IF 4.3 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Methods

Pub Date : 2025-11-19 DOI: 10.1016/j.ymeth.2025.11.007

Gowrang Kasaba Manjunath , Rohit Kumar Verma , Abhijit Berua , Shweta Mahalingam , Tikam Chand Dakal , Abhishek Kumar

Exome sequencing (ES) has transformed genomic research and clinical diagnostics by enabling precise identification of disease-associated variants within protein-coding regions, which, while representing a minority of the genome, include many well-characterized pathogenic mutations. This review provides a comprehensive overview of ES methodology, data analysis pipelines, clinical relevance, and ethical considerations. We describe the ES workflow from DNA extraction and library preparation to target enrichment, sequencing to ES data analysis. We have also evaluated major capture technologies and sequencing platforms, including short-read and emerging long-read systems. Furthermore, we discuss computational analysis tools such as GATK, FreeBayes, DeepVariant, and Platypus, and strategies to improve accuracy through rigorous quality control, coverage optimization, and orthogonal validation. Beyond rare disease and cancer genomics, ES has expanded into pharmacogenomics, population-scale studies, and integrative multi-omics frameworks that combine transcriptomic and proteomic data to enhance functional interpretation. We highlight actionable examples such as CYP2C19 variants influencing clopidogrel metabolism, illustrating ES’s growing role in personalized medicine. Challenges (including variant interpretation complexity, false positives, and data standardization) are critically discussed. The review also addresses ethical, legal, and social dimensions of ES, including informed consent, data privacy, incidental findings, and adherence to ACMG, HIPAA, and GDPR. Finally, we outline future directions emphasizing machine learning–based variant prioritization, single-cell sequencing integration, and scalable bioinformatics infrastructures to enhance accuracy and clinical translation. Collectively, these developments position ES as a pivotal tool bridging genomic discovery, disease diagnostics, and precision healthcare in the era of personalized medicine.

外显子组测序（ES）已经改变了基因组研究和临床诊断，因为它能够精确识别蛋白质编码区域内的疾病相关变异，这些变异虽然只占基因组的一小部分，但包括许多具有良好特征的致病突变。这篇综述提供了ES方法、数据分析管道、临床相关性和伦理考虑的全面概述。我们描述了从DNA提取和文库制备到目标富集，测序到ES数据分析的ES工作流程。我们还评估了主要的捕获技术和测序平台，包括短读和新兴的长读系统。此外，我们还讨论了计算分析工具，如GATK， FreeBayes， DeepVariant和Platypus，以及通过严格的质量控制，覆盖优化和正交验证来提高准确性的策略。除了罕见疾病和癌症基因组学，ES已经扩展到药物基因组学、人群规模研究和整合多组学框架，结合转录组学和蛋白质组学数据来增强功能解释。我们强调了可操作的例子，如CYP2C19变异影响氯吡格雷代谢，说明ES在个性化医疗中的作用越来越大。挑战（包括变体解释的复杂性，误报和数据标准化）进行了批判性的讨论。该审查还涉及ES的伦理、法律和社会层面，包括知情同意、数据隐私、偶然发现以及对ACMG、HIPAA和GDPR的遵守。最后，我们概述了未来的发展方向，强调基于机器学习的变异优先排序，单细胞测序整合和可扩展的生物信息学基础设施，以提高准确性和临床翻译。总的来说，这些发展使ES成为个性化医疗时代连接基因组发现、疾病诊断和精准医疗的关键工具。

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引用次数: 0

Toward accurate breast cancer classification: A review of multi-modal machine learning approaches 乳腺癌准确分类：多模态机器学习方法综述。

IF 4.3 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Methods

Pub Date : 2025-11-18 DOI: 10.1016/j.ymeth.2025.10.011

Archana Mathur , Abbas Mufaddal Dudhiyawala , Sudeepa Roy Dey , Snehanshu Saha

The innovations in classifying breast cancer into malignant and benign categories and further categorizing it into molecular subtypes have reshaped healthcare services, enabling accurate diagnosis of these complex conditions. Identification of molecular subtypes of breast cancer is one of the most important treatment challenges, as these subtypes can have an enormous effect on the prognosis and treatment approaches. Data integration from various modalities, such as transcriptomics, imaging, and genomics, has been crucial in leveraging new opportunities to increase classification accuracy and improve individualized treatment plans. These heterogeneous data sources are examined by applying deep learning algorithms, which provide further insights into the complex patterns that traditional approaches often overlook. In this paper, we explore the various modalities researchers use to investigate breast cancer and the intriguing fusion techniques employed to combine these modalities. We also review the most recent models (traditional, machine learning, and deep learning), emphasizing their improvements over traditional classification methods and the molecular subtype categorization of breast cancer. Furthermore, the emphasis of this review is to examine techniques to process the entire image of the breast tissue slide, which is challenging, particularly due to its size. We explore recent advances in multiple instance learning tasks and the use of attention-based transformers and similar architectures for annotating the WSI slides before using them for cancer classification. We additionally discuss the interpretability tools—attention maps, saliency maps and model explainability— in the context of transformers. In a nutshell, we aim to provide an in-depth look at the revolutionary capabilities of deep learning models in precision oncology and guide future research paths in this crucial field by synthesizing existing studies.

在将乳腺癌分为恶性和良性，并进一步将其分类为分子亚型方面的创新，重塑了医疗保健服务，使其能够准确诊断复杂的疾病。乳腺癌分子亚型的鉴定是最重要的治疗困难之一，因为这些亚型对预后和治疗方法有巨大的影响。来自转录组学、成像和基因组等各种模式的数据集成，对于利用新的机会提高分类准确性和改善个性化治疗计划至关重要。通过应用深度学习算法对这些异构数据源进行检查，这为传统方法经常忽略的复杂模式提供了进一步的见解。在本文中，我们探讨了研究人员用于研究乳腺癌的各种模式以及用于融合模式的有趣融合技术。我们还回顾了最新的模型（传统、机器学习和深度学习），强调了它们对传统分类和乳腺癌分子亚型分类的改进。此外，本综述的重点是研究处理乳腺组织幻灯片的整个图像的技术，这是具有挑战性的，特别是由于它的大小。我们探索了多实例学习任务的最新进展，以及在将WSI幻灯片用于癌症分类之前，使用基于注意力的转换器和类似架构对其进行注释。我们还讨论了可解释性工具-注意图，显著性图和模型可解释性在变压器的背景下。简而言之，我们的目标是深入了解深度学习模型在精确肿瘤学中的革命性能力，并通过结合现有研究指导这一关键领域的未来研究路径。

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引用次数: 0

Unravelling cell heterogeneity and gene regulation mechanisms in multiple myeloma through single-cell RNA-seq 通过单细胞RNA-seq揭示多发性骨髓瘤细胞异质性和基因调控机制

IF 4.3 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Methods

Pub Date : 2025-11-16 DOI: 10.1016/j.ymeth.2025.11.006

Jing Jiang , Junlin Xu , Peng Wang , Yuansheng Liu , Yiping Liu

Multiple myeloma (MM) is the second most common blood cancer in the world, yet its genetic pathology is not fully understood and MM cells have highly heterogeneous regulatory mechanisms. Single-cell RNA-Sequencing (scRNA-Seq) technologies provide an unprecedented opportunity to investigate cell heterogeneity and understand regulatory mechanisms at the single-cell level. Four MM scRNA-Seq datasets were retrieved from the public domain containing 597, 172, 477, and 51,840 cells, respectively. First, they were integrated and jointly analyzed to accurately identify 24 MM cell clusters, using a normal hematopoiesis cells atlas as a control. Then we predicted 651 regulons within the 24 MM cell clusters. The identified regulons can substantially improve the elucidation of heterogeneous gene regulation mechanisms across various cell clusters, and hence can serve as a reference for diagnosis in MM.

多发性骨髓瘤（MM）是世界上第二常见的血癌，但其遗传病理尚不完全清楚，MM细胞具有高度异质性的调节机制。单细胞rna测序（scRNA-Seq）技术为研究细胞异质性和了解单细胞水平的调控机制提供了前所未有的机会。从公共领域检索到4个MM scRNA-Seq数据集，分别包含597、172、477和51840个细胞。首先，将它们整合并联合分析，以准确识别24个MM细胞簇，使用正常造血细胞图谱作为对照。然后我们预测了24个MM细胞簇中的651个调控子。这些确定的调控可以大大提高对不同细胞簇间异质基因调控机制的阐明，因此可以作为MM诊断的参考。

引用次数: 0

Natural language processing 自然语言处理

IF 4.3 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Methods

Pub Date : 2025-11-12 DOI: 10.1016/j.ymeth.2025.11.004

Nguyen Quoc Khanh Le , Matthew Chin Heng Chua

引用次数: 0

Scale-adjusted distance transform and its applications to segmentation of multimodal images 尺度调整距离变换及其在多模态图像分割中的应用。

IF 4.3 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Methods

Pub Date : 2025-11-07 DOI: 10.1016/j.ymeth.2025.11.003

Nirmal Das , Subhadip Basu , Punam K. Saha

Distance transform (DT) is widely used for structural analysis of multi-dimensional (mainly 2-D and 3-D) objects. Association of DT values with local structure scale, often, adds challenges and limits the scope of applications of DT in relative structural analysis among multiple objects with varying scales. In this paper, we introduce a new notion of scale-adjusted distance transform (SADT), conceptually different from traditional DT, which is independent of object scale and offers DT values of scale varying objects on a uniform scale with the value of ‘1’ at ridges. It has been shown that scale-adjusted distance is a metric function in a continuous Euclidean space, and SADT generates a normalized field that is invariant under translation, rotation, and isotropic scaling. The computational method for digital objects traces gradient flow paths on a conventional DT field and uses the change in velocity along a digital path to detect local ridges, which are then used to generate a scale-adjusted density (SAD) field. Finally, SADT is computed using the SAD value. The results of applying the method on 2-D and 3-D multimodal image datasets are presented. Two real-life applications of SADT are shown: 1) segmentation of conjoined nuclei from 2-D microscopic images, and 2) multi-scale separation of conjoined artery–vein in 3-D pulmonary CT image of a pig lung phantom. SADT outperforms the traditional marker-controlled watershed algorithm in conjoined nuclei segmentation from 2-D images and achieves highly accurate multi-scale artery–vein separation in the pig lung phantom experiment. The performance of SADT is invariant to image dimension and imaging modality. Unlike modern deep learning methods, the proposed fuzzy method is transparent and data modality independent. The source code and sample data are freely available at: https://github.com/CMATERJU-BIOINFO/Scale-Adjusted-Distance-Transform.

距离变换（DT）广泛应用于多维（主要是二维和三维）物体的结构分析。将DT值与局部结构尺度相关联，往往会给DT在不同尺度的多物体相对结构分析中的应用范围带来挑战和限制。在本文中，我们引入了一种新的概念，即尺度调整距离变换（SADT），它与传统的DT在概念上有所不同，它不依赖于物体的尺度，并提供了在脊上以1为值的均匀尺度上变化尺度的物体的DT值。尺度调整距离是连续欧几里得空间中的度量函数，SADT生成的归一化域在平移、旋转和各向同性尺度下都是不变的。数字物体的计算方法在传统的DT场上跟踪梯度流动路径，并利用沿数字路径的速度变化来检测局部脊，然后使用这些脊来生成缩放密度（SAD）场。最后，使用SAD值计算SADT。给出了该方法在二维和三维多模态图像数据集上的应用结果。本文给出了SADT在现实生活中的两个应用：1)从二维显微图像中分割连体核；2)在猪肺幻象的三维肺CT图像中多尺度分离连体动静脉。SADT算法在二维图像的连核分割中优于传统的标记控制分水岭算法，在猪肺幻象实验中实现了高精度的多尺度动静脉分离。SADT的性能不受图像维数和成像模态的影响。与现代深度学习方法不同，本文提出的模糊方法具有透明性和数据模态无关性。源代码和示例数据可在https://github.com/CMATERJU-BIOINFO/Scale-Adjusted-Distance-Transform免费获得。

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引用次数: 0

Disease-related omics data analysis 疾病相关组学数据分析。

IF 4.3 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Methods

Pub Date : 2025-11-03 DOI: 10.1016/j.ymeth.2025.11.001

Wei Peng , Zhipeng Cai

引用次数: 0

Carboxyl-PEG modified Fe3O4 nanoparticles as an ultrasensitive SERS substrate for multiplex detection of exogenous hormones related to endometrial cancer 羧基聚乙二醇修饰的Fe3O4纳米颗粒作为超灵敏SERS底物用于子宫内膜癌相关外源激素的多重检测。

IF 4.3 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Methods

Pub Date : 2025-11-03 DOI: 10.1016/j.ymeth.2025.11.002

Biqing Chen, Jiayin Gao, Haizhu Sun, Yinghan Zhao, Yan Liu, Xiaohong Qiu

Traditional surface-enhanced Raman scattering (SERS) technology often struggles to achieve high-precision discrimination in the simultaneous detection of multiple exogenous hormones due to complex spectral overlap and matrix interference, limiting its application in trace analyte analysis within complex matrices (e.g., biological samples). This study developed a SERS substrate based on carboxyl-terminal polyethylene glycol (PEG)-modified iron oxide (Fe₃O₄) nanoparticles (Fe₃O₄@PEG), integrated with artificial intelligence (AI)-driven high-throughput spectral analysis algorithms. This approach successfully enabled ultrasensitive detection and precise discrimination of multiple typical exogenous hormonal drugs, including tamoxifen, drospirenone, cyproterone acetate, medroxyprogesterone acetate, estradiol ester derivatives, and dydrogesterone. By optimizing the surface enhancement effect of Fe₃O₄@PEG nanocomposites and employing machine learning models (e.g., convolutional neural networks, CNN) for collaborative analysis, the weak Raman fingerprint features of target hormones in complex mixtures were effectively extracted and classified, achieving a detection limit at the corresponding to 10⁻⁷–10⁻⁸ mg/mL level. In matrix-spiked serum and urine samples, which mimic complex biological matrices validations, the AI-SERS platform demonstrated exceptional performance in the identification and quantitative analysis of target exogenous hormones. This research provides an intelligent analytical strategy for rapid and highly sensitive detection of multiple trace exogenous hormones in complex matrices.

传统的表面增强拉曼散射（SERS）技术在同时检测多种外源激素时，由于复杂的光谱重叠和基质干扰，往往难以实现高精度的区分，限制了其在复杂基质（如生物样品）内痕量分析物分析中的应用。本研究开发了一种基于羧基末端聚乙二醇（PEG）修饰的氧化铁（Fe3O4）纳米颗粒（Fe3O4@PEG）的SERS底物，并集成了人工智能（AI）驱动的高通量光谱分析算法。该方法成功实现了他莫昔芬、屈螺酮、醋酸环丙孕酮、醋酸甲羟孕酮、雌二醇酯衍生物、地屈孕酮等多种典型外源性激素药物的超灵敏检测和精确鉴别。通过优化Fe3O4@PEG纳米复合材料的表面增强效果，利用卷积神经网络（CNN）等机器学习模型协同分析，有效提取并分类了复杂混合物中目标激素的弱拉曼指纹特征，达到了对应于10-7-10-8 mg/mL水平的检出限。在模拟复杂生物基质验证的基质加标血清和尿液样本中，AI-SERS平台在目标外源激素的鉴定和定量分析中表现出卓越的性能。本研究为复杂基质中多种微量外源激素的快速、高灵敏度检测提供了一种智能分析策略。

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引用次数: 0

Membrane-mediated strategies for efficient intracellular delivery of biologics 膜介导的生物制剂细胞内有效递送策略。

IF 4.3 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Methods

Pub Date : 2025-10-30 DOI: 10.1016/j.ymeth.2025.10.008

Donghyeok Gang, Yeonju Song, Yeonjin Ko

Approximately 80% of drugs developed to date are small molecule compounds. While these compounds can effectively inhibit intracellular targets by crossing cell membranes, their efficacy often depends on stringent conditions, such as the presence of a deep hydrophobic pocket for strong binding. Biologics—including peptides, antibodies, and genetic materials—have fewer binding requirements but cannot penetrate cell membranes, limiting their activity to extracellular targets. Notably, the number of intracellular protein and nucleic acid targets is more than four times that of extracellular targets. Given their potential to treat fundamental disease mechanisms, the intracellular delivery of biologics is of critical importance. In this review, we discuss the generation and application of membrane-based carriers, including cell-derived vesicles and artificial membrane-based carriers, with examples categorized by modality to enhance the therapeutic utility of biologics.

到目前为止，大约80%的药物都是小分子化合物。虽然这些化合物可以通过穿过细胞膜有效地抑制细胞内靶点，但它们的效果通常取决于严格的条件，例如存在一个深疏水口袋以进行强结合。生物制剂——包括多肽、抗体和遗传物质——具有较少的结合要求，但不能穿透细胞膜，限制了它们对细胞外靶标的活性。值得注意的是，细胞内蛋白和核酸靶点的数量是细胞外靶点的4倍以上。鉴于其治疗基本疾病机制的潜力，生物制剂的细胞内递送至关重要。在这篇综述中，我们讨论了膜基载体的产生和应用，包括细胞源性囊泡和人工膜基载体，并按模式分类的例子，以提高生物制剂的治疗效用。

引用次数: 0

Materials-Based spatiotemporal analysis of microbial responses to glyphosate in Winogradsky columns 基于材料的Winogradsky色谱柱中微生物对草甘膦响应的时空分析

IF 4.3 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Methods

Pub Date : 2025-10-28 DOI: 10.1016/j.ymeth.2025.10.009

Ahmad Itani , Marta Velaz Martín , Laura Meisch , Phillip Lemke , Tim Scharnweber , Islam M. Khattab , Kersten S. Rabe , Christof M. Niemeyer

Glyphosate, the active ingredient in many broad-spectrum herbicides, is extensively used in agriculture but has come under increasing scrutiny due to its potential impacts on non-target microbial communities. To investigate these effects within a controlled yet ecologically relevant framework, Winogradsky columns, self-contained sediment-based ecosystems, were employed as a model system. A novel, non-destructive sampling approach was introduced using macroporous elastomeric silicone foam (MESIF) integrated in stainless-steel frames to enable spatiotemporal monitoring of benthic microbial communities. These MESIF-loaded frames were vertically embedded in columns filled with lake sediment and subjected to varying experimental conditions, including light exposure and glyphosate treatment. Microbial colonization of the MESIF was assessed via amplicon sequencing at defined time points. Glyphosate-treated columns exhibited delayed microbial stratification and diminished development of characteristic pigmentation associated with functional groups such as iron-oxidizing and sulfate-reducing bacteria. Although within-column alpha diversity remained relatively constant, glyphosate exposure led to distinct shifts in community composition, including an increased abundance of taxa potentially involved in glyphosate degradation. These findings demonstrate the effectiveness of combining Winogradsky columns with MESIF-based sampling for studying environmental stressors and underscore glyphosate’s influence on microbial succession and functional diversity in sediment ecosystems.

草甘膦是许多广谱除草剂的有效成分，广泛用于农业，但由于其对非目标微生物群落的潜在影响而受到越来越多的关注。为了在一个受控制但与生态相关的框架内研究这些影响，采用Winogradsky柱，自给自足的基于沉积物的生态系统作为模型系统。介绍了一种新型的非破坏性采样方法，将大孔弹性有机硅泡沫（MESIF）集成在不锈钢框架中，以实现对底栖微生物群落的时空监测。这些mesif负载的框架垂直嵌入充满湖泊沉积物的柱中，并经受不同的实验条件，包括光照和草甘膦处理。在确定的时间点通过扩增子测序评估MESIF的微生物定植。草甘膦处理的色谱柱表现出延迟的微生物分层和减少与功能群（如铁氧化菌和硫酸盐还原菌）相关的特征色素沉着的发展。尽管柱内α多样性保持相对稳定，但草甘膦暴露导致群落组成发生明显变化，包括可能参与草甘膦降解的分类群丰度增加。这些发现证明了将Winogradsky柱与mesif采样相结合用于研究环境压力源的有效性，并强调了草甘膦对沉积物生态系统中微生物演替和功能多样性的影响。

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引用次数: 0

Comprehensive analysis of the inhibition of aldehyde dehydrogenase from Flavobacterium PL002 and its coupling with SERS as a path for the selective detection of thiram 综合分析黄杆菌PL002对乙醛脱氢酶的抑制作用及其与SERS的偶联作为选择性检测thiram的途径。

IF 4.3 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Methods

Pub Date : 2025-10-16 DOI: 10.1016/j.ymeth.2025.10.006

Andreea Iuliana Ftodiev , Georgiana Necula Petrareanu , Mihaela Puiu , Gheorghe Proteasa , Cristian V.A. Munteanu , Roberta Maria Banciu , Ruchika Chauhan , Diana Visinescu , Cristina Purcarea , Pablo Fanjul-Bolado , David Ibañez , Ronen Fogel , Janice Limson , Monica Potara , Anca Florina Bonciu , Simion Astilean , Camelia Bala , Alina Vasilescu

The selective detection of dithiocarbamate fungicides in food and agricultural products presents significant analytical challenges. While Surface-enhanced Raman spectroscopy (SERS) has been extensively investigated to address this, detection systems based on enzymatic inhibition remain underexplored. Using thiram as a model dithiocarbamate, the present work explores the potential application of a cold-active aldehyde dehydrogenase from Flavobacterium PL002 for the development of specific, inhibition-based analytical methods. A molecular modelling and docking study confirmed that thiram fits into the binding pocket of the enzyme. An irreversible inhibition mechanism was inferred for thiram based on enzymatic kinetics studies. The mechanism was supported by SERS, mass spectrometry measurements and tests with reducing agents. A simple assay for the detection of the fungicide was developed and compared to a SERS-based procedure. The advantages and the practical limitations of the two methods were revealed by studying the detection of thiram from the surface of fungicide-spiked tomatoes. By coupling enzymatic inhibition with SERS, the selectivity for the detection of individual fungicides can be increased, as illustrated by comparing thiram with ziram, a structurally related compound. The study serves as basis for the development of analytical methods for the selective detection of thiram.

食品和农产品中二硫代氨基甲酸酯杀菌剂的选择性检测提出了重大的分析挑战。虽然表面增强拉曼光谱（SERS）已被广泛研究以解决这一问题，但基于酶抑制的检测系统仍未得到充分探索。利用thiram作为二硫代氨基甲酸酯模型，本研究探索了黄杆菌PL002冷活性醛脱氢酶的潜在应用，以开发特异性的、基于抑制的分析方法。分子模型和对接研究证实，thiram适合酶的结合袋。基于酶动力学研究，推测了一种不可逆的抑制机制。SERS、质谱测定和还原剂试验支持了该机理。开发了一种简单的杀菌剂检测方法，并与基于sers的方法进行了比较。通过对加杀菌剂番茄表面的硫胺检测研究，揭示了这两种方法的优点和实际应用的局限性。通过将酶抑制与SERS耦合，可以提高单个杀菌剂检测的选择性，如将thiram与结构相关的化合物ziram进行比较所示。本研究为发展选择性检测thiram的分析方法奠定了基础。

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引用次数: 0

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