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Results for “transcriptomic platform”

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PubMed2003

Composition and dynamics of the Caenorhabditis elegans early embryonic transcriptome.

Temporal profiles of transcript abundance during embryonic development were obtained by whole-genome expression analysis from precisely staged C. elegans embryos. The result is a highly resolved time course that commences with the zygote and extends into mid-gastrulation, spanning the transition from maternal to embryonic control of development and including the presumptive specification of most major cell fates. Transcripts for nearly half (8890) of the predicted open reading frames are detected and expression levels for the majority of them (>70%) change over time. The transcriptome is stable up to the four-cell stage where it begins rapidly changing until the rate of change plateaus before gastrulation. At gastrulation temporal patterns of maternal degradation and embryonic expression intersect indicating a mid-blastula transition from maternal to embryonic control of development. In addition, we find that embryonic genes tend to be expressed transiently on a time scale consistent with developmental decisions being made with each cell cycle. Furthermore, overall rates of synthesis and degradation are matched such that the transcriptome maintains a steady-state frequency distribution. Finally, a versatile analytical platform based on cluster analysis and developmental classification of genes is provided.

Animals↗

PubMed2025

Stereo-cell: Spatial enhanced-resolution single-cell sequencing with high-density DNA nanoball-patterned arrays.

Single-cell sequencing technologies have advanced our understanding of cellular heterogeneity and biological complexity. However, existing methods face limitations in throughput, capture uniformity, cell size flexibility, and technical extensibility. We present Stereo-cell, a spatial enhanced-resolution single-cell sequencing platform based on high-density DNA nanoball (DNB)-patterned arrays, which enables scalable and unbiased cell capture at a wide input range and supports high-fidelity transcriptome profiling. Stereo-cell further allows integration with imaging-based modalities and multiomics strategies, including immunofluorescence and epitope profiling. This platform is also compatible with profiling extracellular vesicles, microstructures, and large cells, whereas its spatial resolution facilitates in situ analysis of cell-cell interactions, cellular microenvironments, and subcellular transcript localization. Together, Stereo-cell provides a flexible framework for expanding single-cell research applications.

Animals↗

PubMed2026

The Landmark Series: Mutation-Based Therapy of Pancreatic Cancer.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) remains a highly lethal malignancy with limited long-term survival despite advances in surgery and systemic therapy. PATIENTS: The population of interest comprises patients with PDAC characterized by targetable molecular alterations and biologically distinct transcriptomic subtypes. METHODS: We performed a narrative review of landmark and contemporary clinical trials, translational studies, and emerging molecular-classification platforms relevant to precision oncology in PDAC. RESULTS: Growing understanding of PDAC molecular biology has identified putative genetic mutations, including homologous recombination repair deficiency, mismatch repair deficiency, and mutated KRAS, enabling the development of targeted therapies and precision treatment strategies. Concurrently, transcriptomic profiling has revealed biologically distinct molecular subtypes associated with differences in prognosis and therapeutic response. Emerging tools such as molecular classifiers, deep learning models, and multiomic platforms may further refine patient selection and treatment personalization. CONCLUSIONS: This review highlights contemporary efforts of novel targeted therapies, ongoing advances in molecular subtyping, and the evolving role of precision oncology in improving outcomes for patients with PDAC.

Genomic alterations↗

PubMed2003

A new tool for rheumatology: large-scale analysis of gene expression.

Large-scale analysis of gene expression with cDNA arrays is spreading over many biological fields, including rheumatology. In this report, we wish to explain the principle and main advantages of this tool in the context of our discipline. Until 1995, analysis of gene expression was conducted for a few genes at a time but DNA chips now allow one to monitor the expression of thousands of genes in a single experiment and analyze the transcriptome, i.e. the whole of the transcripts in a given cell or tissue. Whatever the platform used (macro- or microarrays, oligo-chips), this technology rests upon the hybridization of i) a set of cDNA clones tethered to a solid support (nylon or glass) as probes, and ii) labelled cDNAs that are reverse-transcribed from bulk mRNAs extracted from a cell or tissue sample as a target. The end result is information on the relative abundance of every mRNA between two or more samples. The transcriptome analysis has two main objectives in rheumatology: i) identifying a gene expression profile that is a hallmark of a pathology and using it for a diagnostic or prognostic purpose, and ii) gathering genes with similar changes of expression, which allows one to specify the identity of novel proteins involved in a well-known intracellular cascade of regulation or even to identify new cascades.

Arthritis, Rheumatoid↗

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↗

PubMed2003

The human proteome organization (HUPO) and environmental health.

The Human Proteome Organization, or HUPO, was formed to promote research and large-scale analysis of the human proteome. By consolidating national proteome organizations into an international body, HUPO will coordinate international initiatives, biological resources, protocols, standards and data for studying the human proteome. HUPO has identified five key areas to advance study of the human proteome, specifically in bioinformatics, new technologies, the plasma proteome, cell models, and a public antibody initiative. Consideration of three major issue areas may help develop HUPO's strategy for human proteome study. First is the need to distinguish the value of high throughput platforms from discovery platforms in proteomics. Second is the importance for international planning on integrating both transcriptome and proteome data and databases. Last is that effects of the environment from chemical, physical, and biological exposures alter the expression and structure of the proteome, which become manifest in long-term adverse health effects and disease. Environmental health research stands to greatly benefit from the shared resources, data, and vision of the HUPO organization as a valuable resource in exploiting knowledge of the human proteome toward improving public health.

Computational Biology↗

PubMed2005

Cross-platform analysis of cancer microarray data improves gene expression based classification of phenotypes.

BACKGROUND: The extensive use of DNA microarray technology in the characterization of the cell transcriptome is leading to an ever increasing amount of microarray data from cancer studies. Although similar questions for the same type of cancer are addressed in these different studies, a comparative analysis of their results is hampered by the use of heterogeneous microarray platforms and analysis methods. RESULTS: In contrast to a meta-analysis approach where results of different studies are combined on an interpretative level, we investigate here how to directly integrate raw microarray data from different studies for the purpose of supervised classification analysis. We use median rank scores and quantile discretization to derive numerically comparable measures of gene expression from different platforms. These transformed data are then used for training of classifiers based on support vector machines. We apply this approach to six publicly available cancer microarray gene expression data sets, which consist of three pairs of studies, each examining the same type of cancer, i.e. breast cancer, prostate cancer or acute myeloid leukemia. For each pair, one study was performed by means of cDNA microarrays and the other by means of oligonucleotide microarrays. In each pair, high classification accuracies (> 85%) were achieved with training and testing on data instances randomly chosen from both data sets in a cross-validation analysis. To exemplify the potential of this cross-platform classification analysis, we use two leukemia microarray data sets to show that important genes with regard to the biology of leukemia are selected in an integrated analysis, which are missed in either single-set analysis. CONCLUSION: Cross-platform classification of multiple cancer microarray data sets yields discriminative gene expression signatures that are found and validated on a large number of microarray samples, generated by different laboratories and microarray technologies. Predictive models generated by this approach are better validated than those generated on a single data set, while showing high predictive power and improved generalization performance.

Animals↗

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↗

PubMed2025

The Annotated Blueprint: Integrated Functional Genomic Resources for a model Tetraploid Wheat Triticum turgidum cv. Kronos.

Triticum turgidum cv. Kronos is a tetraploid wheat cultivar that underpins one of the richest community platforms for functional genomics. Over the past decade, about 3,000 exome- and promoter-capture datasets, linked to mutagenized seed stocks, and transcriptomic and phenotypic resources have accumulated, yet the absence of a reference genome has constrained their impact. Here, we present a chromosome-scale reference genome of Kronos with high-confidence annotations, including manual curation of over 1,000 disease resistance (NLR) genes. This reference revealed previously hidden NLR diversity and clarified their genomic organization at chromosomal ends. Re-analysis of exome- and promoter-capture datasets enabled high-resolution mutation discovery in genes and regulatory regions that were previously inaccessible, uncovering the full standing variation present in Kronos mutant lines. We further re-curated transcriptomic and small RNA datasets, generating improved, genome-wide maps of microRNAs and phasiRNAs important for wheat development. Collectively, these resources elevate Kronos to reference quality and establish it as a versatile platform for functional and translational wheat research.

Journal Article↗

PubMed2006

Construction of quantitative proteome reference maps of mouse spleen and lymph node based on two-dimensional gel electrophoresis.

Quantitative features of the proteome are extremely useful for studying cellular processes at a molecular level. In this study, we attempted to construct quantitative reference proteome maps of the mouse spleen and lymph node based on 2-DE followed by protein identification using MS. We analyzed more than 1000 spots on the 2-DE images and consequently were able to determine that 919 spots were derived from 328 different genes. To obtain statistically reliable information of the protein levels from these 2-DE images, we measured the volumes of the respective spots on 2-DE images obtained by four to six independent experimental runs. These measurements were used to calculate the variability of the volumes of the respective spots on 2-DE following subcellular fractionation, which enabled us to discriminate differentially produced proteins from those within the range of intrinsic variability. More importantly, while the 2-DE data have been traditionally collected in a gel image-based manner, the resultant quantitative 2-DE data could be analyzed using the same procedure as that for mRNA expression profiles. This greatly assists in bridging the gap between the analyses of transcriptomes and proteomes and enables the integration of this data on the same informational platform.

Amino Acid Sequence↗

PubMed2006

Use of gene expression microarrays for the study of acute leukemia.

Genetic lesions found in acute leukemia drive the pathology of the disease in addition to forming reliable classifications of prognosis. However, there is still a reasonable heterogeneity of response among cases with the same genetic lesion. Moreover, many leukemia cases have no detectable genetic marker and these cases have marked heterogeneity of response. How can we learn more about the genes and pathways involved with leukemogenesis and response in the midst of such complexity? Gene expression microarrays are experimental platforms that allow for the simultaneous evaluation of the thousands of mRNA transcripts (the 'transcriptome'). This technology has revolutionized the study of leukemia, giving insight into genes and pathways involved in disease response and the biology involved in specific translocations.

Acute Disease↗

PubMed2027

Systematic Dissection of Key Driver Perturbation Signatures in Single Cells via ECCITE-seq.

CRISPR screens, such as expanded CRISPR-compatible cellular indexing of transcriptomes and epitopes by sequencing (ECCITE-seq), enable the simultaneous measurement of transcriptomes, gRNA identity, and cell-surface protein expression at single-cell resolution to systematically interrogate gene function. This platform provides a powerful and scalable experimental approach for validating disease-associated regulators identified by large-scale association studies and other computational methods, including network-based analyses of multi-omics data. Here, as an example application, we describe an ECCITE-seq framework to characterize the transcriptomic consequences of perturbing multiple neuronal key driver genes associated with Alzheimer's disease (AD) in human-induced pluripotent stem cell (hiPSC)-derived neurons. More broadly, by integrating customized pooled gRNA libraries with different CRISPR effectors across multiple cell types, this approach allows for the assessment of the regulatory impact of candidate genes implicated in development and disease processes.

Humans↗

PubMed2026

Integrative Cross-platform Analysis of Kinase Inhibitor Effects on Statin-relevant Cardioprotective Pathways in Human Cardiomyocytes.

BACKGROUND/AIM: Kinase inhibitors (KIs) can cause cardiotoxicity through mechanisms overlapping with statin cardioprotective pathways, yet their effects on these pathways in cardiomyocytes remain uncertain. We evaluated six literature-defined statin-relevant gene sets using transcriptomic and proteomic data. MATERIALS AND METHODS: Pre-ranked gene set enrichment analysis was performed for 23 KIs in primary cardiac cells (GSE146096; n=319) and iPSC-derived cardiomyocytes (GSE217421; n=541), with cross-platform analysis of 21 KIs by shotgun proteomics (PXD014791; n=300). Pathway-specific concordance was assessed by Spearman correlation with Benjamini-Hochberg correction; protein scores were estimated after adjustment for cell line. RESULTS: KI effects were heterogeneous. The anti-fibrotic pathway showed nominal concordance across the two transcriptomic datasets (ρ=0.495, p=0.016, q=0.098; 91% direction concordance) and significant cell-line-adjusted transcriptomic-proteomic concordance (ρ=0.644, p=0.0016, q=0.0081). Nilotinib reproducibly upregulated NF-κB pathway genes [normalized enrichment score (NES)=+2.29 and +2.18 in discovery and validation], with targeted inter-gene-correlation-adjusted testing supporting higher NF-κB expression than under rosuvastatin (CAMERA p=3.54×10-8). No global cross-omics summary remained significant after harmonizing pathway universes and accounting for repeated pathways. CONCLUSION: KI effects on statin-relevant pathways were pathway-specific. Anti-fibrotic concordance and nilotinib-associated NF-κB upregulation are hypothesis-generating candidates for experimental validation.

Humans↗

PubMed2004

Genome-wide reprogramming of primary and secondary metabolism, protein synthesis, cellular growth processes, and the regulatory infrastructure of Arabidopsis in response to nitrogen.

Transcriptome analysis, using Affymetrix ATH1 arrays and a real-time reverse transcription-PCR platform for >1,400 transcription factors, was performed to identify processes affected by long-term nitrogen-deprivation or short-term nitrate nutrition in Arabidopsis. Two days of nitrogen deprivation led to coordinate repression of the majority of the genes assigned to photosynthesis, chlorophyll synthesis, plastid protein synthesis, induction of many genes for secondary metabolism, and reprogramming of mitochondrial electron transport. Nitrate readdition led to rapid, widespread, and coordinated changes. Multiple genes for the uptake and reduction of nitrate, the generation of reducing equivalents, and organic acid skeletons were induced within 30 min, before primary metabolites changed significantly. By 3 h, most genes assigned to amino acid and nucleotide biosynthesis and scavenging were induced, while most genes assigned to amino acid and nucleotide breakdown were repressed. There was coordinate induction of many genes assigned to RNA synthesis and processing and most of the genes assigned to amino acid activation and protein synthesis. Although amino acids involved in central metabolism increased, minor amino acids decreased, providing independent evidence for the activation of protein synthesis. Specific genes encoding expansin and tonoplast intrinsic proteins were induced, indicating activation of cell expansion and growth in response to nitrate nutrition. There were rapid responses in the expression of many genes potentially involved in regulation, including genes for trehalose metabolism and hormone metabolism, protein kinases and phosphatases, receptor kinases, and transcription factors.

Arabidopsis↗

PubMed2004

[Transcriptome analysis of virus-infected cells].

Human genome project revealed that human genes are derived from 30,000-40,000 species of genetic loci, which had been estimated as approximately 100,000. The project also promoted devising novel tools that enable us to analyze biological phenomena comprehensively. Microarray technology is a representative of the novel tools in genomics and engages us to explore genome-wide expression levels of genes simultaneously (transcriptome analysis). Here we show transcriptomes obtained from 10 species of cells infected with human cytomegalovirus, as a model virus, by a synthetic DNA microarray system that we have established recently. Our system provides simultaneous and parallel description on alteration of expression of viral and host genes that are represented within a single area on a slide glass. Moreover, we propose a project entitled 'comparative virology on cellular responses of infected hosts' that consists of multiple acquisition and integration of transcriptomes from a combination of several cells and viruses as a panel on the identical platform. The attempt should extract a novel concept in virology from investigation on differences and similarities among influence of a virus on a variety of different cells and conversely among responses of a species of cells against a variety of different viruses.

Cytomegalovirus↗

PubMed2026

Transcriptomic responses of gill and intestinal tissues in Nile tilapia (Oreochromis niloticus) to bacterial infection following sequential nanoimmersion and hydrogel-based multivalent vaccination.

Bacterial pathogens, including Flavobacterium oreochromis, Aeromonas veronii, Streptococcus agalactiae, and Edwardsiella tarda, represent major infectious threats to Nile tilapia (Oreochromis niloticus). A multivalent vaccination strategy integrating cationic nanoemulsion immersion with oral hydrogel boosters was developed to investigate tissue-specific immune responses at the transcriptomic level. Gill tissues were collected following immersion challenge and intestinal tissues following intraperitoneal injection challenge, reflecting the physiologically relevant infection biology of each pathogen and the mechanistic rationale of each delivery platform. RNA sequencing (RNA-seq) generated high-quality datasets (mapping rate > 81.64%) with strong concordance to quantitative real-time PCR (qRT-PCR) validation (r = 0.83). Comparative transcriptomic analysis revealed distinct yet complementary immune signatures between tissues. Gill transcriptomes were enriched in phagosome, focal adhesion, extracellular matrix-receptor interaction (ECM-receptor interaction), and cytokine-cytokine receptor interaction pathways, accompanied by increased expression of major histocompatibility complex class I/II (MHC class I/II), mannose receptor, αVβ3 integrin, and calnexin, indicating innate activation, enhanced phagocytic capacity, epithelial barrier reinforcement, and adaptive immune coordination. Intestinal transcriptomes showed predominant enrichment of adaptive immune pathways, including the intestinal immune network for immunoglobulin (Ig) production, Forkhead box O (FoxO) signaling, and mitogen-activated protein kinase (MAPK) signaling, with increased expression of T-cell receptor (TCR), inducible T-cell co-stimulator ligand (ICOS-L), C-X-C chemokine receptor type 4 (CXCR4), and polymeric immunoglobulin receptor (pIgR), reflecting T and B cell coordination, lymphocyte trafficking, and mucosal immunoglobulin transport, alongside innate engagement through phagosome pathway enrichment. Shared upregulation of MHC class II, B-cell receptor (BCR) signaling, integrin alpha M (ITGAM), and immunoglobulin-associated components across both tissues suggests coordinated mucosal immune activation through a conserved immune module, warranting direct experimental validation. Collectively, these findings provide transcriptomic evidence that this vaccination strategy elicits an integrated, tissue-specialized immune response, advancing mechanistic understanding of gill and intestinal immunity in vaccine-induced protection of teleost fish.

Animals↗

PubMed2005

Metabolomics reviewed: a new "omics" platform technology for systems biology and implications for natural products research.

Metabolomics is the study of global metabolite profiles in a system (cell, tissue, or organism) under a given set of conditions. The analysis of the metabolome is particularly challenging due to the diverse chemical nature of metabolites. Metabolites are the result of the interaction of the system's genome with its environment and are not merely the end product of gene expression but also form part of the regulatory system in an integrated manner. Metabolomics has its roots in early metabolite profiling studies but is now a rapidly expanding area of scientific research in its own right. Metabolomics (or metabonomics) has been labeled one of the new "omics", joining genomics, transcriptomics, and proteomics as a science employed toward the understanding of global systems biology. Metabolomics is fast becoming one of the platform sciences of the "omics", with the majority of the papers in this field having been published only in the last two years. In this review metabolomic methodologies are discussed briefly followed by a more detailed review of the use of metabolomics in integrated applications where metabolomics information has been combined with other "omic" data sets (proteomics, transcriptomics) to enable greater understanding of a biological system. The potential of metabolomics for natural product drug discovery and functional food analysis, primarily as incorporated into broader "omic" data sets, is discussed.

Biological Products↗

PubMed2005

Complementary gene and protein expression studies and integrative approaches in toxicogenomics.

Parallel transcript and protein profiling is a strategy to gain further insight into the mechanisms of toxicity and disease. The technologies used to measure expression at the transcript and protein levels each convey different information and have different technical capabilities that can complement each other when combined. In this review, over twenty studies are considered for the use of -Omics platform, the chemical or disease being profiled, tissues, the number of genes and proteins found by each platform and common expression products. A strategy is suggested for toxicant expression profiling that combines the transcriptomics and proteomics of both the target tissue and blood/serum that could provide a more complete characterization of toxicity as well as synergize expression technologies toward biomarker discovery.

Animals↗