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PubMed2000

Comprehensive analyses of prostate gene expression: convergence of expressed sequence tag databases, transcript profiling and proteomics.

Several methods have been developed for the comprehensive analysis of gene expression in complex biological systems. Generally these procedures assess either a portion of the cellular transcriptome or a portion of the cellular proteome. Each approach has distinct conceptual and methodological advantages and disadvantages. We have investigated the application of both methods to characterize the gene expression pathway mediated by androgens and the androgen receptor in prostate cancer cells. This pathway is of critical importance for the development and progression of prostate cancer. Of clinical importance, modulation of androgens remains the mainstay of treatment for patients with advanced disease. To facilitate global gene expression studies we have first sought to define the prostate transcriptome by assembling and annotating prostate-derived expressed sequence tags (ESTs). A total of 55000 prostate ESTs were assembled into a set of 15953 clusters putatively representing 15953 distinct transcripts. These clusters were used to construct cDNA microarrays suitable for examining the androgen-response pathway at the level of transcription. The expression of 20 genes was found to be induced by androgens. This cohort included known androgen-regulated genes such as prostate-specific antigen (PSA) and several novel complementary DNAs (cDNAs). Protein expression profiles of androgen-stimulated prostate cancer cells were generated by two-dimensional electrophoresis (2-DE). Mass spectrometric analysis of androgen-regulated proteins in these cells identified the metastasis-suppressor gene NDKA/nm23, a finding that may explain a marked reduction in metastatic potential when these cells express a functional androgen receptor pathway.

DNA, Complementary↗

PubMed2026

Integrated Genomic and Proteomic Analysis Reveals T-B Lymphocyte Signatures in the MYCN Driven "Immune Desert" of Specific Neuroblastoma Subtypes.

AIMS: This study aims to systematically dissect how MYCN amplification shapes the immunosuppressive tumor microenvironment (TME) in high-risk neuroblastoma, elucidating key mechanisms underlying immune evasion. METHODS: We performed an integrated multi-omics analysis of bulk RNA-seq (n = 721), single-cell RNA-seq (n = 9), proteomic data (n = 49) and spatial transcriptomics (Visium, with external validation in melanoma). Analyses included unsupervised clustering, cell-cell communication inference, transcriptional regulatory network reconstruction, and spatial proximity assessment to map the immune landscape. RESULTS: A distinct molecular subtype (Class C), defined by MYCN amplification and poor prognosis, exhibited a comprehensive "immune desert" phenotype characterized by low immune scores and minimal leukocyte infiltration. Single-cell analysis confirmed significant depletion of T and B lymphocytes within the Class C TME. Dysregulated transcriptional networks were identified, including upregulation of REL and EOMES in T cells-with EOMES potentially driving exhaustion via regulation of Transient Receptor Potential (TRP) genes, and REL inhibition enhancing cytotoxic function in vitro. A unique immunosuppressive B-cell subset (B7) engaged in enhanced crosstalk with exhausted T cells and harbored a MYC-centered network linked to cell cycle dysregulation and poor survival. Spatial transcriptomics revealed significant proximity between B7-active regions and Treg/exhaustion-enriched areas, externally validated in melanoma. Proteomic data validated elevated REL expression in MYCN-amplified tumors. CONCLUSION: This work delineates the immunosuppressive architecture of MYCN-driven neuroblastoma, revealing novel regulatory nodes within specific lymphocyte compartments. Integrating single-cell, spatial, and proteomic evidence, we propose REL inhibition as a therapeutic candidate, the EOMES/TRP axis as a bioinformatically supported hypothesis, and the B7/MYC hub as a hypothesis supported by transcriptomic and spatial evidence.

Humans↗

PubMed2026

jsPCA: fast, scalable, and interpretable identification of spatial domains and variable genes across multi-slice and multi-sample spatial transcriptomics data.

MOTIVATION: Spatial transcriptomics technologies record genome-wide measurements of gene expression with high spatial resolution. These technologies generate large and high-dimensional datasets requiring efficient automated methods for their analysis. We introduce joint spatial PCA (jsPCA), a novel, fast, scalable and interpretable method for the automatic identification of spatial domains and variable genes in multi-slice and multi-sample spatial transcriptomics data. RESULTS: jsPCA relies on a simple mathematical formulation of a spatial covariance defined as the product of the gene expression covariance with the spatial autocorrelation. The principal components of this spatial covariance yield a biologically meaningful low-dimensional representation. From this representation, spatial domains are derived by simple clustering and spatially variable genes are identified directly from the principal component coefficients. A joint representation of multiple slices and samples without spatial alignment is obtained by computing common principal components via joint diagonalization. By leveraging data sparsity and non-convex manifold optimization, jsPCA leads to computing time in the order of seconds to minutes, substantially outperforming state-of-the-art approaches. We benchmarked jsPCA against 10 state-of-the-art methods on two reference databases. Our approach demonstrated excellent performance, comparable or better than state-of-the-art methods, while being much faster, interpretable, and scalable to very large datasets.

Journal Article↗

PubMed2026

Network methods for diagonal integration of unpaired single-cell multiomics data: a review.

MOTIVATION: Advances in single-cell sequencing have enabled multiomics profiling at unprecedented resolution; however, mass spectrometry-based single-cell proteomics (scMS) remains inherently destructive, precluding simultaneous transcriptomic capture. Unlike antibody-based methods such as CITE-seq, which permit paired profiling but are restricted to targeted protein panels, scMS provides unbiased, genome-scale coverage of the intracellular proteome yet necessitates post hoc integration of unpaired datasets. This diagonal integration challenge, where transcriptomes and proteomes are measured in separate cells lacking shared anchors, remains underserved by existing reviews, which focus predominantly on vertical integration strategies enabled by non-destructive assays. RESULTS: We survey the complete computational pipeline for constructing mechanistic proteogenomic networks from unpaired single-cell data, covering: (i) unimodal network inference such as knowledge-based approaches, probabilistic graphical models, temporal directionality inference, and generative and foundation model strategies that establish the transcriptomic scaffold; (ii) cross-modal integration architectures such as network propagation, graph neural networks (scMRDR, scmFormer, scCotag), and consensus frameworks designed explicitly for the unpaired proteomics setting; and (iii) benchmarking paradigms spanning network reconstruction (BEELINE, GRETA, CausalBench) and multi-task integration evaluation (scMultiBench, SCMMIB), with guidance on metric selection under network sparsity and class imbalance. We identify three principal axes of future development: generative proteomic translation from transcriptomic precursors, inductive prior embedding in next-generation architectures, and perturbation-based causal benchmarking. AVAILABILITY AND IMPLEMENTATION: This is a review article; no novel software is distributed. A curated benchmark resource table, methods starter guide, and per-method bottleneck annotations are provided in the Supplementary Material.

Multiomics↗

PubMed2025

UALCAN Mobile, an app for cancer proteogenomic data analysis.

Cancer is a complex disease affecting various organs and is a major cause of death worldwide. During cancer initiation, disease progression, and tumor metastasis, various genomic and proteomic alterations are observed. Recent technological advances have led to the generation of large amounts of molecular data, including genomics and transcriptomics. These large-scale datasets can be utilized to analyze and identify sub-class-specific cancer biomarkers and targets. However, there is a need for the development of user-friendly tools for large-scale data analysis, disseminating the analyzed data in a visualizable format to cancer researchers with no programming skills. We developed UALCAN, a comprehensive platform that allows users to integrate disparate data to better understand the genes, proteins, and pathways perturbed in cancer and make discoveries of potential biomarkers and targets. In the current study, we describe the development of the UALCAN Mobile application (app) that will provide cancer transcriptomic data obtained from The Cancer Genome Atlas (TCGA) project to evaluate protein-coding gene expression based on various stratifications, including stage, grade, race, gender, and molecular-subtypes across over 30 types of cancers. In addition, the UALCAN mobile provides data analysis options for epigenetic changes due to DNA promoter methylation and Clinical Proteomic Tumor Analysis Consortium (CPTAC) cancer proteomic data. The app provides access to large cancer molecular datasets on the go. To find changes in the expression of causative genes and proteins and to identify biomarkers and therapeutic targets, UALCAN mobile app will be extremely valuable. The "UALCAN Mobile" app is free to use and can be downloaded from both the iOS/Apple and the Android Play Store and has been downloaded over 100 times in each of iOS and android app stores.

app↗

PubMed2025

Ulmus minor response to Dutch elm disease: de novo transcriptome assembly and annotation.

Dutch elm disease (DED), caused by Ophiostoma novo-ulmi (ONU), has devastated elm populations across Europe and North America since the 20th century. In this work, a de novo transcriptome assembly of Ulmus minor in response to ONU is presented. We used two DED-resistant genotypes, MDV2.3 and VAD2, and one DED-susceptible genotype, MDV1, to capture responses to ONU at four time points post-inoculation (6, 24, 72, and 144 hours). RNA from collected samples was isolated and sequenced producing 60.88 M 100 bp paired-end reads per sample. We performed a de novo transcriptome assembly combining data from the three genotypes. The assembly was functionally annotated and validated through differential gene expression analysis of the response. This dataset provides a valuable resource for studying molecular mechanisms of DED resistance in elms, contributing to broadening our understanding of tree immunity and facilitating potential applications in functional annotation of future genome assemblies.

Transcriptome↗

PubMed2001

cDNA-AFLP analysis unravels a genome-wide hrpG-regulon in the plant pathogen Xanthomonas campestris pv. vesicatoria.

The Hrp type III protein secretion system is essential for pathogenicity of the Gram-negative plant pathogen Xanthomonas campestris pv. vesicatoria. Expression of the hrp gene cluster is controlled by HrpG, a two-component response regulator, and HrpX, an AraC-type transcriptional activator. Using the cDNA-AFLP technique, 30 hrpG-induced (hgi) and five hrpG-repressed (hgr) cDNA fragments were identified, defining a large hrpG-regulon in X. campestris pv. vesicatoria. Expression of most genes in the hrpG-regulon was dependent on hrpX. Seven cDNA fragments map to the known hrp gene cluster and flanking regions. All other genes appear to be scattered over the chromosome and endogenous plasmids. Sequence analysis identified genes encoding putative extracellular proteases, a putative transcriptional regulator and XopJ and XopB (Xanthomonas outer proteins), homologues of YopJ from Yersinia spp. and the avirulence protein AvrPphD of Pseudomonas syringae respectively. XopB is secreted by the Hrp type III secretion system. Analysis of deletion mutants in several hgi genes revealed a new virulence locus. This study demonstrates that cDNA-AFLP is a powerful tool to study prokaryotic transcriptomes and to identify genes contributing to Xanthomonas virulence and putative effector proteins.

Bacterial Proteins↗

PubMed2006

Layered expression scanning: multiplex molecular analysis of diverse life science platforms.

With the advent of the genomic era, there is an increasing use of high-throughput techniques to generate transcriptome- and proteome-based profiles of biological specimens. Each of these methodologies offers a unique window into the inner workings of cell and tissue samples. Often, these studies generate large data sets and provide investigators with a substantial number of candidate dysregulated genes and pathways. Follow-up studies are then undertaken to independently validate the original findings and to extend the study to additional samples or more quantitative measurements. Although there are several methods available for these validation efforts, they are often tedious and laborious to perform; thus, additional tools that enable this task are needed. One such approach is layered expression scanning (LES), a new technique developed via a cooperative research and development agreement (CRADA) between the National Cancer Institute and 20/20 GeneSystems, Inc. The technique is based on the movement of biomolecules from a two-dimensional life science platform (histological tissue section, electrophoresis gel, multi-well plate, etc.) through a set of analysis membranes while maintaining the original distribution pattern of the molecules. Each membrane measures one analyte and the data are then mapped back to the original specimen, permitting each component of the life science platform to be studied in detail. LES can be configured in several different ways depending on the goals of the study. In this review, we summarize the use of the LES technique for a variety of biological applications.

Membranes, Artificial↗

PubMed2026

HyLnc: a hybrid deep learning and feature-based approach for long non-coding RNA prediction.

Long non-coding RNAs (lncRNAs) play important roles in gene regulation, development and disease, yet accurate identification of lncRNAs from transcriptomic data remains a major computational challenge. Existing methods often rely either on handcrafted sequence features or deep learning approaches, each with their inherent limitations in capturing the full complexity of RNA sequences. In this study, we proposed HyLnc, a computational framework that integrates transformer-based contextual embeddings with biologically meaningful sequence features for improved lncRNA prediction. A custom BERT-based model was first pre-trained on a large corpus of metazoan RNA sequences using a masked language modelling strategy to learn contextual nucleotide dependencies. The model was subsequently fine-tuned on curated datasets of lncRNAs and protein-coding transcripts and 256-dimensional deep sequence embeddings were extracted. Parallelly, 348 handcrafted features, including ORF characteristics, untranslated region (UTR) properties, nucleotide composition and Fickett scores, were computed. A multi-stage feature selection strategy was applied to identify the most informative features, resulting in optimized hybrid feature sets. Multiple machine learning classifiers were evaluated, with the RF model achieving the best performance. The proposed framework attained an accuracy of 91.30%, F1-score of 91.23% and MCC of 82.60 on an independent validation dataset, outperforming several existing lncRNA prediction tools. Thus, HyLnc demonstrates that integrating deep contextual representations with biologically interpretable features enhances lncRNA prediction. This approach provides a robust and scalable solution for large-scale transcriptome annotation and can be extended to other sequence-based prediction.

RNA, Long Noncoding↗

PubMed2004

Soluble factors from human endometrium promote angiogenesis and regulate the endothelial cell transcriptome.

BACKGROUND: Angiogenesis and vascular remodeling play critical roles in the cyclical growth and regression of endometrium. They also appear to play roles in the pathogenesis of endometriosis. METHODS AND RESULTS: Supernatants were collected from cultured endometrium isolated from women with and without endometriosis. These supernatants induced endothelial cell proliferation and angiogenesis in vitro. They contained vascular endothelial growth factor (VEGF)-A, and their proliferative effects on endothelial cells were partially abrogated by a blocking anti-VEGF-A antibody. Gene array analysis showed that culture supernatants from proliferative phase endometrium, and to a lesser extent secretory phase endometrium, induced significant changes in the transcriptome of endothelial cells. We could not detect any association between endometriosis and the ability of endometrial-derived soluble factors to promote angiogenesis or to regulate the endothelial transcriptome. In addition, we could not detect any association between endometriosis and the concentration of VEGF-A in supernatants from cultured endometrium or in menstrual effluent. CONCLUSIONS: We have shown that endometrium cultured in vitro produced soluble factors, including VEGF-A, that promoted angiogenesis. Proliferative phase endometrium promoted significant endothelial cell transcriptome changes that appear overall to be pro-angiogenic. These transcriptome changes provide insight into the dynamic control of vessel structure on which both eutopic endometrium and endometriotic lesions depend.

Case-Control Studies↗

PubMed2001

The human transcriptome map: clustering of highly expressed genes in chromosomal domains.

The chromosomal position of human genes is rapidly being established. We integrated these mapping data with genome-wide messenger RNA expression profiles as provided by SAGE (serial analysis of gene expression). Over 2.45 million SAGE transcript tags, including 160,000 tags of neuroblastomas, are presently known for 12 tissue types. We developed algorithms to assign these tags to UniGene clusters and their chromosomal position. The resulting Human Transcriptome Map generates gene expression profiles for any chromosomal region in 12 normal and pathologic tissue types. The map reveals a clustering of highly expressed genes to specific chromosomal regions. It provides a tool to search for genes that are overexpressed or silenced in cancer.

Algorithms↗

PubMed2006

Applying the SAGE technique to study the effects of electromagnetic field on biological systems.

Identification of genes alternatively expressed in electromagnetic field (EMF)-exposed cells could provide direct evidence for biological effects of EMF. As there are a few indications so far for certain genes to be influenced by EMF, genome-wide scans of the transcriptome appear to be necessary. Among the several technologies used for genome-wide gene expression analysis, serial analysis of gene expression (SAGE) is one promising method, which seems particularly applicable for EMF research. This review provides a brief description of the features of gene expression, illustrates the basic principle of SAGE, and discusses the advantages and limitations of SAGE as well as examples of application. This information should help investigators determine if the SAGE technique is an optimal method for evaluating the biological effects of EMF.

Animals↗

PubMed2026

Chitosan-dsRNA improves tissue stability and delivery for RNAi-mediated Varroa destructor control.

BACKGROUND: Varroa destructor is an ectoparasitic mite and a major threat to honey-bee colony health worldwide. RNA interference (RNAi) offers a potentially species-specific approach for mite control, but practical application is limited by double-stranded RNA (dsRNA) degradation and inefficient delivery to mites. This study evaluated coatomer protein I (COPI) complex subunits as RNAi targets and tested whether chitosan-based dsRNA formulation could improve dsRNA stability, tissue uptake, and delivery from honey-bees to mites. RESULTS: Direct microinjection of dsRNAs targeting COPB, COPD, and COPE significantly reduced target-gene expression and mite survival compared with the double-stranded green fluorescent protein (dsGFP) control, with 72-h survival rates of 8.0%, 12.7%, and 5.3%, respectively, compared with 40.7% in the control group (all log-rank P < 0.0001). Chitosan-conjugated dsRNA remained detectable for longer periods than naked dsRNA in honey-bee tissue fluids, and CNP-Cy3-dsGFP was detected in the honey-bee midgut and fat body. A qualitative fluorescence observation in V. destructor was consistent with host-to-mite dsRNA transfer. Ingestion of COP-targeted chitosan-dsRNAs reduced mite survival, whereas honey-bee survival and expression of honey-bee COP orthologs were not affected. In silico analysis detected no contiguous ≥19-nt matches between Varroa COP dsRNAs and the honey-bee transcriptome or genome. CONCLUSION: COPI subunits are promising RNAi targets in V. destructor, and chitosan formulation may improve dsRNA persistence and uptake while supporting honey-bee-mediated delivery to mites. These laboratory findings support further evaluation of chitosan-formulated dsRNA as a potentially species-selective strategy for Varroa management, while broader safety assessment and field validation remain necessary. © 2026 Society of Chemical Industry.

COPI complex↗

PubMed2007

Changes in gene expression during male meiosis in Petunia hybrida.

We analyzed changes in gene expression during male meiosis in Petunia by combining the meiotic staging of pollen mother cells from a single anther with cDNA-AFLP transcript profiling of mRNA from the synchronously developing sister anthers. The transcript profiling experiments focused on the identification of genes with a modulated expression profile during meiosis, while premeiotic archesporial cells and postmeiotic microspores served as a reference. About 8000 transcript tags, estimated at 30% of the total transcriptome, were generated, of which around 6% exhibited a modulated gene expression pattern at meiosis. Cluster analysis revealed a transcriptional cascade that coincides with the initiation and progression through all stages of the two meiotic divisions. Fragments that exhibited high expression specifically during meiosis I were characterized further by sequencing; 90 out of the 293 sequenced fragments showed homology with known genes, belonging to a wide range of gene classes, including previously characterized meiotic genes. In-situ hybridization experiments were performed to determine the spatial expression pattern for five selected transcript tags. Its concurrence with cDNA-AFLP transcript profiles indicates that this is an excellent approach to study genes involved in specialized processes such as meiosis. Our data set provides the potential to unravel unique meiotic genes that are as yet elusive to reverse genetics approaches.

DNA, Complementary↗

PubMed2005

Circulating transcriptome reveals markers of atherosclerosis.

Circulating monocytes mediate inflammation in atherosclerosis and may serve as easily accessible reporters of disease. To search for markers of atherosclerosis, we compared the in vivo transcriptomes of monocytes purified from patients undergoing carotid endarterectomy and normal subjects by using the serial analysis of gene expression technique. We selected a subset of differentially expressed monocyte-specific genes and confirmed their expression levels. The Finkel-Biskis-Jinkins osteosarcoma (FOS) gene was significantly increased in patients, and the highest levels of FOS associated with patients who had previously undergone coronary revascularization. The correlation between coronary revascularization and FOS was higher than that compared with the cardiac risk marker high sensitivity C-reactive protein. In vitro inhibition of FOS using small interfering RNA and 3-hydroxy-3-methyl-glutaryl CoA reductase inhibitor simvastatin (statin) affected monocyte activation and suggested an important role in pathogenesis. Given the prominent role of FOS in inflammation and calcification, its association with atherosclerosis severity has clear pathophysiologic bases as well as clinical implications as a marker. Our results suggest that analysis of gene expression in circulating cells may provide biological and clinical insights into human atherosclerosis, and that this type of approach may be applicable for studying other types of diseases.

Arteriosclerosis↗

PubMed2005

Defense-related genes expressed in Norway spruce roots after infection with the root rot pathogen Ceratobasidium bicorne (anamorph: Rhizoctonia sp.).

To study the mechanisms of inducible disease resistance in conifers, changes in transcript accumulation in roots of Norway spruce (Picea abies (L.) Karst.) seedlings exposed to the root rot pathogen Ceratobasidium bicorne Erikss. and Ryv. (anamorph: Rhizoctonia sp.) were monitored by differential display (DD). Because C. bicorne attacks root tips, a desiccation treatment was added to exclude genes induced by pathogen-related desiccation stress. The DD analysis was defined by the use of 11 sets of primers, covering about 5% of the transcriptome. A comparison of gene expression in control, desiccation- and pathogen-stressed roots revealed 36 pathogen-induced gene transcripts. Based on database searches, these transcripts were assigned to four groups originating from spruce mRNA (25 transcripts), rRNA (five transcripts), fungal mRNA (two transcripts) and currently unknown cDNAs (four transcripts). Real-time PCR was applied to verify and quantify pathogen-induced changes in transcript accumulation. Of the 18 transcripts tested, nine were verified to be Norway spruce gene transcripts up-regulated from 1.3- to 66-fold in the infected roots. Four germin-like protein isoforms, a peroxidase and a glutathione S-transferase, all implicated in oxidative processes, including the oxidative burst, were predicted from sequence similarity searches. Seven class IV chitinase isoforms implicated in fungal cell wall degradation and a nucleotide binding site-leucine rich repeat (NBS-LRR) disease resistance protein homologue related to pathogen recognition were identified. Several transcript species, such as the NBS-LRR homologue and the germin-like protein homologues, have not previously been identified as pathogen-inducible genes in gymnosperms.

Amino Acid Sequence↗

PubMed2006

A systems approach to model metastatic progression.

Proteomic profiling of human disease has seen much early activity with the accessibility of the newest generation of high-throughput platforms and technologies. Nevertheless, the nature of the dynamic physiologic milieu and high dimensionality of the data has complicated major diagnostic and prognostic breakthroughs. Our recent article in Cancer Cell delineates an integrative model for culling a molecular signature of metastatic progression in prostate cancer from proteomic and transcriptomic analyses and shows its facility as a predictor of prognosis. The study leveraged direct proteomic analysis of tumor tissue extracts, differential feature selection characterizing the proteomic alterations of prostate cancer subclasses, and integration with public and study-derived genomic data to construct a multiplex gene signature representing progression of indolent cancer to aggressive disease. This further predicted clinical outcome in a variety of solid tumors. This review describes the context of the work, the framework for the analysis itself, and a look forward to the promise of this systems approach to human disease.

Computational Biology↗

PubMed2004

A new set of BXD recombinant inbred lines from advanced intercross populations in mice.

BACKGROUND: Recombinant inbred (RI) strains are an important resource for mapping complex traits in many species. While large RI panels are available for Arabidopsis, maize, C. elegans, and Drosophila, mouse RI panels typically consist of fewer than 30 lines. This is a severe constraint on the power and precision of mapping efforts and greatly hampers analysis of epistatic interactions. RESULTS: In order to address these limitations and to provide the community with a more effective collaborative RI mapping panel we generated new BXD RI strains from two independent advanced intercrosses (AI) between C57BL/6J (B6) and DBA/2J (D2) progenitor strains. Progeny were intercrossed for 9 to 14 generations before initiating inbreeding, which is still ongoing for some strains. Since this AI base population is highly recombinant, the 46 advanced recombinant inbred (ARI) strains incorporate approximately twice as many recombinations as standard RI strains, a fraction of which are inevitably shared by descent. When combined with the existing BXD RI strains, the merged BXD strain set triples the number of previously available unique recombinations and quadruples the total number of recombinations in the BXD background. CONCLUSION: The combined BXD strain set is the largest mouse RI mapping panel. It is a powerful tool for collaborative analysis of quantitative traits and gene function that will be especially useful to study variation in transcriptome and proteome data sets under multiple environments. Additional strains also extend the value of the extensive phenotypic characterization of the previously available strains. A final advantage of expanding the BXD strain set is that both progenitors have been sequenced, and approximately 1.8 million SNPs have been characterized. This provides unprecedented power in screening candidate genes and can reduce the effective length of QTL intervals. It also makes it possible to reverse standard mapping strategies and to explore downstream effects of known sequence variants.

Animals↗