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Abstract Multicellular spheroids embedded in 3D hydrogels are prominent in vitro models for 3D cell invasion. Yet, quantification methods for spheroid cell invasion that are high‐throughput, objective and accessible are still lacking. Variations in spheroid sizes and the shapes of the cells within render it difficult to objectively assess invasion extent. The goal of this work is to develop a high-throughput quantification method of cell invasion into 3D matrices that minimizes sensitivity to initial spheroid size and cell spreading and provides precise integrative directionally-dependent metrics of invasion. By analyzing images of fluorescent cell nuclei, invasion metrics are automatically calculated at the pixel level. The initial spheroid boundary is segmented and automated calculations of the nuclear pixel distances from the initial boundary are used to compute common invasion metrics (i.e., the change in invasion area, mean distance) for the same spheroid at a later timepoint. We also introduce the area moment of inertia as an integrative metric of cell invasion that considers the invasion area as well as the pixel distances from the initial spheroid boundary. Further, we show that principal component analysis can be used to quantify the directional influence of a stimuli to invasion (e.g., due to a chemotactic gradient or contact guidance). To demonstrate the power of the analysis for cell types with different invasive potentials and the utility of this method for a variety of biological applications, the method is used to analyze the invasiveness of five different cell types. In all, implementation of this high‐throughput quantification method results in consistent and objective analysis of 3D multicellular spheroid invasion. We provide the analysis code in both MATLAB and Python languages as well as a GUI for ease of use for researchers with a range of computer programming skills and for applications in a variety of biological research areas such as wound healing and cancer metastasis. 

Tissue engineering is an active field and one of its aims is to produce tissues to repair the human body. The Advanced Regenerative Medicine Initiative (ARMI) currently seeks to help increase the manufacturability of tissue engineering products (TEMPs). One of the critical components of large-scale manufacturing is the sensing of information for quality control and critical feedback of tissue growth patterns. Modern sensors that provide information about physical qualities of tissues, however, are invasive or destructive. The goal of this project is to develop noninvasive methodologies to measure the mechanical properties of TEMPs. Our approach is to utilize acoustic waves to induce nano-scale level vibrations in the enginineered tissues in which corresponding displacements are measured in full-field with quantitative optical techniques. In our work, a digital holographic system images the tissue’s vibration at significant modes and provides the displacement patterns of the tissue at various points along the sinusoidal excitation curve. These data are applied to a neural network to compare the experimental vibrational modes to the ones obtained by FEA simulation to estimate the physical properties of the tissue. This methodology has the promise of yielding critical control parameters that would allow technicians to noninvasively and consistently determine when samples are ready to be packaged or if their growth deviates from expected time frames or if there are defects in the tissue. It is expected that this approach will streamline several components of the quality control and production process. 

We propose a method to measure anisotropic stiffness of microtissues and cells by two indentations in orthogonal directions using our novel toroidal probe. Our preliminary results indicate that this approach is applicable in measuring anisotropic stiffness of aligned tissues and cells. This method will provide researchers with a simple and cost-effective means for measuring mechanical anisotropy of micro-scale samples. 

One of the critical components of large-scale manufacturing of bioengineered tissues is the sensing of information for quality control and critical feedback of tissue growth. Modern sensors that measure mechanical qualities of tissues, however, are invasive and destructive. The goal of this project is to develop noninvasive methodologies to measure the mechanical properties of tissue engineering products. Our approach is to utilize acoustic waves to induce nanoscale level vibrations in the engineered tissues in which corresponding displacements are measured in full-field with quantitative optical techniques. A digital holographic system images the tissue’s vibration at significant modes and provides the displacement patterns of the tissue. These data are used to train a supervised learning classifier with a goal of using the comparisons between the experimental vibrational modes and the ones obtained by finite element simulation to estimate the physical properties of the tissue. This methodology has the promise of mechanical properties that would allow technicians to noninvasively determine when samples are ready to be packaged, if their growth deviates from expected time frames, or if there are defects in the tissue. It is expected that this approach will streamline several components of the quality control and production process.