ACD can configure probes for the various manual and automated assays for INS for RNAscope Assay, or for Basescope Assay compatible for your species of interest.
Cell reports
2022 Mar 01
Jeon, H;Lee, H;Kwon, DH;Kim, J;Tanaka-Yamamoto, K;Yook, JS;Feng, L;Park, HR;Lim, YH;Cho, ZH;Paek, SH;Kim, J;
PMID: 35235786 | DOI: 10.1016/j.celrep.2022.110439
Nature.
2018 Feb 28
Augustine V, Gokce SK, Lee S, Wang B, Davidson TJ, Reimann F, Gribble F, Deisseroth K, Lois C, Oka Y.
PMID: 29489747 | DOI: 10.1038/nature25488
Neural circuits for appetites are regulated by both homeostatic perturbations and ingestive behaviour. However, the circuit organization that integrates these internal and external stimuli is unclear. Here we show in mice that excitatory neural populations in the lamina terminalis form a hierarchical circuit architecture to regulate thirst. Among them, nitric oxide synthase-expressing neurons in the median preoptic nucleus (MnPO) are essential for the integration of signals from the thirst-driving neurons of the subfornical organ (SFO). Conversely, a distinct inhibitory circuit, involving MnPO GABAergic neurons that express glucagon-like peptide 1 receptor (GLP1R), is activated immediately upon drinking and monosynaptically inhibits SFO thirst neurons. These responses are induced by the ingestion of fluids but not solids, and are time-locked to the onset and offset of drinking. Furthermore, loss-of-function manipulations of GLP1R-expressing MnPO neurons lead to a polydipsic, overdrinking phenotype. These neurons therefore facilitate rapid satiety of thirst by monitoring real-time fluid ingestion. Our study reveals dynamic thirst circuits that integrate the homeostatic-instinctive requirement for fluids and the consequent drinking behaviour to maintain internal water balance.
The Journal of experimental medicine
2022 Sep 05
Kaiser, FMP;Gruenbacher, S;Oyaga, MR;Nio, E;Jaritz, M;Sun, Q;van der Zwaag, W;Kreidl, E;Zopf, LM;Dalm, VASH;Pel, J;Gaiser, C;van der Vliet, R;Wahl, L;Rietman, A;Hill, L;Leca, I;Driessen, G;Laffeber, C;Brooks, A;Katsikis, PD;Lebbink, JHG;Tachibana, K;van der Burg, M;De Zeeuw, CI;Badura, A;Busslinger, M;
PMID: 35947077 | DOI: 10.1084/jem.20220498
Cell metabolism
2022 Jun 14
Imbernon, M;Saponaro, C;Helms, HCC;Duquenne, M;Fernandois, D;Deligia, E;Denis, RGP;Chao, DHM;Rasika, S;Staels, B;Pattou, F;Pfrieger, FW;Brodin, B;Luquet, S;Bonner, C;Prevot, V;
PMID: 35716660 | DOI: 10.1016/j.cmet.2022.06.002
Neuron
2021 May 21
Pribiag, H;Shin, S;Wang, EH;Sun, F;Datta, P;Okamoto, A;Guss, H;Jain, A;Wang, XY;De Freitas, B;Honma, P;Pate, S;Lilascharoen, V;Li, Y;Lim, BK;
PMID: 34048697 | DOI: 10.1016/j.neuron.2021.05.002
Cell Rep.
2018 Jan 09
Chevée M, Robertson JJ, Cannon GH, Brown SP, Goff LA.
PMID: 29320739 | DOI: 10.1016/j.celrep.2017.12.046
Single-cell RNA sequencing has generated catalogs of transcriptionally defined neuronal subtypes of the brain. However, the cellular processes that contribute to neuronal subtype specification and transcriptional heterogeneity remain unclear. By comparing the gene expression profiles of single layer 6 corticothalamic neurons in somatosensory cortex, we show that transcriptional subtypes primarily reflect axonal projection pattern, laminar position within the cortex, and neuronal activity state. Pseudotemporal ordering of 1,023 cellular responses to sensory manipulation demonstrates that changes in expression of activity-induced genes both reinforced cell-type identity and contributed to increased transcriptional heterogeneity within each cell type. This is due to cell-type biased choices of transcriptional states following manipulation of neuronal activity. These results reveal that axonal projection pattern, laminar position, and activity state define significant axes of variation that contribute both to the transcriptional identity of individual neurons and to the transcriptional heterogeneity within each neuronal subtype.
EMBO Rep
2020 Feb 13
Pereira B, Amaral AL, Dias A, Mendes N, Muncan V, Silva AR, Thibert C, Radu AG, David L, M�ximo V, van den Brink GR, Billaud M, Almeida R
PMID: 32052574 | DOI: 10.15252/embr.201948938
Nature neuroscience
2021 May 17
Biglari, N;Gaziano, I;Schumacher, J;Radermacher, J;Paeger, L;Klemm, P;Chen, W;Corneliussen, S;Wunderlich, CM;Sue, M;Vollmar, S;Klöckener, T;Sotelo-Hitschfeld, T;Abbasloo, A;Edenhofer, F;Reimann, F;Gribble, FM;Fenselau, H;Kloppenburg, P;Wunderlich, FT;Brüning, JC;
PMID: 34002087 | DOI: 10.1038/s41593-021-00854-0
Diabetes.
2018 May 18
Adams JM, Pei H, Sandoval DA, Seeley RJ, Chang RB, Liberles SD, Olson DP.
PMID: 29776968 | DOI: 10.2337/db17-1385
Glucagon-like peptide-1 receptor (GLP-1R) agonists are FDA-approved weight loss drugs. Despite their widespread use, the sites of action through which GLP-1R agonists (GLP1RAs) impact appetite and body weight are still not fully understood. Here, we determined whether GLP-1Rs in either GABAergic or glutamatergic neurons are necessary for the acute and chronic effects of the GLP1RA liraglutide on food intake, visceral illness, body weight and neural network activation. We found that mice lacking GLP-1Rs in vGAT-expressing GABAergic neurons responded identically to controls in all parameters measured, whereas deletion of GLP-1Rs in vGlut2-expressing glutamatergic neurons eliminated liraglutide-induced weight loss and visceral illness and severely attenuated its effects on feeding. Concomitantly, deletion of GLP-1Rs from glutamatergic neurons completely abolished the neural network activation observed after liraglutide administration. We conclude that liraglutide activates a dispersed but discrete neural network to mediate its physiological effects, and that these effects require GLP-1R expression on glutamatergic but not GABAergic neurons.
Nature
2022 Mar 01
Zhao, Q;Yu, CD;Wang, R;Xu, QJ;Dai Pra, R;Zhang, L;Chang, RB;
PMID: 35296859 | DOI: 10.3760/cma.j.cn112151-20210719-00516
Investigative Ophthalmology & Visual Science
2022 Jan 01
Zhu, X;Xu, M;Grachtchouk, M;
Nat Cell Biol.
2017 Apr 10
Turco MY, Gardner L, Hughes J, Cindrova-Davies T, Gomez MJ, Farrell L, Hollinshead M, Marsh SG, Brosens JJ, Critchley HO, Simons BD, Hemberger M, Koo BK, Moffett A, Burton GJ.
PMID: 28394884 | DOI: 10.1038/ncb3516
In humans, the endometrium, the uterine mucosal lining, undergoes dynamic changes throughout the menstrual cycle and pregnancy. Despite the importance of the endometrium as the site of implantation and nutritional support for the conceptus, there are no long-term culture systems that recapitulate endometrial function in vitro. We adapted conditions used to establish human adult stem-cell-derived organoid cultures to generate three-dimensional cultures of normal and decidualized human endometrium. These organoids expand long-term, are genetically stable and differentiate following treatment with reproductive hormones. Single cells from both endometrium and decidua can generate a fully functional organoid. Transcript analysis confirmed great similarity between organoids and the primary tissue of origin. On exposure to pregnancy signals, endometrial organoids develop characteristics of early pregnancy. We also derived organoids from malignant endometrium, and so provide a foundation to study common diseases, such as endometriosis and endometrial cancer, as well as the physiology of early gestation.
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 | |
EnEm | Probe targets exons n and m | |
En-Em | Probe 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 |
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