Background: Critical Blind Spots of Unidirectional Heterogeneous Population Analysis Standards and Dynamic Genome Control Data Bottlenecks in Pluripotent Stem Cell R&D

Existing bulk omics analysis and unidirectional single-cell transcriptomics sequencing standard guidelines have not been able to computationally overcome the transient fate determination branch points and heterogeneity between subclones during stem cell differentiation, which leads to noise from the loss of cellular dissociation structure and the loss of microenvironmental location information. In particular, when inducing differentiation of induced pluripotent stem cells and human embryonic stem cells, the multidimensional interactions of downstream feedback loops and compensatory gene expression networks cause batch-to-batch variations in target differentiation efficiency, and it has consistently failed to establish a precise control baseline for predicting the engraftment stability and in vivo survival rate of cell therapy products. This is a data barrier caused by the failure to dynamically track the intricately intertwined genome regulatory landscape and analyze only a single trajectory, which has led to a chronic R&D bottleneck that cannot bridge the gap between high-throughput screening data and the temporal causal relationship of actual cell fate conversion.

Discovery: Operation of CRISPR-Based Computational Perturbation Screening and Demonstration of Genetic Factor Binding Free Energy Tensor Synchronization

This study (Nature Biotechnology, 2026, doi:10.1038/s41587-026-03199-w) operated a combinatorial CRISPR screening platform targeting tens of thousands of gene regulatory loci within pluripotent stem cells to map the transcriptional regulatory map in ultra-high resolution. A disruptive algorithm was introduced that synchronizes the transcriptome variation matrix according to genome perturbation into a multidimensional tensor form and proactively calculates the transcriptional rate constant and effective chromatin accessibility variation in silico based on differential equations. This completely eliminates the multi-batch effects that can easily occur at the single-cell resolution in a computational manner and quantifies the physical binding free energy change between specific transcription factors and regulatory epitopes. It mathematically formalized the topological variation curve of promoter-enhancer interactions to a level that completely surpasses the limitations of existing simple models, and demonstrated the integrity of molecular biological causality by elucidating the mechanism of regulating the frequency and amplitude of transcriptional bursts during target perturbation.

Establishment of a Reversible Lineage Differentiation Homeostasis Precision Layered Model Based on Transcriptional Regulatory Topology Architecture

This platform presents a precise layered protocol that overcomes the genetic background variations of human induced pluripotent stem cells based on the genome matrix. Based on the map of interactions between transcription factors and epigenetic modification factors located at the key rate-limiting steps of the lineage differentiation pathway, it enables in silico up- and down-regulation simulations of specific control pathways. This completes an operational backbone that allows stem cells to stably maintain pluripotency or inhibit irreversible differentiation into non-target lineages even in external stress and culture microenvironment variation, and reversibly and autonomously regulate effective homeostasis. It has secured industrial precision as a layered model that can early diagnose and filter out the risk of heterogeneous cell inclusion and tumorigenicity of undifferentiated stem cells encountered during differentiation induction.

Prospects: Establishment of a Programmable Stem Cell Genomics Standard and Launch of Next-Generation IND Digital Governance

This research is a major milestone that completely resets the stem cell genome R&D governance from the existing static and post-analysis system to a programmable infrastructure based on AI-based multidimensional tensor. In the development of next-generation pipelines, such as iPSC-based pancreatic beta cell therapy VX-880 or Parkinson's disease therapy Bemdaneprocel, which are being pursued by global multinational pharmaceutical companies (e.g., Vertex Pharmaceuticals, BlueRock Therapeutics), it provides a proprietary technology barrier that can computationally offset batch effects and cell line variations by precisely linking the genome gradient correction coefficient in the high-throughput screening stage. It enables cell quality profiling that meets the digital health companion diagnostics (CDx) standards, and it will be established as a core asset of digital transformation that can automatically generate data to prove cell integrity required for clinical trial protocol (IND) approval and cGMP commercial operation approval from global regulatory agencies such as the U.S. FDA, drastically shortening the approval timeline.