Supplementary material for Integrated analysis of circulating immune cellular and soluble mediators reveals specific COVID19 signatures at hospital admission with utility for prediction of clinical outcomes [Dataset]

Uranga Murillo, Iratxe; Morte Romea, Elena; Hidalgo, Sandra; Pesini, Cecilia; García-Mulero, Sandra; Sierra Monzón, José L.; Santiago, Llipsy; Arias, Maykel; Miguel, Diego de; Encabo-Berzosa, M. Mar; Gracia Tello, Borja; Sanz-Pamplona, Rebeca; Martínez-Lostao, Luis; Gálvez Buerba, Eva Mª; Paño, José Ramón; Ramírez-Labrada, Ariel; Pardo, Julián

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Título:	Supplementary material for Integrated analysis of circulating immune cellular and soluble mediators reveals specific COVID19 signatures at hospital admission with utility for prediction of clinical outcomes [Dataset]
Autor:	Uranga Murillo, Iratxe; Morte Romea, Elena; Hidalgo, Sandra; Pesini, Cecilia; García-Mulero, Sandra; Sierra Monzón, José L.; Santiago, Llipsy CSIC ORCID; Arias, Maykel CSIC ORCID; Miguel, Diego de; Encabo-Berzosa, M. Mar; Gracia Tello, Borja; Sanz-Pamplona, Rebeca; Martínez-Lostao, Luis CSIC ORCID CVN; Gálvez Buerba, Eva Mª CSIC ORCID ; Paño, José Ramón; Ramírez-Labrada, Ariel; Pardo, Julián
Palabras clave:	COVID-19 GzmA GzmB NK cells CXCL9 CXCL10 MIC ULBP
Fecha de publicación:	1-ene-2022
Editor:	Ivyspring International Publisher
Citación:	Uranga Murillo, Iratxe; Morte Romea, Elena; Hidalgo, Sandra; Pesini, Cecilia; García-Mulero, Sandra; Sierra Monzón, José L.; Santiago, Llipsy; Arias, Maykel; Miguel, Diego de; Encabo-Berzosa, M. Mar; Gracia Tello, Borja; Sanz-Pamplona, Rebeca; Martínez Lostao, Luis; Gálvez Buerba, Eva Mª; Paño, José Ramón; Ramírez-Labrada, Ariel; Pardo, Julián; 2022; Supplementary material for Integrated analysis of circulating immune cellular and soluble mediators reveals specific COVID19 signatures at hospital admission with utility for prediction of clinical outcomes [Dataset]; Ivyspring International Publisher; https://doi.org/10.7150/thno.63463
Resumen:	Supplementary materials and methods: sample processing, flow cytometry, high dimensional flow cytometry data analysis, multiplex plasma protein analyses, granzyme activity assay in serum, statistics. Supplementary figure legends (1-5). Supplementary table legends (1-7).
Descripción:	Sample processing: Peripheral blood was collected into sodium heparin tubes and centrifuged for 10 min at 8 2500 rpm at room temperature (RT) to separate the cellular fraction from the plasma. The plasma was removed from the cell pellet and stored at -80 ºC for posterior use. The cell pellet was diluted 1:1 in RPMI 1640 medium and carefully added to Histopaque-1077 (Sigma) for peripheral blood mononuclear cell (PBMC) isolation by centrifugation at 2500 rpm at RT for 10 min. PBMC layer was collected, washed with RPMI 1640, counted and aliquoted for staining and flow cytometry analysis. All flow cytometry analyses were performed using fresh PBMCs. Flow cytometry: For the surface staining, PMBCs were resuspended in 50 μl PBS with 5 % FCS and stained with the different antibody cocktails for 20 min at 4 ºC in dark, washed twice with PBS + 5 % FCS and then fixed for 30 min at 4 ºC in the dark using 2% PFA. Following surface staining, cells that required intracellular staining were fixed/permeabilized for 30 min at 4 ºC in the dark using the FoxP3 transcription factor buffer kit (Miltenyi). Following fixation/permeabilization, cells were washed twice with permeabilization buffer, resuspended in 50 μl permeabilization buffer and stained with intracellular antibodies for 30 min at 4 ºC in the dark. Samples were washed twice with permeabilization buffer following staining and fixed. All samples were acquired on a Gallios (Beckman Coulter) Flow Cytometer. The list of the antibodies used for immune cell phenotyping was: Miltenyi Biotec, CD14-VioBlue (130-110-524), CD16-FITC (130-25 113-392), CD25-APC (130-113-280), CD3-VioGreen (130-113-134), CD38-FITC (130-113-426), Treg detection kit CD4/CD25/CD127 (130-096-082), CD56-PerCP Vio700 (130-100-681), CD57-27 APC-Vio770 (130-111-813), CD8-PerCP Vio700 (130-110-682), FoxP3 Staining Buffer Set(130-28 093-142), GzmB-PE (130-116-486), HLADR-APC-VIO770 (130-111-792), LAG3-APC (130-105-29 453), NKG2A-PE-VIO615 (130-120-035), NKG2C-PE (130-103-635), NKP46-APC (130-092-609), TIM3-PE vio770 (130-121-334); Biolegend, NKp30-PE/Cy7 (325214), PD1-Alexa Fluor700 31 (329952), CD45-Brilliant Violet 421 (304032); BD, NKG2D-BV421 (743558). High dimensional flow cytometry data analysis: viSNE and FlowSOM (Self-organizing map) analyses were performed using Cytobank (https://cytobank.org). We used t-distributed stochastic neighbouring embedding (t-SNE) to reduce the dimensionality of the cell marker datasets generated using the antibody panels indicated above. FlowSOM clustering analysis compared expression of cell markers was used to identify each cluster and perform an unbiased analysis of the PBMC immunophenotyping data. CD56+ cells, CD56+ or CD14+ cells and CD3+CD8+ cells from FACS panels 2, 3 and 4 respectively, were analysed separately. SOM was generated using equal sampling of at least 1000 cells from each FCS file and hierarchical consensus clustering by the following markers: CD3, CD16, CD57, NKp30, NKp46, NKG2C, NKG2D and NKG2A for panel 2 analysis; CD14, CD3, HLA-DR, CD16, GZMB, TIM3, LAG3, PD1 or CD56,CD3, HLA-DR, CD16, GZMB, TIM3, LAG3, PD1 for panel 3 analysis and GzmB, CD38, HLA-DR, TIM3, LAG3, PD1 for panel 3 analysis. For each SOM, 100 clusters and 5, 8 or 10 metaclusters (MTs) were identified for panel 2, panel 3 and 4, which were represented in Minimum Spanning Trees (MTS). Multiplex plasma protein analyses: Luminex assay was run according to manufacturer’s instructions in 100 μl of plasma, using a custom human cytokine panel (RD Systems, catalogue no. LXSAHM). The next proteins were included: IFNα, IFNβ, IFN, IL28A/IFNλ2, IL28B/IFNλ3, IL2, IL1β,IL18/IL1F4,IL1RA,IL33, IL36b/IL1F8, IL7, IL10, IL31, IL6, IL12/IL23 p40, IL15, IL17E/IL25, IL8/CXCL8, CXCL10/IP10, CCL2/MCP1, CCL8/MCP2, CXCL9/MIG, CXCL2/MIP2α, MICA, MICB, ULBP-1, ULBP-2/5/6, ULBP-52 3, TNFα, GzmA and GzmB. Supernatants were mixed with beads coated with capture antibodies and incubated on a 96 well filter plate for 2 hours. Beads were washed and incubated with biotin-labelled detection antibodies for 1 hour, followed by a final incubation with streptavidin-PE. Assay plates were measured using a Luminex 200 instrument (ThermoFisher, catalogue no. APX10031). Data acquisition and analysis were performed using xPONENT software. The standard curve for each analyte had a five-parameter R2 value > 0.95 with or without minor fitting using xPONENT software. Granzyme activity assay in serum: Serum samples were used to evaluate the activity of both GzmA and GzmB using specific quenching FRET fluorescent substrates (FAM-VANRSAS-DABCYL and FAM-IEPDNLV-DABCYL peptides, respectively). In a nutshell, 40 μl of 100 mM Tris-HCl pH 8.5 or 100 mM Tris-HCl 50 mM NaCl pH 7.8 (buffers for GzmA or GzmB respectively) were added to flat bottom, black plates, with 10 μl of the serum samples. 50 μl of GzmA or GzmB substrates were added and the fluorescence of the plate was read at time zero and 1 h for GzmA and 24 h for GzmB using 475 nm excitation and 520 nm emission wavelenghts. Gzm activity was calculated based on a calibration curve with known concentrations of carboxyfluorescein. Statistics: To minimize inter-experimental variability and batch effects between patients, all PBMC samples were acquired, processed, and freshly analysed during four consecutive weeks from April to June 2020. Serum and plasma samples were frozen at -80ºC and later on all of them were thawed and analysed at the same time. Univariate and multivariate logistic regression models were developed using two different groups of variables, representing soluble and immunomodulatory factors (Group 1) or cell populations (Group 2) shown in Table S5. Age, sex and lymphocyte counts were included in all groups except for the comparison between HD and COVID19, since these variables were not known in HDs. First, a univariate logistic regression analysis was performed in the corresponding groups. Variables included in the multivariate discriminant analysis were those with a value of p < 0.1 in the univariate logistic regression analysis and / or with a value of p < 0.1 in the medians comparison tests. The univariate statistic test used has been chi-square or Fisher exact test for qualitative variant comparison and Mann-Whitney (comparison of two groups of variables) or Kruskal-Wallis (comparison of more than two groups of variables) for quantitative variant comparison. The post-test used was Benjamini, Krieger and Yekutieli test. Variable normality has been analysed with Kolmogorov-Smirnov test and Rho’s Spearman has been calculated as correlation coefficients. Statistical models were developed to predict COVID19 of diagnostic and severity. A multivariate logistic regression and discriminant analyses were performed to develop predictive models. Area Under the Curve (AUC), OR and CI95% values were reported for significant variables. Nagelkerke R2 was calculated to analyse sample variability and Hosmer-Lemeshow test was performed to analyse goodness of fit for the logistic regression model. Hosmer-Lemeshow p values higher than 0.05 indicate an adequate calibration of the predictive model. The statistics software used was GraphPad Software 7.0, (Inc. San Diego, CA) and SPSS 26.0 (IBM Corp., Armonk, NY).-- This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions.
Versión del editor:	http://dx.doi.org/10.7150/thno.63463
URI:	http://hdl.handle.net/10261/333721
DOI:	10.7150/thno.63463
Referencias:	Uranga Murillo, Iratxe; Morte Romea, Elena; Hidalgo, Sandra; Pesini, Cecilia; García-Mulero, Sandra; Sierra Monzón, José L.; Santiago, Llipsy; Arias, Maykel; Miguel, Diego de; Encabo-Berzosa, M. Mar; Gracia Tello, Borja; Sanz-Pamplona, Rebeca; Martínez-Lostao, Luis; Gálvez Buerba, Eva Mª; Paño, José Ramón; Ramírez-Labrada, Ariel; Pardo, Julián. Integrated analysis of circulating immune cellular and soluble mediators reveals specific COVID19 signatures at hospital admission with utility for prediction of clinical outcomes. http://dx.doi.org/10.7150/thno.63463. http://hdl.handle.net/10261/254746
Aparece en las colecciones:	(ICB) Conjuntos de datos (PTI Salud Global) Colección Especial COVID-19 (INMA) Conjuntos de datos