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PubMed2025

New Genetic Loci Implicated in Cardiac Morphology and Function Using Three-Dimensional Population Phenotyping.

BACKGROUND: Cardiac remodeling occurs in the mature heart and is a cascade of adaptations in response to stress, which are primed in early life. A key question remains as to the processes that regulate the geometry and motion of the heart and how it adapts to stress. METHODS: We performed spatially resolved phenotyping using machine learning-based analysis of cardiac magnetic resonance imaging in 47 549 UK Biobank participants. We analyzed 16 left ventricular spatial phenotypes, including regional myocardial wall thickness and systolic strain in both circumferential and radial directions. In up to 40 058 participants, genetic associations across the allele frequency spectrum were assessed using genome-wide association studies with imputed genotype participants, and exome-wide association studies and gene-based burden tests using whole-exome sequencing data. We integrated transcriptomic data from the GTEx project and used pathway enrichment analyses to further interpret the biological relevance of identified loci. To investigate causal relationships, we conducted Mendelian randomization analyses to evaluate the effects of blood pressure on regional cardiac traits and the effects of these traits on cardiomyopathy risk. RESULTS: We found 42 loci associated with cardiac structure and contractility, many of which reveal patterns of spatial organization in the heart. Whole-exome sequencing revealed 3 additional variants not captured by the genome-wide association study, including a missense variant in CSRP3 (minor allele frequency 0.5%). The majority of newly discovered loci are found in cardiomyopathy-associated genes, suggesting that they regulate spatially distinct patterns of remodeling in the left ventricle in an adult population. Our causal analysis also found regional modulation of blood pressure on cardiac wall thickness and strain. CONCLUSIONS: These findings provide a comprehensive description of the pathways that orchestrate heart development and cardiac remodeling. These data highlight the role that cardiomyopathy-associated genes have on the regulation of spatial adaptations in those without known disease.

Humans↗

PubMed2026

Long-read sequencing reveals widespread novel splicing and neojunction-derived neoantigens in nasopharyngeal carcinoma.

The widespread transcriptomic diversity driven by alternative splicing (AS) contributes to all hallmarks of cancer and represents a critical source of neoantigens for personalized immunotherapy. However, unlike other major malignancies, the full repertoire of AS in nasopharyngeal carcinoma (NPC) remains underexplored. Here, we employ long-read sequencing (LR-seq) to generate a high-resolution, isoform-level transcriptomic atlas from a cohort of 14 NPC tumor samples and four immortalized nasopharyngeal epithelial cell lines. We identify a substantial number of full-length novel transcripts (22,687; ∼44.38%), which reveal diverse splicing patterns and previously unannotated splicing events. By integrating short-read RNA-seq data to quantify isoform expression, we discover a subset of novel transcripts that are differentially expressed between tumor samples and immortalized nasopharyngeal epithelial cell lines. Furthermore, LR-seq enables precise identification of chimeric readthrough fusion transcripts, such as CLDN15-FIS1 and FOXRED2-TXN2 Finally, we develop a computational framework, tumor-specific splicing neoantigen detection (TS-SNAD), to predict neoantigens originating from novel exon-exon junctions (neojunctions) in tumor-specific novel transcripts. Using this framework, we identify neojunction-derived neoantigens and experimentally validate the immunogenicity of selected HLA-B*40:01-restricted neoantigens. These neojunction-derived peptides constitute a new class of noncanonical neoantigens with significant potential for developing personalized cancer vaccines for NPC.

Humans↗

PubMed2024

Genomic landscape of hepatocellular carcinoma in Egyptian patients by whole exome sequencing.

BACKGROUND: Hepatocellular carcinoma (HCC) is the most common primary liver cancer. Chronic hepatitis and liver cirrhosis lead to accumulation of genetic alterations driving HCC pathogenesis. This study is designed to explore genomic landscape of HCC in Egyptian patients by whole exome sequencing. METHODS: Whole exome sequencing using Ion Torrent was done on 13 HCC patients, who underwent surgical intervention (7 patients underwent living donor liver transplantation (LDLT) and 6 patients had surgical resection}. RESULTS: Mutational signature was mostly S1, S5, S6, and S12 in HCC. Analysis of highly mutated genes in both HCC and Non-HCC revealed the presence of highly mutated genes in HCC (AHNAK2, MUC6, MUC16, TTN, ZNF17, FLG, MUC12, OBSCN, PDE4DIP, MUC5b, and HYDIN). Among the 26 significantly mutated HCC genes-identified across 10 genome sequencing studies-in addition to TCGA, APOB and RP1L1 showed the highest number of mutations in both HCC and Non-HCC tissues. Tier 1, Tier 2 variants in TCGA SMGs in HCC and Non-HCC (TP53, PIK3CA, CDKN2A, and BAP1). Cancer Genome Landscape analysis revealed Tier 1 and Tier 2 variants in HCC (MSH2) and in Non-HCC (KMT2D and ATM). For KEGG analysis, the significantly annotated clusters in HCC were Notch signaling, Wnt signaling, PI3K-AKT pathway, Hippo signaling, Apelin signaling, Hedgehog (Hh) signaling, and MAPK signaling, in addition to ECM-receptor interaction, focal adhesion, and calcium signaling. Tier 1 and Tier 2 variants KIT, KMT2D, NOTCH1, KMT2C, PIK3CA, KIT, SMARCA4, ATM, PTEN, MSH2, and PTCH1 were low frequency variants in both HCC and Non-HCC. CONCLUSION: Our results are in accordance with previous studies in HCC regarding highly mutated genes, TCGA and specifically enriched pathways in HCC. Analysis for clinical interpretation of variants revealed the presence of Tier 1 and Tier 2 variants that represent potential clinically actionable targets. The use of sequencing techniques to detect structural variants and novel techniques as single cell sequencing together with multiomics transcriptomics, metagenomics will integrate the molecular pathogenesis of HCC in Egyptian patients.

Humans↗

PubMed2026

Precision-Based Filtering Facilitates Cross-Referencing of Conventional and Single-Nucleus Transcriptomes to Identify Time- and Temperature-Sensitive Cell Populations.

Transcriptome analysis via RNA sequencing (RNAseq) has become a ubiquitous method of molecular characterization from whole organisms, dissected tissues, and single cells. These experiments continue to provide an extraordinary volume of data describing molecular states and responses to many conditions. However, standard approaches to RNAseq analysis commonly use expression level filters that eliminate potentially useful data in the service of decreasing noise. Here we describe the implementation of a coefficient of variation-based filter for RNAseq gene expression data. This filter prioritizes consistent data across replicates, allowing lowly-expressed genes with low-variation measurements to be retained for downstream analysis. We show, using two independent Arabidopsis RNAseq datasets, that this filter allows for the inclusion of many more transcription factors than even a low-stringency expression level filter. This effect is independent of sequencing depth. We find that these lowly-expressed genes mark specific cell clusters in our single-nucleus (sn)RNAseq dataset and may facilitate future characterization of currently unknown cell types or states. We further characterize communities of co-expressed genes, sampled across the day at two growth temperatures, in relation to snRNAseq cell clusters, finding evidence for a highly photosynthetic cell population, and a cell state marked by high cell division and translation. These methods can be expanded to RNAseq analysis in many systems, facilitating the construction of more detailed models of tissue-specific gene regulatory networks.

Transcriptome analysis↗

PubMed2026

Protocol to predict gene expression from transcriptomic data using PREDICT.

Linking DNA sequence variation to context-specific transcriptional programs is a critical challenge in regulatory genomics, especially for non-model organisms. Here, we present PREDICT, a modular Python package for discovering cis-regulatory elements and transcription factor binding motifs. We describe steps to identify enriched k-mers from differentially expressed genes, map them to known motifs, quantify their impact on gene expression, and visualize motif co-occurrences. PREDICT provides a robust, k-mer-based approach to uncover regulatory logic in diverse genomic systems. For complete details on the use and execution of this protocol, please refer to Yen et al. and Liu et al.1,2.

Gene Expression Profiling↗

PubMed2026

Understanding tumor adaptations and resistance to MET inhibitors in MET-altered non-small cell lung cancer.

AIM: Type Ib MET inhibitors are clinically active in selected MET-altered non-small cell lung cancer, particularly tumors with MET exon 14 skipping or MET amplification, but acquired resistance remains incompletely understood. Here, we investigated resistance across biologically distinct MET-altered contexts, including MET exon 14 skipping, MET amplification, and MET overexpression. METHODS: Paired baseline and progression samples from seven patients treated with tepotinib or capmatinib were analyzed using spatial transcriptomics, whole-exome sequencing, RNA sequencing, CRISPR screening, and drug-combination assays. Patient-derived cultures and resistant cell-line models were used to explore resistance-associated changes. RESULTS: MET inhibitor resistance was heterogeneous, with persistence of the initial MET alteration in most evaluable cases and emergence of patient-specific genomic events. Three main resistance-associated, often overlapping, routes were identified: on-target MET evolution through kinase-domain alterations; extracellular matrix and tumor-microenvironment remodeling, including collagen and fibronectin upregulation, complement-related signaling, and partial EMT-associated programs; and bypass signaling involving EGFR/HER, MAPK, and PI3K/Akt pathways. In vitro models reproduced several tumor-cell-intrinsic features but only partially captured microenvironment-associated changes. CONCLUSIONS: MET inhibitor resistance in this cohort involved overlapping, context-dependent genomic, phenotypic, and signaling adaptations, supporting combination strategies for MET-altered lung cancer.

CRISPR screen↗

PubMed2025

Enhancing and accelerating cell type deconvolution of large-scale spatial transcriptomics slices with dual network model.

MOTIVATION: Cell type deconvolution deciphers spatial distribution of mRNA transcripts at single cell level by integrating single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics data to infer mixture of cell types of spots in slices. Current algorithms are criticized for neglecting connection between scRNA-seq and spatial transcriptomics data, as well as time-consuming, hampering their application to large-scale datasets. RESULTS: In this study, we propose a joint learning nonnegative matrix factorization algorithm for fast cell type deconvolution (aka jMF2D), which integrates scRNA-seq and spatial transcriptomics data with network models. To bridge scRNA-seq and spatial transcriptomics data, jMF2D jointly learns cell type similarity network to enhance quality of signatures of cell types, thereby promoting accuracy and efficiency of deconvolution. Experiments demonstrate that jMF2D outperforms state-of-the-art baselines in terms of accuracy by saving about 90% running time on various datasets generated by different platforms. Furthermore, it can also facilitates the identification of spatial domains and bio-marker genes, providing an efficient and effective model for analyzing spatial transcriptomics data. AVAILABILITY AND IMPLEMENTATION: The software is coded using python, and is free available for academic https://github.com/xkmaxidian/jMF2D.

Algorithms↗

PubMed2025

Multiomics Analysis Reveals Therapeutic Targets for Chronic Kidney Disease With Sarcopenia.

BACKGROUND: The presence of sarcopenia in patients with chronic kidney disease (CKD) is associated with poor prognosis. The mechanism underlying CKD-induced muscle wasting has not yet been fully explored. This study investigates the influence of renal secretions on muscles using multiomics sequencing. METHODS: The kidney transcriptome analysis by RNA-seq and protein profiling by tandem mass tag (TMT), serum TMT and muscle TMT were performed in CKD established using 0.2% adenine and control mice. Spp1 recombinant protein was used to study its effect on myotube atrophy in vitro. In animal experiments on CKD, pharmacological inhibition of Spp1 was used to explore the role of Spp1 in skeletal muscle wasting. Transcriptome analysis was performed to identify differentially expressed genes (DEGs) in the gastrocnemius muscle following Spp1 pharmacological inhibition. RESULTS: In the renal transcriptome and TMT, 503 and 377 proteins/genes respectively were co-upregulated and co-downregulated. In the serum TMT of CKD and normal control (NC) mice, 22 upregulated and 7 downregulated differentially expressed proteins (DEPs) showed the same expression patterns as those in the kidney transcriptome and TMT analysis. Based on bioinformatics analysis and reported studies, we selected Spp1 for further validation. Spp1 recombinant protein was added to C2C12 myotubes in vitro, and the results indicated that Spp1 significantly increased the protein levels of the muscle atrophy marker (Murf-1) and promoted the smaller myotubes (all p < 0.05). Compared with NC mice, Spp1 mRNA and protein levels were significantly upregulated in the kidneys of CKD mice, and the serum concentration of Spp1 was also markedly increased (all p < 0.05). In animal experiments, pharmacological inhibition of Spp1 increased the weights of gastrocnemius and tibialis anterior muscles (p < 0.05) and improved muscle atrophy phenotype. Transcriptome analysis showed that DEGs in the gastrocnemius muscle following Spp1 pharmacological inhibition were enriched in protein digestion and absorption, glucagon signalling pathway, apelin signalling pathway and ECM-receptor interaction pathway. CONCLUSIONS: Our study is the first to establish a regulatory network of kidney-muscle crosstalk to explore the potential mechanism of CKD-related sarcopenia. Employing multiomics analysis, cellular assessment and animal experiments, we have identified that Spp1 could potentialy serve as a promising therapeutic target for CKD patients with sarcopenia.

Sarcopenia↗

PubMed2026

Antagonistic regulation by mango MiSPL9a and MiSPL9b regulates flowering time, drought and salt stress in Arabidopsis.

SQUAMOSA PROMOTER BINDING PROTEIN-LIKE (SPL) transcription factors, which are unique to plants, contain a highly conserved SBP domain that regulates gene expression by binding to downstream targets. They play critical roles in various biological processes, especially in the regulation of flowering in plants. In this study, two SPL-like genes (MiSPL9a and MiSPL9b) were identified from mango genomic and transcriptomic data, and their sequence, expression and function were further analyzed. Sequence analysis revealed that MiSPL9a and MiSPL9b have open reading frames of 1173 bp and 1158 bp, respectively, with slight differences in the number of cis-regulatory elements within their promoter regions. Expression analysis under stress conditions revealed distinct patterns: MiSPL9a expression significantly differed under drought stress but did not significantly differ under salt stress, whereas MiSPL9b expression responded significantly to salt stress but changed minimally under drought stress. Phenotypic analysis of the transgenic Arabidopsis lines revealed that MiSPL9a overexpression delayed flowering, whereas MiSPL9b overexpression promoted early flowering. Under stress conditions, compared with wild-type plants, MiSPL9a-overexpressing plants presented increased drought tolerance but did not significantly differ. In contrast, MiSPL9b-overexpressing plants were sensitive to salt stress, with no notable phenotypic differences observed under drought conditions. Physiological assays revealed that under drought stress, MiSPL9a transgenic plants presented significantly reduced levels of malondialdehyde (MDA) and hydrogen peroxide (H2O2) and increased proline (Pro) content and superoxide dismutase (SOD) activity. Under salt stress, MiSPL9b transgenic plants presented opposite trends in terms of these physiological markers. In summary, both MiSPL9a and MiSPL9b are involved in the regulation of plant flowering time and stress responses, but their functions differ.

Arabidopsis↗

PubMed2024

Innovations Toward Immunopeptidomics.

Over the past 30 years, immunopeptidomics has grown alongside improvements in mass spectrometry technology, genomics, transcriptomics, T cell receptor sequencing, and immunological assays to identify and characterize the targets of activated T cells. Together, multiple research groups with expertise in immunology, biochemistry, chemistry, and peptide mass spectrometry have come together to enable the isolation and sequence identification of endogenous major histocompatibility complex (MHC)-bound peptides. The idea to apply highly sensitive mass spectrometry techniques to study the landscape of peptide antigens presented by cell surface MHCs was innovative and continues to be successfully used and improved upon to deepen our understanding of how peptide antigens are processed and presented to T cells. Multiple research groups were involved in this bringing immunopeptidomics to the forefront of translational research, and we will highlight the contributions of one of the earliest developers, Professor Donald F. Hunt, and his research group at the University of Virginia. The Hunt laboratory applied cutting edge mass spectroscopy-based immunopeptidomics to study cancer, autoimmunity, transplant rejection, and infectious diseases. Across these diverse research areas, the Hunt laboratory and collaborators would characterize previously unknown MHC peptide-binding motifs and identify immunologically active antigens using ultra sensitive mass spectrometry techniques. Amazingly, many of the MHC-bound peptide antigens discovered in collaborations with the Hunt laboratory were sequenced by mass spectrometry before the completion of the human genome using manual de novo sequencing. In this perspective article, we will chronicle the work of the Hunt laboratory and their many collaborators that would be a major part of the foundation for mass spectrometry-based immunopeptidomics and its application to immunology research.

Animals↗

PubMed2026

De novo variants in the poly(rC)-binding protein gene PCBP1 cause a neurodevelopmental disorder.

Poly(rC)-binding protein 1 (PCBP1), a splicing factor and key member of the hnRNP E family, was initially characterized for its tumor suppressive properties. More recently, its role in gene regulation in the brain and nervous system has attracted growing interest. Through an international multicenter collaboration, we identified 16 de novo pathogenic variants in PCBP1 across 17 subjects from 16 unrelated families. All affected individuals exhibited intellectual disability (ID), with autism spectrum disorder (ASD) as a prominent feature. Functional analysis in primary hippocampal mouse neuron cultures indicated that PCBP1 variants impair dendritic arborization, underscoring their deleterious effects. Transcriptomic profiling by RNA sequencing of subject-derived T cells showed a distinctive signature characterized by significantly increased exon skipping. These results highlight the contribution of PCBP1 in neurogenesis and neuritogenesis, which is impacted by loss-of-function variants expressed in neuronal cells, thereby supporting the link between splicing defects and neurodevelopmental disorders. Collectively, our findings demonstrate the prominent role of PCBP1 in neurodevelopment, reaffirming the importance of splicing regulation in mammalian neurodevelopment.

Journal Article↗

PubMed2026

Penicillium melinii promotes root growth through subtle host reprogramming across model and crop species.

Root development is highly responsive to microbial interactions, yet the mechanisms by which beneficial fungi promote root growth remain incompletely understood. Here, we identified Penicillium melinii 'isolate 2' through a screen of endophytic fungi isolated from Arabidopsis and characterized it as a promoter of root development in both Arabidopsis and crop species. We combined phenotyping in vitro, rhizotron, greenhouse and field assays with reporter and mutant analyses, transcriptomics, phytohormone profiling and sequencing and annotation of the fungal genome to investigate the basis of this interaction. P. melinii consistently stimulated root growth and modified root architecture across experimental systems and host species. These effects were associated with subtle but reproducible host transcriptional reprogramming, supporting a model in which the fungus fine-tunes endogenous developmental programmes rather than broadly perturbing stress or growth pathways. Genetic and reporter analyses further suggested that this interaction modulates root branching through localized developmental reprogramming. Genomic analysis provided a framework for understanding the fungal traits associated with this beneficial interaction. The conservation of the response across model and crop species supports the relevance of P. melinii as both a useful experimental system to study beneficial plant-fungus interactions and a promising candidate for improving root traits and crop performance.

Penicillium melinii↗

PubMed2025

Multi-Omics and Integrative Analytics in Natural Products Discovery.

Natural products (NPs) have long been an essential source of new bioactive compounds for drug discovery; however, traditional methods for screening and isolating these compounds can be slow and often yield diminishing returns. Fortunately, advanced multi-omics and computational approaches present powerful solutions to these challenges. This review highlights innovative methodologies that integrate metabolomics, genomics, transcriptomics, and proteomics with bioinformatics and analytical chemistry to accelerate NP discovery. For instance, untargeted metabolomics platforms like high-resolution liquid chromatography-tandem mass spectrometry (LC-MS/MS) and Global Natural Products Social (GNPS) molecular networking allow for comprehensive profiling of new compounds, while targeted isotope-labeling strategies enhance this process. Additionally, genome and metagenome mining tools such as antibiotics and secondary metabolite analysis shell (antiSMASH), Deep Biosynthetic Gene Cluster (DeepBGC), and Pipeline for Reconstructing Integrated Syntheses of Metabolites (PRISM) quickly identify biosynthetic gene clusters (BGCs) in both cultured and uncultured organisms, often using heterologous expression to validate products. Transcriptomic analyses, including RNA sequencing (RNA-seq), co-expression networks, and fluxomics, help clarify how pathways are regulated, while quantitative proteomics techniques like tandem mass tags/isobaric tags for relative and absolute quantitation (TMT/iTRAQ) and label-free methods, along with chemoproteomics approaches such as cellular thermal shift assay and thermal proteome profiling (TPP), uncover molecular targets and their mechanisms of action. This review also places significant emphasis on the role of artificial intelligence (AI) and machine learning (ML) in integrating multi-omics data, spanning activities from constructing gene-metabolite correlation networks to leveraging knowledge graphs and graph neural networks for data fusion and functional prediction. Finally, this review concludes by discussing the synergistic benefits of multi-omics for natural-product discovery, addressing current technical challenges, and exploring future directions toward high-throughput, intelligent data integration for next-generation NP research.

Biological Products↗

PubMed2026

Characterization and analysis of the full-length transcriptome of Frankliniella occidentalis (Thysanoptera: Thripidae).

BACKGROUND: Frankliniella occidentalis, an insect belonging to the order Thysanoptera, causes severe damage to agricultural and horticultural crops, resulting in significant economic losses worldwide. The development of molecular and sequencing technologies has helped elucidate the molecular mechanisms regulating its growth and development as well as its damaging activity. However, much remains to be explored. To further investigate the molecular complexity of this species, we sequenced the full-length transcriptome of mixed samples obtained from specimens at all developmental stages. RESULTS: Of all transcripts, 89.04% matched with the reference genome; additionally, 29,750 alternative splicing events, 2,342 genes with poly(A) sites, and 153 candidate fusion transcript events were identified, and 4,235 long noncoding RNAs were discovered. CONCLUSIONS: This is the first full-length transcriptome of F. occidentalis reported to date. This study greatly contributes to the understanding of the molecular complexity and diversity of this insect, providing a basis to develop specific molecular targets as well as resources for gene function studies in other insects.

Animals↗

PubMed2025

De novo clustering of large long-read transcriptome datasets with isONclust3.

MOTIVATION: Long-read sequencing techniques can sequence transcripts from end to end, greatly improving our ability to study the transcription process. Although there are several well-established tools for long-read transcriptome analysis, most are reference-based. This limits the analysis of organisms without high-quality reference genomes and samples or genes with high variability (e.g. cancer samples or some gene families). In such settings, analysis using a reference-free method is favorable. The computational problem of clustering long reads by region of common origin is well-established for reference-free transcriptome analysis pipelines. Such clustering enables large datasets to be split roughly by gene family and, therefore, an independent analysis of each cluster. There exist tools for this. However, none of those tools can efficiently process the large amount of reads that are now generated by long-read sequencing technologies. RESULTS: We present isONclust3, an improved algorithm over isONclust and isONclust2, to cluster massive long-read transcriptome datasets into gene families. Like isONclust, isONclust3 represents each cluster with a set of minimizers. However, unlike other approaches, isONclust3 dynamically updates the cluster representation during clustering by adding high-confidence minimizers from new reads assigned to the cluster and employs an iterative cluster-merging step. We show that isONclust3 yields results with higher or comparable quality to state-of-the-art algorithms but is 10-100 times faster on large datasets. Also, using a 256 Gb computing node, isONclust3 was the only tool that could cluster 37 million PacBio reads, which is a typical throughput of the recent PacBio Revio sequencing machine. AVAILABILITY AND IMPLEMENTATION: https://github.com/aljpetri/isONclust3.

Algorithms↗

PubMed2026

Epigenetic Profiling for Early Detection and Treatment Response Monitoring in Non-Small Cell Lung Cancer: Protocol for a Prospective Translational Biomarker Study.

BACKGROUND: Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related mortality worldwide and continues to have poor survival outcomes, with most patients diagnosed at advanced stages of disease. In New Zealand, NSCLC contributes substantially to cancer inequities, with Māori communities experiencing disproportionately high incidence and mortality rates. Although low-dose computed tomography screening can improve early detection, major limitations remain, including false-positive findings, overdiagnosis, high infrastructure costs, and limited accessibility for rural and underserved populations. Liquid biopsy approaches using circulating tumor DNA (ctDNA), particularly DNA methylation profiling, have emerged as promising, minimally invasive strategies for improving cancer detection, treatment monitoring, and precision oncology. OBJECTIVE: This study aims to establish integrated genomic and epigenomic predictive and prognostic biomarkers using ctDNA, tumor tissue, and transcriptomic profiling to improve early detection, risk stratification, treatment selection and response prediction, and longitudinal monitoring, with particular emphasis on identifying molecular mechanisms associated with treatment resistance and disease progression. METHODS: This prospective observational translational biomarker study is being conducted through the University of Otago and associated respiratory and oncology services in New Zealand. The study will recruit participants with NSCLC (including squamous and nonsquamous subtypes), individuals referred to fast-track lung nodule assessment clinics, and nonmalignant respiratory controls. Serial peripheral blood sampling will be performed in selected participants at predefined clinical follow-up time points to evaluate treatment response and disease progression. The availability of formalin-fixed paraffin-embedded archival tissues will be recorded, but will not be mandatory for enrollment. Genome-scale DNA methylation profiling will be performed using cell-free reduced representation bisulfite sequencing (cfRRBS), while targeted genomic profiling and transcriptomic analyses will be conducted using targeted sequencing panels and RNA sequencing. Integrative bioinformatic analyses will be used to identify molecular biomarkers associated with early-stage disease, advanced disease, treatment response, and therapeutic resistance. RESULTS: Ethics approval for the study has been obtained from the New Zealand Health and Disability Ethics Committee (2022 EXP 12566). This study commenced in 2022, and recruitment and biospecimen collection are ongoing. The study aims to recruit approximately 450 participants, including patients with NSCLC, individuals referred through respiratory diagnostic pathways, and nonmalignant controls. As of July 31, 2026, 205 participants have been recruited, with recruitment continuing until the target sample size is reached. Molecular and data analyses are ongoing, with additional publications expected as the cohort matures. CONCLUSIONS: This study will generate one of the first integrated genomic, epigenomic, and transcriptomic liquid biopsy datasets for NSCLC in New Zealand. The findings are expected to support the development of sensitive, accessible, and equitable blood-based biomarkers for NSCLC detection and treatment monitoring while also contributing to improved precision oncology approaches and reducing NSCLC inequities among Māori populations.

Humans↗

PubMed2026

Spatial Total RNA Sequencing of Formalin-Fixed Paraffin-Embedded Tissue by spRandom-seq.

The molecular pathogenesis of infectious diseases and cancer is orchestrated by nanoscale of host and microbial RNA transcripts within the tissue microenvironment. Nevertheless, spatially resolving the comprehensive transcriptional landscape within complex clinical tissues, like formalin-fixed paraffin-embedded (FFPE) specimens, still poses a formidable challenge. Here, we present spRandom-seq, a random primer-based spatial total RNA sequencing technology designed to spatially resolve complete transcriptomes from host, bacteria, and even nanoscale viruses in FFPE tissues. Capitalizing on the random primer design, our technology not only facilitated the discovery of specific lncRNAs and alternative splicing events in mouse brain and olfactory bulb, but also delineated pronounced spatial heterogeneity in clinical FFPE sections-across distinct tumor regions in breast cancer and microbial infection sites in Klebsiella pneumoniae-infected tissues. Importantly, integrated analysis of host and viral RNAs in FFPE samples from hepatitis B virus (HBV)‑positive hepatocellular carcinoma (HCC) demonstrated that complement and coagulation pathways were specifically activated across expansive HBV‑infected tumor areas, which also exhibited an increased burden of copy number variations (CNVs). Owing to its compatibility with existing spatial transcriptomics platforms and minimal operational complexity, spRandom-seq represents a practical and scalable approach for clinical pathology applications and infection diagnostics.

Paraffin Embedding↗

PubMed2026

Multi-omics analysis reveals stage-associated differences in gut immunity and microbiota between juvenile and adult common carp (Cyprinus carpio).

In vertebrates, the development of intestinal immunity is closely associated with dynamic changes in the gut microbiota. However, stage-associated differences in intestinal immunity and gut microbial communities remain poorly characterized in teleost fish. In this study, transcriptomic analysis combined with 16S rRNA gene sequencing was employed to characterize intestinal immunity and gut microbial communities in juvenile and adult common carp (Cyprinus carpio). Transcriptomic profiling revealed marked developmental differences in intestinal immune function. Juvenile carp exhibited a predominantly innate immune phenotype, characterized by elevated expression of pro-inflammatory cytokines, antimicrobial peptides, and lysozyme-related genes. This immune profile was accompanied by enhanced mucosal barrier function and a relatively pro-inflammatory intestinal environment. In contrast, adult carp displayed increased expression of genes associated with adaptive immunity, suggesting that adult common carp exhibit relatively stronger adaptive immune characteristics than juvenile fish. Gut microbiota analysis demonstrated significant stage-dependent differences in microbial diversity and community composition. Juvenile fish were enriched with bacterial taxa potentially associated with innate immune activation, whereas adult fish harbored distinct microbial communities linked to intestinal homeostasis and barrier maintenance. Furthermore, correlation analyses identified significant associations between specific microbial taxa and innate immune-related gene expression, suggesting a close association between gut microbiota composition and intestinal immune characteristics in juvenile and adult common carp. Collectively, these findings reveal stage-associated differences in intestinal immunity and gut microbial communities between juvenile and adult common carp, thereby providing insights into intestinal immune characteristics at different developmental stages in teleost fish.

Animals↗