In many cell surgery applications, cell must be oriented properly such that the micro-surgery tool can access the target components with minimum damage to the cell. In this paper, a scheme for out of image plane orientation control of suspended biological cells using robotic controlled optical tweezers is presented for orientation-based cell surgery. Based on our previous work on planar cell rotation using optical tweezers, the dynamic model of cell out-of-plane orientation control is formulated by using the T-matrix approach. Vision-based algorithms are developed to extract the cell out of image plane orientation angles, based on 2D image slices obtained under optical microscope. A robust feedback controller is then proposed to achieve cell out-of-plane rotation. Experiments of automated out of image plane rotational control for cell nucleus extraction surgery are performed to demonstrate the effectiveness of the proposed approach. This approach advances robot-aided single cell manipulation and produces impactful benefits to cell surgery applications such as nucleus transplantation and organelle biopsy in precision medicine.

Mitochondrial transfer is a spontaneous process to restore damaged cells in various pathological conditions. The transfer of mitochondria to cell therapy products before their administration can enhance therapeutic outcomes. However, the low efficiency of previously reported methods limits their clinical application. Here, we developed a droplet microfluidics–based mitochondrial transfer technique that can achieve high-efficiency and high-throughput quantitative mitochondrial transfer to single cells. Because mitochondria are essential for muscles, myoblast cells and a muscle injury model were used as a proof-of-concept model to evaluate the proposed technique. In vitro and in vivo experiments demonstrated that C2C12 cells with 31 transferred mitochondria had significant improvements in cellular functions compared to those with 0, 8, and 14 transferred mitochondria and also had better therapeutic effects on muscle regeneration. The proposed technique can considerably promote the clinical application of mitochondrial transfer, with optimized cell function improvements, for the cell therapy of mitochondria-related diseases.

High dimensionality and small sample sizes, and their inherent risk of overfitting, pose great challenges for constructing efficient classifiers in microarray data classification. Therefore a feature selection technique should be conducted prior to data classification to enhance prediction performance. In general, filter methods can be considered as principal or auxiliary selection mechanism because of their simplicity, scalability, and low computational complexity. However, a series of trivial examples show that filter methods result in less accurate performance because they ignore the dependencies of features. Although few publications have devoted their attention to reveal the relationship of features by multivariate-based methods, these methods describe relationships among features only by linear methods. While simple linear combination relationship restrict the improvement in performance. In this paper, we used kernel method to discover inherent nonlinear correlations among features as well as between feature and target. Moreover, the number of orthogonal components was determined by kernel Fishers linear discriminant analysis (FLDA) in a self-adaptive manner rather than by manual parameter settings. In order to reveal the effectiveness of our method we performed several experiments and compared the results between our method and other competitive multivariate-based features selectors. In our comparison, we used two classifiers (support vector machine, -nearest neighbor) on two group datasets, namely two-class and multi-class datasets. Experimental results demonstrate that the performance of our method is better than others, especially on three hard-classify datasets, namely Wang's Breast Cancer, Gordon's Lung Adenocarcinoma and Pomeroy's Medulloblastoma.

This article reviews the autonomous manipulation strategies of biological cells utilizing optical tweezers, mainly including optical direct and indirect manipulation strategies. The typical and latest achievements in the optical manipulation of cells are presented, and the existing challenges for autonomous optical manipulation of biological cells are also introduced. Moreover, the integrations of optical tweezers with other manipulation tools are presented, which broadens the applications of optical tweezers in the biomedical manipulation areas and will also foster new developments in cell-based physiology and pathology studies, such as cell migration, single cell surgery, and preimplantation genetic diagnosis (PGD).

Single cell surgery such as manipulation or removal of subcellular components or/and organelles from single cells is increasingly used for the study of diseases and their causes in precision medicine. This paper presents a robotic surgery system to achieve automated organelle biopsy of single cells with dimensions of less than 20 μm in diameter. The complexity of spatial detection of the organelle position is reduced by patterning the cells using a microfluidic chip device. A sliding mode nonlinear controller is developed to enable extraction of organelles, such as the mitochondria and the nucleus, from single cells with high precision. An image processing algorithm is also developed to automatically detect the position of the desired organelle. The effectiveness of the proposed robotic surgery system is demonstrated experimentally with automated extraction of mitochondria and nucleus from human acute promyelocytic leukemia cells and human fibroblast cells. Extraction is followed by biological tests to indicate the functionality of biopsied mitochondria as well as the cell viability after removal of mitochondria. The results presented here have revealed that the proposed approach of automated organelle biopsy on single small cells is feasible.

Automated microinjection systems for suspension cells have been studied for years. Nevertheless, microinjection systems for adherent cells still suffer from laborious manual operations and low productivity. This paper presents a new automated microinjection system with high productivity for adherent cells. This system enhances productivity through four approaches. First, cells are detected automatically to replace manual selections. Second, the injection paths of detected cells are optimized rapidly to save time. Third, the penetration depth is adjusted adaptively according to the moving plane of the dish holder plate. Finally, constant outflow-based injection is adopted to minimize clogging. The first three approaches aim to improve the injection speed, and the last one aims to extend the usage time of micropipettes. Experiments of massive injections on MC3T3-E1 cells are performed to evaluate cell detection efficiency, injection speed, success rate, and survival rate. Results confirm that the system allows injections of over 1500 cells in one hour without much training and preparation a priori.