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.
Proceedings of the National Academy of Sciences of the United States of America
2023 Jun 06
van Doeselaar, L;Stark, T;Mitra, S;Yang, H;Bordes, J;Stolwijk, L;Engelhardt, C;Kovarova, V;Narayan, S;Brix, LM;Springer, M;Deussing, JM;Lopez, JP;Czisch, M;Schmidt, MV;
PMID: 37252963 | DOI: 10.1073/pnas.2300722120
Cell reports
2023 Mar 22
Huo, J;Du, F;Duan, K;Yin, G;Liu, X;Ma, Q;Dong, D;Sun, M;Hao, M;Su, D;Huang, T;Ke, J;Lai, S;Zhang, Z;Guo, C;Sun, Y;Cheng, L;
PMID: 36952340 | DOI: 10.1016/j.celrep.2023.112300
Proc Natl Acad Sci U S A.
2017 Nov 02
Spoljarica A, Sejaa P, Spoljaric I, Virtanen MA, Lindfors J, Uvarov P, Summanen M, Crow AK, Hsueh B, Puskarjov M, Ruusuvuori E, Voipio J, Deisseroth K, Kaila K.
PMID: 29183979 | DOI: 10.1073/pnas.1717337114
During birth in mammals, a pronounced surge of fetal peripheral stress hormones takes place to promote survival in the transition to the extrauterine environment. However, it is not known whether the hormonal signaling involves central pathways with direct protective effects on the perinatal brain. Here, we show that arginine vasopressin specifically activates interneurons to suppress spontaneous network events in the perinatal hippocampus. Experiments done on the altricial rat and precocial guinea pig neonate demonstrated that the effect of vasopressin is not dependent on the level of maturation (depolarizing vs. hyperpolarizing) of postsynaptic GABAA receptor actions. Thus, the fetal mammalian brain is equipped with an evolutionarily conserved mechanism well-suited to suppress energetically expensive correlated network events under conditions of reduced oxygen supply at birth.
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.
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.
Science advances
2022 Sep 02
He, Y;Madeo, G;Liang, Y;Zhang, C;Hempel, B;Liu, X;Mu, L;Liu, S;Bi, GH;Galaj, E;Zhang, HY;Shen, H;McDevitt, RA;Gardner, EL;Liu, QS;Xi, ZX;
PMID: 36054363 | DOI: 10.1126/sciadv.abo1440
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 reports
2022 Jun 14
Coverdell, TC;Abraham-Fan, RJ;Wu, C;Abbott, SBG;Campbell, JN;
PMID: 35705034 | DOI: 10.1016/j.celrep.2022.110962
Science. 2015 Jun 12;348(6240):1255-60.
Kumar NN, Velic A, Soliz J, Shi Y, Li K, Wang S, Weaver JL, Sen J, Abbott SB, Lazarenko RM, Ludwig MG, Perez-Reyes E, Mohebbi N, Bettoni C, Gassmann M, Suply T, Seuwen K, Guyenet PG, Wagner CA, Bayliss DA.
Nature communications
2021 Nov 23
Mahadevia, D;Saha, R;Manganaro, A;Chuhma, N;Ziolkowski-Blake, A;Morgan, AA;Dumitriu, D;Rayport, S;Ansorge, MS;
PMID: 34815379 | DOI: 10.1038/s41467-021-27092-z
Science.
2016 Jun 24
Lake BB, Ai R, Kaeser GE, Salathia NS, Yung YC, Liu R, Wildberg A, Gao D, Fung HL, Chen S, Vijayaraghavan R, Wong J, Chen A, Sheng X, Kaper F, Shen R, Ronaghi M, Fan JB, Wang W, Chun J, Zhang K.
PMID: 27339989 | DOI: 10.1126/science.aaf1204
The human brain has enormously complex cellular diversity and connectivities fundamental to our neural functions, yet difficulties in interrogating individual neurons has impeded understanding of the underlying transcriptional landscape. We developed a scalable approach to sequence and quantify RNA molecules in isolated neuronal nuclei from a postmortem brain, generating 3227 sets of single-neuron data from six distinct regions of the cerebral cortex. Using an iterative clustering and classification approach, we identified 16 neuronal subtypes that were further annotated on the basis of known markers and cortical cytoarchitecture. These data demonstrate a robust and scalable method for identifying and categorizing single nuclear transcriptomes, revealing shared genes sufficient to distinguish previously unknown and orthologous neuronal subtypes as well as regional identity and transcriptomic heterogeneity within the human brain.
Neuron.
2017 Jan 31
François A, Low SA, Sypek EI, Christensen AJ, Sotoudeh C, Beier KT, Ramakrishnan C, Ritola KD, Sharif-Naeini R, Deisseroth K, Delp SL, Malenka RC, Luo L, Hantman AW, Scherrer G.
PMID: 28162807 | DOI: 10.1016/j.neuron.2017.01.008
Pain thresholds are, in part, set as a function of emotional and internal states by descending modulation of nociceptive transmission in the spinal cord. Neurons of the rostral ventromedial medulla (RVM) are thought to critically contribute to this process; however, the neural circuits and synaptic mechanisms by which distinct populations of RVM neurons facilitate or diminish pain remain elusive. Here we used in vivo opto/chemogenetic manipulations and trans-synaptic tracing of genetically identified dorsal horn and RVM neurons to uncover an RVM-spinal cord-primary afferent circuit controlling pain thresholds. Unexpectedly, we found that RVM GABAergic neurons facilitate mechanical pain by inhibiting dorsal horn enkephalinergic/GABAergic interneurons. We further demonstrate that these interneurons gate sensory inputs and control pain through temporally coordinated enkephalin- and GABA-mediated presynaptic inhibition of somatosensory neurons. Our results uncover a descending disynaptic inhibitory circuit that facilitates mechanical pain, is engaged during stress, and could be targeted to establish higher pain thresholds.
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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