Kontsek E, Pesti A, Björnstedt M, Üveges T, Szabó E, Garay T, Gordon P, Gergely S, Kiss A
2nd Department of Pathology, Semmelweis University, Budapest, Hungary.
Laboratory for Clinical Pathology and Cytology, Department of Laboratory Medicine, Division of Pathology, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden.
Pathol Oncol Res. 2020 Oct;26(4):2401-2407. doi: 10.1007/s12253-020-00825-z. Epub 2020 Jun 16.
Malignancies are still responsible for a large share of lethalities. Macroscopical evaluation of the surgical resection margins is uncertain. Big data based imaging approaches have emerged in the recent decade (mass spectrometry, two-photon microscopy, infrared and Raman spectroscopy). Indocianine green labelled MS is the most common approach, however, label free mid-infrared imaging is more promising for future practical application. We aimed to identify and separate different transformed (A-375, HT-29) and non-transformed (CCD986SK) cell lines by a label-free infrared spectroscopy method. Our approach applied a novel set-up for label-free mid-infrared range classification method. Transflection spectroscopy was used on aluminium coated glass slides. Both whole range spectra (4000-648 cm) and hypersensitive fingerprint regions (1800-648 cm) were tested on the imaged areas of cell lines fixed in ethanol. Non-cell spectra were possible to be excluded based on mean transmission values being above 90%. Feasibility of a mean transmission based spectra filtering method with principal component analysis and linear discriminant analysis was shown to separate cell lines representing different tissue types. Fingerprint region resulted the best separation of cell lines spectra with accuracy of 99.84% at 70-75 mean transmittance range. Our approach in vitro was able to separate unique cell lines representing different tissues of origin. Proper data handling and spectra processing are key steps to achieve the adaptation of this dye-free technique for intraoperative surgery. Further studies are urgently needed to test this novel, marker-free approach.
恶性肿瘤仍然是导致大量死亡的原因。手术切除边缘的宏观评估并不确定。基于大数据的成像方法在最近十年中出现（质谱、双光子显微镜、红外和拉曼光谱）。吲哚菁绿标记的质谱是最常用的方法，然而，无标记的中红外成像在未来的实际应用中更有前景。我们旨在通过一种无标记的红外光谱方法来识别和区分不同的转化细胞系（A-375、HT-29）和未转化细胞系（CCD986SK）。我们的方法应用了一种用于无标记中红外范围分类方法的新型装置。在涂有铝的载玻片上使用透反射光谱。对固定在乙醇中的细胞系成像区域测试了全范围光谱（4000 - 648 cm）和超灵敏指纹区域（1800 - 648 cm）。基于平均透射率值高于90%，可以排除非细胞光谱。结果表明，基于平均透射率的光谱滤波方法结合主成分分析和线性判别分析能够分离代表不同组织类型的细胞系。指纹区域在70 - 75平均透射率范围内对细胞系光谱的分离效果最佳，准确率达到99.84%。我们的体外方法能够分离代表不同起源组织的独特细胞系。适当的数据处理和光谱处理是使这种无染料技术适用于术中手术的关键步骤。迫切需要进一步的研究来测试这种新型的、无标记的方法。
