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The structure is the message: Preserving experimental context through tensor decomposition. 结构即信息:通过张量分解保留实验背景。
Pub Date : 2024-08-21 DOI: 10.1016/j.cels.2024.07.004
Zhixin Cyrillus Tan, Aaron S Meyer

Recent biological studies have been revolutionized in scale and granularity by multiplex and high-throughput assays. Profiling cell responses across several experimental parameters, such as perturbations, time, and genetic contexts, leads to richer and more generalizable findings. However, these multidimensional datasets necessitate a reevaluation of the conventional methods for their representation and analysis. Traditionally, experimental parameters are merged to flatten the data into a two-dimensional matrix, sacrificing crucial experiment context reflected by the structure. As Marshall McLuhan famously stated, "the medium is the message." In this work, we propose that the experiment structure is the medium in which subsequent analysis is performed, and the optimal choice of data representation must reflect the experiment structure. We review how tensor-structured analyses and decompositions can preserve this information. We contend that tensor methods are poised to become integral to the biomedical data sciences toolkit.

近期的生物学研究在规模和粒度上都发生了革命性的变化,这得益于多重和高通量检测方法。通过对多个实验参数(如扰动、时间和遗传背景)的细胞反应进行剖析,可以获得更丰富、更有普遍意义的发现。然而,这些多维数据集需要重新评估其表示和分析的传统方法。传统的方法是合并实验参数,将数据平铺成二维矩阵,从而牺牲了结构所反映的关键实验背景。正如马歇尔-麦克卢汉的名言:"媒介即信息"。在这项工作中,我们提出实验结构是进行后续分析的媒介,数据表示的最佳选择必须反映实验结构。我们回顾了张量结构分析和分解如何保留这一信息。我们认为,张量方法有望成为生物医学数据科学工具包的组成部分。
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引用次数: 0
Rationally reprogramming single-cell aging trajectories and lifespan through dynamic modulation of environmental inputs. 通过动态调节环境输入,合理重编程单细胞衰老轨迹和寿命。
Pub Date : 2024-08-21 DOI: 10.1016/j.cels.2024.07.008
Matthew Smart, David F Moreno, Murat Acar

How do variations in nutrient levels influence cellular lifespan? A dynamical systems model of a core circuit involved in yeast aging suggests principles underlying lifespan extension observed at static and alternating glucose levels that are reminiscent of intermittent fasting regimens.

营养水平的变化如何影响细胞寿命?一个参与酵母衰老的核心电路的动力学系统模型提出了在静态和交替葡萄糖水平下观察到的寿命延长的基本原理,这让人联想到间歇性禁食疗法。
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引用次数: 0
Deep-learning-based design of synthetic orthologs of SH3 signaling domains. 基于深度学习的 SH3 信号结构域合成同源物设计。
Pub Date : 2024-08-21 Epub Date: 2024-08-05 DOI: 10.1016/j.cels.2024.07.005
Xinran Lian, Nikša Praljak, Subu K Subramanian, Sarah Wasinger, Rama Ranganathan, Andrew L Ferguson

Evolution-based deep generative models represent an exciting direction in understanding and designing proteins. An open question is whether such models can learn specialized functional constraints that control fitness in specific biological contexts. Here, we examine the ability of generative models to produce synthetic versions of Src-homology 3 (SH3) domains that mediate signaling in the Sho1 osmotic stress response pathway of yeast. We show that a variational autoencoder (VAE) model produces artificial sequences that experimentally recapitulate the function of natural SH3 domains. More generally, the model organizes all fungal SH3 domains such that locality in the model latent space (but not simply locality in sequence space) enriches the design of synthetic orthologs and exposes non-obvious amino acid constraints distributed near and far from the SH3 ligand-binding site. The ability of generative models to design ortholog-like functions in vivo opens new avenues for engineering protein function in specific cellular contexts and environments.

基于进化的深度生成模型是理解和设计蛋白质的一个令人兴奋的方向。一个悬而未决的问题是,这类模型能否学习到在特定生物环境中控制适应性的专门功能约束。在这里,我们研究了生成模型生成Src-homology 3(SH3)结构域合成版本的能力,SH3结构域在酵母的Sho1渗透应激反应途径中介导信号传导。我们的研究表明,变异自动编码器(VAE)模型产生的人工序列在实验上再现了天然 SH3 结构域的功能。更广泛地说,该模型对所有真菌 SH3 结构域进行了组织,从而使模型潜在空间中的定位性(而不仅仅是序列空间中的定位性)丰富了合成直向同源物的设计,并暴露了分布在 SH3 配体结合位点附近和远处的非显而易见的氨基酸限制。生成模型设计体内同源物功能的能力为在特定细胞环境中设计蛋白质功能开辟了新途径。
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引用次数: 0
PanIN and CAF transitions in pancreatic carcinogenesis revealed with spatial data integration. 通过空间数据整合揭示胰腺癌发生过程中的 PanIN 和 CAF 转变。
Pub Date : 2024-08-21 Epub Date: 2024-08-07 DOI: 10.1016/j.cels.2024.07.001
Alexander T F Bell, Jacob T Mitchell, Ashley L Kiemen, Melissa Lyman, Kohei Fujikura, Jae W Lee, Erin Coyne, Sarah M Shin, Sushma Nagaraj, Atul Deshpande, Pei-Hsun Wu, Dimitrios N Sidiropoulos, Rossin Erbe, Jacob Stern, Rena Chan, Stephen Williams, James M Chell, Lauren Ciotti, Jacquelyn W Zimmerman, Denis Wirtz, Won Jin Ho, Neeha Zaidi, Elizabeth Thompson, Elizabeth M Jaffee, Laura D Wood, Elana J Fertig, Luciane T Kagohara

This study introduces a new imaging, spatial transcriptomics (ST), and single-cell RNA-sequencing integration pipeline to characterize neoplastic cell state transitions during tumorigenesis. We applied a semi-supervised analysis pipeline to examine premalignant pancreatic intraepithelial neoplasias (PanINs) that can develop into pancreatic ductal adenocarcinoma (PDAC). Their strict diagnosis on formalin-fixed and paraffin-embedded (FFPE) samples limited the single-cell characterization of human PanINs within their microenvironment. We leverage whole transcriptome FFPE ST to enable the study of a rare cohort of matched low-grade (LG) and high-grade (HG) PanIN lesions to track progression and map cellular phenotypes relative to single-cell PDAC datasets. We demonstrate that cancer-associated fibroblasts (CAFs), including antigen-presenting CAFs, are located close to PanINs. We further observed a transition from CAF-related inflammatory signaling to cellular proliferation during PanIN progression. We validate these findings with single-cell high-dimensional imaging proteomics and transcriptomics technologies. Altogether, our semi-supervised learning framework for spatial multi-omics has broad applicability across cancer types to decipher the spatiotemporal dynamics of carcinogenesis.

本研究介绍了一种新的成像、空间转录组学(ST)和单细胞 RNA 序列整合管道,用于描述肿瘤发生过程中肿瘤细胞状态的转变。我们应用半监督分析管道研究了可发展为胰腺导管腺癌(PDAC)的恶性胰腺上皮内瘤(PanINs)。对福尔马林固定和石蜡包埋(FFPE)样本的严格诊断限制了人类 PanINs 在其微环境中的单细胞表征。我们利用全转录组 FFPE ST 对匹配的低级别(LG)和高级别(HG)PanIN 病变进行了罕见的队列研究,以跟踪进展情况并绘制与单细胞 PDAC 数据集相对应的细胞表型图。我们证明,癌相关成纤维细胞(CAFs),包括抗原递呈CAFs,位于PanINs附近。我们进一步观察到,在 PanIN 进展过程中,与 CAF 相关的炎症信号转导向细胞增殖过渡。我们利用单细胞高维成像蛋白质组学和转录组学技术验证了这些发现。总之,我们的空间多组学半监督学习框架可广泛应用于各种癌症类型,以破译癌变的时空动态。
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引用次数: 0
Global transcription regulation revealed from dynamical correlations in time-resolved single-cell RNA sequencing. 从时间分辨单细胞 RNA 测序的动态相关性中揭示全球转录调控。
Pub Date : 2024-08-21 Epub Date: 2024-08-08 DOI: 10.1016/j.cels.2024.07.002
Dimitris Volteras, Vahid Shahrezaei, Philipp Thomas

Single-cell transcriptomics reveals significant variations in transcriptional activity across cells. Yet, it remains challenging to identify mechanisms of transcription dynamics from static snapshots. It is thus still unknown what drives global transcription dynamics in single cells. We present a stochastic model of gene expression with cell size- and cell cycle-dependent rates in growing and dividing cells that harnesses temporal dimensions of single-cell RNA sequencing through metabolic labeling protocols and cel lcycle reporters. We develop a parallel and highly scalable approximate Bayesian computation method that corrects for technical variation and accurately quantifies absolute burst frequency, burst size, and degradation rate along the cell cycle at a transcriptome-wide scale. Using Bayesian model selection, we reveal scaling between transcription rates and cell size and unveil waves of gene regulation across the cell cycle-dependent transcriptome. Our study shows that stochastic modeling of dynamical correlations identifies global mechanisms of transcription regulation. A record of this paper's transparent peer review process is included in the supplemental information.

单细胞转录组学揭示了细胞间转录活性的显著变化。然而,从静态快照中确定转录动态机制仍具有挑战性。因此,我们仍然不知道是什么驱动了单细胞中的全局转录动态。我们提出了一个基因表达的随机模型,该模型通过代谢标记协议和细胞周期报告器,利用单细胞 RNA 测序的时间维度,计算生长和分裂细胞中与细胞大小和细胞周期相关的速率。我们开发了一种并行且高度可扩展的近似贝叶斯计算方法,该方法可纠正技术差异,并在整个转录组范围内准确量化细胞周期中的绝对突变频率、突变大小和降解率。利用贝叶斯模型选择,我们揭示了转录率与细胞大小之间的比例关系,并揭示了依赖于细胞周期的转录组中的基因调控波。我们的研究表明,动态相关性的随机建模可以识别转录调控的全球机制。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
Evaluation of Kernfeld et al.: Toward best practices for tackling false positives in regulatory network inference. 对 Kernfeld 等人的评估:解决调控网络推断中假阳性问题的最佳实践。
Pub Date : 2024-08-21 DOI: 10.1016/j.cels.2024.07.009
Anthony Gitter

One snapshot of the peer review process for "Transcriptome data are insufficient to control false discoveries in regulatory network inference" (Kernfeld et al., 2024).1.

转录组数据不足以控制调控网络推断中的错误发现"(Kernfeld et al.
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引用次数: 0
Transcriptome data are insufficient to control false discoveries in regulatory network inference. 转录组数据不足以控制调控网络推断中的错误发现。
Pub Date : 2024-08-21 DOI: 10.1016/j.cels.2024.07.006
Eric Kernfeld, Rebecca Keener, Patrick Cahan, Alexis Battle

Inference of causal transcriptional regulatory networks (TRNs) from transcriptomic data suffers notoriously from false positives. Approaches to control the false discovery rate (FDR), for example, via permutation, bootstrapping, or multivariate Gaussian distributions, suffer from several complications: difficulty in distinguishing direct from indirect regulation, nonlinear effects, and causal structure inference requiring "causal sufficiency," meaning experiments that are free of any unmeasured, confounding variables. Here, we use a recently developed statistical framework, model-X knockoffs, to control the FDR while accounting for indirect effects, nonlinear dose-response, and user-provided covariates. We adjust the procedure to estimate the FDR correctly even when measured against incomplete gold standards. However, benchmarking against chromatin immunoprecipitation (ChIP) and other gold standards reveals higher observed than reported FDR. This indicates that unmeasured confounding is a major driver of FDR in TRN inference. A record of this paper's transparent peer review process is included in the supplemental information.

从转录组数据推断转录调控网络(TRN)的因果关系时,往往会出现假阳性。控制假阳性发现率(FDR)的方法(例如,通过置换、引导或多元高斯分布)有几个并发症:难以区分直接调控和间接调控、非线性效应,以及因果结构推断需要 "因果充分性",即实验中没有任何未测量的混杂变量。在此,我们使用最近开发的统计框架--X模型山寨版--来控制FDR,同时考虑间接效应、非线性剂量反应和用户提供的协变量。我们对程序进行了调整,即使根据不完整的黄金标准进行测量,也能正确估计 FDR。然而,以染色质免疫沉淀(ChIP)和其他黄金标准为基准,发现观察到的 FDR 比报告的要高。这表明,未测量的混杂因素是 TRN 推断中 FDR 的主要驱动因素。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
Enhanced cellular longevity arising from environmental fluctuations. 环境波动导致细胞寿命延长。
Pub Date : 2024-08-21 DOI: 10.1016/j.cels.2024.07.007
Yuting Liu, Zhen Zhou, Hetian Su, Songlin Wu, Gavin Ni, Alex Zhang, Lev S Tsimring, Jeff Hasty, Nan Hao

Cellular longevity is regulated by both genetic and environmental factors. However, the interactions of these factors in the context of aging remain largely unclear. Here, we formulate a mathematical model for dynamic glucose modulation of a core gene circuit in yeast aging, which not only guided the design of pro-longevity interventions but also revealed the theoretical principles underlying these interventions. We introduce the dynamical systems theory to capture two general means for promoting longevity-the creation of a stable fixed point in the "healthy" state of the cell and the "dynamic stabilization" of the system around this healthy state through environmental oscillations. Guided by the model, we investigate how both of these can be experimentally realized by dynamically modulating environmental glucose levels. The results establish a paradigm for theoretically analyzing the trajectories and perturbations of aging that can be generalized to aging processes in diverse cell types and organisms.

细胞寿命受遗传和环境因素的调节。然而,这些因素在衰老过程中的相互作用在很大程度上仍不清楚。在这里,我们建立了一个数学模型,用于对酵母衰老过程中的核心基因回路进行动态葡萄糖调节,该模型不仅指导了促长寿干预措施的设计,还揭示了这些干预措施的理论基础。我们引入了动力系统理论来捕捉促进长寿的两种一般手段--在细胞的 "健康 "状态下创建一个稳定的固定点,以及通过环境振荡使系统围绕这一健康状态实现 "动态稳定"。在该模型的指导下,我们研究了如何通过动态调节环境中的葡萄糖水平在实验中实现这两种方法。研究结果为从理论上分析衰老的轨迹和扰动建立了一个范例,该范例可推广到不同细胞类型和生物体的衰老过程中。
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引用次数: 0
Brain dynamics supported by a hierarchy of complex correlation patterns defining a robust functional architecture. 复杂相关模式的层次结构支持大脑动态,定义了一个强大的功能架构。
Pub Date : 2024-08-21 Epub Date: 2024-08-13 DOI: 10.1016/j.cels.2024.07.003
Levente Varga, Vasile V Moca, Botond Molnár, Laura Perez-Cervera, Mohamed Kotb Selim, Antonio Díaz-Parra, David Moratal, Balázs Péntek, Wolfgang H Sommer, Raul C Mureșan, Santiago Canals, Maria Ercsey-Ravasz

Functional magnetic resonance imaging (fMRI) provides insights into cognitive processes with significant clinical potential. However, delays in brain region communication and dynamic variations are often overlooked in functional network studies. We demonstrate that networks extracted from fMRI cross-correlation matrices, considering time lags between signals, show remarkable reliability when focusing on statistical distributions of network properties. This reveals a robust brain functional connectivity pattern, featuring a sparse backbone of strong 0-lag correlations and weaker links capturing coordination at various time delays. This dynamic yet stable network architecture is consistent across rats, marmosets, and humans, as well as in electroencephalogram (EEG) data, indicating potential universality in brain dynamics. Second-order properties of the dynamic functional network reveal a remarkably stable hierarchy of functional correlations in both group-level comparisons and test-retest analyses. Validation using alcohol use disorder fMRI data uncovers broader shifts in network properties than previously reported, demonstrating the potential of this method for identifying disease biomarkers.

功能磁共振成像(fMRI)为认知过程提供了重要的临床潜力。然而,在功能网络研究中,脑区通信的延迟和动态变化往往被忽视。我们证明,考虑到信号之间的时滞,从 fMRI 交叉相关矩阵中提取的网络在关注网络属性的统计分布时显示出显著的可靠性。这揭示了一种稳健的大脑功能连接模式,其特点是由强 0 滞后相关和捕捉不同时间延迟下协调的较弱链接组成的稀疏主干。这种动态而稳定的网络结构在大鼠、狨猴、人类以及脑电图(EEG)数据中都是一致的,表明了大脑动态的潜在普遍性。动态功能网络的二阶特性显示,在组级比较和测试-重复分析中,功能相关性的层次结构非常稳定。通过使用酒精使用障碍的 fMRI 数据进行验证,发现了比以前报道的更广泛的网络属性变化,证明了这种方法在确定疾病生物标记物方面的潜力。
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引用次数: 0
Systematic identification of transcriptional activation domains from non-transcription factor proteins in plants and yeast. 系统鉴定植物和酵母中非转录因子蛋白的转录激活结构域。
Pub Date : 2024-07-17 Epub Date: 2024-06-11 DOI: 10.1016/j.cels.2024.05.007
Niklas F C Hummel, Kasey Markel, Jordan Stefani, Max V Staller, Patrick M Shih

Transcription factors can promote gene expression through activation domains. Whole-genome screens have systematically mapped activation domains in transcription factors but not in non-transcription factor proteins (e.g., chromatin regulators and coactivators). To fill this knowledge gap, we employed the activation domain predictor PADDLE to analyze the proteomes of Arabidopsis thaliana and Saccharomyces cerevisiae. We screened 18,000 predicted activation domains from >800 non-transcription factor genes in both species, confirming that 89% of candidate proteins contain active fragments. Our work enables the annotation of hundreds of nuclear proteins as putative coactivators, many of which have never been ascribed any function in plants. Analysis of peptide sequence compositions reveals how the distribution of key amino acids dictates activity. Finally, we validated short, "universal" activation domains with comparable performance to state-of-the-art activation domains used for genome engineering. Our approach enables the genome-wide discovery and annotation of activation domains that can function across diverse eukaryotes.

转录因子可通过激活结构域促进基因表达。全基因组筛选系统地绘制了转录因子的激活结构域,但没有绘制非转录因子蛋白(如染色质调节因子和辅助激活因子)的激活结构域。为了填补这一知识空白,我们利用激活结构域预测工具 PADDLE 分析了拟南芥和酿酒酵母的蛋白质组。我们筛选了这两个物种中超过 800 个非转录因子基因的 18,000 个预测激活结构域,证实 89% 的候选蛋白质含有活性片段。我们的工作使我们能够将数百种核蛋白注释为推定的辅助激活因子,其中许多在植物中从未被赋予任何功能。对肽序列组成的分析揭示了关键氨基酸的分布是如何决定活性的。最后,我们验证了简短的 "通用 "激活结构域,其性能与用于基因组工程的最先进激活结构域相当。我们的方法能够在全基因组范围内发现和注释能够在不同真核生物中发挥作用的激活结构域。
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引用次数: 0
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Cell systems
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