Integrated Single-Cell Profiling System for Dynamic Behavior and Molecular Correlation
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Solution Overview
Problem
Current methods for studying cellular activity lack the ability to integrate dynamic cellular behavior with molecular behavior at the single-cell level, limiting the understanding of cellular functions such as motility, interaction, and protein secretion.
Innovation Solution
The method involves placing a cell population on an area, assaying their dynamic behavior over time, identifying cells of interest based on this behavior, characterizing their molecular profiles, and correlating this information to understand cellular activity and functionality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If single-cell time-lapse microscopy is used for dynamic behavior analysis, then dynamic cellular behavior can be tracked, but molecular profiling capability is lost
Solution Approach 1:
The patent combines time-lapse microscopy for dynamic behavior tracking with single-cell molecular profiling capabilities into a unified platform. This merging allows simultaneous observation of cellular dynamics and molecular characteristics at the single-cell level, resolving the contradiction between tracking speed and measurement precision.
Solution Approach 2:
The system achieves multi-functionality by integrating multiple analytical capabilities (dynamic imaging, molecular profiling, genomic analysis) into a single platform that can perform diverse functions on single cells, thereby maintaining both dynamic tracking and molecular characterization capabilities.
2Reliability
If population-level analysis is performed, then statistical power is improved, but single-cell resolution is lost
Solution Approach 1:
The patent segments the cell population into individual single-cell units for analysis, allowing simultaneous maintenance of single-cell resolution and statistical power through high-throughput processing of many individually resolved cells.
Solution Approach 2:
The system transitions from analyzing either single cells or populations to simultaneously analyzing multiple single cells across multiple dimensions (spatial, temporal, molecular), thereby achieving both single-cell resolution and population-level statistical power.
Data Source
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AI summary
Presented herein are methods of evaluating cellular activity by: placing a cell population on an area; assaying for a dynamic behavior of the cell population as a function of time; identifying cell(s) of interest based on the dynamic behavior; characterizing a molecular profile of the cell(s); and correlating the obtained information. The assayed dynamic behavior can include cellular activation, cellular inhibition, cellular interaction, protein expression, protein secretion, cellular proliferation, changes in cellular morphology, motility, cell death, cell cytotoxicity, cell lysis, and combinations thereof. Sensors associated with the area may be utilized to facilitate assaying. Molecular profiles of the cell(s) can then be characterized by various methods, such as DNA analysis, RNA analysis, and protein analysis. The dynamic behavior and molecular profile can then be correlated for various purposes, such as predicting clinical outcome of a treatment, screening cells, facilitating a treatment, diagnosing a disease, and monitoring cellular activity.