Zhang Lu, Wang Huijun, Liu Jianyi, Chen Shuang, Yang He, Yang Zewen, Zhang Zhenxi, Zhao Hong, Yuan Li, Tian Lifang, Zhong Bo, Liu Xiaolong
Xi'an Jiaotong University, School of Mechanical Engineering, State Key Laboratory for Manufacturing Systems Engineering, Xi'an 710049, China.
Xi'an Jiaotong University, Institute of Artificial Intelligence and Robotics, Xi'an 710049, China.
BME Front. 2022 Nov 8;2022:9867373. doi: 10.34133/2022/9867373. eCollection 2022.
. Distinguishing malignant lymphocytes from normal ones is vital in pathological examination. We proposed an inverse light scattering (ILS) method for label-free suspended lymphocytes with complex fine structures to identify their volumes for pathological state. . Light scattering as cell's "fingerprint" provides valuable morphology information closely related to its biophysical states. However, the detail relationships between the morphology with complex fine structures and its scattering characters are not fully understood. . To quantitatively inverse the volumes of membrane and nucleus as the main scatterers, clinical lymphocyte morphologies were modeled combining the Gaussian random sphere geometry algorithm by 750 reconstructed results after confocal scanning, which allowed the accurate simulation to solve ILS problem. For complex fine structures, the specificity for ILS study was firstly discussed (to our knowledge) considering the differences of not only surface roughness, posture, but also the ratio of nucleus to the cytoplasm and refractive index. . The volumes of membrane and nucleus were proved theoretically to have good linear relationship with the effective area and entropy of forward scattering images. Their specificity deviations were less than 3.5%. Then, our experimental results for microsphere and clinical leukocytes showed the Pearson product-moment correlation coefficients (PPMCC) of this linear relationship were up to 0.9830~0.9926. . Our scattering inversion method could be effectively applied to identify suspended label-free lymphocytes without destructive sample pretreatments and complex experimental systems.
在病理检查中，区分恶性淋巴细胞和正常淋巴细胞至关重要。我们提出了一种用于具有复杂精细结构的无标记悬浮淋巴细胞的反向光散射（ILS）方法，以识别其体积从而判断病理状态。光散射作为细胞的“指纹”，提供了与其生物物理状态密切相关的有价值的形态学信息。然而，具有复杂精细结构的形态与其散射特性之间的详细关系尚未完全了解。为了定量反演作为主要散射体的细胞膜和细胞核的体积，结合高斯随机球体几何算法，通过共聚焦扫描后的750个重建结果对临床淋巴细胞形态进行建模，从而能够精确模拟以解决ILS问题。对于复杂精细结构，首次（据我们所知）考虑表面粗糙度、姿态以及细胞核与细胞质的比例和折射率的差异，讨论了ILS研究的特异性。理论上证明细胞膜和细胞核的体积与前向散射图像的有效面积和熵具有良好的线性关系。它们的特异性偏差小于3.5%。然后，我们对微球和临床白细胞的实验结果表明，这种线性关系的皮尔逊积矩相关系数（PPMCC）高达0.9830~0.9926。我们的散射反演方法可以有效地应用于识别无标记悬浮淋巴细胞，无需进行破坏性样品预处理和复杂的实验系统。
