The intestinal epithelium is maintained by stem cells at the crypt base, however, acute or chronic inflammation can severely disrupt stem cell function and tissue homeostasis. To characterize mucosal inflammation and tissue damage, we evaluated time-course gene expression changes using a dextran sulfate sodium (DSS)-induced mouse model of colitis. By applying normalization, Z-score transformation, and spline curve modeling, we trace the temporal expression dynamics of key intestinal genes. This computational approach offer insights into the molecular responses during inflammation and may help identifying new biomarkers for chronic inflammatory diseases.
Methods in molecular biology (Clifton, N.J.)Jason Ngo, Emily Lee, Marta Olah, Falak Sher
Human induced pluripotent stem cell (iPSC)-derived microglia (iMG) provide an in vitro experimental system for studying human microglial biology, neuroinflammation, and genetic risk mechanisms associated with neurological disease. This chapter describes a standardized, scalable, and reproducible protocol for the differentiation of human iPSCs into functional microglia-like cells, with particular emphasis on applications in transcriptional and epigenomic network analysis. The protocol supports high-viability floating iMG production, compatibility with pooled CRISPR perturbation approaches, and downstream multiomic profiling, including single-cell RNA sequencing, chromatin accessibility assays, and proteomics. Detailed procedures are provided for iPSC maintenance, hematopoietic progenitor cell generation, microglial maturation, functional genomics integration, and quality control.
Methods in molecular biology (Clifton, N.J.)Joseph C F Ng, Raul Ruiz-Hernandez, Isabella Withnell, Tooki Chu, Franca Fraternali
Single-cell RNA sequencing (scRNA-seq) has facilitated the studies of cellular heterogeneity in many different biological contexts, including how B cells function and mediate immune responses to infection, malignancies, and autoimmunity. scRNA-seq as applied to B cells requires specific bioinformatics strategies, which consider the distinct biology of B cells and biological processes unique to these cells, such as somatic hypermutation (SHM) and class switch recombination (CSR). We present here a protocol that analyses scRNA-seq alignments and count matrices, as well as B-cell receptor sequencing data obtained in parallel to scRNA-seq. We highlight computational strategies to ensure cell clusters align with biologically relevant B-cell subsets and cell states. We provide practical guidance on how CSR and SHM information can be extracted from the data and utilized to build robust computational models of B-cell maturation.
Methods in molecular biology (Clifton, N.J.)Lorenzo Fontanelli, Carolina Ferri, Adrian Florentin Suman, Elisa Ceccherini, Giovanni Signore, Fabio Anastasio Recchia, Silvia Rocchiccioli
Cardiac organoids are increasingly used to model human cardiac development and disease, but their small size often limits molecular characterization, especially if different approaches and protocols are necessary to extract and quantify metabolites and lipids. Here, we present a multistep workflow for combined targeted free amino-acid (FAA) based metabolomic and lipidomic profiling from a single pooled cardiac organoid sample. The protocol covers organoid harvesting, detergent-assisted lysis, and a modified Folch extraction that generates an organic phase for lipid analysis and an aqueous phase for FAA metabolite analysis. Lipids are quantified by Liquid Chromatography Electrospray Ionization Tandem Mass Spectrometry (LC-ESI-MS/MS) in Multiple Reaction Monitoring (MRM) mode using class-matched external standards and an internal standard to support calibration and reduce technical variability. Free amino acids and derivatives are analyzed from the same sample after filtration, drying, and AccQ-Tag derivatization, with norvaline as an internal standard. Together, this approach maximizes information yield from limited material and enables integrated analysis of metabolic and lipid pathways within the same biological specimen, facilitating organoid-based studies of cardiac maturation, disease modeling, and pharmacological responses.
Methods in molecular biology (Clifton, N.J.)Benjamin L Kidder
Cancer cell identity is governed by coordinated transcriptional programs that are frequently rewired during tumorigenesis. Systematic identification of cancer type-specific gene regulatory networks provides a framework for understanding oncogenic state transitions and for prioritizing candidate therapeutic targets. Here, we present a reproducible network-based workflow for reconstructing and analyzing transcriptional regulatory programs across human cancer types using publicly available expression datasets. We describe procedures for curating and preprocessing microarray data from the Gene Expression Omnibus, implementing random forest classification, and reconstructing gene regulatory networks using the CellNet platform. Detailed guidance is provided for evaluating classifier performance, quantifying network influence scores, integrating transcription factor, target interaction resources, and performing functional enrichment analyses. In addition, we outline approaches for comparing cancer-specific networks with corresponding normal tissue profiles to identify candidate drivers of malignant cell identity and potential prognostic biomarkers. Together, these protocols provide investigators with a scalable computational framework for defining cancer type-specific transcriptional states and for systematically interrogating regulatory mechanisms underlying tumor heterogeneity.
Methods in molecular biology (Clifton, N.J.)Ece Cakiroglu, Serif Senturk
Clustered regularly interspaced short palindromic repeats (CRISPR) and associated (Cas) systems have revolutionized the field of genome engineering by providing versatile, efficient, and programmable tools for precise genetic manipulation. Originally identified as an adaptive immune mechanism in prokaryotes, CRISPR/Cas systems have been extensively repurposed for a wide range of applications across molecular biology, biotechnology, and medicine. This chapter provides a comprehensive overview of the molecular mechanisms underlying CRISPR/Cas immunity. Furthermore, the classification of CRISPR/Cas systems into distinct types and subtypes is discussed, highlighting their structural and functional diversity. Advances in genome editing technologies, including CRISPR-mediated knockout, base editing, and prime editing, are explored with an emphasis on their mechanisms and applications. The chapter also examines emerging CRISPR-based platforms for transcriptional regulation, epigenome editing, and RNA targeting, which enable precise and reversible modulation of gene expression without altering genomic DNA. In addition, the transformative impact of CRISPR technologies on functional genomics is addressed, particularly through high-throughput screening approaches that facilitate the identification of gene function and genetic vulnerabilities. CRISPR-based diagnostic tools and therapeutic strategies are also reviewed, underscoring their potential in disease detection and treatment. Despite significant progress, challenges such as off-target effects, delivery limitations, and safety concerns remain critical considerations. Overall, this chapter highlights the expanding capabilities of CRISPR/Cas systems and their growing importance in both fundamental research and clinical applications.
Methods in molecular biology (Clifton, N.J.)Amin Alvani, Negar Mottaghi-Dastjerd, Abozar Ghorbani, Ahmad Gholami, Zeinab Pazhoohesh
Conventional treatments often face challenges such as the limited ability to penetrate the blood-brain barrier (BBB). The Doxorubicin-loaded graphene oxide/magnetite (DOX/GO/Fe3O4) nanocomplex offers a promising platform due to GO's high surface area and pH-sensitive release, and Fe3O4's magnetic properties. This protocol describes the methodology for evaluating the cytotoxicity of free DOX versus the DOX/GO/Fe3O4 nanocomplex in the A-172 glioblastoma cell line, followed by advanced bioinformatics analysis to identify gene networks and indirect pathways that enhance the nanomaterial's biocompatibility. The methodology integrates the MTT assay, real-time PCR for apoptosis genes (Casp3, Bax, and Bcl-2), and advanced analysis, including protein-protein interaction (PPI) networking, clustering, and promoter motif analysis. The analysis indicated that miR-92a-2-5p is a potential therapeutic target for preventing myocardial damage and enhancing biocompatibility. The findings highlight key regulatory pathways that indirectly boost nanodrug biocompatibility through the modulation of secondary components like miRNAs and cellular stress mechanisms.
Methods in molecular biology (Clifton, N.J.)Yan Sun, Chengyou Liu, Zihao Jing, Yan Yi Li, Pingzhao Hu
Understanding drug mechanisms of action (MOA) and predicting drug-target interactions (DTIs) are fundamental challenges in modern drug discovery and development, hindered by high costs, long development timelines, and limited knowledge of compound activity and molecular targets. Here, we present two deep learning-based computational protocols designed to address these challenges. The first framework employs directed message passing neural networks (D-MPNN) to predict drug MOA from chemical-genetic interaction profiles (CGIPs), by learning how molecular structures perturb biological pathways through systematic profiling across genetically sensitized strains. The second framework, iNGNN-DTI, utilizes interpretable nested graph neural networks combined with pretrained molecule models to predict DTIs, leveraging cross-attention mechanisms to provide insights into binding determinants. We highlight the application of these methods to key therapeutic areas, including antibacterial drug discovery and drug repurposing for COVID-19 therapeutics. Each protocol provides comprehensive guidance on data preparation, model implementation, validation strategies, and result analysis. These computational approaches offer scalable, cost-effective tools for accelerating therapeutic development by bridging chemical structure, molecular interactions, and systems-level biological responses.
Methods in molecular biology (Clifton, N.J.)Gijs Wilbrink, Roman Pleskot, Peter Grones
The plasma membrane (PM) is the primary interface between plant cells and their environment, and its resident proteins mediate key processes such as extracellular signal perception and downstream cellular reprogramming. Yet, PM proteins are typically underrepresented in total protein extracts, and existing enrichment strategies are often laborious and require extensive optimization. Here, a simple and robust workflow is described for enriching PM proteins from Arabidopsis thaliana seedlings using total microsomal membranes obtained by differential centrifugation as starting material. Sequential low- and high-speed spins are used to isolate total microsomal membranes and progressively deplete contaminating organelles, thereby increasing the relative abundance of PM proteins. Coupled with the rich genetic toolkit available in Arabidopsis, this protocol provides an accessible platform for systematic characterization of the PM proteome.
Methods in molecular biology (Clifton, N.J.)Zhenlan Yao, Melody Li
Affinity purification-mass spectrometry (AP-MS) is a powerful proteomic approach for dissecting the interaction network between virus and host. Traditional AP-MS employs overexpression of viral proteins as baits to enrich host interactors. However, overexpressed viral proteins may mislocalize to inappropriate cellular compartments and trigger endoplasmic reticulum stress by overwhelming the protein-folding machinery, which leads to false identification of host factors. To overcome these limitations, we introduce an AP-MS strategy based on direct infection with an epitope-tagged chikungunya virus (CHIKV/myc-E2), which we used to successfully uncover two new antiviral factors in CHIKV cellular reservoirs-macrophages. In this protocol, we will describe this technique step by step: (1) design and construction of myc-tagged virus by advanced multi-fragment assembly, (2) in vitro transcription and preparation of infectious myc-tagged virus stocks, and (3) immunoprecipitation of myc-tagged viral protein and its interactome for mass spectrometry analysis. This strategy enables accurate identification of viral interactors in a physiologically relevant context, providing a framework for future proteomic studies using tagged viruses.
Methods in molecular biology (Clifton, N.J.)Xiaoqian Shi-Kunne, Jan A L van Kan
The availability of a high-quality genome assembly facilitates the analysis of fungal genomes. This chapter outlines the tools and steps involved in genome sequence assembly and annotation of a plant pathogen, Botrytis cinerea. We describe the use of Illumina short-read and Oxford Nanopore long-read sequencing data to assemble the B. cinerea genome. The steps include the pre-processing of sequencing data, genome assembly using Flye, scaffolding with NtLink, and polishing with Racon, Medaka, and NextPolish. The quality of the final assembly is evaluated using BUSCO, which serves as a benchmark for the completeness of a genome. We also provide details on the identification and masking of repetitive elements using the EarlGrey pipeline, as well as the gene prediction and annotation process with Funannotate. The methodologies and insights described can be applied to genome research in other fungal species.
Methods in molecular biology (Clifton, N.J.)Fanju W Meng, Duy K Nguyen, Patrick J Murphy
Alterations in chromatin state, mediated through histone modifications and the incorporation of histone variants, are fundamental to establishing transcriptional networks and cell identity. Recent advances in low-input epigenome profiling methods, such as CUT&Tag and CUT&RUN, have enabled the study of chromatin states from very limited starting materials. In this chapter, we describe procedures for generating CUT&Tag libraries to profile histone modifications and histone variants in early-developing zebrafish embryos.
Global genomic surveillance has emerged as a foundational pillar of public health in the twenty-first century, enabling real-time tracking of pathogen evolution and informing outbreak response. This chapter examines the strategic architecture of global genomic surveillance, focusing on its application to arboviruses such as chikungunya virus (CHIKV). It explores the integration of genomic data with epidemiological, clinical, and environmental information within a One Health framework, while addressing critical challenges in governance, equity, and interoperability. The discussion covers the entire genomic surveillance workflow, from sample collection and sequencing to bioinformatic analysis and phylogenetic inference, and highlights the transformative role of artificial intelligence (AI) in predictive surveillance. By analyzing global initiatives, operational barriers, and emerging technologies, this chapter underscores the necessity of sustainable, equitable, and interoperable genomic systems to proactively address current and future infectious disease threats.
Methods in molecular biology (Clifton, N.J.)Emiko Desvaux, Amazigh Mokhtari
Recent advances in multiomics technologies have revolutionized the study of immune-driven diseases by enabling high-dimensional, single-cell resolution analyses. This chapter provides a practical guide for integrating CyTOF (mass cytometry) and single-cell RNA sequencing (scRNA-seq) data to address key challenges in this field. The integration of these modalities allows for consistent and reproducible cell type annotation, the transfer of annotations to assist in characterizing difficult-to-identify populations, and the transcriptional characterization of rare and heterogeneous subpopulations. Using tools such as OMIQ and R, the chapter outlines workflows for preprocessing, normalization, and scaling of CyTOF data, as well as dimensionality reduction and clustering techniques. The integration process involves creating Seurat objects, identifying common features, and using anchor-based methods to link CyTOF and scRNA-seq datasets. The chapter also discusses the use of multimodal deep learning techniques for rare subpopulation detection and emphasizes the importance of reproducibility and standardization in multiomics integration. By leveraging these methodologies, researchers can gain deeper insights into cellular heterogeneity and function, ultimately enhancing the understanding of immune-driven diseases. The chapter concludes by addressing integration challenges and proposing future directions for improving model interpretability and capturing nonlinear molecular interactions.
Methods in molecular biology (Clifton, N.J.)Mark Roosjen, Dolf Weijers
Mass spectrometry-based proteomics allows the unbiased identification and quantification of proteins and phosphopeptides in biological materials. The nature of walled plant cells requires specific protocols for effective and efficient protein isolation, and, in general, the plant sciences can benefit from more accessible, optimized proteomics workflows. Advances in MS instrumentation now allow the measurement of large numbers of samples, shifting constraints in proteomics toward the accurate, high-throughput preparation of samples. Here, we describe a high-throughput (phospho)proteomics protocol that enables processing of samples using different filter types in a 96-well format.
Aberrant three-dimensional genome organization is a hallmark of cancer, often driving oncogene activation through mechanisms such as enhancer hijacking. High-throughput chromosome conformation capture (Hi-C) maps these interactions on a genome-wide scale. Unlike earlier dilution-based methods, in situ Hi-C performs proximity ligation within intact nuclei, minimizing random ligation noise and enabling fine-scale structure detection. This chapter describes an optimized in situ Hi-C protocol tailored for cancer cell lines using MboI digestion and biotin-mediated pull-down to generate high-complexity libraries. We further outline a computational workflow that extends beyond standard topological mapping of compartments and topologically associating domains to identify cancer-specific aberrations. Specifically, we focus on detecting chromosomal rearrangements (structural variants) and characterizing the distinct circular topology of extrachromosomal DNA. This integrated experimental and analytical framework provides the necessary tools to dissect the spatial dysregulation underlying tumor evolution.
Methods in molecular biology (Clifton, N.J.)Ana Ruiz-Padilla, Julio L Rodríguez-Romero, Marco Chiapello, María A Ayllón
High-throughput sequencing of total RNA has permitted the detection of novel mycoviruses in fungi with different types of genomes, including mycoviruses with double-stranded RNA, single-stranded positive- or negative-stranded RNA, or single-stranded DNA genomes. However, in silico detection of mycoviruses is not always sufficient to guarantee their presence in sequenced samples, especially in the case of the discovery of unique mycoviruses, and additional analyses are required to validate in vivo the data obtained by bioinformatics analysis. This chapter provides comprehensive protocols for the extraction of total RNA from the plant pathogenic fungus Botrytis cinerea for next-generation sequencing (NGS), outlines the bioinformatics pipeline designed and followed to detect mycoviruses in the sequenced samples, and details the detection in vivo and the complete molecular characterization of the mycoviruses identified in silico.
Single-cell transcriptomics has revolutionized our understanding of cellular heterogeneity by enabling high-resolution gene expression profiling at the individual cell level. However, traditional single-cell RNA sequencing (scRNA-seq) lacks direct protein quantification, limiting comprehensive immunophenotyping. Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq) overcomes this limitation by integrating antibody-derived tag (ADT) quantification with scRNA-seq, allowing simultaneous measurement of surface protein and gene expression from the same cell. This multimodal approach enhances immune cell characterization, revealing new functional states and rare subpopulations in complex biological systems. Here, we provide a detailed protocol for performing CITE-seq, from sample preparation to sequencing and data analysis. We highlight key experimental considerations, discuss challenges related to antibody selection and batch effects, and provide troubleshooting strategies to ensure robust and reproducible results. The integration of transcriptomic and proteomic data through CITE-seq provides unparalleled insights into cellular function, with broad applications in immunology, oncology, and systems biology.
Studying the transcriptome and the proteome of cells is essential for gaining a detailed understanding of cellular behavior, development, drug action, and disease progression. Spatial biology emerges to advance our ability to study the expression of molecules within tissues while preserving their natural spatial context. These cutting-edge technologies enable the mapping of thousands of individual cells in their original environment by detecting the location and biological quantity of cellular contents, such as RNAs and proteins. Here, we present a protocol to combine spatial transcriptomics (Xenium) and spatial proteomics (PhenoCycler-Fusion) within 8 days on the same tissue section to successfully study the expression of hundreds of RNAs and tens of proteins simultaneously. The combination of these two technologies and consequent integration of the two data layers together with high-resolution H&E images allows for the extraction of a maximum of information from a single tissue section. Application of this protocol and the resulting integrated data will help researchers to understand complex biological processes and disease mechanisms, supporting more nuanced research in molecular biology and pathology.
Methods in molecular biology (Clifton, N.J.)Brinda Balasubramanian
Single-cell RNA sequencing (scRNA-seq) has revolutionized the ability to resolve cellular heterogeneity within complex tissues, enabling the identification of discrete cell states. Here, we present an in silico analytical pipeline designed to characterize intestinal stem cells (ISC), transit-amplifying (TA) progenitors, and BEST4⁺ enterocyte precursors from human scRNA-seq datasets, with a focus on inflammatory contexts such as inflammatory bowel disease (IBD). The pipeline integrates dataset acquisition, quality control, normalization, dimensionality reduction, unsupervised clustering, and cell type annotation using a reference cell atlas. We implemented iterative subsetting and re-clustering of ISC and TA compartments to identify inflammation-associated subpopulations and epithelial biomarkers. While demonstrated in the context of IBD, this computational framework is broadly applicable to other tissues and pathological conditions where stem/progenitor dynamics underpin disease progression and tissue repair.
Methods in molecular biology (Clifton, N.J.)Daniela Solano-Galarza, Simone Roeh, Thomas Walzthoeni
Gene regulatory networks (GRNs) represent the complex interplay of transcription factors, regulatory elements, and target genes that orchestrate cellular identity and function, playing a crucial role in the differentiation and maintenance of stem cells. This chapter provides an overview of experimental and computational methodologies for inferring GRNs, with particular emphasis on single-cell approaches. We first review key experimental techniques for detecting transcription factor binding sites, chromatin accessibility, and DNA motifs, alongside essential databases that support GRN reconstruction. We then introduce computational inference methods that can be categorized into four principal frameworks: correlation-based approaches, regression and machine learning models, probabilistic and deep learning methods, and integrative or message-passing frameworks. To illustrate practical application, we present a case study applying the pySCENIC workflow to a peripheral blood mononuclear cell single-cell RNA sequencing dataset from mouse, demonstrating how regulon-based analysis can reveal cell-type-specific regulatory programs. This chapter aims to serve as a practical guide for researchers seeking to understand and implement GRN inference methodologies in stem cell biology and related fields.
Methods in molecular biology (Clifton, N.J.)Mohamed Ali Hussein, Anwar Abdelnaser
.: Nonalcoholic fatty liver disease (NAFLD) is a significant global health concern, impacting roughly 25% of people and leading to chronic liver conditions. It involves excess fat accumulation in the liver without significant alcohol intake and can develop into nonalcoholic steatohepatitis (NASH), fibrosis, or cirrhosis. While liver biopsy remains the gold standard for diagnosis, its invasive nature and associated risks restrict its routine use. Noninvasive biomarkers, such as serum ALT, AST, and various composite scores, are available; however, their clinical usefulness is often limited by variable sensitivity and specificity across different populations and disease stages. To overcome these limitations, this chapter offers a comprehensive, reproducible protocol for identifying and clinically validating circulating long noncoding RNA (lncRNA) biomarkers for NAFLD and NASH. The workflow integrates bioinformatic analysis of four Gene Expression Omnibus (GEO) transcriptomic datasets (two human and two murine cohorts) with network-based inference to construct a NAFLD-related lncRNA-miRNA-mRNA coregulatory network. This is followed by candidate prioritization based on cross-dataset evidence and a literature review. Candidate lncRNAs are then experimentally validated in patient-derived blood samples using quantitative PCR (qPCR), and their diagnostic performance is quantified using receiver operating characteristic (ROC) analysis, both as individual markers and multi-lncRNA panels. Circulating lncRNAs are detected in diverse biofluids, remain stable under standard preanalytical conditions, and are often tissue-specific. This integrated approach facilitates the development of more precise, scalable, and noninvasive biomarkers for NAFLD/NASH. The chapter further emphasizes essential translational steps, including preanalytical standardization, analytical validation, and validation in independent patient cohorts with relevant clinical endpoints.
Proteomics, the large-scale study of proteins, enables the identification, quantification, and functional characterization of proteins, revealing post-translational modifications and protein interactions that are not apparent from transcriptomic data. Human organoids, which recapitulate the structural and functional complexity of native epithelial tissues, provide powerful tools to study disease mechanisms and personalize therapies. However, their culture poses challenges for efficient protein extraction and reproducible analysis. Here, we present a proteomics workflow optimized to maximize protein recovery from Matrigel-encased organoids. Samples were processed using S-Trap microcolumns to minimize losses, followed by liquid chromatography-mass spectrometry (LC-MS) in data-independent acquisition (DIA/SWATH-MS) mode for comprehensive, untargeted quantification. Library-free computational analysis using DIA-NN, combined with differential expression analysis, enabled sensitive detection of key proteins in intestinal organoids.
Methods in molecular biology (Clifton, N.J.)Talita Émile Ribeiro Adelino, Joilson Xavier, Vagner Fonseca, Marta Giovanetti
Nanopore sequencing is transforming viral genomics through real-time, portable, long-read analysis of RNA and DNA. Unlike traditional short-read platforms, it detects nucleotide sequences by measuring ionic current changes as nucleic acids pass through nanoscale pores, enabling direct single-molecule sequencing and base modification detection. Its simplicity, flexibility, and capacity for ultra-long reads make it ideal for resolving complex genomic regions, structural variants, and full viral genomes. These advantages have accelerated its use in pathogen surveillance and outbreak response, especially in resource-limited settings. For chikungunya virus (CHIKV), nanopore sequencing allows rapid, culture-independent recovery of complete genomes from clinical and vector samples, enabling real-time tracking of viral diversity, evolution, and spread. Experiences from Ebola, Zika, and COVID-19 have demonstrated the power of portable sequencing, now applied to CHIKV monitoring. Advances in tools such as Guppy, Dorado, Minimap2, and Medaka enhance read quality, consensus accuracy, and downstream analyses. Despite challenges in basecalling and error correction, robust quality control pipelines ensure reliable results. Ongoing improvements in chemistry, flow cell design, and machine learning will further enhance fidelity and throughput, establishing nanopore sequencing as a cornerstone of CHIKV genomic surveillance and epidemic preparedness.
Methods in molecular biology (Clifton, N.J.)Almudena Escobar-Niño, Olivier Coste, Maria Victoria Calcis-Marzán, Francisco Javier Fernandez-Acero
Proteomics has been revealed as a key set of technologies that provide a detailed description of the molecular processes involved in the development of a specific phenotype. "Omics" technologies can collect an incredible amount of information. Among them, proteomics is an invaluable tool for defining specific biological information by studying the complete set of proteins under specific conditions, the proteome; or specific subsets of proteins, the subproteome. It is a crucial instrument for describing protein post-translational modifications, the functional annotation of the genome, and the detection of orphan genes. Protein extraction procedures are necessary to obtain B. cinerea protein extracts of sufficient quality to be analyzed by LC-MS/MS, avoiding contaminants that interfere with the identification process. After experimental design, collect the samples and replicates as defined in each experimental approach; we will describe protocols and procedures for the next steps of proteome and subproteome extraction and LC-MS analysis.
The Immuno-Oncology Biological Research (IOBR) package is an R-based analysis tool for exploring the tumor microenvironment (TME) and its influence on anti-tumor immunity. Built for high-throughput data-spanning both transcriptomic and genomic profiles-IOBR integrates six analytical modules, including transcriptomic data preprocessing, TME profiling, TME pattern identification, ligand-receptor interaction analysis, genome-TME interaction assessment, and visualization. In this chapter, we walk through a multi-omics workflow using example datasets, illustrating data preparation, distribution analyses, result interpretation, and graphical output. IOBR is open source and is available at https://github.com/IOBR/IOBR and a detailed GitBook ( https://iobr.github.io/book/ ) offers a complete manual and analysis guide for each function.
Methods in molecular biology (Clifton, N.J.)Benjamin L Kidder
Teratoma formation is the gold standard assay for evaluating the developmental pluripotency of human and mouse embryonic stem cells (ESCs) and induced pluripotent stem cells (iPSCs). Following subcutaneous injection into immunodeficient mice, pluripotent stem cells spontaneously differentiate into derivatives representing all three embryonic germ layers-ectoderm, mesoderm, and endoderm. Beyond serving as a functional assay for pluripotency, teratomas provide a unique three-dimensional model system for studying early human development and lineage specification in vivo. This chapter describes comprehensive protocols for teratoma formation in immunodeficient mice, tissue processing for multiple downstream genomic applications, and multi-omics profiling approaches. We detail methods for embryonic stem cell culture, teratoma generation via subcutaneous injection, tissue dissection and processing for chromatin immunoprecipitation followed by sequencing (ChIP-Seq), RNA sequencing (RNA-Seq), single-cell multiome profiling combining chromatin accessibility (ATAC-Seq) and gene expression (scRNA-Seq), and histological analysis using hematoxylin and eosin (H&E) staining. Additionally, we provide bioinformatics workflows for analyzing the resulting genomic datasets to characterize the epigenetic and transcriptional landscapes of teratoma-derived tissues. These methods enable comprehensive molecular characterization of developmental processes and provide valuable resources for stem cell biologists studying pluripotency, differentiation, and early embryonic development.
Methods in molecular biology (Clifton, N.J.)Benjamin L Kidder
The rapid growth of biomedical literature has created an urgent need for computational tools that enable researchers to systematically analyze publication trends, identify emerging research themes, and map the evolution of scientific fields. PubMed Atlas is a command-line and web-enabled workflow for topic-driven bibliometrics and trend intelligence using PubMed E-utilities. The pipeline executes PubMed queries, retrieves matching PMIDs, downloads full metadata records in batches, parses structured information (title, abstract, authors/affiliations, MeSH terms, publication types, grants, keywords, DOI), and stores normalized data in a local SQLite database for rapid querying and visualization. A Streamlit dashboard provides interactive exploration of publication trends, journal distributions, MeSH term summaries, geographic distributions, and recent article browsing with direct PubMed links. This protocol describes the installation, configuration, and operation of PubMed Atlas for cancer stem cell and stem cell transcriptional network research, and other fields, enabling investigators to conduct reproducible bibliometric analyses and identify knowledge gaps in rapidly evolving fields.
Cell-type identification is a crucial step in single-cell RNA-seq (scRNA-seq) data analysis, for which supervised methods are preferred due to their accuracy and efficiency. The quality of the reference data plays an important role in cell-type identification performance, but systematic strategies for selecting and reconstructing reference data remain limited. We present Target-Oriented Reference Construction (TORC), a widely applicable strategy for constructing reference data from available labeled cells given a target dataset. TORC alleviates the differences in data distribution and cell-type composition between the reference and the target. TORC combines initial supervised prediction, optional reference expansion using target cells with high-confidence predicted labels, and reference reconstruction guided by estimated cell-type compositions. Here, we provide detailed, step-by-step instructions describing the input requirements, configurable parameters, and practical considerations for applying TORC in real scRNA-seq analyses. TORC is available at https://github.com/weix21/TORC , where an example implementation using an MLP-based classifier is provided.
Methods in molecular biology (Clifton, N.J.)William Dalleywater
Colonic inflammation induces profound alterations in the intestinal mucosa that are evident at both macroscopic and microscopic levels, including disruption of epithelial barrier integrity, crypt fission, and immune cell infiltration. Recent advances in single-cell and spatial RNA sequencing have greatly expanded our understanding of the cellular diversity of the colonic mucosa, enabling pathological features to be directly linked to underlying cellular and molecular mechanisms. This chapter demonstrates the application of single-cell RNA sequencing (scRNA-seq) analysis techniques to interrogate stem cell dynamics in the context of inflammatory bowel disease. Using publicly available datasets, the chapter provides a step-by-step workflow implemented in Python, covering data access, loading, integration of multiple datasets, and initial preprocessing. Cells are mapped to large single-cell atlas references to infer cell identities, followed by a pseudobulk analysis strategy to assess inflammation-associated changes in cell phenotypes. Finally, trajectory inference approaches are applied to explore potential mechanisms governing the specification and modulation of key cell types during inflammation. Accompanied by an online resource containing fully annotated scripts, this chapter offers guided instruction in contemporary scRNA-seq analysis workflows. It is intended as an accessible introduction for researchers seeking to develop practical skills in single-cell data analysis that can be readily applied to their own biological questions.
This protocol provides a comprehensive, step-by-step workflow for whole-genome sequencing of Chikungunya virus (CHIKV) using an amplicon-based strategy optimized for Oxford Nanopore Technologies (ONT) platforms. The procedure includes detailed instructions for sample handling, viral RNA extraction, quality control, cDNA synthesis, multiplex PCR amplification, library preparation, sequencing, and primary bioinformatic processing. The protocol is designed to maximize reproducibility across laboratories and is suitable for genomic surveillance applications, including outbreak investigation and molecular epidemiology, even when working with low-to-moderate viral loads.
Rapid communications in mass spectrometry : RCMCiara J Sallowicz, Thiago Mattos, Rahul R Deshpande, Bashar Amer, Susan S Bird
RATIONALE: Untargeted lipidomics is commonly performed at analytical flow rates, which consume more solvent and may require higher on-column sample loads when sensitivity is limited by analyte abundance. Nano flow separations use lower flow rates and sample loads, reducing solvent consumption and facilitating improved electrospray ionization. We present here practical considerations for implementing a routine nano flow lipidomics workflow. METHODS: Bovine liver total lipid extract was spiked with SPLASH Lipidomix internal standards and analyzed by nano flow and high flow liquid chromatography coupled to a high-resolution accurate mass Orbitrap-based mass spectrometer. Full-scan polarity switching was used for untargeted profiling, and the AcquireX Deep Scan workflow was applied to support data-dependent MS/MS acquisition in the complex matrix. A SPLASH dilution series was analyzed in triplicate injections to compare analytical response across on-column loads between the two workflows. RESULTS: At 25 ng on-column, nano flow yielded 1266 total lipid annotations and 835 high-quality annotations, compared with 919 total and 518 high-quality annotations for high flow at 100 ng. Unintentional fragmentation decreased under nano flow conditions by 21%-31% across the representative lipid species evaluated. The use of nano flow allowed for the detection of lower on-column loads across several standards, extending the lower end of the response range by up to 40-fold compared with high flow analyses. CONCLUSIONS: Nano flow lipidomics improved sensitivity for untargeted analysis by increasing the number of lipid annotations, reducing unintentional fragmentation, and extending the analytical response to lower on-column loads. Together with practical guidance around sample preparation, injection volume, washing, and equilibration, these results support nano flow chromatography as a sensitive and reliable approach for sample-limited untargeted lipidomics.
The plant genomeSamuel A Adewale, Md Ali Babar, Diego Jarquin, Naeem Khan, Stephen Harrison, Noah DeWitt, Rick Boyles, Shuyu Liu, Ellen Melson, Daniel Hathcoat, Jason D Fiedle…
Genetic gains of oat (Avena sativa L.) grain yield have been historically low compared to other major cereal crops. The use of machine learning models to capture complex interactions and leveraging data types other than genomic information in prediction models has great potential for improving complex traits in oat breeding programs. This study assessed the performance of deep learning model for genomic prediction compared to other statistical models, examined the optimal training set size for grain yield prediction, and investigated the potential of incorporating environmental covariates for enhancing oat grain yield prediction. A total of 463 oat lines were evaluated in five environments in Southern United States, and genotyping of the lines gave 12,657 single-nucleotide polymorphism markers. Our results showed that training set sizes 200-350 could be the optimal size for our panel, indicating the possibility of reducing phenotyping costs by reducing the size of the oat panel tested. The deep learning model was less superior to genomic best linear unbiased prediction and other models for grain yield, test weight, and heading days in the different environments. Incorporating interaction effects (G × E or G × W) into the multikernel prediction models across environments improved predictive abilities for grain yield by up to 0.21 compared to the baseline models. This reveals the potential of incorporating weather data to enhance predictive abilities in genomic prediction models. Our findings provide important information for improving genetic gains in oat breeding programs by integrating genomics and environmental information.
Genetic epidemiologyKristen J Sutton, Julie E Gervis, Moomal Jatoi, Liang-Dar Hwang, Audrey E Hendricks, Debashis Ghosh, Kenneth E Westerman, Joanne B Cole
Most Mendelian randomization (MR) of dietary intake use the full set of genome-wide significant (GWS) variants in the instrumental variable (IV), likely biasing causal estimates due to pleiotropy. To characterize the common methods to handle pleiotropy in dietary intake MR, we conducted a scoping review of the literature on dietary intake MR studies. We extracted information on IV construction, assessment of pleiotropy, and sensitivity analyzes revealing that only 20% of studies used an IV with functional plausibility. In the absence of functionally-informed IVs, we tested if two-sample MR using GWS variants filtered for pleiotropic associations through phenome-wide association studies (PheWAS) could identify diet-health relationships supported by existing nutrition science, focusing on oily fish and alcohol intake, the latter of which has a functionally-informed IV for comparison (rs1229984 in the ADH1B gene). To further explore this question, we performed multivariable MR and employed MR-CAUSE. The numerous models consistently supported that oily fish reduced triglycerides. In contrast, GWS and PheWAS-filtered IVs suggested that alcohol decreased alanine aminotransferase levels, whereas the functional IV (rs1229984) found the opposite expected relationship. Isolating the direct effect of dietary intake from GWS IV remains challenging. Future work should focus on identifying functional variants impacting dietary behavior.
Rapid communications in mass spectrometry : RCMMarco Rolando Aronés Jara, Kirianova Godoy Bautista, Freshsia Ingrid Ortiz Pérez, Edgar Cárdenas Landeo, Edith Eveling Conislla Cáceres, Hugo Roberto Luna Mole…
RATIONALE: Lepidoceras peruvianum Kuijt is an understudied hemiparasitic species endemic to the Peruvian Andes that lacks comprehensive phytochemical characterization. Investigating its metabolome is important for understanding its chemotaxonomic relevance and potential as a source of bioactive compounds. METHODS: Leaves and fruits of L. peruvianum were subjected to untargeted metabolomic profiling using high-resolution UHPLC-ESI-Orbitrap-MS/MS in positive and negative ionization modes. Metabolites were annotated based on accurate mass measurements, isotopic patterns, collision-induced dissociation (CID) fragmentation data, and spectral matching with the Global Natural Products Social Molecular Networking (GNPS), METLIN, and MassBank databases. Structural elucidation employed diagnostic fragmentation pathways, including retro-Diels-Alder (RDA), heterocyclic ring fission (HRF), quinone methide (QM), and benzofuran-forming (BFF) cleavages. Antioxidant activity was evaluated using DPPH●, ABTS●+, and FRAP assays, while antimicrobial activity and toxicity were assessed through antibacterial testing and the Artemia salina lethality assay. RESULTS: Leaves contained 34 metabolites, mainly flavan-3-ols, proanthocyanidins, flavonols, lignans, and isoquinoline alkaloids, whereas fruits were characterized by anthocyanins, organic acids, and polar lipids. Total phenolic content was higher in leaves (292.4 mg GAE/g) than in fruits (216.8 mg GAE/g), corresponding to stronger antioxidant activity. Leaves extracts exhibited greater antibacterial activity against Staphylococcus aureus, while fruit extracts showed stronger effects against Gram-negative bacteria. LC50 values in the A. salina assay were 181.8 μg/mL for leaves and 475.9 μg/mL for fruits. CONCLUSIONS: This study provides the first comprehensive metabolomic characterization of L. peruvianum, revealing organ-specific chemical diversity and notable bioactive properties. The findings demonstrate the utility of HRMS/MS fragmentation analysis for metabolite annotation and identify this endemic species as a promising source of bioactive natural products.
Rapid communications in mass spectrometry : RCMJianping Liu, Ningxing Duan, Tian Xin, Yang Cao, Haiyan Qian
BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a leading cause of death, underscoring the need for improved therapies. Xuanfei Heji (XFHJ), a hospital-prepared herbal formula, has been used clinically in the treatment of COPD. However, its mechanisms remain unclear. METHODS: XFHJ constituents were profiled using UHPLC-HRMS. COPD was induced in rats by intratracheal lipopolysaccharide instillation and cigarette smoke exposure. Treatment effects were assessed using pulmonary function, lung histopathology, and proinflammatory cytokines. Untargeted serum metabolomics and fecal 16S rRNA gene sequencing were performed; associations among differential metabolites, microbial taxa, and inflammatory markers were evaluated using Spearman's rank correlation analysis. RESULTS: Chemical profiling tentatively identified 374 constituents. XFHJ improved pulmonary function and attenuated lung histopathological injury and inflammation. Tryptophan and glycerophospholipid metabolism were the principal treatment-associated pathways. XFHJ also altered gut microbial diversity and composition, with enrichment of potentially beneficial taxa such as Bifidobacterium, Roseburia, and several Clostridia-related taxa. Treatment-responsive taxa correlated positively with indole-related metabolites, which correlated inversely with pulmonary inflammatory markers. CONCLUSIONS: XFHJ exhibited significant therapeutic effects on COPD rats, and its mechanism may be correlated with regulating the intestinal microbiota structure and metabolic profiles of COPD rats, thereby attenuating lung histopathological injury and pulmonary inflammation.
Biomedical chromatography : BMCGuangliang Zhang, Wentao Shao, Junbin Shu, Jianfeng Tong, Jing Zhang, Yi Tao
Brucea javanica oil oral emulsion (BJOE) serves as an adjunctive therapeutic agent for glioblastoma multiforme (GBM); however, the inhibitory effects of its serum metabolites remain unexplored. In this study, ultrahigh performance liquid chromatography-quadrupole-time-of-flight mass spectrometry (UHPLC-Q-TOF-MS/MS) was employed to analyze serum metabolites in rats following BJOE administration. The inhibitory effects of the serum on GBM were assessed using U251 cell bioassays, and the relationship between serum metabolites and cellular inhibitory activity was examined. Six prototype components and six metabolites were identified in the serum, including linoleic acid, linoleic acid-M1, linoleic acid-M2, linoleic acid-M3, oleic acid, oleic acid-M1, oleic acid-M2, oleic acid-M3, sphingosine, palmitoleic acid, oleamide, and 1-stearoyl-rac-glycerol. Rat serum collected 2 h postdosing significantly inhibited U251 cell proliferation. Gray relationship analysis indicated that oleic acid, oleic acid-M1, linoleic acid, linoleic acid-M1, linoleic acid-M2, sphingosine, palmitoleic acid, oleamide, and 1-stearoyl-rac-glycerol were highly correlated with the inhibition rate of U251 cell proliferation, with oleic acid-M1 showing the highest correlation coefficient (0.9806). These findings suggest that BJOE and its metabolites may possess therapeutic potential against GBM, particularly in inhibiting glioma cell proliferation, with oleic acid and its metabolites likely playing a significant role.
Journal of inherited metabolic diseasePatryk Lipiński, Jaak Jaeken
Congenital disorders of glycosylation (CDG) are a rapidly expanding group of inherited metabolic diseases affecting glycoconjugate glycan biosynthesis and attachment. This review provides a structured overview of major advances in human CDG reported from 2023 to 2026, including newly recognized CDG, emerging biochemical and pathophysiological insights, advances in diagnostic strategies and biomarkers, and progress in treatment. Recent developments include the recognition of RPN1-CDG, UGGT1-CDG, and DHRSX-CDG, refinement of phenotypes in established disorders, including PMM2-CDG, ALG13-CDG, ALG8-CDG, GMPPA-CDG, TRAPPC11-CDG, and PGAP3-CDG, and increasing use of glycomics, glycoproteomics, cellular models, and multi-omics approaches. Therapeutic progress remains uneven: no FDA/EMA-approved disease-modifying therapy is currently available for any CDG, and most interventions remain off-label, experimental, or supported by limited observational evidence. Despite substantial progress, most CDG still lack disease-modifying treatment, validated biomarkers, and prospective natural history data.
Heart failure (HF) is an end stage of cardiovascular diseases. Tongmai Qiangshen Medicinal Liquor (TMQSJ) is an over-the-counter traditional Chinese medicinal liquor, but its efficacy-related material basis and mechanisms in HF remain unclear. In this study, UPLC-Q-TOF-MS/MS was used to characterize TMQSJ constituents and prototype compounds in rat plasma. HPLC fingerprinting, spectrum-effect relationship analysis, single-compound activity evaluation, and integrative bioinformatics were combined to screen efficacy-related candidate quality markers and HF-associated candidate genes. A total of 223 compounds were characterized, including 33 prototype plasma components. Chemometric analysis identified caffeic acid, Epimedin A, Epimedin B, Epimedin C, and icariin as characteristic constituents. Spectrum-effect analysis based on 10 batches of TMQSJ and validation in DOX-injured AC16 cells highlighted Epimedin A, Epimedin B, and Epimedin C. Among them, Epimedin A and Epimedin C showed prototype plasma exposure and were prioritized as efficacy-related candidate quality markers. Integration of predicted targets of prototype plasma components with HF transcriptomic data and machine-learning analysis identified BCL2L1, STAT3, HIF1A, MAPK8, and ESR1 as five candidate core genes. Immune cell estimation and molecular docking provided computational clues. Overall, this study establishes an integrated framework linking chemical quality, pharmacological association, in vivo exposure, and mechanistic prediction for TMQSJ quality evaluation.
GliaRobert W Lewis, Breana C Dogan, Amy L Stanek, Elliot B Evans, Madelyn G Coble, Hayli E Spence-Osorio, Karen L G Farizatto, Angie L Mordant, C Allie Mills, Laur…
Megalencephalic leukoencephalopathy with subcortical cysts (MLC) is a rare leukodystrophy characterized by early-onset macrocephaly, white matter edema, seizures, and motor and cognitive decline. Approximately 25% of MLC patients carry HEPACAM pathogenic variants, many of which are dominant missense variants causing remitting MLC Type 2b. HEPACAM encodes hepatic and glial cell adhesion molecule (hepaCAM), also known as GlialCAM, an astrocyte-enriched transmembrane protein with important roles in astrocyte territory establishment, gap junction coupling, branching organization, synaptic function, and development of the gliovascular unit. The molecular mechanisms through which pathogenic variants in HEPACAM alter hepaCAM protein function in vivo and facilitate MLC pathogenesis during brain development remain largely unknown. Here, we used new viral tools and proximity-based proteomics to examine how three different dominant pathogenic variants alter hepaCAM subcellular localization and protein interactome in astrocytes of the developing mouse cortex. We found dramatic changes in hepaCAM distribution throughout the astrocyte, which were common to all mutants tested. We also observed significant changes in protein interactome between wild type and mutant hepaCAM, including decreased association with previously described hepaCAM-interacting proteins Connexin 43 and CLC-2. Moreover, we identified the epilepsy-associate potassium channel KCNQ2 as a novel hepaCAM interaction partner and found reduced association between KCNQ2 and pathogenic variants. Collectively, our data provide new insights into hepaCAM protein function in astrocytes during brain development, reveal altered protein dynamics of pathogenic variants, and provide a new resource to explore the molecular underpinnings of MLC pathogenesis.