Rapid and sensitive mycoplasma detection system using image-based deep learning.

A major concern in the clinical application of cell therapy is the manufacturing cost of cell products, which mainly depends on quality control. The mycoplasma test, an important biological test in cell therapy, takes several weeks to detect a microorganism and is extremely expensive. Furthermore, the manual detection of mycoplasma from images requires high-level expertise. We hypothesized that a mycoplasma identification program using a convolutional neural network could reduce the test time and improve sensitivity. To this end, we developed a program comprising three parts (mycoplasma detection, prediction, and cell counting) that allows users to evaluate the sample and verify infected/non-infected cells identified by the program. In experiments conducted, stained DNA images of positive and negative control using mycoplasma-infected and non-infected Vero cells, respectively, were used as training data, and the program results were compared with those of conventional methods, such as manual counting based on visual observation. The minimum detectable mycoplasma contaminations for manual counting and the proposed program were 10 and 5 CFU (colony-forming unit), respectively, and the test time for manual counting was 20 times that for the proposed program. These results suggest that the proposed system can realize a low-cost and streamlined manufacturing process for cellular products in cell-based research and clinical applications.

Development of an automated two pronuclei detection system on time-lapse embryo images using deep learning techniques.

PURPOSE: To establish an automated pronuclei determination system by analysis using deep learning technology which is able to effectively learn with limited amount of supervised data. METHODS: An algorithm was developed by explicitly incorporating human observation where the outline around pronuclei is being observed in determining the number of pronuclei. Supervised data were selected from the time-lapse images of 300 pronuclear stage embryos per class (total 900 embryos) clearly classified by embryologists as 0PN, 1PN, and 2PN. One-hundred embryos per class (a total of 300 embryos) were used for verification data. The verification data were evaluated for the performance of detection in the number of pronuclei by regarding the results consistent with the judgment of the embryologists as correct answers. RESULTS: The sensitivity rates of 0PN, 1PN, and 2PN were 99%, 82%, and 99%, respectively, and the overlapping 2PN being difficult to determine by microscopic observation alone could also be appropriately assessed. CONCLUSIONS: This study enabled the establishment of the automated pronuclei determination system with the precision almost equivalent to highly skilled embryologists.

Noninvasive total counting of cultured cells using a home-use scanner with a pattern sheet.

The inherent variability in cell culture techniques hinders their reproducibility. To address this issue, we introduce a comprehensive cell observation device. This new approach enhances the features of existing home-use scanners by implementing a pattern sheet. Compared with fluorescent staining, our method over- or underestimated the cell count by a mere 5%. The proposed technique showcased a strong correlation with conventional methodologies, displaying R2 values of 0.91 and 0.99 compared with the standard chamber and fluorescence methods, respectively. Simulations of microscopic observations indicated the potential to estimate accurately the total cell count using just 20 fields of view. Our proposed cell-counting device offers a straightforward, noninvasive means of measuring the number of cultured cells. By harnessing the power of deep learning, this device ensures data integrity, thereby making it an attractive option for future cell culture research.

Pseudo-nuclear staining of cells by deep learning improves the accuracy of automated cell counting in a label-free cellular population.

Deep learning has emerged as a breakthrough tool for the segmentation of images without supporting human experts. Here, we propose an automated approach that uses deep learning to generate pseudo-nuclear staining of cells from phase contrast images. Our proposed approach, which has the feature to generate pseudo-nuclear stained images by simple DNN, showed remarkable advantages over existing approaches in the cell-detection and the detection of the relative position of cells for various cell densities, as well as in counting the exact cell numbers. Pseudo-nuclear staining of cells by deep learning will improve the accuracy of automated cell counting in a label-free cellular population on phase contrast images.

Time-lapse image analysis for whole colony growth curves and daily distribution of the cell number per colony during the expansion of mesenchymal stem cells.

Mesenchymal stem cells from the synovium (synovial MSCs) are attractive for cartilage and meniscus regeneration therapy. We developed a software program that can distinguish individual colonies and automatically count the cell number per colony using time-lapse images. In this study, we investigated the usefulness of the software and analyzed colony formation in cultured synovial MSCs. Time-lapse image data were obtained for 14-day-expanded human synovial MSCs. The cell number per colony (for 145 colonies) was automatically counted from phase-contrast and nuclear-stained images. Colony growth curves from day 1 to day 14 (for 140 colonies) were classified using cluster analysis. Correlation analysis of the distribution of the cell number per colony at 14 days versus that number at 1-14 days revealed a correlation at 7 and 14 days. We obtained accurate cell number counts from phase-contrast images. Individual colony growth curves were classified into three main groups and subgroups. Our image analysis software has the potential to improve the evaluation of cell proliferation and to facilitate successful clinical applications using MSCs.

Intravital imaging with two-photon microscopy reveals cellular dynamics in the ischeamia-reperfused rat heart.

Recent advances in intravital microscopy have provided insight into dynamic biological events at the cellular level in both healthy and pathological tissue. However, real-time in vivo cellular imaging of the beating heart has not been fully established, mainly due to the difficulty of obtaining clear images through cycles of cardiac and respiratory motion. Here we report the successful recording of clear in vivo moving images of the beating rat heart by two-photon microscopy facilitated by cardiothoracic surgery and a novel cardiac stabiliser. Subcellular dynamics of the major cardiac components including the myocardium and its subcellular structures (i.e., nuclei and myofibrils) and mitochondrial distribution in cardiac myocytes were visualised for 4-5 h in green fluorescent protein-expressing transgenic Lewis rats at 15 frames/s. We also observed ischaemia/reperfusion (I/R) injury-induced suppression of the contraction/relaxation cycle and the consequent increase in cell permeability and leukocyte accumulation in cardiac tissue. I/R injury was induced in other transgenic mouse lines to further clarify the biological events in cardiac tissue. This imaging system can serve as an alternative modality for real time monitoring in animal models and cardiological drug screening, and can contribute to the development of more effective treatments for cardiac diseases.

Phase contrast time-lapse microscopy datasets with automated and manual cell tracking annotations.

Phase contrast time-lapse microscopy is a non-destructive technique that generates large volumes of image-based information to quantify the behaviour of individual cells or cell populations. To guide the development of algorithms for computer-aided cell tracking and analysis, 48 time-lapse image sequences, each spanning approximately 3.5 days, were generated with accompanying ground truths for C2C12 myoblast cells cultured under 4 different media conditions, including with fibroblast growth factor 2 (FGF2), bone morphogenetic protein 2 (BMP2), FGF2 + BMP2, and control (no growth factor). The ground truths generated contain information for tracking at least 3 parent cells and their descendants within these datasets and were validated using a two-tier system of manual curation. This comprehensive, validated dataset will be useful in advancing the development of computer-aided cell tracking algorithms and function as a benchmark, providing an invaluable opportunity to deepen our understanding of individual and population-based cell dynamics for biomedical research.