Background: Addressing the Differentiation State Loss Noise of Conventional Double-Strand Break-Inducing Knockouts and the Bottleneck of Cell Line Establishment and Lineage Differentiation Trajectory Data in Human Embryonic R&D

Conventional CRISPR-Cas9-based double-strand break (DSB)-inducing knockout technologies have exposed a critical blind spot in the genetic R&D of human embryonic stem cells (hESCs) and early embryos: the noise associated with the loss of cellular differentiation state. Non-specific cleavage during genome editing induces cytotoxicity, genomic instability, and chromosomal deletions, leading to a sharp decrease in cell viability before screening. In particular, for NANOG, a key gene that controls the rate-limiting steps of human embryonic development, conventional static, unidirectional disruption methods have been unable to capture the subtle gradient changes required for maintaining pluripotency. Furthermore, the evolutionary interspecies differences in the transcriptome network compared to rodent models have limited the ability to predict effective in vivo concentrations in silico. These limitations have created an unreliable data barrier and a bottleneck in human embryo-mimicking R&D, hindering the identification of the intrinsic feedback flux within the transcriptional network.

Discovery: Activating Base Editing-Mediated Single Nucleotide Variation Modality and Demonstrating Single-Cell Resolution Independent Variable Tensor Synchronization

In this study, we activated high-precision adenine/cytosine base editing technology to construct a fine-tuning library of mutations in the essential functional domains of the NANOG gene without inducing double-strand breaks, and applied a multi-dimensional tensor synchronization technique based on single-cell RNA sequencing. Through molecular docking free energy simulations, we proactively calculated the binding energy of amino acid substitutions in the DNA-binding homeodomain of the NANOG protein in silico, and derived protein interaction rate constants based on differential equations. Batch effects were computationally removed to obtain single-cell transcriptome baseline data and precisely fuse multi-dimensional data. Base editing enables precise editing of target codons, thereby clearly elucidating the topological changes in the transcriptional network during human embryonic development, specifically during trophectoderm formation and inner cell mass specification. We validated the integrity by visualizing a human-specific transcriptional factor network tensor that surpasses the topological system of existing mouse models.

Establishing a Multi-Layered, Reversible Homeostatic Precision Stratification Model for Tuning the Structure of Pluripotent Transcriptional Factor Networks

Based on a high-density single-cell omics matrix, we established a precision stratification model for human embryonic stem cell lines and patient-specific molecular phenotypes. This model grades the transcriptional interaction gradient between the NANOG gene and its downstream target genes caused by single nucleotide transitions, predicting phenotypic vulnerability. In particular, we applied a computational control system that artificially up-clamps or down-clamps rate-limiting steps leading to the loss of pluripotency, creating a genetic backbone that can autonomously regulate reversible homeostasis even in aberrant stress conditions. This preemptively simulates the impact of structural stability changes due to mutation location and expression intensity on developmental homeostasis, providing a basis for improving customized reprogramming efficiency.

Prospects: Establishing a Programmable Stem Cell Engineering Standard and Activating a Next-Generation IND Digital Governance System

The combined architecture of base editing and multi-dimensional computational screening has completely reset the governance of stem cell R&D, shifting from the conventional static, post-hoc symptomatic drug screening system to a multi-dimensional omics tensor-based programmable infrastructure. We have established a computational entry barrier and a proprietary computational moat by dynamically linking the genetic gradient correction coefficient in large-scale, high-throughput screening, thereby eliminating functional variations between production batches from multiple institutions. The resulting molecular-level, high-precision cell classification system will accelerate the expansion of global biotech pipelines for intractable disease cell therapies and lead to the development of next-generation cell analysis kits that meet the companion diagnostic (CDx) standards of digital healthcare. Ultimately, the safety prediction matrix based on human embryo modeling research will be consistent with the clinical trial protocol (IND) review guidelines of global regulatory agencies, serving as a core asset of digital R&D governance that disruptively shortens the toxicological assessment approval timeline.