Chopra, A.

Gaudieri, S.

Mpunga, T.

English

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Differential Gene expression profiling of NK cellsAbha Chopra1, Teddy Mpunga1 and Silvana Gaudieri1, 21Institute for Immunology & Infectious Diseases, Murdoch University, Murdoch, Western Australia, 2School of Anatomy, Physiology and Human Biology, University of Western Australia, Crawley, Western AustraliaBackground: Killer cell immunoglobulin-like receptors (KIR) are expressed on NK cells and subsets of T cells. The KIRgenes are polymorphic and the gene complex is polygenic with a varying number of inhibitory (iKIRs) and activating (aKIRs)receptors. Several disease association studies indicate that the interaction between the KIR and HLA loci affects the activatorythreshold of NK cells and may have a role in infectious diseases, autoimmune disorders and cancer. NK cells can be separatedinto distinct subsets (based on level of CD56 expression) that have functional differences in relation to the level of cytotoxicity,however the immunological profile of these NK cell subsets and how it relates to function is not clear. In order to address thisissue, we examined the expression profile of a panel of known immune-related genes on NK cell subsets using the new nCounteranalysis system (NanoString technology). This technology enables the digital quantification of multiplexed target RNA moleculesusing color-coded molecular barcodes and single-molecule imaging. This system gives discrete counts of RNA transcripts and iscapable of providing a high level of precision and sensitivity.Workflow3Brumbaugh et al.: NanoStriDE: Normalization and Differential Expression Analysis of NanoString nCounter Data. BMC Bioinformatics 2011 12:479.Gene expression analysis performed using the nCounter GXHuman Immunology panel of 582 genes on the Nanostringplatform with RNA extracted from these sorted cellpopulations.The raw count data produced by the nCounter analysis isnormalized with internal controls and Housekeeping genesand differential expression analysis is performed on thenormalized data using a web based software NanoStriDE3PBMC are sorted by flow cytometry to isolate NK cellsubsets using the cell surface markers CD3 – T cell, CD56 –NK cell, CD57 – maturationGene Name Avg Count Min Count Max Count %CV Sample 1 Sample 2 Sample 3 Sample 4 Sample 5 Sample 6HLA-A 7008.26 3712.45 11096.21 32.14 7268.89 7597 7568.76 7177.93 6018.91 3712.45HLA-B 6525.88 4073.87 8259.51 19.35 5556.17 7002.76 6508.2 5468.54 5795.37 4073.87HLA-C 3307.57 1394.7 5428.44 44.69 5428.44 5227.16 4373.03 4772.68 3162.88 1959.26KIR3DL1 50.81 15.27 124.54 70.65 23.22 124.54 75.95 56.73 16.66 22.19KIR3DL2 58.49 9.51 142.33 68.18 69.67 142.33 67.52 98.33 27.77 9.51KIR3DL3 22.29 3.15 52.31 67.03 29.03 32.02 14.07 30.25 8.33 15.85KIR_Activating_Subgroup_1101.32 15.27 359.39 101.69 46.45 359.39 220.83 185.31 34.71 34.87KIR_Activating_Subgroup_242.25 7.4 86.98 65.48 52.25 85.4 66.11 86.98 12.5 12.68KIR_Inhibiting_Subgroup_1233.31 18.05 550.81 82.39 545.75 398.53 346.02 226.91 18.05 50.73KIR_Inhibiting_Subgroup_2238.31 30.55 627.03 81.23 627.03 352.27 395.25 234.47 30.55 57.07KIT 110.86 12.49 679.28 166.15 679.28 49.82 126.59 68.07 18.05 104.62HLA-DMA 427.98 77.75 1220.57 86.58 203.2 128.1 175.82 132.36 77.75 1220.57HLA-DMB 477.62 45.82 1756.36 110.81 307.71 81.84 115.34 173.96 45.82 1756.36HLA-DOB 141.13 44.89 478.72 86.82 133.53 92.52 90.02 79.42 80.53 478.72HLA-DPA1 841.15 129.98 1986.92 98.37 232.23 192.15 163.16 253.38 170.78 1867.32HLA-DPB1 2131.41 451.51 5228.24 92.68 476.08 473.26 451.51 832 452.63 3614.17HLA-DQA1 557.02 7.48 4070.7 226.86 92.89 39.14 91.43 86.98 29.16 4070.7Step 3: Data AnalysisStep 2: Nanostring PipelineStep 1: Cell SortingResults1 CD56 Bright2 CD56 Dim 57-3 CD56 Dim 57+4 NKT 5 CD56- CD3+ T cellsKeys1 2 3 4 51 2 3 4 51 2 3 4 51 2 3 4 5Fig 1: CD56 Bright cells express higher levels ofinhibitory KIR as compared to the CD56 DIMswhich express more activating receptors.PBMCs from one sample was taken through the workflow and profiles for each subset wasnormalized. Different patterns were obtained for the subsets including specific profiles for T cells,NKT cells and the NK subsets. Initial analysis revealed CD56+Bright cells expressed low aKIR andhigh iKIR than the CD56+ dim population as might be expected from known cytotoxic properties ofthese two subsets as shown in Fig1. Expression profiles common between different subsetcombinations will be further explored to determine potential functional consequences.1 2 3 4 5ConclusionsNanostring technology has enabled us to identify T cell specific and NK cell specific profiles that contain - as expected - genes known tobe involved in antigen presentation and NK-cell meditated cytotoxicity, respectively. Future work will generate an immunology signaturefor the different NK subsets for the other genes that have been indicated by this technique.1 2 3 4 51 2 3 4 5Acknowledgements: All the members of the IIID team.Website: http://iiid.murdoch.edu.auCD56 dim cells express higherlevels of Class 2 HLA compared tothe non CD56Some more gene profiles that havehigh expression in CD56+ Brightcells.CD56+ bright cells share similarexpression levels of some geneswith CD56 dim CD57+ populationthese genes are likely involved inantiviral responses.Expression of genes sharedbetween NKT cells and CD56brightGene expression in NKT Cells isdistinct from the NK cellsExpression profile of signaturegenes in the T cell population.aKIR Subgroup1 KIR3DS1, KIR2DS1, KIR2DS2, KIR2DS4aKIR Subgroup2KIR2DS1, KIR2DS2, KIR2DS3, KIR2DS4, KIR2DS5iKIR Subgroup1KIR2DL1, KIR2DL2, KIR2DL4, KIR2DL5, KIR3DL1, KIR3DL3iKIR Subgroup2KIR2DL3, KIR2DL4, KIR2DL5, KIR3DL3, KIR2DL1, KIR2DL2, KIR3DL1, KIR3DL2aKIR_Subgroup_1aKIR_Subgroup_2iKIR_Subgroup_1iKIR_Subgroup_2KIR3DL1KIR3DL2KIR3DL3 Gene content of the KIR Subgroups

Differential gene expression profiling of NK cells

https://researchrepository.murdoch.edu.au/id/eprint/36772/1/differential%20poster.pdf

Differential gene expression profiling of NK cells

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