Cell Systems细胞系统
Cell Systems(英文缩写 CELL SYST),ISSN 2405-4712,eISSN 2405-4720,中文译名:细胞系统 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-09-27 至 2026-09-27，按本站收录文献的发表日期统计。
期刊介绍
历年影响因子趋势
| JCR 数据年份 | 影响因子 | JCR 分区 |
|---|---|---|
| 2021 | 11.091 | Q1 |
| 2022 | 9.300 | Q1 |
| 2023 | 9.000 | Q1 |
| 2024 | 7.700 | Q1 |
| 2025 | 7.500 | Q1 |
Cell Systems 最新收录文献
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1. CRISPR-associated enzymes are mislocalized to the cytoplasm in iPSC-derived neurons, resulting in KRAB(KOX1)-specific degradation.
PMID:日期:2026-09-23The use of CRISPR-associated enzymes in induced pluripotent stem cell (iPSC)-derived neurons presents unique challenges compared with dividing cell lines. For example, loss of dCas9-KRAB expression after differentiation has been observed and largely ascribed to transgene silencing. Here, we investigated the expression of CRISPR enzymes in iPSCs and Ngn2-derived neurons. We found that the commonly used dCas9-KRAB(KOX1) displayed a dramatic reduction in protein levels following differentiation, yet nCas9 constructs retained comparable levels. We further found that CRISPR constructs, primarily relying on the SV40 nuclear localization signal (NLS), fail to localize to the nuclei of neurons, despite having robust nuclear levels in iPSCs, leading to KRAB(KOX1)-specific cytoplasmic degradation. By testing other NLSs, we rescued neuronal nuclear localization and protein expression, confirming the contribution of mislocalization to the instability of dCas9-KRAB(KOX1) in neurons. As the lack of nuclear localization can have a profound impact on CRISPR activity, we suggest further investigation across cultured and in vivo postmitotic cell models. A record of this paper's transparent peer review process is included in the supplemental information.
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2. The drifting biochemical basis of an essential molecular interaction.
PMID:日期:2026-09-21Essential biochemical activities are thought to rest on optimized molecular mechanisms that remain conserved during evolution. We traced how the mode of interaction between primary microRNAs and their processing enzyme changed across 460 million years of vertebrate evolution. Through dense phylogenetic reconstruction and high-throughput in vitro processing, we quantified the processing rates of over a thousand ancestral and extant primary microRNAs and all of their possible point mutants. Historical substitutions continuously remodeled the interaction while preserving processing rate. By redistributing base pairs, wobbles, and mismatches and inducing conformational rearrangements, they slightly strengthened or weakened random parts of the interface, gradually reshaping the pattern and biochemical basis of affinity. Each substitution also altered the effects of possible mutations, randomizing the pattern of mutational tolerance within 10%-20% sequence divergence. Overall, the molecular basis of primary microRNA processing is not a fixed outcome of evolutionary optimization but an ever-changing product of drift.
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3. Plasma proteins are integral to cross-tissue gene regulatory networks implicated in cardiometabolic disorders and coronary artery disease.
PMID:日期:2026-09-16The plasma proteome has demonstrated promise for identifying diagnostic markers for cardiometabolic disorders (CMDs) and coronary artery disease (CAD). However, identifying the organ of origin for these biomarkers is critical for establishing biological relevance. We performed a multi-omic integrative analysis across multiple tissues from the STARNET study by profiling 974 plasma proteins in 532 CAD patients, integrating RNA sequencing (RNA-seq) data from the arterial wall, major metabolic organs, and blood. We identified 144 cis-protein quantitative trait loci in plasma, colocalizing with tissue cis-expression quantitative trait loci. Additionally, by mapping tissue mRNA "seed genes," we traced 262 plasma proteins to their source organs, primarily the liver. Crucially, we found that 851 plasma proteins are associated with the activity of cross-tissue gene regulatory networks (GRNs), including GRNs implicated in CMD and CAD development. Our findings demonstrate that plasma proteins are integral components of GRNs, with potential for developing reliable diagnostics and precise therapeutic targets. A record of this paper's transparent peer review process is included in the supplemental information.
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4. Mapping the combinatorial coding between olfactory receptors and perception with deep learning.
PMID:日期:2026-09-16The sense of smell remains poorly understood compared with vision and audition. At its core is an information flow in which odorant molecules activate subsets of olfactory receptors (ORs) and combinations of receptor activations encode distinct percepts. However, predicting molecule-OR interactions, and linking them to perception, remains difficult. Here, we develop MolOR, an approach that maps odorants to their OR-activation profiles and then predicts their odor percepts. Using cross-attention between a graph neural network over molecules and protein-language-model embeddings of receptors, we predict OR activation and-despite no molecular overlap between binding and percept datasets-improve percept prediction by using predicted OR profiles as auxiliary features. Structurally diverse molecules sharing a percept show similar predicted OR profiles, and the model distinguishes protein-coding ORs from pseudogenes across the human subgenome. This may aid the discovery of ligands for orphan ORs and the design of odorants with desired perceptual qualities.
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5. Multiobjective learning and design of bacteriophage specificity.
PMID:日期:2026-09-16Proteins are often optimized for single functions during design and engineering without the consideration of other functionalities that may interfere with the intended outcome. Here, we apply deep learning to understand and design the multifunctional host-targeting landscape of the T7 bacteriophage receptor-binding protein for enhanced infectivity, predefined specificity, and high generality toward unseen strains. We compare four model architectures and experimentally characterize engineered phages optimized for 26 tasks. With multiobjective machine learning, it is possible to engineer complex specificities at success rates that enable low-throughput validation of predicted hits. The targeting capabilities of T7 are highly plastic, with opposite specificities occasionally separated by only a few mutations. This tunability underscores how models trained on multifunctional data can uncover key principles of phage biology and specificity. The same framework can guide multiobjective optimization of other proteins or biological systems, offering a general strategy for modeling multifunctional landscapes.
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6. Synthetic tissue ecology: Ecological control of developmental self-organization across scales.
PMID:日期:2026-09-16Embryonic development builds tissues and organs through the self-organization of heterogeneous cells across spatial and temporal scales, analogous to an ecosystem. Although ecological systems are often stochastic and multistable, embryogenesis is remarkably reproducible. We argue that this contrast is not a contradiction. Developmental robustness arises from evolutionarily filtered ecological interactions embedded within hierarchically organized, multiscale architectures that restrict accessible collective states. Competition, cooperation, niche construction, and density-dependent coupling are not metaphors but core interaction dynamics that shape tissues in vivo. In vitro systems frequently lack these multiscale constraints, allowing more permissive ecological dynamics and increased variability. We propose "synthetic tissue ecology" as a conceptual and engineering framework that treats development as a stabilized regime of ecological organization. By shifting focus to interaction architectures and constraints that direct cross-scale self-organization, this framework enables the decoding of mesoscale modules and tissue-coupling principles while supporting the engineering of robust organoids, embryoids, and regenerative systems.
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7. A diversified consortium of engineered biosensors enables multi-target quantification.
PMID:日期:2026-09-16Whole-cell biosensors (WCBs) offer rapid, cost-effective monitoring of environmental contamination. Efforts to optimize detection of isolated target analytes under laboratory conditions have achieved vastly improved performance and set the stage for WCB deployment in complex environments. We propose a framework that leverages cross-reactivity of single-target WCBs to quantify multiple targets using supervised machine learning. Specifically, we engineer six sensors for heavy metal contaminants in laboratory E. coli. We then evolve the strain to generate five chassis with improved growth in seawater conditions and transform them with the sensors to create a set of 30 variants. The variant responses are characterized with microfluidics, revealing significant diversity. Leveraging this diversity, we combinatorially quantify multiple analytes with a machine learning model that takes an in silico consortium of response inputs and outperforms single-target WCBs in over 90% of test samples. These results form a generalizable framework that facilitates WCB translation toward settings beyond the laboratory. A record of this paper's transparent peer review process is included in the supplemental information.
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8. Interpretable biophysical neural networks of transcriptional activation domains separate roles of protein abundance and coactivator binding.
PMID:日期:2026-09-16Deep neural networks have improved many difficult prediction tasks in biology, but it remains challenging to interpret these networks and learn the molecular mechanisms. Here, we address interpretation challenges by building biophysical neural networks for predicting activation domains, the regions within transcription factors (TFs) that recruit coactivators to drive gene expression. Deep neural networks can now accurately predict acidic activation domains from protein sequences, but these predictors are difficult to interpret. We designed shallow neural networks that incorporated biophysical models and visualized the parameters directly. We found two ways that the arrangement of residues (i.e., sequence grammar) controls function: (1) C-terminal hydrophobic residues increase coactivator binding and decrease protein abundance, and (2) acidic residues at the N terminus promote coactivator binding, while acidic residues at the C terminus promote TF abundance. We demonstrate how combining biophysical and deep neural networks maximizes prediction accuracy and interpretability, revealing biological mechanisms across datasets. A record of this paper's transparent peer review process is included in the supplemental information.
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9. Predicting specificity of TCR-pMHC interactions using machine-learning and biophysical models.
PMID:日期:2026-09-16Understanding T cell receptor (TCR) discrimination of MHC-presented epitope peptides (pMHCs) remains challenging. While machine-learning (ML)-based predictions of TCR specificity have gained attention, their capacity to generalize to unseen peptides is often misinterpreted. Using a proprietary cancer patient dataset, we show that ML methods succeed in predicting TCR specificity for known peptides but fail to generalize to novel peptides. Conversely, physics-based methods outperform ML methods on novel peptides but underperform on known peptides. In light of these observations, we develop a new ML method that leverages protein foundation models to achieve better or comparable performance than existing ML and biophysical methods on both in- and out-of-distribution TCR-pMHC specificity prediction. We furthermore characterize method performance as a function of distance of TCR sequence specificity between training and test sets. Our analysis elucidates the current limitations of modeling TCR-pMHC interactions and outlines new avenues for method development and data acquisition.
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10. Quantifying protein unfolding kinetics with a high-throughput microfluidic platform.
PMID:日期:2026-09-16Even after folding, proteins sample unfolded intermediates at risk of irreversible alteration (e.g., via proteolysis, aggregation, or posttranslational modification). Thus, kinetic stability impacts protein lifetime and abundance. However, we have very few measurements of unfolding rates, largely due to technical challenges. To address this, we developed SPARKfold (simultaneous proteolysis assay revealing kinetics of folding), a microfluidic platform to express, purify, and measure unfolding rate constants at high throughput via native proteolysis. We applied SPARKfold to determine unfolding rate constants for 1,104 protein samples comprising 31 dihydrofolate reductase orthologs with up to 78 chamber replicates each, providing statistical power to resolve subtle effects. SPARKfold rate constants for 5 constructs agreed with traditional measurements across a 150-fold range and provided information about the folding transition state via φ analysis. In future work, SPARKfold can reveal mutations that drive misfolding and aggregation and enable the rational design of kinetically hyperstable variants for industrial use. A record of this paper's transparent peer review process is included in the supplemental information.