Cell Signature Identification via Single-Cell RNA Sequencing
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Solution Overview
Problem
Current methods for categorizing cell types, particularly in diverse neuronal classes like retinal bipolar cells, face challenges due to insufficient cell profiling numbers and the need for scalable validation methods, leading to incomplete and unreliable classifications.
Innovation Solution
A method involving single-cell RNA sequencing (scRNA-seq) and computational algorithms to identify and validate cell types by isolating target cells, quantifying gene expression, and clustering based on differential gene expression, with optional validation against cellular morphology, enabling comprehensive classification of retinal cell types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If single-cell RNA sequencing is performed on a limited number of cells (hundred to few thousand), then the sequencing cost and complexity are controlled, but the cell type classification is incomplete and unreliable
Solution Approach 1:
The patent segments the cell population into distinct types based on gene expression profiles, using computational clustering to divide the continuous data into discrete cell type categories. This allows comprehensive classification even when starting with a limited number of cells, as the segmentation reveals the full diversity of cell types present.
Solution Approach 2:
The patent develops a universal classification framework that can be applied across different cell types and tissues. The computational methods and gene signature approaches are designed to be universally applicable, allowing the same methodology to classify diverse cell populations regardless of their specific biological context.
2Measurement precision
If molecular features are used for cell type classification, then the categorization is quantitative and unbiased, but the validation against orthogonal criteria becomes complex and less scalable
Solution Approach 1:
The patent incorporates feedback loops where computational predictions of cell types are validated against known molecular markers and orthogonal criteria, and the results are used to refine the classification algorithms. This iterative feedback process improves the accuracy of quantitative categorization while systematically managing validation complexity.
Solution Approach 2:
The patent uses computational algorithms and gene signatures as intermediaries between raw molecular data and cell type classification. These computational tools translate complex molecular feature data into interpretable cell type assignments, simplifying the validation process while maintaining quantitative precision.
3Reliability
If comprehensive cell type classification is achieved through increased cell profiling, then the classification completeness improves, but the cost and resource requirements increase significantly
Solution Approach 1:
The patent applies partial action by profiling a subset of cells that is sufficient to capture the full diversity of cell types present in the population. Rather than sequencing every cell, the methodology identifies the minimum number of cells needed to achieve complete classification, reducing resources while maintaining reliability.
Solution Approach 2:
The patent changes key parameters of the sequencing approach, including sequencing depth per cell and the number of cells profiled, to optimize the balance between classification completeness and resource consumption. By adjusting these parameters based on the specific biological question and cell type diversity, the methodology achieves reliable classification with reduced resources.
Data Source
AI summary
The present invention provides for methods of identifying cell types and cell subtypes from a biological sample or population of target cells. The methods further provide for determining cell type or cell subtype signatures. The method further provides for bipolar cell subtypes and markers and cell signatures thereof.