Review




Structured Review

Broad Institute Inc aml cell line transcriptomics data
Overview of the conducted study. <t>AML</t> glycosylation was explored on the level of glycomics (GPST datasets) and <t>transcriptomics</t> (GSE and DepMap datasets). Based on the depicted datasets originating from cell lines and primary cells we sought to explore cellular glycosylation, involved GSTs, and responsible TFs
Aml Cell Line Transcriptomics Data, supplied by Broad Institute Inc, 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/cell+transcriptomic+data/pmc09926860-40-0-14?v=Broad+Institute+Inc
Average 90 stars, based on 1 article reviews
aml cell line transcriptomics data - by Bioz Stars, 2026-08
90/100 stars

Images

1) Product Images from "Transcriptionally imprinted glycomic signatures of acute myeloid leukemia"

Article Title: Transcriptionally imprinted glycomic signatures of acute myeloid leukemia

Journal: Cell & Bioscience

doi: 10.1186/s13578-023-00981-0

Overview of the conducted study. AML glycosylation was explored on the level of glycomics (GPST datasets) and transcriptomics (GSE and DepMap datasets). Based on the depicted datasets originating from cell lines and primary cells we sought to explore cellular glycosylation, involved GSTs, and responsible TFs
Figure Legend Snippet: Overview of the conducted study. AML glycosylation was explored on the level of glycomics (GPST datasets) and transcriptomics (GSE and DepMap datasets). Based on the depicted datasets originating from cell lines and primary cells we sought to explore cellular glycosylation, involved GSTs, and responsible TFs

Techniques Used: Glycoproteomics

Glycomic overview of various AML cell lines. a PCA of glycosylation features derived from glycomics data of 19 AML cell lines. Individual cell lines are annotated and colored by their FAB classifications as assigned earlier . b The associated score plot depicts considered glycan features, which are linked to their respective glycan class ( N -, O -, and GSL) by color (purple, orange, and green) and symbol (triangle, square, and circle). In addition, arrows indicate features that are linked to a specific type of fucosylation. c Radar plots are showing the differences in glycosylation features between AML classes M5 and M6. Again, these features are subdivided into their respective classes based on color and symbols. Data on all AML cell lines were z-transformed prior to visualizing differences between FAB classes in these radar plots. d Spearman correlation of selected glycosylation features between the different glycan classes. Thick connective lines indicate a good correlation whereas thin connective lines show less correlation. Correlation values are depicted
Figure Legend Snippet: Glycomic overview of various AML cell lines. a PCA of glycosylation features derived from glycomics data of 19 AML cell lines. Individual cell lines are annotated and colored by their FAB classifications as assigned earlier . b The associated score plot depicts considered glycan features, which are linked to their respective glycan class ( N -, O -, and GSL) by color (purple, orange, and green) and symbol (triangle, square, and circle). In addition, arrows indicate features that are linked to a specific type of fucosylation. c Radar plots are showing the differences in glycosylation features between AML classes M5 and M6. Again, these features are subdivided into their respective classes based on color and symbols. Data on all AML cell lines were z-transformed prior to visualizing differences between FAB classes in these radar plots. d Spearman correlation of selected glycosylation features between the different glycan classes. Thick connective lines indicate a good correlation whereas thin connective lines show less correlation. Correlation values are depicted

Techniques Used: Glycoproteomics, Derivative Assay, Transformation Assay

Correlation of glycosylation features of N -, O -, and GSL-glycans with the expression of selected TFs in AML cell lines. Correlation coefficients were obtained by Spearman analysis and are indicated by color as indicated in the legend. Of note, due to rather weak correlations of ST6GALs and glycomics data, we did not include these GSTs in our overview. Significant values are marked with * (p ≤ 0.05), ** (p ≤ 0.01,) and *** (p ≤ 0.001). Correlation coefficients and p-values are listed in the Additional file : Table S9
Figure Legend Snippet: Correlation of glycosylation features of N -, O -, and GSL-glycans with the expression of selected TFs in AML cell lines. Correlation coefficients were obtained by Spearman analysis and are indicated by color as indicated in the legend. Of note, due to rather weak correlations of ST6GALs and glycomics data, we did not include these GSTs in our overview. Significant values are marked with * (p ≤ 0.05), ** (p ≤ 0.01,) and *** (p ≤ 0.001). Correlation coefficients and p-values are listed in the Additional file : Table S9

Techniques Used: Glycoproteomics, Expressing

Differences in glycan signatures of M5 and M6 AML cell lines as well as corresponding GST and TF expression. M5 and M6 classes are presented as grey and brown rectangles, respectively. GSTs displayed in the figure present a positive correlation with the corresponding glycosylation feature. The underlined TFs correlate with the glycosylation features. The underlined TFs colored in red are positively correlated with GSTs
Figure Legend Snippet: Differences in glycan signatures of M5 and M6 AML cell lines as well as corresponding GST and TF expression. M5 and M6 classes are presented as grey and brown rectangles, respectively. GSTs displayed in the figure present a positive correlation with the corresponding glycosylation feature. The underlined TFs correlate with the glycosylation features. The underlined TFs colored in red are positively correlated with GSTs

Techniques Used: Glycoproteomics, Expressing

GST and TF expression in primary AML cells. a Determination of the matrix correlation coefficient RV2 (0.49) between expression patterns observed in cell lines and primary samples. b Spearman correlation of selected GSTs with TFs in AML cell lines (left) and primary AML cells (right). c Comparison of the expression of selected GSTs and TFs in primary AML cells grouped by FAB classification. Significances were assessed by one-way ANOVA followed by a Tukey post-hoc test. Significant values are marked with * (p ≤ 0.05), ** (p ≤ 0.01), *** (p ≤ 0.001), and **** (p ≤ 0.0001)
Figure Legend Snippet: GST and TF expression in primary AML cells. a Determination of the matrix correlation coefficient RV2 (0.49) between expression patterns observed in cell lines and primary samples. b Spearman correlation of selected GSTs with TFs in AML cell lines (left) and primary AML cells (right). c Comparison of the expression of selected GSTs and TFs in primary AML cells grouped by FAB classification. Significances were assessed by one-way ANOVA followed by a Tukey post-hoc test. Significant values are marked with * (p ≤ 0.05), ** (p ≤ 0.01), *** (p ≤ 0.001), and **** (p ≤ 0.0001)

Techniques Used: Expressing, Comparison



Similar Products

86
Human Protein Atlas cell transcriptomic data
Cell Transcriptomic Data, supplied by Human Protein Atlas, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/cell+transcriptomic+data/pm42297981-57-1-6?v=Human+Protein+Atlas
Average 86 stars, based on 1 article reviews
cell transcriptomic data - by Bioz Stars, 2026-08
86/100 stars
  Buy from Supplier

86
Human Protein Atlas single cell transcriptomic data
Single Cell Transcriptomic Data, supplied by Human Protein Atlas, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/cell+transcriptomic+data/pm42309270-222-24-29?v=Human+Protein+Atlas
Average 86 stars, based on 1 article reviews
single cell transcriptomic data - by Bioz Stars, 2026-08
86/100 stars
  Buy from Supplier

86
Muris Inc single cell transcriptomic data
Single Cell Transcriptomic Data, supplied by Muris Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/cell+transcriptomic+data/bio_rxiv__64898__2026__04__02__716053-74-14-24?v=Muris+Inc
Average 86 stars, based on 1 article reviews
single cell transcriptomic data - by Bioz Stars, 2026-08
86/100 stars
  Buy from Supplier

86
Human Protein Atlas single cell transcriptomics data
Single Cell Transcriptomics Data, supplied by Human Protein Atlas, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/cell+transcriptomic+data/pm41922271-72-24-29?v=Human+Protein+Atlas
Average 86 stars, based on 1 article reviews
single cell transcriptomics data - by Bioz Stars, 2026-08
86/100 stars
  Buy from Supplier

86
Shanghai Pudong Development Bank Co Ltd single cell transcriptomic data
<t>Single‐cell</t> <t>transcriptome</t> landscape in aging cohort. (A) UMAP visualization of cell‐type‐specific annotation among the aging cohort, showing 9 cell groups in different colors. (B) UMAP visualization of immune cell subpopulation annotation across different age groups, displaying 21 subpopulations in different colors. (C) The proportion of 21 different cell types across age groups.
Single Cell Transcriptomic Data, supplied by Shanghai Pudong Development Bank Co Ltd, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/cell+transcriptomic+data/pmc12961527-63-0-18?v=Shanghai+Pudong+Development+Bank+Co+Ltd
Average 86 stars, based on 1 article reviews
single cell transcriptomic data - by Bioz Stars, 2026-08
86/100 stars
  Buy from Supplier

86
Human Protein Atlas single cell transcriptome data
PTHrP expression in IPF and BLM-induced PF in humans. a Procedure for bioinformatics-based <t>transcriptome</t> analysis. b Identification of 714 commonly up- or downregulated genes in human IPF lungs using publicly available transcriptome datasets. c Top 9 activated gene sets identified by KEGG pathway analysis based on 714 common genes. d Identification of 5 genes through the intersection of genes related to soluble mediators, PTH synthesis, secretion, and action and 714 common genes. e Heatmap of PTHLH expression in normal and IPF samples. f PTHLH mRNA in normal and IPF samples. g Representative images of IF staining of PTHrP and quantification of the intensity of expression of PTHrP in human pulmonary interstitial fibrosis tissue microarrays from patients with IPF ( n = 23) and healthy donors ( n = 4). A magnified view of the region highlighted in the red box is shown. Scale bar: 50 μm and 100 μm (low magnification). a , b , d were created with BioRender.com. Data are shown as the mean ± SEM. P values were determined by two-tailed Student’s t test ( f , g ). *** P < 0.001
Single Cell Transcriptome Data, supplied by Human Protein Atlas, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/cell+transcriptomic+data/pmc12926227-43-16-22?v=Human+Protein+Atlas
Average 86 stars, based on 1 article reviews
single cell transcriptome data - by Bioz Stars, 2026-08
86/100 stars
  Buy from Supplier

96
Broad Clinical Labs cell transcriptomic data
Identification of core genes associated with macrophage immune training and heart failure. (A) Schematic overview of human-derived macrophage trained immunity model and <t>transcriptomic</t> profiling workflow ( GSE235897 ). (B) The volcano plot and (C) DEGs heatmap of hMDMs from trained (n=3) and untrained (n=3) samples in the macrophage-trained immunity dataset GSE235897 (|log2FC| ≥ 0.585, p < 0.05). (D) Sample clustering dendrogram of GSE135055 dataset based on gene expression profiles. (E) Scale-free topology fit index and (F) mean connectivity analysis across a range of soft-thresholding powers. (G) Cluster dendrogram of genes showing co-expression modules identified by WGCNA in database GSE135055 . (H) Module-trait heatmap values represent correlation coefficients between healthy controls and HF samples (* p < 0.05, ** p < 0.01). (I) Venn diagram showing the overlap among heart failure DEGs, trained-immunity DEGs, and WGCNA module genes.
Cell Transcriptomic Data, supplied by Broad Clinical Labs, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/cell+transcriptomic+data/pmc12957145-38-1-11?v=Broad+Clinical+Labs
Average 96 stars, based on 1 article reviews
cell transcriptomic data - by Bioz Stars, 2026-08
96/100 stars
  Buy from Supplier

86
Muris Inc murine single cell transcriptomic data
Identification of core genes associated with macrophage immune training and heart failure. (A) Schematic overview of human-derived macrophage trained immunity model and <t>transcriptomic</t> profiling workflow ( GSE235897 ). (B) The volcano plot and (C) DEGs heatmap of hMDMs from trained (n=3) and untrained (n=3) samples in the macrophage-trained immunity dataset GSE235897 (|log2FC| ≥ 0.585, p < 0.05). (D) Sample clustering dendrogram of GSE135055 dataset based on gene expression profiles. (E) Scale-free topology fit index and (F) mean connectivity analysis across a range of soft-thresholding powers. (G) Cluster dendrogram of genes showing co-expression modules identified by WGCNA in database GSE135055 . (H) Module-trait heatmap values represent correlation coefficients between healthy controls and HF samples (* p < 0.05, ** p < 0.01). (I) Venn diagram showing the overlap among heart failure DEGs, trained-immunity DEGs, and WGCNA module genes.
Murine Single Cell Transcriptomic Data, supplied by Muris Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/cell+transcriptomic+data/pm41651868-390-0-9?v=Muris+Inc
Average 86 stars, based on 1 article reviews
murine single cell transcriptomic data - by Bioz Stars, 2026-08
86/100 stars
  Buy from Supplier

Image Search Results


Single‐cell transcriptome landscape in aging cohort. (A) UMAP visualization of cell‐type‐specific annotation among the aging cohort, showing 9 cell groups in different colors. (B) UMAP visualization of immune cell subpopulation annotation across different age groups, displaying 21 subpopulations in different colors. (C) The proportion of 21 different cell types across age groups.

Journal: Aging Cell

Article Title: The Immune Cell Atlas of “Longevity Molecular Tag”: Identification of Principal Immune Cell Subsets and Their Underlying Molecular Regulatory Mechanisms

doi: 10.1111/acel.70431

Figure Lengend Snippet: Single‐cell transcriptome landscape in aging cohort. (A) UMAP visualization of cell‐type‐specific annotation among the aging cohort, showing 9 cell groups in different colors. (B) UMAP visualization of immune cell subpopulation annotation across different age groups, displaying 21 subpopulations in different colors. (C) The proportion of 21 different cell types across age groups.

Article Snippet: Single‐cell transcriptomic data from 56 healthy individuals aged from birth to over 90 years were acquired from the Shanghai Pudong Cohort ( NCT05206643 ) (Synapse: syn61609846) (Wang, Li, et al. ).

Techniques: Single Cell

Centenarian phenotype‐associated immune cell type analysis at single‐cell resolution. (A) UMAP visualization of cell‐type‐specific annotation among immune cells, showing 9 cell groups in different colors. (B) UMAP visualization of subcellular annotation among immune cell subpopulations, showing 21 subpopulations in different colors. (C) UMAP visualization of Scissor + and Scissor − cells. (D, E) Proportional fractions of identified cell types across Scissor +/− conditions among extracted immune cells.

Journal: Aging Cell

Article Title: The Immune Cell Atlas of “Longevity Molecular Tag”: Identification of Principal Immune Cell Subsets and Their Underlying Molecular Regulatory Mechanisms

doi: 10.1111/acel.70431

Figure Lengend Snippet: Centenarian phenotype‐associated immune cell type analysis at single‐cell resolution. (A) UMAP visualization of cell‐type‐specific annotation among immune cells, showing 9 cell groups in different colors. (B) UMAP visualization of subcellular annotation among immune cell subpopulations, showing 21 subpopulations in different colors. (C) UMAP visualization of Scissor + and Scissor − cells. (D, E) Proportional fractions of identified cell types across Scissor +/− conditions among extracted immune cells.

Article Snippet: Single‐cell transcriptomic data from 56 healthy individuals aged from birth to over 90 years were acquired from the Shanghai Pudong Cohort ( NCT05206643 ) (Synapse: syn61609846) (Wang, Li, et al. ).

Techniques: Single Cell

PTHrP expression in IPF and BLM-induced PF in humans. a Procedure for bioinformatics-based transcriptome analysis. b Identification of 714 commonly up- or downregulated genes in human IPF lungs using publicly available transcriptome datasets. c Top 9 activated gene sets identified by KEGG pathway analysis based on 714 common genes. d Identification of 5 genes through the intersection of genes related to soluble mediators, PTH synthesis, secretion, and action and 714 common genes. e Heatmap of PTHLH expression in normal and IPF samples. f PTHLH mRNA in normal and IPF samples. g Representative images of IF staining of PTHrP and quantification of the intensity of expression of PTHrP in human pulmonary interstitial fibrosis tissue microarrays from patients with IPF ( n = 23) and healthy donors ( n = 4). A magnified view of the region highlighted in the red box is shown. Scale bar: 50 μm and 100 μm (low magnification). a , b , d were created with BioRender.com. Data are shown as the mean ± SEM. P values were determined by two-tailed Student’s t test ( f , g ). *** P < 0.001

Journal: Signal Transduction and Targeted Therapy

Article Title: Parathyroid hormone–related protein is a therapeutic target in idiopathic pulmonary fibrosis

doi: 10.1038/s41392-026-02578-8

Figure Lengend Snippet: PTHrP expression in IPF and BLM-induced PF in humans. a Procedure for bioinformatics-based transcriptome analysis. b Identification of 714 commonly up- or downregulated genes in human IPF lungs using publicly available transcriptome datasets. c Top 9 activated gene sets identified by KEGG pathway analysis based on 714 common genes. d Identification of 5 genes through the intersection of genes related to soluble mediators, PTH synthesis, secretion, and action and 714 common genes. e Heatmap of PTHLH expression in normal and IPF samples. f PTHLH mRNA in normal and IPF samples. g Representative images of IF staining of PTHrP and quantification of the intensity of expression of PTHrP in human pulmonary interstitial fibrosis tissue microarrays from patients with IPF ( n = 23) and healthy donors ( n = 4). A magnified view of the region highlighted in the red box is shown. Scale bar: 50 μm and 100 μm (low magnification). a , b , d were created with BioRender.com. Data are shown as the mean ± SEM. P values were determined by two-tailed Student’s t test ( f , g ). *** P < 0.001

Article Snippet: To confirm the predominance of tissue-specific expression of PTHLH in human tissues, we reanalyzed publicly available single-cell transcriptome data provided by The Human Protein Atlas.

Techniques: Expressing, Staining, Two Tailed Test

Identification of core genes associated with macrophage immune training and heart failure. (A) Schematic overview of human-derived macrophage trained immunity model and transcriptomic profiling workflow ( GSE235897 ). (B) The volcano plot and (C) DEGs heatmap of hMDMs from trained (n=3) and untrained (n=3) samples in the macrophage-trained immunity dataset GSE235897 (|log2FC| ≥ 0.585, p < 0.05). (D) Sample clustering dendrogram of GSE135055 dataset based on gene expression profiles. (E) Scale-free topology fit index and (F) mean connectivity analysis across a range of soft-thresholding powers. (G) Cluster dendrogram of genes showing co-expression modules identified by WGCNA in database GSE135055 . (H) Module-trait heatmap values represent correlation coefficients between healthy controls and HF samples (* p < 0.05, ** p < 0.01). (I) Venn diagram showing the overlap among heart failure DEGs, trained-immunity DEGs, and WGCNA module genes.

Journal: Frontiers in Immunology

Article Title: Identification of MTURN as a trained immunity-related biomarker for heart failure via integrative transcriptomic machine learning analysis and experimental validation

doi: 10.3389/fimmu.2026.1739660

Figure Lengend Snippet: Identification of core genes associated with macrophage immune training and heart failure. (A) Schematic overview of human-derived macrophage trained immunity model and transcriptomic profiling workflow ( GSE235897 ). (B) The volcano plot and (C) DEGs heatmap of hMDMs from trained (n=3) and untrained (n=3) samples in the macrophage-trained immunity dataset GSE235897 (|log2FC| ≥ 0.585, p < 0.05). (D) Sample clustering dendrogram of GSE135055 dataset based on gene expression profiles. (E) Scale-free topology fit index and (F) mean connectivity analysis across a range of soft-thresholding powers. (G) Cluster dendrogram of genes showing co-expression modules identified by WGCNA in database GSE135055 . (H) Module-trait heatmap values represent correlation coefficients between healthy controls and HF samples (* p < 0.05, ** p < 0.01). (I) Venn diagram showing the overlap among heart failure DEGs, trained-immunity DEGs, and WGCNA module genes.

Article Snippet: For single-cell transcriptomic data, we accessed the SCP1303 project from the Broad Institute ( https://singlecell.broadinstitute.org/single_cell ), which includes raw scRNA-seq data from failing human hearts with dilated and hypertrophic cardiomyopathy.

Techniques: Derivative Assay, Gene Expression, Expressing

Five heart failure transcriptomic datasets were integrated with a macrophage-trained immunity model to identify immune-related biomarkers. Through DEGs analysis, WGCNA, CIBERSORT, and six machine learning algorithms, hub genes were prioritized with MTURN emerging as the top candidate. Its potential was further validated by scRNA-seq analysis, which confirmed MTURN enrichment in cardiac macrophages. Finally, MTURN expression was validated using previously published heart failure transcriptomic data and in vitro experiments.

Journal: Frontiers in Immunology

Article Title: Identification of MTURN as a trained immunity-related biomarker for heart failure via integrative transcriptomic machine learning analysis and experimental validation

doi: 10.3389/fimmu.2026.1739660

Figure Lengend Snippet: Five heart failure transcriptomic datasets were integrated with a macrophage-trained immunity model to identify immune-related biomarkers. Through DEGs analysis, WGCNA, CIBERSORT, and six machine learning algorithms, hub genes were prioritized with MTURN emerging as the top candidate. Its potential was further validated by scRNA-seq analysis, which confirmed MTURN enrichment in cardiac macrophages. Finally, MTURN expression was validated using previously published heart failure transcriptomic data and in vitro experiments.

Article Snippet: For single-cell transcriptomic data, we accessed the SCP1303 project from the Broad Institute ( https://singlecell.broadinstitute.org/single_cell ), which includes raw scRNA-seq data from failing human hearts with dilated and hypertrophic cardiomyopathy.

Techniques: Expressing, In Vitro