DeePlexing – Extending Imaging Multiplexity Using Machine Learning

Spatial proteomics and transcriptomics are fast-emerging fields with the potential to revolutionize various branches of biology. In the last five years, various multiplex immunofluorescence and immunohistochemistry imaging methods have been developed to stain 5-60 different protein markers in a given tissue. Nonetheless, most of these techniques are iterative and can image a maximum of 3-8 markers in a single cycle, resulting in processing time of several hours to days.

Devices for Improved Tissue Cryopreservation and Recovery

Problem: Cryopreservation is a process where living biological materials like cells, tissues, and cell therapies (which are susceptible to damage caused by unregulated chemical kinetics) are preserved by cooling to very low temperatures in the presence of specific cryopreservation media that protects the biological material from damage. In order to be used, the biological material ideally should be thawed in a controlled manner that minimizes damage and desirably brings the material back to a viable state.

Machine Learning and/or Neural Networks to Validate Stem Cells and Their Derivatives for Use in Cell Therapy, Drug Delivery, and Diagnostics

Many biological and clinical procedures require functional validation of a desired cell type. Current techniques to validate rely on various assays and methods, such as staining with dyes, antibodies, and nucleic acid probes, to assess stem cell health, death, proliferation, and functionality. These techniques potentially destroy stem cells and risk contaminating cells and cultures by exposing them to the environment; they are low-throughput and difficult to scale-up.