hsa-miR-346 is a potential serum biomarker of Mycobacterium avium complex pulmonary disease activity.

MicroRNA (miRNA) has been recently recognized as a biomarker of various diseases; however, there are no known miRNAs associated with Mycobacterium avium complex (MAC) pulmonary disease. In addition, there are no known biomarkers to precisely reflect disease activity after the diagnosis of MAC pulmonary disease. Thus, we sought to identify a miRNA which is a candidate biomarker of MAC pulmonary disease activity. Serum hsa-miR-346 concentrations of 16 patients with M. avium pulmonary disease were significantly higher than those of 16 healthy controls (p = 0.047). The secretion of hsa-miR-346 increased in a multiplicity of infection-dependent manner in M. avium-infected macrophages. Serum hsa-miR-346 levels of 5 patients with bacterial conversion at the end of follow-up were significantly lower than those at the beginning of the follow-up (p = 0.043). In addition, the longitudinal change in serum hsa-miR-346 concentration correlated with bacterial load in 2 patients with M. avium pulmonary disease. Based on our results, it is supposed that MAC-infected macrophages in pulmonary lesions produce hsa-miR-346, which is then secreted into the bloodstream. The magnitude of this process could be quantitatively controlled by the bacterial load, suggesting that serum hsa-miR-346 is a potentially useful biomarker of MAC pulmonary disease activity.

Characterization of time-course morphological features for efficient prediction of osteogenic potential in human mesenchymal stem cells.

Human bone marrow mesenchymal stem cells (hBMSCs) represents one of the most frequently applied cell sources for clinical bone regeneration. To achieve the greatest therapeutic effect, it is crucial to evaluate the osteogenic differentiation potential of the stem cells during their culture before the implantation. However, the practical evaluation of stem cell osteogenicity has been limited to invasive biological marker analysis that only enables assaying a single end-point. To innovate around invasive quality assessments in clinical cell therapy, we previously explored and demonstrated the positive predictive value of using time-course images taken during differentiation culture for hBMSC bone differentiation potential. This initial method establishes proof of concept for a morphology-based cell evaluation approach, but reveals a practical limitation when considering the need to handle large amounts of image data. In this report, we aimed to scale-down our proposed method into a more practical, efficient modeling scheme that can be more broadly implemented by physicians on the frontiers of clinical cell therapy. We investigated which morphological features are critical during the osteogenic differentiation period to assure the performance of prediction models with reduced burden on image acquisition. To our knowledge, this is the first detailed characterization that describes both the critical observation period and the critical number of time-points needed for morphological features to adequately model osteogenic potential. Our results revealed three important observations: (i) the morphological features from the first 3 days of differentiation are sufficiently informative to predict bone differentiation potential, both activities of alkaline phosphatase and calcium deposition, after 3 weeks of continuous culture; (ii) intervals of 48 h are sufficient for measuring critical morphological features; and (iii) morphological features are most accurately predictive when early morphological features from the first 3 days of differentiation are combined with later features (after 10 days of differentiation).

Flexible Sheet-Type Sensor for Noninvasive Measurement of Cellular Oxygen Metabolism on a Culture Dish.

A novel flexible sensor was developed for the noninvasive oxygen metabolism measurement of cultivated cells and tissues. This device is composed of a transparent double-layered polymer sheet of ethylene-vinyl alcohol (EVOH) and poly(dimethylsiloxane) (PDMS) having an array of microhole structures of 90 µm diameter and 50 µm depth on its surface. All the microhole structures were equipped with a 1-µm-thick optical chemical sensing layer of platinum porphyrin-fluoropolymer on their bottom. The three-dimensional microstructures of the sensor were fabricated by a newly developed simple and low-cost production method named self-aligned hot embossing. The device was designed to be attached slightly above the cells cultivated on a dish to form a temporarily closed microspace over the target cells during measurement. Since the change in oxygen concentration is relatively fast in the microcompartmentalized culture medium, a rapid evaluation of the oxygen consumption rate is possible by measuring the phosphorescence lifetime of the platinum porphyrin-fluoropolymer. The combined use of the device and an automated optical measurement system enabled the high-throughput sensing of cellular oxygen consumption (100 points/min). We monitored the oxygen metabolism of the human breast cancer cell line MCF7 on a Petri dish and evaluated the oxygen consumption rate to be 0.72 ± 0.12 fmol/min/cell. Furthermore, to demonstrate the utility of the developed sensing system, we demonstrated the mapping of the oxygen consumption rate of rat brain slices and succeeded in visualizing a clear difference among the layer structures of the hippocampus, i.e., the cornu ammonis (CA1 and CA3) and dentate gyrus (DG).

Morphology-based prediction of osteogenic differentiation potential of human mesenchymal stem cells.

Human bone marrow mesenchymal stem cells (hBMSCs) are widely used cell source for clinical bone regeneration. Achieving the greatest therapeutic effect is dependent on the osteogenic differentiation potential of the stem cells to be implanted. However, there are still no practical methods to characterize such potential non-invasively or previously. Monitoring cellular morphology is a practical and non-invasive approach for evaluating osteogenic potential. Unfortunately, such image-based approaches had been historically qualitative and requiring experienced interpretation. By combining the non-invasive attributes of microscopy with the latest technology allowing higher throughput and quantitative imaging metrics, we studied the applicability of morphometric features to quantitatively predict cellular osteogenic potential. We applied computational machine learning, combining cell morphology features with their corresponding biochemical osteogenic assay results, to develop prediction model of osteogenic differentiation. Using a dataset of 9,990 images automatically acquired by BioStation CT during osteogenic differentiation culture of hBMSCs, 666 morphometric features were extracted as parameters. Two commonly used osteogenic markers, alkaline phosphatase (ALP) activity and calcium deposition were measured experimentally, and used as the true biological differentiation status to validate the prediction accuracy. Using time-course morphological features throughout differentiation culture, the prediction results highly correlated with the experimentally defined differentiation marker values (R>0.89 for both marker predictions). The clinical applicability of our morphology-based prediction was further examined with two scenarios: one using only historical cell images and the other using both historical images together with the patient's own cell images to predict a new patient's cellular potential. The prediction accuracy was found to be greatly enhanced by incorporation of patients' own cell features in the modeling, indicating the practical strategy for clinical usage. Consequently, our results provide strong evidence for the feasibility of using a quantitative time series of phase-contrast cellular morphology for non-invasive cell quality prediction in regenerative medicine.