Seeing deep to map cell-biomaterial interactions via whole mount light sheet imaging and automated analytics in...

In vitro models aim to improve biomimicry of in vivo tissues and disease processes. Decellularized extracellular matrix (dECM) scaffolds mimic cellular interactions with 3D tissue architecture. These complex 3D models require parallel advancements in analytical methods to quantify functional outputs with respect to scaffold architecture and recellularization while retaining spatial integrity. Current imaging methods optimized in other engineered model systems are limited in their application to dECM due to the inherent thickness, high heterogeneity, opacity and autofluorescence of dECM material. Sacrificial sample preparation methods like digestion and tissue sectioning are tedious and reduce the amount of spatially relevant information that can be extracted. Further, imaging depth and resolution are limited due to light scattering within large opaque scaffolds. We aimed to optimize optical imaging and analysis protocols to overcome imaging challenges and enable quantitative assessments of dECM models, demonstrating a use case in engineered in vitro cancer microenvironments. We first combined a series of established sample preparation methods including tissue clearing agents and cell labeling dyes, to optimize dECM scaffold compatibility with volumetric light sheet fluorescence microscopy (LSFM) imaging. We then developed image analysis algorithms capable of overcoming the segmentation limitations of established methods to accurately quantify scaffold porosity as well as cellular occupation and migration at the single cell level within dECM scaffolds. We automated this analysis to increase usability for large data sets and applied the imaging methods to a porcine liver-derived dECM scaffold model, called a biomatrix. Biomatrices recellularized with colorectal cancer spheroids model liver metastasis. The LSFM imaging and analysis successfully detected increased cell numbers between 3 and 5 days of culture on the dECM biomatrix, and the loss of cells in oxaliplatin-treated biomatrices. With the ability to resolve these key changes in proliferation, invasion and therapeutic response, this optimized set of imaging and computational tools will aid in the mechanistic and therapeutic discovery of colorectal cancer liver metastasis. Broadly, the increased volume and resolution of imaging data from our methods can extract spatially relevant scaffold and cellular information within the context of any dECM model, increasing its adoptability to probe complex biological behaviors across diseases.