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The relationship between non-HDL cholesterol and macrophage phenotypes in human adipose tissue

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ABSTRACT

Data from experimental animal models and in vitro studies suggest that both hyperlipoproteinemia and obesity predispose to development of proinflammatory pathways of macrophages within adipose tissue. The aim of this study was to analyze whether non-HDL cholesterol concentration in healthy living kidney donors (LKDs) is related to the number and phenotype of proinflammatory macrophages in visceral and subcutaneous adipose tissue. Adipose tissue samples were collected by cleansing the kidney grafts of LKDs obtained peroperatively. The stromal vascular fractions of these tissues were analyzed by flow cytometry. Proinflammatory macrophages were defined as CD14+ cells coexpressing CD16+ and high-expression CD36 as well (CD14+CD16+CD36+++), while CD16 negativity and CD163 positivity identified alternatively stimulated, anti-inflammatory macrophages. Non-HDL cholesterol concentration positively correlated to proinflammatory macrophages within visceral adipose tissue, with increased strength with more precise phenotype determination. On the contrary, the proportion of alternatively stimulated macrophages correlated negatively with non-HDL cholesterol. The present study suggests a relationship of non-HDL cholesterol concentration to the number and phenotype proportion of macrophages in visceral adipose tissue of healthy humans.

No MeSH data available.


Related in: MedlinePlus

Example of SVF flow cytometric analysis. A: CD16-positive monocytes were first identified and delineated in the blood sample (left, CD16-positive macrophages in the upper part). The settings were fixed and subsequently used for SVF analysis (B). Total macrophages in SVF were identified by positivity for CD14 (C), and, based on the CD16 marker, two subpopulations were distinguished (B, CD16-positive macrophages in the upper part). The CD16+ subpopulation was divided according to the CD36 marker (D), with the highly positive subpopulation at the top and the low positive in the middle (based on blood macrophage analyses). E: CD163 expression was determined within the CD16-negative subpopulation and divided (CD163-positive macrophages in the upper part). This scheme is partly simplified, as a few minor fractions (already measured) are not mentioned.
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f1: Example of SVF flow cytometric analysis. A: CD16-positive monocytes were first identified and delineated in the blood sample (left, CD16-positive macrophages in the upper part). The settings were fixed and subsequently used for SVF analysis (B). Total macrophages in SVF were identified by positivity for CD14 (C), and, based on the CD16 marker, two subpopulations were distinguished (B, CD16-positive macrophages in the upper part). The CD16+ subpopulation was divided according to the CD36 marker (D), with the highly positive subpopulation at the top and the low positive in the middle (based on blood macrophage analyses). E: CD163 expression was determined within the CD16-negative subpopulation and divided (CD163-positive macrophages in the upper part). This scheme is partly simplified, as a few minor fractions (already measured) are not mentioned.

Mentions: Samples of visceral and subcutaneous adipose tissue were obtained peroperatively after hand-assisted laparoscopic nephrectomy. Adipose tissue samples (∼2 g) were immediately cooled and transferred to the laboratory within 20 min. After removing visible blood vessels and connective tissue, each sample was dissected using scissors to facilitate homogeneous collection of small pieces (∼2 mm2). After shaking incubation of tissue samples with collagenase (2 mg) for 20 min (37°C), the homogenate was filtered (50 μm) and centrifuged. The stromal vascular fraction (SVF) was purified twice by resuspension. The final SVF sample was analyzed immediately by flow cytometry (CyAn; Beckman Coulter, Brea, CA). Monoclonal antibodies and fluorochromes (CD14, Phycoerythrin-Cyanine, CD16, Phycoerythrin-Texas Red X, CD36, FITC and CD163 Phycoerythrin, PE/Clone RM 3/1) were used to define different subsets of monocytes/macrophages. Flow cytometry data were analyzed using Kaluza Software (Beckman Coulter). The viability of analyzed cells was measured for each sample using 7-AAD (7-Aminoactinomycin D), and only samples with a viability higher than 75% were considered. Due to difficulties in delineating CD16-positive cells in the SVF, CD16-positive monocytes were first identified and delineated in the blood samples where a CD16-positive subpopulation was clearly visible. The setting was fixed and subsequently used for SVF analysis. The gating strategy for identifying SVF macrophage subpopulations is shown in Fig. 1.


The relationship between non-HDL cholesterol and macrophage phenotypes in human adipose tissue
Example of SVF flow cytometric analysis. A: CD16-positive monocytes were first identified and delineated in the blood sample (left, CD16-positive macrophages in the upper part). The settings were fixed and subsequently used for SVF analysis (B). Total macrophages in SVF were identified by positivity for CD14 (C), and, based on the CD16 marker, two subpopulations were distinguished (B, CD16-positive macrophages in the upper part). The CD16+ subpopulation was divided according to the CD36 marker (D), with the highly positive subpopulation at the top and the low positive in the middle (based on blood macrophage analyses). E: CD163 expression was determined within the CD16-negative subpopulation and divided (CD163-positive macrophages in the upper part). This scheme is partly simplified, as a few minor fractions (already measured) are not mentioned.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC5036370&req=5

f1: Example of SVF flow cytometric analysis. A: CD16-positive monocytes were first identified and delineated in the blood sample (left, CD16-positive macrophages in the upper part). The settings were fixed and subsequently used for SVF analysis (B). Total macrophages in SVF were identified by positivity for CD14 (C), and, based on the CD16 marker, two subpopulations were distinguished (B, CD16-positive macrophages in the upper part). The CD16+ subpopulation was divided according to the CD36 marker (D), with the highly positive subpopulation at the top and the low positive in the middle (based on blood macrophage analyses). E: CD163 expression was determined within the CD16-negative subpopulation and divided (CD163-positive macrophages in the upper part). This scheme is partly simplified, as a few minor fractions (already measured) are not mentioned.
Mentions: Samples of visceral and subcutaneous adipose tissue were obtained peroperatively after hand-assisted laparoscopic nephrectomy. Adipose tissue samples (∼2 g) were immediately cooled and transferred to the laboratory within 20 min. After removing visible blood vessels and connective tissue, each sample was dissected using scissors to facilitate homogeneous collection of small pieces (∼2 mm2). After shaking incubation of tissue samples with collagenase (2 mg) for 20 min (37°C), the homogenate was filtered (50 μm) and centrifuged. The stromal vascular fraction (SVF) was purified twice by resuspension. The final SVF sample was analyzed immediately by flow cytometry (CyAn; Beckman Coulter, Brea, CA). Monoclonal antibodies and fluorochromes (CD14, Phycoerythrin-Cyanine, CD16, Phycoerythrin-Texas Red X, CD36, FITC and CD163 Phycoerythrin, PE/Clone RM 3/1) were used to define different subsets of monocytes/macrophages. Flow cytometry data were analyzed using Kaluza Software (Beckman Coulter). The viability of analyzed cells was measured for each sample using 7-AAD (7-Aminoactinomycin D), and only samples with a viability higher than 75% were considered. Due to difficulties in delineating CD16-positive cells in the SVF, CD16-positive monocytes were first identified and delineated in the blood samples where a CD16-positive subpopulation was clearly visible. The setting was fixed and subsequently used for SVF analysis. The gating strategy for identifying SVF macrophage subpopulations is shown in Fig. 1.

View Article: PubMed Central - PubMed

ABSTRACT

Data from experimental animal models and in vitro studies suggest that both hyperlipoproteinemia and obesity predispose to development of proinflammatory pathways of macrophages within adipose tissue. The aim of this study was to analyze whether non-HDL cholesterol concentration in healthy living kidney donors (LKDs) is related to the number and phenotype of proinflammatory macrophages in visceral and subcutaneous adipose tissue. Adipose tissue samples were collected by cleansing the kidney grafts of LKDs obtained peroperatively. The stromal vascular fractions of these tissues were analyzed by flow cytometry. Proinflammatory macrophages were defined as CD14+ cells coexpressing CD16+ and high-expression CD36 as well (CD14+CD16+CD36+++), while CD16 negativity and CD163 positivity identified alternatively stimulated, anti-inflammatory macrophages. Non-HDL cholesterol concentration positively correlated to proinflammatory macrophages within visceral adipose tissue, with increased strength with more precise phenotype determination. On the contrary, the proportion of alternatively stimulated macrophages correlated negatively with non-HDL cholesterol. The present study suggests a relationship of non-HDL cholesterol concentration to the number and phenotype proportion of macrophages in visceral adipose tissue of healthy humans.

No MeSH data available.


Related in: MedlinePlus