IF 4.2 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Methods

Pub Date : 2024-10-03 DOI: 10.1016/j.ymeth.2024.10.001

Santosh Bhujbal, Ilva D. Rupenthal, Priyanka Agarwal

A stability-indicating reversed-phase high-performance liquid chromatography (RP-HPLC) method was developed to assay tonabersat and assess its stability in pharmaceutical formulations. Chromatographic separation was achieved using a Kinetex® C18 column (2.6 µm, 150 x 3 mm, 100 Å) at 50 °C, with a 20 µL injection volume. A linear gradient of acetonitrile in water (5 – 33.5 %) was applied for 1 min, followed by a gradual increase to 100 % over 26 min at a flow rate of 0.5 mL/min. Tonabersat and its degradation products were detected at 275 nm and 210 nm, respectively. The optimized method was used to evaluate the stability of tonabersat in lipid-based pharmaceutical formulations at 5 ± 3 °C, 25 ± 2°C/60 ± 5 % RH, and 40 ± 2 °C/75 ± 5 % RH over 3 months. The method was validated as per ICH guidelines and demonstrated linearity in the range of 5 – 200 µg/mL (R² = 0.99994) with good accuracy (98.25 – 101.58 % recovery) and precision (% RSD < 2.5 %). The limits of detection and quantitation were 0.8 µg/mL and 5 µg/mL, respectively. Forced degradation studies showed significant degradation on exposure to alkaline (90.33 ± 0.80 %), acidic (70.60 ± 1.57 %), and oxidative stress (33.95 ± 0.69 %) at 70 °C, but no degradation was observed on exposure to thermal or photolytic stress. No chemical degradation was observed in either formulation on storage. Thus, the method was sensitive, specific, and suitable for stability testing of tonabersat in pharmaceutical formulations.

本研究开发了一种稳定性指示反相高效液相色谱法（RP-HPLC），用于检测托那酯并评估其在药物制剂中的稳定性。色谱分离采用 Kinetex® C18 色谱柱（2.6 µm，150 x 3 mm，100 Å），温度为 50 °C，进样量为 20 µL。乙腈水溶液（5 - 33.5%）的线性梯度为 1 分钟，然后在 26 分钟内逐渐升至 100%，流速为 0.5 mL/min。在 275 纳米和 210 纳米波长下分别检测托那伯沙特及其降解产物。采用优化后的方法评估了托那伯沙特在脂质药物制剂中 5 ± 3 °C、25 ± 2 °C/60 ± 5 % 相对湿度和 40 ± 2 °C/75 ± 5 % 相对湿度条件下 3 个月的稳定性。该方法按照 ICH 指南进行了验证，在 5 - 200 µg/mL 范围内线性关系良好（R2 = 0.99994），准确度（回收率 98.25 - 101.58 %）和精密度（RSD < 2.5 %）良好。检测和定量限分别为 0.8 µg/mL 和 5 µg/mL。强制降解研究表明，在 70 °C的碱性（90.33 ± 0.80 %）、酸性（70.60 ± 1.57 %）和氧化应激（33.95 ± 0.69 %）条件下会出现明显降解，但在热应激或光解应激条件下未观察到降解。两种制剂在储存过程中均未出现化学降解。因此，该方法灵敏、特异，适用于药物制剂中托那酯稳定性的检测。

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引用次数: 0

Addressing heterogeneous sensitivity in biomarker screening with application in NanoString nCounter data 应用 NanoString nCounter 数据解决生物标记物筛选中的异质性灵敏度问题。

IF 4.2 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Methods

Pub Date : 2024-10-01 DOI: 10.1016/j.ymeth.2024.09.007

Chang Yu, Zhijin Wu

Biomarkers are measurable indicators of biological processes and have wide biomedical applications including disease screening and prognosis prediction. Candidate biomarkers can be screened in high-throughput settings, which allow simultaneous measurements of a large number of molecules. For binary biomarkers, the ability to detect a molecule may be hindered by the presence of background noise and the variable signal strength, which lower the sensitivity to a different extent for different target molecules in a sample-specific manner. This heterogeneity in detection sensitivity is often overlooked and leads to an inflated false positive rate. We propose a novel sensitivity adjusted likelihood-ratio test (SALT), which properly controls the false positives and is more powerful than the unadjusted approach. We show that sample-and-feature-specific detection sensitivity can be well estimated from NanoString nCounter data, and using the estimated sensitivity in SALT results in improved biomarker screening.

生物标志物是生物过程的可测量指标，在疾病筛查和预后预测等生物医学领域有着广泛的应用。候选生物标记物可以在高通量环境中进行筛选，从而同时测量大量分子。对于二元生物标记物来说，检测分子的能力可能会受到背景噪声和信号强度变化的阻碍，这些因素会以特定样本的方式在不同程度上降低不同目标分子的灵敏度。检测灵敏度的这种异质性常常被忽视，导致假阳性率升高。我们提出了一种新的灵敏度调整似然比检验（SALT），它能适当地控制假阳性，比未经调整的方法更强大。我们的研究表明，从 NanoString nCounter 数据中可以很好地估算出特定样本和特征的检测灵敏度，在 SALT 中使用估算出的灵敏度可以改进生物标记筛选。

引用次数: 0

Temperature-jump microscopy and interaction of Hsp70 heat shock protein with a client protein in vivo 温度跃迁显微镜和 Hsp70 热休克蛋白与体内客户蛋白的相互作用。

IF 4.2 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Methods

Pub Date : 2024-10-01 DOI: 10.1016/j.ymeth.2024.09.019

Aniket Ravan , Samuel Procopio , Yann R. Chemla , Martin Gruebele

Biomolecular processes such as protein–protein interactions can depend strongly on cell type and even vary within a single cell type. Here we develop a microscope with a Peltier-controlled temperature stage, a laser temperature jump to induce heat stress, and an autofocusing feature to mitigate temperature drift during experiments, to study a protein–protein interaction in a selected cell type within a live organism, the zebrafish larva. As an application of the instrument, we show that there is considerable cell-to-cell variation of the heat shock protein Hsp70 binding to one of its clients, phosphoglycerate kinase in vivo. We adapt a key feature from our previous folding study, rare transformation of cells within the larva, so that individual cells can be imaged and differentiated for cell-to-cell response. Our approach can be extended to other organisms and cell types than the ones demonstrated in this work.

蛋白质-蛋白质相互作用等生物分子过程在很大程度上取决于细胞类型，甚至在单一细胞类型内也会发生变化。在这里，我们开发了一种带有珀尔帖（Peltier）控温平台、激光温度跃迁以诱导热应力、自动聚焦功能以减轻实验过程中的温度漂移的显微镜，用于研究活生物体（斑马鱼幼体）内所选细胞类型中的蛋白质-蛋白质相互作用。作为该仪器的一项应用，我们发现热休克蛋白 Hsp70 与其客户之一磷酸甘油酸激酶的结合在活体细胞间存在相当大的差异。我们调整了先前折叠研究的一个关键特征，即幼虫体内细胞的罕见转化，这样就可以对单个细胞进行成像和分化，以了解细胞间的反应。我们的方法可扩展到其他生物体和细胞类型，而非本研究中展示的生物体和细胞类型。

引用次数: 0

Identification of target genes co-regulated by four key histone modifications of five key regions in hepatocellular carcinoma 确定肝细胞癌中五个关键区域的四个关键组蛋白修饰共同调控的靶基因。

IF 4.2 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Methods

Pub Date : 2024-09-28 DOI: 10.1016/j.ymeth.2024.09.017

Yu-Xian Liu , Jia-Le Song , Xiao-Ming Li , Hao Lin , Yan-Ni Cao

Hepatocellular carcinoma (HCC) is a cancer with high morbidity and mortality. Studies have shown that histone modification plays an important regulatory role in the occurrence and development of HCC. However, the specific regulatory effects of histone modifications on gene expression in HCC are still unclear. This study focuses on HepG2 cell lines and hepatocyte cell lines. First, the distribution of histone modification signals in the two cell lines was calculated and analyzed. Then, using the random forest algorithm, we analyzed the effects of different histone modifications and their modified regions on gene expression in the two cell lines, four key histone modifications (H3K36me3, H3K4me3, H3K79me2, and H3K9ac) and five key regions that co-regulate gene expression were obtained. Subsequently, target genes regulated by key histone modifications in key regions were screened. Combined with clinical data, Cox regression analysis and Kaplan-Meier survival analysis were performed on the target genes, and four key target genes (CBX2, CEBPZOS, LDHA, and UMPS) related to prognosis were identified. Finally, through immune infiltration analysis and drug sensitivity analysis of key target genes, the potential role of key target genes in HCC was confirmed. Our results provide a theoretical basis for exploring the occurrence of HCC and propose potential biomarkers associated with histone modifications, which may be potential drug targets for the clinical treatment of HCC.

肝细胞癌（HCC）是一种发病率和死亡率都很高的癌症。研究表明，组蛋白修饰在 HCC 的发生和发展中起着重要的调控作用。然而，组蛋白修饰对 HCC 基因表达的具体调控作用仍不清楚。本研究以 HepG2 细胞系和肝细胞系为研究对象。首先，计算并分析组蛋白修饰信号在两种细胞系中的分布。然后，利用随机森林算法分析了不同组蛋白修饰及其修饰区域对两种细胞系基因表达的影响，得到了四种关键组蛋白修饰（H3K36me3、H3K4me3、H3K79me2和H3K9ac）和五个共同调控基因表达的关键区域。随后，筛选出受关键区域中关键组蛋白修饰调控的靶基因。结合临床数据，对靶基因进行了Cox回归分析和Kaplan-Meier生存分析，确定了与预后相关的四个关键靶基因（CBX2、CEBPZOS、LDHA和UMPS）。最后，通过对关键靶基因的免疫浸润分析和药物敏感性分析，证实了关键靶基因在 HCC 中的潜在作用。我们的研究结果为探讨HCC的发生提供了理论依据，并提出了与组蛋白修饰相关的潜在生物标志物，这些生物标志物可能成为临床治疗HCC的潜在药物靶点。

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引用次数: 0

The application of advanced deep learning in biomedical graph analysis 高级深度学习在生物医学图谱分析中的应用。

IF 4.2 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Methods

Pub Date : 2024-09-27 DOI: 10.1016/j.ymeth.2024.09.013

Wen Zhang , Shikui Tu , Xiaopeng Zhu , Shichao Liu

引用次数: 0

Model-agnostic confidence measurement for aggregating multimodal ensemble models in automatic diagnostic systems 在自动诊断系统中聚合多模态集合模型的模型诊断可信度测量。

IF 4.2 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Methods

Pub Date : 2024-09-26 DOI: 10.1016/j.ymeth.2024.09.012

Chan-Yang Ju, Dong-Ho Lee

Automatic diagnostic systems (ADSs) have been garnering increased attention because they can alleviate the workload of clinicians by assisting in diagnosis and offering low-cost access to healthcare for people in medically underserved areas. ADS can suggest potential diseases by analyzing a patient's self-report. Previous research on ADS has leveraged diagnostic case data from various patients and medical knowledge to diagnose diseases, with multimodal ensemble methods proving particularly effective. However, the existing multimodal ensemble method combines the probabilities of different models in the aggregating process, which can not properly combine the probabilities that are produced by different criteria. To address these issues, we propose an effective aggregation framework for multimodal ensembles that can properly aggregate model-agnostic confidence scores and predictions from each model. Our framework transforms probability scores from different criteria into unified aggregation rule-based scores and reflects the gap between the probabilities that may be blurred in the aggregation process through the confidence score. In particular, The proposed confidence measurement method employs a post-analysis approach with the developed model or algorithm, making it adaptable in a model-agnostic manner and suitable for multimodal ensemble learning that utilizes heterogeneous prediction results. Our experimental results demonstrate that our framework outperforms existing approaches by more effectively leveraging the strengths of each ensemble member.

自动诊断系统（ADS）能够通过辅助诊断减轻临床医生的工作量，并为医疗服务不足地区的人们提供低成本的医疗服务，因此受到越来越多的关注。ADS 可以通过分析病人的自我报告来提示潜在的疾病。以往的 ADS 研究利用来自不同患者的诊断病例数据和医学知识来诊断疾病，其中多模态组合方法尤其有效。然而，现有的多模态集合方法在聚合过程中结合了不同模型的概率，无法正确结合不同标准产生的概率。为了解决这些问题，我们为多模态集合提出了一个有效的集合框架，它可以正确地集合与模型无关的置信度得分和每个模型的预测结果。我们的框架将来自不同标准的概率分数转化为统一的基于聚合规则的分数，并通过置信度分数反映聚合过程中可能模糊的概率之间的差距。特别是，所提出的置信度测量方法采用了一种与所开发的模型或算法相结合的后分析方法，使其具有与模型无关的适应性，适用于利用异构预测结果的多模态集合学习。实验结果表明，我们的框架能更有效地利用每个集合成员的优势，因此优于现有方法。

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引用次数: 0

Cancer subtype identification by multi-omics clustering based on interpretable feature and latent subspace learning 基于可解释特征和潜在子空间学习的多组学聚类癌症亚型识别。

IF 4.2 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Methods

Pub Date : 2024-09-24 DOI: 10.1016/j.ymeth.2024.09.014

Tianyi Shi, Xiucai Ye, Dong Huang, Tetsuya Sakurai

In recent years, multi-omics clustering has become a powerful tool in cancer research, offering a comprehensive perspective on the diverse molecular characteristics inherent to various cancer subtypes. However, most existing multi-omics clustering methods directly integrate heterogeneous features from different omics, which may struggle to deal with the noise or redundancy of multi-omics data and lead to poor clustering results. Therefore, we propose a novel multi-omics clustering method to extract interpretable and discriminative features from various omics before data integration. The clinical information is used to supervise the process of feature extraction based on SHAP (SHapley Additive exPlanation) values. Singular value decomposition (SVD) is then applied to integrate the extracted features of different omics by constructing a latent subspace. Finally, we utilize shared nearest neighbor-based spectral clustering on the latent representation to obtain the clustering result. The proposed method is evaluated on several cancer datasets across three levels of omics, in comparison to several state-of-the-art multi-omics clustering methods. The comparison results demonstrate the superior performance of the proposed method in multi-omics data analysis for cancer subtyping. Additionally, experiments reveal the efficacy of utilizing clinical information based on SHAP values for feature extraction, enhancing the performance of clustering analyses. Moreover, enrichment analysis of the identified gene signatures in different subtypes is also performed to further demonstrate the effectiveness of the proposed method.

Availability: The proposed method can be freely accessible at https://github.com/Tianyi-Shi-Tsukuba/Multi-omics-clustering-based-on-SHAP. Data will be made available on request.

近年来，多组学聚类技术已成为癌症研究的有力工具，可全面透视各种癌症亚型固有的不同分子特征。然而，现有的多组学聚类方法大多直接整合来自不同组学的异构特征，可能难以处理多组学数据的噪声或冗余，导致聚类结果不佳。因此，我们提出了一种新颖的多组学聚类方法，在数据整合之前从不同的组学数据中提取可解释和可判别的特征。临床信息用于监督基于 SHAP（SHapley Additive exPlanation）值的特征提取过程。然后应用奇异值分解（SVD），通过构建一个潜在子空间来整合不同 omics 的提取特征。最后，我们在潜在表征上使用基于共享近邻的光谱聚类来获得聚类结果。与几种最先进的多组学聚类方法相比，我们在多个癌症数据集上对所提出的方法进行了评估。对比结果表明，所提出的方法在癌症亚型的多组学数据分析中表现出色。此外，实验还揭示了利用基于 SHAP 值的临床信息进行特征提取的功效，从而提高了聚类分析的性能。此外，还对不同亚型中已识别的基因特征进行了富集分析，进一步证明了所提方法的有效性。可用性：建议的方法可在 https://github.com/Tianyi-Shi-Tsukuba/Multi-omics-clustering-based-on-SHAP 免费获取。数据将应要求提供。

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引用次数: 0

Production, analysis, and safety assessment of a soil and plant-based natural material with microbiome- and immune-modulatory effects 一种以土壤和植物为基础、具有微生物和免疫调节作用的天然材料的生产、分析和安全评估。

IF 4.2 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Methods

Pub Date : 2024-09-19 DOI: 10.1016/j.ymeth.2024.09.011

Anirudra Parajuli , Iida Mäkelä , Marja I. Roslund , Emma Ringqvist , Juulia Manninen , Yan Sun , Noora Nurminen , Sami Oikarinen , Olli H. Laitinen , Heikki Hyöty , Malin Flodström-Tullberg , Aki Sinkkonen

It has been suggested that reduced contact with microbiota from the natural environment contributes to the rising incidence of immune-mediated inflammatory disorders (IMIDs) in western, highly urbanized societies. In line with this, we have previously shown that exposure to environmental microbiota in the form of a blend comprising of soil and plant-based material (biodiversity blend; BDB) enhances the diversity of human commensal microbiota and promotes immunoregulation that may be associated with a reduced risk for IMIDs. To provide a framework for future preclinical studies and clinical trials, this study describes how the preparation of BDB was standardized, its microbial content analysed and safety assessments performed. Multiple batches of BDB were manufactured and microbial composition analysed using 16S rRNA gene sequencing. We observed a consistently high alpha diversity and relative abundance of bacteria normally found in soil and vegetation. We also found that inactivation of BDB by autoclaving effectively inactivates human and murine bacteria, viruses and parasites. Finally, we demonstrate that experimental mice prone to develop IMIDs (non-obese diabetic, NOD, mouse model) can be exposed to BDB without causing adverse effects on animal health and welfare. Our study provides insights into a potentially safe, sustainable, and cost-effective approach for simulating exposure to natural microbiota, which could have substantial impacts on health and socio-economic factors.

有人认为，在西方高度城市化的社会中，与自然环境中的微生物群接触减少是导致免疫介导的炎症性疾病（IMIDs）发病率上升的原因之一。有鉴于此，我们以前的研究表明，接触由土壤和植物材料组成的混合物形式的环境微生物群（生物多样性混合物；BDB）可提高人类共生微生物群的多样性并促进免疫调节，这可能与降低 IMIDs 的风险有关。为了给未来的临床前研究和临床试验提供一个框架，本研究介绍了如何标准化制备 BDB、分析其微生物含量以及进行安全性评估。我们生产了多个批次的 BDB，并使用 16S rRNA 基因测序分析了微生物成分。我们观察到，通常存在于土壤和植被中的细菌始终具有较高的α多样性和相对丰度。我们还发现，用高压灭菌法灭活 BDB 能有效地灭活人类和鼠类细菌、病毒和寄生虫。最后，我们证明，易患 IMIDs 的实验小鼠（非肥胖糖尿病小鼠模型）可以接触 BDB，而不会对动物健康和福利造成不良影响。我们的研究提供了一种潜在的安全、可持续和具有成本效益的方法，用于模拟自然微生物群的暴露，这可能会对健康和社会经济因素产生重大影响。

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引用次数: 0

GATDE: A graph attention network with diffusion-enhanced protein-protein interaction for cancer classification GATDE：用于癌症分类的具有扩散增强蛋白质-蛋白质相互作用的图注意网络

IF 4.2 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Methods

Pub Date : 2024-09-18 DOI: 10.1016/j.ymeth.2024.09.003

Ruike Song , Xiaofeng Wang , Jiahao Zhang , Shengquan Chen , Jianyu Zhou

Cancer classification is crucial for effective patient treatment, and recent years have seen various methods emerge based on protein expression levels. However, existing methods oversimplify by assuming uniform interaction strengths and neglecting intermediate influences among proteins. Addressing these limitations, GATDE employs a graph attention network enhanced with diffusion on protein-protein interactions. By constructing a weighted protein-protein interaction network, GATDE captures the diversity of these interactions and uses a diffusion process to assess multi-hop influences between proteins. This information is subsequently incorporated into the graph attention network, resulting in precise cancer classification. Experimental results on breast cancer and pan-cancer datasets demonstrate that GATDE surpasses current leading methods. Additionally, in-depth case studies further validate the effectiveness of the diffusion process and the attention mechanism, highlighting GATDE's robustness and potential for real-world applications.

癌症分类对病人的有效治疗至关重要，近年来出现了各种基于蛋白质表达水平的方法。然而，现有的方法过于简化，假定蛋白质之间的相互作用强度一致，忽略了蛋白质之间的中间影响。为了解决这些局限性，GATDE 采用了图注意网络，通过扩散增强蛋白质-蛋白质相互作用。通过构建加权蛋白质-蛋白质相互作用网络，GATDE 可以捕捉到这些相互作用的多样性，并利用扩散过程来评估蛋白质之间的多跳影响。这些信息随后被纳入图注意网络，从而实现精确的癌症分类。在乳腺癌和泛癌症数据集上的实验结果表明，GATDE 超越了当前的领先方法。此外，深入的案例研究进一步验证了扩散过程和注意力机制的有效性，凸显了 GATDE 的鲁棒性和在现实世界中的应用潜力。

引用次数: 0

AtML: An Arabidopsis thaliana root cell identity recognition tool for medicinal ingredient accumulation AtML：拟南芥根细胞身份识别工具，用于药用成分的积累。

IF 4.2 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Methods

Pub Date : 2024-09-16 DOI: 10.1016/j.ymeth.2024.09.010

Shicong Yu , Lijia Liu , Hao Wang , Shen Yan , Shuqin Zheng , Jing Ning , Ruxian Luo , Xiangzheng Fu , Xiaoshu Deng

Arabidopsis thaliana synthesizes various medicinal compounds, and serves as a model plant for medicinal plant research. Single-cell transcriptomics technologies are essential for understanding the developmental trajectory of plant roots, facilitating the analysis of synthesis and accumulation patterns of medicinal compounds in different cell subpopulations. Although methods for interpreting single-cell transcriptomics data are rapidly advancing in Arabidopsis, challenges remain in precisely annotating cell identity due to the lack of marker genes for certain cell types. In this work, we trained a machine learning system, AtML, using sequencing datasets from six cell subpopulations, comprising a total of 6000 cells, to predict Arabidopsis root cell stages and identify biomarkers through complete model interpretability. Performance testing using an external dataset revealed that AtML achieved 96.50% accuracy and 96.51% recall. Through the interpretability provided by AtML, our model identified 160 important marker genes, contributing to the understanding of cell type annotations. In conclusion, we trained AtML to efficiently identify Arabidopsis root cell stages, providing a new tool for elucidating the mechanisms of medicinal compound accumulation in Arabidopsis roots.

拟南芥能合成多种药用化合物，是药用植物研究的模式植物。单细胞转录组学技术对于了解植物根系的发育轨迹至关重要，有助于分析药用化合物在不同细胞亚群中的合成和积累模式。虽然拟南芥单细胞转录组学数据的解读方法在迅速发展，但由于缺乏某些细胞类型的标记基因，在精确标注细胞身份方面仍面临挑战。在这项工作中，我们使用来自六个细胞亚群（共 6000 个细胞）的测序数据集训练了一个机器学习系统 AtML，通过完整的模型可解释性预测拟南芥根细胞阶段并识别生物标记。使用外部数据集进行的性能测试表明，AtML 的准确率达到 96.50%，召回率达到 96.51%。通过 AtML 提供的可解释性，我们的模型确定了 160 个重要的标记基因，有助于理解细胞类型注释。总之，我们训练的 AtML 能有效识别拟南芥根细胞阶段，为阐明拟南芥根中药用化合物的积累机制提供了一种新工具。

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引用次数: 0

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