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Endocrine Cells

Endocrine Cells: Specialized cells within glandular tissue that secrete hormones directly into the bloodstream.
These cells play a crucial role in regulating physiological processes, such as metabolism, growth, development, and homeostasis.
Endocrine cells are found in various organs, includig the pituitary, thyroid, parathyroid, adrenal, and pancreatic glands.
Their dysfunction can lead to endocrine disorders, making them an important target for research and therapeutic interventions.
PubCompare.ai offers powerful tools to optimize your endocrine cells research, enhancing reproducibility and accuracy through AI-driven protocol comparisons and data-driven insights.

Most cited protocols related to «Endocrine Cells»

1
Publicly Available RNA-seq and scRNA-seq Data
RNA-seq data of hPSC-LOs and lung autopsy samples is publicly available on the GEO repository database under the accession number GSE155241. scRNA-seq data of hPSC-LOs is publicly available on the GEO repository database under the accession number GSE148113. scRNA-seq data of hPSC-COs are publicly available on the GEO repository database, accession number GSE147975. RNA-seq of hPSC-derived endocrine cells and liver organoids are available on the GEO repository database, accession number GSE151803.
Source Data behind Figs. 1c, d, g, k; 2i; 3a, e, k; 4a, c, g, i; Extended Data Figs 1d, e; 5a; 6a, c, d; 7a, c; 8a, c, e; 9a, d; 10a, c, are available within the manuscript files.
Han Y., Duan X., Yang L., Nilsson-Payant B.E., Wang P., Duan F., Tang X., Yaron T.M., Zhang T., Uhl S., Bram Y., Richardson C., Zhu J., Zhao Z., Redmond D., Houghton S., Nguyen D.H., Xu D., Wang X., Jessurun J., Borczuk A., Huang Y., Johnson J.L., Liu Y., Xiang J., Wang H., Cantley L.C., tenOever B.R., Ho D.D., Pan F.C., Evans T., Chen H.J., Schwartz R.E, & Chen S. (2020). Identification of SARS-CoV-2 Inhibitors using Lung and Colonic Organoids. Nature, 589(7841), 270-275.
Publication 2020
Autopsy Endocrine Cells Figs Liver Lung Organoids RNA-Seq Single-Cell RNA-Seq
2
Publicly Available RNA-seq and scRNA-seq Data
RNA-seq data of hPSC-LOs and lung autopsy samples is publicly available on the GEO repository database under the accession number GSE155241. scRNA-seq data of hPSC-LOs is publicly available on the GEO repository database under the accession number GSE148113. scRNA-seq data of hPSC-COs are publicly available on the GEO repository database, accession number GSE147975. RNA-seq of hPSC-derived endocrine cells and liver organoids are available on the GEO repository database, accession number GSE151803.
Source Data behind Figs. 1c, d, g, k; 2i; 3a, e, k; 4a, c, g, i; Extended Data Figs 1d, e; 5a; 6a, c, d; 7a, c; 8a, c, e; 9a, d; 10a, c, are available within the manuscript files.
Han Y., Duan X., Yang L., Nilsson-Payant B.E., Wang P., Duan F., Tang X., Yaron T.M., Zhang T., Uhl S., Bram Y., Richardson C., Zhu J., Zhao Z., Redmond D., Houghton S., Nguyen D.H., Xu D., Wang X., Jessurun J., Borczuk A., Huang Y., Johnson J.L., Liu Y., Xiang J., Wang H., Cantley L.C., tenOever B.R., Ho D.D., Pan F.C., Evans T., Chen H.J., Schwartz R.E, & Chen S. (2020). Identification of SARS-CoV-2 Inhibitors using Lung and Colonic Organoids. Nature, 589(7841), 270-275.
Publication 2020
Autopsy Endocrine Cells Figs Liver Lung Organoids RNA-Seq Single-Cell RNA-Seq
3
Comprehensive Single-Nucleus Profiling of PDAC
Treatment-naïve and neoadjuvant-treated specimens were aggregated into a single dataset. The log2(TP10K+1) expression matrix was constructed for downstream analyses. We identified the top 2,000 highly-variable genes across the entire dataset using the Scanpy 1.7.291 (link)highly_variable_genes function with sample ID as input for the batch. We then performed a Principal Component Analysis (PCA) over the top 2,000 highly variable genes and identified the top 40 principal components (PCs) beyond which negligible additional variance was explained in the data. Subsequently, we performed batch correction using Harmony-Pytorch v0.1.792 (link) and built a k-nearest neighbors graph of nuclei profiles (k = 10) based on the top 40 batch corrected components and performed community detection on this neighborhood graph using the Leiden graph clustering method93 (link) with resolution set to 1 to identify distinct cell population clusters. Individual nucleus profiles were visualized using the Uniform Manifold Approximation and Projection (UMAP)94 . Doublets were identified and removed in part using Scrublet v0.2.3. Distinct cell populations identified from the previous steps were annotated using known cell type-specific gene expression signatures and representative gene markers19 (link),26 (link),95 (link)–97 (link). We used the Adjusted Mutual Information (AMI) score to quantify the similarity in single cell assignments between the partitions imposed by the Leiden clustering labels and patient ID labels. The AMI was computed using the adjusted_mutual_info_score function in the scikit-learn v0.22.2 package.
While earlier scRNA-seq studies in PDAC did not fully capture the stromal milieu and necessitated enrichment strategies for CAFs such as fluorescence-activated cell sorting41 (link),72 (link),98 (link),99 (link), they were well-represented in our samples. Specifically, our snRNA-seq had a higher yield of high quality nuclei per patient in the untreated group (6,054 ± 1,529) than a recent scRNA-seq study of primary untreated PDAC72 (link) (1,718 ± 773), despite comparable quantities of loaded cells/nuclei (p = 1.92 x 10−9, Mann-Whitney U test; Extended Data Figure 10), recovered six additional cell types absent in scRNA-seq, and captured significantly higher proportions of CAFs, pericytes, and endocrine cells and lower proportions of vascular smooth muscle cells, myeloid cells, lymphoid cells, and endothelial cells (p < 0.05; Mann-Whitney U Test; comparable results using Dirichlet-multinomial regression; Extended Data Figure 10).
Delaney WE I.V., Edwards R., Colledge D., Shaw T., Torresi J., Miller T.G., Isom H.C., Bock C.T., Manns M.P., Trautwein C, & Locarnini S. (2001). Cross-Resistance Testing of Antihepadnaviral Compounds Using Novel Recombinant Baculoviruses Which Encode Drug-Resistant Strains of Hepatitis B Virus. Antimicrobial Agents and Chemotherapy, 45(6), 1705-1713.
Publication 2022
Anophthalmia with pulmonary hypoplasia Blood Vessel Cell Nucleus Cells Conotruncal Anomaly Face Syndrome Endocrine Cells Endothelial Cells Fluorescence Genes Lymphoid Cells Muscle, Smooth, Vascular Myeloid Cells Myocytes, Smooth Muscle Neoadjuvant Therapy Patients Pericytes Single-Cell RNA-Seq Small Nuclear RNA
4
Integrative Genomic Analysis of Human Islet Cells
We split the genome into 5 kb windows and removed windows overlapping blacklisted regions (v2) from ENCODE86 (link),87 (link). For each experiment, we created a sparse m x n matrix containing read depth for m cells passing read depth thresholds at n windows. Using scanpy88 (link) (v.1.4.4.post1), we extracted highly variable windows using mean read depth and normalized dispersion (‘min_mean=0.01, min_disp=0.25’). After normalization to uniform read depth and log-transformation, for each experiment, we regressed out the log-transformed read depth within highly variable windows for each cell. We then performed principal component analysis (PCA) and extracted the top 50 principal components. We used Harmony24 (link) to correct the principal components and remove batch effects across experiments, using donor-of-origin as a covariate. We used Harmony-corrected components to calculate the nearest 30 neighbors using the cosine metric, which were subsequently used for UMAP dimensionality reduction (‘min_dist=0.3’) and Leiden clustering89 (link) (‘resolution=1.5’).
We performed iterative clustering to identify and remove cells with abnormal features prior to the final clustering results (see Supplementary Note). After removing these cells, we ended up with 15,298 cells mapping to 12 clusters. We used chromatin accessibility at windows overlapping promoters for marker hormones to assign cell types for the endocrine islet cell types and chromatin accessibility at windows around marker genes from scRNA-seq to assign cluster labels for non-endocrine islet clusters.
Chiou J., Zeng C., Cheng Z., Han J.Y., Schlichting M., Miller M., Mendez R., Huang S., Wang J., Sui Y., Deogaygay A., Okino M.L., Qiu Y., Sun Y., Kudtarkar P., Fang R., Preissl S., Sander M., Gorkin D.U, & Gaulton K.J. (2021). Single cell chromatin accessibility identifies pancreatic islet cell type- and state-specific regulatory programs of diabetes risk. Nature genetics, 53(4), 455-466.
Publication 2021
Cells Chromatin Endocrine Cells Genetic Markers Genome Hormones Islets of Langerhans M Cells Single-Cell RNA-Seq System, Endocrine Tissue Donors
5
Immunostaining and Quantitative Analysis
Tissue fixation, processing and immunostaining were performed essentially as described [45 (link)]. Tissues were fixed with 4% paraformaldehyde (PFA) in PBS for 1-2 hrs at 4°C, embedded in OCT and cryosectioned at 7-8 μm thickness. Primary antibodies used in this study are listed in Table 3. Secondary antibodies were purchased from Jackson Immunoresearch. To calculate labeling efficiencies, we photographed 5-12 randomly selected 20× fields per stained specimen, across 4-8 sections separated by 100-150 μm. The total number of each cell type (DAPI for total cells per field, LacZ or GFP for Muc1IC2-labeled cells, insulin and glucagon for endocrine cells, amylase, cytokeratin-19 and DBA lectin for exocrine cells) was determined using the Analyze Particles function of ImageJ (NIH). Double-positive cells were detected by additive image overlay, in ImageJ, of the DAPI channel with lineage+ and marker+ staining. Accuracy of counts was confirmed by eye in Adobe Photoshop for random samples. Calculations and graphs were generated with Microsoft Excel and R http://www.r-project.org.
Kopinke D, & Murtaugh L.C. (2010). Exocrine-to-endocrine differentiation is detectable only prior to birth in the uninjured mouse pancreas. BMC Developmental Biology, 10, 38.
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Publication 2010
Amylase Antibodies Cells DAPI Endocrine Cells Glucagon Insulin Keratin-19 LacZ Genes Lectin paraform Tissue Fixation Tissues

Most recents protocols related to «Endocrine Cells»

1
Deconvolution of Pancreatic Cell Types
Deconvolution was performed using the CibersortX algorithm at cibersortx.stanford.edu [74 (link)]. Single-cell transcriptomic profiling dataset of cells in the embryonic pancreas [39 (link)] was used as a reference, including count matrix and metadata labels. Particularly, only cells with pancreatic epithelial or endocrine cell fate were used, corresponding to the annotation of five broader cell types—α cells, β cells, endocrine progenitors, trunk epithelium and tip epithelium [39 (link)]. The reference matrix was built out of the 2589 cells and gene list of 18,565 gene features, as deposited by [39 (link)]. Each cell population counted > 250 cells. The units of the reference matrix were UMI counts. Calculation of the scRNA-seq signature matrix was done in default mode (quantile normalization disabled, minimal expression of 0.75, replicates of 5, sampling of 0.5). Imputation of cell fractions and group-mode expression were used in default settings, with S-mode batch correction enabled, quantile normalization disabled and n = 100 permutations for significance analysis. Sample mixture file was submitted with unfiltered gene list 27,124 features for Isl1CKO and in UMI counts.
Bohuslavova R., Fabriciova V., Lebrón-Mora L., Malfatti J., Smolik O., Valihrach L., Benesova S., Zucha D., Berkova Z., Saudek F., Evans S.M, & Pavlinkova G. (2023). ISL1 controls pancreatic alpha cell fate and beta cell maturation. Cell & Bioscience, 13, 53.
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Publication 2023
Cells Embryo Endocrine Cells Epithelial Cells Epithelium Genes Pancreas Single-Cell RNA-Seq System, Endocrine
2
Transcriptional Profiling of Endocrine Cells
RNA-seq libraries were prepared from 100 FACS-sorted cells/sample obtained from the pancreases of reporter Isl1CKO-Ai14 mutant (n = 5 samples) and reporter control-Ai14 (n = 6) from E14.5 embryos; and Isl1CKO-Ai14 mutant (n = 6) and reporter control-Ai14 (n = 5) from P9 mice. Each sample contained 100 tdTomato+ endocrine cells. Following the manufacturer's instructions, the NEB Next single-cell low input RNA library prep kit for Illumina was used for cDNA synthesis, amplification, and library generation [67 (link)] at the Gene Core Facility (Institute of Biotechnology CAS, Czechia). Fragment Analyzer assessed the quality of cDNA libraries. The libraries were sequenced on an Illumina NextSeq 500 next-generation sequencer. NextSeq 500/550 High Output kit 75 cycles (Illumina #200,024,906) were processed at the Genomics and Bioinformatics Core Facility (Institute of Molecular Genetics CAS, Czechia). RNA-Seq reads in FASTQ files were mapped to the mouse genome using STAR [version 2.7.0c [68 (link)]] GRCm38 primary assembly and annotation version M8. The raw data of RNA sequencing were processed with a standard pipeline. Using cutadapt v1.18 [69 (link)], the number of reads (minimum, 32 million; maximum, 73 million) was trimmed by Illumina sequencing adaptor and of bases with reading quality lower than 20, subsequently reads shorter than 20 bp were filtered out TrimmomaticPE version 0.36 [70 (link)].
Ribosomal RNA and reads mapping to UniVec database were filtered out using bowtie v1.2.2. with parameters -S -n 1 and SortMeRNA [71 (link)]. A count table was generated by Rsubread v2.0.1 package using default parameters without counting multi mapping reads. The raw RNA-seq data were deposited at GEO: (https://www.ncbi.nlm.nih.gov/geo/).
DESeq2 [v1.26.0 [72 (link)]] default parameters were used to normalize data and compare the different groups. Genes were then filtered using the criteria of an adjusted P-value Padj < 0.05, and a base mean ≥ 50, and Fold change > 1.5 for upregulated genes and < 0.5 for downregulated genes for both E14.5 and P9 data to identify differentially expressed genes between Isl1CKO and control endocrine cells. The enrichment of the functional categories and functional annotation clustering of the differentially expressed genes was performed using g: Profiler [73 (link)] using version e104_eg51_p15_3922dba with g: SCS multiple testing correction methods applying a significance threshold of 0.05. Transcription factor (TF) enrichment analysis (TFEA) [41 (link)] was used to identify the enrichment of TF target genes in our set of differentially expressed genes. The top seven enriched TFs are listed (Additional file 6: Dataset S1c).
Bohuslavova R., Fabriciova V., Lebrón-Mora L., Malfatti J., Smolik O., Valihrach L., Benesova S., Zucha D., Berkova Z., Saudek F., Evans S.M, & Pavlinkova G. (2023). ISL1 controls pancreatic alpha cell fate and beta cell maturation. Cell & Bioscience, 13, 53.
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Publication 2023
Anabolism cDNA Library DNA, Complementary DNA Library Embryo Endocrine Cells Gene Annotation Genes Mus Pancreas Ribosomal RNA RNA-Seq tdTomato Transcription, Genetic Transcription Factor
3
Single-cell RNA editing profiling
Cell Ranger (https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/what-is-cell-ranger) and Seurat (https://satijalab.org/seurat/) were applied to cluster and identify the cell types based on known marker expression. The marker genes were PRSS1, CTRB1, CTRB2, and REG1B for acinar cells, MS4A1, CD79A, CD79B, and CD52 for B cells, AMBP, CFTR, and MMP7 for type1 ductal cells, KRT19, KRT7, TSPAN8, and SLPI for type 2 ductal cells, CHGB, CHGA, INS, and IAPP for endocrine cells, CDH5, PLVAP, VWF, and CLDN5 for endothelial cells, LUM, DCN, and COL1A1 for fibroblast cells, AIF1, CD64, CD14, and CD68 for macrophage cells, ACTA2, PDGFRB, and ADIRF for stellate cells, and CD3E, CD4, and CD8 T cells (21 (link)). The reads for each cell type were extracted and combined. RNA editing events for each type were identified by REDItools. Then, credible RNA editing sites were filtered by satisfying stringent requirements (total reads for each site in each sample ≥10, 1 > editing level for each site in each sample ≥0.1, remove SNP sites).
Mei Y., Liang D., Ai B., Wang T., Guo S., Jin G, & Yu D. (2023). Genome-wide identification of A-to-I RNA editing events provides the functional implications in PDAC. Frontiers in Oncology, 13, 1092046.
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Publication 2023
Acinar Cell ACTA2 protein, human Amylin B-Lymphocytes CD8-Positive T-Lymphocytes CD79A protein, human CD79B protein, human CDH5 protein, human Cells CHGB protein, human CTRB1 protein, human CTRB2 protein, human Cystic Fibrosis Transmembrane Conductance Regulator Endocrine Cells Endothelial Cells Fibroblasts Gene Expression Genes KRT19 protein, human Macrophage MMP7 protein, human PRSS1 protein, human SLPI protein, human Strains
4
Pituitary Cell Type Identification

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Publication 2023
ACTA2 protein, human CD31 Antigens Cells CHGB protein, human COL1A2 protein, human DRD2 protein, human Endocrine Cells Endothelial Cells Fibroblasts FLT1 protein, human Gene, c-fms Gene Expression GNRHR protein, human KRT19 protein, human Lymphocyte Myeloid Cells NR5A1 protein, human PTPRC protein, human RGS1 protein, human SCG2 protein, human SLPI protein, human SNAP25 protein, human SOX2 protein, human SOX9 protein, human Stem, Plant Stem Cells UCHL1 protein, human
5
Distinguishing Non-Tumor Neuroendocrine Cells in PitNET Samples

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Publication 2023
Cells Endocrine Cells Genes, Neoplasm Genetic Heterogeneity Neoplasms Neuroendocrine Cells Neuroendocrine Tumors Reproduction

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Matrigel is a complex mixture of extracellular matrix proteins derived from Engelbreth-Holm-Swarm (EHS) mouse sarcoma cells. It is widely used as a basement membrane matrix to support the growth, differentiation, and morphogenesis of various cell types in cell culture applications.
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Lipofectamine rnaimax reagent by Thermo Fisher Scientific
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Lipofectamine RNAiMAX reagent is a lipid-based transfection reagent designed for efficient delivery of small interfering RNA (siRNA) and microRNA (miRNA) into a variety of mammalian cell types for gene silencing applications. The reagent is formulated to complex with and deliver nucleic acids into cells, enabling effective knockdown of target gene expression.
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DAPI is a fluorescent dye that binds strongly to adenine-thymine (A-T) rich regions in DNA. It is commonly used as a nuclear counterstain in fluorescence microscopy to visualize and locate cell nuclei.
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More about "Endocrine Cells"

Endocrine cells, also known as secretory cells or hormone-producing cells, are specialized cells found within glandular tissues that secrete hormones directly into the bloodstream.
These cells play a crucial role in regulating a wide range of physiological processes, such as metabolism, growth, development, and homeostasis.
Endocrine cells are present in various organs, including the pituitary, thyroid, parathyroid, adrenal, and pancreatic glands.
Dysfunction or imbalance in endocrine cell function can lead to a variety of endocrine disorders, making them an important target for research and therapeutic interventions.
Researchers often utilize tools and techniques like CellSens imaging software, Matrigel, Lipofectamine RNAiMAX reagent, and DAPI staining to study the structure, function, and regulation of endocrine cells.
One example of an endocrine cell line commonly used in research is the MCF-7 cell line, which is derived from a human breast adenocarcinoma and exhibits many characteristics of mammary epithelial cells.
These cells can be cultured in DPBS and MTeSR1 medium, and their behavior can be analyzed using advanced microscopy techniques like the Axiovert microscope.
Optimizing endocrine cell research is crucial for improving our understanding of endocrine system function and developing effective treatments for endocrine disorders.
PubCompare.ai offers powerful tools to enhance the reproducibility and accuracy of your endocrine cell research, allowing you to identify the best protocols and products for your experiments and gain data-driven insights to improve your research outcomes.