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Species

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Gene

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Platform

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Channel

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HiPlex Channel

  • T1 (85058) Apply T1 filter
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  • T11 (85039) Apply T11 filter
  • T9 (82563) Apply T9 filter
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  • S1 (32) Apply S1 filter
  • 8 (17) Apply 8 filter
  • 1 (1) Apply 1 filter
  • 10 (1) Apply 10 filter
  • 6 (1) Apply 6 filter

Product

  • RNAscope Multiplex Fluorescent Assay (1035) Apply RNAscope Multiplex Fluorescent Assay filter
  • RNAscope (998) Apply RNAscope filter
  • RNAscope Fluorescent Multiplex Assay (732) Apply RNAscope Fluorescent Multiplex Assay filter
  • RNAscope 2.5 HD Red assay (704) Apply RNAscope 2.5 HD Red assay filter
  • RNAscope 2.0 Assay (497) Apply RNAscope 2.0 Assay filter
  • RNAscope 2.5 HD Brown Assay (293) Apply RNAscope 2.5 HD Brown Assay filter
  • TBD (193) Apply TBD filter
  • RNAscope 2.5 LS Assay (191) Apply RNAscope 2.5 LS Assay filter
  • RNAscope 2.5 HD Duplex (160) Apply RNAscope 2.5 HD Duplex filter
  • RNAscope 2.5 HD Reagent Kit - BROWN (108) Apply RNAscope 2.5 HD Reagent Kit - BROWN filter
  • RNAscope Multiplex Fluorescent v2 (97) Apply RNAscope Multiplex Fluorescent v2 filter
  • BASEscope Assay RED (91) Apply BASEscope Assay RED filter
  • RNAscope 2.5 VS Assay (85) Apply RNAscope 2.5 VS Assay filter
  • Basescope (53) Apply Basescope filter
  • RNAscope HiPlex v2 assay (30) Apply RNAscope HiPlex v2 assay filter
  • miRNAscope (26) Apply miRNAscope filter
  • DNAscope HD Duplex Reagent Kit (15) Apply DNAscope HD Duplex Reagent Kit filter
  • RNAscope 2.5 HD duplex reagent kit (13) Apply RNAscope 2.5 HD duplex reagent kit filter
  • BaseScope Duplex Assay (12) Apply BaseScope Duplex Assay filter
  • RNAscope Multiplex fluorescent reagent kit v2 (6) Apply RNAscope Multiplex fluorescent reagent kit v2 filter
  • RNAscope Fluorescent Multiplex Reagent kit (5) Apply RNAscope Fluorescent Multiplex Reagent kit filter
  • RNAscope ISH Probe High Risk HPV (5) Apply RNAscope ISH Probe High Risk HPV filter
  • CTCscope (4) Apply CTCscope filter
  • RNAscope 2.5 HD Reagent Kit (4) Apply RNAscope 2.5 HD Reagent Kit filter
  • RNAscope HiPlex12 Reagents Kit (3) Apply RNAscope HiPlex12 Reagents Kit filter
  • DNAscope Duplex Assay (2) Apply DNAscope Duplex Assay filter
  • RNAscope 2.5 HD Assay (2) Apply RNAscope 2.5 HD Assay filter
  • RNAscope 2.5 LS Assay - RED (2) Apply RNAscope 2.5 LS Assay - RED filter
  • RNAscope Multiplex Fluorescent Assay v2 (2) Apply RNAscope Multiplex Fluorescent Assay v2 filter
  • BOND RNAscope Brown Detection (1) Apply BOND RNAscope Brown Detection filter
  • HybEZ Hybridization System (1) Apply HybEZ Hybridization System filter
  • miRNAscope Assay Red (1) Apply miRNAscope Assay Red filter
  • RNA-Protein CO-Detection Ancillary Kit (1) Apply RNA-Protein CO-Detection Ancillary Kit filter
  • RNAscope 2.0 HD Assay - Chromogenic (1) Apply RNAscope 2.0 HD Assay - Chromogenic filter
  • RNAscope 2.5 HD- Red (1) Apply RNAscope 2.5 HD- Red filter
  • RNAscope 2.5 LS Reagent Kits (1) Apply RNAscope 2.5 LS Reagent Kits filter
  • RNAScope HiPlex assay (1) Apply RNAScope HiPlex assay filter
  • RNAscope HiPlex Image Registration Software (1) Apply RNAscope HiPlex Image Registration Software filter
  • RNAscope LS Multiplex Fluorescent Assay (1) Apply RNAscope LS Multiplex Fluorescent Assay filter
  • RNAscope Multiplex Fluorescent Reagent Kit V3 (1) Apply RNAscope Multiplex Fluorescent Reagent Kit V3 filter
  • RNAscope Multiplex Fluorescent Reagent Kit v4 (1) Apply RNAscope Multiplex Fluorescent Reagent Kit v4 filter
  • RNAscope Multiplex Fluorescent v1 (1) Apply RNAscope Multiplex Fluorescent v1 filter
  • RNAscope Target Retrieval Reagents (1) Apply RNAscope Target Retrieval Reagents filter

Research area

  • Neuroscience (1849) Apply Neuroscience filter
  • Cancer (1385) Apply Cancer filter
  • Development (509) Apply Development filter
  • Inflammation (472) Apply Inflammation filter
  • Infectious Disease (410) Apply Infectious Disease filter
  • Other (406) Apply Other filter
  • Stem Cells (258) Apply Stem Cells filter
  • Covid (237) Apply Covid filter
  • Infectious (220) Apply Infectious filter
  • HPV (187) Apply HPV filter
  • lncRNA (135) Apply lncRNA filter
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  • Immunotherapy (72) Apply Immunotherapy filter
  • Other: Methods (67) Apply Other: Methods filter
  • HIV (64) Apply HIV filter
  • CGT (62) Apply CGT filter
  • Pain (62) Apply Pain filter
  • diabetes (57) Apply diabetes filter
  • LncRNAs (46) Apply LncRNAs filter
  • Aging (43) Apply Aging filter
  • Other: Heart (40) Apply Other: Heart filter
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  • Obesity (29) Apply Obesity filter
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  • Behavior (27) Apply Behavior filter
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  • Other: Kidney (27) Apply Other: Kidney filter
  • Alzheimer's Disease (26) Apply Alzheimer's Disease filter
  • Bone (24) Apply Bone filter
  • Stress (21) Apply Stress filter
  • Other: Zoological Disease (20) Apply Other: Zoological Disease filter
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  • Fibrosis (17) Apply Fibrosis filter
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  • Other: Endocrinology (16) Apply Other: Endocrinology filter
  • Other: Skin (16) Apply Other: Skin filter
  • Injury (15) Apply Injury filter
  • Anxiety (14) Apply Anxiety filter
  • Memory (14) Apply Memory filter
  • Reproductive Biology (14) Apply Reproductive Biology filter

Product sub type

  • Target Probes (256568) Apply Target Probes filter
  • Control Probe - Automated Leica (409) Apply Control Probe - Automated Leica filter
  • Control Probe - Automated Leica Multiplex (284) Apply Control Probe - Automated Leica Multiplex filter
  • Control Probe - Automated Leica Duplex (168) Apply Control Probe - Automated Leica Duplex filter
  • Control Probe- Manual RNAscope Multiplex (148) Apply Control Probe- Manual RNAscope Multiplex filter
  • Control Probe - Automated Ventana (143) Apply Control Probe - Automated Ventana filter
  • Control Probe - Manual RNAscope Singleplex (142) Apply Control Probe - Manual RNAscope Singleplex filter
  • Control Probe - Manual RNAscope Duplex (137) Apply Control Probe - Manual RNAscope Duplex filter
  • Control Probe (73) Apply Control Probe filter
  • Control Probe - Manual BaseScope Singleplex (51) Apply Control Probe - Manual BaseScope Singleplex filter
  • Control Probe - VS BaseScope Singleplex (41) Apply Control Probe - VS BaseScope Singleplex filter
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  • L-HBsAG (15) Apply L-HBsAG filter
  • Cancer (13) Apply Cancer filter
  • Automated Assay 2.5: Leica System (8) Apply Automated Assay 2.5: Leica System filter
  • Control Probe- Manual BaseScope Duplex (8) Apply Control Probe- Manual BaseScope Duplex filter
  • 1765 (8) Apply 1765 filter
  • 1379 (8) Apply 1379 filter
  • 2184 (8) Apply 2184 filter
  • 38322 (8) Apply 38322 filter
  • Manual Assay 2.5: Pretreatment Reagents (5) Apply Manual Assay 2.5: Pretreatment Reagents filter
  • Controls: Manual Probes (5) Apply Controls: Manual Probes filter
  • Control Probe- Manual RNAscope HiPlex (5) Apply Control Probe- Manual RNAscope HiPlex filter
  • Manual Assay RNAscope Brown (4) Apply Manual Assay RNAscope Brown filter
  • Manual Assay RNAscope Duplex (4) Apply Manual Assay RNAscope Duplex filter
  • Manual Assay RNAscope Multiplex (4) Apply Manual Assay RNAscope Multiplex filter
  • Manual Assay BaseScope Red (4) Apply Manual Assay BaseScope Red filter
  • IA: Other (4) Apply IA: Other filter
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  • Manual Assay miRNAscope Red (4) Apply Manual Assay miRNAscope Red filter
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  • Control Probe - Automated Ventana Duplex (3) Apply Control Probe - Automated Ventana Duplex filter
  • Manual Assay BaseScope Duplex (3) Apply Manual Assay BaseScope Duplex filter
  • Manual Assay RNAscope Red (2) Apply Manual Assay RNAscope Red filter
  • Controls: Control Slides (2) Apply Controls: Control Slides filter
  • Control Probe- Manual BaseScope Singleplex (2) Apply Control Probe- Manual BaseScope Singleplex filter
  • Control Probe - Manual BaseScope™Singleplex (2) Apply Control Probe - Manual BaseScope™Singleplex filter
  • Manual Assay: Accessory Reagent (1) Apply Manual Assay: Accessory Reagent filter
  • Accessory Reagent (1) Apply Accessory Reagent filter
  • Controls: Manual RNAscope Multiplex (1) Apply Controls: Manual RNAscope Multiplex filter
  • IA: HybEZ (1) Apply IA: HybEZ filter
  • Automated Assay BaseScope: LS (1) Apply Automated Assay BaseScope: LS filter
  • Automated Assay BaseScope: VS (1) Apply Automated Assay BaseScope: VS filter
  • Software: RNAscope HiPlex Image Registration (1) Apply Software: RNAscope HiPlex Image Registration filter
  • miRNAscope Automated Assay: Leica System (1) Apply miRNAscope Automated Assay: Leica System filter
  • Automated Assay: VS (1) Apply Automated Assay: VS filter
  • Control Probe - VS BaseScope™Singleplex (1) Apply Control Probe - VS BaseScope™Singleplex filter
  • Controls:2.5VS Probes (1) Apply Controls:2.5VS Probes filter
  • Control Probe - Manual RNAscope Multiplex (1) Apply Control Probe - Manual RNAscope Multiplex filter

Sample Compatibility

  • Cell pellets (49) Apply Cell pellets filter
  • FFPE (41) Apply FFPE filter
  • Fixed frozen tissue (31) Apply Fixed frozen tissue filter
  • TMA (31) Apply TMA filter
  • Adherent cells (26) Apply Adherent cells filter
  • Freshfrozen tissue (18) Apply Freshfrozen tissue filter
  • Fresh frozen tissue (13) Apply Fresh frozen tissue filter
  • Cell Cultures (12) Apply Cell Cultures filter
  • TMA(Tissue Microarray) (9) Apply TMA(Tissue Microarray) filter
  • FFPE,Freshfrozen tissue,Fixed frozen tissue,TMA,Cell pellets,Adherent cells (7) Apply FFPE,Freshfrozen tissue,Fixed frozen tissue,TMA,Cell pellets,Adherent cells filter
  • CTC (4) Apply CTC filter
  • PBMC's (4) Apply PBMC's filter
  • Adherent or Cultured Cells (1) Apply Adherent or Cultured Cells filter
  • Fixed frozen (1) Apply Fixed frozen filter
  • FFPE,TMA (1) Apply FFPE,TMA filter
  • Fixed frozen tissues (for chromogenic assays) (1) Apply Fixed frozen tissues (for chromogenic assays) filter

Category

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Application

  • Cancer (139875) Apply Cancer filter
  • Neuroscience (51010) Apply Neuroscience filter
  • Cancer, Neuroscience (32227) Apply Cancer, Neuroscience filter
  • Non-coding RNA (24365) Apply Non-coding RNA filter
  • Cancer, Inflammation (16436) Apply Cancer, Inflammation filter
  • Cancer, Inflammation, Neuroscience (12591) Apply Cancer, Inflammation, Neuroscience filter
  • Inflammation (9879) Apply Inflammation filter
  • Cancer, Stem Cell (7932) Apply Cancer, Stem Cell filter
  • Cancer, Neuroscience, Stem Cell (7028) Apply Cancer, Neuroscience, Stem Cell filter
  • Cancer, Immunotherapy, Inflammation, Neuroscience, Stem Cell (6854) Apply Cancer, Immunotherapy, Inflammation, Neuroscience, Stem Cell filter
  • Cancer, Inflammation, Neuroscience, Stem Cell (5424) Apply Cancer, Inflammation, Neuroscience, Stem Cell filter
  • Immunotherapy (5368) Apply Immunotherapy filter
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  • Cancer, Immunotherapy, Inflammation (2844) Apply Cancer, Immunotherapy, Inflammation filter
  • Cancer, Immunotherapy, Inflammation, Neuroscience (1878) Apply Cancer, Immunotherapy, Inflammation, Neuroscience filter
  • Cancer, Immunotherapy, Neuroscience (1786) Apply Cancer, Immunotherapy, Neuroscience filter
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Heart Regeneration in the Mexican Cavefish.

Cell Rep.

2018 Nov 20

Stockdale WT, Lemieux ME, Killen AC, Zhao J, Hu Z, Riepsaame J, Hamilton N, Kudoh T, Riley PR, van Aerle R, Yamamoto Y, Mommersteeg MTM.
PMID: 30462998 | DOI: 10.1016/j.celrep.2018.10.072

Although Astyanax mexicanus surface fish regenerate their hearts after injury, their Pachón cave-dwelling counterparts cannot and, instead, form a permanent fibrotic scar, similar to the human heart. Myocardial proliferation peaks at similar levels in both surface fish and Pachón 1 week after injury. However, in Pachón, this peak coincides with a strong scarring and immune response, and ultimately, cavefish cardiomyocytes fail to replace the scar. We identified lrrc10 to be upregulated in surface fish compared with Pachón after injury. Similar to cavefish, knockout of lrrc10 in zebrafish impairs heart regeneration without affecting wound cardiomyocyte proliferation. Furthermore, using quantitative trait locus (QTL) analysis, we have linked the degree of heart regeneration to three loci in the genome, identifying candidate genes fundamental to the difference between scarring and regeneration. Our study provides evidence that successful heart regeneration entails a delicate interplay between cardiomyocyte proliferation and scarring.

SHP2 regulates intramembranous ossification by modifying the TGFβ and BMP2 signaling pathway

Bone.

2018 Nov 22

Wang L, Huang J, Moore DC, Song Y, Ehrlich MG, Yang W.
PMID: 30471432 | DOI: 10.1016/j.bone.2018.11.014

SHP2 is a ubiquitously expressed protein tyrosine phosphatase, which is involved in many signaling pathways to regulate the skeletal development. In endochondral ossification, SHP2 is known to modify the osteogenic fate of osteochondroprogenitors and to impair the osteoblastic transdifferentiation of hypertrophic chondrocytes. However, how SHP2 regulates osteoblast differentiation in intramembranous ossification remains incompletely understood. To address this question, we generated a mouse model to ablate SHP2 in the Prrx1-expressing mesenchymal progenitors by using "Cre-loxP"-mediated gene excision and examined the development of calvarial bone, in which the main process of bone formation is intramembranous ossification. Phenotypic characterization showed that SHP2 mutants have severe defects in calvarial bone formation. Cell lineage tracing and in situ hybridization data showed less osteoblast differentiation of mesenchymal cells and reduced osteogenic genes expression, respectively. Further mechanistic studies revealed enhanced TGFβ and suppressed BMP2 signaling in SHP2 ablated mesenchymal progenitors and their derivatives. Our study uncovered the critical role of SHP2 in osteoblast differentiation through intramembranous ossification and might provide a potential target to treat craniofacial skeleton disorders.

Single-Cell RNA Sequencing of Microglia throughout the Mouse Lifespan and in the Injured Brain Reveals Complex Cell-State Changes

Immunity.

2018 Nov 21

Hammond TR, Dufort C, Dissing-Olesen L, Giera S, Young A, Wysoker A, Walker AJ, Gergits F, Segel M, Nemesh J, Marsh SE, Saunders A, Macosko E, Ginhoux F, Chen J, Franklin RJM, Piao X, McCarroll SA, Stevens B.
PMID: 30471926 | DOI: 10.1016/j.immuni.2018.11.004

Microglia, the resident immune cells of the brain, rapidly change states in response to their environment, but we lack molecular and functional signatures of different microglial populations. Here, we analyzed the RNA expression patterns of more than 76,000 individual microglia in mice during development, in old age, and after brain injury. Our analysis uncovered at least nine transcriptionally distinct microglial states, which expressed unique sets of genes and were localized in the brain using specific markers. The greatest microglial heterogeneity was found at young ages; however, several states-including chemokine-enriched inflammatory microglia-persisted throughout the lifespan or increased in the aged brain. Multiple reactive microglial subtypes were also found following demyelinating injury in mice, at least one of which was also found in human multiple sclerosis lesions. These distinct microglia signatures can be used to better understand microglia function and to identify and manipulate specific subpopulations in health and disease.

Quantitative Clonal Analysis and Single-Cell Transcriptomics Reveal Division Kinetics, Hierarchy, and Fate of Oral Epithelial Progenitor Cells.

Cell Stem Cell.

2018 Nov 21

Jones KB, Furukawa S, Marangoni P, Ma H, Pinkard H, D’Urso R, Zilionis R, Klein AM, Klein OD.
PMID: 30472156 | DOI: 10.1016/j.stem.2018.10.015

The oral mucosa is one of the most rapidly dividing tissues in the body and serves as a barrier to physical and chemical insults from mastication, food, and microorganisms. Breakdown of this barrier can lead to significant morbidity and potentially life-threatening infections for patients. Determining the identity and organization of oral epithelial progenitor cells (OEPCs) is therefore paramount to understanding their roles in homeostasis and disease. Using lineage tracing and label retention experiments, we show that rapidly dividing OEPCs are located broadly within the basal layer of the mucosa throughout the oral cavity. Quantitative clonal analysis demonstrated that OEPCs undergo population-asymmetrical divisions following neutral drift dynamics and that they respond to chemotherapy-induced damage by altering daughter cell fates. Finally, using single-cell RNA-seq, we establish the basal layer population structure and propose a model that defines the organization of cells within the basal layer.

Single-Cell Transcriptome Analysis Reveals Estrogen Signaling Coordinately Augments One-Carbon, Polyamine, and Purine Synthesis in Breast Cancer.

Cell Rep.

2018 Nov 20

Zhu D, Zhao Z, Cui G, Chang S, Hu L, See YX, Lim MGL, Guo D, Chen X, Robson P, Luo Y, Cheung E.
PMID: 30463022 | DOI: 10.1016/j.celrep.2018.10.093

Estrogen drives breast cancer (BCa) progression by directly activating estrogen receptor α (ERα). However, because of the stochastic nature of gene transcription, it is important to study the estrogen signaling pathway at the single-cell level to fully understand how ERα regulates transcription. Here, we performed single-cell transcriptome analysis on ERα-positive BCa cells following 17β-estradiol stimulation and reconstructed the dynamic estrogen-responsive transcriptional network from discrete time points into a pseudotemporal continuum. Notably, differentially expressed genes show an estrogen-stimulated metabolic switch that favors biosynthesis but reduces estrogen degradation. Moreover, folate-mediated one-carbon metabolism is reprogrammed through the mitochondrial folate pathway and polyamine and purine synthesis are upregulated coordinately. Finally, we show AZIN1 and PPAT are direct ERα targets that are essential for BCa cell survival and growth. In summary, our study highlights the dynamic transcriptional heterogeneity in ERα-positive BCa cells upon estrogen stimulation and uncovers a mechanism of estrogen-mediated metabolic switch.

Hypothalamic POMC or MC4R deficiency impairs counterregulatory responses to hypoglycemia in mice.

Molecular Metabolism

2018 Nov 20

Tooke BP, Yu H, Adams JM, Jones GL, Sutton-Kennedy T, Mundada L, Qi NR, Low MJ, Chhabra KH.
PMID: - | DOI: 10.1016/j.molmet.2018.11.004

Abstract

Objective

Life-threatening hypoglycemia is a major limiting factor in the management of diabetes. While it is known that counterregulatory responses to hypoglycemia are impaired in diabetes, molecular mechanisms underlying the reduced responses remain unclear. Given the established roles of the hypothalamic proopiomelanocortin (POMC)/melanocortin 4 receptor (MC4R) circuit in regulating sympathetic nervous system (SNS) activity and the SNS in stimulating counterregulatory responses to hypoglycemia, we hypothesized that hypothalamic POMC as well as MC4R, a receptor for POMC derived melanocyte stimulating hormones, is required for normal hypoglycemia counterregulation.

Methods

To test the hypothesis, we induced hypoglycemia or glucopenia in separate cohorts of mice deficient in either POMC or MC4R in the arcuate nucleus (ARC) or the paraventricular nucleus of the hypothalamus (PVH), respectively, and measured their circulating counterregulatory hormones. In addition, we performed a hyperinsulinemic-hypoglycemic clamp study to further validate the function of MC4R in hypoglycemia counterregulation. We also measured Pomc and Mc4r mRNA levels in the ARC and PVH, respectively, in the streptozotocin-induced type 1 diabetes mouse model and non-obese diabetic (NOD) mice to delineate molecular mechanisms by which diabetes deteriorates the defense systems against hypoglycemia. Finally, we treated diabetic mice with the MC4R agonist MTII, administered stereotaxically into the PVH, to determine its potential for restoring the counterregulatory response to hypoglycemia in diabetes.

Results

Stimulation of epinephrine and glucagon release in response to hypoglycemia or glucopenia was diminished in both POMC- and MC4R-deficient mice, relative to their littermate controls. Similarly, the counterregulatory response was impaired in association with decreased hypothalamic Pomc and Mc4r expression in the diabetic mice, a phenotype that was not reversed by insulin treatment which normalized glycemia. In contrast, infusion of an MC4R agonist in the PVH restored the counterregulatory response in diabetic mice.

Conclusion

In conclusion, hypothalamic Pomc as well as Mc4r, both of which are reduced in type 1 diabetic mice, are required for normal counterregulatory responses to hypoglycemia. Therefore, enhancing MC4R function may improve hypoglycemia counterregulation in diabetes.

LncRNA Meg3 protects endothelial function by regulating the DNA damage response.

Nucleic Acids Research

2018 Nov 22

Shihabudeen Haider Ali MS, Cheng X, Moran M, Haemmig S, Naldrett MJ, Alvarez S, Feinberg MW, Sun X.
PMID: - | DOI: 10.1093/nar/gky1190

Abstract

The role of long non-coding RNAs (lncRNAs) in regulating endothelial function through the DNA damage response (DDR) remains poorly understood. In this study, we demonstrate that lncRNA maternally expressed gene 3 (Meg3) interacts with the RNA binding protein polypyrimidine tract binding protein 3 (PTBP3) to regulate gene expression and endothelial function through p53 signaling ─ a major coordinator of apoptosis and cell proliferation triggered by the DDR. Meg3 expression is induced in endothelial cells (ECs) upon p53 activation. Meg3 silencing induces DNA damage, activates p53 signaling, increases the expression of p53 target genes, promotes EC apoptosis, and inhibits EC proliferation. Mechanistically, Meg3 silencing reduces the interaction of p53 with Mdm2, induces p53 expression, and promotes the association of p53 with the promoters of a subset of p53 target genes. PTBP3 silencing recapitulates the effects of Meg3 deficiency on the expression of p53 target genes, EC apoptosis and proliferation. The Meg3-dependent association of PTBP3 with the promoters of p53 target genes suggests that Meg3 and PTBP3 restrain p53 activation. Our studies reveal a novel role of Meg3 and PTBP3 in regulating p53 signaling and endothelial function, which may serve as novel targets for therapies to restore endothelial homeostasis.

Beyond the H&E: Advanced Technologies for in situ Tissue Biomarker Imaging.

ILAR J.

2018 Nov 21

Himmel LE, Hackett TA, Moore JL, Adams WR, Thomas G, Novitskaya T, Caprioli RM, Zijlstra A, Mahadevan-Jansen A, Boyd KL.
PMID: 30462242 | DOI: 10.1093/ilar/ily004

For decades, histopathology with routine hematoxylin and eosin staining has been and remains the gold standard for reaching a morphologic diagnosis in tissue samples from humans and veterinary species. However, within the past decade, there has been exponential growth in advanced techniques for in situ tissue biomarker imaging that bridge the divide between anatomic and molecular pathology. It is now possible to simultaneously observe localization and expression magnitude of multiple protein, nucleic acid, and molecular targets in tissue sections and apply machine learning to synthesize vast, image-derived datasets. As these technologies become more sophisticated and widely available, a team-science approach involving subspecialists with medical, engineering, and physics backgrounds is critical to upholding quality and validity in studies generating these data. The purpose of this manuscript is to detail the scientific premise, tools and training, quality control, and data collection and analysis considerations needed for the most prominent advanced imaging technologies currently applied in tissue sections: immunofluorescence, in situ hybridization, laser capture microdissection, matrix-assisted laser desorption ionization imaging mass spectrometry, and spectroscopic/optical methods. We conclude with a brief overview of future directions for ex vivo and in vivo imaging techniques.

Molecular and immunologic analysis of laryngeal squamous cell carcinoma in smokers and non-smokers.

American Journal of Otolaryngology

2018 Nov 22

Malm IJ, Rooper LM, Bishop JA, Ozgursoy SK, Hillel AT, Akst LM, Best SR.
PMID: - | DOI: 10.1016/j.amjoto.2018.11.009

Abstract

Background

Laryngeal squamous cell carcinoma (LSCC) is strongly associated with tobacco use, but recent reports suggest an increasing incidence of LSCC in patients without traditional risk factors, suggesting an alternative etiology of tumorigenesis. The purpose of this study is to characterize this non-smoking population and to compare immunohistochemical markers in tumor specimens from non-smokers and smokers with LSCC.

Methods

A retrospective chart review of patients with LSCC at Johns Hopkins Hospital (JHH) was performed. A tissue microarray (TMA) was constructed with tumor specimen from non-smokers with stage and age-matched smokers and stained for a variety of immunologic and molecular targets.

Results

In the JHH cohort of 521 patients, 12% (n = 63) were non-smokers. Non-smokers were more likely to be <45 years old at time of diagnosis (OR 4.13, p = 0.001) and to have glottic tumors (OR 2.46, p = 0.003). The TMA was comprised of tumors from 34 patients (14 non-smokers, 20 smokers). Only 2 patients (6%) were human-papillomavirus (HPV) positive by high-risk RNA in situ hybridization (ISH). There was no correlation between smoking status and p16 (p = 0.36), HPV-ISH positivity (p = 0.79), phosphatase and tensin homolog (PTEN, p = 0.91), p53 (p = 0.14), or programmed death-ligand 1 (PD-L1, p = 0.27) expression.

Conclusions

Non-smokers with LSCC are more likely to be younger at the time of diagnosis and have glottic tumors than smokers with LSCC. In TMA analysis of stage and age-matched specimens from smoker and non-smokers with LSCC, the pattern of expression for common molecular and immunologic markers is similar. Further, HPV does not appear to be a major causative etiology of LSCC in either smokers or non-smokers in our cohort of patients.

Indian Hedgehog links obesity to development of hepatocellular carcinoma.

Oncogene.

2018 Nov 23

Chong YC, Lim TE, Fu Y, Shin EM, Tergaonkar V, Han W.
PMID: 30470823 | DOI: 10.1038/s41388-018-0585-5

Obesity increases the risk of hepatocellular carcinoma (HCC), but precise identification and characterization of druggable oncogenic pathways that contribute to the progression of NAFLD to HCC, and hence to the increased incidence and aggressiveness of HCC in obese individuals is lacking. In this regard, we demonstrate that the Indian Hedgehog (Ihh) signaling pathway is upregulated in the fatty livers of mice consuming a high fat diet, and furthermore sustained in HCC tumors specifically within the context of a NAFLD microenvironment. Using a diet-induced mouse model of HCC wherein only obese mice develop HCC, targeted ablation of hepatocyte-secreted Ihh results in a decreased tumor burden and lower grade tumors. Ihh activation regulates the transdifferentiation of ciliated stellate cells and proliferation of Epcam+ ductal cells to promote fibrosis. Mechanistically, increased expression of hitherto uncharacterized effectors of Hh pathway, namely Myc and Tgf-β2 is critical to the observed physiology. This pro-tumorigenic response is driven by increased expression of Wnt5a to effect a poorly-differentiated and invasive tumor phenotype. Wnt5a secreted from activated stellate cells act on Ror2-expressing hepatocytes. We further demonstrate that Wnt5a expression is also elevated in poorly-differentiated HCC cells, suggesting that these ligands are also able to function in an autocrine positive feedback manner to sustain poorly-differentiated tumors. Taken together, our study provides a mechanistic understanding for how Ihh signaling promotes HCC tumorigenesis specifically in obese mice. We propose that therapeutic targeting of the Hh pathway offers benefit for patients with dietary / NAFLD-driven steatotic HCC.

Intratumoral heterogeneity of esophageal squamous cell carcinoma and its clinical significance.

Pathology - Research and Practice

2018 Nov 24

Wang X, Jia Y, Deng H, Liu Y, Liu Y.
PMID: - | DOI: 10.1016/j.prp.2018.11.019

Recent studies have shown that intratumoral heterogenity is prevalent in esophageal squamous cell cancer (ESCC) based on DNA sequencing and chromosome analysis in multiple regions from the same tumor. This study aimed to investigate the expression of ZNF750, EP300, MTOR and KMT2D and their intratumoral heterogeneity(ITH) in patients with ESCC. A total of 106 cases, who underwent esophagectomy from 2008 to 2010, with two foci from each case, were tested by immunohistochemistry(IHC) as well as 12 cases were tested by RNAscope in this study.We found that 58/106 (54.72%), 66/106 (62.26%), 75/106 (70.75%%) of ESCC showed high expression of ZNF750, EP300, MTOR, respectively by IHC, and 8/12 (66.67%), 10/12 (83.33%), 4/12 (33.33%) and 6/12 (50%) showed high expression of ZNF750, EP300, MTOR and KMT2D, respectively by RNAscope. Multivariate analysis showed that MTOR expression was an independent infavorable prognostic factor of overall survival(OS) (HR = 1.921; P = 0.000). This study also found that 44/106(41,51%), 37/106 (34.91%), 39/106(36.79%) of ESCC showed heterogeneous expression of ZNF750, EP300 and MTOR respectively by IHC, 8/12(66.67%), 8/12(66.67%), 4/12(33.33%), 4/12(33.33%) of ZNF750, EP300, MTOR and KMT2D respectively by RNAscope, IHC and RNAscope could successfully detect a high prevalence of ITH. In conclusion, findings of this study showed that ZNF750, EP300, MTOR and KMT2D heterogeneously expressed in ESCC. High expression of ZNF750 related to a better outcome,while EP300 and MTOR related to a poor prognosis.

Cytopathologic characteristics of HPV‐related small cell carcinoma of the oropharynx

Cancer Cytopathol.

2018 Nov 23

Allison DB, Rooper LM, Mustafa S, Maleki Z, Wakely PE Jr, Ali SZ.
PMID: 30468701 | DOI: 10.1002/cncy.22078

Abstract

BACKGROUND:

Human papillomavirus (HPV)-related squamous cell carcinoma (SqCC) of the oropharynx is an epidemiologically and clinically distinct form of SqCC that is associated with an improved prognosis. However, HPV-related small cell carcinoma of the oropharynx is a rare and newly described variant that is associated with aggressive clinical behavior and poor outcomes. To date, fewer than 2 dozen reports of this entity exist in the literature, and there is no discussion of cytopathologic features. This article reports 6 cases and discusses the salient cytomorphologic findings, ancillary studies, and challenges when this entity is encountered.

METHODS:

Anatomic pathology archives were searched to identify patients with a diagnosis of HPV-related small cell carcinoma of the oropharynx. Medical records were reviewed to document the following: age, sex, smoking status, other relevant clinical history, primary location, treatment, and clinical outcome. Both p16 and high-risk HPV in situ hybridization (ISH) studies were positive in at least 1 specimen from each patient. The pathologic diagnoses, cytomorphologic characteristics, immunocytochemical stains, and HPV ISH studies were reviewed and recorded for all available cases.

RESULTS:

Six patients with 11 cytopathology specimens of HPV-related small cell carcinoma of the oropharynx were identified. The mean age was 61.3 years, and all patients died with widely metastatic disease (mean, 23 months; range, 12-48 months). Mixed small cell carcinoma and SqCC components were present in half of the cases.

CONCLUSIONS:

The identification of a small cell component can be reliably performed with cytology preparations and is crucial because this (and not the HPV status) determines the prognosis.

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Description
sense
Example: Hs-LAG3-sense
Standard probes for RNA detection are in antisense. Sense probe is reverse complent to the corresponding antisense probe.
Intron#
Example: Mm-Htt-intron2
Probe targets the indicated intron in the target gene, commonly used for pre-mRNA detection
Pool/Pan
Example: Hs-CD3-pool (Hs-CD3D, Hs-CD3E, Hs-CD3G)
A mixture of multiple probe sets targeting multiple genes or transcripts
No-XSp
Example: Hs-PDGFB-No-XMm
Does not cross detect with the species (Sp)
XSp
Example: Rn-Pde9a-XMm
designed to cross detect with the species (Sp)
O#
Example: Mm-Islr-O1
Alternative design targeting different regions of the same transcript or isoforms
CDS
Example: Hs-SLC31A-CDS
Probe targets the protein-coding sequence only
EnEmProbe targets exons n and m
En-EmProbe targets region from exon n to exon m
Retired Nomenclature
tvn
Example: Hs-LEPR-tv1
Designed to target transcript variant n
ORF
Example: Hs-ACVRL1-ORF
Probe targets open reading frame
UTR
Example: Hs-HTT-UTR-C3
Probe targets the untranslated region (non-protein-coding region) only
5UTR
Example: Hs-GNRHR-5UTR
Probe targets the 5' untranslated region only
3UTR
Example: Rn-Npy1r-3UTR
Probe targets the 3' untranslated region only
Pan
Example: Pool
A mixture of multiple probe sets targeting multiple genes or transcripts

Enabling research, drug development (CDx) and diagnostics

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