Environmental chemical exposure, such as pesticides and heavy metals, may contribute to neurodegenerative disorders through neuroinflammation. This study aims to identify suitable in vitro microglial models for assessing cytokine responses to potential neurotoxicants, particularly focusing on human induced pluripotent stem cell-derived microglia (hiMG). In this study, we evaluated the cytokine secretion profiles of four microglial cell types-hiMG, HMC3, IM-HM, and BV2-upon stimulation with lipopolysaccharides (LPS) using cytokine arrays. Our findings showed cytokine response patterns in hiMG cells that most closely resemble in vivo conditions, with significant increases in interleukin 6 (IL-6) and tumor necrosis factor-alpha (TNF-α) levels, the latter being uniquely expressed after LPS treatment. Consequently, we developed a homogeneous time-resolved fluorescence (HTRF) assay platform in a 1536-well plate format for high-throughput screening of environmental chemicals using hiMG cells. After LPS treatment, the assay window for secretion of IL-6 and TNF-α increased 3.71-fold and 2.62-fold over the vehicle control group, respectively, with respective EC(50) values of approximately 50 ng/mL and 90 ng/mL for IL-6 and TNF-α. We also assessed the response activity of hiMG to other stimuli, including interferon gamma and various catecholamine compounds, and nine environmental chemicals with evidence of cytokine-inducing potential in other in vitro assays. While all nine tested agents stimulated IL-6 and TNF-α production, three compounds (e.g., picoxystrobin) showed significant stimulation of both cytokines. âThis study establishes a reliable high-throughput platform for detecting inflammatory effects of environmental toxicants in a microglial cell assay, contributing valuable insights into their neuroinflammatory potential and possible implications for neurodegenerative disorders.
High-throughput cytokine detection platform for evaluation of chemical induced microglial activation.
用于评估化学诱导的小胶质细胞活化的高通量细胞因子检测平台。
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| 期刊: | SLAS Technology | 影响因子: | 3.700 |
| 时间: | 2025 | 起止号: | 2025 Dec;35:100347 |
| doi: | 10.1016/j.slast.2025.100347 | ||
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