Abstract
Changes in the multi-level physical structure of biological features going from cellular to tissue level composition is a key factor in many major diseases. However, we are only beginning to understand the role of these structural changes because there are few dedicated multiscale imaging platforms with sensitivity at both the cellular and macrostructural spatial scale. A single platform reduces bias and complications from multiple sample preparation methods and can ease image registration. In order to address these needs, we have developed a multiscale imaging system using a range of imaging modalities sensitive to tissue composition: Ultrasound, Second Harmonic Generation Microscopy, Multiphoton Microscopy, Optical Coherence Tomography, and Enhanced Backscattering. This paper details the system design, the calibration for each modality, and a demonstration experiment imaging a rabbit eye.
Highlights
The multi-level structure of tissue, from the arrangement of cells to the composition of organs, is a key factor in many major diseases
Cancer mortality is due to the damage the disease does to organ systems yet 90% of cancer deaths are due to metastasis [1]
The microscale alignment of collagen fibers around a tumor boundary increases the risk of metastasis [3], while the macroscale density of collagen is related to risk of developing breast cancer [4]
Summary
The multi-level structure of tissue, from the arrangement of cells to the composition of organs, is a key factor in many major diseases. Cancer mortality is due to the damage the disease does to organ systems yet 90% of cancer deaths are due to metastasis [1]. Differences in these micro-environments determine how likely a metastasis is to occur and how the disease will progress on the tissue-mesoscale and organ-macroscale [2]. The microscale alignment of collagen fibers around a tumor boundary increases the risk of metastasis [3], while the macroscale density of collagen is related to risk of developing breast cancer [4]. Many diseases have analogous interactions across spatial scales and, reports have identified integrating data across multiple scales as a key step forward for improving patient care [6,7]
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