18071943. SINGLE CELL IDENTIFICATION FOR CELL SORTING simplified abstract (SONY GROUP CORPORATION)

SINGLE CELL IDENTIFICATION FOR CELL SORTING

Organization Name

SONY GROUP CORPORATION

Inventor(s)

Haipeng Tang of Sunnyvale CA (US)

Michael Zordan of Boulder Creek CA (US)

Ming-Chang Liu of San Jose CA (US)

SINGLE CELL IDENTIFICATION FOR CELL SORTING - A simplified explanation of the abstract

This abstract first appeared for US patent application 18071943 titled 'SINGLE CELL IDENTIFICATION FOR CELL SORTING

Simplified Explanation

The single cell identification described herein utilizes cell image information and extracts cell features with a neural network model to subtly distinguish the noise events from single cells, allowing the user to choose which different types of noise events to exclude depending on the requirement of applications. The fast neural network model is able to extract more abundant and specific cell features than handpicked features, which enables the model to be equipped with higher accuracy and higher discriminative capability of distinguishing noise events and identifying the single cells in real-time. Utilization of a neural network model for real-time single cell identification represents a novel technique never applied before. It allows high discriminative capability and high accuracy compared to traditional FACS (Fluorescence-activated Cell Sorting). The usefulness of this technique is to integrate with any brightfield (BF) model and fluorescence (FL) model to identify single cells for different downstream applications.

• Utilizes cell image information and neural network model for single cell identification
• Distinguishes noise events from single cells in real-time
• Higher accuracy and discriminative capability compared to traditional methods
• Integrates with brightfield and fluorescence models for various applications

Potential Applications

The technology can be applied in various fields such as biomedical research, drug discovery, and clinical diagnostics for accurate single cell identification.

Problems Solved

The technology solves the challenge of accurately identifying single cells in real-time and distinguishing them from noise events, improving the efficiency and reliability of cell analysis processes.

Benefits

The benefits of this technology include higher accuracy, faster identification, and the ability to customize exclusion of noise events based on specific requirements, leading to more precise results in cell analysis.

Potential Commercial Applications

The technology can be commercialized in the biotechnology and pharmaceutical industries for high-throughput screening, cell-based assays, and personalized medicine applications.

Possible Prior Art

Prior art may include traditional methods of single cell identification using manual feature extraction or flow cytometry techniques, which may lack the accuracy and speed provided by neural network models.

How does this technology compare to other automated single cell identification methods currently available in the market?

The article does not provide a direct comparison with other automated single cell identification methods, leaving the reader wondering about the specific advantages and limitations of this technology compared to existing solutions.

What are the potential limitations or challenges in implementing this technology in different laboratory settings?

The article does not address the potential challenges or limitations that users may face when implementing this technology in diverse laboratory settings, leaving room for uncertainty regarding its practical applicability.

Original Abstract Submitted

The single cell identification described herein utilizes cell image information and extracts cell features with a neural network model to subtly distinguish the noise events from single cells, allowing the user to choose which different types of noise events to exclude depending on the requirement of applications. The fast neural network model is able to extract more abundant and specific cell features than handpicked features, which enables the model to be equipped with higher accuracy and higher discriminative capability of distinguishing noise events and identifying the single cells in real-time. Utilization of a neural network model for real-time single cell identification represents a novel technique never applied before. It allows high discriminative capability and high accuracy compared to traditional FACS (Fluorescence-activated Cell Sorting). The usefulness of this technique is to integrate with any brightfield (BF) model and fluorescence (FL) model to identify single cells for different downstream applications.