Spatial Cell Image Analysis for Single-Cell Transcriptome Mapping
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Solution Overview
Problem
Current spatial biology technologies face a challenge in achieving whole-transcriptome tissue analysis with single-cell resolution, as existing methods struggle to balance the breadth of genes analyzed with spatial and cellular detail, often blurring the boundaries between different cell types and limiting the detection of gene expressions.
Innovation Solution
A system and method involving a high-depth cell gene panel for cell classification followed by unsupervised clustering, combined with a whole-transcriptome panel for broader genetic analysis, and data aggregation based on spatial proximity or cell-to-cell interactions, enabling detailed insights into cellular compositions and interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current spatial biology technologies are used to achieve whole-transcriptome tissue analysis, then the breadth of genes analyzed is improved, but the spatial and cellular resolution deteriorates, blurring boundaries between different cell types
Solution Approach 1:
The patent divides the tissue section into discrete spatial locations or regions, analyzing gene expression at each location separately. This segmentation allows the system to maintain high spatial resolution while performing whole-transcriptome analysis, as each segmented region can be analyzed independently to preserve cellular boundaries and spatial context.
Solution Approach 2:
The patent introduces spatial coordinates as an additional dimension to the gene expression data. By mapping transcriptome data onto spatial dimensions (x, y, z coordinates), the system simultaneously achieves comprehensive gene coverage and precise spatial localization, transforming the analysis from a two-dimensional gene space to a multi-dimensional space that includes spatial information.
2Adaptability or versatility
If current technologies increase the number of genes analyzed by in situ platforms, then the breadth of analysis is improved, but the detection of all protein-coding genes is limited by the finite cell volume
Solution Approach 1:
The patent performs preliminary capture of mRNA molecules at their native spatial locations within the tissue section before any analysis occurs. By pre-capturing and preserving the spatial distribution of transcripts in situ, the system ensures that all protein-coding genes can be detected with high reliability, as the finite cell volume constraint is overcome by maintaining the original spatial context of each transcript.
3Ease of operation
If existing methods are used for tissue analysis, then the process is simpler, but the ability to distinguish cell types and maintain single-cell resolution deteriorates
Solution Approach 1:
The patent introduces spatial barcodes or location identifiers as intermediary elements that link gene expression data to specific spatial locations and cell types. These intermediaries enable the system to automatically distinguish different cell types based on their spatial positions and molecular signatures, maintaining single-cell resolution without requiring complex manual analysis procedures.
Data Source
AI summary
Disclosed herein, inter alia, are computer-implemented methods for analyzing electronic images of a tissue sample.