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 methods
2022 Oct 24
Ali Marandi Ghoddousi, R;Magalong, VM;Kamitakahara, AK;Levitt, P;
PMID: 36313803 | DOI: 10.1016/j.crmeth.2022.100316
Biological Psychiatry
2018 Oct 05
Shukla R, Prevot TD, French L, Isserlin R, Rocco BR, Banasr M, Bader GD, Sibille E.
PMID: - | DOI: 10.1016/j.celrep.2018.09.034
Background Aging is accompanied by altered thinking (cognition) and feeling (mood), functions that depend on information processing by brain cortical cell microcircuits. We hypothesized that age-associated long-term functional and biological changes are mediated by gene transcriptomic changes within neuronal cell-types forming cortical microcircuits, namely excitatory pyramidal cells (PYC) and inhibitory GABAergic neurons expressing vasoactive intestinal peptide (Vip), somatostatin (Sst) and parvalbumin (Pvalb). Methods To test this hypothesis, we assessed locomotor, anxiety-like and cognitive behavioral changes between young (2 months, n=9) and old (22 months, n=12) male C57BL/6 mice, and performed frontal cortex cell-type specific molecular profiling, using laser-capture microscopy and RNA sequencing. Results were analyzed by neuroinformatics and validated by fluorescent in situ hybridization. Results Old-mice displayed increased anxiety and reduced working memory. The four cell-types displayed distinct age-related transcriptomes and biological pathway profiles, affecting metabolic and cell signaling pathways, and selective markers of neuronal vulnerability (Ryr3), resilience (Oxr1), and mitochondrial dynamics (Opa1), suggesting high age-related vulnerability of PYCs, and variable degree of adaptation in GABAergic neurons. Correlations between gene expression and behaviors suggest that changes in cognition and anxiety associated with age are partly mediated by normal age-related cell changes, and that additional age-independent decreases in synaptic and signaling pathways, notably in PYC and SST-neurons further contribute to behavioral changes. Conclusions Our study demonstrates cell-dependent differential vulnerability and coordinated cell-specific cortical microcircuit molecular changes with age. Collectively, the results suggest intrinsic molecular links between aging, cognition and mood-related behaviors with SST-neurons contributing evenly to both behavioral conditions.
Neuron
2023 Apr 18
Liu, Y;Savier, EL;DePiero, VJ;Chen, C;Schwalbe, DC;Abraham-Fan, RJ;Chen, H;Campbell, JN;Cang, J;
PMID: 37086721 | DOI: 10.1016/j.neuron.2023.03.036
Cell Rep
2019 May 21
Kupari J, Häring M, Agirre E, Castelo-Branco G, Ernfors P.
PMID: 31116992 | DOI: 10.1016/j.celrep.2019.04.096
Sensory functions of the vagus nerve are critical for conscious perceptions and for monitoring visceral functions in the cardio-pulmonary and gastrointestinal systems. Here, we present a comprehensive identification, classification, and validation of the neuron types in the neural crest (jugular) and placode (nodose) derived vagal ganglia by single-cell RNA sequencing (scRNA-seq) transcriptomic analysis. Our results reveal major differences between neurons derived from different embryonic origins. Jugular neurons exhibit fundamental similarities to the somatosensory spinal neurons, including major types, such as C-low threshold mechanoreceptors (C-LTMRs), A-LTMRs, Aδ-nociceptors, and cold-, and mechano-heat C-nociceptors. In contrast, the nodose ganglion contains 18 distinct types dedicated to surveying the physiological state of the internal body. Our results reveal a vast diversity of vagal neuron types, including many previously unanticipated types, as well as proposed types that are consistent with chemoreceptors, nutrient detectors, baroreceptors, and stretch and volume mechanoreceptors of the respiratory, gastrointestinal, and cardiovascular systems.
Nature neuroscience
2023 Jan 23
Zhou, H;Li, M;Zhao, R;Sun, L;Yang, G;
PMID: 36690899 | DOI: 10.1038/s41593-022-01250-y
Neuron
2023 Jan 04
Schroeder, A;Pardi, MB;Keijser, J;Dalmay, T;Groisman, AI;Schuman, EM;Sprekeler, H;Letzkus, JJ;
PMID: 36610397 | DOI: 10.1016/j.neuron.2022.12.010
Cell.
2018 Aug 09
Saunders A, Macosko EZ, Wysoker A, Goldman M, Krienen FM, de Rivera H, Bien E, Baum M, Bortolin L, Wang S, Goeva A, Nemesh J, Kamitaki N, Brumbaugh S, Kulp D, McCarroll SA.
PMID: 30096299 | DOI: 10.1016/j.cell.2018.07.028
The mammalian brain is composed of diverse, specialized cell populations. To systematically ascertain and learn from these cellular specializations, we used Drop-seq to profile RNA expression in 690,000 individual cells sampled from 9 regions of the adult mouse brain. We identified 565 transcriptionally distinct groups of cells using computational approaches developed to distinguish biological from technical signals. Cross-region analysis of these 565 cell populations revealed features of brain organization, including a gene-expression module for synthesizing axonal and presynaptic components, patterns in the co-deployment of voltage-gated ion channels, functional distinctions among the cells of the vasculature and specialization of glutamatergic neurons across cortical regions. Systematic neuronal classifications for two complex basal ganglia nuclei and the striatum revealed a rare population of spiny projection neurons. This adult mouse brain cell atlas, accessible through interactive online software (DropViz), serves as a reference for development, disease, and evolution.
Nature genetics
2021 Apr 08
Kameneva, P;Artemov, AV;Kastriti, ME;Faure, L;Olsen, TK;Otte, J;Erickson, A;Semsch, B;Andersson, ER;Ratz, M;Frisén, J;Tischler, AS;de Krijger, RR;Bouderlique, T;Akkuratova, N;Vorontsova, M;Gusev, O;Fried, K;Sundström, E;Mei, S;Kogner, P;Baryawno, N;Kharchenko, PV;Adameyko, I;
PMID: 33833454 | DOI: 10.1038/s41588-021-00818-x
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
Description | ||
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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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