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Abstract

Developmental Biology

Two-Photon Microscopy for the Study of Tendons

Published: March 29th, 2024

DOI:

10.3791/65853

1Center for Regenerative Medicine, Department of Orthopedic Surgery, Massachusetts General Hospital, Harvard Medical School, 2Harvard Stem Cell Institute, 3Center Of Regeneration and Longevity (CORAL), Tel-Aviv Sourasky Medical Center

Abstract

Two-photon microscopy has emerged as a potent tool for evaluating deep tissue cells and characterizing the alignment of the extracellular matrix (ECM) in various biological systems. This technique relies on nonlinear light-matter interactions to detect two distinct signals: the second harmonic generated (SHG) diffusion signal, which facilitates the visualization of collagen fibers and their orientation, and the near-infrared excitation signal for imaging ultraviolet excited autofluorescence.

SHG imaging proves especially effective in visualizing collagen fibers due to the non-centrosymmetric crystalline structure of fibrillar collagen I. Given that tendons are matrix-rich tissues with a limited number of cells, their high collagen content makes them ideal candidates for analysis using two-photon microscopy. Consequently, two-photon microscopy offers a valuable means to analyze and characterize collagen abnormalities in tendons. Its application extends to studying tendon development, injuries, healing, and aging, enabling the comprehensive characterization of tendon cells and their interactions with the ECM under various conditions using two-photon microscopy tools. This protocol outlines the use of two-photon microscopy in tendon biology and presents an adapted methodology to achieve effective imaging and characterization of tendon cells during development and after injury. The method allows the utilization of thin microscopic sections to create a comprehensive image of the ECM within tendons and the cells that interact with this matrix. Most notably, the article showcases a technique to generate 3D images using two-photon microscopy in animal models.

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