Summary:
A UCLA researcher in the Department of Medicine, Hematology Oncology has developed a computational framework to model subcellular orientation for spatial biology
Background:
Spatial biology allows researchers to map the coordinates of proteins with subcellular resolution, revolutionizing our understanding of disease. Critical cellular processes are inherently directional, emphasizing the importance of mapping the vector quantities of molecular arrangement. For example, vector-based data is critical for detecting metastasis, evaluating immune cell efficacy, and studying neurobiology. Current spatial biology technologies treat protein expression data as scalar quantities, neglecting the directional orientation or flow of protein distribution within its subcellular environment. As a result, these scalar-based technologies miss critical structural information on how proteins are polarized and trafficked. Currently, there are no bioinformatics tools that convert spatial coordinates into a vector field to model directionality, forcing researchers to rely on manual methods of observing these dynamic states. Thus, there is a need for a bioinformatics solution that models the dynamic, vector-based nature of subcellular protein expression.
Innovation:
Researchers at UCLA have developed a novel bioinformatics tool capable of transforming spatial protein expression data into continuous vector fields. Unlike current methods, this technology enables the calculation of the magnitude and direction of protein distribution. This provides an automatic, quantitative framework to model subcellular orientation, with the potential to transform analytical approaches to spatial biology.
Potential Applications:
● Oncology
○ Metastasis Prediction
● Immunology
○ Synapse Formation
● Neurobiology
● Intracellular Transport
● Developmental Biology
● Drug Discovery
● Phenotypic Screening
Advantages:
● Structural and directional information
○ Produces vector quantities
● Automation
○ Previously manual & qualitative
○ High-throughput
● Hardware Agnostic
Reference:
UCLA Case No. 2026-139
Lead Inventor:
Katie Campbell