Microscopy|3 Article(s)
Progress and Prospect of Research on Single-molecule Localization Super-resolution Microscopy(Invited Review)
Sha AN, Dan DAN, Xiang-hua YU, Tong PENG, and Bao-li YAO
Super-resolution optical microscopy breaks through the diffraction limit and becomes a powerful tool for the modern biomedical research with the development of novel fluorescence probes, advanced lasers, high sensitivity photodetectors, etc. Single-molecule Localization Microscopy (SMLM), as one of the super-resolution technologies, can resolve the subcellular structures in nanoscale by using the photoswitching effect of certain fluorescence probes. In this paper, the principle and implementation of SMLM are introduced, the applications in the fields of cytobiology, tissue biology and neuroscience are presented, furthermore, the development trends and the futher investigated directions of this technique are discussed, providing references for the relevant research fields. The continuous innovation of super-resolution microscopy will promote the development of life science.
Acta Photonica Sinica
  • Publication Date: Sep. 01, 2020
  • Vol. 49, Issue 9, 0918001 (2020)
In Vivo,Dual-color Fluorescent Imaging Miniature Microscope
Kaiqiu LAN, Xibin YANG, Baoteng XU, Jialin LIU, Wei ZHOU, and Daxi XIONG
In recent years, neuroscientists have become more and more interested in brain imaging of conscious and free-behaving animals, hoping to obtain nerve impulse signals in the brains of free-behaving experimental animals, especially certain types of cellular activity may be inhibited by anesthesia. Combined with the Genetically Encoded Calcium Indicator (GECI), the miniature microscope has the ability to image the brain of free-behaving animals and obtain the signal of nerve impulse. The miniature microscope is then widely used in the study of brain science. Currently, most optical systems of miniature microscopes are limited by chromatic aberration due to the use of Gradient Index Lenses (GRIN), which does not meet the experimental requirement of the two-color fluorescent imaging effect. Dual-color fluorescence imaging miniature microscopes have a number of advantages, such as the ability to compare the activities of two different cell populations in the same brain region of a free-behaving animal combined with GECIs which have distinguishable color spectrums, or it can be used for the motion correction. Therefore, a Dual-color Fluorescent Imaging Miniature Microscope (DCFIMM) is developed. Firstly, in order to enhance the dual-color fluorescent imaging capability of the miniature microscope, a micro achromatic lens is designed to replace the gradient index lens. The miniature achromatic lens is composed of double cemented lenses, which forms an infinity correction optical system with the imaging lens. And according to the application direction of cerebral cortex imaging and deep brain imaging, DCFIMM-SBI (superficial brain imaging) and DCFIMM-DBI (deep brain imaging) are designed, both have a larger imaging field of view than the monochromatic fluorescence imaging miniature microscope with grin lens, which are 1.10 mm×1.10 mm and 0.77 mm×0.77 mm respectively. Meanwhile, the dual-band filter for green and near-infrared is used to reduce fluorescent crosstalk. Secondly, a data acquisition circuit is designed to alternately trigger two LEDs with different wavelengths with the frame rate of the CMOS camera. Therefore, the green fluorescent information and near-infrared fluorescent information can be obtained in odd-numbered frames and even-numbered frames, respectively. Our system can realize the imaging speed of 10 fps with the ability of dual-color fluorescence imaging. Thirdly, the video data is stored in a micro SD card. DCFIMM is not limited by the wire transmission. Finally, the structure design of our DCFIMM is optimized. The whole weight of our DCFIMM is 4.8 g (6.2 g with a battery). The experimental results of the USAF 1951 high-resolution target show that the achievable resolution of our DCFIMM is 3.47 μm, which is comparable with monochromatic fluorescent imaging using a miniature microscope with GRIN lens. In the dual-color fluorescent imaging experiment for the hybrid microsphere, DCFIMM can distinguish the fluorescent microspheres of different colors. Compared with the experimental results of monochromatic fluorescence imaging miniature microscope with grin lens, it is found that the chromatic aberration of the DCFIMM optical system has also been well corrected, which demonstrates that our DCFIMM has the ability to distinguish fluorescence of different wavelengths. The proposed DCFIMM in this paper shows promising and wide applications for brain science research.
Acta Photonica Sinica
  • Publication Date: Jun. 25, 2022
  • Vol. 51, Issue 6, 0618001 (2022)
A Fluorescence Lifetime Retrieval Algorithm Based on LSTM Neural Network
Biyu YANG, and Yue XU
Fluorescence lifetime imaging technology utilizes the decay difference of the emitted fluorescences to distinguish different fluorescent molecules, which is widely used in biomedicine, chemical analysis, and life science. The quality of fluorescence lifetime imaging depends on fluorescence lifetime measurement techniques and retrieval algorithms. The measurement method based on time-correlated single-photon counting technology has become the main stream fluorescence lifetime measurement method in the field of biological research because of its high accuracy and easy low-light detection. Traditional fluorescence lifetime retrieval algorithms based on time-correlated single-photon counting technology are not suitable for the extraction of fast, high-precision, and long fluorescence lifetime. Most of the long fluorescence lifetime substances are quantum dots. In recent years, emerging deep learning techniques have also been gradually used for fluorescence lifetime retrieval, mainly realizing fluorescence lifetime imaging with fluorescence lifetimes in the range of 10 ns. Therefore, it is urgent to develop a new fluorescence lifetime retrieval algorithm to solve the constraints of retrieval accuracy and speed in a wide fluorescence lifetime range.To solve the problem of low accuracy of fluorescence lifetime retrieval in a large dynamic range, a fluorescence lifetime retrieval algorithm based on long short-term memory neural network is proposed in this paper. The algorithm uses a multi-layer long-short-term memory neural network with a time-series memory function to realize the feature extraction of the fluorescence lifetime decay histogram data which is based on time-correlated single photon counting. The unique gate structure of long-short-term memory neural network can realize the protection and control of time series information. What's more, deep learning technology is used to learn a large number of various fluorescence lifetime decay information, establish a corresponding relationship between histogram and fluorescence lifetime, and then the weight value and bias coefficients of the network are updated to make the training model more suitable for fluorescence lifetime retrieval. To train the model, the grid search method is used to select the hyperparameters of the neural network model, including a number of neurons and network layers. To make the simulated data closer to the real experimental data, the data set for model training is a time series generated by a computer simulation of the time-correlated single-photon counting process in the presence of Poisson noise. The generated time series is the series corresponding to 20 000 fluorescence lifetime decay histograms uniformly distributed in the range of 100 ns. The data were normalized to eliminate the order-of-magnitude differences, and to avoid large order-of-magnitude differences which would reduce the accuracy of the predictions. The prediction accuracy of the randomly generated 1~100 ns fluorescence lifetime outside the training data set is supposed as the evaluation standard, and the optimal model including 3 layers of LSTM network is selected for the subsequent fluorescence lifetime retrieval. Monte Carlo simulation results indicate that the proposed retrieval algorithm achieves a retrieval accuracy of 95% in the fluorescence lifetime range of 1~90 ns even when the number of photons is 5 000 which is conducive to the fluorescence lifetime imaging. In the case of the same number of photons, the retrieval range is increased by 4.5 times in comparison with the center-of-mass method. Moreover, the proposed method achieves higher retrieval accuracy of the long lifetimes than the traditional least squares method. For the imaging of 32×32 arrays, after several experimental calculations, it is shown that the center-of-mass method can complete the computing in 0.07 s, the least-squares method takes about 77 s, and the proposed algorithm takes about 9.7 s under the conditions of Windows11 (64-bit) operating system, 16 GB memory, and Intel(R) Core(TM) i5-1157G7 processor. The results reveal that neural network not only provides comparable or even better performances but also offers much faster high-throughput data analysis. A shorter time will be used to complete the array imaging when the hardware conditions are improved which provides the possibility for real-time imaging. The proposed algorithm can significantly broaden the fluorescence lifetime reduction range with high retrieval accuracy, thus, it is suitable for accurate fluorescence lifetime retrieval imaging with a single exponential large dynamic range.
Acta Photonica Sinica
  • Publication Date: Jun. 25, 2022
  • Vol. 51, Issue 6, 0618002 (2022)