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PubMed2026

Development and evaluation of a multiplex PCR-based dual-platform targeted sequencing framework for precise differentiation of lumpy skin disease virus.

BACKGROUND: Lumpy skin disease virus (LSDV) shares over 96% genomic identity with goatpox and sheeppox viruses, presenting severe diagnostic challenges due to cross-reactivity. METHODS: To address this bottleneck, we established a targeted sequencing framework integrating multiplex PCR with short-read and long-read platforms. By sequentially screening target pathogens, identifying low-homology genes, and designing short and gradient long-fragment primer pools, we evaluated these dual-platform panels using highly homologous poxvirus samples. RESULTS: The short-read panel stably detected target viruses at inputs as low as 5.26 ×101 copies/μL. Under strict alignment criteria, LSDV mapping rates reached 42.91%, suppressing non-target signals to 3.05%. The Nanopore-Targeted Sequencing (NTS) long-amplicon strategy successfully eliminated homologous interference. By applying length-dependent diagnostic thresholds (≥ 100 reads for short amplicons; ≥ 50 reads for long amplicons), precise species-level identification was achieved, maintaining near-zero cross-reads (0-5) in ultra-long regions. Crucially, the field-deployable NTS workflow enabled complete detection in approximately 4 h. CONCLUSION: This complementary strategy seamlessly meets both laboratory demands for high-sensitivity enrichment and frontline requirements for rapid typing, providing a reliable tool for LSDV surveillance, mutation tracking, and outbreak control.

Capripoxvirus differentiation↗

PubMed2026

Mapping the de-implementation of traditional diagnostic tests in pediatric acute lymphoblastic leukemia.

INTRODUCTION: Advances in cancer diagnostics raise questions about when and how to de-implement traditional approaches; however, these processes remain poorly described. At St. Jude Children's Research Hospital (SJCRH), routine conventional cytogenetics for pediatric acute lymphoblastic leukemia (ALL) diagnosis was de-implemented in 2018 following adoption of clinical genomics. This study aimed to map this process to inform future diagnostic de-implementation initiatives. METHODS: Interviews were conducted with SJCRH staff involved or impacted by cytogenetics de-implementation. Data were analyzed using thematic and rapid qualitative analysis informed by the Consolidated Framework for Implementation Research. Member-checking was used to verify and refine process maps, which were subsequently reviewed by an external expert panel, representing diverse settings, through focus group discussions. RESULTS: Thirteen SJCRH clinicians participated. De-implementation was described as successful, with no negative impact on patient outcomes. Decision-making began with internal correlation studies that demonstrated superior diagnostic performance of clinical genomics. De-implementation was viewed as a natural evolution that improved molecular classification, resource allocation, and workflow efficiency. Perceived risks included loss of cytogenetics competency, delayed turnaround time, and career insecurity, all addressed institutionally. Lessons learned highlighted the importance of deliberate discussion about logic and evidence supporting de-implementation. Fifteen external experts offered suggestions to improve process map generalizability, highlighting institutional- and system-level considerations. CONCLUSION: De-implementation of cytogenetics in ALL in favor of clinical genomics was successful at SJCRH. This study offers an example of diagnostic de-implementation in cancer care and proposes a structured approach to guide future efforts. De-implementation should be considered alongside introduction of novel diagnostic approaches.

cancer diagnostics↗

PubMed2026

Comparative chloroplast genomics of six Bupleurum (Apiaceae) accessions: candidate barcodes, phylogeny based on available plastomes, and candidate RNA-editing sites.

INTRODUCTION: Bupleurum L. (Apiaceae), a taxonomically intricate genus of about 190 species and a source of Radix Bupleuri (Chai Hu), is difficult to discriminate because of convergent morphology, infraspecific variation, and limited genomic sampling. This study aimed to characterize plastome variation, identify and validate candidate molecular markers, reconstruct plastid phylogenetic relationships, and assess candidate plastid RNA-editing sites in Bupleurum. METHODS: We assembled six plastomes from subgenus Bupleurum, screened 51 Bupleurum plastomes for diagnostic loci, reconstructed whole-plastome and partitioned protein-coding-sequence phylogenies, and predicted plastid C-to-U RNA-editing candidates across the six newly assembled plastomes using a PREP-Cp-compatible workflow. Candidate barcode performance was evaluated against the reference plastome phylogenies, and codon-based models were used to test for positive selection. RESULTS: The plastomes were 154,496-155,778 bp with the canonical quadripartite structure and GC contents of 37.67-37.73%. Gene content was stable (131-132 genes; 86-87 protein-coding genes); B. falcatum subsp. cernuum lacked ycf15 but contained an additional inverted-repeat-associated ycf1 annotation. A/U-ending synonymous codons were favoured. Finite pairwise Ka/Ks estimates were below 1 for most genes, and site-specific codon models detected no positive selection. Each plastome contained 55-61 pure microsatellites, dominated by A/T mononucleotide motifs. MarkerSeek ranked 265 features and identified atpF-atpH, petA-psbJ, rpl32-trnL-UAG, and ycf1 as leading candidate barcodes. ycf1 recovered 38 of 41 nodes strongly supported by both reference trees, whereas a partitioned four-locus analysis recovered 40 of 41 and distinguished all 51 accession sequences. However, only one of seven multi-accession operational binomial groups was monophyletic, and only one showed a positive local barcode gap. The whole-plastome phylogeny recovered Bupleurum as monophyletic relative to Chamaesium. The two sampled Penninervia accessions occupied early-diverging positions without forming an exclusive clade. B. falcatum subsp. cernuum was sister to B. ranunculoides, with B. ranunculoides subsp. telonense sister to that pair. A partitioned 74-CDS analysis recovered the same key relationships and 45 of 50 internal bipartitions. Across the six newly assembled plastomes, 57-63 nonsynonymous C-to-U candidates were predicted per accession (367 total) in 21-22 genes; 269 affected the second codon position and 98 the first. DISCUSSION: Bupleurum plastomes are structurally conservative but retain localised divergence useful for marker development. Concordant whole-plastome and CDS genealogies support genus monophyly, whereas sparse Penninervia sampling and maternal plastid inheritance preclude rejecting traditional subgeneric classification. The predicted RNA-editing sites represent candidates for future experimental validation rather than an established Bupleurum editome. These genomic resources support authentication, conservation, and evolutionary research in Bupleurum.

Apiaceae↗

PubMed2026

A pan-cancer multi-omic SuperLearner for regulated cell death survival topologies.

INTRODUCTION: Regulated cell death (RCD) pathways influence tumor progression and immune modulation. We previously constructed a signature database mapping 25 RCD forms across seven multi-omic layers and 33 tumor types (CancerRCDShiny). Despite their ability to identify risk populations, translating these signatures into personalized clinical workflows requires a shift from cohort stratification to individualized risk mapping by modeling patient risk (survival topologies) to capture the non-linear dynamics of RCD signatures. METHODS: We engineered a pan-cancer multi-omic SuperLearner pipeline across 33 cancer types. Phase I performed zero-leakage harmonization and groupwise imputation to prevent cross-cohort amalgamation. Phase II deployed Elastic Net-regularized Cox regression as a CANARY diagnostic to map proportional hazards failures. Strata with a 35% missingness barrier entered Phase III, deploying a Quadripartite ensemble: Random Survival Forests, XGBoost, Survival-Boruta, and Multi-Task Logistic Regression, fused within an Elastic Net Multi-View Meta-Learner (MVL), with post-hoc TreeSHAP and LIME interpretability. RESULTS: The CANARY diagnostic demonstrated the structural invalidity of pan-cancer geometric proportional hazards. Across 96 admissible strata, Phase III executed algorithmic displacement: continuous multi-omic topologies suppressed static genomic mutations and copy number variations (85.7% vs. 0.0% apex retention). The MVL stabilized predictions against extreme variance; LIME surrogate validations (R 2 < 0.10) confirmed the systematic failure of linear interpretative proxies. N-dimensional TreeSHAP interaction mapping exposed synergistic and antagonistic rescue trajectories defining individualized Survival Topologies, which were invisible to additive models. The architecture was deployed as CancerRCDPredictor, a digital molecular tumor board with integrated LLM capabilities. The MVL SuperLearner achieved a median C-index of 0.749 (IQR: 0.722-0.836) across 96 modelable strata, with 95% bootstrap confidence intervals confirming precision (median width: 0.052) and permutation significance in 93.8% of strata (p < 0.001). External CPTAC validation across ten cancer types demonstrated significant cross-cohort generalizability in clear cell renal carcinoma (KIRC; C-index 0.675, p = 0.017) and modest performance across the remaining adequately powered cancers (median 0.582), underscoring the need for larger multi-institutional validation cohorts. CONCLUSION: This pan-cancer multi-omic SuperLearner bypasses linear topological failures, advancing beyond generalized stratification to establish a deterministically mapped architecture for predicting RCD-related survival topologies. Through the CancerRCDPredictor interface, multi-omic insights translate into individualized survival topology exploration, providing a foundation for future precision oncology validation.

SuperLearner↗

PubMed2026

The Computational Revolution in Natural Product Research: A Data-Driven Roadmap for Next-Generation Drug Development.

Natural products (NPs) have historically provided the foundational scaffolds for drug development, yet traditional bioprospecting faces critical limitations: high rediscovery rates, laborious isolation workflows, and substantial attrition during clinical translation. The emergence of big data technologies is fundamentally transforming this landscape, enabling a shift from serendipity-based discovery toward systematic, data-driven approaches. This review examines how the integration of artificial intelligence (AI), machine learning (ML), and multi-omics datasets is accelerating natural product research across three key domains: (1) genome mining for biosynthetic gene cluster identification using platforms such as antiSMASH, (2) cheminformatics-driven prediction of structure-activity relationships and ADMET properties, and (3) metabolomics-guided dereplication to prioritize novel bioactive scaffolds. We evaluate the convergence of genomics, metabolomics, and computational chemistry in enabling in silico lead optimization and the discovery of cryptic metabolites from previously inaccessible microbial taxa. While challenges in data standardization and scalability persist, the synergy between big data and NP research is accelerating clinical translation. Despite persistent challenges in data standardization, scalability, and equitable benefit-sharing, the convergence of big data and NP research is poised to redefine drug development. These advances position computational NP research as a cornerstone of next-generation drug development.

big data analytics↗

PubMed2026

BioEMMA: Automated Generation of Model-Specific Escher-Compatible Maps from KEGG Pathways.

Genome-scale metabolic models are widely used to investigate cellular metabolism, but their interpretation and comparison are limited by the lack of reproducible pathway-level visualizations with a common spatial organization. This study presents BioEMMA, a Python-based tool for the automated generation of model-specific metabolic pathway maps in the Escher JSON format using coordinate information from curated KEGG pathway maps. BioEMMA parses KGML files, map reaction and metabolite identifiers to model database namespaces, filters pathway elements according to an input SBML model, adds non-primary metabolites, reconstructs Escher-compatible layouts, and supports flux visualization. The tool was integrated into a reproducible BioUML workflow for metabolic model reconstruction. BioEMMA was evaluated using the e_coli_core model and the KEGG glycolysis/gluconeogenesis pathway while generating a model-specific map with overlaid FBA fluxes. It was then applied to compare E. coli reconstructions generated by gapseq, ModelSEEDpy, and Reconstructor across three central carbon metabolism pathways. To broaden the evaluation, BioEMMA was applied using 87 prokaryotic BiGG models and three eukaryotic models. The analysis revealed pathway-specific differences in reaction coverage, shared and model-specific reactions, and predicted flux activity. BioEMMA therefore provides a reproducible framework for pathway-level visualization and comparison of genome-scale metabolic reconstructions within a common spatial coordinate system.

Escher maps↗

PubMed2026

Blood Plasma Analysis in Ovarian Cancer Patients Using an AFM/MS Approach: Effect of Sample Dilution on Proteome Depth.

Early detection of ovarian cancer remains challenging because of the lack of sensitive and reproducible blood-based biomarkers. A major challenge in plasma proteomics is the extremely wide dynamic range of protein concentrations, which prevents simultaneous detection of both high- and low abundance proteins and limits the identification of disease-associated signals. In this study, we applied a combined atomic force microscopy and mass spectrometry (AFM/MS) approach to investigate how sample dilution affects plasma proteome coverage and the detection of differences between healthy donors and patients with stage I and stage III ovarian cancer. Plasma samples were analyzed at two dilution levels (1:100 and 1:10,000). At 1:100 dilution, a total of 235 proteins were identified across all samples, representing the union of all replicates and groups. The reproducible CORE proteome comprised 169 proteins in the Healthy group, 183 in the Stage I group, and 193 in the Stage III group. Differential analysis revealed distinct, non-overlapping protein sets at each dilution level. At 1:100 dilution, most altered proteins were decreased in patients and corresponded to major plasma components, including complement proteins and protease inhibitors. At 1:10,000 dilution, most altered proteins were increased and were predominantly immunoglobulin-related proteins, along with complement regulatory components. These findings show that sample dilution determines which fraction of the plasma proteome is observable. Here, proteome depth refers to the total number of non-redundant proteins accessible within the analytical workflow. When CORE sets from all groups were combined, 216 proteins were identified at 1:100 and 149 at 1:10,000, with 133 shared between the two dilution conditions. The higher dilution contributed 16 additional CORE proteins not detected in the 1:100 CORE union, increasing the combined CORE set to 232 proteins. Thus, higher dilution alone did not increase proteome depth, but provided complementary protein identifications that increased cumulative proteome depth when both dilution conditions were considered together. This effect reflects dilution-dependent selectivity in the composition of the detectable protein subset.

Humans↗

PubMed2026

Integrating Radiogenomics and CSF-Based Liquid Biopsy Sequencing for Precision Neuro-Oncology.

Glioblastoma and diffuse gliomas pose major therapeutic challenges due to marked intratumoral heterogeneity, limited tissue accessibility, and the blood-brain barrier. Tissue-based next-generation sequencing (NGS) remains essential for WHO CNS5 molecular classification, yet it is invasive and poorly suited to serial monitoring. Two complementary non- or minimally invasive approaches have advanced rapidly: radiogenomics, which correlates multiparametric MRI features with genomic alterations, and cerebrospinal fluid (CSF) liquid biopsy sequencing, which detects circulating tumor DNA with high tissue concordance. This review examines the independent progress and synergistic integration of radiogenomics and CSF-NGS. Imaging signatures can non-invasively predict key drivers (IDH1/2, EGFR, TERT, PTEN, TP53) and molecular subtypes, while CSF-ctDNA sequencing enables real-time assessment of clonal evolution, therapy resistance (including post-temozolomide hypermutation), and residual disease. We discuss technical considerations, performance metrics, multimodal artificial-intelligence fusion, and emerging clinical applications for diagnosis, prognosis, treatment selection, and longitudinal surveillance. Critical challenges, standardization, prospective validation, and workflow integration are highlighted. By combining the spatial phenotypic information of radiogenomics with the temporal genomic resolution of CSF sequencing, this multimodal strategy offers a promising path toward precision neuro-oncology and reduced reliance on repeated invasive sampling.

Humans↗

PubMed2024

Heat Inactivation of Nipah Virus for Downstream Single-Cell RNA Sequencing Does Not Interfere with Sample Quality.

Single-cell RNA sequencing (scRNA-seq) technologies are instrumental to improving our understanding of virus-host interactions in cell culture infection studies and complex biological systems because they allow separating the transcriptional signatures of infected versus non-infected bystander cells. A drawback of using biosafety level (BSL) 4 pathogens is that protocols are typically developed without consideration of virus inactivation during the procedure. To ensure complete inactivation of virus-containing samples for downstream analyses, an adaptation of the workflow is needed. Focusing on a commercially available microfluidic partitioning scRNA-seq platform to prepare samples for scRNA-seq, we tested various chemical and physical components of the platform for their ability to inactivate Nipah virus (NiV), a BSL-4 pathogen that belongs to the group of nonsegmented negative-sense RNA viruses. The only step of the standard protocol that led to NiV inactivation was a 5 min incubation at 85 °C. To comply with the more stringent biosafety requirements for BSL-4-derived samples, we included an additional heat step after cDNA synthesis. This step alone was sufficient to inactivate NiV-containing samples, adding to the necessary inactivation redundancy. Importantly, the additional heat step did not affect sample quality or downstream scRNA-seq results.

Nipah Virus↗

PubMed2026

Meta-QTL Analysis Reveals Consensus Genomic Regions and Candidate Genes for Resistance to Sudden Death Syndrome in Soybean.

Sudden death syndrome (SDS), caused by Fusarium virguliforme, is one of the most economically important diseases limiting soybean production worldwide. Although numerous quantitative trait loci (QTL) associated with SDS resistance have been reported, inconsistencies among mapping populations, marker systems, and experimental conditions have hindered the identification of robust resistance loci for soybean improvement. In this study, a comprehensive meta-analysis was conducted to integrate published QTL and identify stable consensus genomic regions associated with SDS resistance. After a systematic literature survey and data curation, 153 QTL derived from 14 linkage-mapping studies were analyzed using a custom R-based workflow, resulting in the identification of 23 consensus meta-QTL (MQTL) distributed across 17 chromosomes. Several MQTL, particularly those located on chromosomes 6, 8, 18, and 20, were supported by multiple independent studies and represented major genomic hotspots for SDS resistance. Physical localization and functional annotation of these MQTL identified 217 candidate genes, including genes predicted to be involved in plant defense, signal transduction, transcriptional regulation, and secondary metabolism. Gene Ontology enrichment analysis identified response to salicylic acid as the only biological process that remained significant after FDR correction, whereas Kyoto Encyclopedia of Genes and Genomes pathway analysis did not identify significantly enriched pathways. Independent support using five published genome-wide association studies further supported several MQTL, especially those on chromosomes 6, 18, and 20, thereby increasing confidence in these genomic regions. The identified MQTL and prioritized candidate genes provide potential genomic resources for future marker development, improvement applications, and functional validation aimed at improving soybean resistance to SDS.

Fusarium virguliforme↗

PubMed2026

Reference-Free Microsatellite Instability Detection from Tumor Sequencing Using Intrasample Variability Modeling.

Microsatellite instability (MSI) is a predictive biomarker in several tumor types. However, many next-generation sequencing-based callers require matched normal samples, reference panels, or pretrained models, limiting their portability across assays and sequencing centers. We developed PROMIS (PROfiling of Microsatellite InStability), a tumor-only, reference-free pipeline that uses a discrete mixture model to characterize intrasample repeat-length distributions at predefined microsatellite loci. Locus-level classifications are then aggregated into a continuous MSI score. We benchmarked PROMIS in colorectal (CRC), endometrial (UCEC), and gastric (STAD) cancers from The Cancer Genome Atlas. PROMIS achieved an overall area under the receiver operating characteristic curve (AUC) of 0.995 and cohort-specific AUCs of 1.00 in CRC and stomach adenocarcinoma and 0.999 in uterine corpus endometrial carcinoma, comparable to established tools despite not using matched normals or pretrained models. Subsampling demonstrated robust performance with substantially fewer loci. In silico dilution showed progressively reduced MSI-microsatellite-stable discrimination, with the pooled AUC declining from 0.83 at 10% tumor fraction to 0.53 at 1%. At low tumor fractions, tumor-type-specific baseline microsatellite variability increasingly influenced PROMIS scores. Finally, in prostate and CRC cell-free DNA cohorts, including Illumina TSO500 data and an 18-gene panel, PROMIS yielded MSI scores concordant with orthogonal tissue- and panel-based classifications across the evaluated Illumina-based sequencing contexts. Accordingly, the present validation should be considered limited to Illumina-based sequencing platforms. PROMIS is intended to complement existing genomic profiling workflows by enabling MSI assessment from sequencing data already generated for broader molecular analyses. Prospective clinical validation remains necessary before clinical implementation.

Journal Article↗

PubMed2026

Quantitative Fluorescence Imaging of Alphavirus Infection for Antiviral Screenings.

Fluorescence microscopy offers a highly sensitive and versatile approach for investigating alphavirus infection at the cellular level. By combining fluorescently labeled viruses with quantitative image analysis, this method enables detailed spatial and temporal characterization of infection dynamics, including the detection of subtle differences in replication kinetics and cell-to-cell spread. A central aim of this protocol is its application in antiviral screening assays. Image-based quantification of fluorescence intensity provides a robust and reproducible means to assess the efficacy of antiviral compounds, allowing early and sensitive detection of inhibitory effects in infected cells. This facilitates the identification of promising antiviral hits and supports the evaluation of dose-dependent responses. The approach is also well-suited for comparative studies of different alphavirus strains or mutants, as variations in replication behavior and dissemination patterns become readily apparent. Its flexibility, compatibility with multiple cell lines, and straightforward integration into automated imaging platforms makes the method scalable and suitable for high-throughput screening campaigns. Overall, this protocol advances the discovery and evaluation of antiviral strategies. Given that several alphaviruses cause significant human and veterinary diseases, lack approved antiviral therapies, and continue to expand geographically with emerging outbreaks, the identification of novel antivirals remains an urgent priority. Therefore, this fluorescence-based workflow represents a valuable and timely contribution to modern alphavirus research.

Antiviral Agents↗

PubMed2026

Characterizing Riboglow Probes In Vitro as the Basis for Fluorescence Lifetime Imaging In Live Mammalian Cells and Three-Dimensional Cellular Models.

Nearly 80% of the human genome is transcribed into RNA, while less than 2% encode for proteins, indicating that the majority of mammalian transcripts are noncoding and participate in diverse regulatory processes. Therefore, sensing and visualizing RNA molecules in live mammalian cell systems quantitatively are critical to understanding RNA dynamics and interactions, yet remains technically challenging, especially in complex cellular environments. Riboglow is a genetically encoded RNA biosensor in which a short RNA aptamer binds a small-molecule probe, producing a quantifiable fluorescence lifetime turn-on detectable by fluorescence lifetime imaging microscopy (FLIM). Here, we present a detailed workflow for Riboglow-FLIM, including sample preparation, image acquisition, and quantitative analysis of FLIM datasets. The goal of this protocol is to enable quantitative fluorescence lifetime-based RNA detection using Riboglow in controlled and live-cell environments. The protocol is demonstrated in vitro, where RNA dependent lifetime changes are measured, and in live mammalian cells, where FLIM acquisition, region of interest selection, and subcellular analysis are established. Successful implementation requires careful control of experimental and acquisition parameters. Key considerations for reproducible implementation are highlighted. Together, this protocol serves as a practical reference for implementing Riboglow-FLIM and quantitatively assessing RNA visualization in live cells.

Humans↗

PubMed2026

Generation of Fibrin-Based Three-Dimensional Engineered Vascular Tissues from Human Aortic Smooth Muscle Cells for Proteomic Analysis.

Vascular smooth muscle cells (SMCs) reside within the medial layer of blood vessels, where they interact with an extracellular matrix (ECM) composed of collagen, elastin, and proteoglycans to maintain vascular structure and function. Aberrant ECM remodeling contributes to multiple vascular diseases; however, conventional two-dimensional culture systems do not adequately recapitulate the three-dimensional (3D) cellular and matrix environment required to study SMC-ECM interactions and matrix remodeling. This protocol describes the generation of engineered vascular tissues (EVTs) from primary human aortic SMCs cultured within fibrin-based 3D hydrogels. Following casting between flexible polydimethylsiloxane posts, EVTs undergo cellular alignment, contraction, and deposit de novo ECM, providing a physiologically relevant platform for studying vascular matrix biology. The protocol details tissue fabrication, culture, harvesting, and downstream analysis of newly deposited ECM by immunofluorescence staining. In addition, a workflow is presented for qualitative and quantitative characterization of EVT-derived proteins using Western blotting and mass spectrometry-based proteomics. Sequential protein extraction enables assessment of soluble and ECM-enriched protein fractions, facilitating in-depth evaluation of ECM composition. This platform provides a reproducible approach for investigating ECM production and remodeling by human SMCs in a 3D environment.

Humans↗

PubMed2026

Clinical Variant Interpretation with the Integrative Genomics Viewer (IGV) for Molecular Pathologists.

The integrative genomics viewer (IGV) is a pivotal tool in clinical genomics, enabling the visualization and interpretation of complex sequencing data. Bringing clinical knowledge to bear with visual evaluation of sequencing results is the primary means by which molecular pathologists and other professionals assess and finalize cases. A variety of software tools can assist, but their relationship to the underlying data must be understood and applied systematically. This study includes essential background on next-generation sequencing (NGS) data file types (e.g., FASTQ, BAM, VCF) with a discussion of their format and purpose. We then describe features of IGV that derive nuances from these files. We utilize a series of curated practical cases based on clinical vignettes through which the reader will interact with clinical NGS sequencing data using the IGV software to review various types of clinically relevant variants relative to the human reference genome. These clinical vignettes have been curated to describe examples of some of the complexities of interpretation of genomic data, and how utilizing IGV as part of a routine workflow can provide additional interpretive information for variants beyond routine bioinformatic software algorithm variant calls. The visual inspection of genomic variants utilizing the tools within IGV can unmask subtle contextual cues (i.e., variant allele frequency, strand bias, tissue-specific context) that can influence the interpretation of genomic variants. Although this study focuses on using IGV for the detection and interpretation of somatic variants, the provided applications can be extrapolated for use in the germline setting, including analysis of complex variants and detection of mosaicism.

Humans↗

PubMed2026

DNA-FISH Metaphase Spreads to Distinguish Extrachromosomal DNA from Homogeneously Staining Regions in Human Cancer Cell Lines.

Whole-genome sequencing identifies focal DNA amplifications with base-pair resolution but cannot determine whether amplified sequences reside on extrachromosomal DNA (ecDNA, also known as double minutes) or within chromosomally integrated homogeneously staining regions (HSRs). DNA fluorescence in situ hybridization (DNA-FISH) metaphase spreads remain the gold standard for distinguishing these amplification states at single-cell resolution. Here, we present a detailed protocol for DNA-FISH metaphase spreads using human cancer cell lines, encompassing cell culture, metaphase arrest, hypotonic treatment, fixation, chromosome spreading, fluorescent probe hybridization, and fluorescence imaging. The protocol incorporates intermediate quality-control steps to verify successful chromosome dispersion and optimize metaphase spread quality, making the workflow accessible to laboratories without specialized cytogenetics expertise. Results demonstrate clear visualization of ecDNA and HSR amplification states using locus-specific probes and illustrate common technical artifacts that can affect interpretation. This protocol provides a robust and reproducible approach for studying the structural organization of oncogene amplification in cancer cells.

Humans↗

PubMed2026

Generation of Aneuploid Human Induced Pluripotent Stem Cells from Primary Amniotic Fluid Cells via Episomal Plasmid Electroporation.

The generation of patient-specific induced pluripotent stem cells (iPSCs) from amniotic fluid cells (AFCs) carrying defined chromosomal aneuploidies provides a powerful platform for modeling genetic disorders. However, establishing a reliable and reproducible reprogramming pipeline for aneuploid AFCs remains technically challenging due to the intrinsic genomic instability and variable proliferative capacity of these cells. Here, we present a comprehensive, non-integrating method for generating aneuploid human iPSCs from primary AFCs using episomal plasmid electroporation. This protocol details the complete workflow, encompassing cell thawing and expansion with a gradual media adaptation strategy, optimized plasmid delivery via electroporation system, sequential post-electroporation culture with mesenchymal-to-epithelial transition (MET)-directed media changes, and mechanical colony picking based on defined morphological criteria. We further describe validation procedures, including immunofluorescence staining for core pluripotency markers, G-banding karyotype analysis to confirm aneuploid karyotype maintenance, and PCR-based episomal vector clearance verification. This feeder-free, integration-free protocol yields aneuploid iPSC lines suitable for disease modeling, drug screening, and studies of chromosome biology.

Humans↗

PubMed2026

Protocol for Duplex Sequencing of Mitochondrial DNA in Single Human Oocytes.

Oocytes are densely packed with mitochondria, the energy-producing organelles that contain their own genome, mitochondrial DNA (mtDNA). Each cell contains multiple copies of mtDNA, with copy number varying among tissue types. Oocytes possess the highest mtDNA copy number, containing hundreds of thousands of mtDNA molecules per cell. Because mitochondria are inherited exclusively through the maternal lineage, accurate detection of mtDNA variants is essential for studies of inheritance, aging, and disease. The presence of multiple mtDNA copies allows wild-type and mutant molecules to coexist within the same cell, a condition known as heteroplasmy, in which low-frequency and de novo variants may occur at frequencies below 1%. Conventional next-generation sequencing (NGS) lacks sufficient accuracy to reliably distinguish these rare variants from errors introduced during library preparation and sequencing. Here, we present a protocol for enriching mtDNA from single human oocytes using Exonuclease V to remove linear DNA, followed by duplex sequencing library preparation for highly accurate mtDNA analysis. This workflow enables error-corrected sequencing of individual oocytes, facilitating reliable detection of low-frequency mtDNA variants and analysis of heteroplasmy and de novo mutagenesis. The protocol provides a reproducible approach for investigating mitochondrial genome variation in single oocytes using Illumina-compatible sequencing platforms.

Humans↗