cell marker genes Search Results


opti-klear ™ live cell imaging buffer  (Marker Gene Technologies)
90
Marker Gene Technologies opti-klear ™ live cell imaging buffer
Opti Klear ™ Live Cell Imaging Buffer, supplied by Marker Gene Technologies, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Average 90 stars, based on 1 article reviews
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live cell fluorescent reactive oxygen species detection kit  (Marker Gene Technologies)
90
Marker Gene Technologies live cell fluorescent reactive oxygen species detection kit
Live Cell Fluorescent Reactive Oxygen Species Detection Kit, supplied by Marker Gene Technologies, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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live cell fluorescent reactive oxygen species detection kit - by Bioz Stars, 2026-06
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cell lysis buffer  (Marker Gene Technologies)
90
Marker Gene Technologies cell lysis buffer
Cell Lysis Buffer, supplied by Marker Gene Technologies, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Average 90 stars, based on 1 article reviews
cell lysis buffer - by Bioz Stars, 2026-06
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dapi fluorescent cell count normalization kit  (Marker Gene Technologies)
90
Marker Gene Technologies dapi fluorescent cell count normalization kit
Dapi Fluorescent Cell Count Normalization Kit, supplied by Marker Gene Technologies, 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/result/dapi fluorescent cell count normalization kit/product/Marker Gene Technologies
Average 90 stars, based on 1 article reviews
dapi fluorescent cell count normalization kit - by Bioz Stars, 2026-06
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schwann cell marker gene mgp  (Schmid GmbH)
90
Schmid GmbH schwann cell marker gene mgp
Validation of RNA-seq by droplet digital PCR, and gene expression profiling of <t>neuron-/Schwann</t> <t>cell-/fibroblast-specific</t> genes. (A) Heatmap showing Pearson correlations among samples in terms of the common logarithmic counts of genes with non-zero counts. Samples in each group correlated well with each other and the largest difference was between PAN and VC. (B) Principal component analysis (PCA). The regularized logarithmic transformation was applied to the normalized counts and PCA was performed on the top 500 most variable genes across samples. Three samples in each group were well clustered. (C,D) Validation of RNA-seq data using droplet digital PCR (ddPCR). Relative value of each group compared to iN (= 1.00) is shown. (C) Relative value of TPM was calculated for known neuron markers (TuJ1 and Prph), glial marker (Sox2), and fibroblast marker (Tnc). (D) Relative value of ddPCR expression data for the same 4 genes validated our RNA-seq data. Error bars represent standard error of the mean (SEM). (E) A heatmap for reportedly specific genes for pan-neuronal, synaptic, ion-channel, transcription factors, cytoskeletal reorganization, oligodendrocytes, Schwann cells, mesenchymal cells and mitosis. Red-white-blue spectrum indicates high-moderate-low expression level for each gene.
Schwann Cell Marker Gene Mgp, supplied by Schmid GmbH, 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/result/schwann cell marker gene mgp/product/Schmid GmbH
Average 90 stars, based on 1 article reviews
schwann cell marker gene mgp - by Bioz Stars, 2026-06
90/100 stars
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dyrect live-cell neutral lipid imaging kit  (Marker Gene Technologies)
90
Marker Gene Technologies dyrect live-cell neutral lipid imaging kit
Validation of RNA-seq by droplet digital PCR, and gene expression profiling of <t>neuron-/Schwann</t> <t>cell-/fibroblast-specific</t> genes. (A) Heatmap showing Pearson correlations among samples in terms of the common logarithmic counts of genes with non-zero counts. Samples in each group correlated well with each other and the largest difference was between PAN and VC. (B) Principal component analysis (PCA). The regularized logarithmic transformation was applied to the normalized counts and PCA was performed on the top 500 most variable genes across samples. Three samples in each group were well clustered. (C,D) Validation of RNA-seq data using droplet digital PCR (ddPCR). Relative value of each group compared to iN (= 1.00) is shown. (C) Relative value of TPM was calculated for known neuron markers (TuJ1 and Prph), glial marker (Sox2), and fibroblast marker (Tnc). (D) Relative value of ddPCR expression data for the same 4 genes validated our RNA-seq data. Error bars represent standard error of the mean (SEM). (E) A heatmap for reportedly specific genes for pan-neuronal, synaptic, ion-channel, transcription factors, cytoskeletal reorganization, oligodendrocytes, Schwann cells, mesenchymal cells and mitosis. Red-white-blue spectrum indicates high-moderate-low expression level for each gene.
Dyrect Live Cell Neutral Lipid Imaging Kit, supplied by Marker Gene Technologies, 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/result/dyrect live-cell neutral lipid imaging kit/product/Marker Gene Technologies
Average 90 stars, based on 1 article reviews
dyrect live-cell neutral lipid imaging kit - by Bioz Stars, 2026-06
90/100 stars
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visualization of cell typology with ‘canonical’ marker genes and ‘references’ from the hra  (CellGuide Ltd)
90
CellGuide Ltd visualization of cell typology with ‘canonical’ marker genes and ‘references’ from the hra
Validation of RNA-seq by droplet digital PCR, and gene expression profiling of <t>neuron-/Schwann</t> <t>cell-/fibroblast-specific</t> genes. (A) Heatmap showing Pearson correlations among samples in terms of the common logarithmic counts of genes with non-zero counts. Samples in each group correlated well with each other and the largest difference was between PAN and VC. (B) Principal component analysis (PCA). The regularized logarithmic transformation was applied to the normalized counts and PCA was performed on the top 500 most variable genes across samples. Three samples in each group were well clustered. (C,D) Validation of RNA-seq data using droplet digital PCR (ddPCR). Relative value of each group compared to iN (= 1.00) is shown. (C) Relative value of TPM was calculated for known neuron markers (TuJ1 and Prph), glial marker (Sox2), and fibroblast marker (Tnc). (D) Relative value of ddPCR expression data for the same 4 genes validated our RNA-seq data. Error bars represent standard error of the mean (SEM). (E) A heatmap for reportedly specific genes for pan-neuronal, synaptic, ion-channel, transcription factors, cytoskeletal reorganization, oligodendrocytes, Schwann cells, mesenchymal cells and mitosis. Red-white-blue spectrum indicates high-moderate-low expression level for each gene.
Visualization Of Cell Typology With ‘Canonical’ Marker Genes And ‘References’ From The Hra, supplied by CellGuide 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/result/visualization of cell typology with ‘canonical’ marker genes and ‘references’ from the hra/product/CellGuide Ltd
Average 90 stars, based on 1 article reviews
visualization of cell typology with ‘canonical’ marker genes and ‘references’ from the hra - by Bioz Stars, 2026-06
90/100 stars
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cell surface markers and chromosome or fusion gene detection tests  (srl inc)
90
srl inc cell surface markers and chromosome or fusion gene detection tests
Validation of RNA-seq by droplet digital PCR, and gene expression profiling of <t>neuron-/Schwann</t> <t>cell-/fibroblast-specific</t> genes. (A) Heatmap showing Pearson correlations among samples in terms of the common logarithmic counts of genes with non-zero counts. Samples in each group correlated well with each other and the largest difference was between PAN and VC. (B) Principal component analysis (PCA). The regularized logarithmic transformation was applied to the normalized counts and PCA was performed on the top 500 most variable genes across samples. Three samples in each group were well clustered. (C,D) Validation of RNA-seq data using droplet digital PCR (ddPCR). Relative value of each group compared to iN (= 1.00) is shown. (C) Relative value of TPM was calculated for known neuron markers (TuJ1 and Prph), glial marker (Sox2), and fibroblast marker (Tnc). (D) Relative value of ddPCR expression data for the same 4 genes validated our RNA-seq data. Error bars represent standard error of the mean (SEM). (E) A heatmap for reportedly specific genes for pan-neuronal, synaptic, ion-channel, transcription factors, cytoskeletal reorganization, oligodendrocytes, Schwann cells, mesenchymal cells and mitosis. Red-white-blue spectrum indicates high-moderate-low expression level for each gene.
Cell Surface Markers And Chromosome Or Fusion Gene Detection Tests, supplied by srl 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/result/cell surface markers and chromosome or fusion gene detection tests/product/srl inc
Average 90 stars, based on 1 article reviews
cell surface markers and chromosome or fusion gene detection tests - by Bioz Stars, 2026-06
90/100 stars
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compositions and methods for targeted enzymatic release of cell regulatory compounds  (Marker Gene Technologies)
90
Marker Gene Technologies compositions and methods for targeted enzymatic release of cell regulatory compounds
Validation of RNA-seq by droplet digital PCR, and gene expression profiling of <t>neuron-/Schwann</t> <t>cell-/fibroblast-specific</t> genes. (A) Heatmap showing Pearson correlations among samples in terms of the common logarithmic counts of genes with non-zero counts. Samples in each group correlated well with each other and the largest difference was between PAN and VC. (B) Principal component analysis (PCA). The regularized logarithmic transformation was applied to the normalized counts and PCA was performed on the top 500 most variable genes across samples. Three samples in each group were well clustered. (C,D) Validation of RNA-seq data using droplet digital PCR (ddPCR). Relative value of each group compared to iN (= 1.00) is shown. (C) Relative value of TPM was calculated for known neuron markers (TuJ1 and Prph), glial marker (Sox2), and fibroblast marker (Tnc). (D) Relative value of ddPCR expression data for the same 4 genes validated our RNA-seq data. Error bars represent standard error of the mean (SEM). (E) A heatmap for reportedly specific genes for pan-neuronal, synaptic, ion-channel, transcription factors, cytoskeletal reorganization, oligodendrocytes, Schwann cells, mesenchymal cells and mitosis. Red-white-blue spectrum indicates high-moderate-low expression level for each gene.
Compositions And Methods For Targeted Enzymatic Release Of Cell Regulatory Compounds, supplied by Marker Gene Technologies, 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/result/compositions and methods for targeted enzymatic release of cell regulatory compounds/product/Marker Gene Technologies
Average 90 stars, based on 1 article reviews
compositions and methods for targeted enzymatic release of cell regulatory compounds - by Bioz Stars, 2026-06
90/100 stars
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cell marker genes  (Raredon Resources)
86
Raredon Resources cell marker genes
Validation of RNA-seq by droplet digital PCR, and gene expression profiling of <t>neuron-/Schwann</t> <t>cell-/fibroblast-specific</t> genes. (A) Heatmap showing Pearson correlations among samples in terms of the common logarithmic counts of genes with non-zero counts. Samples in each group correlated well with each other and the largest difference was between PAN and VC. (B) Principal component analysis (PCA). The regularized logarithmic transformation was applied to the normalized counts and PCA was performed on the top 500 most variable genes across samples. Three samples in each group were well clustered. (C,D) Validation of RNA-seq data using droplet digital PCR (ddPCR). Relative value of each group compared to iN (= 1.00) is shown. (C) Relative value of TPM was calculated for known neuron markers (TuJ1 and Prph), glial marker (Sox2), and fibroblast marker (Tnc). (D) Relative value of ddPCR expression data for the same 4 genes validated our RNA-seq data. Error bars represent standard error of the mean (SEM). (E) A heatmap for reportedly specific genes for pan-neuronal, synaptic, ion-channel, transcription factors, cytoskeletal reorganization, oligodendrocytes, Schwann cells, mesenchymal cells and mitosis. Red-white-blue spectrum indicates high-moderate-low expression level for each gene.
Cell Marker Genes, supplied by Raredon Resources, 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/result/cell marker genes/product/Raredon Resources
Average 86 stars, based on 1 article reviews
cell marker genes - by Bioz Stars, 2026-06
86/100 stars
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exprofile human cell surface markers related gene qpcr array  (Genecopoeia)
N/A
The ExProfile human cell surface markers related gene qPCR array profiles the expression of 84 human genes related to cell surface markers. These genes are carefully chosen for their close correlation based on a thorough
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Image Search Results


Validation of RNA-seq by droplet digital PCR, and gene expression profiling of neuron-/Schwann cell-/fibroblast-specific genes. (A) Heatmap showing Pearson correlations among samples in terms of the common logarithmic counts of genes with non-zero counts. Samples in each group correlated well with each other and the largest difference was between PAN and VC. (B) Principal component analysis (PCA). The regularized logarithmic transformation was applied to the normalized counts and PCA was performed on the top 500 most variable genes across samples. Three samples in each group were well clustered. (C,D) Validation of RNA-seq data using droplet digital PCR (ddPCR). Relative value of each group compared to iN (= 1.00) is shown. (C) Relative value of TPM was calculated for known neuron markers (TuJ1 and Prph), glial marker (Sox2), and fibroblast marker (Tnc). (D) Relative value of ddPCR expression data for the same 4 genes validated our RNA-seq data. Error bars represent standard error of the mean (SEM). (E) A heatmap for reportedly specific genes for pan-neuronal, synaptic, ion-channel, transcription factors, cytoskeletal reorganization, oligodendrocytes, Schwann cells, mesenchymal cells and mitosis. Red-white-blue spectrum indicates high-moderate-low expression level for each gene.

Journal: Frontiers in Cell and Developmental Biology

Article Title: Direct Reprogramming of Spiral Ganglion Non-neuronal Cells into Neurons: Toward Ameliorating Sensorineural Hearing Loss by Gene Therapy

doi: 10.3389/fcell.2018.00016

Figure Lengend Snippet: Validation of RNA-seq by droplet digital PCR, and gene expression profiling of neuron-/Schwann cell-/fibroblast-specific genes. (A) Heatmap showing Pearson correlations among samples in terms of the common logarithmic counts of genes with non-zero counts. Samples in each group correlated well with each other and the largest difference was between PAN and VC. (B) Principal component analysis (PCA). The regularized logarithmic transformation was applied to the normalized counts and PCA was performed on the top 500 most variable genes across samples. Three samples in each group were well clustered. (C,D) Validation of RNA-seq data using droplet digital PCR (ddPCR). Relative value of each group compared to iN (= 1.00) is shown. (C) Relative value of TPM was calculated for known neuron markers (TuJ1 and Prph), glial marker (Sox2), and fibroblast marker (Tnc). (D) Relative value of ddPCR expression data for the same 4 genes validated our RNA-seq data. Error bars represent standard error of the mean (SEM). (E) A heatmap for reportedly specific genes for pan-neuronal, synaptic, ion-channel, transcription factors, cytoskeletal reorganization, oligodendrocytes, Schwann cells, mesenchymal cells and mitosis. Red-white-blue spectrum indicates high-moderate-low expression level for each gene.

Article Snippet: In contrast, we found the Schwann cell marker gene Mgp (Schmid et al., ) and the fibroblast specific gene Dcn (Danielson et al., ) in the iN-low, VC-high cluster, demonstrating iNs lost characteristics of SGNNCs (Figure -lower).

Techniques: Biomarker Discovery, RNA Sequencing, Digital PCR, Gene Expression, Transformation Assay, Marker, Expressing