Imaging Cytometry of Human Leukocytes with Third Harmonic Generation Microscopy.

Cheng-Ham Wu,Yao-Ming Wu,Hau-Tieng Wu,Jong-Wei Lin,Tzu-Ming Liu,Chia-Hung Hsieh,Shih-Hung Huang,Szu-Chun Hsu,Tzung-Dau Wang

doi:10.1038/srep37210

Abstract

Based on third-harmonic-generation (THG) microscopy and a k-means clustering algorithm, we developed a label-free imaging cytometry method to differentiate and determine the types of human leukocytes. According to the size and average intensity of cells in THG images, in a two-dimensional scatter plot, the neutrophils, monocytes, and lymphocytes in peripheral blood samples from healthy volunteers were clustered into three differentiable groups. Using these features in THG images, we could count the number of each of the three leukocyte types both in vitro and in vivo. The THG imaging-based counting results agreed well with conventional blood count results. In the future, we believe that the combination of this THG microscopy-based imaging cytometry approach with advanced texture analysis of sub-cellular features can differentiate and count more types of blood cells with smaller quantities of blood.

Highlights

Leukocytes called white blood cells (WBCs), play important roles in the immunity of humans
To acquire ground-truth third harmonic generation (THG) images of leukocytes for in vivo imaging flow cytometry, WBCs need to be isolated with the least perturbation to their physiological properties right after blood sampling
Compared with hematologic examination using standard Liu’s stain (Fig. S1a), THG sectioning images of red blood cells (RBCs), polymorphonuclear neutrophils, monocytes, smaller lymphocytes, and scattered platelets can be clearly identified in a blood smear (Fig. 1)

Summary

Introduction

Leukocytes called white blood cells (WBCs), play important roles in the immunity of humans. We made further steps to ensure the THG imaging cytometry of human leukocytes has differentiable features for the three major types of WBCs. using the average THG intensity within cells and the cell size estimated from the cross-sectional area, we confirmed in the scatter plot that the distribution of each type was clustered and separated from others. Exploiting the threshold learned from those data, we differentiated and counted the number of WBCs in a whole-blood sample Both the percentages and number densities agreed well with the results from clinical laboratory examination. We found these THG features can differentiate circulating human leukocytes in vivo. With these ground-truth images and differentiable features, combined with texture analyses and machine-learning algorithms, we believe that in vivo WBC differentiation and automatic counting can be achieved in the near future

Methods

Results

Conclusion