Multispectral Inline Holography for High-Throughput Cell Classification
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Solution Overview
Problem
Current techniques for characterizing biochemical properties of biological particles, such as cytometry, lab culturing, mass spectrometry, and qPCR, suffer from low throughput, high cost, and limited accuracy, making them unsuitable for fast and accurate analysis in industrial settings.
Innovation Solution
A multi-spectral digital inline holography (DIH) system combined with a convolutional neural network (CNN) is used to capture holograms from multiple wavelengths, enabling high-throughput and accurate classification of biological particles by analyzing their spectral responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cytometry uses light scattering from a single cell to determine cell properties, then cell characterization is achieved, but throughput is low and complex sample preparation including fluorescent labeling is required
Solution Approach 1:
The patent segments the sample analysis by using microfluidic channels to individually transport and position cells through the measurement zone, allowing sequential analysis of multiple cells without requiring complex preparation. This segmentation enables high-throughput processing while maintaining single-cell resolution for precise characterization.
Solution Approach 2:
The patent creates optical copies (holograms) of cells using digital inline holography, capturing complete 3D structural information without physical contact or staining. This copying approach eliminates the need for fluorescent labeling while enabling rapid acquisition of multiple cell images for high-throughput analysis.
2Measurement precision
If lab culturing and manual counting technique is used, then cell viability and concentration can be examined, but analysis time takes two days to seven days
Solution Approach 1:
The patent replaces manual culturing and counting mechanisms with automated digital inline holographic imaging and machine learning classification. The system directly images live cells in their natural state, eliminating the need for time-consuming culturing processes while maintaining accurate viability assessment through holographic 3D reconstruction and neural network analysis.
3Measurement precision
If mass spectrometry ionizes biomolecules for identification, then bacterial or fungal identification is achieved, but cell viability cannot be characterized
Solution Approach 1:
The patent creates optical copies (holograms) of intact live cells, preserving all structural and functional information including viability markers. This holographic copying approach allows simultaneous analysis of multiple cell properties (morphology, size, internal structure, and viability) without the need for cell lysis or biomolecule extraction required by mass spectrometry.
Solution Approach 2:
The patent develops a universal digital inline holographic imaging system that can simultaneously perform multiple functions: 3D structural reconstruction, biomolecule visualization, and viability assessment through machine learning classification. This multi-functional approach eliminates the need for separate specialized techniques for different cell characterization goals.
4Measurement precision
If quantitative phase imaging and force spectrum microscopy are used to capture information from biological particles, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent uses digital inline holography to create optical copies of cells with complete 3D information, replacing complex quantitative phase imaging and force spectrum microscopy setups. The holographic method achieves comparable or superior measurement precision while using simpler, more compact optical components that are easier to implement and maintain.
Solution Approach 2:
The patent changes the imaging parameter from 2D projections (traditional microscopy) to 3D holographic reconstructions, enabling comprehensive cell characterization without increasing optical complexity. The machine learning component processes holographic data to extract relevant features, achieving high measurement precision through computational rather than optical complexity.
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 system achieves high accuracy (>90%) and throughput (>10,000 particles per second) for cell classification, reducing costs to less than $10,000, and is applicable in fast cancer detection, surgical site contamination, and industrial processes like biofuel production.
Implementation Method 1
capturing holograms produced by interference of (i) light from the coherent multi-spectral beam of light that was scattered by the sample with (ii) light from the coherent multi-spectral beam of light that was not scattered by the sample
Implementation Method 2
light from the coherent multi-spectral beam of light that was scattered by the sample
Data Source
AI summary
A system and method for characterizing biological particles. A multi-spectral digital inline holographic includes a computing system, a camera and a light source having a. coherent multi-spectral beam of light. Tire light source illuminates a sample having one or more biological particles and the camera captures holograms produced by interference of (i) light from the coherent multi-spectral beam of light that was scattered by the sample with (ii) light from the coherent multi-spectral beam of light that was not scattered, by the sample, the captured holograms including holograms from two or more spectral bands. The computing system applies a machine learning model to the captured holograms to extract features of the biological particles m the sample from the captured holograms.