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Search

Probes for INS

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.

Your search for "INS" returned results. Search for our Top genes LGR5, vglut2, gad67, brca1

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    • Publications (64) Apply Publications filter
    Lymphocyte networks are dynamic cellular communities in the immunoregulatory landscape of lung adenocarcinoma

    Cancer cell

    2023 Apr 08

    Gaglia, G;Burger, ML;Ritch, CC;Rammos, D;Dai, Y;Crossland, GE;Tavana, SZ;Warchol, S;Jaeger, AM;Naranjo, S;Coy, S;Nirmal, AJ;Krueger, R;Lin, JR;Pfister, H;Sorger, PK;Jacks, T;Santagata, S;
    PMID: 37059105 | DOI: 10.1016/j.ccell.2023.03.015

    Lymphocytes are key for immune surveillance of tumors, but our understanding of the spatial organization and physical interactions that facilitate lymphocyte anti-cancer functions is limited. We used multiplexed imaging, quantitative spatial analysis, and machine learning to create high-definition maps of lung tumors from a Kras/Trp53-mutant mouse model and human resections. Networks of interacting lymphocytes ("lymphonets") emerged as a distinctive feature of the anti-cancer immune response. Lymphonets nucleated from small T cell clusters and incorporated B cells with increasing size. CXCR3-mediated trafficking modulated lymphonet size and number, but T cell antigen expression directed intratumoral localization. Lymphonets preferentially harbored TCF1+ PD-1+ progenitor CD8+ T cells involved in responses to immune checkpoint blockade (ICB) therapy. Upon treatment of mice with ICB or an antigen-targeted vaccine, lymphonets retained progenitor and gained cytotoxic CD8+ T cell populations, likely via progenitor differentiation. These data show that lymphonets create a spatial environment supportive of CD8+ T cell anti-tumor responses.
    Positional influence on cellular transcriptional identity revealed through spatially segmented single-cell transcriptomics

    Cell systems

    2023 Jun 21

    Morse, DB;Michalowski, AM;Ceribelli, M;De Jonghe, J;Vias, M;Riley, D;Davies-Hill, T;Voss, T;Pittaluga, S;Muus, C;Liu, J;Boyle, S;Weitz, DA;Brenton, JD;Buenrostro, JD;Knowles, TPJ;Thomas, CJ;
    PMID: 37348462 | DOI: 10.1016/j.cels.2023.05.003

    Single-cell RNA sequencing (scRNA-seq) is a powerful technique for describing cell states. Identifying the spatial arrangement of these states in tissues remains challenging, with the existing methods requiring niche methodologies and expertise. Here, we describe segmentation by exogenous perfusion (SEEP), a rapid and integrated method to link surface proximity and environment accessibility to transcriptional identity within three-dimensional (3D) disease models. The method utilizes the steady-state diffusion kinetics of a fluorescent dye to establish a gradient along the radial axis of disease models. Classification of sample layers based on dye accessibility enables dissociated and sorted cells to be characterized by transcriptomic and regional identities. Using SEEP, we analyze spheroid, organoid, and in vivo tumor models of high-grade serous ovarian cancer (HGSOC). The results validate long-standing beliefs about the relationship between cell state and position while revealing new concepts regarding how spatially unique microenvironments influence the identity of individual cells within tumors.
    Gut-brain communication by distinct sensory neurons differently controls feeding and glucose metabolism

    Cell metabolism

    2021 May 21

    Borgmann, D;Ciglieri, E;Biglari, N;Brandt, C;Cremer, AL;Backes, H;Tittgemeyer, M;Wunderlich, FT;Brüning, JC;Fenselau, H;
    PMID: 34043943 | DOI: 10.1016/j.cmet.2021.05.002

    Sensory neurons relay gut-derived signals to the brain, yet the molecular and functional organization of distinct populations remains unclear. Here, we employed intersectional genetic manipulations to probe the feeding and glucoregulatory function of distinct sensory neurons. We reconstruct the gut innervation patterns of numerous molecularly defined vagal and spinal afferents and identify their downstream brain targets. Bidirectional chemogenetic manipulations, coupled with behavioral and circuit mapping analysis, demonstrated that gut-innervating, glucagon-like peptide 1 receptor (GLP1R)-expressing vagal afferents relay anorexigenic signals to parabrachial nucleus neurons that control meal termination. Moreover, GLP1R vagal afferent activation improves glucose tolerance, and their inhibition elevates blood glucose levels independent of food intake. In contrast, gut-innervating, GPR65-expressing vagal afferent stimulation increases hepatic glucose production and activates parabrachial neurons that control normoglycemia, but they are dispensable for feeding regulation. Thus, distinct gut-innervating sensory neurons differentially control feeding and glucoregulatory neurocircuits and may provide specific targets for metabolic control.
    Spatially organized multicellular immune hubs in human colorectal cancer

    Cell

    2021 Aug 24

    Pelka, K;Hofree, M;Chen, JH;Sarkizova, S;Pirl, JD;Jorgji, V;Bejnood, A;Dionne, D;Ge, WH;Xu, KH;Chao, SX;Zollinger, DR;Lieb, DJ;Reeves, JW;Fuhrman, CA;Hoang, ML;Delorey, T;Nguyen, LT;Waldman, J;Klapholz, M;Wakiro, I;Cohen, O;Albers, J;Smillie, CS;Cuoco, MS;Wu, J;Su, MJ;Yeung, J;Vijaykumar, B;Magnuson, AM;Asinovski, N;Moll, T;Goder-Reiser, MN;Applebaum, AS;Brais, LK;DelloStritto, LK;Denning, SL;Phillips, ST;Hill, EK;Meehan, JK;Frederick, DT;Sharova, T;Kanodia, A;Todres, EZ;Jané-Valbuena, J;Biton, M;Izar, B;Lambden, CD;Clancy, TE;Bleday, R;Melnitchouk, N;Irani, J;Kunitake, H;Berger, DL;Srivastava, A;Hornick, JL;Ogino, S;Rotem, A;Vigneau, S;Johnson, BE;Corcoran, RB;Sharpe, AH;Kuchroo, VK;Ng, K;Giannakis, M;Nieman, LT;Boland, GM;Aguirre, AJ;Anderson, AC;Rozenblatt-Rosen, O;Regev, A;Hacohen, N;
    PMID: 34450029 | DOI: 10.1016/j.cell.2021.08.003

    Immune responses to cancer are highly variable, with mismatch repair-deficient (MMRd) tumors exhibiting more anti-tumor immunity than mismatch repair-proficient (MMRp) tumors. To understand the rules governing these varied responses, we transcriptionally profiled 371,223 cells from colorectal tumors and adjacent normal tissues of 28 MMRp and 34 MMRd individuals. Analysis of 88 cell subsets and their 204 associated gene expression programs revealed extensive transcriptional and spatial remodeling across tumors. To discover hubs of interacting malignant and immune cells, we identified expression programs in different cell types that co-varied across tumors from affected individuals and used spatial profiling to localize coordinated programs. We discovered a myeloid cell-attracting hub at the tumor-luminal interface associated with tissue damage and an MMRd-enriched immune hub within the tumor, with activated T cells together with malignant and myeloid cells expressing T cell-attracting chemokines. By identifying interacting cellular programs, we reveal the logic underlying spatially organized immune-malignant cell networks.

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    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
    EnEmProbe targets exons n and m
    En-EmProbe 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

    Enabling research, drug development (CDx) and diagnostics

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