Stain-Free Approach to Determine and Monitor Cell Heath Using Supervised and Unsupervised Image-Based Deep Learning.

Cell-based medicinal products (CBMPs) are a growing class of therapeutics that promise new treatments for complex and rare diseases. Given the inherent complexity of the whole human cells comprising CBMPs, there is a need for robust and fast analytical methods for characterization, process monitoring, and quality control (QC) testing during their manufacture. Existing techniques to evaluate and monitor cell quality typically constitute labor-intensive, expensive, and highly specific staining assays. In this work, we combine image-based deep learning with flow imaging microscopy (FIM) to predict cell health metrics using cellular morphology "fingerprints" extracted from images of unstained Jurkat cells (immortalized human T-lymphocyte cells). A supervised (i.e., algorithm trained with human-generated labels for images) fingerprinting algorithm, trained on images of unstained healthy and dead cells, provides a robust stain-free, non-invasive, and non-destructive method for determining cell viability. Results from the stain-free method are in good agreement with traditional stain-based cytometric viability measurements. Additionally, when trained with images of healthy cells, dead cells and cells undergoing chemically induced apoptosis, the supervised fingerprinting algorithm is able to distinguish between the three cell states, and the results are independent of specific treatments or signaling pathways. We then show that an unsupervised variational autoencoder (VAE) algorithm trained on the same images, but without human-generated labels, is able to distinguish between samples of healthy, dead and apoptotic cells along with cellular debris based on learned morphological features and without human input. With this, we demonstrate that VAEs are a powerful exploratory technique that can be used as a process monitoring analytical tool.

Comparison of cation and anion-mediated resolution enhancement of bioprinted hydrogels for membranous tissue fabrication.

Fabrication of engineered thin membranous tissues (TMTs) presents a significant challenge to researchers, as these structures are small in scale, but present complex anatomies containing multiple stratified cell layers. While numerous methodologies exist to fabricate such tissues, many are limited by poor mechanical properties, need for post-fabrication, or lack of cytocompatibility. Extrusion bioprinting can address these issues, but lacks the resolution necessary to generate biomimetic, microscale TMT structures. Therefore, our goal was to develop a strategy that enhances bioprinting resolution below its traditional limit of 150 µm and delivers a viable cell population. We have generated a system to effectively shrink printed gels via electrostatic interactions between anionic and cationic polymers. Base hydrogels are composed of gelatin methacrylate type A (cationic), or B (anionic) treated with anionic alginate, and cationic poly-L-lysine, respectively. Through a complex coacervation-like mechanism, the charges attract, causing compaction of the base GelMA network, leading to reduced sample dimensions. In this work, we evaluate the role of both base hydrogel and shrinking polymer charge on effective print resolution and cell viability. The alginate anion-mediated system demonstrated the ability to reach bioprinting resolutions of 70 µm, while maintaining a viable cell population. To our knowledge, this is the first study that has produced such significant enhancement in extrusion bioprinting capabilities, while also remaining cytocompatible.