Quantum Generative Modeling of Single-Cell transcriptomes: Capturing Gene-Gene and Cell-Cell Interactions.

Area of Science:

• Computational Biology
• Quantum Computing
• Genomics

Background:

• Classical single-cell RNA sequencing (scRNA-seq) data simulation methods rely on linear correlations, failing to capture complex, nonlinear biological interactions.
• Existing simulators do not jointly model gene-gene and cell-cell interactions, limiting the realism of synthetic single-cell data.

Purpose of the Study:

• To introduce qSimCells, a novel quantum computing-based simulator for scRNA-seq data.
• To leverage quantum entanglement for modeling intra- and inter-cellular interactions, capturing cellular heterogeneity and complex gene regulatory networks (GRNs).

Main Methods:

• Developed a quantum kernel using parameterized quantum circuits with CNOT gates to encode nonlinear GRNs and cell-cell communication topologies.
• Utilized quantum entanglement to model gene-gene and cell-cell interactions with explicit causal directionality.
• Generated synthetic single-cell transcriptomic data using the qSimCells simulator.

Main Results:

• The simulated data exhibited non-classical dependencies, where standard correlation analyses failed to identify programmed causal pathways.
• Quantum entanglement was essential for capturing true mechanistic links, showing up to a 75-fold increase in inferred cell-cell communication probability.
• The quantum kernel successfully modeled complex, nonlinear gene regulatory networks and cell-cell communication.

Conclusions:

• The quantum kernel is crucial for generating high-fidelity ground-truth scRNA-seq datasets.
• Advanced inference techniques are necessary to analyze complex, non-classical dependencies in biological data simulated with quantum methods.
• qSimCells provides a powerful new approach for realistic single-cell data simulation, accounting for intricate biological interactions.