Cell systems最新文献

Unlike genome editing, RNA editing offers the ability to transiently alter cells with minimal risk from off-target effects. While exon-skipping technologies can influence splice site selection, many desired perturbations to the transcriptome require replacement or addition of exogenous exons to target mRNAs, such as replacing disease-causing exons, repairing truncated proteins, or engineering protein fusions. Here, we report the development of RNA-guided trans-splicing with Cas editor (RESPLICE). RESPLICE uses two orthogonal RNA-targeting CRISPR effectors to co-localize a trans-splicing pre-mRNA and to inhibit the cis-splicing reaction, respectively. We demonstrate efficient, specific, and programmable trans-splicing of RNA cargo (up to 2.1 kb) into 11 endogenous transcripts across 3 cell types, achieving up to 45% trans-splicing efficiency in bulk or 90% when sorting for high effector expression. Our results present RESPLICE as a mode of RNA editing that could provide fine-tuned and transient control of cellular programs.

Two-component systems (TCSs) are ubiquitous multi-step signal sensing systems in prokaryotes and are promising platforms for building cellular sensors. However, their programmability remains underexplored, limiting broader applications in synthetic biology. Here, we refactor TCSs to systematically elucidate the functional properties of response regulator (RR) and histidine kinase (HK) as the concentration-dependent activator and inhibitor for TCS sensor output, respectively. By decoupling HK expression from native feedback circuitry, we engineer ultrasensitive TCS sensors with tunable detection thresholds. By leveraging RR as a transducer, we couple one-component system (OCS) and TCS to create a synergistic sensing system (SSS) characterized by both a low detection limit and a high dynamic range. We further show that RR alone serves as a biological-low noise amplifier (LNA), substantially upgrading performance of diverse genetically encoded biosensors. Our study demonstrates TCS's high plasticity and programmability for customizing gene expression regulation in synthetic circuits, providing modular toolkits for biosensor optimization. A record of this paper's transparent peer review process is included in the supplemental information.

Advancements in single-cell technologies and deep sequencing have expanded the B cell repertoire available for antibody discovery. However, selecting the highest-affinity antibodies from many sequences remains challenging, reflecting our incomplete understanding of the mechanisms sustaining affinity maturation and associated molecular markers. Here, we generated datasets of antigen-specific B cells after mouse immunization and reanalyzed public data to identify "High Signature" (HS), a transcriptomic signature predictive of high-affinity antibodies. HS was derived through differential expression analyses and machine learning by integrating antibody sequences, gene expression, and affinity measurements of expressed antibodies. HS enabled sub-nanomolar-affinity antibody selection without prior sequence analysis in de novo immunization campaigns. HS-expressing B cells were 3 times more likely to yield high-affinity antibodies than randomly picked cells. HS demonstrated translatability to two human PBMC datasets from COVID patients, resulting in enriched high-affinity antibody selection, highlighting its antibody discovery potential across species. A record of this paper's transparent peer review process is included in the supplemental information.

Integrating single-cell or spatial transcriptomic and epigenomic data enables scrutinizing the transcriptional regulatory mechanisms controlling cell fate. Current integration methods usually align multi-omics data into a shared latent space but fail to reveal the underlying connections between genes and regulatory elements. The correlation- or regression-based regulatory inference methods cannot dissect different transcriptional regulation codes for cells under different spatial and temporal states. To address both problems, we develop a feature-guided optimal transport (FGOT) method, which simultaneously uncovers cellular heterogeneity and their associated transcriptional regulatory links. FGOT also provides post hoc interpretability for existing integration methods. FGOT is applicable for paired/unpaired single-cell multi-omics data and paired spatial multi-omics data. Benchmarking and validating via histone modification data or three-dimensional (3D) genomics data show good robustness and accuracy in integration and inference of regulatory links. The method allows systematic screening of cell-state and spatial-location-specific regulatory elements in diseases at the single-cell level. A record of this paper's transparent peer review process is included in the supplemental information.

The native microbiome influences numerous host processes, including neurological function. However, its impacts on diverse brain cell types remain poorly understood. Here, we performed single-nucleus RNA sequencing on the hippocampus of wild-type, germ-free mice, revealing the microbiome-dependent transcriptional landscape across all major neural cell types. We found conserved impacts on key adaptive immune and neurodegenerative transcriptional pathways. Mono-colonization with select indigenous microbes identified organism-specific effects on brain myeloid cell transcriptional state. Escherichia coli colonization induced a distinct myeloid cell activation state, increased brain-resident CD8⁺ T cells, and shaped amyloid phagocytic capacity, suggesting heightened disease susceptibility. Finally, E. coli-exposed 5xFAD mice displayed exacerbated cognitive decline and amyloid pathology, demonstrating the sufficiency of intestinal E. coli to worsen Alzheimer's disease-relevant outcomes. Together, these results emphasize the broad, species-specific, microbiome-dependent consequences on neural cell states and highlight the capacity of specific microbes to modulate disease susceptibility.

In aged humans and mice, hypobranched glycogen aggregates, known as polyglucosan bodies (PGBs), accumulate in hippocampal astrocytes. While PGBs are linked to cognitive decline in neurological diseases, they remain largely unstudied in the context of typical aging. We show that PGBs arise in autophagy-dysregulated astrocytes in the aged hippocampus, with substantial variation among 32 inbred BXD mouse strains. Genetic mapping through quantitative trait locus analysis identified a major locus (Pgb1) that modulates hippocampal PGB burden. Extensive transcriptomic and proteomic datasets were produced for the aged hippocampus of the BXD family to investigate the mechanism by which the Pgb1 locus modulates PGB burden. We identified that Pgb1 contains allelic Smarcal1 and Usp37 variants and influences PGB burden through trans-regulation of mRNA and protein expression levels, including abundance of glycogen-mobilizing factor PYGB. Furthermore, comprehensive phenome-wide association scans, transcriptomic analyses, and direct behavioral testing demonstrated that cognition remains intact despite age-related PGB burden. A record of this paper's transparent peer review process is included in the supplemental information.

Pathogens have evolved complex strategies that exploit the unique intracellular niches of organelles to establish a favorable replication environment that promotes infection and associated diseases. Defining how pathogens remodel organelle structures and compositions to redirect their functions is a major goal in cell biology. Recent technological advancements now enable structural characterizations of remodeled organelles in exquisite detail, as well as quantitative mapping of relocalized protein constituents and suborganellar interacting proteins. We describe emerging advances in complementary approaches for spatially and temporally profiling organelle rearrangements dictated by pathogen infection, with a focus on state-of-the-art microscopy, quantitative proteomics, and the integration of computational developments during virus infection. We examine the organellar resolutions and subcellular scales of these methodologies and recent applications during viral infections. We discuss how existing biochemical and bioinformatic tools can be integrated for systems-level mapping of organelle remodeling dynamics to dissect structure-function relationships of rewired organelles induced by microbes.

Classical phylogenetics assumes site independence, potentially overlooking epistasis. Protein language models capture dependencies in conserved structural and functional domains across the protein universe. Here, we ask whether MSA Transformer, which takes a multiple sequence alignment (MSA) as input, captures evolutionary distance and to what extent its representations reflect epistasis in protein sequence evolution, neither of which are explicitly available during training. Systematic shuffling of natural and simulated MSAs demonstrates that the model exploits column-wise conservation to distinguish phylogenetic relationships. Using internal embeddings, we reconstruct trees that are markedly consistent with those generated by maximum likelihood inference. Applying this approach to both the RNA-dependent RNA polymerase of RNA viruses and the nucleo-cytoplasmic large DNA virus domain, we recover both established and novel evolutionary relationships. We conclude that MSA Transformer complements, rather than replaces, classical inference for more accurate histories of protein families.

The maintenance of cellular homeostasis requires tight regulation of proteome concentration and composition. To achieve this, protein production and elimination must be robustly coordinated. However, the mechanistic basis of this coordination remains unclear. Here, we address this question using quantitative live-cell imaging, computational modeling, transcriptomics, and proteomics approaches. We found that protein decay rates systematically adapt to global alterations of protein synthesis rates. This adaptation is driven by a core passive mechanism supplemented by facultative changes in mechanistic/mammalian target of rapamycin (mTOR) signaling. Passive adaptation hinges on changes in the production rate of the machinery governing protein decay and allows for partial maintenance of the cellular proteome. Sustained changes in mTOR signaling provide an additional layer of adaptation unique to naive pluripotent stem cells, allowing for near-perfect maintenance of proteome composition. Our work unravels the mechanisms protecting the integrity of mammalian proteomes upon variations in protein synthesis rates. A record of this paper's transparent peer review process is included in the supplemental information.