WARF: P190306US02

  • Patent applied for.

Algorithms to Classify T Cell Activation by Autofluorescence Imaging


Melissa Skala, Anthony Gitter, Zijie Wang, Alexandra Walsh

The Wisconsin Alumni Research Foundation (WARF) is seeking commercial partners interested in developing a method to non-invasively detect T cell activation by imaging NAD(P)H. These algorithms can be applied to NAD(P)H images taken with commercial imaging flow cytometers/sorters, and fluorescence microscopes.
OVERVIEWThe importance of T cells in immunotherapy is driving the development of technologies to better characterize T cells and improve therapeutic efficacy. One specific objective is assessing antigen-induced T cell activation because only functionally active T cells are capable of killing the desired targets. Autofluorescence imaging can assess functional activity of individual T cells in a non-destructive manner by detecting endogenous changes in metabolic co-enzymes such as NAD(P)H. However, recognizing robust patterns of T cell activity is computationally challenging in the absence of exogenous labels or information-rich autofluorescence lifetime measurements.

Non-invasive, contrast-agent free determination of T cell behavior is imperative for advancing studies of T cells in vivo and for quality control of clinical adoptive immune transfer procedures (e.g., CAR T cell therapies).
THE INVENTIONBuilding on award-winning work, UW–Madison researchers have discovered that autofluorescence intensity images of NAD(P)H can accurately classify T cells as activated or not activated (‘naïve’ or ‘quiescent’), and have developed algorithms to classify T cell activation based on the images. Specifically, adapting pre-trained convolutional neural networks (CNNs) for the T cell activity classification task, T cells can be classified with 92 percent accuracy. These pre-trained CNNs perform better than classification based on summary statistics (e.g., cell size) or CNNs trained on the autofluorescence images alone.

This invention provides a way to non-invasively detect T cell activation by imaging NAD(P)H intensity. These algorithms can be applied to NAD(P)H images taken with commercial imaging flow cytometers / sorters, and fluorescence microscopes. If increased accuracy of T cell activation is needed for a specific application, additional measurements of the other NAD(P)H and FAD fluorescence endpoints can be obtained and used for classification.
  • Screening and sorting activated T cells
  • Algorithms can be applied to existing commercial systems
  • Method is non-invasive and contrast-agent free, hence can reduce reagent cost and variability
  • Current methods to determine T cell activation require contrast agents and may require tissue/cell fixation
STAGE OF DEVELOPMENTThe researchers have demonstrated that advanced machine learning models can accurately classify T cell activity from NAD(P)H intensity images and that those image-based signatures transfer across human donors.
Contact Information
For current licensing status, please contact Jeanine Burmania at or 608-960-9846.
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