Reconstructing 3D Cell Positions from scRNA-seq Data
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Solution Overview
Problem
Current single-cell RNA sequencing methods (scRNA-seq) lose positional information of cells in three-dimensional tissues, while methods like Slide-Seq can only reconstruct spatial arrangements in two-dimensional tissues, and are costly and labor-intensive.
Innovation Solution
A method using machine learning to assign gene expression profiles from single cell layers with positional information to those without, leveraging the similarity of adjacent cells' profiles and minimizing costs by reducing the need for barcoded beads and multiple Slide-Seq applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If scRNA-seq methods are used to sequence individual cells, then transcriptomic information of cells is obtained, but positional information of cells in the tissue is lost
Solution Approach 1:
The patent introduces an intermediary computational model (deep learning framework) that mediates between scRNA-seq data without spatial information and Slide-seq data with spatial information. This model learns the mapping relationship and enables reconstruction of 3D tissue cell positions without directly measuring spatial coordinates during sequencing
Solution Approach 2:
The patent transforms the problem by changing parameters from direct spatial measurement to computational inference. It uses gene expression profiles as input parameters and reconstructs positional parameters through trained neural networks, converting an unmeasurable quantity (original position) into a computable output
2Loss of information
If Slide-Seq method is used to obtain positional information of cells in single cell layer, then spatial information is obtained, but it only works for two-dimensional tissue samples and requires time-consuming application of barcoded beads
Solution Approach 1:
The patent creates a computational copy of the spatial mapping process. Instead of physically applying barcoded beads to each tissue section, it learns a mapping model from limited Slide-seq data and applies this computational copy to reconstruct positions of cells in scRNA-seq data from multiple layers
Solution Approach 2:
The patent performs preliminary action by training the deep learning model on a small subset of Slide-seq data with known positions before applying it to reconstruct positions in the majority of scRNA-seq data. This preliminary training phase captures the spatial relationships that can then be transferred to other samples
3Loss of information
If multiple Slide-Seq approaches are applied to single cell layers of three-dimensional tissue to reconstruct 3D positions, then positional information is obtained, but costs and labor increase significantly
Solution Approach 1:
The patent creates a universal deep learning model that can reconstruct 3D positions for multiple different tissue samples and cell layers using a single trained model. The model learns general spatial mapping principles that apply across different samples, eliminating the need to perform expensive Slide-seq experiments on each individual layer
Solution Approach 2:
The patent applies partial action by using Slide-seq method on only a fraction of the tissue layers (e.g., one or a few representative layers) rather than all layers. The deep learning model then extrapolates this partial spatial information to reconstruct positions in all other layers, achieving complete 3D reconstruction with minimal expensive measurements
Data Source
AI summary
A method for determining an assignment rule in order to merge gene expression profiles which are very similar is disclosed. The method includes (i) adjusting a model, e.g. a linear regression model, using the model to predict the gene expression profiles, (ii) calculating a cost matrix from the predictions, and (iii) applying the Hungarian algorithm to the cost matrix to obtain a new assignment rule and repeating these steps several times.