SLAS Technology最新文献

英文中文

Corrigendum to “Early Detection of Bronchopulmonary Dysplasia (BPD) in Preterm Infants Using Doppler Ultrasound Technology” [SLAS Technology Volume 31, April 2025, 100249] “使用多普勒超声技术早期检测早产儿支气管肺发育不良（BPD）”的更正[SLAS技术卷31，April 2025, 100249]。

IF 3.7 4区医学 Q3 BIOCHEMICAL RESEARCH METHODS

SLAS Technology

Pub Date : 2026-01-01 Epub Date: 2025-09-28 DOI: 10.1016/j.slast.2025.100354

Pin Wang , Lihong Duan , Congxin Sun , Yu Chen , Yanyan Peng , Guihong Chen , Lixia Wu , Yan Li

引用次数: 0

Spatial transcriptomic modeling of vascular remodeling in aortic aneurysm using integrated single-cell RNA sequencing analysis 基于单细胞RNA测序分析的主动脉瘤血管重构的空间转录组学建模。

IF 3.7 4区医学 Q3 BIOCHEMICAL RESEARCH METHODS

SLAS Technology

Pub Date : 2026-01-01 Epub Date: 2025-12-06 DOI: 10.1016/j.slast.2025.100377

Shaodong Xiong , Ludong Liang , Tao Liu

Background

The phenotypic regulation of vascular smooth muscle cells (VSMCs) is a critical characteristic of aortic aneurysm formation, although its spatial and transcriptional dynamics remain incompletely understood.

Methods

We developed a computational workflow integrating single-cell RNA sequencing (scRNA-seq) with pseudo-spatial transcriptomic inference to model vascular remodeling at cellular resolution. Using the publicly available SCP1361 dataset from the Broad Institute's Single Cell Portal (15,698 high-quality cells from ascending aorta of normal and high- fat diet mice), we employed Seurat v4.3.0 for quality control and clustering, Tangram v1.0 for pseudo-spatial projection onto synthetic tissue scaffolds, and Monocle3 for trajectory inference. Statistical analyses included chi-square tests for cell proportion differences, Wilcoxon rank-sum tests for differential expression, and Moran's I for spatial variability (α = 0.05, adjusted for multiple testing).

Results

We identified 25 transcriptionally distinct cell populations including 5 VSMC subtypes and 4 fibroblast subtypes showing region-specific localization patterns. High-fat diet significantly increased synthetic VSMC_2 populations (+13.1 %, p = 0.008) and monocyte infiltration (+16.3 %, p = 0.042) while decreasing contractile VSMC_3 (-8.2 %, p = 0.041). Pseudo-spatial reconstruction revealed anatomically compartmentalized cell states with contractile VSMCs localizing to the media and synthetic/inflammatory phenotypes enriching in adventitial regions. Trajectory analysis identified 847 genes with pseudotemporal dynamics (q < 0.05) associated with VSMC phenotypic transitions.

Conclusions

This framework demonstrates how publicly available scRNA-seq data can be leveraged for hypothesis-generating spatial modeling of vascular disease. The approach reveals cell-type-specific transcriptional programs and phenotypic transitions that warrant experimental validation through immunohistochemistry and true spatial transcriptomics.

背景：血管平滑肌细胞（VSMCs）的表型调控是动脉瘤形成的一个关键特征，尽管其空间和转录动力学尚不完全清楚。方法：我们开发了一个计算工作流，将单细胞RNA测序（scRNA-seq）与伪空间转录组推断结合起来，在细胞分辨率上模拟血管重构。使用来自Broad研究所单细胞门户网站的公开可用的SCP1361数据集（来自正常和高脂肪饮食小鼠升主动脉的15698个高质量细胞），我们使用Seurat v4.3.0进行质量控制和聚类，Tangram v1.0用于伪空间投影到合成组织支架上，Monocle3用于轨迹推断。统计分析包括细胞比例差异的卡方检验，差异表达的Wilcoxon秩和检验，空间变异性的Moran's I检验（α = 0.05，经多重检验调整）。结果：我们鉴定了25个转录不同的细胞群，包括5个VSMC亚型和4个显示区域特异性定位模式的成纤维细胞亚型。高脂饲料显著增加了合成VSMC_2 （+13.1%, p=0.008）和单核细胞浸润（+16.3%,p=0.042），显著降低了收缩VSMC_3 （-8.2%, p=0.041）。伪空间重建显示解剖上的细胞状态区隔化，收缩的VSMCs定位于介质，合成/炎症表型在外膜区丰富。轨迹分析确定了847个具有伪时间动力学的基因(结论：该框架证明了如何利用公开可用的scRNA-seq数据进行血管疾病的假设生成空间建模。该方法揭示了细胞类型特异性转录程序和表型转变，需要通过免疫组织化学和真正的空间转录组学进行实验验证。

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引用次数: 0

Life sciences and aging 生命科学与老龄化。

IF 3.7 4区医学 Q3 BIOCHEMICAL RESEARCH METHODS

SLAS Technology

Pub Date : 2026-01-01 Epub Date: 2025-11-14 DOI: 10.1016/j.slast.2025.100367

引用次数: 0

An explainable hybrid CNN-LSTM framework for accurate sequence-based classification of RNA N6-methyladenosine (m6A) modification 基于序列的RNA n6 -甲基腺苷（m6A）修饰精确分类的可解释的CNN-LSTM混合框架

IF 3.7 4区医学 Q3 BIOCHEMICAL RESEARCH METHODS

SLAS Technology

Pub Date : 2026-01-01 Epub Date: 2025-12-05 DOI: 10.1016/j.slast.2025.100383

Kainat Ali Rehman , Muhammad Sohail Khan , Faiza Tila , Inayat Khan , Jawad Khan , Mohammad Shabaz , Ahlam Almusharraf , Mohammad Tabrez Quasim

N6-methyladenosine (m6A) is one of the most prevalent and functionally significant RNA modifications in eukaryotic transcriptomes, playing critical roles in post-transcriptional gene regulation. Accurate identification of m6A sites remains a considerable challenge due to complex sequence dependencies and limited labeled data. To address these issues, this paper proposed a hybrid Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) framework integrated with Shapley additive explanations (SHAP) for feature selection and interpretability. The proposed scheme aims to improve both predictive performance and biological insight by identifying and emphasizing the most informative features in RNA sequences. Firstly, multiple biologically relevant feature extraction methods are employed to encode RNA sequences into numerical representations. Secondly, SHAP is used to quantify each feature's contribution, enabling the selection of the most impactful subset while enhancing model interpretability. Thirdly, the selected data is reshaped and standardized to ensure Conv1D compatibility and capture local sequence motifs effectively. Fourthly, CNN layers extract high-level spatial features, which are then fed into LSTM layers to model long-range dependencies within the sequences. Finally, the output is processed through fully connected layers with a sigmoid activation function to perform binary prediction. The experimental results indicate that the CNN-LSTM model, combined with SHAP-based feature selection, outperforms traditional classifiers and standalone deep learning models. The proposed CNN-LSTM framework, evaluated using 10-fold stratified cross-validation, achieved an accuracy of 87.39 %, sensitivity of 83.25 %, specificity of 91.52 %, and an MCC of 0.7534. The results of the proposed model demonstrate its strong ability to accurately classify RNA m6A modification sites, highlighting its potential for large-scale transcriptome-wide epitranscriptomics analysis. The proposed CNN-LSTM model was rigorously compared with several traditional machine learning classifiers and state-of-the-art deep learning approaches, including DT, SVM, KNN, AdaBoost Classifier, Gaussian NB, and DNN.

n6 -甲基腺苷（m6A）是真核生物转录组中最普遍和功能最重要的RNA修饰之一，在转录后基因调控中起着关键作用。由于复杂的序列依赖性和有限的标记数据，准确识别m6A位点仍然是相当大的挑战。为了解决这些问题，本文提出了一个混合卷积神经网络和长短期记忆（CNN-LSTM）框架，并结合Shapley加性解释（SHAP）进行特征选择和可解释性。该方案旨在通过识别和强调RNA序列中最具信息量的特征来提高预测性能和生物学洞察力。首先，采用多种生物学相关特征提取方法将RNA序列编码为数值表示。其次，使用SHAP来量化每个特征的贡献，在增强模型可解释性的同时选择最具影响力的子集。再次，对选取的数据进行重构和标准化，以保证Conv1D的兼容性，有效捕获局部序列基元。第四，CNN层提取高级空间特征，然后将其馈送到LSTM层中，以模拟序列内的长期依赖关系。最后，用sigmoid激活函数对输出进行全连接层处理，进行二值预测。实验结果表明，CNN-LSTM模型结合基于shap的特征选择，优于传统的分类器和独立的深度学习模型。采用10倍分层交叉验证对提出的CNN-LSTM框架进行评估，准确率为87.39%，灵敏度为83.25%，特异性为91.52%，MCC为0.7534。该模型的结果表明，其具有准确分类RNA m6A修饰位点的强大能力，突出了其在大规模转录组全表转录组学分析方面的潜力。提出的CNN-LSTM模型与几种传统的机器学习分类器和最先进的深度学习方法进行了严格的比较，包括DT、SVM、KNN、AdaBoost Classifier、高斯NB和DNN。

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引用次数: 0

High-throughput dissection of inter-organ genetic networks: A multi-omic systems biology approach 器官间遗传网络的高通量解剖：多组学系统生物学方法。

IF 3.7 4区医学 Q3 BIOCHEMICAL RESEARCH METHODS

SLAS Technology

Pub Date : 2026-01-01 Epub Date: 2025-12-11 DOI: 10.1016/j.slast.2025.100376

Rana Alabdan , Hechmi Shili , Ghada Moh. Samir Elhessewi , Mukhtar Ghaleb , Eman M Alanazi , Nouf Helal Alharbi , Rowida Mohammed Alharbi , Asma A. Alhashmi

The existing multi-omic analyses are frequently confined to individual tissues, and the regulatory picture of the systemic regulator of complex physiology and disease is hidden. To fill this gap, we have created a unified systems biology model of the high-throughput dissection of inter-organ genetic networks. Our model incorporates transcriptomic, epigenomic and proteomic analysis of five major organs (liver, kidney, heart, lung, brain) using the Multi-Omics Factor Analysis (MOFA+) tool, specifically, cross-tissue coordination. We characterized 27 evidence-heavy cross-tissue modules (FDR < 0.05) that are major hubs such as *HNF4Aenda NRF2cheng8loadmasterregulatingconstitutionembryonicstemcellularinfoncogenes recognize them. One notable observation was liver-kidney metabolic axis, significant cross-talks in hepatocyte organoids are confirmed with CRISPR knockdown, which suppresses the expression of transporters expressed by the kidney. Our work offers a scalable validated framework that goes beyond organ-centric perspectives, which can be used as a potent tool of systemic disease modelling and precision medicine.

现有的多组学分析往往局限于单个组织，而隐藏了复杂生理和疾病的系统性调节机制。为了填补这一空白，我们创建了一个统一的高通量解剖器官间遗传网络的系统生物学模型。我们的模型结合了五个主要器官（肝、肾、心、肺、脑）的转录组学、表观基因组学和蛋白质组学分析，使用多组学因子分析（MOFA+）工具，特别是跨组织协调。我们鉴定了27个证据丰富的跨组织模块（FDR < 0.05），这些模块是主要的枢纽，如*HNF4Aenda nrf2cheng8loadmaster调控结构，胚胎系统细胞信息癌基因识别它们。一个值得注意的观察是肝-肾代谢轴，肝细胞类器官的显著交叉对话被CRISPR敲低证实，这抑制了肾脏表达的转运蛋白的表达。我们的工作提供了一个可扩展的验证框架，超越了以器官为中心的观点，它可以作为系统性疾病建模和精准医学的有力工具。

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引用次数: 0

Retraction notice to “Clinical Observation and Evaluation of Health Management Intervention in Controlling Senile Chronic Diseases such as Hyperlipidemia” [SLAS Technology 33 (2025) 100318] 《健康管理干预控制老年高脂血症等慢性病的临床观察与评价》撤稿通知[sla科技33(2025)100318]。

IF 3.7 4区医学 Q3 BIOCHEMICAL RESEARCH METHODS

SLAS Technology

Pub Date : 2026-01-01 Epub Date: 2025-12-13 DOI: 10.1016/j.slast.2025.100375

Hongxia Liu

引用次数: 0

2nd EUOS/SLAS joint challenge: Prediction of spectral properties of compounds 第二届EUOS/SLAS联合挑战：化合物光谱性质预测。

IF 3.7 4区医学 Q3 BIOCHEMICAL RESEARCH METHODS

SLAS Technology

Pub Date : 2026-01-01 Epub Date: 2025-11-22 DOI: 10.1016/j.slast.2025.100374

Katholiki Skopelitou , Federica Rossella , Rawdat Awuku Larbi , Philip Gribbon , Thalita Cirino , Igor V. Tetko

引用次数: 0

Toward full automation in synthetic biology: A progressive conceptual framework integrating robotics and intelligent agents 迈向合成生物学的完全自动化：一个整合机器人和智能代理的渐进概念框架。

IF 3.7 4区医学 Q3 BIOCHEMICAL RESEARCH METHODS

SLAS Technology

Pub Date : 2026-01-01 Epub Date: 2025-12-06 DOI: 10.1016/j.slast.2025.100378

Mirco Plante , Antoine Champie , François Michaud , Sébastien Rodrigue

Synthetic biology is a rapidly evolving discipline that seeks to understand, modify, design, and build biological systems by applying modular and systemic principles inspired by engineering. Automation in synthetic biology offers significant gains in efficiency, reproducibility, and standardization, enabling more reliable and scalable experiments while reducing human fatigue and health risks. This shift allows researchers to focus on experimental design, data analysis, and innovation rather than repetitive tasks. More recently, artificial intelligence has begun to reshape laboratory work at a cognitive level, enabling machines to analyze data, make decisions, and learn from experience. Artificial intelligence in biology has the potential to accelerate discovery, optimize experimental design, and enhance data analysis by identifying patterns beyond human capabilities. The convergence of robotics and artificial intelligence offers a promising future for synthetic biology but also raises ethical concerns. As the creation of engineered life becomes increasingly automated and shaped by intelligent agents, questions about governance, responsibility, and transparency become more pressing. In this article, we examine the progress and prospects of both physical (robotic) and cognitive (intelligent agent) automation in synthetic biology. We begin with an overview of automation technologies in industrial and laboratory settings, then discuss the objectives and challenges of synthetic biology from an automation perspective. Finally, we propose a dual conceptual framework: one for total automation of the Design–Build–Test–Learn (DBTL) cycle, and another for progressive automation adaptable to diverse laboratory contexts. Our aim is to support the development and responsible implementation of automation systems in synthetic biology laboratories.

合成生物学是一门快速发展的学科，旨在通过应用受工程学启发的模块化和系统化原则来理解、修改、设计和构建生物系统。合成生物学中的自动化大大提高了效率、可重复性和标准化，实现了更可靠和可扩展的实验，同时减少了人类的疲劳和健康风险。这种转变使研究人员能够专注于实验设计、数据分析和创新，而不是重复的任务。最近，人工智能已经开始在认知层面重塑实验室工作，使机器能够分析数据、做出决策并从经验中学习。生物学中的人工智能有可能通过识别超出人类能力的模式来加速发现、优化实验设计和增强数据分析。机器人和人工智能的融合为合成生物学提供了一个充满希望的未来，但也引发了伦理问题。随着工程生命的创造变得越来越自动化，并受到智能代理的影响，有关治理、责任和透明度的问题变得更加紧迫。在本文中，我们研究了合成生物学中物理（机器人）和认知（智能体）自动化的进展和前景。我们首先概述了工业和实验室环境中的自动化技术，然后从自动化的角度讨论合成生物学的目标和挑战。最后，我们提出了一个双重概念框架：一个用于设计-构建-测试-学习（DBTL）周期的完全自动化，另一个用于适应不同实验室环境的渐进自动化。我们的目标是支持合成生物学实验室自动化系统的开发和负责任的实施。

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引用次数: 0

Editorial: Robotics in laboratory automation 社论：实验室自动化中的机器人技术。

IF 3.7 4区医学 Q3 BIOCHEMICAL RESEARCH METHODS

SLAS Technology

Pub Date : 2026-01-01 Epub Date: 2025-12-13 DOI: 10.1016/j.slast.2025.100373

Kerstin Thurow , Oliver Peter , Patrick Courtney , Károly Széll , Ádám Wolf

The increasing complexity of modern life science laboratories presents unique challenges for automation and robotics that extend beyond traditional industrial applications. As laboratory workflows become increasingly intricate, the integration of robotic systems has become essential to improve efficiency, reproducibility, and scalability. This special issue highlights recent advances in laboratory automation, focusing on innovative robotic solutions that enhance experimental precision and operational throughput. We explore key technological developments, standardization efforts, and emerging trends that are shaping the future of automation. By addressing both the opportunities and current limitations of robotic systems in laboratory environments, this editorial provides insights into the evolution of intelligent automation in life sciences.

日益复杂的现代生命科学实验室提出了超越传统工业应用的自动化和机器人的独特挑战。随着实验室工作流程变得越来越复杂，机器人系统的集成对于提高效率、可重复性和可扩展性变得至关重要。本期特刊重点介绍了实验室自动化的最新进展，重点介绍了提高实验精度和操作吞吐量的创新机器人解决方案。我们探讨了关键的技术发展、标准化工作以及正在塑造自动化未来的新兴趋势。通过解决实验室环境中机器人系统的机遇和当前的局限性，这篇社论提供了对生命科学中智能自动化发展的见解。

引用次数: 0

Single-cell RNA insights and densely scaled vision transformer-based MRI classification for precision brain tumors 单细胞RNA洞察和基于密集尺度视觉转换器的精确脑肿瘤MRI分类。

IF 3.7 4区医学 Q3 BIOCHEMICAL RESEARCH METHODS

SLAS Technology

Pub Date : 2025-12-01 Epub Date: 2025-11-20 DOI: 10.1016/j.slast.2025.100371

Pratikkumar Chauhan, Munindra Lunagaria, Deepak Kumar Verma

There are >1600 evolutionarily conserved RNA-binding proteins (RBPs) in the human genome. Many multi-omics studies have demonstrated that these proteins are often not working properly in malignancies like glioblastoma and melanoma. These RBPs are very important for the complex regulatory networks that govern the activities that are typical of cancer. RBPs’ intricate control of RNA activity at many levels and their post-translational modifications, which make them more functional, make things even more convoluted. Additionally, other RBP-based therapies have emerged, each underpinned by distinct molecular mechanisms, including genomic analysis and the inhibition of RBP functionality. This paper reports findings from patients with brain tumours undergoing experimental RNA interference treatment. We also suggest a Densely Scaled Vision Transformer (DSViT) made to find and locate brain tumors of different types. The model is evaluated on the FigShare Brain Tumor Dataset comprising 3064 MRI images categorized into Glioma, Meningioma, and Pituitary tumors, with final testing conducted on 614 samples. Experimental results show that DSViT achieves an accuracy of 96.09 %, precision of 96.57 %, recall of 95.97 %, and an F1-score of 96.27 %, significantly outperforming the ViT-Baseline and ablation variants. Future directions include extending DSViT into multimodal pipelines that fuse imaging with molecular profiles, thereby enhancing precision neuro-oncology. Its modular structure also enables integration into radiological reporting systems for automated annotation and clinician-guided decision support. This innovative RNA interference (iRNAi) based therapeutic intervention has significant therapeutic potential and is, as far as we are aware, the first time RNA interference has been used to treat human disease.

人类基因组中有超过1600种进化上保守的rna结合蛋白（rbp）。许多多组学研究表明，这些蛋白质在恶性肿瘤如胶质母细胞瘤和黑色素瘤中通常不能正常工作。这些rbp对于控制典型癌症活动的复杂调控网络非常重要。rbp在许多层面上对RNA活性的复杂控制，以及它们的翻译后修饰（使它们更有功能），使事情变得更加复杂。此外，已经出现了其他基于RBP的疗法，每种疗法都有不同的分子机制，包括基因组分析和抑制RBP功能。本文报道了脑肿瘤患者接受实验性RNA干扰治疗的结果。我们还建议使用密集缩放视觉变压器（DSViT）来发现和定位不同类型的脑肿瘤。该模型在FigShare脑肿瘤数据集上进行评估，该数据集包含3064张MRI图像，分为胶质瘤、脑膜瘤和垂体瘤，并对614个样本进行了最终测试。实验结果表明，DSViT的准确率为96.09%，精密度为96.57%，召回率为95.97%，f1评分为96.27%，显著优于ViT-Baseline和消融变体。未来的方向包括将DSViT扩展到融合成像和分子谱的多模态管道，从而提高神经肿瘤学的精确性。它的模块化结构还可以集成到放射报告系统中，用于自动注释和临床指导决策支持。这种创新的基于RNA干扰（iRNAi）的治疗干预具有显著的治疗潜力，据我们所知，这是RNA干扰首次用于治疗人类疾病。

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