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.
As a kind of microalgae, Spirulina plays an important role in fish culture, food processing industry, medical treatment and bioenergetic development due to its reasonable nutritional composition and high hydrogenase activity. However, the purity of Spirulina , which could be significantly affected by virus infection and miscellaneous algal issues, has great impact on the quality of the product. Thus, periodic Spirulina detection is necessary for quality control of Spirulina culture. Currently, there are two main methods of Spirulina detection: the optical microscopic method and the fluorescence detection method. The former has higher accuracy and a lower speed while the latter has a higher speed in a sample destructing mode. Deep learning-based method has the ability to accelerate data processing. Meanwhile, it can achieve high accuracy by model training and validation. In this work, we have applied deep learning to Spirulina detection to achieve a higher accuracy rate. The process was divided into four main steps: Spirulina culture, image acquisition, image preprocessing and YOLO-v3 model training. The hyperparametric modulation was carried out to determine the appropriate training parameters, providing a trained model with mAP of 0.839 at a detection speed of 20.53 fps. It has great application potential in quantity detection and size detection of cultured Spirulina .
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.
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