Mitochondria functions as a major power source in diverse cell physiological processes. Mitochondria dysfunction is associated with many diseases, such as neurodegenerative diseases and various cancers. It is crucial to understand mitochondrial metabolism in live cells and related dysfunction in diseases. Currently, three-dimensional (3D) cultured tumor spheroids have been widely used in drug development in cancer therapy fields, due to their similarities with the animal model compared with 2D cultured cells. However, characterization methods to monitor metabolism status in tumor spheroids are limited by the lack of techniques to measure mitochondrial metabolic fluxes in a noninvasive and real-time way. Here, we proposed a Fluorescence Lifetime Imaging Microscopy (FLIM) based method to monitor the mitochondria status in 3D cultured cells. MCF-7 tumor spheroids or 2D cultured cells were labelled with a mitochondria specific dye Rhodamine123 (R123). Images of cultured cells were captured with a FLIM system and analyzed with software SPCImage, providing the size and shape of each tumor spheroid as well as the fluorescence lifetime values of mitochondria. In 2D cultured cells, the fluorescence lifetime values of each dye had little fluctuation, indicating similar physiological status. In tumor spheroids labelled with R123, the fluorescence lifetime values of the inner part of spheroids were uneven compared with those of the outer edge. The reason for this uneven lifetime span of mitochondria may be associated with the difference of status between the inner and outer layers of tumor spheroids. In conclusion, this FLIM based method has shown great advantages to characterize the mitochondria status in 3D cultured tumor spheroids, providing a powerful quantitative method in cellular physiology research fields.
Chlorella is a unicellular spherical green microalga with alternate colors from blue green to yellowish or red due to different components of innate pigments. Light and salinity are two important environmental factors in Chlorella culture. Light conditions directly affect the growth and biochemical composition of microalgae, while salinity change could influence the pigment composition of Chlorella. Therefore, it has crucial research significance to monitor the response of Chlorella to salinity stress under different light conditions. Recently, Fluorescence Lifetime Imaging Microscopy (FLIM) technology has been widely applied into biological fields, providing fluorescence lifetime values for quantitative analysis. Here, FLIM method was used to observe the autofluorescence of a freshwater microalga, Chlorella sp.. Chlorella cells were treated with a series of salinity concentrations (control sample in normal culture medium, 3S sample with an additional 3× salinity, 7S sample with an additional 7× salinity, respectively) under light (12 h/12 h light/dark cycles) or dark (0 h/24 h light/dark cycles) treatments. After one day, images of the microalgae cells from each group were obtained with FLIM system, followed by an analysis with SPCImage software. The results showed that 3× salinity condition had little effect on Chlorella in both light/dark conditions, suggesting the adaptive capacity of Chlorella to seawater salinity. By contrast, the mean fluorescence lifetime values in 7S samples under light conditions were significantly decreased compared to that of the control. Interestingly, similar lifetime values were observed in 7S samples and the control samples under dark conditions, which indicated a potential high salinity resistance induced by different light/dark conditions. In conclusion, FLIM could work as a fast evaluation method of the physiological status of living Chlorella sp. under different culture conditions in a quantitative way.
Inorganic arsenic (iAs) is one of the most toxic metalloids which could accumulate in marine species, especially in clams, causing serious ecological risk. Marine clams accumulate high level of iAs in different tissues. Currently, Fluorescence Lifetime Microscopy (FLIM) technique has provided quantitative information in biochemical diagnosis. In this study, we applied FLIM method into analyzing Hematoxylin and eosin (H&E) stained sections of arsenic exposed Ruditapes philippinarum. The clams were exposed under different concentrations of As(Ⅲ) and As(V) for thirty days. Fluorescent images of the H&E stained hepatopancreas tissue were obtained with FLIM system, followed with data analysis for fluorescence lifetime values. The average fluorescence lifetime of sections in the control group was around 250 ps. The average lifetime value in the As(V) group was slightly increased to around 280-300 ps. The average lifetime value in the As(III) group achieved a significant increase to around 340 ps. These results suggested a higher extent of structural change in As(Ⅲ) exposed group than As(V) group. As a result, this work has provided quantitative evaluation standard for the toxicity of marine mollusk based on fluorescence lifetime imaging method.
There is a high demand of novel monitoring methods for apple content measurement, especially sugar content (SC). Two traditional methods are widely used: one obtains sugar degree of apple juicy; the other identifies SC from intact apples by using near-infrared reflectance and optical fiber sensing techniques. The former is destructive and cumbersome. The latter requires expensive spectrometer equipment. Recently, deep learning has played an important role in image recognition. Convolutional Neural Network (CNN) has stronger capabilities of feature extraction and model formulation. Here, we have applied CNN into evaluate apple SC. Firstly, images of apples with SC in the range between 10 to 15 were sampled to generate data sets, which were used for data augmentation to generate larger data sets. In image processing, semantic segmentation was used to separate the target apple image from ambient noise. In the following training process, the extracted data sets were input into CNN-based deep learning model to provide the apple SC prediction model and the accuracy of prediction yield. After that, the network structure and hyperparameters were optimized to a satisfactory level, ensuring this apple sugar degree prediction model to achieve an accuracy of about 90% on the test set of apple images. Moreover, this CNN-based apple SC model was deployed on the mobile phone for achieving high portability. In conclusion, the CNN-based prediction method of apple sugar content has the advantages of non-invasive property, low cost, fast speed, high accuracy and flexibility, indicating great potential in practical applications of fruit industry.
Chlorella is a single-celled blue-green spherical microalga, whose color could change from green to red or yellowish due to the components of different types of innate pigments. Salinity change is one important environmental stressor that may influence the pigment composition of Chlorella. Therefore, it is necessary to monitor the salinity stress on Chlorella in a real-time mode. Recently, fluorescence lifetime imaging microscopy (FLIM) technology has been widely applied into biological fields, which could provide fluorescence lifetime values for quantitative analysis. Here, we used FLIM method to investigate a freshwater microalga, Chlorella sp. based on its autofluorescence. Chlorella cells were treated with a series of salinity concentrations (control sample in normal culture medium, 3S sample with an additional 3× salinity, 7S sample with an additional 7× salinity, respectively) for one day. Then images of the microalgae cells from each group were obtained with FLIM system and analyzed with SPCImage software, providing the fluorescence lifetime data. The results of fluorescence lifetime data showed that 3× salinity condition had little effect on Chlorella, which indicated that Chlorella had a strong adaptive capacity in environments close to seawater salinity. However, the significant left shift of lifetime distribution peak and decreased mean lifetime values were observed in 7S samples compared with the control. In conclusion, FLIM method has shown great potential as a fast identification method of living Chlorella sp. under high salinity conditions in a quantitative and non-invasive way.
With the rapid development of modern industry, heavy metal pollution is becoming one of the most severe marine environmental problems among the world. Mussels are considered as suitable models to monitor heavy metal pollutions. The hematoxylin and eosin (H&E) sections of mussels could be observed with fluorescence lifetime imaging microscopy (FLIM) method, generating quantitative data of images for improved analysis with higher accuracy. In this study, we used FLIM method to investigate the fluorescence lifetime of digestive glands of adult mussels Mytilus galloprovincialis treated with two environmentally relevant concentrations (5 μg/L and 50 μg/L) of cadmium (Cd) for 14 days. After exposure, the H&E stained sections of the digestive glands were observed with FLIM system. The images were analyzed with SPCImage software, providing both the structural images with fluorescence intensity information and the pseudo-color images with fluorescence lifetime information. The fluorescence lifetime values were presented as total τ, which could be divided into τ1 (the shorter part) and τ2 (the longer part), respectively. Results showed that the τ2 values in 50 μg/L Cd treatments were significantly lower than those of the control group, suggesting that the high concentration of Cd treatment has a high impact on the digestive glands of M. galloprovincialis. The significance of the fluorescence lifetime data of mussel digestive glands induced by Cd at a high concentration has proved the sensitivity of FLIM method to distinguish the polluted mussels from the healthy individuals. In conclusion, FLIM has high potential in further applications of marine environment pollution monitoring.
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