UCLA researchers in the Department of Chemistry and Biochemistry have developed a novel technology for spatial cell-type mapping in 2D and 3D tissue samples.
BACKGROUND:
Cell type mapping has become an integral component for many biomedical research studies and diagnostic technologies. However, a major challenge in single cell data is accurately mapping the spatial organization and architecture of tissues and organ and how it is altered in a diseased state. RNA imaging through hybridization of RNA probes results in low resolution and slow imaging inherent to the nature of RNA molecules. Antibody-based approaches also do not penetrate well into the tissue and antibodies can be unreliable and require optimization for each sample type. Thus, there is a great need for faster and sensitive cell mapping methods for tissue samples.
INNOVATION:
UCLA researchers from the laboratory of Dr. Roy Wollman have developed a novel method to extract cell classification and mapping data from single-cell high-dimensional data. This new method is based on novel application of dimensionality reduction that is implemented experimentally. This is achieved by measuring a specific weighted linear combination of the expression of genes such that the measurement per cell represents its position in the dimensionality reduced space. Applying this method can result in much faster processing time and increased signal compared to microscopy methods for accurate 2-D cell type classification. With appropriate instrumentation, one can easily scale up 3D tissue samples for accurate and fast cell type classification.
POTENTIAL APPLICATIONS:
ADVANTAGES:
DEVELOPMENT-TO-DATE: Proof of concept for 2-D applications, protocol and method development underway