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Tissue Microarray Analysis

Tissue Microarray Analysis is a powerful technique that allows for the high-throughput analysis of tissue samples.
It involves the systematic arrangement of small tissue samples from multiple sources on a single slide, enabling the simultaneous examination of a large number of tissue specimens.
This approach facilitates the study of gene expression, protein localization, and other molecular markers across a diverse set of tissues, contributing to advancements in disease diagnosis, prognosis, and targeted theraputic development.
PubCompare.ai's AI-driven platform provides a seamless solution for locating and leveraging tissue microarray analysis protocols from literature, pre-prints, and patents, while offering AI-driven comparisons to identify the best protocols and products.
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Most cited protocols related to «Tissue Microarray Analysis»

1
Genome-wide genetic association analysis
Cited 19 times
Genome-wide gene association analysis was performed using MAGMA v1.0815 (http://ctg.cncr.nl/software/magma). All variants in the GWAS outside of the MHC region (GRCh37: 6:28,477,797–33,448,354) that positionally map within one of the 19,019 protein coding genes were included to estimate the significance value of that gene. Genes were considered significant if the P-value was <0.05 after Bonferroni correction for 19,019 genes. All MAGMA analyses utilized 1KG43 (link) LD information. MAGMA gene-set analysis was performed where variants map to 15,496 gene-sets from the MSigDB v7.0 database52 (link). Gene-sets were considered significant if the P-value was <0.05 after Bonferroni correction for the number of tested gene-sets. Forward selection of significantly associated gene-sets was performed using MAGMA v1.08 conditional analysis53 (link). Initially the most significant gene-set was selected as a covariate and the remaining gene-sets were analyzed. The most significant gene-set from this conditional analysis was added as a covariate in addition to the previous gene-set and a new analysis was run. This process was repeated until no gene-set met the significance threshold (PBonferroni<0.05). MAGMA tissue specificity analysis was performed in FUMA using 30 general tissue type gene expression profiles (from GTEx v8). Tissues were considered significant if the P-value was < 0.05 after Bonferroni correction for 30 tissues.
FUMA cell type specificity analysis16 (link) utilises the MAGMA gene association results to identify cell types enriched in expression of trait associated genes. We focused on brain and immune related cell types with the inclusion of pancreas as a control, therefore selecting the following scRNA-seq datasets: Allen_Human_LGN_level154 (link), Allen_Human_LGN_level254 (link), Allen_Human_MTG_level154 (link), Allen_Human_MTG_level254 (link), DroNc_Human_Hippocampus55 (link), DroNc_Mouse_Hippocampus55 (link), GSE104276_Human_Prefrontal_cortex_all_ages56 (link), GSE67835_Human_Cortex57 (link), GSE81547_Human_Pancreas58 (link), Linnarsson_GSE101601_Human_Temporal_cortex59 (link), MouseCellAtlas_all60 (link), PBMC_10x_68k61 (link), and PsychENCODE_Adult62 . Within-dataset corrected results were reported to indicate which single cells are most likely to be disease relevant. The gene-based and gene-set analyses were also performed without the larger APOE region (19:40000000–50000000).
Wightman D.P., Jansen I.E., Savage J.E., Shadrin A.A., Bahrami S., Holland D., Rongve A., Børte S., Winsvold B.S., Drange O.K., Martinsen A.E., Skogholt A.H., Willer C., Bråthen G., Bosnes I., Nielsen J.B., Fritsche L.G., Thomas L.F., Pedersen L.M., Gabrielsen M.E., Bakke Johnsen M., Meisingset T.W., Zhou W., Proitsi P., Hodges A., Dobson R., Velayudhan L., Heilbron K., Auton A., Sealock J.M., Davis L.K., Pedersen N.L., Reynolds C.A., Karlsson I.K., Magnusson S., Stefansson H., Thordardottir S., Jonsson P.V., Snaedal J., Zettergren A., Skoog I., Kern S., Waern M., Zetterberg H., Blennow K., Stordal E., Hveem K., Zwart J.A., Athanasiu L., Selnes P., Saltvedt I., Sando S.B., Ulstein I., Djurovic S., Fladby T., Aarsland D., Selbæk G., Ripke S., Stefansson K., Andreassen O.A, & Posthuma D. (2021). A genome-wide association study with 1,126,563 individuals identifies new risk loci for Alzheimer’s disease. Nature genetics, 53(9), 1276-1282.
Publication 2021
ApoE protein, human Brain Cells Chromosome Mapping Cytoplasmic Inclusion Gene Expression Gene Products, Protein Genes Genome-Wide Association Study Homo sapiens Mus Pancreas Prefrontal Cortex Proteins Single-Cell RNA-Seq Tissue Microarray Analysis Tissues Tissue Specificity
2
Validating AD-related Transcriptional Changes
Cited 18 times
For data validation, we obtained datasets from published studies and compared them with our findings. We obtained DEG analysis results from a recent snRNA-seq study (12 (link)). We selected a list of cell type-specific DEGs between healthy and diseased samples with an adjusted P < 0.01 (two-sided Wilcoxon rank-sum test) and log2 fold change ≥ 0.25 or ≤ −0.25. However, the proportion of endothelial cells in the previous dataset was low, rendering it unsuitable for validating our findings in endothelial cells.
We also obtained the dataset from a study by Narayanan et al. (Gene Expression Omnibus [GEO] accession no. GSE33000), who performed bulk transcriptome microarray analysis of prefrontal cortical tissues in a large cohort (AD: n = 310; NC: n = 157) (18 (link)). We first filtered the samples according to disease status and kept only “Alzheimer’s disease” and “non-demented” samples for subsequent analysis. We removed genes that failed to be mapped to Entrez gene IDs. For genes mapped by multiple probes, we used the median value. To scale the variance, we performed log2 transformation and quantile normalization using the R limma package (54 (link)). For differential expression analysis, we fitted the gene expression profiles by linear regression, adjusting for age and sex. We used the empirical Bayes method provided in limma to calculate t-statistics and log-fold changes in differential expression. We adjusted the P values using the Benjamini–Hochberg procedure. We also used the same pipeline to process the microarray dataset of AD temporal cortical samples from Webster et al. (GEO accession no. GSE15222) (19 (link)).
We obtained transcriptome data for mouse models of amyloid-beta deposition and Tau hyperphosphorylation from MOUSEAC (55 (link)).
Lau S.F., Cao H., Fu A.K, & Ip N.Y. (2020). Single-nucleus transcriptome analysis reveals dysregulation of angiogenic endothelial cells and neuroprotective glia in Alzheimer’s disease. Proceedings of the National Academy of Sciences of the United States of America, 117(41), 25800-25809.
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Publication 2020
Amyloid beta-Peptides Cells Dietary Fiber Endothelial Cells Gene Expression Genes Microarray Analysis Mus Prefrontal Cortex Small Nuclear RNA Temporal Lobe Tissue Microarray Analysis Transcriptome
3
Metabolomics and Proteomics of C. elegans
Cited 9 times

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Publication 2016
Caenorhabditis elegans Helminths Microarray Analysis Tissue Microarray Analysis Tissues
4
Deconvolving Cell Type Expression
Cited 9 times
Input: Expression data on tissue samples and a set of gene symbols that is known to be highly expressed in a specific cell type.
Output: Expression profile for each of the cell types in a tissue.
Step I: If W is known, proceed to step II, else estimate W using XS and equation 3.
Step II: Estimate S through quadratic programming.
minS‖O−SW‖2s.tS≺t1andS≻t2
where O is the gene expression profile on tissue samples, S is the expression profile for pure cell types, W is the weight matrix estimated using the marker genes, and t1 and t2 is the maximum and minimum measurable gene expression level. R package ‘quadprog’ is used to solve the quadratic programming problem.
Zhong Y., Wan Y.W., Pang K., Chow L.M, & Liu Z. (2013). Digital sorting of complex tissues for cell type-specific gene expression profiles. BMC Bioinformatics, 14, 89.
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Publication 2013
Cells Gene Expression Genes Genes, vif Tissue Microarray Analysis Tissues
5
Prostate Cancer Tissue Microarray Database
Cited 8 times
A set of prostate cancer tissue microarrays (TMA) was used in this study containing one tissue core each from 12,427 consecutive radical prostatectomy specimens from patients undergoing surgery at the Department of Urology, and the Martini Clinic, Prostate Cancer Center, University Medical Center Hamburg-Eppendorf. This TMA is based on our previous 3,261 samples prostate prognosis TMA [10 (link)], with additional 9,166 tumors and updated clinical data from 12,344 patients with a median follow-up of 36.4 months (range: 1 to 241 months; Table 4). In all patients, prostate specific antigen (PSA) values were measured quarterly in the first year, followed by biannual measurements in the second and annual measurements after the third year following surgery. Recurrence was defined as a postoperative PSA of 0.2 ng/ml and rising thereafter. The first PSA value above or equal to 0.2 ng/ml was used to define the time of recurrence. Patients without evidence of tumor recurrence were censored at the time of the last follow-up. All prostate specimens were diagnosed according to a standard procedure, including complete embedding of the entire prostate for histological analysis [47 (link)]. The TMA manufacturing process was described earlier in detail [48 (link), 49 (link)]. In short, one 0.6 mm core was taken from a representative tissue block from each patient. The tissues were distributed among 27 TMA blocks, each containing 144 to 522 tumor samples. Presence or absence of cancer tissue was validated by immunohistochemical AMACR and 34BE12 analysis on adjacent TMA sections. For internal controls, each TMA block also contained various control tissues, including normal prostate tissue. The molecular database attached to this TMA contained results on ERG expression in 10,678, ERG break apart fluorescence in-situ hybridization (FISH) analysis in 7,099 (expanded from [27 (link), 50 (link)]), and deletion status of PTEN in 6,704 (expanded from [38 (link)]) tumors.
The usage of archived diagnostic left-over tissues for manufacturing of tissue microarrays and their analysis for research purposes as well as patient data analysis has been approved by local laws (HmbKHG, §12,1) and by the local ethics committee (Ethics commission Hamburg, WF-049/09 and PV3652). All work has been carried out in compliance with the Helsinki Declaration.
Kluth M., Amschler N.N., Galal R., Möller-Koop C., Barrow P., Tsourlakis M.C., Jacobsen F., Hinsch A., Wittmer C., Steurer S., Krech T., Büscheck F., Clauditz T.S., Beyer B., Wilczak W., Graefen M., Huland H., Minner S., Schlomm T., Sauter G, & Simon R. (2016). Deletion of 8p is an independent prognostic parameter in prostate cancer. Oncotarget, 8(1), 379-392.
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Publication 2016
Alpha-Methylacyl-CoA Racemase Deficiency Cardiac Arrest Deletion Mutation Diagnosis Fluorescent in Situ Hybridization Malignant Neoplasms Microarray Analysis Neoplasms Operative Surgical Procedures Patients Prognosis Prostate Prostate-Specific Antigen Prostate Cancer Prostatectomy PTEN protein, human Recurrence Regional Ethics Committees Tissue Microarray Analysis Tissues Urologic Surgical Procedures

Most recents protocols related to «Tissue Microarray Analysis»

1
GPRC5D Expression in Multiple Myeloma
Not available on PMC !

Example 1

The Expression of human GPRC5D was evaluated in various malignant and normal tissues by investigating gene expression profiles in databases such as the cancer cell line encyclopedia and BioGPS. As shown in FIG. 2, human GPRC5D was highly expressed in multiple myeloma, but not in other malignant tissues. Normal expression appeared limited to plasma cells. Potential GPRC5D targeted CAR T cell eradication of this normal cell type may not have significant adverse effects based on inventors' patient experience with CD19 targeted CAR T cells. Any lack of physiologic antibody production can be addressed with intravenous immunoglobulin treatment.

US11866478B2. Nucleic acid molecules encoding chimeric antigen receptors targeting G-protein coupled receptor (2024-01-09). MEMORIAL SLOAN KETTERING CANCER CENTER [US], EUREKA THERAPEUTICS, INC. [US]. Inventors: Renier J. Brentjens [US], Eric L Smith [US], Cheng Liu [US].
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Patent 2024
Antibody Formation Cell Lines Cells GPRC5D protein, human Homo sapiens Immunoglobulins Intravenous Infusion Inventors Malignant Neoplasms Multiple Myeloma Patients physiology Plasma Cells T-Lymphocyte Tissue Microarray Analysis Tissues
2
Transcriptome Profiling of Human Tissues
GTEx Release V8 (dbGaP Accession phs000424.v8. p2) was used to retrieve the tissue expression profiles of genes, in which the expression levels were shown in transcripts per million.
Qi J., Mo F., An N.A., Mi T., Wang J., Qi J., Li X., Zhang B., Xia L., Lu Y., Sun G., Wang X., Li C, & Hu B. (2023). A Human‐Specific De Novo Gene Promotes Cortical Expansion and Folding. Advanced Science, 10(7), 2204140.
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Publication 2023
Tissue Microarray Analysis
3
Colorectal Adenocarcinoma Tissue Analysis
In total, 215 patients diagnosed with colorectal adenocarcinoma at the Taipei Municipal Wan Fang Hospital of Taiwan from 1998 to 2005 were included in this study. We retrieved the CRC tissues from the Department of Pathology, Taipei Municipal Wan Fang Hospital (Taipei, Taiwan), with Institutional Review Board approval. All experiments were approved by the Ethical Committee (Taipei Medical University‐Joint IRB, approval number: TMU‐IRB 99049). All methodologies conformed to the standards set by the Declaration of Helsinki. Informed consent was signed by all patients and all experiments were approved by the Ethical Committee. We fixed the surgical specimens in 10% buffered neutral formalin and embedded them in paraffin. We reviewed the histological diagnoses, tumor sizes, levels of tumor invasiveness, and lymph node statuses of all the cases, and two pathologists (M.H. and C.L.C.) confirmed them. We determined the final disease stages according to the Cancer Staging System of the American Joint Committee of Cancer (AJCC). We retrospectively collected the clinical data, including data on the follow‐up period, overall survival period, and disease‐free survival period, from each patient's medical record. We followed the patients for more than 152 months or until their deaths. We excluded the patients who died of postoperative complications within 30 days of the surgery from the survival analysis [29 (link)].
We used a tissue microarray (TMA) for the immunohistochemistry (IHC) analysis of the RAB3C expression in this study [22 (link)]. We prepared the TMA containing the CRC tissues and corresponding adjacent noncancerous colon tissues, as previously described [22 (link)]. For each case, we selected three 1‐mm cores from different areas of the tumor tissue. In addition, if available, we also selected two 1‐mm cores of adjacent noncancerous normal colon mucosa for each case. In total, we assembled 243 archival CRC samples for the TMA. The antibodies that we used for the IHC staining included antihuman RAB3C (1:100; Cat # 15029‐1‐AP, Proteintech, Rosemont, IL, USA) and dystrophin (1:50; Cat # HPA023885, Atlas Antibodies, Bromma, Sweden). We performed the immunodetection with an EnVision dual‐link‐system horseradish peroxidase (HRP) detection kit (DAKO, Glostrup, Denmark). The detailed process and classification were introduced in our previous articles [22 (link)].
Chang Y., Li C., Chan M., Fang C., Zhang Z., Chen C, & Hsiao M. (2023). Overexpression of synaptic vesicle protein Rab GTPase 3C promotes vesicular exocytosis and drug resistance in colorectal cancer cells. Molecular Oncology, 17(3), 422-444.
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Publication 2023
Adenocarcinoma Antibodies Arthropathy Colon Diagnosis Dystrophin Formalin GIT1 protein, human Horseradish Peroxidase Joints Malignant Neoplasms Microarray Analysis Mucous Membrane Neoplasm Invasiveness Neoplasms Nodes, Lymph Operative Surgical Procedures Paraffin Pathologists Patients Postoperative Complications Tissue Microarray Analysis Tissues
4
RNA-seq Analysis of Lymphoma Immune Repertoire
RNA-seq reads were aligned to mouse genome mm10 using STAR/2.7.1a with basic two pass mode for realigning splice junctions enabled. Picard tools (version 2.18.16) were then used to mark duplicate reads, split CIGAR reads with Ns at the splice junctions. Immune repertoire of lymphomas (V(D)J enrichment) were reconstructed using TRUST4 following its suggested pipeline.53 (link) RNA-seq expression levels were calculated using HISAT254 (link) and Cufflinks.55 (link) To compare A3B transcript expression levels across tissues, CHURP was used to map and quantify the expression in mouse tissues,56 (link) while gene expression profiles from TCGA human tumor tissues were downloaded from the Broad Institute Firehose.
Durfee C., Temiz N.A., Levin-Klein R., Argyris P.P., Alsøe L., Carracedo S., de la Vega A.A., Proehl J., Holzhauer A.M., Seeman Z.J., Lin Y.H., Vogel R.I., Sotillo R., Nilsen H, & Harris R.S. (2023). Human APOBEC3B promotes tumor heterogeneity in vivo including signature mutations and metastases. bioRxiv.
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Publication Preprint 2023
Genome Homo sapiens Lymphoma Mus Neoplasms RNA-Seq Tissue Microarray Analysis Tissues Transcription, Genetic
5
Comprehensive Analysis of ZmRBOH Genes
RNA-seq data of different maize tissues and developmental stages were downloaded from the Maize Efp [74 (link)] database for ZmRBOH genes. The correlation heatmap was generated using the expression correlation matrix by TBtools. Based on the extractions of the ZmRBOH genes upstream 2000 bp sequences with TBtools, the cis-elements in gene promoter regions were explored with PlantCARE online tools (http://bioinformatics.psb.ugent.be/webtools/plantcare/html/, accessed on 15 December 2022) [75 (link)]. To better exhibit gene expression profiles in tissues, a heatmap of phenotype simulation was illustrated using TBtools. The data of transcriptome of 2-week-old maize seedlings of B73 and Oh43 under various stresses from NCBI database with three replicates was downloaded to analyze the expression profiles of ZmRBOH genes [76 (link)]. The stress treatment was conducted at low temperature (4 °C) for 16 h, high temperature (50 °C) treatment for 4 h, salt (300 mmol/L NaCl) treatment for 20 h and UV treatment for 2 h.
Zhang H., Wang X., Yan A., Deng J., Xie Y., Liu S., Liu D., He L., Weng J, & Xu J. (2023). Evolutionary Analysis of Respiratory Burst Oxidase Homolog (RBOH) Genes in Plants and Characterization of ZmRBOHs. International Journal of Molecular Sciences, 24(4), 3858.
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Publication 2023
Base Sequence Cold Temperature Fever Gene Components Genes Maize Phenotype RNA-Seq Seedlings Sodium Chloride Sodium Chloride, Dietary Tissue Microarray Analysis Tissues Transcriptome

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Human genome u133 plus 2.0 array by Thermo Fisher Scientific
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The Human Genome U133 Plus 2.0 Array is a high-density oligonucleotide microarray designed to analyze the expression of over 47,000 transcripts and variants from the human genome. It provides comprehensive coverage of the human transcriptome and is suitable for a wide range of gene expression studies.
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Agilent 2100 bioanalyzer by Agilent Technologies
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TRIzol reagent is a monophasic solution of phenol, guanidine isothiocyanate, and other proprietary components designed for the isolation of total RNA, DNA, and proteins from a variety of biological samples. The reagent maintains the integrity of the RNA while disrupting cells and dissolving cell components.
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The Affymetrix Human Genome U133 Plus 2.0 Array is a high-density oligonucleotide array that provides comprehensive coverage of the human transcriptome. The array contains over 54,000 probe sets, representing approximately 38,500 well-characterized human genes.
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Humanht 12 v4.0 expression beadchip by Illumina
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Agilent feature extraction software by Agilent Technologies
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More about "Tissue Microarray Analysis"

Tissue Microarray Analysis (TMA) is a cutting-edge technique that revolutionizes the way researchers study gene expression, protein localization, and other molecular markers across a diverse set of tissue samples.
This powerful approach involves the systematic arrangement of small tissue specimens from multiple sources on a single slide, enabling the simultaneous examination of a large number of tissue samples.
TMA is widely used in disease diagnosis, prognosis, and targeted therapeutic development, thanks to its high-throughput capabilities.
Researchers can leverage this technique in conjunction with other advanced tools, such as the Human Genome U133 Plus 2.0 Array, Agilent 2100 Bioanalyzer, and TRIzol reagent, to gain deeper insights into molecular mechanisms and pathways.
The Affymetrix Human Genome U133 Plus 2.0 Array, for example, can be used in conjunction with TMA to analyze gene expression patterns across a wide range of tissues.
Antibodies like Anti-Ki67 and Anti-c-Myc can also be employed to study the localization and abundance of specific proteins within the tissue samples.
The HumanHT-12 V4.0 expression beadchip is another powerful tool that can be leveraged alongside TMA, enabling researchers to profile the expression of thousands of genes simultaneously.
The RecoverAll Total Nucleic Acid Isolation Kit can be used to extract high-quality RNA and DNA from the tissue samples for downstream analysis.
To analyze the data generated from TMA experiments, researchers can utilize the Agilent Feature Extraction software, which provides advanced image analysis and data normalization capabilities.
Additionally, the Anti-β-catenin antibody can be used to study the localization and abundance of this key signaling molecule within the tissue samples.
PubCompare.ai's AI-driven platform offers a seamless solution for locating and leveraging TMA protocols from literature, pre-prints, and patents, while providing AI-driven comparisons to identify the best protocols and products.
Expereinced the difference today and enhance your research reproducibility and accuracy with this innovative solution.