Cell systems最新文献

A key challenge in synthetic biology is achieving durable amplification of low-level inputs in gene regulation systems. Current RNA-based tools primarily operate post-transcriptionally and often yield limited, transient responses. An underexplored feature of lowly expressed long non-coding RNAs (lncRNAs) is their ability to induce outsized effects on chromatin regulation across large genomic regions. Mechanistic insights from basic research are bringing the field closer to designing lncRNAs for epigenetic engineering. We review foundational studies on ectopic expression to uncover lncRNA-mediated epigenetic mechanisms and state-of-the-art transgenic systems for studying lncRNA-driven epigenetic regulation. We present perspectives on strategies for testing the composability of modular lncRNA elements to build rationally designed systems with programmable chromatin-modifying functions and potential biomedical applications such as gene dosage correction. Deepening mechanistic insights into lncRNA function, combined with the development of lncRNA-based technologies for genome regulation, will pave the way for significant advances in cell state control.

Synthetic biology offers control over cellular and tissue functions. As it moves beyond microbes into humans, synthetic biology enables precise control over gene expression, cell fate, and tissue organization across heart, lung, blood, and sleep systems. By integrating genome engineering, dynamic gene circuits, and high-dimensional biosensors, these advances support scalable, quantitative models of multicellular biology, expanding the need for systems-level models and integration. We highlight emerging systems such as tunable transcriptional regulators, synthetic organizers, and feedback circuits that bridge molecular control with functional outcomes. Furthermore, by combining omics data with artificial intelligence (AI)-guided circuit design, synthetic biology enables high-resolution cellular and tissue-scale models of development, cellular interactions, drug development, gene therapy, and therapeutic response. Key challenges remain-including delivery, transgene stability, and robust spatiotemporal control in physiologically relevant models. This perspective synthesizes field-spanning progress and defines shared priorities for engineering cells and tissues that function reliably across dynamic, multi-organ environments.

Synthetic gene circuits represent a transformative approach in gene- and cell-based therapies, offering dynamic and precise control of therapeutic functions to address the limitations inherent in conventional treatments. Despite significant preclinical advancements, their clinical translation has been predominantly confined to relatively simple circuit designs, with few complex systems progressing into clinical trials. This perspective discusses current clinical applications of synthetic gene circuits, particularly their roles in solid tumor therapy, T cell-mediated immunomodulation, and metabolic disease management. We outline the therapeutic potential of these circuits and address the challenges impeding their clinical applications, including safety, specificity, immunogenicity, and delivery efficiency. To advance translation, we emphasize the importance of the development of humanized animal models, advanced delivery platforms, AI-driven optimization of circuit components, and the strategic selection of clinically target scenarios. Furthermore, we highlight emerging cybergenetics principles-intelligent and programmable genetic control systems-as a cornerstone for future smart living therapeutics and cell-based therapies.

DIAL is a novel framework for temporal control of transcript abundances in engineered cells. Targeted excision of DNA spacers in transgenic promoters permits controlled transitions of protein expression between setpoints. DIAL expands the repertoire of bioengineering tools for controlling protein expression, cell fates, and biological systems in general.

Society needs alternatives to painful, expensive, and cumbersome injections for diseases like diabetes. Franko et al. developed cells that sense melatonin to deliver glucagon-like peptide-1 (GLP-1) therapy during sleep. This circadian-synchronized approach restored normal blood sugar in diabetic mice, advancing the field of smart cells for patient-centered circadian medicine.

Single-cell RNA sequencing provides detailed insights into cellular heterogeneity and responses to external stimuli. However, distinguishing inherent cellular variation from extrinsic effects induced by external stimuli remains a major analytical challenge. Here, we present scCausalVI, a causality-aware generative model designed to disentangle these sources of variation. scCausalVI decouples intrinsic cellular states from treatment effects through a deep structural causal network that explicitly models the causal mechanisms governing cell-state-specific responses to external perturbations while accounting for technical variations. Our model integrates structural causal modeling with cross-condition in silico prediction to infer gene expression profiles under hypothetical scenarios. Comprehensive benchmarking demonstrates that scCausalVI outperforms existing methods in disentangling causal relationships, quantifying treatment effects, generalizing to unseen cell types, and separating biological signals from technical variation in multi-source data integration. Applied to COVID-19 datasets, scCausalVI effectively identifies treatment-responsive populations and delineates molecular signatures of cellular susceptibility.

High-content image-based phenotypic profiling combines automated microscopy and analysis to identify phenotypic alterations in cell morphology and provide insight into the cell's physiological state. Classical representations of the phenotypic profile cannot capture the full underlying complexity in cell organization, while recent weakly supervised machine-learning-based representation-learning methods are hard to biologically interpret. We used the abundance of control wells to learn the in-distribution of control experiments and used it to formulate a self-supervised reconstruction anomaly-based representation that encodes the intricate morphological inter-feature dependencies while preserving the representation interpretability. The performance of our anomaly-based representations was evaluated for downstream tasks with respect to two classical representations across four public Cell Painting datasets. Anomaly-based representations improved reproducibility and mechanism of action classification and complemented classical representations. Unsupervised explainability of autoencoder-based anomalies identified specific inter-feature dependencies causing anomalies. The general concept of anomaly-based representations can be adapted to other applications in cell biology. A record of this paper's transparent peer review process is included in the supplemental information.

To begin deciphering the hierarchical structure of the cell, we need to integrate multiple types of data of different scales on subcellular organization. To this end, we developed MIRAGE, a multi-modal generative model for integrating protein sequence, protein-protein interaction, and protein localization data. Our adversarial approach successfully learns a joint embedding space that captures the complex relationships among these diverse modalities and allows us to generate missing modalities. We evaluate our model's performance against existing methods, obtaining superior performance in protein function prediction and protein complex detection. We apply MIRAGE to construct a hierarchical map of subcellular organization in HEK293T cells, recovering known protein assemblies across multiple scales.

AI-enabled functional annotation reveals hidden enzyme diversity and distribution in fermented food microbiomes, shedding light on their ecological roles and biotechnological potential.

Microbial bioactivity is essential for the flavor, appearance, quality, and safety of fermented foods. However, the diversity and distribution of enzymatic resources in fermentation remain poorly understood. This study explored 10,202 metagenome-assembled genomes from global fermented foods using machine learning, identifying over 5 million enzyme sequences grouped into 98,693 homologous clusters, representing over 3,000 enzyme types. Functional analysis revealed that 84.4% of these clusters were unannotated in current databases, with high novelty in terpenoid and polyketide metabolism enzymes. Peptide hydrolases exhibited broad environmental adaptability based on predicted optimal temperatures and pH, and niche breadth calculations indicated 31.3% of enzyme clusters displayed food-type specificity. Additionally, we developed a machine learning model to classify fermented food sources by enzyme clusters, highlighting key enzymes differentiating habitats. Our findings emphasize the untapped potential of fermented food environments for enzyme resource exploration, offering valuable insights into microbial functions for future food research. A record of this paper's transparent peer review process is included in the supplemental information.