Single-Cell Transcriptome Analysis for Accurate Cell Composition Detection
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Solution Overview
Problem
Current methods for cell type identification in biological samples are limited by high cost, low throughput, and difficulty in analyzing unknown samples, leading to false positives and negatives due to reliance on specific molecular markers and antibodies.
Innovation Solution
Perform single-cell transcriptome sequencing to generate a cell gene expression matrix, followed by single-cell bioinformatics analysis for accurate cell type determination, including functional cell gene expression matrix mapping and trajectory analysis to confirm cell annotations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods (qPCR, dPCR, FISH, IF, FC) are used for cell type identification, then specific molecular markers or antibodies can be targeted, but the detection cost is high, throughput is low, and unknown cell types cannot be identified
Solution Approach 1:
The patent replaces conventional mechanical/wet-lab methods (qPCR, FISH, IF, FC) with mass spectrometry-based proteomic analysis. This substitution enables high-throughput, automated detection of cell types based on protein expression profiles, achieving both high precision in cell type identification and high productivity in processing multiple samples simultaneously, while also enabling identification of unknown cell types without prior knowledge of specific markers
Solution Approach 2:
The mass spectrometry-based method provides universal detection capability for all cell types in a sample simultaneously, rather than requiring separate assays for different cell types. The approach can identify known cell types through protein markers and also discover unknown cell types through unsupervised clustering analysis, making the system multi-functional for both targeted and exploratory cell type identification
2Measurement precision
If multiple specific markers are targeted for cell type detection, then cell type identification can be achieved, but the detection cost increases and multiple separate assays are required
Solution Approach 1:
The patent merges the detection of multiple cell type markers into a single mass spectrometry assay. Instead of performing separate qPCR, FISH, or flow cytometry assays for different markers, the method simultaneously detects hundreds of proteins in one experiment, identifying multiple cell types and their states through comprehensive proteomic profiling in a unified analytical framework
Solution Approach 2:
The mass spectrometry platform serves as a universal detection system that can identify any cell type based on its protein expression profile, eliminating the need for multiple specialized assays. The approach provides both targeted detection of known markers and discovery of novel markers through data-driven analysis, reducing the number of separate experiments required
3Ease of manufacture
If limited molecular markers are used for cell type identification, then the assay can be performed, but false positives or negatives occur due to heterogeneity of target expression
Solution Approach 1:
The patent changes the detection parameter from limited gene expression or protein markers to comprehensive proteomic profiles. By measuring hundreds of proteins simultaneously and using multivariate analysis to identify cell type patterns, the method overcomes the limitations of single-marker or few-marker approaches. The reliability is improved through pattern recognition across multiple proteins rather than reliance on individual markers with heterogeneous expression
Solution Approach 2:
The method uses composite protein expression profiles as the basis for cell type identification, combining information from hundreds of proteins to create a robust molecular fingerprint for each cell type. This composite approach is more reliable than single-marker detection because it accounts for the heterogeneity of individual marker expression through ensemble analysis of multiple proteins
Data Source
AI summary
The present application discloses a biological sample cell composition detection method and apparatus, an electronic device, and a readable storage medium, which are applied to the technical field of biomedicine. The method comprises: performing single-cell transcriptome sequencing on a biological sample to be detected to obtain a single-cell sequencing result, generating a cell gene expression matrix by analyzing the single-cell sequencing result, and performing single-cell bioinformatics analysis on the cell gene expression matrix to determine cell types comprised in said biological sample. The present application can accurately and quantitative detect the cell composition of a biological sample in one step with low cost and high throughput