Guihai Feng

The functional integrity of a mammalian chromosome is shaped by its long-term, co-evolution with species-specific nuclear environment. How chromosomes co-adapt with their native environment to define 3D architecture and transcriptional activity remain poorly understood, largely due to a lack of experimental models capable of systematically dissecting this co-evolution relationship. Here, we report a cross-species chromosome substitution (CROSS) method, a robust genomic engineering method that enables the stable, scarless replacement of host chromosomes with evolutionarily divergent orthologs. By integrating microcell-mediated chromosome transfer with CRISPR/Cas9, we imported the intact 158-Mb rat X chromosome into mouse embryonic stem cells, and subsequently achieved targeted substitution of its endogenous mouse counterpart, maintaining stability and integrity. Using this model, we found that rat-specific LINE1 and RatSatRep2 repeats failed to adequately recruit host SETDB1 in mouse cells, leading to localized erosion of H3K9me3 heterochromatin. This further triggered 3D structural remodeling, characterized by the de novo formation of topologically associating domain (TAD) boundaries that aberrantly activated adjacent genes—including Rhox5 , the master regulator of the Rhox cluster—impairing cellular differentiation. Our method provides a powerful chromosome engineering platform for dissecting how genomic sequences and epigenetic mechanisms cooperate in regulating chromosome architecture and function, and for evaluating the structural and functional fidelity of large-scale synthetic or heterologous DNA across species.

Why eukaryotic genomes are universally divided among multiple chromosomes remains an unresolved question. Although yeast and mouse cells can tolerate chromosomal fusions without impairing viability, we show here that chromosome length in mammalian cells is constrained by a biophysical limit governed by spindle geometry. Using engineered mouse cells carrying fused chromosomes of defined sizes, we identify ~308 Mb as the maximal length tolerated for faithful mitosis. Chromosomes exceeding this threshold disrupt segregation, leading to daughter cell re-coalescence and polyploidization. Aurora B kinase regulates this process by modulating spindle elongation; its inhibition induces mitotic failure even in chromosome configurations within the tolerated threshold of ~308 Mb. These findings explain the structural basis for genome fragmentation in animals and reveal a general mechanism linking chromosome size, spindle dynamics, and genome stability.

Cell fate decisions are orchestrated by intricate gene regulatory networks (GRNs), which govern gene expression with precise spatiotemporal control. However, accurately capturing context‐specific nature of gene regulation remains challenging, particularly when integrating multi‐omics data at bulk and single‐cell level across diverse cellular contexts. Here, we present CellPolaris, a unified computational framework designed to decode the roles of transcription factors (TFs) in developmental processes. CellPolaris performs TF‐centered GRN construction, master TF identification, and TF perturbation simulation. By leveraging transfer learning, the framework generates tissue‐specific or cell‐type‐specific GRNs using pre‐constructed high‐confidence GRNs of diverse contexts and requires only transcriptomic data as input. Using these learned GRNs, CellPolaris identifies underlying master TFs critical for cell fate transitions and simulates the effects of TF perturbations on developmental processes. Benchmarking tests demonstrate the robust performance of CellPolaris in GRN construction. The efficacy of CellPolaris is supported by the significant overlap between predicted top‐ranked master regulators and known TF combinations experimentally validated in cell fate conversion experiments. Furthermore, CellPolaris accurately simulates the developmental consequences of Rfx2 knockout during round spermatid differentiation. In summary, we present CellPolaris, a comprehensive framework that enables GRN construction through transfer learning, identification of key TFs driving cell fate transitions, and simulation of TF perturbations. This tool allows us to further elucidate the regulatory mechanisms underlying developmental processes and cell state transitions.

Embryo development undergoes critical morphological transformations post-implantation, largely driven by the complex and dynamic microenvironment of the uterus. Despite advances, current 3D culture models inadequately recapitulate the uterine environment necessary for studying embryo-uterus interactions. In this work, we engineer a hydrogel inspired by the properties of the decidua, incorporating Matrigel to support blastocyst implantation and embryo development in vitro. Our findings reveal that embryos cultured within this hydrogel system successfully progress to an early organogenesis-like stage, including the development of first and second heart fields, mimicking natural embryogenesis. Moreover, we identify that the mechanical properties, particularly stress relaxation, play a crucial role in facilitating focal adhesion (FA) formation between the trophoblast and the hydrogel. Additionally, the degradation of the hydrogel by embryo-secreted metalloproteinases (MMP2 and MMP9) creates a favorable environment for continued embryonic growth and development. These insights contribute to a deeper understanding of how the external environment regulates embryo development and offer an enhanced approach for in vitro embryo culture.

Researching genes and their interactions is crucial for deciphering the fundamental laws of cellular activity, advancing disease treatment, drug discovery, and more. Large language Models (LLMs), with their profound text comprehension and generation capabilities, have made significant strides across various natural science fields. However, their application in cell biology remains limited and a systematic evaluation of their performance is lacking. To address this gap, in this article, we select seven mainstream LLMs and evaluate their performance across nine gene-related problem scenarios. Our findings indicate that LLMs possess a certain level of understanding of genes and cells, but still lag behind domain-specific models in comprehending transcriptional expression profiles. Moreover, we have improved the current method of textual representation of cells, enhancing the LLMs’ ability to tackle cell annotation tasks. We encourage cell biology researchers to leverage LLMs for problem-solving while being mindful of the associated challenges. We release our code and data at https://github.com/epang-ucas/Evaluate_LLMs_to_Genes .