A main oscillator in the suprachiasmatic nucleus (SCN) conveys circadian information to the peripheral clock systems for the regulation of fundamental physiological functions. Although polysynaptic autonomic neural pathways between the SCN and the liver were observed in rats, whether activation of the sympathetic nervous system entrains clock gene expression in the liver has yet to be understood. To assess sympathetic innervation from the SCN to liver tissue, we investigated whether injection of adrenaline͞ noradrenaline (epinephrine͞norepinephrine) or sympathetic nerve stimulation could induce mPer gene expression in mouse liver. Acute administration of adrenaline or noradrenaline increased mPer1 but not mPer2 expression in the liver of mice in vivo and in hepatic slices in vitro. Electrical stimulation of the sympathetic nerves or adrenaline injection caused an elevation of bioluminescence in the liver area of transgenic mice carrying mPer1 promoterluciferase. Under a light-dark cycle, destruction of the SCN flattened the daily rhythms of not only mPer1, mPer2, and mBmal1 genes but also noradrenaline content in the liver. Daily injection of adrenaline, administered at a fixed time for 6 days, recovered oscillations of mPer2 and mBmal1 gene expression in the liver of mice with SCN lesion on day 7. Sympathetic nerve denervation by 6-hydroxydopamine flattened the daily rhythm of mPer1 and mPer2 gene expression. Thus, on the basis of the present results, activation of the sympathetic nerves through noradrenaline and͞or adrenaline release was a factor controlling the peripheral clock.

A microfluidic on-chip imaging cell sorter has several advantages over conventional cell sorting methods, especially to identify cells with complex morphologies such as clusters. One of the remaining problems is how to efficiently discriminate targets at the species level without labelling. Hence, we developed a label-free microfluidic droplet-sorting system based on image recognition of cells in droplets. To test the applicability of this method, a mixture of two plankton species with different morphologies (Dunaliella tertiolecta and Phaeodactylum tricornutum) were successfully identified and discriminated at a rate of 10 Hz. We also examined the ability to detect the number of objects encapsulated in a droplet. Single cell droplets sorted into collection channels showed 91 ± 4.5% and 90 ± 3.8% accuracy for D. tertiolecta and P. tricornutum, respectively. Because we used image recognition to confirm single cell droplets, we achieved highly accurate single cell sorting. The results indicate that the integrated method of droplet imaging cell sorting can provide a complementary sorting approach capable of isolating single target cells from a mixture of cells with high accuracy without any staining.

An on-chip multi-imaging flow cytometry system has been developed to obtain morphometric parameters of cell clusters such as cell number, perimeter, total cross-sectional area, number of nuclei and size of clusters as “imaging biomarkers”, with simultaneous acquisition and analysis of both bright-field (BF) and fluorescent (FL) images at 200 frames per second (fps); by using this system, we examined the effectiveness of using imaging biomarkers for the identification of clustered circulating tumor cells (CTCs). Sample blood of rats in which a prostate cancer cell line (MAT-LyLu) had been pre-implanted was applied to a microchannel on a disposable microchip after staining the nuclei using fluorescent dye for their visualization, and the acquired images were measured and compared with those of healthy rats. In terms of the results, clustered cells having (1) cell area larger than 200 µm2 and (2) nucleus area larger than 90 µm2 were specifically observed in cancer cell-implanted blood, but were not observed in healthy rats. In addition, (3) clusters having more than 3 nuclei were specific for cancer-implanted blood and (4) a ratio between the actual perimeter and the perimeter calculated from the obtained area, which reflects a shape distorted from ideal roundness, of less than 0.90 was specific for all clusters having more than 3 nuclei and was also specific for cancer-implanted blood. The collected clusters larger than 300 µm2 were examined by quantitative gene copy number assay, and were identified as being CTCs. These results indicate the usefulness of the imaging biomarkers for characterizing clusters, and all of the four examined imaging biomarkers—cluster area, nuclei area, nuclei number, and ratio of perimeter—can identify clustered CTCs in blood with the same level of preciseness using multi-imaging cytometry.