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Simple visualization of submicroscopic protein clusters with a phase-separation-based fluorescent reporter. 利用基于相分离的荧光报告器实现亚显微蛋白质团簇的简单可视化。
Pub Date : 2024-06-19 Epub Date: 2024-05-25 DOI: 10.1016/j.cels.2024.05.003
Thomas R Mumford, Diarmid Rae, Emily Brackhahn, Abbas Idris, David Gonzalez-Martinez, Ayush Aditya Pal, Michael C Chung, Juan Guan, Elizabeth Rhoades, Lukasz J Bugaj
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引用次数: 0
Tissue module discovery in single-cell-resolution spatial transcriptomics data via cell-cell interaction-aware cell embedding. 通过细胞-细胞交互感知细胞嵌入在单细胞分辨率空间转录组学数据中发现组织模块
Pub Date : 2024-06-19 Epub Date: 2024-05-31 DOI: 10.1016/j.cels.2024.05.001
Yuzhe Li, Jinsong Zhang, Xin Gao, Qiangfeng Cliff Zhang

Computational methods are desired for single-cell-resolution spatial transcriptomics (ST) data analysis to uncover spatial organization principles for how individual cells exert tissue-specific functions. Here, we present ST data analysis via interaction-aware cell embedding (SPACE), a deep-learning method for cell-type identification and tissue module discovery from single-cell-resolution ST data by learning a cell representation that captures its gene expression profile and interactions with its spatial neighbors. SPACE identified spatially informed cell subtypes defined by their special spatial distribution patterns and distinct proximal-interacting cell types. SPACE also automatically discovered "cell communities"-tissue modules with discernible boundaries and a uniform spatial distribution of constituent cell types. For each cell community, SPACE outputs a characteristic proximal cell-cell interaction network associated with physiological processes, which can be used to refine ligand-receptor-based intercellular signaling analyses. We envision that SPACE can be used in large-scale ST projects to understand how proximal cell-cell interactions contribute to emergent biological functions within cell communities. A record of this paper's transparent peer review process is included in the supplemental information.

单细胞分辨率空间转录组学(ST)数据分析需要计算方法来揭示单个细胞如何发挥组织特异性功能的空间组织原理。在这里,我们介绍了通过交互感知细胞嵌入(SPACE)进行的空间转录组学数据分析,这是一种深度学习方法,可从单细胞分辨率的空间转录组学数据中识别细胞类型和发现组织模块。SPACE 通过特殊的空间分布模式和不同的近邻相互作用细胞类型,识别出了有空间信息的细胞亚型。SPACE 还自动发现了 "细胞群落"--具有明显边界和统一空间分布的组成细胞类型的组织模块。对于每个细胞群落,SPACE 都会输出一个与生理过程相关的近端细胞-细胞相互作用网络,该网络可用于完善基于配体-受体的细胞间信号分析。我们设想 SPACE 可用于大规模 ST 项目,以了解近端细胞-细胞相互作用如何促进细胞群落内新出现的生物功能。这篇论文的同行评审过程非常透明,相关记录见补充信息。
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引用次数: 0
Stimulus-response signaling dynamics characterize macrophage polarization states. 刺激-反应信号动态描述了巨噬细胞的极化状态。
Pub Date : 2024-06-19 Epub Date: 2024-06-05 DOI: 10.1016/j.cels.2024.05.002
Apeksha Singh, Supriya Sen, Michael Iter, Adewunmi Adelaja, Stefanie Luecke, Xiaolu Guo, Alexander Hoffmann

The functional state of cells is dependent on their microenvironmental context. Prior studies described how polarizing cytokines alter macrophage transcriptomes and epigenomes. Here, we characterized the functional responses of 6 differentially polarized macrophage populations by measuring the dynamics of transcription factor nuclear factor κB (NF-κB) in response to 8 stimuli. The resulting dataset of single-cell NF-κB trajectories was analyzed by three approaches: (1) machine learning on time-series data revealed losses of stimulus distinguishability with polarization, reflecting canalized effector functions. (2) Informative trajectory features driving stimulus distinguishability ("signaling codons") were identified and used for mapping a cell state landscape that could then locate macrophages conditioned by an unrelated condition. (3) Kinetic parameters, inferred using a mechanistic NF-κB network model, provided an alternative mapping of cell states and correctly predicted biochemical findings. Together, this work demonstrates that a single analyte's dynamic trajectories may distinguish the functional states of single cells and molecular network states underlying them. A record of this paper's transparent peer review process is included in the supplemental information.

细胞的功能状态取决于其微环境背景。之前的研究描述了极化细胞因子如何改变巨噬细胞转录组和表观基因组。在这里,我们通过测量转录因子核因子κB(NF-κB)在8种刺激下的动态变化,描述了6种不同极化巨噬细胞群的功能反应。由此产生的单细胞 NF-κB 轨迹数据集通过三种方法进行了分析:(1)对时间序列数据进行机器学习,发现刺激的可区分性随着极化的消失而消失,这反映了渠化效应器功能。(2)确定了驱动刺激可分辨性的信息轨迹特征("信号密码子"),并将其用于绘制细胞状态图,从而定位受无关条件制约的巨噬细胞。(3) 利用机理 NF-κB 网络模型推断的动力学参数提供了另一种细胞状态图谱,并正确预测了生化结果。总之,这项工作表明,单个分析物的动态轨迹可以区分单个细胞的功能状态及其基础的分子网络状态。本文的同行评审过程透明,其记录见补充信息。
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引用次数: 0
The substrate quality of CK2 target sites has a determinant role on their function and evolution. CK2 目标位点的底物质量对其功能和进化具有决定性作用。
Pub Date : 2024-06-19 Epub Date: 2024-06-10 DOI: 10.1016/j.cels.2024.05.005
David Bradley, Chantal Garand, Hugo Belda, Isabelle Gagnon-Arsenault, Moritz Treeck, Sabine Elowe, Christian R Landry

Most biological processes are regulated by signaling modules that bind to short linear motifs. For protein kinases, substrates may have full or only partial matches to the kinase recognition motif, a property known as "substrate quality." However, it is not clear whether differences in substrate quality represent neutral variation or if they have functional consequences. We examine this question for the kinase CK2, which has many fundamental functions. We show that optimal CK2 sites are phosphorylated at maximal stoichiometries and found in many conditions, whereas minimal substrates are more weakly phosphorylated and have regulatory functions. Optimal CK2 sites tend to be more conserved, and substrate quality is often tuned by selection. For intermediate sites, increases or decreases in substrate quality may be deleterious, as we demonstrate for a CK2 substrate at the kinetochore. The results together suggest a strong role for substrate quality in phosphosite function and evolution. A record of this paper's transparent peer review process is included in the supplemental information.

大多数生物过程都是由与短线性基团结合的信号模块调控的。对于蛋白激酶来说,底物可能与激酶识别基序完全匹配,也可能仅部分匹配,这种特性被称为 "底物质量"。然而,目前还不清楚底物质量的差异是代表中性变异还是具有功能性后果。我们研究了具有多种基本功能的激酶 CK2 的这一问题。我们的研究表明,最佳 CK2 位点以最大的化学计量单位磷酸化,并且在许多条件下都能发现,而最小底物的磷酸化程度较弱,并且具有调节功能。最佳 CK2 位点往往更为保守,底物的质量往往通过选择来调整。对于中间位点,底物质量的增加或降低可能是有害的,正如我们在研究动核上的 CK2 底物时所证明的那样。这些结果共同表明,底物质量在磷酸根功能和进化中发挥着重要作用。补充信息中包含了本文透明的同行评审过程记录。
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引用次数: 0
How has the AI boom impacted algorithmic biology? 人工智能热潮对算法生物学有何影响?
Pub Date : 2024-06-19 DOI: 10.1016/j.cels.2024.05.008
Mona Singh, Cenk Sahinalp, Jianyang Zeng, Wei Vivian Li, Carl Kingsford, Qiangfeng Zhang, Teresa Przytycka, Joshua Welch, Jian Ma, Bonnie Berger

This Voices piece will highlight the impact of artificial intelligence on algorithm development among computational biologists. How has worldwide focus on AI changed the path of research in computational biology? What is the impact on the algorithmic biology research community?

这篇网络文章将重点介绍人工智能对计算生物学家算法开发的影响。全世界对人工智能的关注如何改变了计算生物学的研究路径?对算法生物学研究界有何影响?
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引用次数: 0
To modulate or to skip: De-escalating PARP inhibitor maintenance therapy in ovarian cancer using adaptive therapy. 调节或跳过:利用适应性疗法降低卵巢癌中 PARP 抑制剂维持疗法的等级。
Pub Date : 2024-06-19 Epub Date: 2024-05-20 DOI: 10.1016/j.cels.2024.04.003
Maximilian A R Strobl, Alexandra L Martin, Jeffrey West, Jill Gallaher, Mark Robertson-Tessi, Robert Gatenby, Robert Wenham, Philip K Maini, Mehdi Damaghi, Alexander R A Anderson

Toxicity and emerging drug resistance pose important challenges in poly-adenosine ribose polymerase inhibitor (PARPi) maintenance therapy of ovarian cancer. We propose that adaptive therapy, which dynamically reduces treatment based on the tumor dynamics, might alleviate both issues. Utilizing in vitro time-lapse microscopy and stepwise model selection, we calibrate and validate a differential equation mathematical model, which we leverage to test different plausible adaptive treatment schedules. Our model indicates that adjusting the dosage, rather than skipping treatments, is more effective at reducing drug use while maintaining efficacy due to a delay in cell kill and a diminishing dose-response relationship. In vivo pilot experiments confirm this conclusion. Although our focus is toxicity mitigation, reducing drug use may also delay resistance. This study enhances our understanding of PARPi treatment scheduling and illustrates the first steps in developing adaptive therapies for new treatment settings. A record of this paper's transparent peer review process is included in the supplemental information.

多腺苷核糖聚合酶抑制剂(PARPi)对卵巢癌的维持治疗面临着毒性和新出现的耐药性这两个重要挑战。我们建议采用自适应疗法,即根据肿瘤动态动态减少治疗次数,从而缓解这两个问题。利用体外延时显微镜和逐步模型选择,我们校准并验证了一个微分方程数学模型,并利用该模型测试了不同的合理适应性治疗方案。我们的模型表明,由于细胞杀伤延迟和剂量-反应关系减弱,调整剂量比跳过治疗更能有效减少药物用量,同时保持疗效。体内试点实验证实了这一结论。虽然我们的重点是减轻毒性,但减少用药也可能延迟耐药性的产生。这项研究加深了我们对 PARPi 治疗计划的理解,并说明了为新的治疗环境开发适应性疗法的第一步。补充信息中包含了本文透明的同行评审过程记录。
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引用次数: 0
Systems genetics of metabolic health in the BXD mouse genetic reference population. BXD 小鼠遗传参考群体代谢健康的系统遗传学。
Pub Date : 2024-06-19 Epub Date: 2024-06-11 DOI: 10.1016/j.cels.2024.05.006
Xiaoxu Li, Jean-David Morel, Jonathan Sulc, Alessia De Masi, Amélia Lalou, Giorgia Benegiamo, Johanne Poisson, Yasmine Liu, Giacomo V G Von Alvensleben, Arwen W Gao, Maroun Bou Sleiman, Johan Auwerx

Susceptibility to metabolic syndrome (MetS) is dependent on genetics, environment, and gene-by-environment interactions, rendering the study of underlying mechanisms challenging. The majority of experiments in model organisms do not incorporate genetic variation and lack specific evaluation criteria for MetS. Here, we derived a continuous metric, the metabolic health score (MHS), based on standard clinical parameters and defined its molecular signatures in the liver and circulation. In human UK Biobank, the MHS associated with MetS status and was predictive of future disease incidence, even in individuals without MetS. Using quantitative trait locus analyses in mice, we found two MHS-associated genetic loci and replicated them in unrelated mouse populations. Through a prioritization scheme in mice and human genetic data, we identified TNKS and MCPH1 as candidates mediating differences in the MHS. Our findings provide insights into the molecular mechanisms sustaining metabolic health across species and uncover likely regulators. A record of this paper's transparent peer review process is included in the supplemental information.

代谢综合征(MetS)的易感性取决于遗传、环境以及基因与环境之间的相互作用,因此研究其潜在机制具有挑战性。大多数模式生物实验并不包含遗传变异,也缺乏代谢综合征的具体评估标准。在此,我们根据标准临床参数得出了一种连续性指标--代谢健康评分(MHS),并定义了其在肝脏和血液循环中的分子特征。在人类英国生物库中,MHS 与 MetS 状态相关,并可预测未来疾病的发病率,即使没有 MetS 的个体也是如此。通过对小鼠进行定量性状位点分析,我们发现了两个与 MHS 相关的基因位点,并在无关联的小鼠群体中进行了复制。通过对小鼠和人类基因数据进行优先排序,我们确定 TNKS 和 MCPH1 为介导 MHS 差异的候选基因。我们的研究结果让我们深入了解了维持不同物种代谢健康的分子机制,并发现了可能的调节因子。补充信息中包含了本文透明的同行评审过程记录。
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引用次数: 0
Decoupled degradation and translation enables noise modulation by poly(A) tails. 去耦降解和平移可实现多(A)尾噪音调制。
Pub Date : 2024-06-19 DOI: 10.1016/j.cels.2024.05.004
Carmen Grandi, Martin Emmaneel, Frank H T Nelissen, Laura W M Roosenboom, Yoanna Petrova, Omnia Elzokla, Maike M K Hansen

Poly(A) tails are crucial for mRNA translation and degradation, but the exact relationship between tail length and mRNA kinetics remains unclear. Here, we employ a small library of identical mRNAs that differ only in their poly(A)-tail length to examine their behavior in human embryonic kidney cells. We find that tail length strongly correlates with mRNA degradation rates but is decoupled from translation. Interestingly, an optimal tail length of ∼100 nt displays the highest translation rate, which is identical to the average endogenous tail length measured by nanopore sequencing. Furthermore, poly(A)-tail length variability-a feature of endogenous mRNAs-impacts translation efficiency but not mRNA degradation rates. Stochastic modeling combined with single-cell tracking reveals that poly(A) tails provide cells with an independent handle to tune gene expression fluctuations by decoupling mRNA degradation and translation. Together, this work contributes to the basic understanding of gene expression regulation and has potential applications in nucleic acid therapeutics.

多聚(A)尾对 mRNA 的翻译和降解至关重要,但尾长与 mRNA 动力学之间的确切关系仍不清楚。在这里,我们利用一个小型的相同 mRNA 文库,研究了它们在人类胚胎肾细胞中的行为。我们发现,尾部长度与 mRNA 降解率密切相关,但与翻译无关。有趣的是,最佳尾长度为 100 nt 的翻译率最高,这与纳米孔测序法测得的平均内源尾长度相同。此外,poly(A)-尾长度的可变性--内源性mRNA的特征--影响翻译效率,但不影响mRNA降解率。随机建模结合单细胞追踪发现,poly(A)尾通过将mRNA降解与翻译解耦,为细胞提供了调节基因表达波动的独立控制手段。总之,这项工作有助于对基因表达调控的基本理解,并有可能应用于核酸治疗。
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引用次数: 0
Accurate single-molecule spot detection for image-based spatial transcriptomics with weakly supervised deep learning. 利用弱监督深度学习为基于图像的空间转录组学提供精确的单分子点检测。
Pub Date : 2024-05-15 DOI: 10.1016/j.cels.2024.04.006
Emily Laubscher, Xuefei Wang, Nitzan Razin, Tom Dougherty, Rosalind J Xu, Lincoln Ombelets, Edward Pao, William Graf, Jeffrey R Moffitt, Yisong Yue, David Van Valen

Image-based spatial transcriptomics methods enable transcriptome-scale gene expression measurements with spatial information but require complex, manually tuned analysis pipelines. We present Polaris, an analysis pipeline for image-based spatial transcriptomics that combines deep-learning models for cell segmentation and spot detection with a probabilistic gene decoder to quantify single-cell gene expression accurately. Polaris offers a unifying, turnkey solution for analyzing spatial transcriptomics data from multiplexed error-robust FISH (MERFISH), sequential fluorescence in situ hybridization (seqFISH), or in situ RNA sequencing (ISS) experiments. Polaris is available through the DeepCell software library (https://github.com/vanvalenlab/deepcell-spots) and https://www.deepcell.org.

基于图像的空间转录组学方法能够利用空间信息测量转录组尺度的基因表达,但需要复杂的人工调整分析管道。我们介绍的 Polaris 是一种基于图像的空间转录组学分析流水线,它将用于细胞分割和斑点检测的深度学习模型与概率基因解码器相结合,可准确量化单细胞基因表达。Polaris 提供了一个统一的交钥匙解决方案,用于分析来自多重误差校正 FISH (MERFISH)、连续荧光原位杂交 (seqFISH) 或原位 RNA 测序 (ISS) 实验的空间转录组学数据。Polaris可通过DeepCell软件库(https://github.com/vanvalenlab/deepcell-spots)和https://www.deepcell.org。
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引用次数: 0
Multilevel relations among plankton stitched together with an eco-evolutionary needle. 用生态进化针缝合浮游生物之间的多层次关系。
Pub Date : 2024-05-15 DOI: 10.1016/j.cels.2024.04.007
Van M Savage

Power-law relationships between population abundances, energy use, and other factors are often referred to as macroecological scaling. A recent study convincingly shows that these relationships emerge from individual physiology but only after the population distribution is shaped by trophic interactions that are subject to both ecological and evolutionary pressures.

种群丰度、能量消耗和其他因素之间的幂律关系通常被称为宏观生态缩放。最近的一项研究令人信服地表明,这些关系产生于个体生理学,但只有在受到生态和进化压力的营养相互作用影响后,种群分布才会发生变化。
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引用次数: 0
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Cell systems
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