Singh Dhananjay Kumar, Ahrens Caroline C, Li Wei, Vanapalli Siva A
Department of Chemical Engineering, Texas Tech University, Lubbock, Texas, USA.
Biomed Opt Express. 2017 Jan 4;8(2):536-554. doi: 10.1364/BOE.8.000536. eCollection 2017 Feb 1.
Large-scale and label-free phenotyping of cells holds great promise in medicine, especially in cancer diagnostics and prognosis. Here, we introduce inline digital holography microscopy for volumetric imaging of cells in bulk flow and fingerprinting of flowing tumor cells based on two metrics, in-focus scattered intensity and cell diameter. Using planar distribution of immobilized particles, we identify the optimal recording distance and microscope objective magnification that minimizes the error in measurement of particle position, size and scattered intensity. Using the optimized conditions and the two metrics, we demonstrate the capacity to enumerate and fingerprint more than 100,000 cells. Finally, we highlight the power of our label-free and high throughput technology by characterizing breast tumor cell lines with different metastatic potentials and distinguishing drug resistant ovarian cancer cells from their parental cell line.
细胞的大规模无标记表型分析在医学领域,尤其是癌症诊断和预后方面具有巨大潜力。在此,我们引入在线数字全息显微镜,用于对大量流动细胞进行体积成像,并基于两个指标——焦内散射强度和细胞直径,对流动肿瘤细胞进行指纹识别。利用固定化颗粒的平面分布,我们确定了能使颗粒位置、大小和散射强度测量误差最小化的最佳记录距离和显微镜物镜放大倍数。使用优化后的条件和这两个指标,我们展示了对超过10万个细胞进行计数和指纹识别的能力。最后,我们通过对具有不同转移潜能的乳腺肿瘤细胞系进行表征,并区分耐药卵巢癌细胞与其亲本细胞系,突出了我们无标记高通量技术的强大功能。