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.
Cellular and molecular gastroenterology and hepatology
2021 Dec 29
Kim, TY;Kim, S;Kim, Y;Lee, YS;Lee, S;Lee, SH;Kweon, MN;
PMID: 34971821 | DOI: 10.1016/j.jcmgh.2021.12.015
J Comp Neurol.
2019 Feb 27
Hultman K, Scarlett JM, Baquero AF, Cornea A, Zhang Y, Salinas CBG, Brown J, Morton GJ, Whalen EJ, Grove KL, Koegler FH, Schwartz MW, Mercer AJ.
PMID: 30809795 | DOI: 10.1002/cne.24668
Central activation of fibroblast growth factor (FGF) receptors regulates peripheral glucose homeostasis and reduces food intake in preclinical models of obesity and diabetes. The current work was undertaken to advance our understanding of the receptor expression, as sites of ligand action by FGF19, FGF21, and FGF1 in the mammalian brain remains unresolved. Recent advances in automated RNAscope in situ hybridization and droplet digital PCR (ddPCR) technology allowed us to interrogate central FGFR/beta klotho (Klb) system at the cellular level in the mouse, with relevant comparisons to nonhuman primate and human brain. FGFR1-3 gene expression was broadly distributed throughout the CNS in Mus musculus, with FGFR1 exhibiting the greatest heterogeneity. FGFR4 expression localized only in the medial habenula and subcommissural organ of mice. Likewise, Klb mRNA was restricted to the suprachiasmatic nucleus (SCh) and select midbrain and hindbrain nuclei. ddPCR in the rodent hypothalamus confirmed that, although expression levels are indeed low for Klb, there is nonetheless a bonafide subpopulation of Klb+ cells in the hypothalamus. In NHP and human midbrain and hindbrain, Klb + cells are quite rare, as is expression of FGFR4. Collectively, these data provide the most robust central map of the FGFR/Klb system to date and highlight central regions that may be of critical importance to assess central ligand effects with pharmacological dosing, such as the putative interactions between the endocrine FGFs and FGFR1/Klb, or FGF19 with FGFR4.
Clin Cancer Res.
2019 Apr 19
Piskol R, Huw LY, Sergin I, Klijn C, Modrusan Z, Kim D, Kljavin NM, Tam R, Patel R, Burton J, Penuel E, Qu X, Koeppen H, Sumiyoshi T, de Sauvage FJ, Lackner MR, de Sousa E Melo F, Kabbarah O.
PMID: 31004000 | DOI: 10.1158/1078-0432.CCR-18-3032
Abstract
PURPOSE:
Four consensus molecular subtypes (CMS1-4) of colorectal cancer (CRC) were identified in primary tumors and found to be associated with distinctive biological features and clinical outcomes. Given that distant metastasis largely accounts for CRC-related mortality, we examined the molecular and clinical attributes of CMS in metastatic CRC (mCRC).
EXPERIMENTAL DESIGN:
We developed a CRC-focused Nanostring based CMS classifier that is ideally suited to interrogate archival tissues. We successfully employ this panel in the CMS classification of FFPE tissues from mCRC cohorts, one of which is comprised of paired primary tumors and metastases. Finally, we developed novel mouse implantation models to enable modelling of CRC in vivo at relevant sites.
RESULTS:
Using our classifier we find that the biological hallmarks of mCRC, including CMS, are in general highly similar to those observed in non-metastatic early stage disease. Importantly, our data demonstrate that CMS1 has the worst outcome in relapsed disease, compared to other CMS. Assigning CMS to primary tumors and their matched metastases revealed mostly concordant subtypes between primary and metastasis. Molecular analysis of matched discordant pairs revealed differences in stromal composition at each site. The development of two novel in vivo orthotopic implantation models further reinforces the notion that extrinsic factors may impact on CMS identification in matched primary and metastatic CRC.
CONCLUSION:
We describe the utility of a Nanostring panel for CMS classification of FFPE clinical samples. Our work reveals the impact of intrinsic and extrinsic factors on CRC heterogeneity during disease progression.
Cell Rep.
2018 Oct 09
Timper K, Paeger L, Sánchez-Lasheras C, Varela L, Jais A, Nolte H, Vogt MC, Hausen AC, Heilinger C, Evers N, Pospisilik JA, Penninger JM, Taylor EB, Horvath TL, Kloppenburg P, Brüning JC.
PMID: 30304679 | DOI: 10.1016/j.celrep.2018.09.034
Mitochondrial oxidative phosphorylation (OXPHOS) and substrate utilization critically regulate the function of hypothalamic proopiomelanocortin (POMC)-expressing neurons. Here, we demonstrate that inactivation of apoptosis-inducing factor (AIF) in POMC neurons mildly impairs mitochondrial respiration and decreases firing of POMC neurons in lean mice. In contrast, under diet-induced obese conditions, POMC-Cre-specific inactivation of AIF prevents obesity-induced silencing of POMC neurons, translating into improved glucose metabolism, improved leptin, and insulin sensitivity, as well as increased energy expenditure in AIFΔPOMC mice. On a cellular level, AIF deficiency improves mitochondrial morphology, facilitates the utilization of fatty acids for mitochondrial respiration, and increases reactive oxygen species (ROS) formation in POMC neurons from obese mice, ultimately leading to restored POMC firing upon HFD feeding. Collectively, partial impairment of mitochondrial function shifts substrate utilization of POMC neurons from glucose to fatty acid metabolism and restores their firing properties, resulting in improved systemic glucose and energy metabolism in obesity.
International journal of molecular sciences
2022 Mar 17
Torz, L;Niss, K;Lundh, S;Rekling, JC;Quintana, CD;Frazier, SED;Mercer, AJ;Cornea, A;Bertelsen, CV;Gerstenberg, MK;Hansen, AMK;Guldbrandt, M;Lykkesfeldt, J;John, LM;Villaescusa, JC;Petersen, N;
PMID: 35328681 | DOI: 10.3390/ijms23063260
Cell metabolism
2022 Feb 01
Ramírez, S;Haddad-Tóvolli, R;Radosevic, M;Toledo, M;Pané, A;Alcolea, D;Ribas, V;Milà-Guasch, M;Pozo, M;Obri, A;Eyre, E;Gómez-Valadés, AG;Chivite, I;Van Eeckhout, T;Zalachoras, I;Altirriba, J;Bauder, C;Imbernón, M;Garrabou, G;Garcia-Ruiz, C;Nogueiras, R;Soto, D;Gasull, X;Sandi, C;Brüning, JC;Fortea, J;Jiménez, A;Fernández-Checa, JC;Claret, M;
PMID: 35108514 | DOI: 10.1016/j.cmet.2021.12.023
Psychoneuroendocrinology
2022 Jan 19
Brix, LM;Häusl, AS;Toksöz, I;Bordes, J;van Doeselaar, L;Engelhardt, C;Narayan, S;Springer, M;Sterlemann, V;Deussing, JM;Chen, A;Schmidt, MV;
PMID: 35091292 | DOI: 10.1016/j.psyneuen.2022.105670
Nat Commun
2020 Jan 23
Engstr�m Ruud L Pereira MMA, de Solis AJ, Fenselau H Br�ning JC
PMID: 31974377 | DOI: 10.1038/s41467-020-14291-3
Nature metabolism
2023 Jan 01
Liu, H;He, Y;Bai, J;Zhang, C;Zhang, F;Yang, Y;Luo, H;Yu, M;Liu, H;Tu, L;Zhang, N;Yin, N;Han, J;Yan, Z;Scarcelli, NA;Conde, KM;Wang, M;Bean, JC;Potts, CHS;Wang, C;Hu, F;Liu, F;Xu, Y;
PMID: 36593271 | DOI: 10.1038/s42255-022-00701-x
Development (Cambridge, England)
2021 May 15
Kaiser, K;Jang, A;Kompanikova, P;Lun, MP;Prochazka, J;Machon, O;Dani, N;Prochazkova, M;Laurent, B;Gyllborg, D;van Amerongen, R;Fame, RM;Gupta, S;Wu, F;Barker, RA;Bukova, I;Sedlacek, R;Kozmik, Z;Arenas, E;Lehtinen, MK;Bryja, V;
PMID: 34032267 | DOI: 10.1242/dev.192054
Developmental biology
2021 Nov 06
Bertozzi, A;Wu, CC;Hans, S;Brand, M;Weidinger, G;
PMID: 34748730 | DOI: 10.1016/j.ydbio.2021.11.001
Nature (2015)
Wang B, Zhao L, Fish M, Logan CY, Nusse R.
PMID: 26245375 | DOI: 10.1038/nature14863
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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