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.
Nat Neurosci. 2018 Nov;21(11):1530-1540.
2018 Oct 22
Piñol RA, Zahler SH, Li C, Saha A, Tan BK, Škop V, Gavrilova O, Xiao C, Krashes MJ, Reitman ML.
PMID: 30349101 | DOI: 10.1038/s41593-018-0249-3
Cell Stem Cell.
2019 May 09
Pepe-Mooney BJ, Dill MT, Alemany A, Ordovas-Montanes J, Matsushita Y, Rao A, Sen A, Miyazaki M, Anakk S, Dawson PA, Ono N, Shalek AK, van Oudenaarden A, Camargo FD.
PMID: 31080134 | DOI: 10.1016/j.stem.2019.04.004
The liver can substantially regenerate after injury, with both main epithelial cell types, hepatocytes and biliary epithelial cells (BECs), playing important roles in parenchymal regeneration. Beyond metabolic functions, BECs exhibit substantial plasticity and in some contexts can drive hepatic repopulation. Here, we performed single-cell RNA sequencing to examine BEC and hepatocyte heterogeneity during homeostasisand after injury. Instead of evidence for a transcriptionally defined progenitor-like BEC cell, we found significant homeostatic BEC heterogeneity that reflects fluctuating activation of a YAP-dependent program. This transcriptional signature defines a dynamic cellular state during homeostasis and is highly responsive to injury. YAP signaling is induced by physiological bile acids (BAs), required for BEC survival in response to BA exposure, and is necessary for hepatocyte reprogramming into biliary progenitors upon injury. Together, these findings uncover molecular heterogeneity within the ductal epithelium and reveal YAP as a protective rheostat and regenerative regulator in the mammalian liver.
Nature
2022 Jun 22
Hill, RZ;Loud, MC;Dubin, AE;Peet, B;Patapoutian, A;
PMID: 35732741 | DOI: 10.1038/s41586-022-04860-5
Nature metabolism
2021 Apr 01
Ludwig, MQ;Cheng, W;Gordian, D;Lee, J;Paulsen, SJ;Hansen, SN;Egerod, KL;Barkholt, P;Rhodes, CJ;Secher, A;Knudsen, LB;Pyke, C;Myers, MG;Pers, TH;
PMID: 33767443 | DOI: 10.1038/s42255-021-00363-1
Proc Natl Acad Sci U S A.
2016 Mar 07
Xin Y, Kim J, Ni M, Wei Y, Okamoto H, Lee J, Adler C, Cavino K, Murphy AJ, Yancopoulos GD, Lin HC, Gromada J.
PMID: 26951663 | DOI: -
This study provides an assessment of the Fluidigm C1 platform for RNA sequencing of single mouse pancreatic islet cells. The system combines microfluidic technology and nanoliter-scale reactions. We sequenced 622 cells, allowing identification of 341 islet cells with high-quality gene expression profiles. The cells clustered into populations of α-cells (5%), β-cells (92%), δ-cells (1%), and pancreatic polypeptide cells (2%). We identified cell-type-specific transcription factors and pathways primarily involved in nutrient sensing and oxidation and cell signaling. Unexpectedly, 281 cells had to be removed from the analysis due to low viability, low sequencing quality, or contamination resulting in the detection of more than one islet hormone. Collectively, we provide a resource for identification of high-quality gene expression datasets to help expand insights into genes and pathways characterizing islet cell types. We reveal limitations in the C1 Fluidigm cell capture process resulting in contaminated cells with altered gene expression patterns. This calls for caution when interpreting single-cell transcriptomics data using the C1 Fluidigm system.
Nature.
2017 Nov 16
Haber AL, Biton M, Rogel N, Herbst RH, Shekhar K, Smillie C, Burgin G, Delorey TM, Howitt MR, Katz Y, Tirosh I, Beyaz S, Dionne D, Zhang M, Raychowdhury R, Garrett WS, Rozenblatt-Rosen O, Shi HN, Yilmaz O, Xavier RJ, Regev A.
PMID: 29144463 | DOI: 10.1038/nature24489
Intestinal epithelial cells absorb nutrients, respond to microbes, function as a barrier and help to coordinate immune responses. Here we report profiling of 53,193 individual epithelial cells from the small intestine and organoids of mice, which enabled the identification and characterization of previously unknown subtypes of intestinal epithelial cell and their gene signatures. We found unexpected diversity in hormone-secreting enteroendocrine cells and constructed the taxonomy of newly identified subtypes, and distinguished between two subtypes of tuft cell, one of which expresses the epithelial cytokine Tslp and the pan-immune marker CD45, which was not previously associated with non-haematopoietic cells. We also characterized the ways in which cell-intrinsic states and the proportions of different cell types respond to bacterial and helminth infections: Salmonella infection caused an increase in the abundance of Paneth cells and enterocytes, and broad activation of an antimicrobial program; Heligmosomoides polygyrus caused an increase in the abundance of goblet and tuft cells. Our survey highlights previously unidentified markers and programs, associates sensory molecules with cell types, and uncovers principles of gut homeostasis and response to pathogens.
Cell.
2018 Apr 10
Cembrowski MS, Phillips MG, DiLisio SF, Shields BC, Winnubst J, Chandrashekar J, Bas E, Spruston N.
PMID: 29681453 | DOI: 10.1016/j.cell.2018.03.031
The mammalian hippocampus, comprised of serially connected subfields, participates in diverse behavioral and cognitive functions. It has been postulated that parallel circuitry embedded within hippocampal subfields may underlie such functional diversity. We sought to identify, delineate, and manipulate this putatively parallel architecture in the dorsal subiculum, the primary output subfield of the dorsal hippocampus. Population and single-cell RNA-seq revealed that the subiculum can be divided into two spatially adjacent subregions associated with prominent differences in pyramidal cell gene expression. Pyramidal cells occupying these two regions differed in their long-range inputs, local wiring, projection targets, and electrophysiological properties. Leveraging gene-expression differences across these regions, we use genetically restricted neuronal silencing to show that these regions differentially contribute to spatial working memory. This work provides a coherent molecular-, cellular-, circuit-, and behavioral-level demonstration that the hippocampus embeds structurally and functionally dissociable streams within its serial architecture.
Nature Neuroscience
2018 May 25
Stagkourakis S, Spigolon G, Williams P, Protzmann J, Fisone G, Broberger C.
PMID: - | DOI: 10.1038/s41593-018-0153-x
Intermale aggression is used to establish social rank. Several neuronal populations have been implicated in aggression, but the circuit mechanisms that shape this innate behavior and coordinate its different components (including attack execution and reward) remain elusive. We show that dopamine transporter-expressing neurons in the hypothalamic ventral premammillary nucleus (PMvDAT neurons) organize goal-oriented aggression in male mice. Activation of PMvDATneurons triggers attack behavior; silencing these neurons interrupts attacks. Regenerative PMvDAT membrane conductances interacting with recurrent and reciprocal excitation explain how a brief trigger can elicit a long-lasting response (hysteresis). PMvDAT projections to the ventrolateral part of the ventromedial hypothalamic and the supramammillary nuclei control attack execution and aggression reward, respectively. Brief manipulation of PMvDAT activity switched the dominance relationship between males, an effect persisting for weeks. These results identify a network structure anchored in PMvDAT neurons that organizes aggressive behavior and, as a consequence, determines intermale hierarchy.
Neuron
2022 Jun 10
Trendafilova, T;Adhikari, K;Schmid, AB;Patel, R;Polgár, E;Chisholm, KI;Middleton, SJ;Boyle, K;Dickie, AC;Semizoglou, E;Perez-Sanchez, J;Bell, AM;Ramirez-Aristeguieta, LM;Khoury, S;Ivanov, A;Wildner, H;Ferris, E;Chacón-Duque, JC;Sokolow, S;Saad Boghdady, MA;Herchuelz, A;Faux, P;Poletti, G;Gallo, C;Rothhammer, F;Bedoya, G;Zeilhofer, HU;Diatchenko, L;McMahon, SB;Todd, AJ;Dickenson, AH;Ruiz-Linares, A;Bennett, DL;
PMID: 35705078 | DOI: 10.1016/j.neuron.2022.05.017
Cell Metab.
2016 Oct 11
Segerstolpe Å, Palasantza A, Eliasson P, Andersson EM, Andréasson AC, Sun X, Picelli S, Sabirsh A, Clausen M, Bjursell MK, Smith DM, Kasper M, Ämmälä C, Sandberg R.
PMID: 27667667 | DOI: 10.1016/j.cmet.2016.08.020
Hormone-secreting cells within pancreatic islets of Langerhans play important roles in metabolic homeostasis and disease. However, their transcriptional characterization is still incomplete. Here, we sequenced the transcriptomes of thousands of human islet cells from healthy and type 2 diabetic donors. We could define specific genetic programs for each individual endocrine and exocrine cell type, even for rare δ, γ, ε, and stellate cells, and revealed subpopulations of α, β, and acinar cells. Intriguingly, δ cells expressed several important receptors, indicating an unrecognized importance of these cells in integrating paracrine and systemic metabolic signals. Genes previously associated with obesity or diabetes were found to correlate with BMI. Finally, comparing healthy and T2D transcriptomes in a cell-type resolved manner uncovered candidates for future functional studies. Altogether, our analyses demonstrate the utility of the generated single-cell gene expression resource.
Proc Natl Acad Sci U S A.
2017 Jan 23
Okamoto H, Cavino K, Na E, Krumm E, Kim SY, Cheng X, Murphy AJ, Yancopoulos GD, Gromada J.
PMID: 28115707 | DOI: 10.1073/pnas.1621069114
Inactivating mutations in the insulin receptor results in extreme insulin resistance. The resulting hyperglycemia is very difficult to treat, and patients are at risk for early morbidity and mortality from complications of diabetes. We used the insulin receptor antagonist S961 to induce severe insulin resistance, hyperglycemia, and ketonemia in mice. Using this model, we show that glucagon receptor (GCGR) inhibition with a monoclonal antibody normalized blood glucose and β-hydroxybutyrate levels. Insulin receptor antagonism increased pancreatic β-cell mass threefold. Normalization of blood glucose levels with GCGR-blocking antibody unexpectedly doubled β-cell mass relative to that observed with S961 alone and 5.8-fold over control. GCGR antibody blockage expanded α-cell mass 5.7-fold, and S961 had no additional effects. Collectively, these data show that GCGR antibody inhibition represents a potential therapeutic option for treatment of patients with extreme insulin-resistance syndromes.
Cell Rep.
2019 Mar 05
Mehta P, Kreeger L, Wylie DC, Pattadkal JJ, Lusignan T, Davis MJ, Turi GF, Li WK, Whitmire MP, Chen Y, Kajs BL, Seidemann E, Priebe NJ, Losonczy A, Zemelman BV.
PMID: 30840900 | DOI: 10.1016/j.celrep.2019.02.011
Viral vectors enable foreign proteins to be expressed in brains of non-genetic species, including non-human primates. However, viruses targeting specific neuron classes have proved elusive. Here we describe viral promoters and strategies for accessing GABAergic interneurons and their molecularly defined subsets in the rodent and primate. Using a set intersection approach, which relies on two co-active promoters, we can restrict heterologous protein expression to cortical and hippocampal somatostatin-positive and parvalbumin-positive interneurons. With an orthogonal set difference method, we can enrich for subclasses of neuropeptide-Y-positive GABAergic interneurons by effectively subtracting the expression pattern of one promoter from that of another. These methods harness the complexity of gene expression patterns in the brain and significantly expand the number of genetically tractable neuron classes across mammals.
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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