Background: Technical Limitations in Single-Cell Spatial Transcriptomics Control and the Polarization Data Bottleneck in Giant Cell Regeneration R&D

• Existing cell dissociation-based single-cell transcriptomics guidelines suffer from a critical blind spot: they fail to address the noise caused by cell dissociation-induced structural disruption and the resulting loss of transcript polarity during individual cell extraction. This static baseline approach does not preserve the intracellular microenvironment and cytoplasmic polarity information, repeatedly failing to ensure the effective delivery and target prevention concentration of mRNA. In particular, for Stentor coeruleus, which autonomously reconstructs complex structures in a single-cell state, it has been difficult to precisely track the dynamic flux during fragmentation and regeneration. In the field of computational biology, relying solely on existing 2D cultures or bulk data prevents in silico computational control of the thermodynamic rate constants of intracellular microtubule motor proteins and mRNA-binding proteins, leading to a significant data barrier in therapeutic target discovery. This inability to predict intramolecular feedback fluxes during mRNA-based LNP delivery architecture design has created a massive data bottleneck in next-generation platform R&D.

Discovery: Implementation of a Single-Cell Compartment-Specific mRNA Profiling Algorithm and Demonstration of Cell-Resolution Tensor Synchronization

• This study computationally demonstrated the synchronization of single-cell spatial transcriptomics data into a multi-dimensional tensor space. A library of Stentor apex-to-base fragments was constructed, and RNA folding free energy was precisely tuned to restore the intracellular coordinate vector of mRNA transcripts. Differential equation-based rate constants were in silico proactively calculated to elucidate physical transport mechanisms, and spatial arrangement effects were completely removed as a correction factor. As a result, the topological variation curve of downstream transcript networks, which move along the microtubule network during regeneration, was elucidated. This drastically surpasses existing simple statistical models and demonstrates the integrity of single-cell polarity reconstruction. Through tensor operations of transcript signatures before and after cell damage and regeneration, it was successfully demonstrated that the geometric localization of mRNA is governed by charge transport and active compartmentalization rather than simple diffusion.

Establishment of a Precision-Layered Model for Intracellular Location-Based Polarity Regulation and Reversible Regeneration Homeostasis

• Based on the constructed 3D transcriptomics matrix, a precision-layered numerical model of single-cell regeneration phenotype was established. The spatiotemporal trajectory of the regeneration-incompetent state induced by intracellular mRNA mislocalization was layered to identify target prediction pathways. In particular, by performing up-clamping and down-clamping simulations that artificially vary the rate constants of the rate-limiting step of cytoplasmic regeneration, a backbone network architecture was secured to maintain reversible homeostasis even under aberrant stress conditions. Furthermore, by using a correlation matrix between cell membrane potential variables and transcript local density, a dynamic control mechanism was established to correct the temporal gradient of gene expression, successfully mathematically layering the physical limitations and homeostasis recovery ability of single-cell regeneration.

Prospects: Establishment of a Programmable Single-Cell Engineering Standard and Launch of a Next-Generation IND Digital Governance System

• The mapping of intracellular mRNA compartmentalization landscapes in this study completely resets the existing static symptomatic treatment R&D governance to a real-time AI-based, multi-dimensional tensor-based programmable infrastructure. When developing the next-generation RNA therapeutic pipeline of global biotech companies such as Alnylam and Moderna, a computational moat can be secured by linking the gene gradient correction coefficient to the design tensor, effectively zeroing out the variance between sample batches. This platform provides an analytical tool that standardizes intracellular mRNA density as a core component of digital healthcare's companion diagnostic panel, and it will become a master asset that drastically shortens the regulatory approval timeline by generating predictive data required for FDA clinical trial applications and cGMP certification.