18580053. CELL-TYPE OPTIMIZATION METHOD AND SCANNER simplified abstract (The Regents of the University of California)

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CELL-TYPE OPTIMIZATION METHOD AND SCANNER

Organization Name

The Regents of the University of California

Inventor(s)

Roy Wollman of Los Angeles CA (US)

Zachary Hemminger of Los Angeles CA (US)

CELL-TYPE OPTIMIZATION METHOD AND SCANNER - A simplified explanation of the abstract

This abstract first appeared for US patent application 18580053 titled 'CELL-TYPE OPTIMIZATION METHOD AND SCANNER

Abstract: Methods are described for cell-type mapping utilizing direct measurement of low-dimensional representation of single-cell transcriptomics with a supervised machine learning algorithm to spatially map cell types, bypassing the need to measure expression of single genes. Such methods are useful to identify locations of cell types in biological specimens for purposes including tissue-based diagnostics.

  • Simplified Explanation:

The patent application discusses a method for mapping cell types in biological specimens using a supervised machine learning algorithm based on single-cell transcriptomics.

  • Key Features and Innovation:

- Utilizes low-dimensional representation of single-cell transcriptomics - Spatially maps cell types without measuring expression of single genes - Enhances tissue-based diagnostics - Bypasses the need for traditional gene expression measurements

  • Potential Applications:

- Tissue-based diagnostics - Cell type identification in biological specimens - Spatial mapping of cell types in various tissues - Research in cell biology and pathology

  • Problems Solved:

- Eliminates the need for measuring expression of single genes - Provides a more efficient and accurate method for cell-type mapping - Enhances the understanding of cell types in biological specimens

  • Benefits:

- Faster and more accurate cell-type mapping - Improved tissue-based diagnostics - Enhanced research capabilities in cell biology and pathology

  • Commercial Applications:

Title: Innovative Cell-Type Mapping Technology for Tissue Diagnostics Potential commercial uses include: - Biomedical research institutions - Pharmaceutical companies for drug development - Clinical laboratories for diagnostic purposes

  • Questions about Cell-Type Mapping Technology:

1. How does this method compare to traditional gene expression-based cell-type mapping techniques? 2. What are the potential limitations of using a supervised machine learning algorithm for cell-type mapping?


Original Abstract Submitted

Methods are described for cell-type mapping utilizing direct measurement of low-dimensional representation of single-cell transcriptomics with a supervised machine learning algorithm to spatially map cell types, bypassing the need to measure expression of single genes. Such methods are useful to identify locations of cell types in biological specimens for purposes including tissue based diagnostics.