies of regenerative mechanisms and stem cell biology. However, current research faces challenges in automated culture, long-term tracking, and real-time in vivo imaging at single-worm level, limiting efficient acquisition of accurate, individual-specific data and impeding deeper investigation into regeneration dynamics. To address this, we developed a planarian microfluidic chip and an integrated microdevice for automated cultivation, individual tracking, and in vivo imaging of planarian fragments. The PlanarianChip architecture incorporates parallel analytical channels, each comprising a combination of loading channel/detection chamber. A novel loading/semi-closing strategy enables precise and efficient loading and confinement of single planarian fragments from different body parts (head, pharynx, and tail) within detection chambers with 100% success rate, while allowing controlled medium renewal. We demonstrate automated cultivation, reliable retention and parallel manipulation of individual fragments, supporting long-term tracking and live imaging throughout regeneration. Performance of the planarian microdevice was validated by systematically investigating effects of liquid-renewal frequency, detection chamber size, and fragmented body parts on regeneration. With its flexible design, scalable fabrication, and support for automated fluidic control and individualized monitoring, this microdevice establishes a versatile and robust platform for regeneration studies.

Article information

Article type
Paper
Submitted
13 Mar 2026
Accepted
01 Jun 2026
First published
02 Jun 2026

Analyst, 2026, Accepted Manuscript

Regeneration-on-a-chip: a planarian microfluidic device enabling automated cultivation, individual tracking and in vivo imaging for regeneration study

R. Zhong, X. Wang, H. Li and Q. Zhang, Analyst, 2026, Accepted Manuscript , DOI: 10.1039/D6AN00281A

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