Please wait a minute...

中国生物工程杂志

China Biotechnology
China Biotechnology  2019, Vol. 39 Issue (9): 68-83    DOI: 10.13523/j.cb.20190910
Orginal Article     
Development, Application and Prospection of Flow Cytometry
Hang Hai-ying1,Liu Chun-chun2,Ren Dan-dan1,**
1 Key Laboratory for Protein and Peptide Pharmaceuticals, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
2 Core Facility for Protein Research Technology, Tsinghua University, Beijing 100084, China
Download: HTML   PDF(1845KB) HTML
Export: BibTeX | EndNote (RIS)      

Abstract  

Flow cytometry, which measures cells,particles or aggregates in flow, is able to provide a large number of single-cell measurements with statistical significance within a short time. After more than 70 years of development, it has become an indispensable technology in many fields including biology, medicine and environmental surveillance. This technology was put forward in the 1940s and formed in the 1970s. In the following 4 decades, the detection performance, multi-parameter measurement ability and sorting ability have been significantly improved. Especially its application fields have expanded rapidly from bacteria measurement, environmental microorganism detection, conventional cell function detection to the diagnosis and monitoring of many clinical diseases, and up to the cutting-edge fields of cancer immune mechanism research, immunotherapy and biopharmaceuticals discovery. At present, a series of new technologies and new types of corresponding flow cytometers have been developed. Although their working principles are different more or less from the traditional method, the concept of obtaining large-scale single cell multi-parameter measurements has never changed. Currently the field of flow cytometry is on the edge of a major development and revolution.



Key wordsFlow cytometry      Cluster of differentiation (CD)      Escherichia Coli (E.coli)      Exosome      High-dimensional data automatic analysis technology     
Received: 24 August 2019      Published: 20 September 2019
ZTFLH:  Q819  
Corresponding Authors: Dan-dan Ren   
Cite this article:

Hang Hai-ying,Liu Chun-chun,Ren Dan-dan. Development, Application and Prospection of Flow Cytometry. China Biotechnology, 2019, 39(9): 68-83.

URL:

https://manu60.magtech.com.cn/biotech/10.13523/j.cb.20190910     OR     https://manu60.magtech.com.cn/biotech/Y2019/V39/I9/68

Fig.1 Basic structure of flow cytometer
Fig.2 The One-Laser flow cytometer developed by Institute of Biophysics, Chinese Academy of sciences in 1980s
年代 代表性仪器和应用技术进展
仪器技术 应用
20世纪40年代
及以前
? 原始光电探测器件(PMT,1933)
? 流式细胞仪概念原型(1934)
? Gucker空气层流系统计数气溶胶颗粒(气体流式,1947年解密)
? Coulter原理
? 甲基绿与派洛宁组合使用进行核酸染色
? 荧光素(fluorescin)偶联抗血清(首次获得抗原特异性)
? Gucker系统用于细菌计数
20世纪50年代 ? 液体层流鞘液与流体动力学聚焦原理(液体流式)
? 计算机应用
? 商用Coulter计数器
? 基于流式散射光进行红细胞计数
? 基于双色光吸收进行红、白细胞分类计数
? 吖啶橙(AO)染色的异色荧光鉴别和定量组织中RNA含量
20世纪60年代 ? 液滴加电分选技术;机械抓取分选技术
? 激光光源
? 三轴垂直立体激发结构原理
? 基于显微镜方式的流式
? AO染色全血,结合散射光和荧光区分红白细胞(首例荧光流式和双参数流式实验)
? 基于细胞荧光的分选
? DNA细胞周期检测
? 粒、淋巴群分选纯度>95%
? 首台商业化流式推出(德国Partec公司)
20世纪70年代 ? 缝扫描流式细胞仪
? 各项技术集成,结构完善,形成沿用至今的三轴垂直立体激发的结构原理
? 3激光8参数流式(计算机控制完备,多参数圈门;未商业化)
? EB/FITC对DNA和蛋白质双染
? 染色体核型分析(karyotyping)
? 杂交瘤技术出现并产生单克隆抗体
? 人类淋巴细胞表面抗原反应的单克隆抗体推出
? 2激光2色免疫荧光实验
20世纪80年代 ? 封闭式液流系统
? 石英杯流动池
? 光胶耦合一体化镜头收集光信号
? 气冷激光器
? 光纤传导激光光束
? 在低流速流式系统上达到溶液中单分子荧光测量的极限灵敏度
? 高速流式分选系统
? 第一代临床荧光流式分析仪推出,仪器小型化
? AIDS被流式检测发现(随后HIV病毒被发现)
? PE、APC应用,首个串联染料推出
? 3色免疫荧光检测,多参数设门
? 大量应用于艾滋病临床检测
? GFP
? 酶切荧光底物
? 膜电位、Ca2+、pH、氧化酶等许多染料被开发
? 特异核酸序列检测
? 染色体分选应用于早期人类基因组计划
20世纪90年代 ? 20Bit高精度ADC
? 微机软件数据分析
? 离线补偿
? 小型固体激光器应用
? 14荧光通道配置仪器(实验室)
? 性能、质控理论和技术实用化
? PE、APC系列串联染料
? 10色免疫荧光实验
? Tn、各类Tm细胞群等被发现
? 胞内细胞因子检测
21世纪00年代 ? 全数字化现代流式
? 自动化台式分选仪
? 低鞘液压力高速分选(Influx)
? 7激光32通道传统流式配置
? 成像流式发布(Imaging Cytometry)
? 质谱流式发布(Mass Cytomery)
? 自动数据分析技术初现
? Alexa、Qdot、efluor系列染料
? 20色免疫荧光实验
? Tfh/Treg等亚群被发现
? 胞内磷酸化表位检测(phoph-flow)
? 三荧光蛋白级联FRET检测活细胞内蛋白质相互作用
年代 代表性仪器和应用技术进展
仪器技术 应用
21世纪10年代 ? 声波聚焦技术
? 光谱流式(Spectra Cytometry)推出
? 大量样本自动高通量上样
? 10激光50通道传统流式
? 更高自动化小型高速分选仪
? 多种微流控分选仪推向市场
? 高维数据自动分析技术迅速发展,机器学习应用于数据分析
? Genomic Cytometry概念被提出(https://genomiccytometry.com)
? BV、BUV、BB系列及SB(SuperBright)系列有机聚合物染料商用推出
? 28色免疫荧光实验
? 52参数质谱流式
? Tscm、NKB(Natural killer B)等亚群被发现
? 液相芯片阵列;细胞二维码技术
? 分选单细胞深度测序(单细胞转录组)
Table 1 A brief chronology of the flow cytometry development
检测参数 检测方法和探针
内在结构参数 不使用探针
细胞大小 小角度散射光、图像分析(成像流式)、电阻抗(少用,有的微流控流式使用)、消光(少用,有的微流控流式使用)
细胞性状 信号波型分析、图像分析(成像流式)
细胞质内颗粒性 大角度散射光、电阻抗(少用)
双折射(如血嗜酸粒细胞颗粒) 偏振光散射、吸收
血红蛋白、光合色素、卟啉类 自发荧光、多角度散射
内在功能参数 不使用探针
氧化还原(能量代谢)状态 吡啶、黄素核苷酸类自发荧光
外在结构参数 外加荧光探针
DNA含量 Propidium iodide、DAPI、Hoechst、DRAQ5/7等染料
DNA碱基比率 Hoechst33258(AT偏好)、Chromomycin A(CG偏好)
核酸序列 荧光标记的寡聚核苷酸
染色体结构或状态 用酸处理或加热细胞(不破坏染色体状态),然后用acridine orange、7-aminoactinomycin D等染色,不同种类染料结合不同状态染色体,发出不同荧光
RNA含量 对细胞适当前处理后用acridine orange或pyronin Y染RNA
特异序列mRNA PrimeFlow RNA Assay(特异探针与mRNA序列杂交,经分支DNA技术构成一个树形预放大分子,再以偶联荧光染料的多重标签探针结合,进一步放大信号)
蛋白质总量 共价(如FITC)和离子键酸性染料(如Rhodamine10)
碱性蛋白质含量 酸性染料(如brilliant sulfaflavine)在碱性条件下染碱性蛋白
细胞表面及细胞内抗原 荧光标记抗体
细胞表面糖基 荧光标记凝集素
脂质 脂质荧光染料如Nile red、Filipin
自噬体 荧光抗体标记自噬体膜表面LC3-II蛋白;LC3-II/LC3-I(胞浆可溶型)比值可估计自噬水平
溶酶体 酸性细胞器高度选择性染料(如 LysoTracker(Thermo)、Lyso-ID(Enzo)),荧光抗体标记LAMP1(溶酶体相关膜蛋白)
高尔基体 荧光神经酰胺(fluorescentceramide)
检测参数 检测方法和探针
外在功能参数 外加荧光探针
细胞表面及细胞内受体 荧光标记配体
细胞表面电荷 荧光标记多离子分子
细胞膜流动性 1,6-diphenyl-l,3,5-hexatriene (DPH)
细胞膜完整性 用Propidium、fluorescein diacetate能通过不完整的膜,细胞发荧光
膜融合/迁移 长链脂肪酸衍生物标记的荧光染料(如dioctadecyl- Indocarbocyanine和dioctadecyloxacarboyanin)
细胞膜状态 Merocyanine 540染非致密组织的脂质,Annexin V染质膜外翻露出的磷脂酰丝氨酸
膜的流动性或微粘度 反映膜脂极化分布状态的荧光染料(dephenylhexantriene)
膜的可渗透性(检测染料/药物的细胞摄取或排除) Anthracyclines、rhodamine 123、cyanines
内吞作用 荧光标记的微珠或细菌
细胞分裂代数跟踪 亲脂或共价标记荧光染料
细胞骨架组织状态 NBD-PHALLOIDIN荧光染料(定量标记F肌动蛋白)
酶活性 可被酶转化成生光产物的染料
有氧代谢 Dichlorofluorescein(染过氧化氢物)
巯基/谷胱甘肽 Bimanes(联苯类,可被谷胱甘肽转化为荧光发生物)
DNA合成 荧光标记抗BrdU抗体(BrdU作为脱氧胸腺嘧啶替代物在DNA合成时掺入DNA),EdU(5-乙炔-2'-脱氧尿苷)掺入法(核苷类似物EdU掺入合成中的DNA,再以小分子荧光叠氮化物与之共价结合)
DNA降解(如细胞凋亡时) DNA荧光染料
细胞质基质的结构性 通过检测完整细胞内荧光偏振强度来判断细胞基质的结构性(如用fluorescein diacetate,FDA)
细胞膜/线粒体膜跨膜电势 亲脂性阳离子或阴离子染料荧光染料如cyannines、rhodamine 123、oxonols
膜结合钙离子 疏水性抗生素(螯合剂)荧光染料chlortetracycline
细胞质钙离子浓度 钙离子螯合荧光染料如indo-1、 fluo-3/4、Fura Red、calcium Green
细胞内pH值 荧光染料如BCECF、SNARF-1
基因表达 荧光蛋白(与目标基因形成融合基因)、常用表达标签的荧光抗体检测等
Table 2 Celluar compoments and functional activities that can be detected by flow cytometry
Fig.3 Comparison of Influx and Calibur in detecting forward scattering (FSC),side scattering (SSC) lights and green fluorescence of microbeads (a) The detection of FSC and SSC of beads of three different sizes by Calibur (left) and Influx (right) (b) Detection results of the green fluorescence of rainbow beads by Calibur (left) and Influx (Right)
Fig.4 Comparison of the FSC vs SSC distribution of E.coli populations expressing heterogenous proteins detected by Calibur and Influx (a) Detection of scattering signals from filtered phosphate buffer (PBS) by Calibur and Influx to assess their background noise level. The PBS was filtered through a 0.1- m filter,and the sample running time was 60s on both cytometers (b) Scattering signals distribution of the E.coli cells that were not induced to express β-glucosidase. The collected cell numbers on the two cytometers are equivalent (c) Scattering signals distribution of the E.Coli cells that were induced to express β-glucosidase. The collected cell numbers are equivalent. Inducion condition: 0.5mM IPTG, 37℃, incubated for 5.5 hours
 
[1]   Gucker F T Jr, O’Konski C T, Pickard H B , et al. A photoelectronic counter for colloidal particles. J Am Chem SOC, 1947,69:2422-2431.
[2]   Kamentsky L A, Melamed M R, Derman H . Spectrophotometer, new instrument for ultrarapid cell analysis. Science, 1965,150(3696):630-631.
[3]   Van Dilla M A, Trujillo T T, Mullaney P F , et al. Cell microfluorometry: a method for rapid fluorescence measurement. Science, 1969,163(3872):1213-1214.
[4]   Dittrich W, Gohde W . Impulsfluoromerrie bei einzelzellen in suspensionen. Z Naturforsch, 1969,24b:360-361.
[5]   Howard M S . Practical flow cytometry. 4th ed,Hoboken: John Wiley & Sons,Inc., 2003.
[6]   Fulwyler M J . Electronic separation of biological cells by volume. Science, 1965,150(3698):910-911.
[7]   Hulett H R, Bonner W A, Barrett J , et al. Cell sorting: automated separation of Mammalian cells as a function of intracellular fluorescence. Science, 1969,166(3906):747-749.
[8]   Bonner W A, Hulett H R, Sweet R G , et al. Fluorescence activated cell sorting. Rev Sci Instrum, 1972,43(3):404-409.
[9]   Herzenberg L A, Sweet R G, Herzenberg L A . Fluorescence activated cell sorting. Scientific American, 1976,237(3):108-117.
[10]   Shrirao A B, Fritz Z, Novik E M , et al. Microfluidic flow cytometry: The role of microfabrication methodologies, performance and functional specificiation. Technology, 2018,6(1):1-23.
[11]   Gray J W, Lucas J, Peters D , et al. Flow karyotyping and sorting of human chromosomes. Cold Spring Harb Symp Quant Biol, 1986,51(1):141-149.
[12]   Ibrahim S F, van den Engh G . High-speed cell sorting: fundamentals and recent advances. Curr Opin Biotechnol, 2003,14(1):5-12.
[13]   Bol S, van den EnghG, Visser J . A technique for staining haemopoietic colonies in agar cultures. Exp Hematol, 1977,5(6):551-553.
[14]   Van den Engh G, Visser J . Light scattering properties of pluripotent and committed haemopoietic stem cells. Acta Haematol, 1979,62(5-6):289-298.
[15]   Bol S ,Visser J,van den Engh G. The physical separation of three subpopulations of granulocyte/macrophage progenitor cells from mouse bone marrow. Exp Hematol, 1979,7(10):541-553.
[16]   Bendall S C, Simonds E F, Qiu P , et al. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science, 2011,332(6030):687-696.
doi: 10.1126/science.1198704
[17]   Furlong E E, Profitt D, Scott M P . Automated sorting of live transgenic embryos. Nat Biotechnol, 2001,19(2):153-156.
[18]   Van Dongen J J M, Lhermitte L, B?ttcher S , et al. EuroFlow antibody panels for standardized n-dimensional flow cytometric immunophenotyping of normal reactive and malignant leukocytes. Leukemia, 2012,26:1908-1975.
[19]   Finak G, Langweiler M, Jaimes M , et al. Standardizing flow cytometry immunophenotyping analysis from the Human ImmunoPhenotyping Consortium. Sci Rep, 2016,6:20686.
[20]   Hasan M, Beitz B, Rouilly V , et al. Semiautomated and standardized cytometric procedures for multi-panel and multi-parametric whole blood immunophenotyping. Clin Immunol, 2015,157(2):261-276 .
[21]   Darzynkiewicz Z ,Huang X. Analysis of cellular DNA content by flow cytometry. Curr Protoc Immunol, 2004, Chapter 5: Unit 5 . 7, doi: 10.1002/0471142735.im0507s60.
doi: 10.1002/0471142735.im0507s60
[22]   Hang H, Fox M H . Analysis of the mammalian cell cycle by flow cytometry. Methods Mol Biol, 2004,241:23-35.
[23]   Bailey J E, Fazel-Makilessi J ,McQuitty D N, et al. Characterization of bacterial growth by means of flow microfluorometry. Science, 1977,198(4322):1175-1176.
[24]   Paau A S, Cowles J R, Oro J . Flow-microfluorometric analysis of Escherichia coli, Rhizobium meliloti, and Rhizobium japonicum at different stages of the growth cycle. Can J Microbiol, 1977,23(9):1165-1169.
[25]   Martin G W. Flow cytometry in microbiology:technology and applications, Norfolk: Caister Academic Press, 2015.
[26]   Petersen T W, Brent Harrison C, Horner D N , et al. Flow cytometric characterization of marine microbes. Methods. 2012,57(3):350-358.
[27]   刘春春, 赵云, 杭海英 . 流式细胞术揭示出枯草芽孢杆菌多态异质性. 生物化学与生物物理进展, 2014,41(4):393-402.
[27]   Liu C C, Zhao Y, Hang H Y . Multiple states of bacillus subtillis revealed by flow cytometry. Prog Biochem Biophys, 2014,41(4):393-402.
[28]   Sartory D P . Heterotrophic plate count monitoring of treated drinking water in the UK: a useful operational tool. Int J Food Microbiol, 2004,92(3):297-301.
[29]   WHO, 2003a. Expert consensus. //Bartram J, Cotruvo J A, Exner M , et al. Heterotrophic Plate Counts and Drinking-water Safety -the Significance of HPCs for Water Quality and Human Health.London: IWA Publishing on behalf of the World Health Organisation, 2003: 1-11.
[30]   Keller M, Zengler K . Tapping into microbial diversity. Nat Rev Microbiol, 2004,2(2):141-150.
[31]   Michael M J . Where are we with monoclonal antibodies for multidrug-resistant infections? Drug Discovery Today, 2019,24(5):1132-1138.
[32]   Gill S, Catchpole R, Forterre P . Extracellular membrane vesicles in the three domains of life and beyond. FEMS Microbiol Rev. 2019,43(3):273-303.
[33]   Maas S L N, Breakefield X O, Weaver A M . Extracellular vesicles: unique intercellular delivery vehicle. Trends Cell Biol, 2017,27(3):172-188.
[34]   Zhao M, Nanbo A, Sun L, et al. Extracellular vesicles in epstein-barr Virus’ life cycle and pathogenesis.Microorganisms , 2019,7(2),pii: E48.
[35]   Todorova D, Simoncini S, Lacroix R , et al. Extracellular vesicles in angiogenesis. Circ Res, 2017,120(10):1658-1673.
[36]   Zhang Y, Kim M S, Jia B , et al. Hypothalamic stem cells control ageing speed partly through exosomal miRNAs. Nature, 2017,548(7665):52-57.
[37]   Shi M, Sheng L, Stewart T , et al. New windows into the brain, central nervous system-derived extracellular vesicles in blood. Prog Neurobiol, 2019,175:96-106.
[38]   Bebelman M P, Smit M J, Pegte D M , et al. Biogenesis and function of extracellular vesicles in cancer. Pharmacol Ther, 2018,188:1-11.
[39]   Nolan J P, Duggan E. Analysis of individual extracellular vesicles by flow cytometry. // Hawley T, Hawley R. Flow cytometry protocols. 4th edition. Methods in Molecular Biology, Vol 1678, New York: Humana Press, 2018: 79-92.
[40]   Chandler W L . Measurement of microvesicle levels in human blood using flow cytometry. Cytometry B Clin Cytom, 2016,90(4):326-336.
[41]   Lian H, He S, Chen C , et al. Flow cytometric analysis of nanoscale biological particles and organelles. Annu Rev Anal Chem (PaloAlto Calif), 2019,12(1):389-409.
[42]   Van der Pol E, Hoekstra AG, Sturk A , et al. Optical and nonoptical methods for detection and characterization of microparticles and exosomes. J Thromb Haemost, 2010,8:2596-2607.
[43]   Krishnan V V, Selvan S R, Parameswaran N , et al. Proteomic profiles by multiplex microsphere suspension array. J Immunol Methods, 2018,461:1-14.
[44]   Juncker D, Bergeron S, Laforte V , et al. Cross-reactivity in antibody microarrays and multiplexed sandwich assays, shedding light on the dark side of multiplexing. Curr Opin Chem Biol, 2014,18:29-37.
[45]   Nettey L, Giles A J, Chattopadhyay P K . OMIP-050: A 28-color/30-parameter fluorescence flow cytometry panel to enumerate and characterize cells expressing a wide array of immune checkpoint molecules. Cytometry Part A, 2018,93A:1094-1096.
[46]   Lo K, Brinkman R R, Gottardo R . Automated gating of flow cytometry data via robust model-based clustering. Cytometry Part A, 2008,73A:321-332.
[47]   Bendall S C, Nolan G P, Roederer M , et al. A deep profiler’s guide to cytometry. Trends Immunol, 2012,33(7):323-332.
doi: 10.1016/j.it.2012.02.010
[48]   Rahim A, Meskas J, Drissler S , et al.High throughput automated analysis of big flow cytometry data. Methods, 2018, 134-135:164-176.
[49]   O’Neill K, Aghaeepour N, Spidlen J , et al. Flow cytometry bioinformatics. PLoS Comput Biol, 2013,9(12):e1003365.
[50]   Verschoor C P, Lelic A, Bramson J L , et al. An introduction to automated flow cytometry gating tools and their implementation. Front Immunol, 2015,6:380. doi: 10.3389/fimmu.2015.00380.
doi: 10.3389/fimmu.2015.00380
[51]   Montante S, Brinkman R R . Flow cytometry data analysis: Recent tools and algorithms. Int J Lab Hematol, 2019,41(Suppl. 1):56-62.
[52]   Bagwell C B. High-dimensional modeling for cytometry:building rock solid models using gemStoneTM and verity cen-se’TM high-definition t-SNE mapping. // Hawley T, Hawley R. Flow cytometry protocols. 4th edition. Methods in Molecular Biology, Vol 1678, New York, NY: Humana Press, 2018: 11-36.
[53]   Saeys Y, Van Gassen S, Lambrecht B N . Computational flow cytometry: helping to make sense of high-dimensional immunology data. Nat Rev Immunol, 2016,16(7):449-462.
[1] WANG Yu-xuan,CHEN Ting,ZHANG Yong-liang. Research Progress on the Biological Function of MiR-148[J]. China Biotechnology, 2021, 41(7): 74-80.
[2] LI Kai-xiu,SI Wei. Progress in the Treatment of Inflammatory Bowel Diseases by Exosomes Derived from Mesenchymal Stem Cells[J]. China Biotechnology, 2021, 41(7): 66-73.
[3] LV Hui-zhong,ZHAO Chen-chen,ZHU Lian,XU Na. Progress of Using Exosome for Drug Targeted Delivery in Tumor Therapy[J]. China Biotechnology, 2021, 41(5): 79-86.
[4] QIU Jin-ge,LIU De-wu,SUN Bao-li,LI Yao-kun,GUO Yong-qing,DENG Ming,LIU Guang-bin. Research Progress on Animal Exosome Isolation Methods[J]. China Biotechnology, 2020, 40(9): 36-42.
[5] WU You,XIN Lin. New Drug Delivery System: Delivery of Exosomes as Drug Carriers[J]. China Biotechnology, 2020, 40(9): 28-35.
[6] MAO Hui,LV Yu-hua,ZHU Li-hui,LIN Yue-xia,LIAO Rong-rong. The Role of Exosomes in the Diagnosis and Treatment of Viral Infection[J]. China Biotechnology, 2020, 40(3): 104-110.
[7] WU Jia-han,JIANG Lin,CHEN Ting,SUN Jia-jie,ZHANG Yong-liang,XI Qian-yun. Research on the Interaction between Adipose Tissue Exosomes and Other Tissues[J]. China Biotechnology, 2020, 40(3): 111-116.
[8] YUAN Xiao-ying,WANG Ya-zhe,SHI Wei-hua,CHANG Yan,HAO Le,HE Ling-ling,SHI Hong-xia,HUANG Xiao-jun,LIU Yan-rong. Methodological Study on Flow Detection of PNH Clone and Its Clinical Screening Significance[J]. China Biotechnology, 2019, 39(9): 33-40.
[9] HUI Yi-hua,WANG Hai-na,QI Yu-feng,CAO Xue-ling,GUAN Xue-mei,DUAN Jing-jing,DUAN Yi- jun,WANG Yan- feng,SU Wen. Normal Reference Range of Lymphocyte Subsets in Healthy Adults in Shanxi Province[J]. China Biotechnology, 2019, 39(9): 41-49.
[10] PENG Xian-gui,YANG Wu-chen,LI Jia,GOU Yang,WANG Ping,LIU Si-heng,ZHANG Yun,LI Yi,ZHANG Xi. The Application of Related Cytomorphological Technology in Hematological Neoplasms Research Progress[J]. China Biotechnology, 2019, 39(9): 84-90.
[11] HE Ling-ling,LUO Ting-ting,CHANG Yan,WANG Ya-zhe,YUAN Xiao-ying,SHI Wei-hua,LAI Yue-yun,SHI Hong-xia,QIN Ya-zhen,HUANG Xiao-jun,LIU Yan-rong. Analysis on the Laboratory Examination Characteristics in 28 Patients with Acute Megakaryoblastic Leukemia[J]. China Biotechnology, 2019, 39(9): 2-10.
[12] ZHAO Si-shu,LIU Lu,LIU Fang,QIU Hai-rong,FAN Lei,LI Jian-yong,WU Yu-jie. Diagnostic Value of CD11c Antigen in Patients with Chronic Lymphocytic Leukemia[J]. China Biotechnology, 2019, 39(9): 19-24.
[13] LIU Yan,DAI Peng,ZHU Yun-feng. Research Progress of Exosome as Tumor Marker[J]. China Biotechnology, 2019, 39(8): 74-79.
[14] Yan LIU,Peng DAI,Yun-feng ZHU. Research Progress of Relationship between Exosomes and Autophagosomes[J]. China Biotechnology, 2019, 39(6): 78-83.
[15] WEI Jin-mei, FAN Xiao-qin, XIONG Hai-ting, GAO Xue-juan, LIU Xiao-hui, LIU Lang-xia. hnRNPK Interacts with Nef and Facilitates the Cell Surface Expression of CD4[J]. China Biotechnology, 2015, 35(4): 17-22.