Cell systems最新文献

Engineering biological systems with specified functions requires navigating an extensive design space, which is challenging to achieve with wet-lab experiments alone. To expedite the design process, mathematical modeling is typically employed to predict circuit function in silico ahead of implementation, which, when coupled with computational optimization, can be used to automatically identify promising designs. However, circuit models are inherently inaccurate, which can result in suboptimal or non-functional in vivo performance. To mitigate this, we propose combining Bayesian inference, Thompson sampling, and risk management to find optimal circuit designs. Our approach employs data from non-functional designs to estimate the distribution of model parameters and then employs risk-averse optimization to select design parameters that are expected to perform well, given parameter uncertainty and biomolecular noise. We illustrate the approach by designing adaptation circuits and genetic oscillators using real and simulated data, with models of varied complexity. A record of this paper's transparent peer review process is included in the supplemental information.

Direct reprogramming of immune cells holds promise for immunotherapy but is constrained by limited knowledge of transcription factor (TF) networks. Here, we developed REPROcode, a combinatorial single-cell screening platform to identify TF combinations for immune cell reprogramming. We first validated REPROcode by inducing type-1 conventional dendritic cells (cDC1s) with multiplexed sets of 9, 22, and 42 factors. With cDC1-enriched TFs, REPROcode enabled identification of optimal TF stoichiometry, fidelity enhancers, and regulators of cDC1 states. We then constructed an arrayed lentiviral library of 408 barcoded immune TFs to explore broader reprogramming capacity. Screening 48 TFs enriched in dendritic cell subsets yielded myeloid and lymphoid phenotypes and enabled the construction of a TF hierarchy map to guide immune reprogramming. Finally, we validated REPROcode's discovery power by inducing natural killer (NK)-like cells. This study deepens our understanding of immune transcriptional control and provides a versatile toolbox for engineering immune cells to advance immunotherapy.

Understanding antibody recognition and adaptation to viral evolution is central to vaccine and therapeutic development. Over 1,100 SARS-CoV-2 antibody structures have been resolved, marking the largest structural biology effort for a single pathogen. We present a comprehensive analysis of this landmark dataset to investigate the principles of antibody recognition and immune escape. Human immunoglobulins and camelid single-chain antibodies dominate, collectively mapping 99% of the receptor-binding domain. Despite remarkable sequence and conformational diversity, antibodies exhibit convergence in their paratope structures, revealing evolutionary constraints in epitope selection. Analyses reveal near-universal immune escape of antibodies, including all clinical monoclonals, by advanced variants such as KP3.1.1. On average, over one-third of antibody epitope residues are mutated. These findings support pervasive immune escape, underscoring the need to effectively leverage multi-epitope-targeting strategies to achieve durable immunity. To support community accessibility, we developed an interactive web server for visualization and analysis of antibody-antigen complexes and mutational data.

RNA regulation is central to tuning gene expression and is controlled by thousands of RNA-binding proteins (RBPs). While many RBPs require their full sequence to function, some act through modular domains that recruit larger regulatory complexes. Mapping these RNA-regulatory effector domains is important for understanding RBP function and designing compact RNA regulators. We developed a high-throughput recruitment assay (HT-RNA-Recruit) to identify RNA-downregulatory effector domains within human RBPs. By recruiting over 30,000 protein tiles from 367 RBPs to a reporter mRNA, we discovered over 100 RNA-downregulatory effector domains in 86 RBPs. Certain domains-for instance, KRABs-suppress gene expression upon recruitment to both DNA and RNA. We engineered inducible synthetic RNA regulators based on NANOS that can downregulate endogenous RNAs or maintain reporter expression at defined intermediate levels, as predicted by mathematical modeling. This work serves as a resource for understanding RNA regulators and expands the repertoire of RNA control tools. A record of this paper's transparent peer review process is included in the supplemental information.

Robust and stable expression of genes of interest is crucial for studying and engineering biology. While expressing desired gene products in some microorganisms is well established, achieving stable expression in mammalian systems is still a complex task. Over the years, various methods have been developed to integrate transgenes into mammalian cells, including the use of viral vectors, transposases, nucleases, and recombinases. This review aims to provide an overview of some of the commonly used integration strategies in mammalian cells, with a particular focus on methods toward site-specific integration, highlighting respective advantages and limitations and providing a summary of recent advances. Additionally, it also explores some of the challenges in the field, offering insights into potential directions for future development.

Mammalian cells utilize intercellular signals to regulate physiological processes such as development and homeostasis. Synthetic signaling systems emulate these processes using orthogonal signals like small molecules, which offer benefits including rapid diffusion, controllability, and reduced immunogenicity compared with proteins. However, prior synthetic small molecule systems exhibited limited sensitivity (50% effective concentrations [EC₅₀] > 10^-7 mol/L) and imposed high metabolic burdens due to precursor biosynthesis. To address this, we engineered a super-sensitive (EC₅₀ ∼10^-9 mol/L) and low-burden cell-cell communication platform comprising de novo designed sender, receiver, and degrader modules. The sender produces signal molecules from the endogenous amino acid phenylalanine, the receiver integrates cis-regulatory elements from genomic data and AI-assisted trans-regulatory factor optimization to minimize leakage, and the degrader employs screened enzymes for highly efficient signal control. Finally, this precursor-free system facilitates robust, long-range morphogen gradient formation. The intercellular communication system reported herein holds great potential for future applications in tissue engineering.

Protein secretion in mammalian cells is the active transport of proteins from the cytoplasm to the extracellular space. It plays a fundamental role in mammalian physiology and signaling, as well as biotherapeutics production and cell and gene therapies. The efficacy of protein secretion, however, is impacted by features of the secreted protein itself, and the host-cell machinery that supports each step of the secretion process. High-throughput techniques such as microfluidics, cell display, and cell encapsulation assays for the study and engineering of secreted proteins are transforming biomedical knowledge and our ability to modulate protein secretion. In addition, computational advances, including signal peptide modeling, whole-protein machine learning models, and genome-scale simulations, are opening new pathways for rational design of protein secretion. Here, we highlight recent developments in secretion engineering that are leading to the convergence of high-throughput experimentation and machine learning methods and can help address current challenges in bioproduction and support future efforts in cell and gene therapy while enabling new modalities.

The secretory pathway processes >30% of mammalian proteins, orchestrating their synthesis, modification, trafficking, and quality control across multiple organelles via coordinated interactions, making its regulation difficult to decipher. To advance such research, we present secRecon, a reconstruction of the mammalian secretory pathway, comprising 1,127 manually curated genes organized within 77 secretory process terms, annotated with functional roles, subcellular localization, protein interactions, and complexes. Applying secRecon to omics data revealed distinct secretory topologies in antibody-producing plasma cells versus Chinese hamster ovary (CHO) cells, with CHO-specific deficiencies in proteostasis, translocation, and N-glycosylation genes, highlighting targets to enhance secretion. Analysis of single-cell SEC-seq data uncovered diversity in IgG-secreting plasma cells that is shaped by the unfolded protein response, endoplasmic reticulum (ER)-associated degradation, and vesicle trafficking and identified distinct secretory machinery genes as markers of plasma cell differentiation. These results show that secRecon enables the discovery of mechanisms controlling protein secretion and supports applications in both biomedical research and biotechnology. A record of this paper's transparent peer review process is included in the supplemental information.

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.