Common-path diffraction optical tomography for investigation of three-dimensional structures and dynamics of biological cells: erratum

Compensation of aberration in quantitative phase imaging using lateral shifting and spiral phase integration

We present a simple and effective method to eliminate system aberrations in quantitative phase imaging. Using spiral phase integration, complete information about system aberration is calculated from three laterally shifted phase images. The present method is especially useful when measuring confluent samples in which acquisition of background area is challenging. To demonstrate validity and applicability, we present measurements of various types of samples including microspheres, HeLa cells, and mouse brain tissue. Working conditions and limitations are systematically analyzed and discussed.

Identification of non-activated lymphocytes using three-dimensional refractive index tomography and machine learning

Identification of lymphocyte cell types are crucial for understanding their pathophysiological roles in human diseases. Current methods for discriminating lymphocyte cell types primarily rely on labelling techniques with magnetic beads or fluorescence agents, which take time and have costs for sample preparation and may also have a potential risk of altering cellular functions. Here, we present the identification of non-activated lymphocyte cell types at the single-cell level using refractive index (RI) tomography and machine learning. From the measurements of three-dimensional RI maps of individual lymphocytes, the morphological and biochemical properties of the cells are quantitatively retrieved. To construct cell type classification models, various statistical classification algorithms are compared, and the k-NN (k = 4) algorithm was selected. The algorithm combines multiple quantitative characteristics of the lymphocyte to construct the cell type classifiers. After optimizing the feature sets via cross-validation, the trained classifiers enable identification of three lymphocyte cell types (B, CD4+ T, and CD8+ T cells) with high sensitivity and specificity. The present method, which combines RI tomography and machine learning for the first time to our knowledge, could be a versatile tool for investigating the pathophysiological roles of lymphocytes in various diseases including cancers, autoimmune diseases, and virus infections.