Accurate 3D morphological computational model reconstruction of suspended cells imaged through stratified media by the precise depth-varying point spread function method.

Accurate three-dimensional (3D) morphological computational models of cells are important in a number of biological studies. This study proposes a precise depth-varying point spread function (PDV-PSF) method for reconstructing 3D computational models of suspended cells from two-dimensional (2D) confocal image stacks. Our approach deblurs the 2D images in horizontal plane and corrects the deformation in vertical direction to overcome the refractive index mismatch problem caused by suspended cells imaging through stratified media. Standard fluorescent polystyrene spheres and Jurkat T-lymphocytes are selected to evaluate the validity and accuracy of this PDV-PSF method. Qualitative and quantitative results demonstrate that our approach has superior performance in 3D morphological computational models reconstruction of suspended cells.

Label-free biological sample detection and non-contact separation system based on microfluidic chip.

The detection and separation of biological samples are of great significance for achieving accurate diagnoses and state assessments. Currently, the detection and separation of cells mostly adopt labeling methods, which will undoubtedly affect the original physiological state and functions of cells. Therefore, in this study, a label-free cell detection method based on microfluidic chips is proposed. By measuring the scattering of cells to identify cells and then using optical tweezers to separate the target cells, the whole process without any labeling and physical contact could realize automatic cell identification and separation. Different concentrations of 15 µm polystyrene microspheres and yeast mixed solution are used as samples for detection and separation. The detection accuracy is over 90%, and the separation accuracy is over 73%.