Drug-Responsive Cell Sorting via Gene Regulatory Network Perturbation
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Solution Overview
Problem
Current methods for sorting drug-responsive cell populations using single-cell RNA sequencing (scRNA-seq) data are inaccurate due to not considering prior knowledge of drug targets and requiring data from two different states, limiting their application to data from only disease states.
Innovation Solution
A method that involves acquiring scRNA-seq data and drug target information, constructing target gene regulatory networks (GRN) and target-perturbed GRNs (tpGRN), and using manifold alignment to score drug responses based on Euclidean distances, thereby sorting drug-responsive cell populations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing methods score and sort drug-responsive cell populations using the number of differentially expressed genes (DEGs) between paired cell populations under different conditions, then the sorting can be performed, but the accuracy of inferring drug-responsive cell populations is low because these methods do not take into account the prior knowledge of drug targets
Solution Approach 1:
The patent applies preliminary action by integrating drug target information into the gene regulatory network construction before performing differential expression analysis. The method pre-processes the scRNA-seq data to annotate genes with their drug target relationships, then uses this annotated information to weight and prioritize genes in the network. This preliminary integration of prior knowledge (drug target annotations) into the analytical framework enables more accurate identification of drug-responsive cell populations without requiring complex post-processing steps.
2Adaptability or versatility
If existing methods require scRNA-seq data in two different states (e.g., treated and control), then the differential expression analysis can be performed, but the application scope is severely limited because they cannot be applied to scRNA-seq data of only disease states
Solution Approach 1:
The patent applies segmentation by decomposing the drug response prediction problem into multiple independent computational steps that can be performed sequentially on single-state data. Instead of requiring paired treated and control samples, the method segments the analysis into: (1) constructing gene regulatory networks from disease-state scRNA-seq data, (2) integrating drug target information to identify relevant network nodes, (3) calculating perturbation scores based on network topology changes, and (4) ranking cell populations. This segmentation enables the method to work with only disease-state data while maintaining prediction accuracy.
3Measurement precision
If the method constructs gene regulatory networks and performs manifold alignment to calculate Euclidean distances, then the accuracy of drug response prediction is improved, but the computational complexity and time required increases
Solution Approach 1:
The patent applies the extraction principle by isolating and focusing computational resources on the most informative aspects of the data. Instead of performing exhaustive comparisons across all genes and all cell populations, the method extracts and prioritizes: (1) genes annotated as drug targets or related to drug mechanisms, (2) key regulatory nodes in the gene regulatory network with high betweenness centrality or hub status, and (3) cell populations showing the largest perturbation scores. This selective extraction of critical information reduces the computational burden of manifold alignment and distance calculations while preserving prediction accuracy.
Data Source
AI summary
Method and systems for sorting drug-responsive cell population based on single-cell RNA sequencing (scRNA-seq) data are disclosed. In some examples, the method includes: constructing a target gene regulatory network (GRN) for each of the cell populations based on scRNA-seq data of cell populations in a disease state; virtually knocking out targets in the GRN, and constructing a target-perturbed gene regulatory network (tpGRN); acquiring a low-dimensional representation of each of network nodes from the GRN in the GRN and the tpGRN through manifold alignment; calculating a Euclidean distance for each of the network nodes between the GRN and the tpGRN through a Euclidean distance; scoring a drug response of each of the cell populations by comprehensively considering changing trends of a drug target, a 2-hop node, and an edge of the 2-hop node in the GRN and the tpGRN, and determining a sorting result of the drug-responsive cell population.