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Structured Review

Human Protein Atlas immunohistochemical staining images
Age‐dependent NTS molecule expression patterns across cell types. (A) Relative expression of NTS across cell types (ExN, InN, MOL, OPC, Astro, Micro, Endo, Peri) as a function of age. NTS include acetylcholine, cannabinoid, dopamine, GABA, glutamate, glycine, histamine, MAT (monoamine transporters), norepinephrine, opioid, progestin, prostaglandin, and serotonin. Notable trends include age‐associated increases or decreases in expression levels of specific neurotransmitters within distinct cell types. (B) <t>Immunohistochemical</t> staining of HTR1E in cerebral cortex tissue (HPA004931). Images show representative staining in male (age 45) and female (age 54) samples. Scale bar: 100 μm. (C) Scatter plot of normalized TPM (nTPM) values showing HTR1E expression levels in frontal cortex samples (BA9, n = 209). Samples are stratified into three clusters: HTR1E high , HTR1E middle , and HTR1E low . (D) Bar plot showing the distribution of HTR1E expression clusters across young and old groups. The proportion of samples in each cluster is displayed. (E) Immunohistochemical staining of PGRMC1 in cerebral cortex tissue (HPA002877). Images show representative staining in female (age 45) and female (age 54) samples. Insets highlight specific regions with detailed staining patterns. Scale bar: 100 μm. (F) Scatter plot of normalized TPM (nTPM) values showing PGRMC1 expression levels in frontal cortex samples (BA9, n = 209). Samples are stratified into three clusters: PGRMC1 high , PGRMC1 middle , and PGRMC1 low . (G) Bar plot showing the distribution of PGRMC1 expression clusters across young and old groups. The proportion of samples in each cluster is displayed.
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Images

1) Product Images from "Multi‐Omics Reveals Dysregulated Neurotransmitter Systems in Aging and CNS Disorders"

Article Title: Multi‐Omics Reveals Dysregulated Neurotransmitter Systems in Aging and CNS Disorders

Journal: Aging Cell

doi: 10.1111/acel.70544

Age‐dependent NTS molecule expression patterns across cell types. (A) Relative expression of NTS across cell types (ExN, InN, MOL, OPC, Astro, Micro, Endo, Peri) as a function of age. NTS include acetylcholine, cannabinoid, dopamine, GABA, glutamate, glycine, histamine, MAT (monoamine transporters), norepinephrine, opioid, progestin, prostaglandin, and serotonin. Notable trends include age‐associated increases or decreases in expression levels of specific neurotransmitters within distinct cell types. (B) Immunohistochemical staining of HTR1E in cerebral cortex tissue (HPA004931). Images show representative staining in male (age 45) and female (age 54) samples. Scale bar: 100 μm. (C) Scatter plot of normalized TPM (nTPM) values showing HTR1E expression levels in frontal cortex samples (BA9, n = 209). Samples are stratified into three clusters: HTR1E high , HTR1E middle , and HTR1E low . (D) Bar plot showing the distribution of HTR1E expression clusters across young and old groups. The proportion of samples in each cluster is displayed. (E) Immunohistochemical staining of PGRMC1 in cerebral cortex tissue (HPA002877). Images show representative staining in female (age 45) and female (age 54) samples. Insets highlight specific regions with detailed staining patterns. Scale bar: 100 μm. (F) Scatter plot of normalized TPM (nTPM) values showing PGRMC1 expression levels in frontal cortex samples (BA9, n = 209). Samples are stratified into three clusters: PGRMC1 high , PGRMC1 middle , and PGRMC1 low . (G) Bar plot showing the distribution of PGRMC1 expression clusters across young and old groups. The proportion of samples in each cluster is displayed.
Figure Legend Snippet: Age‐dependent NTS molecule expression patterns across cell types. (A) Relative expression of NTS across cell types (ExN, InN, MOL, OPC, Astro, Micro, Endo, Peri) as a function of age. NTS include acetylcholine, cannabinoid, dopamine, GABA, glutamate, glycine, histamine, MAT (monoamine transporters), norepinephrine, opioid, progestin, prostaglandin, and serotonin. Notable trends include age‐associated increases or decreases in expression levels of specific neurotransmitters within distinct cell types. (B) Immunohistochemical staining of HTR1E in cerebral cortex tissue (HPA004931). Images show representative staining in male (age 45) and female (age 54) samples. Scale bar: 100 μm. (C) Scatter plot of normalized TPM (nTPM) values showing HTR1E expression levels in frontal cortex samples (BA9, n = 209). Samples are stratified into three clusters: HTR1E high , HTR1E middle , and HTR1E low . (D) Bar plot showing the distribution of HTR1E expression clusters across young and old groups. The proportion of samples in each cluster is displayed. (E) Immunohistochemical staining of PGRMC1 in cerebral cortex tissue (HPA002877). Images show representative staining in female (age 45) and female (age 54) samples. Insets highlight specific regions with detailed staining patterns. Scale bar: 100 μm. (F) Scatter plot of normalized TPM (nTPM) values showing PGRMC1 expression levels in frontal cortex samples (BA9, n = 209). Samples are stratified into three clusters: PGRMC1 high , PGRMC1 middle , and PGRMC1 low . (G) Bar plot showing the distribution of PGRMC1 expression clusters across young and old groups. The proportion of samples in each cluster is displayed.

Techniques Used: Expressing, Immunohistochemical staining, Staining

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Expressing:

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Article Snippet: 1 Laboratory Medicine Center, Department of Transfusion Medicine, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China 2 The Third School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China 3 Department of Thoracic Surgery, The Second Affiliated Hospital of Zhejiang, Chinese Medical University, Hangzhou, Zhejiang, China 4 School of Medical Imaging, Hangzhou Medical College, Hangzhou, Zhejiang, China 5 Cancer Center, Department of Hematology, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China 6 Faculty of Data Science, City University of Macau, TaipaMacao SAR, China Abstract Purpose Colorectal Cancer (CRC) exhibits considerable heterogeneity.. Circadian Rhythm (CR) disruption is increasingly implicated in tumorigenesis and cancer progression.. Methods We identified Circadian Rhythm-Related Genes (CRRGs) significantly associated with prognosis through differential expression analysis and univariate Cox regression from 1,184 samples, and established the molecular subtypes based on unsupervised clustering.

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Article Snippet: .. To achieve this, the following data were collected: From the TCGA database: RNA-seq data, DNA methylation data, and clinical data from 501 RNA-seq datasets LGG patients(Tables S1); From the CCGA database: 142 microarray datasets, 403 RNA-seq datasets, and clinical data from LGG patients(Tables S2,3); From the GEO database: one dataset ( GSE43378 ) containing 18 LGG microarray datasets, along with information on patient survival status and duration; From the Human Protein Atlas (HPA): the study aimed to investigate CCDC34 protein expression levels by utilizing immunohistochemical (IHC) staining images from HPA ( https://www.proteinatlas.org/ ). ..

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Immunohistochemical staining:

Article Title: Integrative Machine-Learning Molecular Subtyping and Risk Scores of Circadian Rhythm-Related Prognostic Signatures in Colorectal Cancer.
Article Snippet: 1 Laboratory Medicine Center, Department of Transfusion Medicine, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China 2 The Third School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China 3 Department of Thoracic Surgery, The Second Affiliated Hospital of Zhejiang, Chinese Medical University, Hangzhou, Zhejiang, China 4 School of Medical Imaging, Hangzhou Medical College, Hangzhou, Zhejiang, China 5 Cancer Center, Department of Hematology, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China 6 Faculty of Data Science, City University of Macau, TaipaMacao SAR, China Abstract Purpose Colorectal Cancer (CRC) exhibits considerable heterogeneity.. Circadian Rhythm (CR) disruption is increasingly implicated in tumorigenesis and cancer progression.. Methods We identified Circadian Rhythm-Related Genes (CRRGs) significantly associated with prognosis through differential expression analysis and univariate Cox regression from 1,184 samples, and established the molecular subtypes based on unsupervised clustering.

Article Title: Machine learning-based prognostic signature integrating mitochondrial function and programmed cell death patterns in pancreatic adenocarcinoma.
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Article Snippet: Bisphenols are widely used industrial chemicals with endocrine-disrupting properties, and their potential association with carcinogenesis has drawn considerable attention.. Cervical cancer, as a prevalent gynecological malignancy, has a pathogenesis that is not yet fully understood, particularly regarding the influence of environmental factors.. In this study, we systematically investigated the molecular effects of bisphenols on cervical cancer using multi-dimensional bioinformatics approaches.

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Immunohistochemistry:

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Article Title: The oncogenic role of CCDC34 in lower-grade gliomas: prognostic significance and therapeutic potential
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Article Title: Effect of Semaglutide on High-Fat-Diet-Induced Liver Cancer in Obese Mice
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Article Title: Genes From Epithelial-Mesenchymal Transition Predict Overall Survival Effectively in Breast Cancer: A Novel Risk Model Based on Initial Step of Tumor Metastasis
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Staining:

Article Title: Integrative Machine-Learning Molecular Subtyping and Risk Scores of Circadian Rhythm-Related Prognostic Signatures in Colorectal Cancer.
Article Snippet: 1 Laboratory Medicine Center, Department of Transfusion Medicine, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China 2 The Third School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China 3 Department of Thoracic Surgery, The Second Affiliated Hospital of Zhejiang, Chinese Medical University, Hangzhou, Zhejiang, China 4 School of Medical Imaging, Hangzhou Medical College, Hangzhou, Zhejiang, China 5 Cancer Center, Department of Hematology, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China 6 Faculty of Data Science, City University of Macau, TaipaMacao SAR, China Abstract Purpose Colorectal Cancer (CRC) exhibits considerable heterogeneity.. Circadian Rhythm (CR) disruption is increasingly implicated in tumorigenesis and cancer progression.. Methods We identified Circadian Rhythm-Related Genes (CRRGs) significantly associated with prognosis through differential expression analysis and univariate Cox regression from 1,184 samples, and established the molecular subtypes based on unsupervised clustering.

Article Title: Machine learning-based prognostic signature integrating mitochondrial function and programmed cell death patterns in pancreatic adenocarcinoma.
Article Snippet: Significant differences were evaluated by one-way ANOVA and t-test with GraphPad Prism 9.0.0 (San Diego, CA, USA). .. Immunohistochemical (IHC) staining images for the eight model genes were obtained from the Human Protein Atlas (https://www.proteinatlas.org/), enabling comparisons between normal and tumor tissues. ..

Article Title: Mechanisms of bisphenols-induced cervical cancer: A multidimensional bioinformatics analysis.
Article Snippet: Bisphenols are widely used industrial chemicals with endocrine-disrupting properties, and their potential association with carcinogenesis has drawn considerable attention.. Cervical cancer, as a prevalent gynecological malignancy, has a pathogenesis that is not yet fully understood, particularly regarding the influence of environmental factors.. In this study, we systematically investigated the molecular effects of bisphenols on cervical cancer using multi-dimensional bioinformatics approaches.

Article Title: The oncogenic role of CCDC34 in lower-grade gliomas: prognostic significance and therapeutic potential
Article Snippet: .. To achieve this, the following data were collected: From the TCGA database: RNA-seq data, DNA methylation data, and clinical data from 501 RNA-seq datasets LGG patients(Tables S1); From the CCGA database: 142 microarray datasets, 403 RNA-seq datasets, and clinical data from LGG patients(Tables S2,3); From the GEO database: one dataset ( GSE43378 ) containing 18 LGG microarray datasets, along with information on patient survival status and duration; From the Human Protein Atlas (HPA): the study aimed to investigate CCDC34 protein expression levels by utilizing immunohistochemical (IHC) staining images from HPA ( https://www.proteinatlas.org/ ). ..

Article Title: Effect of Semaglutide on High-Fat-Diet-Induced Liver Cancer in Obese Mice
Article Snippet: In our study, we utilized two databases, GEPIA (gene expression profiling interactive analysis) and the Xiangtan database, to compare the expression levels of several protein mRNAs in liver cancer tissue. .. To further investigate the protein expression patterns, we collected immunohistochemical (IHC) staining images of liver cancer (LIHC) and normal tissues from the Human Protein Atlas (HPA) database. ..

Article Title: Genes From Epithelial-Mesenchymal Transition Predict Overall Survival Effectively in Breast Cancer: A Novel Risk Model Based on Initial Step of Tumor Metastasis
Article Snippet: .. Immunohistochemical (IHC) staining images of these genes from the risk score model were downloaded from the Human Protein Atlas (HPA) database ( https://www.proteinatlas.org/ ) for exploratory and descriptive purposes. ..

RNA Sequencing:

Article Title: The oncogenic role of CCDC34 in lower-grade gliomas: prognostic significance and therapeutic potential
Article Snippet: .. To achieve this, the following data were collected: From the TCGA database: RNA-seq data, DNA methylation data, and clinical data from 501 RNA-seq datasets LGG patients(Tables S1); From the CCGA database: 142 microarray datasets, 403 RNA-seq datasets, and clinical data from LGG patients(Tables S2,3); From the GEO database: one dataset ( GSE43378 ) containing 18 LGG microarray datasets, along with information on patient survival status and duration; From the Human Protein Atlas (HPA): the study aimed to investigate CCDC34 protein expression levels by utilizing immunohistochemical (IHC) staining images from HPA ( https://www.proteinatlas.org/ ). ..

DNA Methylation Assay:

Article Title: The oncogenic role of CCDC34 in lower-grade gliomas: prognostic significance and therapeutic potential
Article Snippet: .. To achieve this, the following data were collected: From the TCGA database: RNA-seq data, DNA methylation data, and clinical data from 501 RNA-seq datasets LGG patients(Tables S1); From the CCGA database: 142 microarray datasets, 403 RNA-seq datasets, and clinical data from LGG patients(Tables S2,3); From the GEO database: one dataset ( GSE43378 ) containing 18 LGG microarray datasets, along with information on patient survival status and duration; From the Human Protein Atlas (HPA): the study aimed to investigate CCDC34 protein expression levels by utilizing immunohistochemical (IHC) staining images from HPA ( https://www.proteinatlas.org/ ). ..

Microarray:

Article Title: The oncogenic role of CCDC34 in lower-grade gliomas: prognostic significance and therapeutic potential
Article Snippet: .. To achieve this, the following data were collected: From the TCGA database: RNA-seq data, DNA methylation data, and clinical data from 501 RNA-seq datasets LGG patients(Tables S1); From the CCGA database: 142 microarray datasets, 403 RNA-seq datasets, and clinical data from LGG patients(Tables S2,3); From the GEO database: one dataset ( GSE43378 ) containing 18 LGG microarray datasets, along with information on patient survival status and duration; From the Human Protein Atlas (HPA): the study aimed to investigate CCDC34 protein expression levels by utilizing immunohistochemical (IHC) staining images from HPA ( https://www.proteinatlas.org/ ). ..



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Human Protein Atlas immunohistochemical staining images
Age‐dependent NTS molecule expression patterns across cell types. (A) Relative expression of NTS across cell types (ExN, InN, MOL, OPC, Astro, Micro, Endo, Peri) as a function of age. NTS include acetylcholine, cannabinoid, dopamine, GABA, glutamate, glycine, histamine, MAT (monoamine transporters), norepinephrine, opioid, progestin, prostaglandin, and serotonin. Notable trends include age‐associated increases or decreases in expression levels of specific neurotransmitters within distinct cell types. (B) <t>Immunohistochemical</t> staining of HTR1E in cerebral cortex tissue (HPA004931). Images show representative staining in male (age 45) and female (age 54) samples. Scale bar: 100 μm. (C) Scatter plot of normalized TPM (nTPM) values showing HTR1E expression levels in frontal cortex samples (BA9, n = 209). Samples are stratified into three clusters: HTR1E high , HTR1E middle , and HTR1E low . (D) Bar plot showing the distribution of HTR1E expression clusters across young and old groups. The proportion of samples in each cluster is displayed. (E) Immunohistochemical staining of PGRMC1 in cerebral cortex tissue (HPA002877). Images show representative staining in female (age 45) and female (age 54) samples. Insets highlight specific regions with detailed staining patterns. Scale bar: 100 μm. (F) Scatter plot of normalized TPM (nTPM) values showing PGRMC1 expression levels in frontal cortex samples (BA9, n = 209). Samples are stratified into three clusters: PGRMC1 high , PGRMC1 middle , and PGRMC1 low . (G) Bar plot showing the distribution of PGRMC1 expression clusters across young and old groups. The proportion of samples in each cluster is displayed.
Immunohistochemical Staining Images, 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
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Human Protein Atlas immunohistochemical ihc staining images
Age‐dependent NTS molecule expression patterns across cell types. (A) Relative expression of NTS across cell types (ExN, InN, MOL, OPC, Astro, Micro, Endo, Peri) as a function of age. NTS include acetylcholine, cannabinoid, dopamine, GABA, glutamate, glycine, histamine, MAT (monoamine transporters), norepinephrine, opioid, progestin, prostaglandin, and serotonin. Notable trends include age‐associated increases or decreases in expression levels of specific neurotransmitters within distinct cell types. (B) <t>Immunohistochemical</t> staining of HTR1E in cerebral cortex tissue (HPA004931). Images show representative staining in male (age 45) and female (age 54) samples. Scale bar: 100 μm. (C) Scatter plot of normalized TPM (nTPM) values showing HTR1E expression levels in frontal cortex samples (BA9, n = 209). Samples are stratified into three clusters: HTR1E high , HTR1E middle , and HTR1E low . (D) Bar plot showing the distribution of HTR1E expression clusters across young and old groups. The proportion of samples in each cluster is displayed. (E) Immunohistochemical staining of PGRMC1 in cerebral cortex tissue (HPA002877). Images show representative staining in female (age 45) and female (age 54) samples. Insets highlight specific regions with detailed staining patterns. Scale bar: 100 μm. (F) Scatter plot of normalized TPM (nTPM) values showing PGRMC1 expression levels in frontal cortex samples (BA9, n = 209). Samples are stratified into three clusters: PGRMC1 high , PGRMC1 middle , and PGRMC1 low . (G) Bar plot showing the distribution of PGRMC1 expression clusters across young and old groups. The proportion of samples in each cluster is displayed.
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Human Protein Atlas representative 518 immunohistochemical staining images
Age‐dependent NTS molecule expression patterns across cell types. (A) Relative expression of NTS across cell types (ExN, InN, MOL, OPC, Astro, Micro, Endo, Peri) as a function of age. NTS include acetylcholine, cannabinoid, dopamine, GABA, glutamate, glycine, histamine, MAT (monoamine transporters), norepinephrine, opioid, progestin, prostaglandin, and serotonin. Notable trends include age‐associated increases or decreases in expression levels of specific neurotransmitters within distinct cell types. (B) <t>Immunohistochemical</t> staining of HTR1E in cerebral cortex tissue (HPA004931). Images show representative staining in male (age 45) and female (age 54) samples. Scale bar: 100 μm. (C) Scatter plot of normalized TPM (nTPM) values showing HTR1E expression levels in frontal cortex samples (BA9, n = 209). Samples are stratified into three clusters: HTR1E high , HTR1E middle , and HTR1E low . (D) Bar plot showing the distribution of HTR1E expression clusters across young and old groups. The proportion of samples in each cluster is displayed. (E) Immunohistochemical staining of PGRMC1 in cerebral cortex tissue (HPA002877). Images show representative staining in female (age 45) and female (age 54) samples. Insets highlight specific regions with detailed staining patterns. Scale bar: 100 μm. (F) Scatter plot of normalized TPM (nTPM) values showing PGRMC1 expression levels in frontal cortex samples (BA9, n = 209). Samples are stratified into three clusters: PGRMC1 high , PGRMC1 middle , and PGRMC1 low . (G) Bar plot showing the distribution of PGRMC1 expression clusters across young and old groups. The proportion of samples in each cluster is displayed.
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Human Protein Atlas representative immunohistochemical staining images
TMEM132A is upregulated in breast cancer and correlates with clinicopathological features ( A ) Differential expression of TMEM132A between breast cancer tissues and normal breast tissues in the TCGA_BRCA cohort. ( B ) TMEM132A expression comparison between tumor samples from TCGA_BRCA and normal breast tissues from the integrated TCGA and GTEx cohort. ( C-E ) Validation of elevated TMEM132A expression in breast cancer using independent GEO datasets, including GSE20711 ( C ), GSE22820 ( D ), and GSE162228 ( E ). ( F , G ) ROC curve analyses evaluating the diagnostic performance of TMEM132A in distinguishing breast cancer tissues from normal tissues in the TCGA_BRCA cohort ( F ) and the TCGA+GTEx_BRCA cohort ( G ). ( H ) Paired analysis of TMEM132A expression between matched tumor and adjacent normal breast tissues in the TCGA_BRCA cohort. ( I , J ) Association between TMEM132A expression and clinical stage in the TCGA_BRCA cohort. ( K ) Distribution of TMEM132A expression across different molecular subtypes of breast cancer in the TCGA_BRCA cohort. ( L-T ) Relationship between TMEM132A expression and histological grade in the TCGA_BRCA cohort (L) and multiple independent GEO datasets, including GSE7390 ( M ), GSE11121 ( N ), GSE20711 ( O ), GSE21653 ( P ), GSE22219 ( Q ), GSE25055 ( R ), GSE45255 ( S ), GSE42568 ( T ), and GSE61304 ( U ). ( U , V ) Comparison of TMEM132A expression between non-metastatic (M0) and metastatic (M1) breast cancer samples in the TCGA_BRCA cohort ( U ) and the GSE20685 dataset ( V ). ( W ) Distribution of TMEM132A expression levels across breast cancer molecular. subgroups in the TCGA_BRCA cohort. ( X ) Representative <t>immunohistochemical</t> staining images showing TMEM132A protein expression in normal breast tissue and breast cancer tissue obtained from the Human Protein Atlas. ( Y ) Immunofluorescence staining showing TMEM132A expression (green), nuclei (blue), microtubules (red), and merged images in the MCF7 breast cancer cell line
Representative Immunohistochemical Staining Images, 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
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Image Search Results


Age‐dependent NTS molecule expression patterns across cell types. (A) Relative expression of NTS across cell types (ExN, InN, MOL, OPC, Astro, Micro, Endo, Peri) as a function of age. NTS include acetylcholine, cannabinoid, dopamine, GABA, glutamate, glycine, histamine, MAT (monoamine transporters), norepinephrine, opioid, progestin, prostaglandin, and serotonin. Notable trends include age‐associated increases or decreases in expression levels of specific neurotransmitters within distinct cell types. (B) Immunohistochemical staining of HTR1E in cerebral cortex tissue (HPA004931). Images show representative staining in male (age 45) and female (age 54) samples. Scale bar: 100 μm. (C) Scatter plot of normalized TPM (nTPM) values showing HTR1E expression levels in frontal cortex samples (BA9, n = 209). Samples are stratified into three clusters: HTR1E high , HTR1E middle , and HTR1E low . (D) Bar plot showing the distribution of HTR1E expression clusters across young and old groups. The proportion of samples in each cluster is displayed. (E) Immunohistochemical staining of PGRMC1 in cerebral cortex tissue (HPA002877). Images show representative staining in female (age 45) and female (age 54) samples. Insets highlight specific regions with detailed staining patterns. Scale bar: 100 μm. (F) Scatter plot of normalized TPM (nTPM) values showing PGRMC1 expression levels in frontal cortex samples (BA9, n = 209). Samples are stratified into three clusters: PGRMC1 high , PGRMC1 middle , and PGRMC1 low . (G) Bar plot showing the distribution of PGRMC1 expression clusters across young and old groups. The proportion of samples in each cluster is displayed.

Journal: Aging Cell

Article Title: Multi‐Omics Reveals Dysregulated Neurotransmitter Systems in Aging and CNS Disorders

doi: 10.1111/acel.70544

Figure Lengend Snippet: Age‐dependent NTS molecule expression patterns across cell types. (A) Relative expression of NTS across cell types (ExN, InN, MOL, OPC, Astro, Micro, Endo, Peri) as a function of age. NTS include acetylcholine, cannabinoid, dopamine, GABA, glutamate, glycine, histamine, MAT (monoamine transporters), norepinephrine, opioid, progestin, prostaglandin, and serotonin. Notable trends include age‐associated increases or decreases in expression levels of specific neurotransmitters within distinct cell types. (B) Immunohistochemical staining of HTR1E in cerebral cortex tissue (HPA004931). Images show representative staining in male (age 45) and female (age 54) samples. Scale bar: 100 μm. (C) Scatter plot of normalized TPM (nTPM) values showing HTR1E expression levels in frontal cortex samples (BA9, n = 209). Samples are stratified into three clusters: HTR1E high , HTR1E middle , and HTR1E low . (D) Bar plot showing the distribution of HTR1E expression clusters across young and old groups. The proportion of samples in each cluster is displayed. (E) Immunohistochemical staining of PGRMC1 in cerebral cortex tissue (HPA002877). Images show representative staining in female (age 45) and female (age 54) samples. Insets highlight specific regions with detailed staining patterns. Scale bar: 100 μm. (F) Scatter plot of normalized TPM (nTPM) values showing PGRMC1 expression levels in frontal cortex samples (BA9, n = 209). Samples are stratified into three clusters: PGRMC1 high , PGRMC1 middle , and PGRMC1 low . (G) Bar plot showing the distribution of PGRMC1 expression clusters across young and old groups. The proportion of samples in each cluster is displayed.

Article Snippet: Immunohistochemical staining images and frontal cortex RNA‐seq data from the Human Protein Atlas (HPA) provided supportive cross‐dataset validation for the snRNA‐seq findings.

Techniques: Expressing, Immunohistochemical staining, Staining

TMEM132A is upregulated in breast cancer and correlates with clinicopathological features ( A ) Differential expression of TMEM132A between breast cancer tissues and normal breast tissues in the TCGA_BRCA cohort. ( B ) TMEM132A expression comparison between tumor samples from TCGA_BRCA and normal breast tissues from the integrated TCGA and GTEx cohort. ( C-E ) Validation of elevated TMEM132A expression in breast cancer using independent GEO datasets, including GSE20711 ( C ), GSE22820 ( D ), and GSE162228 ( E ). ( F , G ) ROC curve analyses evaluating the diagnostic performance of TMEM132A in distinguishing breast cancer tissues from normal tissues in the TCGA_BRCA cohort ( F ) and the TCGA+GTEx_BRCA cohort ( G ). ( H ) Paired analysis of TMEM132A expression between matched tumor and adjacent normal breast tissues in the TCGA_BRCA cohort. ( I , J ) Association between TMEM132A expression and clinical stage in the TCGA_BRCA cohort. ( K ) Distribution of TMEM132A expression across different molecular subtypes of breast cancer in the TCGA_BRCA cohort. ( L-T ) Relationship between TMEM132A expression and histological grade in the TCGA_BRCA cohort (L) and multiple independent GEO datasets, including GSE7390 ( M ), GSE11121 ( N ), GSE20711 ( O ), GSE21653 ( P ), GSE22219 ( Q ), GSE25055 ( R ), GSE45255 ( S ), GSE42568 ( T ), and GSE61304 ( U ). ( U , V ) Comparison of TMEM132A expression between non-metastatic (M0) and metastatic (M1) breast cancer samples in the TCGA_BRCA cohort ( U ) and the GSE20685 dataset ( V ). ( W ) Distribution of TMEM132A expression levels across breast cancer molecular. subgroups in the TCGA_BRCA cohort. ( X ) Representative immunohistochemical staining images showing TMEM132A protein expression in normal breast tissue and breast cancer tissue obtained from the Human Protein Atlas. ( Y ) Immunofluorescence staining showing TMEM132A expression (green), nuclei (blue), microtubules (red), and merged images in the MCF7 breast cancer cell line

Journal: Clinical and Experimental Medicine

Article Title: TMEM132A is associated with metabolic reprogramming, macrophage-oriented immune remodeling, and breast cancer progression

doi: 10.1007/s10238-026-02125-3

Figure Lengend Snippet: TMEM132A is upregulated in breast cancer and correlates with clinicopathological features ( A ) Differential expression of TMEM132A between breast cancer tissues and normal breast tissues in the TCGA_BRCA cohort. ( B ) TMEM132A expression comparison between tumor samples from TCGA_BRCA and normal breast tissues from the integrated TCGA and GTEx cohort. ( C-E ) Validation of elevated TMEM132A expression in breast cancer using independent GEO datasets, including GSE20711 ( C ), GSE22820 ( D ), and GSE162228 ( E ). ( F , G ) ROC curve analyses evaluating the diagnostic performance of TMEM132A in distinguishing breast cancer tissues from normal tissues in the TCGA_BRCA cohort ( F ) and the TCGA+GTEx_BRCA cohort ( G ). ( H ) Paired analysis of TMEM132A expression between matched tumor and adjacent normal breast tissues in the TCGA_BRCA cohort. ( I , J ) Association between TMEM132A expression and clinical stage in the TCGA_BRCA cohort. ( K ) Distribution of TMEM132A expression across different molecular subtypes of breast cancer in the TCGA_BRCA cohort. ( L-T ) Relationship between TMEM132A expression and histological grade in the TCGA_BRCA cohort (L) and multiple independent GEO datasets, including GSE7390 ( M ), GSE11121 ( N ), GSE20711 ( O ), GSE21653 ( P ), GSE22219 ( Q ), GSE25055 ( R ), GSE45255 ( S ), GSE42568 ( T ), and GSE61304 ( U ). ( U , V ) Comparison of TMEM132A expression between non-metastatic (M0) and metastatic (M1) breast cancer samples in the TCGA_BRCA cohort ( U ) and the GSE20685 dataset ( V ). ( W ) Distribution of TMEM132A expression levels across breast cancer molecular. subgroups in the TCGA_BRCA cohort. ( X ) Representative immunohistochemical staining images showing TMEM132A protein expression in normal breast tissue and breast cancer tissue obtained from the Human Protein Atlas. ( Y ) Immunofluorescence staining showing TMEM132A expression (green), nuclei (blue), microtubules (red), and merged images in the MCF7 breast cancer cell line

Article Snippet: Fig. 2 TMEM132A is upregulated in breast cancer and correlates with clinicopathological features ( A ) Differential expression of TMEM132A between breast cancer tissues and normal breast tissues in the TCGA_BRCA cohort. ( B ) TMEM132A expression comparison between tumor samples from TCGA_BRCA and normal breast tissues from the integrated TCGA and GTEx cohort. ( C-E ) Validation of elevated TMEM132A expression in breast cancer using independent GEO datasets, including GSE20711 ( C ), GSE22820 ( D ), and GSE162228 ( E ). ( F , G ) ROC curve analyses evaluating the diagnostic performance of TMEM132A in distinguishing breast cancer tissues from normal tissues in the TCGA_BRCA cohort ( F ) and the TCGA+GTEx_BRCA cohort ( G ). ( H ) Paired analysis of TMEM132A expression between matched tumor and adjacent normal breast tissues in the TCGA_BRCA cohort. ( I , J ) Association between TMEM132A expression and clinical stage in the TCGA_BRCA cohort. ( K ) Distribution of TMEM132A expression across different molecular subtypes of breast cancer in the TCGA_BRCA cohort. ( L-T ) Relationship between TMEM132A expression and histological grade in the TCGA_BRCA cohort (L) and multiple independent GEO datasets, including GSE7390 ( M ), GSE11121 ( N ), GSE20711 ( O ), GSE21653 ( P ), GSE22219 ( Q ), GSE25055 ( R ), GSE45255 ( S ), GSE42568 ( T ), and GSE61304 ( U ). ( U , V ) Comparison of TMEM132A expression between non-metastatic (M0) and metastatic (M1) breast cancer samples in the TCGA_BRCA cohort ( U ) and the GSE20685 dataset ( V ). ( W ) Distribution of TMEM132A expression levels across breast cancer molecular. subgroups in the TCGA_BRCA cohort. ( X ) Representative immunohistochemical staining images showing TMEM132A protein expression in normal breast tissue and breast cancer tissue obtained from the Human Protein Atlas. ( Y ) Immunofluorescence staining showing TMEM132A expression (green), nuclei (blue), microtubules (red), and merged images in the MCF7 breast cancer cell line We next assessed the potential diagnostic value of TMEM132A in distinguishing tumor from normal tissues.

Techniques: Quantitative Proteomics, Expressing, Comparison, Biomarker Discovery, Diagnostic Assay, Immunohistochemical staining, Staining, Immunofluorescence