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.
Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology
2021 Apr 16
Venkataraman, A;Hunter, SC;Dhinojwala, M;Ghebrezadik, D;Guo, J;Inoue, K;Young, LJ;Dias, BG;
PMID: 33864008 | DOI: 10.1038/s41386-021-01006-5
Front Cell Neurosci. 2018 Oct 9;12:341.
2018 Oct 09
Yoo T, Cho H, Lee J, Park H, Yoo YE, Yang E, Kim JY, Kim H, Kim E.
PMID: 30356810 | DOI: 10.3389/fncel.2018.00341
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.
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.
Communications biology
2022 Aug 18
Noh, YW;Yook, C;Kang, J;Lee, S;Kim, Y;Yang, E;Kim, H;Kim, E;
PMID: 35982261 | DOI: 10.1038/s42003-022-03813-y
Molecular therapy : the journal of the American Society of Gene Therapy
2022 May 05
Tadokoro, T;Bravo-Hernandez, M;Agashkov, K;Kobayashi, Y;Platoshyn, O;Navarro, M;Marsala, S;Miyanohara, A;Yoshizumi, T;Shigyo, M;Krotov, V;Juhas, S;Juhasova, J;Nguyen, D;Kupcova Skalnikova, H;Motlik, J;Studenovska, H;Proks, V;Reddy, R;Driscoll, SP;Glenn, TD;Kemthong, T;Malaivijitnond, S;Tomori, Z;Vanicky, I;Kakinohana, M;Pfaff, SL;Ciacci, J;Belan, P;Marsala, M;
PMID: 35524407 | DOI: 10.1016/j.ymthe.2022.04.023
Kidney Int.
2018 Sep 21
Kleczko EK, Marsh KH, Tyler LC, Furgeson SB, Bullock BL, Altmann CJ, Miyazaki M, Gitomer BY, Harris PC, Weiser-Evans MCM, Chonchol MB, Clambey ET, Nemenoff RA, Hopp K.
PMID: 30249452 | DOI: 10.1016/j.kint.2018.06.025
Autosomal dominant polycystic kidney disease (ADPKD) is the most prevalent inherited nephropathy. To date, therapies alleviating the disease have largely focused on targeting abnormalities in renal epithelial cell signaling. ADPKD has many hallmarks of cancer, where targeting T cells has brought novel therapeutic interventions. However, little is known about the role and therapeutic potential of T cells in ADPKD. Here, we used an orthologous ADPKD model, Pkd1 p.R3277C (RC), to begin to define the role of T cells in disease progression. Using flow cytometry, we found progressive increases in renal CD8+ and CD4+ T cells, correlative with disease severity, but with selective activation of CD8+ T cells. By immunofluorescence, T cells specifically localized to cystic lesions and increased levels of T-cell recruiting chemokines (CXCL9/CXCL10) were detected by qPCR/in situ hybridization in the kidneys of mice, patients, and ADPKD epithelial cell lines. Importantly, immunodepletion of CD8+ T cells from one to three months in C57Bl/6 Pkd1RC/RC mice resulted in worsening of ADPKD pathology, decreased apoptosis, and increased proliferation compared to IgG-control, consistent with a reno-protective role of CD8+ T cells. Thus, our studies suggest a functional role for T cells, specifically CD8+ T cells, in ADPKD progression. Hence, targeting this pathway using immune-oncology agents may represent a novel therapeutic approach for ADPKD.
Cancer immunology research
2022 Oct 04
Reschke, R;Shapiro, JW;Yu, J;Rouhani, SJ;Olson, DJ;Zha, Y;Gajewski, TF;
PMID: 35977003 | DOI: 10.1158/2326-6066.CIR-22-0362
Experimental neurology
2021 Nov 24
Sartori, AM;Hofer, AS;Scheuber, MI;Rust, R;Kessler, TM;Schwab, ME;
PMID: 34826427 | DOI: 10.1016/j.expneurol.2021.113937
Biological psychiatry
2023 May 26
Leithead, AB;Godino, A;Barbier, M;Harony-Nicolas, H;
PMID: 37245781 | DOI: 10.1016/j.biopsych.2023.05.016
PNAS 2018
2018 Feb 07
Clarke LE, Liddelow SA, Chakraborty C, Münch AE, Heiman M, Barres BA.
PMID: - | DOI: 10.1073/pnas.1800165115
The decline of cognitive function occurs with aging, but the mechanisms responsible are unknown. Astrocytes instruct the formation, maturation, and elimination of synapses, and impairment of these functions has been implicated in many diseases. These findings raise the question of whether astrocyte dysfunction could contribute to cognitive decline in aging. We used the Bac-Trap method to perform RNA sequencing of astrocytes from different brain regions across the lifespan of the mouse. We found that astrocytes have region-specific transcriptional identities that change with age in a region-dependent manner. We validated our findings using fluorescence in situ hybridization and quantitative PCR. Detailed analysis of the differentially expressed genes in aging revealed that aged astrocytes take on a reactive phenotype of neuroinflammatory A1-like reactive astrocytes. Hippocampal and striatal astrocytes up-regulated a greater number of reactive astrocyte genes compared with cortical astrocytes. Moreover, aged brains formed many more A1 reactive astrocytes in response to the neuroinflammation inducer lipopolysaccharide. We found that the aging-induced up-regulation of reactive astrocyte genes was significantly reduced in mice lacking the microglial-secreted cytokines (IL-1α, TNF, and C1q) known to induce A1 reactive astrocyte formation, indicating that microglia promote astrocyte activation in aging. Since A1 reactive astrocytes lose the ability to carry out their normal functions, produce complement components, and release a toxic factor which kills neurons and oligodendrocytes, the aging-induced up-regulation of reactive genes by astrocytes could contribute to the cognitive decline in vulnerable brain regions in normal aging and contribute to the greater vulnerability of the aged brain to injury.
Proc Natl Acad Sci U S A.
2018 Apr 23
Meng D, Li HQ, Deisseroth K, Leutgeb S, Spitzer NC.
PMID: 29686073 | DOI: 10.1073/pnas.1801598115
Neurotransmitter switching in the adult mammalian brain occurs following photoperiod-induced stress, but the mechanism of regulation is unknown. Here, we demonstrate that elevated activity of dopaminergic neurons in the paraventricular nucleus of the hypothalamus (PaVN) in the adult rat is required for the loss of dopamine expression after long-day photoperiod exposure. The transmitter switch occurs exclusively in PaVN dopaminergic neurons that coexpress vesicular glutamate transporter 2 (VGLUT2), is accompanied by a loss of dopamine type 2 receptors (D2Rs) on corticotrophin-releasing factor (CRF) neurons, and can lead to increased release of CRF. Suppressing activity of all PaVN glutamatergic neurons decreases the number of inhibitory PaVN dopaminergic neurons, indicating homeostatic regulation of transmitter expression in the PaVN.
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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