Autofluorescence T Cell Classification via Convolutional Neural Network
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Solution Overview
Problem
Current methods for determining T cell activation, such as flow cytometry and immunofluorescence imaging, require contrast agents and may involve tissue or cell fixation, limiting their applicability for classifying and sorting T cells effectively, especially in complex treatments like CAR T cell therapy.
Innovation Solution
A device and method utilizing a single-cell autofluorescence image sensor and a trained convolutional neural network to classify and sort T cells based on autofluorescence intensity images, allowing for the identification and isolation of activated T cells without the need for fluorescent labels or cell fixation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If flow cytometry or immunofluorescence imaging is used to determine T cell activation, then activation can be detected, but contrast agents are required and tissue or cell fixation is needed
Solution Approach 1:
The patent extracts and eliminates the requirement for external contrast agents by utilizing the intrinsic autofluorescence properties of T cells. The system captures autofluorescence signals naturally emitted by cellular components, removing the need for additional reagents and simplifying the overall detection process while maintaining activation detection capability
Solution Approach 2:
The T cells themselves serve as the contrast source through their inherent autofluorescence. The cellular components naturally emit fluorescent signals that can be detected and used for activation state determination, making the cells self-sufficient for detection purposes without requiring external fixation or labeling procedures
2Measurement precision
If current methods are used for T cell classification, then activation state can be determined, but the process is complex and requires multiple steps including contrast agent application and fixation
Solution Approach 1:
The patent removes the time-consuming steps of contrast agent application and cell fixation from the workflow by relying on autofluorescence. This extraction of unnecessary steps directly reduces the total processing time while preserving the essential function of activation state determination
Solution Approach 2:
The system is designed to capture autofluorescence signals directly from live cells without requiring preliminary fixation or labeling steps. The methodology is prepared in advance to work with cells in their native state, eliminating the need for time-consuming preparatory procedures
3Measurement precision
If fluorescent labels are used for T cell identification, then activation state can be visualized, but the procedure becomes more complex and may affect cell viability
Solution Approach 1:
The T cells provide their own fluorescent signal through autofluorescence from endogenous cellular components. This self-service approach eliminates the need for external fluorescent labels that could potentially harm cell viability, allowing for non-invasive activation state assessment
Solution Approach 2:
The patent converts the typically weak and hard-to-detect autofluorescence signal into a beneficial detection mechanism. By using sensitive imaging systems and appropriate filtering, the naturally occurring autofluorescence is transformed into a reliable indicator of T cell activation without introducing harmful external substances
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The approach achieves high accuracy in classifying activated T cells, with classification accuracy of up to 98%, enabling the effective sorting and administration of activated T cells for therapeutic applications like CAR T cell therapy.
Implementation Method 1
The single-cell autofluorescence image sensor is configured to acquire an autofluorescence intensity image of a T cell positioned in the observation zone
Data Source
AI summary
Systems and methods for classifying and/or sorting T cells by activation state are disclosed. The system includes a cell classifying pathway, a single-cell autofluorescence image sensor, a processor, and a non-transitory computer-readable memory. The memory is accessible to the processor and has stored thereon a trained convolutional neural network and instructions. The instructions, when executed by the processor, cause the processor to: a) receive the autofluorescence intensity image; b) optionally pre-process the autofluorescence intensity image to produce an adjusted autofluorescence intensity image; c) input the autofluorescence intensity image or the adjusted autofluorescence intensity image into the trained convolutional neural network to produce an activation prediction for the T cell.