Biology Methods and Protocols最新文献

{"title":"Cannabinoid GPCRs, ectopic olfactory GPCRs and TRP channels: a prespecified baseline-edit-rescue framework for testing higher-order membrane-conditioned integration.","authors":"Erhan Yarar","doi":"10.1093/biomethods/bpag034","DOIUrl":"10.1093/biomethods/bpag034","url":null,"abstract":"<p><p>Cell membranes are not mere platforms for signalling proteins; they can shape how receptor inputs are assembled into local responses. In membrane-rich microdomains, receptor identification and pathway mapping do not reveal the logic of a measured effect. That effect may arise from independent receptor activity, pairwise crosstalk or higher-order integration governed by membrane state. The membrane-encoded chemosensory system (MECS) is introduced as a conceptual framework for addressing the inferential gap between receptor co-expression mapping and mechanistic crosstalk claims in territories with cannabinoid GPCRs, ectopic olfactory GPCRs and TRP channels. Its operational method, MECS baseline-edit-rescue (MECS-BER), fixes one membrane prior, one locked proximal outcome and one three-arm candidate assembly. Eligibility gates test arm engagement and outcome competence. Combinatorial responses are analysed with κ, the third-order interaction under a pairwise-only null within a complete three-factor perturbation design, to distinguish lower-order explanation from higher-order interpretation and test edit-rescue reversibility. Deterministic matrices and simulations establish classification logic and tolerance handling; biological adjudication awaits fully compliant MECS-BER datasets. The workflow provides a prespecified and formalized methodological route, not biological proof of any specific receptor triad. It keeps nomination separate from adjudication and requires co-localization, distal phenotypes, shared downstream signals and nonlinear mixtures to be tested against a locked proximal outcome before biological interpretation. Renal micro-niches specify prospective deployment across renin, transport, barrier and flow-sensitive calcium control without claiming validated cannabinoid-olfactory-TRP assemblies. Membrane lipids may shape both the signalling vocabulary of individual receptors and the local language through which receptors communicate.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"11 1","pages":"bpag034"},"PeriodicalIF":2.4,"publicationDate":"2026-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13316429/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148362794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction.","authors":"","doi":"10.1093/biomethods/bpag030","DOIUrl":"https://doi.org/10.1093/biomethods/bpag030","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.1093/biomethods/bpaf077.].</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"11 1","pages":"bpag030"},"PeriodicalIF":2.4,"publicationDate":"2026-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13268962/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148273207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transmission electron microscopy of cultured cells: Optimized protocols for ultrastructural studies and post-embedding immunogold labeling.","authors":"Anna Richert, Robert Lenartowski, Jolanta Nowak, Piotr Wasąg, Marta Lenartowska","doi":"10.1093/biomethods/bpag033","DOIUrl":"10.1093/biomethods/bpag033","url":null,"abstract":"<p><p>Nanometer-scale localization of specific proteins within cells is essential for understanding their roles in diverse physiological and pathological cellular processes. Such high-resolution localization of protein epitopes can be achieved by combining transmission electron microscopy (TEM) with immunogold labeling. Several immuno-TEM approaches are available, including pre-embedding and post-embedding techniques, each offering distinct advantages and limitations when applied to cultured cells. One of the major challenges is preparation of cell culture samples embedded in resin in a manner that enables reliable ultrathin sectioning and efficient collection of sections. Here, we describe an optimized post-embedding immunogold labeling procedure for the localization of one or more proteins in both adherent and non-adherent cells cultured on plastic coverslips. This approach has been successfully applied in our previous studies and proved suitable for routine ultrastructural immunolabeling of cultured cells. Although the protocol is designed to achieve an optimal balance between the preservation of cellular ultrastructure and antigenicity, optimal results may still depend on the specific characteristics of the proteins under investigation and their localization within particular cellular compartments. Therefore, we also present a complementary procedure for the ultrastructural analysis of cultured cells that can support and extend immuno-TEM investigations.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"11 1","pages":"bpag033"},"PeriodicalIF":2.4,"publicationDate":"2026-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13326743/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148377527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ClinAgent: AI-assisted methodology for clinical trial data processing and statistical programming.","authors":"Jaime Yan","doi":"10.1093/biomethods/bpag032","DOIUrl":"10.1093/biomethods/bpag032","url":null,"abstract":"<p><p>Clinical trial statistical programming requires 12-24 full-time-equivalent months per Phase 3 study and remains a bottleneck in pharmaceutical research. Modern artificial intelligence coding agents reason capably but lack domain-specific tools: they cannot read proprietary statistical software datasets, parse analysis specifications, or generate standards-compliant code without extensive guidance. We present ClinAgent, a skill and tool layer that augments any artificial intelligence coding agent with clinical programming capabilities through Model Context Protocol tools. Its design separates minimal data access from rich domain logic: skills package prompts, rule engines, and decision trees encoding expert knowledge, while tools provide stateless input-output for statistical software datasets, spreadsheet specifications, and log files. In this single-study proof-of-concept evaluation, we validate ClinAgent's nine skills on artifacts from a production Phase 2 cardiovascular study, with synthetic datasets spanning 13 analysis domains and 102 109 observations. All skills pass functional validation. On this small sample, deterministic components identify one error and seven warnings without false positives and match all 56 subject-level variables; corresponding confidence intervals are wide, so these point estimates should be read as upper bounds pending replication. Prompt-based specification generation, dependent on the underlying language model, reaches 72.1% derivation accuracy overall, above 96% in simple domains and below 55% in complex ones, indicating that generated specifications require expert review. Our contributions include an agent-augmentation architecture, nine validated skills, tool implementations for clinical data formats, and a validation methodology distinguishing deterministic tool correctness from language-model-dependent output.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"11 1","pages":"bpag032"},"PeriodicalIF":2.4,"publicationDate":"2026-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13555824/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148920445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gap junction communication regulates luminal-myoepithelial crosstalk and cell differentiation in a bilayered human mammary epithelial cell model.","authors":"Alec McDermott, Melany Nicole Juarez, David Tovar Parra, Capucine Duval, Shunmoogum A Patten, Isabelle Plante","doi":"10.1093/biomethods/bpag031","DOIUrl":"10.1093/biomethods/bpag031","url":null,"abstract":"<p><p>Interactions between luminal and myoepithelial cells are essential for proper mammary gland development and function. However, only a few <i>in vitro</i> models allow the study of these interactions and their modulation by extrinsic factors. We developed a layered co-culture system (LCS) that mimics the bilayered architecture of the human breast epithelium, enabling direct contact and bidirectional crosstalk between luminal (MCF-12A) and myoepithelial (MYO1089) cells. We confirmed the formation of adherens and functional gap junctions across the layers. Transcriptomic analysis revealed that co-culture altered gene expression in pathways related to extracellular matrix remodeling, mRNA processing, response to external stimuli, hormonal signaling, receptor activity, and cell cycle regulation. In the LCS, MCF-12A cells exhibited a more luminal-like phenotype, with increased keratin-18 and decreased keratin-14, α-smooth muscle actin (α-SMA), and caldesmon-1 expression compared to 2D monoculture. Conversely, MYO1089 cells showed enhanced expression of myoepithelial markers, including α-SMA, keratin-14, and caldesmon-1. Inhibition of gap junction communication by carbenoxolone disrupted these lineage-specific differentiation patterns. These findings highlight the importance of direct communication in regulating epithelial identity and underscore the LCS as a physiologically relevant model for studying mammary gland biology and external influences that can impact the epithelial differentiation.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"11 1","pages":"bpag031"},"PeriodicalIF":2.4,"publicationDate":"2026-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13294455/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148346552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the feasibility of machine learning for ancient DNA age prediction: Limitations and insights.","authors":"Maksim Kazanskii, Maria Golikova, Artem Kasianov, Anna Ivanova, Alibek Suraganov, Leonid Uroshlev","doi":"10.1093/biomethods/bpag028","DOIUrl":"10.1093/biomethods/bpag028","url":null,"abstract":"<p><p>We investigated the possibility of estimating the age of ancient biological samples directly from their DNA damage profiles using supervised machine learning. Traditional dating methods such as radiocarbon dating or dendrochronology rely on either isotope composition or material context, while our approach exploits intrinsic molecular degradation signatures. Using damage statistics obtained from ancient DNA sequencing data, we trained several regression models to predict sample age over a temporal range of up to 10 000 years. Despite initial correlations between specific damage features and age, cross-validation and external testing revealed no statistically significant predictive signal beyond mean-based baselines. These findings indicate that, in the current formulation, DNA damage information alone is insufficient for reliable age estimation. However, this negative result empirically validates the extent to which environmental and biochemical factors dominate damage variation, effectively masking the chronological signal, thereby limiting the predictive power of current supervised learning models. We suggest that integrating contextual data, expanding labeled datasets, and incorporating physical models of DNA decay may improve future attempts. Our study thus contributes to a transparent assessment of the limitations and prospects of DNA-based fossil dating.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"11 1","pages":"bpag028"},"PeriodicalIF":2.4,"publicationDate":"2026-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13283477/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148303326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advances in the detection of antimicrobial resistance in aquatic environments: a methodological perspective.","authors":"Olukayode Adebola Ibitoye, Chinyere Nkemjika Anyanwu, Abdulganiy Babatunde Agbaje, Ilemobayo Victor Fasogbon, Reuben Samson Dangana, Saheed Adekunle Akinola, Julius Tibyangye, Adam Ahmed Adam, Patrick Maduabuchi Aja","doi":"10.1093/biomethods/bpag029","DOIUrl":"10.1093/biomethods/bpag029","url":null,"abstract":"<p><p>Antimicrobial resistance (AMR) is a global health and environmental challenge, driven by complex interactions among microbial communities, resistance genes, and selective pressures in various ecological niches. Traditional surveillance procedures often fall short in capturing the full diversity and dynamics of resistance reservoirs in the environment. This review examines the integration of artificial intelligence (AI) and machine learning (ML) with next-generation sequencing (NGS) technologies for comprehensive resistome profiling. We discuss advances in multi-omics approaches, particularly metagenomics, microbiome-based analytics, and metatranscriptomics. We also highlight computational workflows that enable high-resolution mapping of resistance genes, their mobile genetic elements, and host associations. The role of AI/ML in resistome prediction, classification, and source tracking, as well as the incorporation of environmental metadata for contextual interpretation is discussed based on the selected literature. Moreover, we assess current challenges and propose future directions for developing standardized, scalable, and interpretable bioinformatic pipelines in AMR surveillance. This review primarily elucidates the potential of integrated AI-omics platforms to revolutionize aquatic environmental AMR monitoring and inform risk assessment and mitigation strategies.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"11 1","pages":"bpag029"},"PeriodicalIF":2.4,"publicationDate":"2026-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13283427/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148303374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to: Enhanced formulation of precision probiotics through active machine learning.","authors":"","doi":"10.1093/biomethods/bpag018","DOIUrl":"https://doi.org/10.1093/biomethods/bpag018","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.1093/biomethods/bpag007.].</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"11 1","pages":"bpag018"},"PeriodicalIF":1.3,"publicationDate":"2026-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13229923/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148164863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"sc-rDSeq: Droplet-based single-cell full-length total RNA-seq method.","authors":"Xue Sun, Oren Ram","doi":"10.1093/biomethods/bpag027","DOIUrl":"10.1093/biomethods/bpag027","url":null,"abstract":"<p><p>This protocol describes sc‑rDSeq, a scalable, droplet‑based method for full‑length, strand‑specific total RNA sequencing at single‑cell resolution. The protocol uses a refined set of 220 ribosomal‑depleted sequences (rDS) primers that selectively exclude ribosomal RNA during initial reverse transcription, enabling capture of both polyadenylated and non‑polyadenylated RNAs such as histone RNAs, noncoding RNAs, and enhancer RNAs, without requiring costly post‑amplification depletion steps. This method is useful for researchers who would like to detect not only gene expression variations, but also alternative splicing events and single nucleotide variations in complex heterogenous cellular systems, providing a more complete view of cellular heterogeneity and regulatory programs that remain invisible to conventional polyadenylated‑only sequencing approaches. Compared with existing full‑length protocols, which are often limited by high reagent costs or reliance on complex multistep microfluidics, sc-rDSeq provides a simpler, single-step microfluidic workflow compatible with standard inDrops platforms, which may reduce experimental complexity and cost relative to existing full-length total-RNA methods. A key improvement is the 10-fold increase in unique molecular identifiers per cell relative to 3' end‑based methods, at a reported reagent cost of approximately $0.08 per cell, making deep total transcriptome analysis more accessible. The protocol includes three major parts: sc‑rDSeq barcode synthesis, single‑cell co‑encapsulation, and library construction.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"11 1","pages":"bpag027"},"PeriodicalIF":2.4,"publicationDate":"2026-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13275127/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148279112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Viral Sentry AI-Automated zoonotic surveillance and drug repurposing agent.","authors":"Cristian R Munteanu, Jose Vázquez-Naya, Eduardo Tejera","doi":"10.1093/biomethods/bpag026","DOIUrl":"10.1093/biomethods/bpag026","url":null,"abstract":"<p><p>Zoonotic viruses capable of jumping from animal reservoirs into human populations represent a persistent and unpredictable menace to global health. To confront this challenge, we developed Viral Sentry AI, an autonomous agent designed to close the gap between viral emergence and therapeutic response. Unlike static analysis tools, Viral Sentry AI operates as a continuous sentinel, automatically scanning the National Center for Biotechnology Information public databases for new viral genomes and executing a three-stage agentic surveillance workflow, with distinct, specialized artificial intelligence architectures for generated text, macromolecule sequences, and drug chemical data. First, the system is using a Large Language Model (Gemma4) to parse unstructured submission records and extract the host information if it is not available in the dedicated field. In the second stage, the system employs a novel deep-learning topology, virsentai-v3-hyena-dna-16k, a fine-tuned HyenaDNA model capable of processing complete viral genomes up to 160 000 bases. This architecture captures subtle, long-range genomic dependencies to predict human infectivity with high precision. Upon predicting the possible human infection of the scanned viruses, the agent autonomously triggers a downstream therapeutic module as the stage three. It extracts National Center for Biotechnology Information RefSeq viral protein sequences and utilizes a pretrained Protein-Ligand Affinity Prediction Transformer model to calculate affinity interactions against 2092 ChEMBL-approved drugs, instantly identifying candidates for drug repurposing. In rigorous cross-validation on a curated dataset of 33 426 complete viral genomes, the surveillance module demonstrated robust discriminatory power, achieving an Area Under the Receiver Operating Characteristic Curve of 0.88 in classifying human host potential. By integrating state-of-the-art genomic modeling with automated lead compound screening, Viral Sentry AI offers a proactive, end-to-end research prototype for pandemic preparedness. The platform is freely accessible at https://muntisa.github.io/virsentai (source code: https://github.com/muntisa/virsentai).</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"11 1","pages":"bpag026"},"PeriodicalIF":2.4,"publicationDate":"2026-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13256000/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148253702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}