PubMed Health⌕ Search

SEARCH · PubMed Health

Results for “transcriptomic platform”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 235 records · Page 13Linked to original sources

Tumor-like proliferation of CCM3 knockout endothelial cells: insights from semaxinib treatment and transcriptome profiling of co-cultures.

Cerebral cavernous malformations (CCMs) are vascular lesions associated with severe neurological complications. Increasing evidence suggests that cancer-like mechanisms, like an abnormal expansion of CCM3 knockout (KO) endothelial cells (ECs) in co-culture with wild-type (WT) cells, contribute to lesion formation. Yet, the underlying processes remain poorly understood. Here, we employed a human induced pluripotent stem cell (iPSC)-derived EC co-culture model to screen a cytokine inhibitor library for modulators of this tumor-like behavior. We identified the known VEGFR2 inhibitor semaxinib which selectively suppressed proliferation of WT ECs in co-culture, but not in monoculture. In contrast, CCM3 KO cells maintained their abnormal expansion under semaxinib treatment which was unaffected by modulation of extracellular VEGFA levels. RNA-seq profiling revealed distinct transcriptional responses to semaxinib including extracellular matrix remodeling, stress signaling, and overexpression of growth factors and receptors in CCM3 KO cells, which may contribute to their survival advantage. These findings advance our understanding of the complex interplay between WT and KO cells in CCM pathogenesis and demonstrate that the proliferative advantage of CCM3-deficient cells is not solely driven by CCM3 loss. Finally, our iPSC-based EC co-culture assay provides a scalable platform to study KO/WT interactions and may accelerate the identification of effective therapeutic strategies for CCM disease.

Humans↗

Noncontact laser microdissection and pressure catapulting: sample preparation for genomic, transcriptomic, and proteomic analysis.

The understanding of the molecular mechanisms of cellular metabolism and proliferation necessitates accurate identification, isolation, and finally characterization of a specific cell or a population of cells and subsequently their subsets of biomolecules. For the simultaneous analysis of thousands of molecular parameters within a single experiment, as realized by DNA, RNA, and protein microarray technologies, a defined number of homogeneous cells derived from a distinct morphological origin is required. Sample preparation is therefore a very crucial step for high-resolution downstream applications. Laser microdissection and laser pressure catapulting (LMPC) enables such pure and homogeneous sample preparation, resulting in an eminent increase in the specificity of molecular analyses. For microdissection, the force of focused laser light is used to excise selected cells or large tissue areas from object slides or from living cell culture down to a resolution of individual single cells and subcellular components like organelles or chromosomes, respectively. After microdissection this sample is directly catapulted into an appropriate collection device. As the entire process works without any mechanical contact, it enables pure sample retrieval from morphologically defined origin without cross contamination. Wherever homogenous samples are required for subsequent analysis of, e.g., cell areas, single cells, or chromosomes, the PALM MicroBeam system is an indispensable tool. The integration of image analysis platforms fully automates screening, identification, and finally subsequent high-throughput sample handling. These samples can be directly linked into versatile downstream applications, such as single-cell mRNA-extraction, different PCR methods, microarray techniques, and many others. Acceleration in sample generation vastly increases the throughput in molecular laboratories and leads to an increasing knowledge about differentially regulated mRNAs and expressed proteins, providing new insights into cellular mechanisms and therefore enabling the development of systems for tumor biomarker identification, early detection of disease-causing alterations, therapeutic targeting and/or patient-tailored therapy.

Animals↗

CEBS object model for systems biology data, SysBio-OM.

MOTIVATION: To promote a systems biology approach to understanding the biological effects of environmental stressors, the Chemical Effects in Biological Systems (CEBS) knowledge base is being developed to house data from multiple complex data streams in a systems friendly manner that will accommodate extensive querying from users. Unified data representation via a single object model will greatly aid in integrating data storage and management, and facilitate reuse of software to analyze and display data resulting from diverse differential expression or differential profile technologies. Data streams include, but are not limited to, gene expression analysis (transcriptomics), protein expression and protein-protein interaction analysis (proteomics) and changes in low molecular weight metabolite levels (metabolomics). RESULTS: To enable the integration of microarray gene expression, proteomics and metabolomics data in the CEBS system, we designed an object model, Systems Biology Object Model (SysBio-OM). The model is comprehensive and leverages other open source efforts, namely the MicroArray Gene Expression Object Model (MAGE-OM) and the Proteomics Experiment Data Repository (PEDRo) object model. SysBio-OM is designed by extending MAGE-OM to represent protein expression data elements (including those from PEDRo), protein-protein interaction and metabolomics data. SysBio-OM promotes the standardization of data representation and data quality by facilitating the capture of the minimum annotation required for an experiment. Such standardization refines the accuracy of data mining and interpretation. The open source SysBio-OM model, which can be implemented on varied computing platforms is presented here. AVAILABILITY: A universal modeling language depiction of the entire SysBio-OM is available at http://cebs.niehs.nih.gov/SysBioOM/. The Rational Rose object model package is distributed under an open source license that permits unrestricted academic and commercial use and is available at http://cebs.niehs.nih.gov/cebsdownloads. The database and interface are being built to implement the model and will be available for public use at http://cebs.niehs.nih.gov.

Database Management Systems↗

Gene expression profiling of peripheral blood mononuclear cells (PBMC) from Mycobacterium bovis infected cattle after in vitro antigenic stimulation with purified protein derivative of tuberculin (PPD).

Microarray analysis of messenger RNA (mRNA) abundance was used to investigate the gene expression program of peripheral blood mononuclear cells (PBMC) from cattle infected with Mycobacterium bovis, the causative agent of bovine tuberculosis. An immunospecific bovine microarray platform (BOTL-4) with spot features representing 1336 genes was used for transcriptional profiling of PBMC from six M. bovis-infected cattle stimulated in vitro with bovine purified protein derivative of tuberculin (PPD-bovine). Cells were harvested at four time points (3 h, 6 h, 12 h and 24 h post-stimulation) and a split-plot design with pooled samples was used for the microarray experiment to compare gene expression between PPD-bovine stimulated PBMC and unstimulated controls for each time point. Statistical analyses of these data revealed 224 genes (approximately 17% of transcripts on the array) differentially expressed between stimulated and unstimulated PBMC across the 24 h time course (P<0.05). Of the 224 genes, 87 genes were significantly upregulated and 137 genes were significantly downregulated in M. bovis-infected PBMC stimulated with PPD-bovine across the 24 h time course. However, perturbation of the PBMC transcriptome was most apparent at time points 3 h and 12 h post-stimulation, with 81 and 84 genes differentially expressed, respectively. In addition, a more stringent statistical threshold (P<0.01) revealed 35 genes (approximately 3%) that were differentially expressed across the time course. Real-time quantitative reverse transcription PCR (qRT-PCR) of selected genes validated the microarray results and demonstrated a wide range of differentially expressed genes in PPD-bovine-, PPD-avian- and Concanavalin A (ConA) stimulated PBMC, including the interferon-gamma gene (IFNG), which was upregulated in PBMC stimulated with PPD-bovine (40-fold), PPD-avian (10-fold) and ConA (8-fold) after in vitro culture for 12 h. The pattern of expression of these genes in PPD-bovine stimulated PBMC provides the first description of an M. bovis-specific signature of infection that may provide insights into the molecular basis of the host response to infection. Although the present study was carried out with mixed PBMC cell populations, it will guide future studies to dissect immune cell-specific gene expression patterns in response to M. bovis infection.

Animals↗

Gene expression profiling of human cancers.

DNA microarrays allow us to visualize simultaneously the expression of potentially all genes within a cell population or tissue sample-revealing the "transcriptome." The analysis of this type of data is commonly called "gene expression profiling" (GEP) because it provides a comprehensive picture of the pattern of gene expression in a particular biological sample. For this reason microarrays are revolutionizing life sciences research and are leading to the development of novel and powerful methods for investigating cancer biology, classifying cancers, and predicting clinical outcome of cancers. Several recent high-profile reports have revealed how clustering of GEP data can clearly identify clinically (and prognostically) important subtypes of cancer among patients considered by established clinicopathological criteria to have similar tumors. Accurate "prognostic signatures" can be obtained from GEP data, which represent relatively small numbers of genes. These signatures can be valuable in directing appropriate treatment and in predicting clinical outcome, and they generally outperform other systems based on clinical and histological criteria. In this paper the basic principles of DNA microarray technology and the different types of microarray platforms available will be introduced, and the power of the technique will be illustrated by reviewing some recent GEP studies on selected cancers, including a preliminary analysis of hepatocellular carcinoma from our Palermo laboratory. GEP is likely to be adopted in the future as a key decision-making tool in the clinical arena. However, several issues relating to data analysis, reproducibility, cross-comparability, validation, and cost need to be resolved before the technology can be adopted broadly in this context.

Carcinoma, Hepatocellular↗

Antisense intronic non-coding RNA levels correlate to the degree of tumor differentiation in prostate cancer.

A large fraction of transcripts are expressed antisense to introns of known genes in the human genome. Here we show the construction and use of a cDNA microarray platform enriched in intronic transcripts to assess their biological relevance in pathological conditions. To validate the approach, prostate cancer was used as a model, and 27 patient tumor samples with Gleason scores ranging from 5 to 10 were analyzed. We find that a considerably higher fraction (6.6%, [23/346]) of intronic transcripts are significantly correlated (P< or =0.001) to the degree of prostate tumor differentiation (Gleason score) when compared to transcripts from unannotated genomic regions (1%, [6/539]) or from exons of known genes (2%, [27/1369]). Among the top twelve transcripts most correlated to tumor differentiation, six are antisense intronic messages as shown by orientation-specific RT-PCR or Northern blot analysis with strand-specific riboprobe. Orientation-specific real-time RT-PCR with six tumor samples, confirmed the correlation (P=0.024) between the low/high degrees of tumor differentiation and antisense intronic RASSF1 transcript levels. The need to use intron arrays to reveal the transcriptome profile of antisense intronic RNA in cancer has clearly emerged.

Cell Differentiation↗

Myeloid landscape of BRAF-mutant papillary thyroid cancer and thyroiditis.

Papillary thyroid cancer (PTC) is less aggressive when associated with lymphocytic thyroiditis (LT), even in the presence of oncogenic BRAF, including smaller tumours, less lymph node involvement and reduced extrathyroidal extension. To investigate possible immune mechanisms underlying this association, we compared the tumour microenvironment of PTC-BRAF with LT and that without LT using single-cell RNA sequencing (scRNA-seq). Single-cell libraries were generated from fresh and fixed tumour samples with post-dissociation viability >70% using the 10x Genomics Chromium Platform and sequenced on an Illumina NovaSeq 6000. We analysed scRNA-seq data from 11 PTC-BRAF tumours: four with LT (one publicly available sample) and seven without LT. Downstream analyses included quality control, batch correction, dimensionality reduction, and differential gene expression analysis. We found that neutrophils were the predominant myeloid cell type in PTCs without LT. Thyrocytes without LT showed significant expression of the neutrophil recruitment chemokine ECRG4. In the absence of LT, neutrophils expressed oncogenic genes with poor clinical outcomes. In contrast, thyrocytes from tumours with LT showed increased expression of MHC-II antigen presentation, consistent with effective immune surveillance. Thyrocytes and macrophages in the presence of LT showed enrichment of interferon gamma response pathways. Our data suggest that LT in thyroid cancer is associated with enhanced antigen presentation and fewer features of pro-tumourigenic innate immune activity. These results identify previously under-recognised innate immune cell population and associated transcriptomic features, which suggest new mechanisms to target immune treatments in PTC refractory to other therapies.

Humans↗

Engineered Transformer Base Editor with Enhanced Editing Efficiency.

Canonical cytosine base editors (CBEs) achieve precise C-to-T conversions without inducing DNA double-strand breaks (DSBs), yet their clinical potential remains hampered by substantial off-target (OT) mutations. The recently developed transformer base editor (tBE) significantly reduces both genomic and transcriptomic OT mutations by using a cleavable deoxycytidine deaminase inhibitor (dCDI) domain. However, the modest base editing efficiency limits its broader applications. Here, through rational deaminase engineering and fusion of a uracil DNA glycosylase inhibitor (UGI) domain, we developed the engineered tBE (etBE). The etBE exhibited substantially enhanced editing efficiencies compared with the parental tBE (up to 35.11-fold improvement), while maintaining high editing fidelity and background levels of OT mutations. As a therapeutic proof-of-concept, dual adeno-associated virus (AAV)-mediated delivery of etBE targeting proprotein convertase subtilisin/kexin type 9 (PCSK9), a well-established therapeutic target for cardiovascular diseases, was evaluated in a humanized mouse model. The treatment achieved efficient in vivo base editing (up to 35.13%), resulting in substantial reductions in plasma PCSK9 protein (24%) and low-density lipoprotein cholesterol (LDL-C) levels (33%), while inducing only minimal OT mutations. Collectively, etBE represents a highly efficient and specific base editing platform with enormous potential for both basic research and clinical applications.

CRISPR&#x2010;Cas9↗

Scalable single-cell total RNA-seq reveals non-coding programs in immunity, infection, and brain development.

Non-coding RNAs represent a widespread and diverse layer of post-transcriptional regulation across cell types and states, yet much of their diversity remains uncharted at single-cell resolution. This gap stems from the limitations of widely used single-cell RNA-sequencing protocols, which focus on polyadenylated transcripts and miss many short or non-polyadenylated RNAs. Here, we adapted single-cell RNA-sequencing on the 10x Genomics platform to capture a broad complement of coding and non-coding RNAs-including miRNAs, tRNAs, lncRNAs, histone RNAs, and non-adenylated viral transcripts. This approach enabled the discovery of rich, dynamic non-coding RNA programs across immune cells, virally infected hepatocytes, and the developing human brain. In dengue virus-infected hepatocytes, we detect non-adenylated viral transcripts and distinguish active from transcriptionally quiescent infected states, each with distinct host regulatory signatures. In brain tissue, we identify biotype-specific, cell-type-restricted non-coding RNAs, including miRNAs whose expression anticorrelates with predicted targets, consistent with post-transcriptional regulatory relationships. We show that MIR137, one of the strongest GWAS loci associated with schizophrenia and intellectual disability, is expressed specifically in Cajal-Retzius cells, an early-born but transient population that guides subsequent cortical neuron migration. These findings demonstrate the importance of non-coding RNAs in defining cell identity and state, and show how expanded transcriptome coverage can reveal additional layers of gene control-now accessible through practical and scalable single-cell profiling.

Journal Article↗

A refined MASH-HCC model identifies macrophage Gadd45b as a key orchestrator of inflammation-driven neoplastic progression.

Metabolic dysfunction-associated steatohepatitis (MASH) is emerging as a leading driver of hepatocellular carcinoma (HCC), yet the molecular mechanisms linking metabolic stress, chronic inflammation and tumorigenesis remain poorly understood. Here we established a metabolically relevant, time-efficient MASH-to-HCC model in C57BL/6N mice by combining a MASH diet with controlled CCl4 administration, enabling stepwise recapitulation of MASH-associated neoplastic progression. Using this model, we identified growth arrest and DNA damage 45b (Gadd45b) as a novel MASH-derived protumorigenic regulator selectively activated under metabolic stress. Integrated analyses of human bulk and single-cell transcriptomic datasets and mouse transcriptomic deconvolution revealed concordant macrophage remodeling and GADD45B/Gadd45b expression dynamics during MASH-to-HCC progression. Mechanistically, fatty acids and TNF&#x3b1; preferentially induced Gadd45b in macrophages, where it amplified TNF&#x3b1;-NF-&#x3ba;B signaling. Macrophage-derived inflammatory signals subsequently induced Gadd45b and NF-&#x3ba;B activation in hepatocytes, establishing a feed-forward inflammatory loop that promoted fibrogenic and partial EMT-like programs and tumor spheroid formation. Importantly, temporal profiling during spheroid formation and progression revealed transient induction of Gadd45b during early spheroid establishment, but not during later progression, indicating that Gadd45b-mediated inflammatory signaling primarily promotes tumor initiation rather than subsequent growth. Consistent with human data, Gadd45b expression increased with disease severity and positively correlated with inflammatory factors in the MASH-HCC model, whereas pharmacological inhibition attenuated the Gadd45b-inflammation signaling axis. Collectively, our findings establish macrophage Gadd45b as a key orchestrator linking metabolic stress, chronic inflammation, and neoplastic transformation during MASH-to-HCC progression. Our refined MASH-HCC model provides a robust platform for mechanistic studies and preclinical evaluation of inflammation-targeted therapies.

Journal Article↗

Advancing the Deciphering of Host-Microbe Crosstalk with Spatial Omics: A Mini-Review.

Host-microbe crosstalk refers to the reciprocal influences between a host and its resident or invading microorganisms. This crosstalk plays important roles in maintaining host health, regulating physiological functions, and coordinating responses to infection. The rapid rise of spatial omics is transforming how this crosstalk is studied in both animals and plants. Unlike traditional bulk omics, which homogenize tissues and erase spatial context, spatial methods preserve in situ organization and can simultaneously capture molecular information from hosts and microbes. As a result, researchers can characterize the spatial organization of colonization and infection, identify spatial associations between microbial niches and host cell states, and visualize local host response gradients across intact tissues. Current spatial omics technologies encompass sequencing-based, imaging-based, and hybrid platforms. Spatial multi-omics approaches enable the joint measurement or integration of gene expression, protein abundance, and metabolite distributions. Although spatial association alone does not establish causality, spatial omics provides a high-resolution framework for characterizing host-microbe relationships within intact tissues and generating spatially constrained, testable hypotheses. When combined with perturbation experiments and complementary experimental evidence, these hypotheses can contribute to mechanistic interpretation of host-microbe crosstalk. Here, we review spatial omics technologies, compare their suitability and major trade-offs for host-microbe studies, and discuss computational strategies, analytical challenges, and future prospects.

Multiomics↗

Dogme: a nextflow pipeline for reprocessing nanopore RNA and DNA modifications.

MOTIVATION: Oxford Nanopore (ONT) sequencing allows for the direct detection of RNA and DNA modifications from unamplified nucleic acids, which is a significant advantage over other platforms. However, the rapid updates to ONT basecalling models and the evolving landscape of computational tools for modification detection bring about challenges for reproducible and standardized analyses. To address these challenges, we developed Dogme to automate basecalling, alignment, modification detection, and transcript quantification. Dogme automates the reprocessing of ONT POD5 files by integrating basecalling using Dorado, read mapping using minimap2 and subsequent analysis steps such as running modkit. The pipeline supports three major types of sequencing data-direct RNA (dRNA), complementary DNA (cDNA), and genomic DNA (gDNA). Dogme facilitates detection of diverse RNA modifications supported by Dorado such as N6-methyladenosine (m6A), 5-methylcytosine (m5C), inosine, pseudouridine, 2'-O-methylation (Nm) and DNA methylation, while concurrently quantifying full-length transcript isoforms LR-Kallisto for transcript quantification for dRNA and cDNA. RESULTS: We applied Dogme to three separate mouse C2C12 myoblast replicates using direct RNA sequencing on MinION flow cells. We detected 96&#xa0;603 m6A, 43&#xa0;476 m5C, 8829 inosine, 10&#xa0;055 pseudouridine, and 30&#xa0;320 Nm sites in three biological replicates. The pipeline produced reproducible modification profiles and transcript expression levels across replicates, demonstrating its utility for integrative long-read transcriptomic and epigenomic analyses. AVAILABILITY AND IMPLEMENTATION: Dogme is implemented in Nextflow and is freely available under the MIT license at https://github.com/mortazavilab/dogme, with documentation provided for installation and usage.

RNA↗

The current and future perspective of ChickenGTEx project and its applications in precision breeding.

The Chicken Genotype-Tissue Expression (ChickenGTEx) project was established to systematically characterize the regulatory landscape of the chicken genome and to accelerate the translation of functional genomics into precision breeding. By integrating whole-genome sequencing with multi-tissue transcriptomic profiling, ChickenGTEx provides a comprehensive atlas of gene expression regulation across diverse tissues and physiological systems. Current findings demonstrate that complex production traits are governed by coordinated regulatory networks rather than isolated loci, with substantial contributions from tissue-specific gene expression, structural variation, and genotype-by-sex interactions. Sex-dependent regulatory effects further refine the genetic architecture of metabolic, immune, and reproductive traits, highlighting the importance of incorporating sex as a biological variable in genomic analyses. Application of integrative omics frameworks within elite layer populations has revealed multilayer regulatory mechanisms underlying extended laying performance, feed efficiency, metabolic health, and eggshell quality. By partitioning phenotypic variance into genetic, regulatory, and host-microbiome components, these approaches move beyond association-based mapping toward causal inference and biological interpretation. Importantly, validated regulatory loci identified through ChickenGTEx and related analyses provide actionable markers for genomic selection and rational targets for precision genome modification. Looking forward, continued expansion of regulatory atlases, incorporation of single-cell and longitudinal data in diverse environmental conditions, and integration of functional annotation into breeding pipelines will further enhance prediction accuracy and sustainable genetic improvement. The ChickenGTEx project thus represents a foundational platform bridging functional genomics and practical poultry breeding.

Animals↗

Quantitative proteomic and genomic profiling reveals metastasis-related protein expression patterns in gastric cancer cells.

Gastric cancer is a leading cause of death worldwide, and patients have an overall 5-year survival rate of less than 10%. Using quantitative proteomic techniques together with microarray chips, we have established comprehensive proteome and transcriptome profiles of the metastatic gastric cancer TMC-1 cells and the noninvasive gastric cancer SC-M1 cell. Our qualitative protein profiling strategy offers the first comprehensive analysis of the gastric cancer cell proteome, identifying 926 and 909 proteins from SC-M1 and TMC-1 cells, respectively. Cleavable isotope-coded affinity tagging analysis allows quantitation of a total of 559 proteins (with a protein false-positive rate of <0.005), and 240 proteins were differentially expressed (>1.3-fold) between the SC-M1 and TMC-1 cells. We identified numerous proteins not previously associated with gastric cancer. Notably, a large subset of differentially expressed proteins was associated with tumor metastasis, including proteins functioning in cell-cell and cell-extracellular matrix (cell-ECM) adhesion, cell motility, proliferation, and tumor immunity. Gene expression profiling by DNA microarray revealed differential expression (of >2-fold) of about 1000 genes. The weak correlation observed between protein and mRNA profiles highlights the important complementarities of DNA microarray and proteomics approaches. These comparative data enabled us to map the disease-perturbed cell-cell and cell-ECM adhesion and Rho GTPase-mediated cytoskeletal pathways. Further validation of a subset of genes suggests the potential use of vimentin and galectin 1 as markers for metastasis. We demonstrate that combining proteomic and genomic approaches not only provides a rapid, robust, and sensitive platform to elucidate the molecular mechanisms underlying gastric cancer metastasis but also may identify candidate diagnostic markers and therapeutic targets.

Biomarkers, Tumor↗

Establishment of a CRISPR-Cas9 Library for Indica Rice and Identification of OsOPR5 (LOC_Os06g11210) as a Regulator of Root Architecture.

Functional characterization of a large number of rice genes remains a major challenge despite the availability of genome sequences and large-scale transcriptomic datasets. CRISPR-Cas9 library is a powerful approach for high-throughput targeted mutagenesis; however, its application in indica rice cultivars remains limited due to low transformation and regeneration efficiencies. In this study, we developed a CRISPR-Cas9 library targeting 12,000 rice genes and evaluated its utility for functional genomics in the indica cultivar MTU-1010. Sanger sequencing and NGS analysis of the plasmid library revealed high sgRNA coverage and more than 80% accuracy. Transformation of the developed library into the indica cultivar MTU-1010 resulted in a high target editing efficiency, with 90% of analyzed transgenic plants carrying mutations at the intended target site. Functional analysis of one homozygous mutant identified a previously uncharacterized role for OsOPR5 (LOC_Os06g11210), a member of the 12-oxophytodienoate reductase family in root architecture. The opr5 mutants exhibited significant reductions in lateral root number, seminal and crown root number, and root length, demonstrating that OsOPR5 positively regulates root system architecture in rice. Notably, endogenous jasmonic acid (JA) and JA-isoleucine levels were not significantly altered in the mutant, suggesting potential functional specialization or redundancy among rice OPR family members for JA accumulation. The root system architecture is a key determinant of water and nutrient acquisition; our results suggest that OsOPR5 may play an important role in adaptation under adverse environmental conditions. Collectively, this study establishes an efficient genome-editing platform for indica rice and identifies OsOPR5 as a novel regulator of root development.

Oryza↗

"Per cell" normalization method for mRNA measurement by quantitative PCR and microarrays.

BACKGROUND: Transcriptome data from quantitative PCR (Q-PCR) and DNA microarrays are typically obtained from a fixed amount of RNA collected per sample. Therefore, variations in tissue cellularity and RNA yield across samples in an experimental series compromise accurate determination of the absolute level of each mRNA species per cell in any sample. Since mRNAs are copied from genomic DNA, the simplest way to express mRNA level would be as copy number per template DNA, or more practically, as copy number per cell. RESULTS: Here we report a method (designated the "Percellome" method) for normalizing the expression of mRNA values in biological samples. It provides a "per cell" readout in mRNA copy number and is applicable to both quantitative PCR (Q-PCR) and DNA microarray studies. The genomic DNA content of each sample homogenate was measured from a small aliquot to derive the number of cells in the sample. A cocktail of five external spike RNAs admixed in a dose-graded manner (dose-graded spike cocktail; GSC) was prepared and added to each homogenate in proportion to its DNA content. In this way, the spike mRNAs represented absolute copy numbers per cell in the sample. The signals from the five spike mRNAs were used as a dose-response standard curve for each sample, enabling us to convert all the signals measured to copy numbers per cell in an expression profile-independent manner. A series of samples was measured by Q-PCR and Affymetrix GeneChip microarrays using this Percellome method, and the results showed up to 90 % concordance. CONCLUSION: Percellome data can be compared directly among samples and among different studies, and between different platforms, without further normalization. Therefore, "percellome" normalization can serve as a standard method for exchanging and comparing data across different platforms and among different laboratories.

Animals↗

Generation and validation of a Myh11Dre-Spp1Cre intersectional mouse model for lineage tracing of disease-associated smooth muscle cell states.

BACKGROUND: Phenotypic modulation of vascular smooth muscle cells (VSMCs) is a hallmark of vascular remodeling and cardiovascular disease. Recent lineage-tracing and single-cell transcriptomic studies have identified secreted phosphoprotein 1 (SPP1) as a prominent marker associated with disease-associated VSMC states, particularly those linked to fibrotic remodeling and vascular calcification. However, the cellular origins and fate of SPP1-associated VSMC populations remain incompletely understood. METHODS AND RESULTS: We generated a novel Spp1-rSTOPr-Cre (Spp1Cre) knock-in mouse line in which Cre recombinase is expressed from the endogenous Spp1 locus following Dre-mediated excision of a rox-flanked transcriptional STOP cassette. Correct targeting of the knock-in allele was validated by internal, 5' junction, 3' junction, and long-range PCR analyses, as well as Sanger sequencing. To establish an intersectional lineage-tracing strategy, Spp1Cre mice were crossed with Myh11DreERT2 and Rosa26-RSR-LSL-tdTomato-LSL-eGFP reporter mice, enabling permanent labeling of VSMC-derived populations following activation of the endogenous Spp1 locus. Under physiological conditions, eGFP-positive cells were detected at low frequency within the vascular wall and were predominantly negative for the contractile markers ACTA2 and MYH11. As a proof-of-principle application, eGFP-positive cells markedly expanded within atherosclerotic lesions induced by AAV-PCSK9D377Y and high-fat diet feeding. These lineage-traced cells remained largely ACTA2- and MYH11-negative, consistent with a modulated phenotype. Notably, only a minority of eGFP-positive cells expressed SPP1 or fibronectin at the time of analysis, demonstrating the utility of permanent lineage tracing for tracking cells with a history of endogenous Spp1 activation during vascular remodeling. CONCLUSION: We report the generation and validation of a novel Myh11Dre-Spp1Cre intersectional mouse model for lineage tracing of VSMC-derived populations that have activated the endogenous Spp1 locus. This genetic resource provides a valuable platform for investigating the origin, fate, and phenotypic evolution of Spp1-associated VSMC populations during vascular remodeling and cardiovascular disease.

Animals↗

Investigation of protein functions through data-mining on integrated human transcriptome database, H-Invitational database (H-InvDB).

H-Invitational Database (H-InvDB; ) is a human transcriptome database, containing integrative annotation of 41,118 full-length cDNA clones originated from 21,037 loci. H-InvDB is a product of the H-Invitational project, an international collaboration to systematically and functionally validate human genes by analysis of a unique set of high quality full-length cDNA clones using automatic annotation and human curation under unified criteria. Here, 19,574 proteins encoded by these cDNAs were classified into 11,709 function-known and 7865 function-unknown hypothetical proteins by similarity with protein databases and motif prediction (InterProScan). The proportion of "hypothetical proteins" in H-InvDB was as high as 40.4%. In this study, we thus conducted data-mining in H-InvDB with the aim of assigning advanced functional annotations to those hypothetical proteins. First, by data-mining in the H-InvDB version of GTOP, we identified 337 SCOP domains within 7865 H-Inv hypothetical proteins. Second, by data-mining of predicted subcellular localization by SOSUI and TMHMM in H-InvDB, we found 1032 transmembrane proteins within H-Inv hypothetical proteins. These results clearly demonstrate that structural prediction is effective for functional annotation of proteins with unknown functions. All the data in H-InvDB are shown in two main views, the cDNA view and the Locus view, and five auxiliary databases with web-based viewers; DiseaseInfo Viewer, H-ANGEL, Clustering Viewer, G-integra and TOPO Viewer; the data also are provided as flat files and XML files. The data consists of descriptions of their gene structures, novel alternative splicing isoforms, functional RNAs, functional domains, subcellular localizations, metabolic pathways, predictions of protein 3D structure, mapping of SNPs and microsatellite repeat motifs in relation with orphan diseases, gene expression profiling, and comparisons with mouse full-length cDNAs in the context of molecular evolution. This unique integrative platform for conducting in silico data-mining represents a substantial contribution to resources required for the exploration of human biology and pathology.

Amino Acid Sequence↗