Modified Cell Standards for Impedance Cytometry and Separation
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Solution Overview
Problem
Existing impedance-based cytometry and dielectrophoresis methods lack reliable and reproducible model particles with desirable subcellular electrical phenotypes for benchmarking the electrical physiology of unknown cell types, leading to inaccuracies in cell identification and separation.
Innovation Solution
Utilize modified mammalian cells, such as red blood cells (RBCs) or cancer cells, with systematically modulated subcellular electrophysiology and fluorescence levels, to serve as multimodal standards for impedance-based cytometry and dielectrophoretic separation, enabling accurate estimation and separation of unknown cell types by fitting impedance metrics to dielectric models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional impedance-based cytometry methods are used without modified model particles, then the measurement process is simpler, but the reliability and accuracy of cell identification and separation benchmarking deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-modifying model particles (such as red blood cells or microbeads) with specific electrical phenotypes before use in impedance cytometry. These modifications include altering membrane capacitance, cytoplasmic conductivity, and other electrical properties to create standardized reference particles. This preliminary preparation enables reliable benchmarking of unknown cell types without requiring complex modifications during the actual measurement process.
Solution Approach 2:
The patent employs parameter changes by systematically varying electrical parameters of model particles, such as membrane capacitance values and cytoplasmic conductivity levels. By creating a library of model particles with different controlled parameter combinations, the system can accurately benchmark unknown cells across multiple electrical phenotypes, resolving the contradiction between reliability and complexity through standardized parameter variations.
2Measurement precision
If time-consuming algorithms with unknown fitting parameters are used to estimate biophysical information, then measurement precision may improve, but productivity and analysis speed deteriorate
Solution Approach 1:
The patent applies copying by creating model particles that replicate the electrical phenotypes of target cell types. Instead of using complex algorithms to infer biophysical properties of unknown cells, the system uses copies (model particles) with known, characterized electrical properties as direct references. This copying approach maintains measurement precision while dramatically improving productivity by eliminating time-consuming computational fitting processes.
Solution Approach 2:
The patent uses preliminary action by pre-characterizing model particles with known biophysical properties before deployment. The electrical phenotypes of model particles are established in advance through controlled modifications and comprehensive characterization, allowing direct comparison with unknown cells during rapid analysis without requiring iterative fitting algorithms during the actual measurement process.
3Measurement precision
If no standardized model particles with known electrical phenotypes are available, then the system is easier to operate, but the accuracy of cell type identification and separation benchmarking deteriorates
Solution Approach 1:
The patent applies universality by creating a standardized library of model particles that can serve multiple functions across different impedance cytometry applications. These model particles with characterized electrical phenotypes can be used for benchmarking various cell types, validating separation performance, and calibrating instruments, thereby improving measurement precision while maintaining ease of operation through a reusable, standardized reference system.
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, single-cell sensitivity identification and separation of unknown cell types by mapping their impedance data to known model cell types, reducing the need for time-consuming algorithms and improving the accuracy and resolution of biophysical information determination.
Implementation Method 1
measuring a plurality of electrical impedance parameters of the target biological specimen
Implementation Method 2
fitting impedance metrics to dielectric models
Implementation Method 3
triggering generation of an alternating current (AC) electrical stimulus
Data Source
AI summary
Inline classification of a biological specimen including mammalian cells can include generating an alternating current (AC) electrical stimulus to an electrode structure. The electrode structure can be electrically coupled with a flow cell. A response, elicited by the electrical stimulus, can be received when a model specimen class traverses the flow cell. Using the received response, a corresponding impedance parameter value can be determined, the value indicative of a specified biophysical characteristic corresponding to the model specimen class. The first impedance parameter can be translated to a value corresponding to the specified biophysical characteristic.