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MetWare Ltd transcriptome sequencing
Transcriptome Sequencing, supplied by MetWare Ltd, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/transcriptome+sequencing/transcriptome+sequencing/pmc09555243-60-7-17
Average 90 stars, based on 1 article reviews
transcriptome sequencing - by Bioz Stars, 2026-11
90/100 stars

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Article Title: Transcriptome and Metabolome Analyses of Short-Term Responses of Populus talassica × Populus euphratica to Leaf Damage.
Article Snippet: High-throughput transcriptome sequencing and secondary metabolome analyses were performed by Metware Biotechnology Co., Ltd., Wuhan, China.

Sequencing:

Article Title: Tea polyphenol mediated CsMYB77 regulation of CsPOD44 to promote tea plant ( Camellia sinensis ) root drought resistance
Article Snippet: .. Twelve tea root samples from the four experimental treatments were sent to Metware Biotechnology Co., Ltd. (China) for transcriptome sequencing, with three biological replicates per group. ..

Article Title: Exploring the Influence Mechanism of Low/High Temperatures on Carotenoid Production in Sporobolomyces pararoseus: Insights From Physiological and Transcriptomic Analyses.
Article Snippet: Carotenoids, a class of lipid‐soluble isoprenoid pigments, play essential roles in determining coloration and enhancing nutritional value across various food products.. Sporobolomyces pararoseus has emerged as a promising microbial platform for industrial‐scale biosynthesis of high‐value carotenoids, particularly β‐carotene, torulene, and torularhodin.. The study evaluated the specific impacts of low and high temperatures on carotenoid production in S. pararoseus.

Article Title: Bamboo Leaf Flavonoids from Phyllostachys glauca McClure Suppress the Progression of Alzheimer's Disease Induced by Circadian Rhythm Disruption Through Regulating Hif3α/Rab7/TNFα/IL1β Pathway.
Article Snippet: Nuclei were stained with DAPI, and fluorescence images were captured using an FV10i microscope (Olympus, Japan) with appropriate filters for DAPI (nuclei), Alexa Fluor 488 (GFAP), and Alexa Fluor 594 (Iba-1). n = 5 per group. .. Transcriptome sequencing was outsourced to Wuhan MetWare Biotechnology Inc. (Wuhan, China). .. Eight brain tissue samples were randomly selected from each group: normal control, normal CD (no treatment), normal (no CD with treatment), normal CD (with treatment), AD control, AD (no CD with treatment), AD CD (no treatment), and AD CD (with treatment). mRNA with polyA tails was isolated from total RNA using Oligo(dT) magnetic beads (Solarbio).

Article Title: Identification and functional analysis of terpene synthases revealing the aroma variations among different Chrysanthemum indicum var. aromaticum ploidy germplasm.
Article Snippet: In both natural and cultivated populations of Chrysanthemum indicum var. aromaticum (CIA), diploid and tetraploid germplasms coexist, yet the comparative aroma profiles and their biosynthetic mechanisms remain poorly characterized.. This study used a combination of ultra-fast gas-phase electronic nose, widely targeted flavor-omics and sensory evaluation assays, and identified 15 core aroma compounds in diploid CIA and 10 in tetraploid CIA.. Notably, the aroma differences between these two groups were primarily attributed to variations in terpenoids.

Article Title: Deciphering Grape Berry Peel Resistance to Botrytis cinerea : A Transcriptomic and Metabolomic Perspective.
Article Snippet: The fruit peel serves as the frontline defense of grapes against pathogens like Botrytis cinerea, yet its defense mechanisms remain poorly understood.. This study reveals novel resistance mechanisms underlying peel immunity through comparative transcriptomic and metabolomic analysis of Vitis amurensis “Bei Binghong” (BH) and V. vinifera “Red Globe” (RG).. The analysis identified 1277 differentially expressed genes (DEGs) and 38 differentially accumulated metabolites (DAMs), primarily associated with secondary metabolic processes and plant hormone signaling pathways.

Article Title: Deletion of GhSCY2D Causes Impaired Chloroplast Development and Temperature-Dependent Leaf Yellowing in Cotton (Gossypium hirsutum L.).
Article Snippet: Transcriptome sequencing and analysis were performed by Metware. .. Transcriptome sequencing and analysis were performed by Metware. .. For qRT‐PCR, total RNA was extracted using the Plant RNA EASYspin Plus Kit (Aidlab Biotechnologies Co. Ltd.).

RNA Extraction:

Article Title: Exploring the Influence Mechanism of Low/High Temperatures on Carotenoid Production in Sporobolomyces pararoseus: Insights From Physiological and Transcriptomic Analyses.
Article Snippet: Carotenoids, a class of lipid‐soluble isoprenoid pigments, play essential roles in determining coloration and enhancing nutritional value across various food products.. Sporobolomyces pararoseus has emerged as a promising microbial platform for industrial‐scale biosynthesis of high‐value carotenoids, particularly β‐carotene, torulene, and torularhodin.. The study evaluated the specific impacts of low and high temperatures on carotenoid production in S. pararoseus.



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Transcriptomic and TME characteristics associated with ECMSig in TCGA-GBM cohort (A) Volcano plot showing DEGs between ECMSig-high and ECMSig-low groups. Red dots: upregulated in high-risk; blue dots: upregulated in low-risk. Benjamini-Hochberg adjusted. (B) Gene set enrichment analysis (GSEA) plots showing enrichment of hallmark pathways. Pathways enriched in ECMSig-high and ECMSig-low groups are shown with their running enrichment scores (ESs) and ranked gene lists. Benjamini-Hochberg adjusted. (C) Heatmap showing the activity scores of selected oncogenic and tumor-related signaling pathways (rows) across TCGA-GBM samples (columns), annotated by ECMSig group and ECMSig score. Red indicates high activity, blue indicates low activity. ∗ p < 0.05. Wilcoxon signed-rank test. (D) Heatmap depicting the estimated infiltration levels of various immune and stromal cell types (rows) in TCGA-GBM samples (columns), stratified by ECMSig group and score. Red indicates high infiltration, blue indicates low infiltration. Cells significantly highly infiltrated in ECMSig-high are labeled in red, and those high in ECMSig-low group are in blue. ∗q < 0.05, ∗∗q < 0.01, ∗∗∗q < 0.001. Wilcoxon signed-rank test. Benjamini-Hochberg adjusted. (E and F) Scatterplots showing the spearman correlation between ECMSig score and (E) Macrophage_XCELL infiltration score and (F) immune_score_XCELL. The blue line represents the linear regression fit with 95% confidence interval bands. Spearman correlation test.

Journal: iScience

Article Title: Multi-omics profiling-derived signature links cellular ecosystem to glioblastoma prognosis

doi: 10.1016/j.isci.2026.115982

Figure Lengend Snippet: Transcriptomic and TME characteristics associated with ECMSig in TCGA-GBM cohort (A) Volcano plot showing DEGs between ECMSig-high and ECMSig-low groups. Red dots: upregulated in high-risk; blue dots: upregulated in low-risk. Benjamini-Hochberg adjusted. (B) Gene set enrichment analysis (GSEA) plots showing enrichment of hallmark pathways. Pathways enriched in ECMSig-high and ECMSig-low groups are shown with their running enrichment scores (ESs) and ranked gene lists. Benjamini-Hochberg adjusted. (C) Heatmap showing the activity scores of selected oncogenic and tumor-related signaling pathways (rows) across TCGA-GBM samples (columns), annotated by ECMSig group and ECMSig score. Red indicates high activity, blue indicates low activity. ∗ p < 0.05. Wilcoxon signed-rank test. (D) Heatmap depicting the estimated infiltration levels of various immune and stromal cell types (rows) in TCGA-GBM samples (columns), stratified by ECMSig group and score. Red indicates high infiltration, blue indicates low infiltration. Cells significantly highly infiltrated in ECMSig-high are labeled in red, and those high in ECMSig-low group are in blue. ∗q < 0.05, ∗∗q < 0.01, ∗∗∗q < 0.001. Wilcoxon signed-rank test. Benjamini-Hochberg adjusted. (E and F) Scatterplots showing the spearman correlation between ECMSig score and (E) Macrophage_XCELL infiltration score and (F) immune_score_XCELL. The blue line represents the linear regression fit with 95% confidence interval bands. Spearman correlation test.

Article Snippet: The single-cell transcriptomic sequencing dataset utilizing technology from the 10X Genomics platform was available under the accession number GEO: GSE182109 at the Gene Expression Omnibus (GEO) repository.

Techniques: Activity Assay, Protein-Protein interactions, Labeling

Single-cell RNA sequencing analysis revealing ECMSig expression across cell types and identification of prognostically relevant cell states in GBM (A) UMAP visualization of major cell types identified in GBM scRNA-seq data. (B) Dot plot showing the scaled average expression (color intensity) and percentage of cells expressing (dot size) canonical marker genes for each major cell type. (C) Dot plot showing the scaled average expression and percentage of cells expressing the seven ECMSig genes across major cell types. (D) UMAP plots showing the expression levels of individual ECMSig genes and overall ECMSig score across all cells. (E–G) UMAP plots illustrating Scissor-identified prognostically unfavorable (Scissor_Pos, red dashed circle) and favorable (Scissor_Neg, blue dashed circle; Scissor_Others, gray) cell subpopulations within (E) tumor cells, (F) myeloid cells, and (G) endothelial cells. (H–K) Violin plots comparing ECMSig scores among tumor cells grouped by Scissor status (H) and tumor type (I), and myeloid cells (J) or endothelial cells (K) grouped by Scissor status. ∗∗∗∗ p < 0.0001. Wilcoxon signed-rank test. (L) Dot plot showing differentially expressed marker genes between myeloid Scissor_Pos and other myeloid cells. Dot size indicates the fraction of cells in the group expressing the gene; color indicates average expression level.

Journal: iScience

Article Title: Multi-omics profiling-derived signature links cellular ecosystem to glioblastoma prognosis

doi: 10.1016/j.isci.2026.115982

Figure Lengend Snippet: Single-cell RNA sequencing analysis revealing ECMSig expression across cell types and identification of prognostically relevant cell states in GBM (A) UMAP visualization of major cell types identified in GBM scRNA-seq data. (B) Dot plot showing the scaled average expression (color intensity) and percentage of cells expressing (dot size) canonical marker genes for each major cell type. (C) Dot plot showing the scaled average expression and percentage of cells expressing the seven ECMSig genes across major cell types. (D) UMAP plots showing the expression levels of individual ECMSig genes and overall ECMSig score across all cells. (E–G) UMAP plots illustrating Scissor-identified prognostically unfavorable (Scissor_Pos, red dashed circle) and favorable (Scissor_Neg, blue dashed circle; Scissor_Others, gray) cell subpopulations within (E) tumor cells, (F) myeloid cells, and (G) endothelial cells. (H–K) Violin plots comparing ECMSig scores among tumor cells grouped by Scissor status (H) and tumor type (I), and myeloid cells (J) or endothelial cells (K) grouped by Scissor status. ∗∗∗∗ p < 0.0001. Wilcoxon signed-rank test. (L) Dot plot showing differentially expressed marker genes between myeloid Scissor_Pos and other myeloid cells. Dot size indicates the fraction of cells in the group expressing the gene; color indicates average expression level.

Article Snippet: The single-cell transcriptomic sequencing dataset utilizing technology from the 10X Genomics platform was available under the accession number GEO: GSE182109 at the Gene Expression Omnibus (GEO) repository.

Techniques: Single Cell, RNA Sequencing, Expressing, Marker

Spatial transcriptomic analysis revealing co-localization of ECMSig, hypoxia, Scissor-Positive cells, and pericytes in GBM (A) Spatial feature plots for four GBM samples. Each row represents a sample. Columns show spatial heatmaps of: ECMSig score, hypoxia signature score, tumor Scissor_Pos signature score, myeloid Scissor_Pos signature score, endothelial Scissor Pos signature score, and pericyte marker signature score. Color scale indicates scaled expression or score (low to high). Each dot represents a spatial barcoded spot.

Journal: iScience

Article Title: Multi-omics profiling-derived signature links cellular ecosystem to glioblastoma prognosis

doi: 10.1016/j.isci.2026.115982

Figure Lengend Snippet: Spatial transcriptomic analysis revealing co-localization of ECMSig, hypoxia, Scissor-Positive cells, and pericytes in GBM (A) Spatial feature plots for four GBM samples. Each row represents a sample. Columns show spatial heatmaps of: ECMSig score, hypoxia signature score, tumor Scissor_Pos signature score, myeloid Scissor_Pos signature score, endothelial Scissor Pos signature score, and pericyte marker signature score. Color scale indicates scaled expression or score (low to high). Each dot represents a spatial barcoded spot.

Article Snippet: The single-cell transcriptomic sequencing dataset utilizing technology from the 10X Genomics platform was available under the accession number GEO: GSE182109 at the Gene Expression Omnibus (GEO) repository.

Techniques: Marker, Expressing

(A) Heatmap of DEG cluster analysis for DT and CK transcriptomes. The black dendrogram shows hierarchical clustering of samples and genes with similar expression patterns. The color scale represents normalized gene expression levels (yellow/orange = high expression, blue = low expression), demonstrating clear separation between CK and DT groups. (B) Volcano plot of differentially expressed genes. Orange dots indicate up-regulated genes (log 2 FC > 1, p < 0.05), blue dots indicate down-regulated genes (log 2 FC < −1, p < 0.05), and gray dots indicate non-significant genes.

Journal: PeerJ

Article Title: A cellular view of drought adaptation in sugarcane: multi-omics integration reveals a quadruple module network linking water regulation, oxidative defense, cell wall remodeling, and cell cycle regulation

doi: 10.7717/peerj.21396

Figure Lengend Snippet: (A) Heatmap of DEG cluster analysis for DT and CK transcriptomes. The black dendrogram shows hierarchical clustering of samples and genes with similar expression patterns. The color scale represents normalized gene expression levels (yellow/orange = high expression, blue = low expression), demonstrating clear separation between CK and DT groups. (B) Volcano plot of differentially expressed genes. Orange dots indicate up-regulated genes (log 2 FC > 1, p < 0.05), blue dots indicate down-regulated genes (log 2 FC < −1, p < 0.05), and gray dots indicate non-significant genes.

Article Snippet: The transcriptome sequences and annotation information of Saccharum spp. suspension cells under drought stress determined in this study have been uploaded to the National Center for Biotechnology Information ( PRJNA1403770 ) for access by relevant researchers.

Techniques: Expressing, Gene Expression

(A) The abscissa indicates the selected DEGs related to drought stress in sugarcane, and the ordinate indicates the log 2 fold change of gene expression measured by RNA-seq (orange bars) and RT-qPCR (blue bars). The column height represents the differential expression multiple, and the smaller the height difference between orange and blue bars, the more consistent the expression trend between the two methods. (B) Correlation analysis of log 2 fold change values from RNA-seq ( x -axis) and RT-qPCR ( y -axis). Each blue square represents one DEG. The linear regression equation and R 2 value ( R 2 = 0.97581) indicate a high positive correlation between the two techniques, verifying the reliability of the transcriptome sequencing data.

Journal: PeerJ

Article Title: A cellular view of drought adaptation in sugarcane: multi-omics integration reveals a quadruple module network linking water regulation, oxidative defense, cell wall remodeling, and cell cycle regulation

doi: 10.7717/peerj.21396

Figure Lengend Snippet: (A) The abscissa indicates the selected DEGs related to drought stress in sugarcane, and the ordinate indicates the log 2 fold change of gene expression measured by RNA-seq (orange bars) and RT-qPCR (blue bars). The column height represents the differential expression multiple, and the smaller the height difference between orange and blue bars, the more consistent the expression trend between the two methods. (B) Correlation analysis of log 2 fold change values from RNA-seq ( x -axis) and RT-qPCR ( y -axis). Each blue square represents one DEG. The linear regression equation and R 2 value ( R 2 = 0.97581) indicate a high positive correlation between the two techniques, verifying the reliability of the transcriptome sequencing data.

Article Snippet: The transcriptome sequences and annotation information of Saccharum spp. suspension cells under drought stress determined in this study have been uploaded to the National Center for Biotechnology Information ( PRJNA1403770 ) for access by relevant researchers.

Techniques: Gene Expression, RNA Sequencing, Quantitative RT-PCR, Quantitative Proteomics, Expressing, Sequencing

Transcriptomic profiling of the pretreatment biopsy sample. RNA-seq was used to obtain gene expression profiles of tumor samples. A global embedding of cancer types was calculated by transforming these gene expression profiles using PCA, followed by a t-SNE transformation to obtain a 2-dimensional approximate “map” of transcriptional similarity. The new patient sample is projected onto a pre-calculated PCA embedding space based on historical samples analyzed at Caris. Its proximity to other samples on the map reflects shared transcriptional programs with these samples. Only the immediate neighborhoods of samples should be considered for further interpretation since t-SNE embeddings do not preserve similarity correlations over long distances. Each point on the t-SNE map represents a different sample. The target symbol represents the present patient’s pretreatment biopsy sample. PCA, principal component analysis; t-SNE, t-stochastic neighbor embedding.

Journal: Therapeutic Advances in Medical Oncology

Article Title: Lineage infidelity in FH-deficient RCC with secondary somatic alterations: a case report and implications for diagnosis and treatment

doi: 10.1177/17588359261456676

Figure Lengend Snippet: Transcriptomic profiling of the pretreatment biopsy sample. RNA-seq was used to obtain gene expression profiles of tumor samples. A global embedding of cancer types was calculated by transforming these gene expression profiles using PCA, followed by a t-SNE transformation to obtain a 2-dimensional approximate “map” of transcriptional similarity. The new patient sample is projected onto a pre-calculated PCA embedding space based on historical samples analyzed at Caris. Its proximity to other samples on the map reflects shared transcriptional programs with these samples. Only the immediate neighborhoods of samples should be considered for further interpretation since t-SNE embeddings do not preserve similarity correlations over long distances. Each point on the t-SNE map represents a different sample. The target symbol represents the present patient’s pretreatment biopsy sample. PCA, principal component analysis; t-SNE, t-stochastic neighbor embedding.

Article Snippet: To confirm the diagnosis, molecular testing (next-generation sequencing (NGS); whole exome and whole transcriptome sequencing, Caris Life Sciences (Irving, Texas, United States)) revealed an S419P mutation in the FH gene, along with pathogenic mutations in KMT2A , NF2 , and TP53 , discussed in greater detail below.

Techniques: RNA Sequencing, Gene Expression, Transformation Assay