Label-Free Lymphocyte Classification via Optical Metabolic Imaging
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Solution Overview
Problem
Current methods for assessing lymphocyte activation and subtype in single cells are invasive, time-consuming, and destructive, lacking the ability to provide non-destructive, label-free, and high-resolution measurements necessary for immune profiling and cell therapy applications.
Innovation Solution
A device and method utilizing optical metabolic imaging (OMI) that measures autofluorescence intensity and lifetime of NAD(P)H and FAD to classify lymphocyte activation and subtype without labels, using machine learning to analyze metabolic shifts and distinguish between quiescent and activated states of B cells, NK cells, and T cells.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If flow cytometry is used to provide single-cell resolution, then measurement precision is improved, but device complexity and time consumption increase due to requirement of fluorescent antibody labeling
Solution Approach 1:
The patent extracts and eliminates the labeling step from the measurement process by using label-free Raman spectroscopy. Instead of requiring fluorescent antibodies, the system directly detects endogenous molecular vibrations in lymphocytes, removing the complex labeling procedure while maintaining single-cell measurement capability
Solution Approach 2:
The lymphocytes serve themselves by providing intrinsic Raman scattering signals from their own molecular composition. The measurement system utilizes endogenous biomolecules (proteins, lipids, nucleic acids) as the signal source, eliminating the need for external fluorescent labels and simplifying the overall measurement process
2Measurement precision
If single-cell RNA sequencing or CyTOF is used to provide extensive single-cell information, then measurement precision is improved, but the sample is destroyed
Solution Approach 1:
The patent replaces destructive mechanical and chemical processing (cell lysis, RNA extraction, protein digestion) with non-invasive optical Raman spectroscopy. The measurement uses photon-matter interactions that do not alter or destroy the cell structure, allowing live cells to be measured and subsequently used for therapy
Solution Approach 2:
Instead of consuming expensive reagents and destroying valuable cell samples, the system uses inexpensive photons for measurement. The Raman scattering process is non-destructive, preserving the cells for potential clinical application while providing comprehensive metabolic and functional information
3Measurement precision
If fluorescent antibody labeling is used to identify lymphocyte subtypes, then measurement precision is improved, but the process becomes time-consuming and disruptive to cells
Solution Approach 1:
The patent removes the time-consuming fluorescent labeling step entirely by using label-free Raman spectral fingerprinting. Different lymphocyte subtypes (T cells, B cells, NK cells) are identified based on their unique endogenous molecular composition and metabolic profiles, eliminating hours of labeling and washing procedures
Solution Approach 2:
The system performs preliminary classification of lymphocyte subtypes based on intrinsic metabolic signatures before any functional activation assessment. This preliminary identification is achieved rapidly through spectral analysis of endogenous molecules, providing a foundation for subsequent functional studies without time loss
4Ease of operation
If bulk cytokine release measurements are used, then ease of operation is improved, but single-cell measurement capability is lost
Solution Approach 1:
The patent segments the bulk cell population into individual single-cell measurements by using flow-based Raman spectroscopy. Each lymphocyte passes through the measurement zone individually, allowing independent spectral acquisition and analysis, thereby providing single-cell resolution while maintaining operational simplicity
Solution Approach 2:
The Raman spectroscopy system provides multiple functions simultaneously: it identifies lymphocyte subtypes, assesses activation state, and measures metabolic function all through a single non-destructive measurement. This multi-functionality replaces multiple separate assays while maintaining ease of operation
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
Enables accurate, non-destructive, and label-free classification of lymphocyte activation and subtype with high accuracy, facilitating immune profiling and cell therapy by capturing metabolic changes and heterogeneity within populations.
Implementation Method 1
The autofluorescence spectrometer is configured to acquire an autofluorescence data set for the lymphocyte located in the cell analysis observation zone
Data Source
AI summary
Devices and methods for label-free sensing of lymphocyte activation and identify are disclosed. The activation status of B cells and NK cells can be reliably determined. A general classifier capable of determining the activation status of lymphocytes having unknown identity (i.e., unknown whether a T cell, B cell, or NK cell) is disclosed. An identity classifier capable of differentiating T cells from B cells from NK cells is disclosed. A six-class classifier is disclosed which is capable of identifying both lymphocyte identity and activation status.