Institution of Analytical Chemistry, Department of Chemistry, Zhejiang University, Hangzhou, 310058, China.
Well-Healthcare Technologies, Co. LTD, Hangzhou, 310051, China.
Anal Chim Acta. 2020 Apr 22;1107:92-100. doi: 10.1016/j.aca.2020.02.019. Epub 2020 Feb 12.
Cell heterogeneity of tumor tissues is one of the causes of cancer recurrence after chemotherapy. Cell subtype identification in tumor tissues of specific cancer is critical for precision medicine and prognosis. As the structural and functional components of cells, lipids are closely related to the apparent morphology of cells. They are potential biomarkers of species of cancers and can be used to classify different cancer cell types, but it remains a challenge to establish a stable cell differentiation model and extend it to tumor tissue cell subtype differentiation. Here we describe a lipid profiling method based on nanostructure assisted laser desorption/ionization mass spectrometry (NALDI-MS), which could classify five hepatocellular carcinoma (HCC) cell lines and discriminate subtype of mixed cells and tumor tissues. The NALDI target was patterned with array of sample spots containing vertical silicon nanowires (Si NWs). Owing to its high ability to absorb laser energy, the vertical Si NWs can help to generate abundant lipid ions of cell extracts without need of organic matrix. Combined with statistical analysis methods, twenty-two ion peaks distributed in four MS peak clusters were selected as potential biomarkers to distinguish the subtype of the five HCC cell lines. Peak normalization was performed within each MS peak cluster to reduce the variation of peak intensity in batch to batch analysis. Compared to full-spectrum normalization method, the inner-cluster normalization method could help to distinguish cell subtype more stably and accurately. The molecular structure of these biomarkers was identified and sorted into two classes including phosphatidylcholine (PE, PI, PG, PA, PS) and glycosphingolipid (LacCer, ST). Furthermore, the established method was successfully applied to identify the major HCC cell subtype in mixed cell samples and xenograft tumor tissues as well as drug response test, showing great potential in precision medicine and prognosis.
肿瘤组织的细胞异质性是化疗后癌症复发的原因之一。在特定癌症的肿瘤组织中识别细胞亚型对于精准医学和预后至关重要。作为细胞的结构和功能组成部分，脂质与细胞的明显形态密切相关。它们是癌症物种的潜在生物标志物，可用于对不同的癌细胞类型进行分类，但建立稳定的细胞分化模型并将其扩展到肿瘤组织细胞亚型分化仍然是一个挑战。在这里，我们描述了一种基于纳米结构辅助激光解吸/电离质谱（NALDI-MS）的脂质分析方法，该方法可对五种肝癌（HCC）细胞系进行分类，并区分混合细胞和肿瘤组织的亚型。NALDI 靶标采用含有垂直硅纳米线（SiNWs）的样品点阵列进行图案化。由于其吸收激光能量的能力很高，垂直 SiNWs 有助于生成丰富的细胞提取物脂质离子，而无需有机基质。结合统计分析方法，从四个 MS 峰簇中选择了分布在 22 个离子峰作为潜在的生物标志物，用于区分五种 HCC 细胞系的亚型。在每个 MS 峰簇内进行峰归一化，以减少批处理分析中峰强度的变化。与全谱归一化方法相比，内簇归一化方法可以更稳定、更准确地帮助区分细胞亚型。鉴定了这些生物标志物的分子结构，并将其分为两类，包括磷脂酰胆碱（PE、PI、PG、PA、PS）和糖脂（LacCer、ST）。此外，该方法还成功地应用于鉴定混合细胞样本和异种移植肿瘤组织中的主要 HCC 细胞亚型以及药物反应测试，在精准医学和预后方面具有很大的潜力。
