SLAS Technology最新文献

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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 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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引用次数: 0

A machine learning-driven robotic system for autonomous nucleic acid extraction and library preparation 自主核酸提取和文库制备的机器学习驱动机器人系统。

IF 3.7 4区医学 Q3 BIOCHEMICAL RESEARCH METHODS

SLAS Technology

Pub Date : 2025-12-01 DOI: 10.1016/j.slast.2025.100370

Jun Lu , Zhizhong Zhang , Sheng Zhang , Qingqing Ye , Yuanhua Wang , Jianqiang Chen , Guodong Sun , Zihui Huang , Youcong Hou , Zhiyong Sun , Jianhua Dong , Jisheng Qin , Xueping Chen , Shiwen Mai , Minghui Gao , Fengxiang Zhang , Xiaohui Wen , Wenshen Gu , Ruiguo Yu , Guanglei Zhao , Xiao Zhang

Nucleic acids, the fundamental building blocks of life, serve as versatile tools in genetic information retrieval, disease diagnosis, and biotechnological applications. The automated Intelligent Robotic System for Nucleic Acid Extraction and Library Preparation (iRoNAEaLP) tool represents a significant advancement in nucleic acid extraction and library preparation in an automated manner, addressing complexity and diversity while minimizing human involvement. Utilising machine learning algorithms and a Long Short-Term Memory (LSTM) architecture, iRoNAEaLP autonomously generates process flowcharts, predetermined reagents and consumable quantities, and aligns process steps with specific module actions via strategy-guided segmented program file arrangements. As a result, the biological outcome from this system has demonstrated high efficiency and large-scale data quality in various types of samples in terms of trace nucleic acid extraction, plasmid/genetic construct extraction, and single-cell and spatial omics, which require mRNA library preparation for smart-seq2 sequencing. This innovation paves the way for more efficient and accessible bioprocesses in various life science applications.

核酸是生命的基本组成部分，在遗传信息检索、疾病诊断和生物技术应用中发挥着多功能工具的作用。自动化核酸提取和文库制备智能机器人系统（iRoNAEaLP）工具代表了自动化核酸提取和文库制备的重大进步，在最大限度地减少人类参与的同时解决了复杂性和多样性。利用机器学习算法和长短期记忆（LSTM）架构，iRoNAEaLP自动生成工艺流程图、预定试剂和消耗品数量，并通过策略指导的分段程序文件安排将工艺步骤与特定模块动作对齐。因此，该系统在痕量核酸提取、质粒/遗传构建体提取、单细胞组学和空间组学等需要制备用于smart-seq2测序的mRNA文库的各类样品中均显示出高效率和大规模的数据质量。这一创新为在各种生命科学应用中更有效和更容易获得的生物过程铺平了道路。

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引用次数: 0

Automated endpoint titer ELISAs for high-throughput immunogenicity evaluations using a BioMek i7 liquid handler 使用BioMek i7液体处理器进行高通量免疫原性评估的自动终点滴度elisa。

IF 3.7 4区医学 Q3 BIOCHEMICAL RESEARCH METHODS

SLAS Technology

Pub Date : 2025-12-01 DOI: 10.1016/j.slast.2025.100368

Barbara Theriot , Andrew N. Macintyre

引用次数: 0

Single cell and multi omics dissection of periodontal ligament cells identifies regulatory networks and therapeutic targets in skeletal class II malocclusion 牙周韧带细胞单细胞和多组学解剖鉴定骨骼ⅱ类错颌畸形的调节网络和治疗靶点。

IF 3.7 4区医学 Q3 BIOCHEMICAL RESEARCH METHODS

SLAS Technology

Pub Date : 2025-11-29 DOI: 10.1016/j.slast.2025.100372

Yirui Niu , Quan Dai , Min Li

Skeletal Class II malocclusion is a common dentofacial deformity often associated with dysregulation in the growth and remodeling of periodontal tissues. Understanding the cellular heterogeneity and molecular pathways of periodontal ligament (PDL) cells is crucial for identifying novel therapeutic targets. However, traditional bulk sequencing methods lack the resolution to distinguish cell-type-specific gene expression and epigenetic regulation, limiting insights into the pathogenesis of this condition. To address these limitations, we propose an integrated framework utilizing 5200 high-quality PDL Single-Cell RNA combined with Assay for Transposase-Accessible Chromatin using sequencing (scRNA-ATAC-seq) to perform a multi-omics dissection of PDL cells in Class II malocclusion. This approach enables the simultaneous profiling of transcriptomic and chromatin accessibility landscapes at single-cell resolution, uncovering cell-specific regulatory networks. Using this scRNA-ATAC-seq method, we successfully identified 12 distinct PDL cell subpopulations and revealed key transcription factors and signaling pathways involved in aberrant skeletal development. Data integration achieved a 0.94 accuracy score, enabling confident regulatory mapping. The findings provide critical insights into the gene regulatory architecture underlying Class II malocclusion and highlight potential cell-specific therapeutic targets for clinical intervention.

骨骼II类错牙合是一种常见的牙面畸形，通常与牙周组织生长和重塑的失调有关。了解牙周韧带（PDL）细胞的细胞异质性和分子通路对于确定新的治疗靶点至关重要。然而，传统的批量测序方法缺乏区分细胞类型特异性基因表达和表观遗传调控的分辨率，限制了对这种疾病发病机制的了解。为了解决这些限制，我们提出了一个综合框架，利用5200个高质量的PDL单细胞RNA结合转座酶可及染色质测序（scRNA-ATAC-seq）对II类错颌错的PDL细胞进行多组学解剖。这种方法能够在单细胞分辨率下同时分析转录组学和染色质可及性景观，揭示细胞特异性调节网络。使用scRNA-ATAC-seq方法，我们成功鉴定了12个不同的PDL细胞亚群，并揭示了参与异常骨骼发育的关键转录因子和信号通路。数据集成的准确率达到了0.94分，实现了自信的监管映射。这些发现为II类错颌畸形的基因调控结构提供了重要的见解，并强调了临床干预的潜在细胞特异性治疗靶点。

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引用次数: 0

Life sciences and accountability. 生命科学与责任。

IF 3.7 4区医学 Q3 BIOCHEMICAL RESEARCH METHODS

SLAS Technology

Pub Date : 2025-11-25 DOI: 10.1016/j.slast.2025.100369

Kerstin Thurow

引用次数: 0

Life sciences and anxiety - Between enlightenment and uncertainty. 生命科学与焦虑——在启蒙与不确定性之间。

IF 3.7 4区医学 Q3 BIOCHEMICAL RESEARCH METHODS

SLAS Technology

Pub Date : 2025-11-14 DOI: 10.1016/j.slast.2025.100366

引用次数: 0

Technology-enabled integration of single-cell transcriptomics and microbiome data identifies RNA-targetable host-microbiota networks in colorectal adenoma 技术支持的单细胞转录组学和微生物组数据整合鉴定结直肠腺瘤中rna靶向宿主-微生物群网络。

IF 3.7 4区医学 Q3 BIOCHEMICAL RESEARCH METHODS

SLAS Technology

Pub Date : 2025-11-13 DOI: 10.1016/j.slast.2025.100365

Boyang Ma , Haiyan Hu , Yu Lin , Zhiheng Wang , Qingyu Song

Although mechanism-to-intervention processes are becoming possible because to the convergence of single-cell technologies with RNA treatment methods, combined host-microbiome analysis with systematic target identification for colorectal adenoma is still fragmented. Here, we created a repeatable computational pipeline that combines MaAsLin2 for host-microbiome association modeling, QIIME2/DADA2 for microbiome processing, and Seurat/Harmony for single-cell analysis. Under strict statistical control (FDR < 0.05), three single-cell RNA sequencing datasets (GSE117875, GSE178341, and GSE144735; totaling 426,425 cells) were combined with parallel microbiome datasets (PRJNA397906, PRJNA541510, and PRJNA672605; 975 samples). In adenoma-associated microbiomes, we measured a 26.8 % decrease in Shannon diversity (4.21→3.08), with a notable enrichment of Fusobacterium nucleatum and a depletion of Faecalibacterium prausnitzii. Immune activation patterns, goblet cell malfunction (MUC2 2.4-fold drop), and stem cell expansion (LGR5 3.2-fold increase) were all identified by single-cell analysis. 847 significant host-microbiome interactions were found by integration analysis, and F. nucleatum showed a substantial correlation with markers of inflammatory signaling (NFKB1: β=0.64, FDR<0.001) and stem cell proliferation (LGR5: β=0.72, FDR<0.001). 25 RNA-targetable candidates were identified by systematic prioritizing, including mRNA restoration targets (MUC2, FOXP3) and ASO/siRNA suppression targets (NFKB1, IL1B). By converting host-microbiome interaction networks into systematic RNA therapeutic options, this technology framework creates a template for the translation of transcriptomics into therapeutics and offers a repeatable pipeline for the creation of precision interventions in colorectal disease.

尽管由于单细胞技术与RNA治疗方法的融合，机制到干预的过程正在成为可能，但将宿主-微生物组分析与结直肠腺瘤的系统靶点识别相结合仍然是碎片化的。在这里，我们创建了一个可重复的计算管道，将MaAsLin2用于宿主-微生物组关联建模，QIIME2/DADA2用于微生物组处理，Seurat/Harmony用于单细胞分析。在严格的统计控制（FDR < 0.05）下，将3个单细胞RNA测序数据集（GSE117875、GSE178341和GSE144735，共426,425个细胞）与平行微生物组数据集（PRJNA397906、PRJNA541510和PRJNA672605, 975个样本）进行组合。在腺瘤相关微生物组中，Shannon多样性下降了26.8%(4.21→3.08)，其中核梭杆菌显著富集，prausnitzii粪杆菌明显减少。免疫激活模式、杯状细胞功能障碍（MUC2下降2.4倍）和干细胞扩增（LGR5增加3.2倍）均通过单细胞分析确定。整合分析发现了847个显著的宿主-微生物组相互作用，核核F. nucleatum与炎症信号标志物（NFKB1: β=0.64， FDR）存在显著相关性

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引用次数: 0

Literature highlights column: From the literature: Life Sciences Discovery and Technology Highlights. 文献亮点栏目：来自文献：生命科学发现与技术亮点。

IF 3.7 4区医学 Q3 BIOCHEMICAL RESEARCH METHODS

SLAS Technology

Pub Date : 2025-11-12 DOI: 10.1016/j.slast.2025.100364

Jamien Lim, Tal Murthy

引用次数: 0

Nanomedicine for prostate cancer: Modern therapies based on green synthesis of nanoparticles 前列腺癌的纳米医学：基于纳米颗粒绿色合成的现代疗法。

IF 3.7 4区医学 Q3 BIOCHEMICAL RESEARCH METHODS

SLAS Technology

Pub Date : 2025-11-06 DOI: 10.1016/j.slast.2025.100362

Mohammad Aatif , Mohamed S. AboHoussien , Ahmed T. Elhendawy , Ghazala Muteeb , Eduardo L. Fabella , Doaa S.R. Khafaga

Prostate cancer remains one of the leading causes of cancer-related illness and death in men globally. Despite advancements in diagnostics and traditional therapies, significant challenges such as drug resistance, systemic toxicity, and restricted specificity persist in impeding successful management. In recent years, nanotechnology has emerged as a revolutionary method in cancer treatment, offering targeted drug delivery, enhanced bioavailability, and reduced off-target side effects. Eco-friendly or green-synthesized nanomaterials have gained significant attention due to their biocompatibility, sustainability, and reduced environmental impact. This review delineates the present state of prostate cancer treatment and the constraints of traditional pharmacological approaches. We subsequently investigate the burgeoning role of nanomedicine in addressing these difficulties, focusing specifically on eco-friendly nanomaterials synthesized from plant extracts, microbial systems, and natural polymers. These biosynthesized nanoparticles (NPs) provide dual benefits: medicinal effectiveness and diminished environmental impact, consistent with the tenets of green chemistry and sustainable medicine. Additionally, we examine diverse drug delivery systems employing green NPs for prostate cancer, including liposomes, polymeric NPs, and metal-based systems synthesized through environmentally friendly methods. Recent in vitro and in vivo research is rigorously examined to assess the clinical potential of these methodologies. The review identifies significant translational hurdles, such as large-scale repeatability, regulatory constraints, and stability concerns, while proposing potential future approaches to enhance the therapeutic application of eco-friendly nanomedicine in prostate cancer treatment.

前列腺癌仍然是全球男性癌症相关疾病和死亡的主要原因之一。尽管诊断和传统治疗方法取得了进步，但诸如耐药性、全身毒性和限制性特异性等重大挑战仍然阻碍着成功的治疗。近年来，纳米技术已成为癌症治疗的一种革命性方法，提供靶向药物输送，提高生物利用度，减少脱靶副作用。生态友好型或绿色合成的纳米材料因其生物相容性、可持续性和减少对环境的影响而受到广泛关注。本文综述了前列腺癌治疗的现状和传统药理学方法的局限性。我们随后研究了纳米医学在解决这些困难方面的新兴作用，特别关注从植物提取物、微生物系统和天然聚合物合成的环保纳米材料。这些生物合成纳米颗粒（NPs）提供了双重好处：药物有效性和减少对环境的影响，符合绿色化学和可持续医学的原则。此外，我们研究了多种采用绿色NPs治疗前列腺癌的药物输送系统，包括脂质体、聚合物NPs和通过环保方法合成的金属基系统。最近的体外和体内研究经过严格检查，以评估这些方法的临床潜力。这篇综述指出了重大的转化障碍，如大规模的可重复性、监管限制和稳定性问题，同时提出了潜在的未来方法来加强生态友好型纳米药物在前列腺癌治疗中的治疗应用。

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Integrated multi-omics identifies GZMA targeting NK cells as a novel therapeutic strategy for hidradenitis suppurativa 整合多组学鉴定GZMA靶向NK细胞作为化脓性汗腺炎的新治疗策略。

IF 3.7 4区医学 Q3 BIOCHEMICAL RESEARCH METHODS

SLAS Technology

Pub Date : 2025-11-03 DOI: 10.1016/j.slast.2025.100363

Jizhao Yao , Mingfeng Ye , Taosheng Miao , Lusheng Miao

Background

Hidradenitis suppurativa (HS) represents a prevalent inflammatory dermatosis that not only triggers persistent local inflammation but also imposes significant psychosocial burdens. Given that the pathogenesis of HS remains incompletely elucidated, there is an urgent need necessitating the development of precision intervention strategies grounded in patients' immune profiles.

Methods

To investigate the pathogenesis of hidradenitis suppurativa, we employed an integrative approach combining bulk transcriptomics, Mendelian randomization (MR), and single-cell transcriptomic profiling. Specifically, transcriptomic analysis utilized Weighted Gene Co-Expression Network Analysis(WGCNA) for co-expression network construction and Least Absolute Shrinkage and Selection Operator(LASSO) regression for feature selection. Mendelian randomization applied IVW, weighted median, simple mode, weighted mode, and Bayesian MR approaches to ensure robust causal inference, with mediation analysis identifying potential metabolites. Molecular docking simulations were conducted to validate drug candidates targeting core genes. For single-cell transcriptomic analysis, we leveraged Gene Expression Omnibus(GEO) datasets followed by dispersion reduction, clustering, and cell-cell communication analysis to elucidate underlying cellular mechanisms.

Result

Our transcriptomic analysis identified Granzyme A(GZMA) as a central therapeutic target (Discovery cohort ROC: 0.945; Validation cohort ROC: 0.819). Furthermore, Mendelian randomization analysis indicated a heightened causal association between GZMA and HS risk: IVW (OR = 1.62, 95 %CI:1.24–2.11; p = 0.0003) and BWMR (OR = 1.62, 95 %CI:1.26–2.08; p = 0.0002). Critically,mediation analysis established N-Acetylputrescine as a potential mediating metabolite. Shifting to the cellular level, single-cell sequencing revealed prominent GZMA expression specifically within NK cells. Analysis of cell-cell interactions revealed communication between NK cells and both T cells and B cells, and highlighted differences in the communication dynamics between GZMA-positive and GZMA-negative subpopulations.

Conclusion

Integrative analysis of transcriptomic, MR, and scRNA-seq data strongly implicates GZMA as a potential therapeutic target and highlights the crucial role of NK cells in HS pathogenesis. These findings provide novel insights into HS immunopathology and pave the way for targeted therapeutic development.

背景：化脓性汗腺炎（HS）是一种常见的炎症性皮肤病，不仅会引发持续的局部炎症，还会造成严重的社会心理负担。鉴于HS的发病机制仍未完全阐明，迫切需要发展基于患者免疫谱的精确干预策略。方法：为了研究化脓性汗腺炎的发病机制，我们采用了一种综合方法，结合大量转录组学、孟德尔随机化（MR）和单细胞转录组学分析。具体而言，转录组学分析使用加权基因共表达网络分析（WGCNA）构建共表达网络，使用最小绝对收缩和选择算子（LASSO）回归进行特征选择。孟德尔随机化应用IVW、加权中位数、简单模式、加权模式和贝叶斯MR方法来确保稳健的因果推断，并通过中介分析确定潜在的代谢物。通过分子对接模拟来验证靶向核心基因的候选药物。对于单细胞转录组学分析，我们利用基因表达综合（GEO）数据集，随后进行分散减少、聚类和细胞-细胞通信分析，以阐明潜在的细胞机制。结果：我们的转录组学分析确定颗粒酶A（GZMA）为中心治疗靶点（发现队列ROC: 0.945；验证队列ROC: 0.819）。此外，孟德尔随机化分析表明，GZMA与HS风险之间的因果关系较高：IVW （OR=1.62, 95%CI:1.24-2.11; p=0.0003）和BWMR （OR=1.62, 95%CI:1.26-2.08; p=0.0002）。重要的是，中介分析确定n -乙酰腐胺是潜在的中介代谢物。转移到细胞水平，单细胞测序显示GZMA在NK细胞中特异性表达。细胞间相互作用的分析揭示了NK细胞与T细胞和B细胞之间的通信，并强调了gzma阳性和gzma阴性亚群之间通信动力学的差异。结论：转录组学、MR和scRNA-seq数据的综合分析强烈暗示GZMA是一个潜在的治疗靶点，并强调了NK细胞在HS发病机制中的关键作用。这些发现为HS免疫病理学提供了新的见解，并为靶向治疗开发铺平了道路。

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