ACD can configure probes for the various manual and automated assays for SPP1 for RNAscope Assay, or for Basescope Assay compatible for your species of interest.
Nature neuroscience
2023 Feb 06
De Schepper, S;Ge, JZ;Crowley, G;Ferreira, LSS;Garceau, D;Toomey, CE;Sokolova, D;Rueda-Carrasco, J;Shin, SH;Kim, JS;Childs, T;Lashley, T;Burden, JJ;Sasner, M;Sala Frigerio, C;Jung, S;Hong, S;
PMID: 36747024 | DOI: 10.1038/s41593-023-01257-z
Cell reports
2023 Feb 17
Hoeft, K;Schaefer, GJL;Kim, H;Schumacher, D;Bleckwehl, T;Long, Q;Klinkhammer, BM;Peisker, F;Koch, L;Nagai, J;Halder, M;Ziegler, S;Liehn, E;Kuppe, C;Kranz, J;Menzel, S;Costa, I;Wahida, A;Boor, P;Schneider, RK;Hayat, S;Kramann, R;
PMID: 36807143 | DOI: 10.1016/j.celrep.2023.112131
Nat Neurosci.
2018 Apr 23
Häring M, Zeisel A, Hochgerner H, Rinwa P, Jakobsson JET, Lönnerberg P, La Manno G, Sharma N, Borgius L, Kiehn O, Lagerström MC, Linnarsson S, Ernfors P.
PMID: 29686262 | DOI: 10.1038/s41593-018-0141-1
The dorsal horn of the spinal cord is critical to processing distinct modalities of noxious and innocuous sensation, but little is known of the neuronal subtypes involved, hampering efforts to deduce principles governing somatic sensation. Here we used single-cell RNA sequencing to classify sensory neurons in the mouse dorsal horn. We identified 15 inhibitory and 15 excitatory molecular subtypes of neurons, equaling the complexity in cerebral cortex. Validating our classification scheme in vivo and matching cell types to anatomy of the dorsal horn by spatial transcriptomics reveals laminar enrichment for each of the cell types. Neuron types, when combined, define a multilayered organization with like neurons layered together. Employing our scheme, we find that heat and cold stimuli activate discrete sets of both excitatory and inhibitory neuron types. This work provides a systematic and comprehensive molecular classification of spinal cord sensory neurons, enabling functional interrogation of sensory processing.
Nat Med.
2018 Sep 17
Zanconato F, Battilana G, Forcato M, Filippi L, Azzolin L, Manfrin A, Quaranta E, Di Biagio D, Sigismondo G, Guzzardo V, Lejeune P, Haendler B, Krijgsveld J, Fassan M, Bicciato S, Cordenonsi M, Piccolo S.
PMID: 30224758 | DOI: 10.1038/s41591-018-0158-8
Cancer cells rely on dysregulated gene expression. This establishes specific transcriptional addictions that may be therapeutically exploited. Yet, the mechanisms that are ultimately responsible for these addictions are poorly understood. Here, we investigated the transcriptional dependencies of transformed cells to the transcription factors YAP and TAZ. YAP/TAZ physically engage the general coactivator bromodomain-containing protein 4 (BRD4), dictating the genome-wide association of BRD4 to chromatin. YAP/TAZ flag a large set of enhancers with super-enhancer-like functional properties. YAP/TAZ-bound enhancers mediate the recruitment of BRD4 and RNA polymerase II at YAP/TAZ-regulated promoters, boosting the expression of a host of growth-regulating genes. Treatment with small-molecule inhibitors of BRD4 blunts YAP/TAZ pro-tumorigenic activity in several cell or tissue contexts, causes the regression of pre-established, YAP/TAZ-addicted neoplastic lesions and reverts drug resistance. This work sheds light on essential mediators, mechanisms and genome-wide regulatory elements that are responsible for transcriptional addiction in cancer and lays the groundwork for a rational use of BET inhibitors according to YAP/TAZ biology.
Nature. 2018 Nov;563(7729):72-78.
2018 Oct 31
Tasic B, Yao Z, Graybuck LT, Smith KA, Nguyen TN, Bertagnolli D, Goldy J, Garren E, Economo MN, Viswanathan S, Penn O, Bakken T, Menon V, Miller J, Fong O, Hirokawa KE, Lathia K, Rimorin C, Tieu M, Larsen R, Casper T, Barkan E, Kroll M, Parry S, Shapovalova NV, Hirschstein D, Pendergraft J, Sullivan HA, Kim TK, Szafer A, Dee N, Groblewski P, Wickersham I, Cetin A, Harris JA, Levi BP, Sunkin SM, Madisen L, Daigle TL, Looger L, Bernard A, Phillips J, Lein E, Hawrylycz M, Svoboda K, Jones AR, Koch C, Zeng H.
PMID: 30382198 | DOI: 10.1038/s41586-018-0654-5
Cell Rep. 2018 Oct 16;25(3):585-597.e7.
2018 Oct 16
Joost S, Jacob T, Sun X, Annusver K, La Manno G, Sur I, Kasper M.
PMID: 30332640 | DOI: 10.1016/j.celrep.2018.09.059
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.
Neuron (2018)
2018 Dec 31
Li Q, Cheng Z, Zhou L, Darmanis S, Neff NF, Okamoto J, Gulati G, Bennett ML, Sun LO, Clarke LE, Marschallinger J, Yu G, Quake SR, Wyss-Coray T, Barres BA.
| DOI: 10.1016/j.neuron.2018.12.006
Cellular & molecular immunology
2023 Jun 26
Silvin, A;Qian, J;Ginhoux, F;
PMID: 37365324 | DOI: 10.1038/s41423-023-01053-6
Nature communications
2022 Dec 21
Lim, RG;Al-Dalahmah, O;Wu, J;Gold, MP;Reidling, JC;Tang, G;Adam, M;Dansu, DK;Park, HJ;Casaccia, P;Miramontes, R;Reyes-Ortiz, AM;Lau, A;Hickman, RA;Khan, F;Paryani, F;Tang, A;Ofori, K;Miyoshi, E;Michael, N;McClure, N;Flowers, XE;Vonsattel, JP;Davidson, S;Menon, V;Swarup, V;Fraenkel, E;Goldman, JE;Thompson, LM;
PMID: 36543778 | DOI: 10.1038/s41467-022-35388-x
Nature communications
2022 Sep 26
Matson, KJE;Russ, DE;Kathe, C;Hua, I;Maric, D;Ding, Y;Krynitsky, J;Pursley, R;Sathyamurthy, A;Squair, JW;Levi, BP;Courtine, G;Levine, AJ;
PMID: 36163250 | DOI: 10.1038/s41467-022-33184-1
Nature communications
2022 Sep 17
Lee, SH;Kim, N;Kim, M;Woo, SH;Han, I;Park, J;Kim, K;Park, KS;Kim, K;Shim, D;Park, SE;Zhang, JY;Go, DM;Kim, DY;Yoon, WK;Lee, SP;Chung, J;Kim, KW;Park, JH;Lee, SH;Lee, S;Ann, SJ;Lee, SH;Ahn, HS;Jeong, SC;Kim, TK;Oh, GT;Park, WY;Lee, HO;Choi, JH;
PMID: 36115863 | DOI: 10.1038/s41467-022-33202-2
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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