Journal: medRxiv

Article Title: Hydroxychloroquine inhibits trained immunity – implications for COVID-19

doi: 10.1101/2020.06.08.20122143

Figure Lengend Snippet: a-g, PBMCs isolated from COVID-19 patients at admission and from healthy controls were analyzed using flow cytometry (n = 10 for COVID-19 patients, n = 7 for healthy controls). a, tSNE plots showing unsupervised clustering on the expression of 10 markers (CD45, CD14, CD16, CD3, CD19, CD56, HLA-DR, CD11b, CCR2 and CX3CR1) in controls and COVID-19 patients. b, Quantification of lymphocytes using gating strategy shown in Extended Data showed decreased amounts of T-cells in COVID-19 patients. c,d, Quantification of monocytes showed overall higher counts in COVID-19 patients that was due to higher number of classical monocytes (CD14 ++ ,CD16 − ), whereas non-classical monocytes(CD14 + , CD16 ++ ) were reduced in COVID-19 patients. e-g, Analysis of marker expression on monocytes revealed reduced expression of HLA-DR (e), reduced number of CX3CR1 expressing monocytes (f) and increased number of CD11b expressing monocytes (g) in COVID-19 patients. h,i, Isolated PBMCs were stimulated with LPS (h) or R848 (i) for 24 hours after which production of IL-1β, IL-6 and TNFα was quantified in the supernatant using ELISA. COVID-19 patient PBMCs show increased cytokine production upon stimulation with either stimulus (n = 13 for COVID-19 patients, n = 10 for healthy controls). j, Isolated PBMCs were stimulated with heat-killed Staphylococcus aureus (HKSA) for 7 days after which production of IFNγ and IL-17 was quantified using ELISA. IFNγ response was reduced, whereas IL-17 production was elevated in COVID-19 patients. (n = 12 for COVID-19 patients, n = 10 for healthy controls) Data are presented as mean ± SEM.*p< 0.05, **p< 0.01, ***p< 0.001 for two-sided student’s ttest (for normally distributed data) or Kruskal-Wallis test.

Article Snippet: Antibodies and dilutions used are CD45-BV510 (Biolegend, 1:100), CD14-PC7 (eBioscience, 1:100), CD16-FITC (eBioscience, 1:100), CD3-APC750 (Beckman Coulter, 1:50), CD19-APC-R700 (Becton Dickinson, 1:100), CD56-APC (Beckman Coulter, 1:50), HLADR-PE (Beckman Coulter, 1:20), CD11b-BV785 (Biolegend, 1:100), CCR2-BV421 (Becton Dickinson, 1:50), CX3CR1-BV650 (Biolegend, 1:50) and Live/Dead FVS620 (Becton Dickinson, according to manufacturers’ instructions).

Techniques: Isolation, Flow Cytometry, Expressing, Marker, Enzyme-linked Immunosorbent Assay

Journal: medRxiv

Article Title: Hydroxychloroquine inhibits trained immunity – implications for COVID-19

doi: 10.1101/2020.06.08.20122143

Figure Lengend Snippet: a-g, PBMCs isolated from COVID-19 patients at admission were analyzed using flow cytometry (n = 7 for COVID-19 patients with good outcome, n = 3 for COVID-19 patients with poor outcome). a, tSNE plots showing unsupervised clustering on the expression of 10 markers (CD45, CD14, CD16, CD3, CD19, CD56, HLA-DR, CD11b, CCR2 and CX3CR1) in COVID-19 patients with good and poor outcome. b, Quantification of lymphocytes using gating strategy shown in Extended Data showed minor differences in B-cells between COVID-19 patients with good or poor outcome. c,d, Quantification of monocytes showed no difference between COVID-19 patients with good or poor outcome. e-g, Expression of HLA-DR (e), CX3CR1 (f) and CD11b (g) on monocytes did not differ between COVID-19 patients with good or poor outcome. h,i, Transcriptome analysis was performed on isolated monocytes of COVID-19 patients at admission. h, Heatmap of differentially expressed genes (p< 0.005) between COVID-19 patients with good and poor outcome. i, Heatmap of GO-pathways and hallmark-pathways that are significantly enriched (FDR< 0.05) in COVID-19 patients with good and poor outcome. (* HALLMARK pathways; NES: normalized enrichment score) Data are presented as mean ± SEM. ^p< 0.06, *p< 0.05 for two-sided student’s t-test (for normally distributed data) or Kruskal-Wallis test.

Article Snippet: Antibodies and dilutions used are CD45-BV510 (Biolegend, 1:100), CD14-PC7 (eBioscience, 1:100), CD16-FITC (eBioscience, 1:100), CD3-APC750 (Beckman Coulter, 1:50), CD19-APC-R700 (Becton Dickinson, 1:100), CD56-APC (Beckman Coulter, 1:50), HLADR-PE (Beckman Coulter, 1:20), CD11b-BV785 (Biolegend, 1:100), CCR2-BV421 (Becton Dickinson, 1:50), CX3CR1-BV650 (Biolegend, 1:50) and Live/Dead FVS620 (Becton Dickinson, according to manufacturers’ instructions).

Techniques: Isolation, Flow Cytometry, Expressing

Journal: medRxiv

Article Title: Hydroxychloroquine inhibits trained immunity – implications for COVID-19

doi: 10.1101/2020.06.08.20122143

Figure Lengend Snippet: a-g, PBMCs isolated from COVID-19 patients five days after admission were analyzed using flow cytometry (n = 2 for COVID-19 patients with good outcome, n = 3 for COVID-19 patients with poor outcome). a, tSNE plots showing unsupervised clustering on the expression of 10 markers (CD45, CD14, CD16, CD3, CD19, CD56, HLA-DR, CD11b, CCR2 and CX3CR1) in COVID-19 patients with good and poor outcome. b, Quantification of lymphocytes using gating strategy shown in Extended Data showed no differences between COVID-19 patients with good or poor outcome. c, Quantification of overall monocytes showed no difference between COVID-19 patients with good or poor outcome. d, Subset analysis of monocytes revealed increased amounts of non-classical monocytes (CD14 + , CD16 ++ ) in COVID-19 patients with good outcome. e,f, Expression of HLA-DR (e) and numbers of CX3CR1 expressing monocytes (f) were increased in COVID-19 patients with good outcome. g, CD11b expression on monocytes did not differ between COVID-19 patients with good or poor outcome. h,i, Transcriptome analysis was performed on isolated monocytes of COVID-19 patients five days after admission. (n = 3 for COVID-19 patients with good outcome, n = 4 for COVID-19 patients with poor outcome) h, Heatmap of differentially expressed genes (p< 0.001) between COVID-19 patients with good and poor outcome. i, Heatmap of GO-pathways and hallmark-pathways that are significantly enriched (FDR< 0.01) in COVID-19 patients with good and poor outcome.(* HALLMARK pathways; NES: normalized enrichment score) Data are presented as mean ± SEM. ^p< 0.06, *p< 0.05 for two-sided student’s t-test (for normally distributed data) or Kruskal-Wallis test.

Article Snippet: Antibodies and dilutions used are CD45-BV510 (Biolegend, 1:100), CD14-PC7 (eBioscience, 1:100), CD16-FITC (eBioscience, 1:100), CD3-APC750 (Beckman Coulter, 1:50), CD19-APC-R700 (Becton Dickinson, 1:100), CD56-APC (Beckman Coulter, 1:50), HLADR-PE (Beckman Coulter, 1:20), CD11b-BV785 (Biolegend, 1:100), CCR2-BV421 (Becton Dickinson, 1:50), CX3CR1-BV650 (Biolegend, 1:50) and Live/Dead FVS620 (Becton Dickinson, according to manufacturers’ instructions).

Techniques: Isolation, Flow Cytometry, Expressing

Journal: Cell Reports Medicine

Article Title: Hydroxychloroquine Inhibits the Trained Innate Immune Response to Interferons

doi: 10.1016/j.xcrm.2020.100146

Figure Lengend Snippet:

Article Snippet: Anti-CD14 PC7 , eBioscience , Cat#25-0149; RRID: AB_1582277.

Techniques: Staining, Virus, Isolation, Recombinant, Protease Inhibitor, Enzyme-linked Immunosorbent Assay, Lactate Assay, Sensitive Assay, In Vitro, Software

Journal: Biomedicines

Article Title: Statins Directly Influence the Polarization of Adipose Tissue Macrophages: A Role in Chronic Inflammation

doi: 10.3390/biomedicines9020211

Figure Lengend Snippet: Blood gating strategy and the isolated stromal vascular fraction of the visceral adipose tissue. After gate-out debris and non-singlets, only viable cells were included in the analysis. Viable CD14+ cells were divided into CD16+ and CD16- subpopulations based on the blood sample gate where the transition between the CD16+ and CD16- population was obvious. Next, both cell populations were further differentiated according to CD36 expression into CD16-, CD36low, and CD36high. In this manner, we defined the three main adipose tissue macrophage subtypes as follows: pro-inflammatory (CD14+CD16+CD36high), transient (CD14+CD16+CD36low), and anti-inflammatory (CD14+CD16-CD36low).

Article Snippet: Monoclonal antibodies and fluorochromes (CD14-phycoerythrin-cyanine 7 (PC7), CD16-phycoerythrin-Texas Red-X, ECD, CD 36-fluorescein isothiocyanate, FITC, and Fixable Viability Dye eFluorTM 780; Thermo Fisher, Waltham, MA, USA) were used for the specific individual subsets of macrophages.

Techniques: Isolation, Expressing

Journal: Biomedicines

Article Title: Statins Directly Influence the Polarization of Adipose Tissue Macrophages: A Role in Chronic Inflammation

doi: 10.3390/biomedicines9020211

Figure Lengend Snippet: Polarization of adipose tissue macrophages and its relationship to low-density lipoprotein (LDL) cholesterol. Linear regression between LDL cholesterol and the percentage of pro-inflammatory (CD14+CD16+CD36high), transient (CD14+CD16+CD36low), and anti-inflammatory (CD14+CD16-CD36low) macrophages in visceral adipose tissue (VAT).

Article Snippet: Monoclonal antibodies and fluorochromes (CD14-phycoerythrin-cyanine 7 (PC7), CD16-phycoerythrin-Texas Red-X, ECD, CD 36-fluorescein isothiocyanate, FITC, and Fixable Viability Dye eFluorTM 780; Thermo Fisher, Waltham, MA, USA) were used for the specific individual subsets of macrophages.

Techniques:

Journal: Biomedicines

Article Title: Statins Directly Influence the Polarization of Adipose Tissue Macrophages: A Role in Chronic Inflammation

doi: 10.3390/biomedicines9020211

Figure Lengend Snippet: Effects of hypercholesterolemia ( A ) and statin therapy ( B ) in the 118 living kidney donors (LKDs) included in the Bayesian analysis. The data are expressed as the difference in the relative numbers of pro-inflammatory, transient, and anti-inflammatory phenotypes in VAT. The results are shown as the mean ± 95% confidential interval pro-inflammatory (CD14+CD16+CD36high), transient (CD14+CD16+CD36low), and anti-inflammatory (CD14+CD16-CD36low) macrophage subpopulations. The clinically irrelevant ±1.5% delta margins of the differences are shown as grey areas. The differences were obtained from multivariate linear normal regressions with Bayesian estimations using the Metropolis-Hastings algorithm.

Article Snippet: Monoclonal antibodies and fluorochromes (CD14-phycoerythrin-cyanine 7 (PC7), CD16-phycoerythrin-Texas Red-X, ECD, CD 36-fluorescein isothiocyanate, FITC, and Fixable Viability Dye eFluorTM 780; Thermo Fisher, Waltham, MA, USA) were used for the specific individual subsets of macrophages.

Techniques: