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.

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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.

Mitochondrial dysfunction is considered to be an important factor that leads to aging and premature aging diseases. Transferring mitochondria to cells is an emerging and promising technique for the therapy of mitochondrial deoxyribonucleic acid (mtDNA)‐related diseases. This paper presents a unique method of controlling the quality and quantity of mitochondria transferred to single cells using an automated optical tweezer‐based micromanipulation system. The proposed method can automatically, accurately, and efficiently collect and transport healthy mitochondria to cells, and the recipient cells then take up the mitochondria through endocytosis. The results of the study reveal the possibility of using mitochondria from fetal mesenchymal stem cells (fMSCs) as a potential source to reverse the aging‐related phenotype and improve metabolic activities in adult mesenchymal stem cells (aMSCs). The results of the quantitative polymerase chain reaction analysis show that the transfer of isolated mitochondria from fMSCs to a single aMSC can significantly increase the antiaging and metabolic gene expression in the aMSC. The proposed mitochondrial transfer method can greatly promote precision medicine for cell therapy of mtDNA‐related diseases.

Highly precise micromanipulation tools that can manipulate and interrogate cell organelles and components must be developed to support the rapid development of new cell-based medical therapies, thereby facilitating in-depth understanding of cell dynamics, cell component functions, and disease mechanisms. This paper presents a literature review on micro/nanomanipulation tools and their control methods for single-cell surgery. Micromanipulation methods specifically based on laser, microneedle, and untethered micro/nanotools are presented in detail. The limitations of these techniques are also discussed. The biological significance and clinical applications of single-cell surgery are also addressed in this paper.