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Updated: Mar 29, 2026

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment
Published on: June 3, 2022
Portable microrobotic platform for non-prehensile mechanophenotyping of biopsy-derived human cancer tissues
Yuvaraj Kamble1, Shreekant Bharti2, Abhishek Raj3
1Bio-Robotics and Artificial Intelligence (BRAIn) Lab, Department of Mechanical Engineering, Indian Institute of Technology Patna, Bihta, Patna, 801106, Bihar, India.
Abstract:
Mechanical characterization of cancer tissues is crucial for understanding tumor progression and response to therapy. However, common mechanophenotyping methods such as atomic force microscopy (AFM), compression testing, and elastography require expensive setups, involve complex sample handling, and are often performed in non-physiological conditions that alter tissue properties. Many are tethered, non-portable, operator-dependent, and may damage or consume samples, preventing repeatable measurements on the same specimen. To address these limitations, we present a portable, non-prehensile microrobotic indentation platform for localized stiffness measurement of biological tissues. The microrobots are actuated by magnetic fields generated from electromagnetic coils, enabling precise control during indentation. Tissue samples are immobilized using gentle, active flow-based stabilization, which secures the sample without causing mechanical damage. We applied the system to Adenoid Cystic Carcinoma (AdCC), Invasive Breast Cancer (IBC), and Squamous Cell Carcinoma (SCC) biopsies. Using the Hertzian contact model, the measured Young's moduli were 194, 323, and 71 kPa, respectively, which agree with reported values. The platform showed high repeatability and lower variability than operator-dependent methods. This work provides a physiologically relevant, sample-preserving, and accessible approach for cancer tissue mechanophenotyping, with potential applications in mechanobiology studies, therapy monitoring, and drug-response evaluation.

