JoVE Journal > Immunology and Infection
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Immunologie et infection

用三维定量相位成像和机器学习技术对淋巴细胞亚型进行无标签鉴定

Published: November 19, 2018
doi:
10.3791/58305
, , , , , , ,
1Department of Physics,University of Cambridge, 2Department of Physics,Korea Advanced Institute of Science and Technology, 3KAIST Institute for Health Science and Technology,Korea Advanced Institute of Science and Technology, 4Tomocube, Inc., 5Department of Chemical and Biomolecular Engineering,Korea Advanced Institute of Science and Technology, 6Department of Biological Sciences,Korea Advanced Institute of Science and Technology, 7Department of Applied Physics,Stanford University

Summary

我们描述了一种使用定量相位成像和机器学习算法对淋巴细胞亚型进行无标签识别的协议。对淋巴细胞三维折射率图的测量为单个细胞提供了三维形态和生化信息, 然后用机器学习算法进行分析, 以识别细胞类型。

Abstract

我们在这里描述了一个协议的无标签识别淋巴细胞亚型使用定量相位成像和机器学习。淋巴细胞亚型的鉴定对免疫学的研究以及各种疾病的诊断和治疗都很重要。目前, 对淋巴细胞类型进行分类的标准方法依赖于通过抗原抗体反应标记特定的膜蛋白。然而, 这些标记技术具有改变细胞功能的潜在风险。通过利用三维定量相位成像和机器学习算法测量的内在光学对比来克服这些挑战。测量淋巴细胞的三维折射率 (ri) 断层扫描物提供了有关单个细胞的三维形态学和表型的定量信息。然后, 使用机器学习算法对从测量到的 3d ri 断层扫描图中提取的生物物理参数进行定量分析, 从而能够在单细胞水平上对淋巴细胞类型进行无标签识别。我们测量 b、cd4+ t 和 cd8+ t 淋巴细胞的 3d ri 断层扫描, 并以80% 以上的准确率确定它们的细胞类型。在本协议中, 我们描述了淋巴细胞分离、三维定量相位成像和机器学习的详细步骤, 以识别淋巴细胞类型。

Introduction

淋巴细胞可分为多种亚型, 包括 b、辅助 (cd4 +) t、细胞毒性 (cd8 +) t 和调节 t 细胞。每种淋巴细胞类型在适应性免疫系统中都有不同的作用;例如, b 淋巴细胞产生抗体, 而 t 淋巴细胞检测到特定的抗原, 消除异常细胞, 并调节 b 淋巴细胞。淋巴细胞功能和调节受到各种疾病的严格控制, 并与之相关, 包括癌症1、自身免疫性疾病2和病毒感染 3。因此, 确定淋巴细胞类型对于了解其在此类疾病中的病理生理作用以及在临床上进行免疫治疗具有重要意义。

目前, 淋巴细胞类型的分类方法依靠抗原抗体反应, 靶向特定的表面膜蛋白或表面标记4。靶向表面标记是确定淋巴细胞类型的一种精确和准确的方法。但是, 它需要昂贵的试剂和耗时的程序。此外, 它还具有改变膜蛋白结构和改变细胞功能的风险。

为了克服这些挑战, 这里描述的协议介绍了使用三维定量相位成像 (qpi) 和机器学习5的淋巴细胞类型的无标签识别.这种方法可以根据从单个淋巴细胞的无标签三维成像中提取的形态信息, 在单细胞水平上对淋巴细胞类型进行分类。与传统的荧光显微镜技术不同, qpi 利用折射率 (ri) 分布 (活细胞和组织的固有光学特性) 作为光学对比度6,7。单个淋巴细胞的 ri 图是特定于淋巴细胞亚型的表型信息。在这种情况下, 为了系统地利用单个淋巴细胞的三维 ri 断层扫描, 采用了一种有监督的机器学习算法。

利用各种 qpi 技术, 细胞的三维 ri 图像已被积极用于细胞病理生理学的研究, 因为它们提供了一个无标签, 定量成像能力8,9,10, 11,12,13。此外, 单个细胞的三维 ri 分布可以提供有关细胞的形态、生化和生物力学信息。3d ri 断层扫描术以前曾被用于血液学领域 14,15,16, 17, 传染病18,19, 20、免疫学21、细胞生物学22、23、炎症24、癌症25、神经科学26、27、发育生物学 28、毒理学29, 微生物学12,30,31,32。

尽管 3d ri 断层扫描物提供了细胞的详细形态和生化信息, 但通过简单地成像 3d ri 图5很难实现淋巴细胞亚型的分类.为了系统、定量地利用测量到的三维 ri 图进行细胞类型分类, 我们采用了机器学习算法。最近, 有几项工作是用各种机器学习算法33分析细胞的定量相位图像, 包括微生物的检测34、细菌属的分类 35,36、炭疽孢子的快速无标签检测 37, 精子细胞的自动分析 38, 癌细胞 39,40 的分析, 巨噬细胞活化41 的检测.

该协议提供了详细的步骤, 使用 3d qpi 和机器学习在单个细胞级别执行无标签的淋巴细胞类型识别。这包括: 1) 从小鼠血液中分离淋巴细胞, 2) 通过流式细胞仪进行淋巴细胞分选, 3) 3d qpi, 4) 从 3d ri 肿瘤中提取定量特征, 5) 有监督的学习, 以识别淋巴细胞类型。

Protocol

动物护理和实验程序是在 kaist 机构动物照料和使用委员会 (ka2010-21、kaa160001 和 ka2015-03) 的批准下进行的。本研究中的所有实验都是按照批准的准则进行的。 1. 小鼠血液淋巴细胞分离 一旦 c57bl/6j 小鼠通过吸入二氧化碳实现安乐死 , 将26-g 针插入小鼠心脏并收集0.3 毫升的血液。直接将血液放入用1毫升磷酸盐缓冲盐水 (pbs) 稀释的 100 u/ml 肝素溶液管中。注: 或?…

Representative Results

图 11显示整个协议的原理图过程。使用这里介绍的程序, 我们分离 b (n = 149)、cd4+ t (n = 95) 和 cd8 + t (n = 112) 淋巴细胞。为了在不同的光照角度获得相位和振幅信息, 通过改变光照角度 (从-60°到 60°) 测量了每个淋巴细胞的多个二维全息图。通常情况下, 50个全息图可用于重建三维 ri 断层扫描图, 但2d 全息图的数量可以根据成像速度和质量进行调整。利…

Discussion

我们提出了一个协议, 使标签免费识别淋巴细胞类型结合3d 定量相位成像和机器学习。该协议的关键步骤是定量相位成像和特征选择。为了获得最佳的全息成像, 应按上述方式控制细胞的密度。细胞的机械稳定性对于获得精确的 3d ri 分布也很重要, 因为浮动或振动细胞运动会干扰全息图测量时, 照明角度的变化。因此, 我们等待了几分钟, 直到样品在成像室变得稳定和静态, 然后再测量全息图。最后,…

Divulgations

The authors have nothing to disclose.

Acknowledgements

这项工作得到了 kaist bk21+ 计划、tomocube 公司和韩国国家研究基金会的支持 (2015ra3a2066, 2017m3c1a3013923, 2018k0003996)。y. jo 感谢 kaist 总统研究金和阿桑基金会生物医学科学奖学金的支持。

Materials

Mouse Daehan Biolink C57BL/6J mice  gender and age-matched, 6 – 8 weeks
Falcon conical centrifuge tube ThermoFisher Scientific 14-959-53A 15 mL
Phosphate-buffered saline  Sigma-Aldrich 806544-500ML
Ammonium-chloride-potassium lysing buffer  ThermoFisher Scientific A1049201
RPMI-1640 medium Sigma-Aldrich R8758
Fetal bovine serum ThermoFisher Scientific 10438018
Antibody BD Biosciences 553140 (RRID:AB_394655) CD16/32 (clone 2.4G2)
Antibody BD Biosciences 555275 (RRID:AB_395699) CD3ε (clone 17A2)
Antibody Biolegnd 100734 (RRID:AB_2075238) CD8α (clone 53-6.7)
Antibody BD Biosciences 557655 (RRID:AB_396770) CD19 (clone 1D3)
Antibody BD Biosciences 557683 (RRID:AB_396793) CD45R/B220 (clone RA3-6B2)
Antibody BD Biosciences 552878 (RRID:AB_394507) NK1.1 (clone PK136)
Antibody eBioscience 11-0041-85 (RRID:AB_464893) CD4 (clone GK1.5)
DAPI  Roche 10236276001 4,6-diamidino-2-phenylindole
Flow cytometry  BD Biosciences Aria II or III 
Imaging chamber Tomocube, Inc. TomoDish
Holotomography Tomocube, Inc. HT-1H
Holotomography imaging software Tomocube, Inc. TomoStudio
Image professing software MathWorks Matlab R2017b
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Tags

Label-freeLymphocyte Subtypes3D Quantitative Phase ImagingMachine LearningFluorescence LabelingFlow CytometryRefractive Index TomographyBlood CancerAutoimmune DiseaseSingle-cell AnalysisBacteria3D Quantitative Phase MicroscopeRPMI MediumDigital Micromirror Device3D Reconstruction

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Yoon, J., Jo, Y., Kim, Y. S., Yu, Y., Park, J., Lee, S., Park, W. S., Park, Y. Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning. J. Vis. Exp. (141), e58305, doi:10.3791/58305 (2018).
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