Methods (San Diego, Calif.)[JOURNAL]
Sun
200 papers
IF-CRIB: A 3D-printable device to facilitate immunofluorescence experiments and its application in screening and characterizing cells expressing a degradable form of ERK2.
Berliocchi E, Onesto C, Pagès G … +2 more , Lenormand P, Buscà R
Methods · 2025 Nov · PMID 40789436 · Publisher ↗
Immunofluorescence-based detection of proteins in fixed cells is a powerful tool for research in cell and developmental biology. While a variety of immunofluorescence protocols exist, they can be time consuming or requir...
Immunofluorescence-based detection of proteins in fixed cells is a powerful tool for research in cell and developmental biology. While a variety of immunofluorescence protocols exist, they can be time consuming or require expensive equipment which may not be accessible to all laboratories. A common challenge in these protocols is the numerous washing steps, particularly in experiments with numerous conditions. To address this, here we introduce the IF-CRIB device, a 3D-printable wash rack specifically designed for applications involving a high number of round coverslips with adherent cultured cells. We detail its design and the 3D printing process which can be easily used by any laboratory and we highlight that it facilitates the numerous washing steps. In addition, we present the IF-Express protocol, an optimized and effective method that enables fast and consistent immunofluorescence results. As an example of the utility of the IF-CRIB device and the IF-Express protocol, we describe their application in the screening and characterization of several NIH3T3 cell clones expressing a degradable form of ERK2 kinase (ERK2-dTAG) after treatment with the dTAG-13 compound. The generation of ERK2-dTAG clones involves a knock-in strategy. We provide a detailed methodology for clone selection, immunofluorescence screening, and characterization of ERK2-dTAG, including degradation kinetics, dose-response analysis, and nuclear translocation assays to assess ERK2-dTAG functionality. The IF-CRIB device and IF-Express protocol has been proven to be efficient for the obtention and characterization of ERK2dTAG-expressing clones thereby offering a powerful framework for studying ERK2 dynamics in cell biology and disease models.
DEF-DSVM: A deep ensemble feature learning and deepSVM approach for multifaceted analysis and diagnosis of Alzheimer's disease from EEG signals.
Hesari S, Ghaffari H, Rezaee K
Methods · 2025 Oct · PMID 40769494 · Publisher ↗
Early detection of Alzheimer's disease (AD) and its precursor, mild cognitive impairment (MCI), is paramount for timely intervention and effective disease management. This study introduces a novel computer-aided diagnost...
Early detection of Alzheimer's disease (AD) and its precursor, mild cognitive impairment (MCI), is paramount for timely intervention and effective disease management. This study introduces a novel computer-aided diagnostic model that leverages electroencephalogram (EEG) data to precisely identify and classify AD and MCI. A comprehensive preprocessing pipeline is employed, incorporating discrete wavelet transform (DWT) for EEG signal decomposition into relevant subbands and subsequent signal windowing to address non-stationarity. Spectrograms derived from these preprocessed signals serve as input for a deep ensemble feature learning and deep support vector machine (DEF-DSVM) architecture. The DEF-DSVM model significantly enhances the accuracy of diagnosing both MCI and AD, achieving an impressive 98.17% accuracy rate that surpasses contemporary state-of-the-art methods. Beyond diagnostic precision, the model effectively identifies specific EEG subbands-namely alpha, theta, and delta-instrumental in elucidating the pathophysiology of AD and MCI. The structure's generalizability and robustness are validated using the Figshare dataset, encompassing, AD, MCI, and control classes. To ensure a rigorous assessment of the model's performance, the Leave-One-Subject-Out (LOSO) cross-validation procedure is employed in lieu of the traditional K-fold approach, mitigating the risk of overoptimistic performance estimates and providing a more accurate reflection of the model's ability to generalize to novel, unseen subjects. Further evaluation of the method's generalizability through its application to an EEG dataset related to attention deficit hyperactivity disorder (ADHD) highlights its broader clinical utility across various neurodegenerative disorders. These findings establish the DEF-DSVM model as a reliable and potent tool for the early diagnosis and monitoring of AD and MCI, offering substantial accuracy gains and demonstrating its potential for widespread application across different neurological conditions.
A newly developed ferroptosis-related gene signature for forecasting prognosis in uveal melanoma.
Zhang Y, Li J, Zhang Y … +1 more , Qu Y
Methods · 2025 Oct · PMID 40763838 · Publisher ↗
BACKGROUND: Uveal melanoma is the most common primary intraocular malignancy in adults, characterized by significant inter-patient heterogeneity and poor long-term prognosis. Ferroptosis, an iron-dependent form of regula...
BACKGROUND: Uveal melanoma is the most common primary intraocular malignancy in adults, characterized by significant inter-patient heterogeneity and poor long-term prognosis. Ferroptosis, an iron-dependent form of regulated cell death, has emerged as a promising avenue for cancer therapy, yet its role in UVM remains insufficiently explored. METHODS: We systematically screened ferroptosis-related genes (FRGs) associated with UVM prognosis by integrating univariate Cox regression, LASSO regression, and Random Forest algorithms. A prognostic signature was constructed and validated using data from The Cancer Genome Atlas and Gene Expression Omnibus cohorts. We further evaluated the model's association with clinicopathological features, tumor immune microenvironment, and drug sensitivity. In vitro experiments were conducted to validate the functional role of SIRT3, the most pivotal gene in the signature. RESULTS: A robust FRG-based prognostic model comprising SIRT3, RRM2, and PANX2 was developed and successfully stratified patients into high- and low-risk groups with significantly different survival outcomes. Multivariate Cox regression confirmed the risk score as an independent prognostic factor. High-risk patients exhibited immunosuppressive microenvironmental features, including elevated regulatory T cells and macrophage infiltration, as well as lower predicted response to immune checkpoint blockade. Drug sensitivity analysis identified ten compounds with lower predicted IC50 values in the high-risk group, suggesting potential therapeutic relevance. Functional assays demonstrated that SIRT3 knockdown promoted UVM cell proliferation and migration, while attenuating susceptibility to ferroptosis, highlighting its tumor-suppressive role. CONCLUSION: This study presents a novel FRG signature with strong prognostic and immunological implications in UVM. The model offers a promising tool for risk stratification and may assist in guiding ferroptosis- and immunotherapy-based precision treatment. Our findings also suggest that SIRT3 acts as a ferroptosis sensitizer in UVM, representing a potential therapeutic target warranting further investigation.
Identification of immunoregulatory peptides from gut microbiota modulating Epstein-Barr virus-induced gene 3 (EBI3) using a novel specific AlphaLISA™ assay.
Touch S, Orsini Delgado ML, Evouna-Mengue P … +6 more , Surand L, Luangsay S, Anesary NM, Strozzi F, Chêne L, Cultrone A
Methods · 2025 Oct · PMID 40763837 · Publisher ↗
Gut microbiota-derived compounds are pivotal in modulating host immunity by regulating the functions of various key innate and adaptive immune cells. Epstein-Barr virus-induced gene 3 (EBI3) serves as the beta subunit sh...
Gut microbiota-derived compounds are pivotal in modulating host immunity by regulating the functions of various key innate and adaptive immune cells. Epstein-Barr virus-induced gene 3 (EBI3) serves as the beta subunit shared by the heterodimeric cytokines interleukin (IL)-27 and IL-35. Both these cytokines have been documented to inhibit the development of T helper 2 (Th2) and T helper 17 (Th17) cells, while enhancing the function of regulatory T cells (Tregs). EBI3, itself, has also been shown to regulate cell-mediated immune responses. Despite their critical roles in maintaining immune homeostasis, there is a significant lack of robust, high-throughput-compatible assays to evaluate the secretion of IL-27, IL-35, or EBI3. In this study, we detail the development of a novel amplified luminescent proximity homogeneous assay (AlphaLISA™) to quantify EBI3 secretion by tolerogenic dendritic cells. We utilized this assay to screen a library of 9739 small proteins derived from the human gut microbiota to identify compounds that could stimulate EBI3 secretion. Our findings revealed the immunoregulatory potential of VAC18, an unknown protein from Fusicatenibacter saccharivorans (Clostridiumcluster XIVa) which significantly induces the secretion of both EBI3 and IL-27. This is the first study to demonstrate the effect of gut microbiota derived peptides on the balanced secretion of EBI3 and IL-27.
Development of capillary electrophoresis method to measure albumin thiol oxidation in dystrophic humans and animal models of Duchenne muscular dystrophy.
Bautista APR, Terrill JR, Duong MN … +6 more , Angelica G, Tsioutsias I, James CP, Lafoux A, Huchet C, Arthur PG
Methods · 2025 Oct · PMID 40752756 · Publisher ↗
Inflammatory responses evident in many diseases involve the generation of oxidants which can cause oxidant-induced post-translational modifications to proteins. Albumin, the most abundant plasma protein, contains a free...
Inflammatory responses evident in many diseases involve the generation of oxidants which can cause oxidant-induced post-translational modifications to proteins. Albumin, the most abundant plasma protein, contains a free thiol group which is susceptible to oxidative modification. We propose that albumin thiol oxidation (AlbOx) could be a useful biomarker to monitor changes in inflammatory activity and oxidative stress. To measure AlbOx in humans and animal models, we developed a fast, sensitive, simple, and reproducible capillary electrophoresis method (CE-AlbOx). This method can analyse total, reversible, and irreversible oxidation of albumin. The method only requires a small volume of sample (<10 μL blood), has an intra/interday variation of <2 %, and has a total run time of 17 min. We validated the usefulness of AlbOx as a biomarker of chronic inflammation by analysing samples from patients with, and animal models of, Duchenne muscular dystrophy (DMD), a disease associated with chronic inflammation. The main findings in this study are (1) dystrophic humans and animals have higher oxidised albumin compared to healthy controls, (2) mouse albumin has two reactive cysteine groups, and (3) our method is the first to quantify the different oxidation states of mouse albumin. In conclusion, we have developed a new method to measure albumin oxidation in humans and animals using capillary electrophoresis. The simple methodology of the CE-AlbOx method makes it advantageous to current methods and can be readily used as a biomarker of inflammation and oxidative stress in both humans and animal models.
Recent advances in biosensors: structure, principles, classification, and application in bio-manufacturing.
Gao W, Wang Q, Gong W … +4 more , Zheng L, Liu Q, Zhang L, Ma Y
Methods · 2025 Oct · PMID 40752755 · Publisher ↗
Bio-manufacturing, as a frontier field of the new round of global scientific and technological revolution and industrial change, is becoming a core driving force for the future development of the bioeconomy. However, the...
Bio-manufacturing, as a frontier field of the new round of global scientific and technological revolution and industrial change, is becoming a core driving force for the future development of the bioeconomy. However, the bio-manufacturing process itself contains complex metabolic mechanisms and fine process control, which undoubtedly increases its technical difficulty. Especially at a time when the field of intelligent bio-manufacturing is developing rapidly, more stringent requirements have been placed on the ability to sense key biochemical information during the bio-manufacturing process. Biosensors based on biomolecular recognition provide powerful technical support for real time monitoring and precise control of key biochemical information that can rapidly sense the production process, and their applications have made remarkable progress. This article reviews the structure, classification, and working principle of biosensors and discusses their applications in bio-manufacturing, such as real-time monitoring of key biochemical parameters, intracellular and extracellular metabolite concentrations, and high-throughput screening for precise control and optimization of bioprocesses. The article also analyses the challenges facing biosensors, including the need for stability and reliability enhancements, and future directions. Researchers are developing new biometric components and sensor materials with advanced signal conversion techniques, while microelectronics and nanotechnology are driving the miniaturization and integration of sensors. These advances are expected to make biosensors more useful in microbial fermentation and biotechnology.
3DCellComposer - A versatile pipeline utilizing 2D cell segmentation methods for 3D cell segmentation.
Chen H, Zhang T, Ruffalo M … +1 more , Murphy RF
Methods · 2025 Oct · PMID 40738209 · Full text
Cell segmentation is crucial in bioimage informatics, as its accuracy directly impacts conclusions drawn from cellular analyses. While many approaches to 2D cell segmentation have been described, 3D cell segmentation has...
Cell segmentation is crucial in bioimage informatics, as its accuracy directly impacts conclusions drawn from cellular analyses. While many approaches to 2D cell segmentation have been described, 3D cell segmentation has received much less attention. 3D segmentation faces significant challenges, including limited training data availability due to the difficulty of the task for human annotators, and inherent three-dimensional complexity. As a result, existing 3D cell segmentation methods often lack broad applicability across different imaging modalities. To address this, we developed a generalizable approach for using 2D cell segmentation methods to produce accurate 3D cell segmentations. We implemented this approach in 3DCellComposer, a versatile, open-source package that allows users to choose any existing 2D segmentation model appropriate for their tissue or cell type(s) without requiring any additional training. Importantly, we have enhanced our open source CellSegmentationEvaluator quality evaluation tool to support 3D images. It provides metrics that allow selection of the best approach for a given imaging source and modality, without the need for human annotations to assess performance. Using these metrics, we demonstrated that our approach produced high-quality 3D segmentations of multichannel tissue images. 3DCellComposer, when paired with well-trained 2D segmentation models, provides an important alternative to acquiring human-annotated 3D images for new sample types or imaging modalities and then training 3D segmentation models using them. It is expected to be of significant value for large scale projects such as the Human BioMolecular Atlas Program.
A simple, accurate method for the measurement of lysosomal activity.
Iwai Y, Furuya Y, Oguro Y … +1 more , Yamamoto K
Methods · 2025 Oct · PMID 40714181 · Publisher ↗
Lysosomes are responsible for the degradation of intra- and extracellular components and are thus essential for the quality control of proteins and organelles. Lysosomal dysfunction leads to lysosomal storage diseases, a...
Lysosomes are responsible for the degradation of intra- and extracellular components and are thus essential for the quality control of proteins and organelles. Lysosomal dysfunction leads to lysosomal storage diseases, and it is therefore important to identify which types of stress cause functional abnormalities. Lysosomal function is generally evaluated by measuring the enzyme activity of lysosomes with fluorescent dyes. However, fluorescence microscopy can lead to different outcomes due to variations in the field of view, the analysis software used, and the parameter settings. We therefore developed a method that uses only a microplate reader and DQ Green BSA, a dye that emits fluorescence upon lysosomal degradation, to ascertain lysosomal activity. HEK293 cells were treated with DQ Green BSA with or without bafilomycin A1 and lysates extracted using cell lysis buffer. Fluorescence intensities and protein concentrations in the cell lysates were then measured using a microplate reader and the bicinchoninic acid method, respectively, and the fluorescence intensity divided by the protein concentration. Results indicated a significant lysosome inhibitor-induced dose-dependent decrease in the lysosomal activity. The Z'-factor of 0.77 obtained using the proposed method is a significant improvement over the - 0.06 obtained using the conventional method. The versatility of the method was evaluated with different cell types, cell lysis buffers, inhibitors, and protease substrates. These results suggest that the method works regardless of the cells or reagents used, and indicates the relative simplicity and accuracy of the proposed method compared to the currently utilized method.
Bio-impedance spectroscopy-based classification of mental acuity in university students via machine-learning and deep-learning approaches.
Tara K, Sakib S, Islam MH … +3 more , Mohonta SC, Anower MS, Sugi T
Methods · 2025 Oct · PMID 40714180 · Publisher ↗
Mental acuity detection is crucial for identifying cognitive impairments linked to body composition imbalances and ensuring overall mental and physical fitness. This study introduces a deep-learning neural network (NN) m...
Mental acuity detection is crucial for identifying cognitive impairments linked to body composition imbalances and ensuring overall mental and physical fitness. This study introduces a deep-learning neural network (NN) model with MobileNetV2 deep learning architecture to classify mental acuity levels of university students-excellent, good, and average-using bioelectrical impedance spectroscopy (BIS)-based body composition and bio-impedance measurements. Body composition and bio-impedance features such as basal metabolic rate (BMR), body cell mass (BCM), total body water (TBW), bioelectrical impedance (BI), and phase angle (PA) were utilized as inputs for a feature-based random forest (RF) machine-learning model, achieving an accuracy of 88.26% and an F1-score of 84.89%. However, an image-based NN model with MobileNetV2 deep learning architecture, leveraging 2D impedance spectrum images, outperformed RF model, achieving exceptional accuracy of 98.39% and an F1-score of 97.83%. Additionally, the Nyquist diagram showed that excellent mental acuity had the smallest semicircle, average mental acuity had the largest, and good mental acuity level was intermediate. Similarly, feature analysis revealed that excellent mental acuity level corresponded to high BMR, BCM, TBW, and PA with low BI, while average mental acuity level had the opposite trend, while good mental acuity levels fell in between. The reliability and performance of the NN model in detecting mental acuity using 2D impedance spectrum analysis highlights its potential for this task. These results emphasize the value of deep-learning approaches in integrating BIS data for accurate mental acuity assessment and their broader implications for monitoring cognitive health.
Interpretable multimodal learning for tumor protein-metal binding: Progress, challenges, and perspectives.
Liu X, Rastegari S, Huang Y … +12 more , Cheong SC, Liu W, Zhao W, Tian Q, Wang H, Guo Y, Zhou S, Tabakhi S, Liu X, Zhu Z, Sang W, Lu H
Methods · 2025 Oct · PMID 40701309 · Publisher ↗
In cancer therapeutics, protein-metal binding mechanisms critically govern the pharmacokinetics and targeting efficacy of drugs, thereby fundamentally shaping the rational design of anticancer metallodrugs. While convent...
In cancer therapeutics, protein-metal binding mechanisms critically govern the pharmacokinetics and targeting efficacy of drugs, thereby fundamentally shaping the rational design of anticancer metallodrugs. While conventional laboratory methods used to study such mechanisms are often costly, low throughput, and limited in capturing dynamic biological processes, machine learning (ML) has emerged as a promising alternative. Despite increasing efforts to develop protein-metal binding datasets and ML algorithms, the application of ML in tumor protein-metal binding remains limited. Key challenges include a shortage of high-quality, tumor-specific datasets, insufficient consideration of multiple data modalities, and the complexity of interpreting results due to the "black box" nature of complex ML models. This paper summarizes recent progress and ongoing challenges in using ML to predict tumor protein-metal binding, focusing on data, modeling, and interpretability. We present multimodal protein-metal binding datasets and outline strategies for acquiring, curating, and preprocessing them for training ML models. Moreover, we explore the complementary value provided by different data modalities and examine methods for their integration. We also review approaches for improving model interpretability to support more trustworthy decisions in cancer research. Finally, we offer our perspective on research opportunities and propose strategies to address the scarcity of tumor protein data and the limited number of predictive models for tumor protein-metal binding. We also highlight two promising directions for effective metal-based drug design: integrating protein-protein interaction data to provide structural insights into metal-binding events and predicting structural changes in tumor proteins after metal binding.
Circularization enhances RNA aptamer binding and Stability: Evidence from in-cell NMR.
Eladl O
Methods · 2025 Oct · PMID 40681131 · Publisher ↗
RNA aptamers are emerging as promising molecular probes due to their high specificity and low immunogenicity. However, their clinical potential is often limited by instability under physiological conditions, primarily du...
RNA aptamers are emerging as promising molecular probes due to their high specificity and low immunogenicity. However, their clinical potential is often limited by instability under physiological conditions, primarily due to exonucleolytic degradation and structural flexibility. To address these challenges in a model system, we designed a circular RNA aptamer targeting the HIV-1 Tat protein. Enzymatic circularization of the aptamer was performed using T4 RNA ligase, and circularization was confirmed by mobility shift assays and RNase R digestion. Binding affinity was assessed via filter dot blot assay, while structural stability was evaluated using 1D imino proton NMR and CLEANEX-PM experiments. Intracellular stability was monitored using in-cell NMR following transfection into HeLa cells. In our study, the circular aptamer showed approximately tenfold higher binding affinity compared to its linear counterpart, as determined by filter binding assay. NMR analysis indicated improved structural rigidity, preservation of native base pairing, and reduced solvent exchange. Notably, in-cell NMR revealed that the circular aptamer remained detectable up to 18 h post-transfection, whereas the linear aptamer degraded within a few hours. In this system, circularization substantially improved the binding performance and intracellular persistence of the RNA aptamer. These findings demonstrate the feasibility of applying RNA circularization to enhance aptamer functionality in living cells and lay the groundwork for future therapeutic exploration.
Ultrasensitive detection of MMP-2 via T7 RNA polymerase and CRISPR/Cas13a-Enhanced electrochemiluminescence biosensor for COPD diagnosis.
Li W, Tang Z, Zhu X … +4 more , Wang B, Zhang X, Ji Z, Cheng S
Methods · 2025 Oct · PMID 40675426 · Publisher ↗
In this work, an electrochemiluminescence (ECL) biosensor integrating T7 RNA polymerase amplification and CRISPR/Cas13a-mediated signal enhancement was developed for the ultrasensitive detection of matrix metalloproteina...
In this work, an electrochemiluminescence (ECL) biosensor integrating T7 RNA polymerase amplification and CRISPR/Cas13a-mediated signal enhancement was developed for the ultrasensitive detection of matrix metalloproteinase-2 (MMP-2), a key biomarker associated with chronic inflammatory diseases such as COPD. A peptide nucleic acid (PNA) probe was designed to respond specifically to MMP-2 cleavage, enabling the release of DNA templates for subsequent T7 RNA polymerase-driven transcription amplification. The generated RNA triggers the collateral cleavage activity of CRISPR/Cas13a, resulting in a significant amplification of the ECL signal. The biosensor's surface was constructed using a AuNPs/TiCTx/Ru(II)-PEI nanocomposite, which enhanced signal transduction and stability. Under optimized conditions, the proposed biosensor achieved a detection limit as low as 62.05 fM, demonstrating superior sensitivity compared to conventional methods, as summarized in Table 1. The platform also exhibited excellent specificity and anti-interference capability, ensuring reliable detection of MMP-2 in complex biological samples. This study provides a simple yet highly efficient strategy for enzymatic biomarker detection, offering great potential for clinical applications in early disease diagnosis and monitoring.
A photoactivatable chemical lipidomics approach for local sphingolipid metabolic analysis.
Wang X, Zhang Z, Tian S … +1 more , Feng S
Methods · 2025 Oct · PMID 40639503 · Publisher ↗
In eukaryotic cells, lipid metabolism is tightly regulated depending on the subcellular localization, which is essential for maintaining lipid homeostasis. However, understanding compartmentalized lipid metabolism remain...
In eukaryotic cells, lipid metabolism is tightly regulated depending on the subcellular localization, which is essential for maintaining lipid homeostasis. However, understanding compartmentalized lipid metabolism remains challenging due to limited availability of suitable techniques. In this study, we present a chemical lipidomics approach that combines photoactivatable probes with high resolution mass spectrometry and stable-isotope labelling to analyze lipid dynamics at subcellular resolution. We applied this method to analyze the metabolism of 1-deoxysphingolipid (DoxSL), a non-canonical lipid species linked to various metabolic diseases and neuropathy, whose metabolism remains largely unexplored. Using the photoactivatable probes, we selectively delivered 1-deoxysphinganine, a key DoxSL intermediate, to mitochondria upon photo-illumination and subsequently analyzed its local metabolic products over time. Our data show that most 1-deoxysphinganine delivered to mitochondria is rapidly converted into 1-deoxyceramides, while only a small fraction forms oxidized products. Further lipidomic analysis revealed that 1-deoxyceramides are transported to the extracellular space and that DoxSL is also present in mouse and human serum samples. In summary, we developed novel probes to track lipid dynamics with high spatiotemporal resolution in a non-invasive manner and provided new insights into sphingolipid metabolism.
Fragile methods, fractured trust: rethinking scientific responsibility.
Bustin SA, Wittwer CT
Methods · 2025 Oct · PMID 40633818 · Publisher ↗
Science has a credibility problem, and it is not just the fault of politicians, journalists, or conspiracy theorists. It begins within science itself. This review examines how flawed methods and selective reporting, comb...
Science has a credibility problem, and it is not just the fault of politicians, journalists, or conspiracy theorists. It begins within science itself. This review examines how flawed methods and selective reporting, combined with overly polished communications that prioritise image over clarity, have normalised bad practice in molecular biology, diagnostics, and related applied sciences. The quantitative real-time polymerase chain reaction (qPCR) offers a clear example: a conceptually simple, technically mature technology that is nonetheless routinely misused, despite published standards and repeated calls for methodological rigour over the past two decades. If qPCR is so often misapplied, what does that suggest about confidence in more complex, less transparent technologies? An additional problem lies in the way scientific findings are misreported or exaggerated. Such distortions have far-reaching consequences beyond individual studies. From the MMR-autism scare to COVID-19 testing and vaccine hesitancy, they have fuelled confusion, eroded public trust, and endangered public health. Consequently, when flawed or overstated findings shape public policy or clinical decisions, the damage undermines science's role as a reliable source of knowledge and informed choice. Credibility must rest on transparent practice, ethical responsibility, and attention to both how results are produced and how they are communicated. Until scientists recognise that communication is not value-neutral, and that our public voice carries consequences far beyond the lab, public scepticism will be justified.
Immunoinformatics-based multi-epitope vaccine design using transforming growth factor beta-2 proprotein (TGFB2) for glioblastoma multiforme (GBM): GVac.
Tülümen D, Aydemir E, Ayaz F
Methods · 2025 Oct · PMID 40628361 · Publisher ↗
Glioblastoma multiforme (GBM), a malignant glioma, is a central nervous system tumor with a high mortality rate in the world. Despite its high mortality rate, there is no effective treatment yet. Classical treatment meth...
Glioblastoma multiforme (GBM), a malignant glioma, is a central nervous system tumor with a high mortality rate in the world. Despite its high mortality rate, there is no effective treatment yet. Classical treatment methods are usually applied to patients, but patients lose their lives in a short time. A strong vaccine or drug that will extend the life of patients has not yet emerged. In this study, various bioinformatic analyses were performed on the Transforming growth factor beta-2 proprotein (TGFB2) belonging to GBM, and a multi-epitope vaccine design was made. These analyses include allergenicity, antigenicity and toxicity tests, various epitope selections, molecular docking, molecular dynamics simulation and immune simulation analyses. As a result of all analyses, a vaccine candidate called GVac was revealed. Gvac is enhanced with an adjuvant called batroxicidin (BatxC), an antimicrobial peptide. While analyses of Gvac generally yield strong results, it offers the potential to be used in various clinical studies if carried forward. With developing technologies, it is now necessary to move away from classical treatment methods and apply to treatment methods that can provide faster and more effective results. This is also the aim of this study. Gvac may offer hope to GBM patients awaiting treatment around the world and the studies need to be carried forward.
A prognostic gene signature and subtype-specific drug sensitivity in TNBC revealed by single-cell and bulk RNA sequencing: Insights into stemness and tumor heterogeneity.
Minh Xuan DT, Quy Nguyen DP, Ngoc Tram VT … +1 more , Khoa Ta HD
Methods · 2025 Oct · PMID 40541706 · Publisher ↗
Triple-negative breast cancer (TNBC) remains one of the most aggressive Triple-negative breast cancer (TNBC) remains one of the most aggressive and therapeutically challenging breast cancer subtypes, largely due to its l...
Triple-negative breast cancer (TNBC) remains one of the most aggressive Triple-negative breast cancer (TNBC) remains one of the most aggressive and therapeutically challenging breast cancer subtypes, largely due to its lack of targetable receptors and its intrinsic chemoresistance. In this study, we applied an integrative multi-omics approach - combining single-cell RNA sequencing (scRNA-seq) with bulk transcriptomic, epigenomic, and mutational analyses, to investigate the cellular heterogeneity and underlying mechanisms of drug resistance in TNBC. Analysis of the scRNA-seq dataset (GSE176078) revealed a complex tumor microenvironment with a highly plastic cancer epithelial subpopulation (Cluster C4) exhibiting elevated multipotency and distinct intercellular communication patterns. Concurrently, unsupervised clustering of TCGA-BRCA data delineated three molecular subtypes (CS1, CS2, and CS3) with unique biological and metabolic profiles, where CS3 notably exhibited unique molecular features associated with chromatin remodeling and high proliferative activity, suggesting distinct therapeutic vulnerabilities. An overlap analysis between scRNA-seq and bulk RNA-seq data identified 220 common differentially expressed genes (DEGs), from which a four-gene prognostic signature (CTSF, GBP1, BCL2A1, and EMP1) was derived. This signature robustly stratified patients by overall survival across both internal and external cohorts. Overall, our findings provide critical insights into the molecular drivers of chemoresistance in TNBC and offer a foundation for personalized therapeutic strategies.
Multifunctional nanophotonic photoacoustic biosensors: a new era in molecular imaging-guided deep-tissue cancer monitoring.
Taha BA, Sulaiman GM, Addie AJ … +4 more , Khalil KAA, Ahmed EM, Chaudhary V, Arsad N
Methods · 2025 Oct · PMID 40516638 · Publisher ↗
Monitoring cancer therapy is difficult because of restricted imaging depth, inadequate molecular specificity, and delayed response evaluation. Moreover, conventional imaging techniques fail to provide high-resolution, re...
Monitoring cancer therapy is difficult because of restricted imaging depth, inadequate molecular specificity, and delayed response evaluation. Moreover, conventional imaging techniques fail to provide high-resolution, real-time views of the dynamic tumor microenvironment during therapy. Among emerging technologies, nanophotonic photoacoustic biosensors have gained prominence as multifunctional platforms that enable real-time, non-invasive imaging and dynamic monitoring of cancer therapy. This review discusses advances in nanophotonic engineering, including plasmonic nanostructures, NIR-II fluorophore-integrated systems, SERS-active materials, fiber-optic probes, and hybrid nanosystems, all tailored to enhance molecular targeting and signal specificity. In addition, biomimetic and biologically inspired nanosystems with enhanced tissue penetration and reduced autofluorescence in the NIR-II spectrum can be specifically highlighted. The key aspects of clinical translation are examined including biosafety, molecular specificity, and scalability. Furthermore, further explore the convergence of these biosensors with artificial intelligence and Internet of Things (IoT) frameworks to support adaptive, patient-specific decision-making in oncology. As a result of these multifunctional systems that combine nanophotonics, machine learning, and molecular diagnostics, oncology could shift towards precision-guided treatment. Finally, it proposes strategic avenues for clinical adoption, placing PAS at the vanguard of the next generation of cancer diagnostics.
Inside the cell: Approaches to evaluating mRNA internalization and trafficking.
Del Toro Runzer C, Plank C, van Griensven M … +1 more , Balmayor ER
Methods · 2025 Sep · PMID 40513751 · Publisher ↗
With the growing prominence of mRNA-based therapeutics and vaccines, accurately assessing the cellular uptake of mRNA complexes is a critical first step in evaluating both the efficiency of delivery systems and their dow...
With the growing prominence of mRNA-based therapeutics and vaccines, accurately assessing the cellular uptake of mRNA complexes is a critical first step in evaluating both the efficiency of delivery systems and their downstream therapeutic potential. This is especially important when working with novel mRNA constructs, comparing different delivery vectors, or targeting diverse cell types. In this study, we present a suite of methods to quantify and visualize mRNA internalization following transfection of three types of human primary cells: mesenchymal stromal cells, fibroblasts, and osteoblasts. We highlight the utility of fluorescent probes for both qualitative and quantitative assessment of mRNA uptake and intracellular trafficking. To dissect the pathways involved in uptake, we employed three distinct endocytic inhibitors-chlorpromazine, wortmannin, and genistein-each targeting specific endocytic mechanisms. Additionally, we provide protocols for the lipid-based transfection agents Lipofectamine 3000 and 3DFect, which can be adapted for use with similar vectors. Key methodologies such as flow cytometry and correlative light and electron microscopy, known as CLEM, are described in detail for their effectiveness in analyzing mRNA internalization. A deeper understanding of the internalization and intracellular fate of mRNA is essential for the advancement of more efficient and safer mRNA-based delivery platforms.
Characterization of targeted knock-in achieved via tandem paired nicking mediated by CRISPR/Cas9 nickases.
Shihan MTA, Hyodo T, Fujino T … +13 more , Rahman ML, Hasan MN, Biswas M, Vu LQ, Jahan N, Mihara Y, Karnan S, Ota A, Tsuzuki S, Toyoda A, Hosokawa Y, Kasahara M, Konishi H
Methods · 2025 Sep · PMID 40505881 · Publisher ↗
Targeted knock-in of specific DNA sequences using CRISPR/Cas9 is an advanced technology that enables programmed genome alterations including insertions, deletions, and base substitutions exactly as designed. Despite its...
Targeted knock-in of specific DNA sequences using CRISPR/Cas9 is an advanced technology that enables programmed genome alterations including insertions, deletions, and base substitutions exactly as designed. Despite its utility in life sciences and promise for medical and industrial applications, it remains critical to establish a methodology for highly precise and efficient targeted knock-in to facilitate the practical use of this technology. Tandem paired nicking (TPN) is a genome editing methodology leveraging nicking variants of CRISPR/Cas9 nucleases (Cas9 nickases) to create site-specific nicks within the homologous region of the genome and donor DNA. Such nicking configuration promotes precise and efficient targeted knock-in while repressing the formation of unintended insertions and deletions and p53-mediated DNA damage response. In this study, we conducted a detailed characterization of TPN-based targeted knock-in by performing genome editing assays with various nicking configurations modified from TPN. Our results demonstrated that genomic nicks remarkably contribute to TPN-based targeted knock-in, whereas donor nicks play a less critical role. The introduction of additional nicks beyond the standard two-nick configuration did not further improve the efficiency of TPN-based targeted knock-in. Comparison with other Cas9 nickase-based methodologies for targeted knock-in demonstrated largely equivalent knock-in efficiencies achieved by these methodologies. High-throughput long-read sequencing confirmed a lower incidence of undesired insertions and deletions of various lengths by TPN, in comparison with a conventional Cas9 nuclease-based approach. These findings underscore TPN as a methodology for precise and efficient targeted knock-in, and highlight the broad potential of Cas9 nickase-based targeted knock-in for clinical and industrial applications.
Enhancing biliary tract cancer diagnosis using AI-driven 3D optical diffraction tomography.
Park SW, Moon HC, Hong SJ … +9 more , Choi A, Lee SL, Park DH, Shin E, Jo JH, Koh DH, Lee J, Hou JU, Lee KJ
Methods · 2025 Sep · PMID 40484187 · Publisher ↗
Biliary tract cancer is associated with distinct metabolic alterations, particularly in lipid metabolism. This study aimed to classify biliary tract cancer cells automatically based on lipid droplet (LD) characteristics...
Biliary tract cancer is associated with distinct metabolic alterations, particularly in lipid metabolism. This study aimed to classify biliary tract cancer cells automatically based on lipid droplet (LD) characteristics using three-dimensional (3D) optical diffraction tomography (ODT) combined with convolutional neural networks (CNNs). Human biliary tract cancer cell lines (SNU1196, SNU308, and SNU478) and a normal cholangiocyte cell line (H69) were cultured to evaluate the LD volume, mass, and count. We generated 3D refractive index tomograms and developed a CNN-based diagnostic system for automated classification. The biliary tract cancer cells exhibited significantly increased LD volume, mass, and count compared with those of normal cholangiocytes, reflecting distinct metabolic profiles. The EfficientNet-b3 model achieved an area under the curve (AUC) of 0.982 and an accuracy of 93.79%. Incorporating LD metadata, such as volume and dry mass, improved performance, yielding an AUC of 0.997 and an accuracy of 97.94%. Combining LD metadata with multi-view score fusion enhanced diagnostic performance (AUC: 0.999, accuracy: 98.61%). Further, LayerCAM analysis revealed that the model focused on LD-rich cytoplasmic regions, thereby aligning with known metabolic phenotypes. Overall, our findings demonstrate the diagnostic potential of LD characteristics and support the clinical utility of 3D ODT combined with deep learning for early detection of biliary tract cancer and future multimodal applications.
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