Cell Status Inference via Genomic Binning
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Solution Overview
Problem
Current technologies face challenges in interpreting and utilizing the high-throughput transcriptome data from biological samples to accurately infer cell status, particularly in heterogeneous cell populations, which is crucial for diagnosing diseases like cancer and evaluating treatment responses.
Innovation Solution
A method involving single cell sequencing to map nucleic acid sequences onto bins representing the reference genome, allowing for the assignment of cells into mitotic stages and the inference of cell cycle statuses, enabling the characterization of cell populations and evaluating the impact of perturbations such as drug treatments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If high-throughput transcriptome sequencing is performed to quantify gene expression in hundreds or thousands of individual cells, then the quantity of data generated increases, but the ability to accurately interpret and infer cell status in heterogeneous cell populations deteriorates
Solution Approach 1:
The patent segments the reference genome into multiple bins and analyzes nucleic acid sequence read counts in each bin for individual cells. This segmentation allows the complex task of inferring cell status from high-throughput data to be broken down into manageable units, enabling accurate characterization of cell cycle stages and DNA replication status in heterogeneous cell populations
Solution Approach 2:
The patent introduces an intermediary computational model that maps sequence read count patterns across genomic bins to cell status categories. This intermediary layer translates raw sequencing data into biologically meaningful cell status inferences, bridging the gap between high-throughput data generation and accurate cell status determination
2Measurement precision
If single cell sequencing is used to obtain nucleic acid sequence reads from each cell, then measurement precision for cell status improves, but device complexity and computational requirements increase
Solution Approach 1:
By dividing the reference genome into bins and analyzing read counts in discrete units, the patent simplifies the computational analysis of single-cell sequencing data. This segmentation reduces the complexity of processing high-dimensional data while maintaining precision in cell status determination
Solution Approach 2:
The patent employs automated computational algorithms that automatically assign cells to mitotic stages and identify DNA replication status based on sequence read count patterns. This self-service approach eliminates the need for manual analysis, reducing computational complexity while maintaining high measurement precision
Data Source
AI summary
Systems and methods for inferring a status of a cell population are provided. Described techniques allow deconvolving a first clonal population comprising a first plurality of cells of a species, wherein nucleic acid sequence reads from each cell in the first plurality of cells are obtained. The nucleic acid sequence reads are mapped into bins representing portions of a reference genome, and a pattern of sequence read counts for each cell across the multiple bins is used to assign a cell to a group, thereby inferring a mitotic status of the cell. The assignment of nucleic acid sequence reads into bins is also be used for segregating cells into classes based on a status of a certain biological marker in each cell. Comparison of sequence read counts for a subset of bins across the cell classes allows evaluating effect of a compound on a cell status.