CN113917678A - Ultra-fast large-field super-resolution fluorescence microscopy imaging system and imaging method - Google Patents
Ultra-fast large-field super-resolution fluorescence microscopy imaging system and imaging method Download PDFInfo
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- 238000003384 imaging method Methods 0.000 title claims abstract description 126
- 238000010857 super resolution fluorescence microscopy Methods 0.000 title claims abstract description 15
- 238000000799 fluorescence microscopy Methods 0.000 claims abstract description 44
- 238000005286 illumination Methods 0.000 claims abstract description 25
- 238000003062 neural network model Methods 0.000 claims abstract description 23
- 239000000919 ceramic Substances 0.000 claims description 16
- 238000012549 training Methods 0.000 claims description 12
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- 238000005516 engineering process Methods 0.000 abstract description 19
- 238000010859 live-cell imaging Methods 0.000 abstract description 4
- 206010034972 Photosensitivity reaction Diseases 0.000 abstract description 3
- 208000007578 phototoxic dermatitis Diseases 0.000 abstract description 3
- 231100000018 phototoxicity Toxicity 0.000 abstract description 3
- 239000000523 sample Substances 0.000 description 43
- 210000004027 cell Anatomy 0.000 description 4
- 238000000034 method Methods 0.000 description 4
- 238000013135 deep learning Methods 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 239000007850 fluorescent dye Substances 0.000 description 2
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Abstract
一种超快速大视场超分辨率荧光显微成像系统，包括激光器系统、照明光路、显微镜主体、三维纳米级样品锁定模块、样品扫描模块、成像模块；本发明通过成像模块对样品进行荧光宽场成像、超分辨率荧光成像，利用超分辨率荧光成像结果训练深度神经网络模型，并基于训练好的深度神经网络模型和所述荧光宽场成像结果、超分辨率荧光成像结果得到大视场的超分辨率图像，可以在整体低强度的照明条件下，获得大视野超分辨率的成像结果，与单纯使用超分辨率成像方法相比，成像的速度可以提升n/m倍；可以解决超分辨率荧光显微成像技术拍摄速度慢，成像视野受限，高光毒性光漂白的缺点，在生命科学领域具有广泛的应用前景，可以实现高通量活细胞成像。
An ultra-fast large-field super-resolution fluorescence microscopy imaging system, comprising a laser system, an illumination light path, a microscope main body, a three-dimensional nanoscale sample locking module, a sample scanning module, and an imaging module; Field imaging, super-resolution fluorescence imaging, using super-resolution fluorescence imaging results to train a deep neural network model, and obtaining a large field of view based on the trained deep neural network model and the fluorescence wide-field imaging results and super-resolution fluorescence imaging results Compared with the super-resolution imaging method, the imaging speed can be increased by n/m times; it can solve the problem of super-resolution imaging. High-resolution fluorescence microscopy imaging technology has the disadvantages of slow shooting speed, limited imaging field, and high phototoxicity photobleaching. It has broad application prospects in the field of life sciences and can achieve high-throughput live cell imaging.
Description
Technical Field
The invention relates to the technical field of microscopic imaging, in particular to an ultra-fast large-field-of-view super-resolution fluorescence microscopic imaging system and an imaging method.
Background
The super-resolution fluorescence microscopic imaging technology is a microscopic imaging technology which is developed very rapidly in recent years and is applied more and more widely in the field of life science, however, various super-resolution fluorescence microscopic imaging technologies are inevitably selected from other parameters in order to obtain resolution exceeding the diffraction limit.
Firstly, most of super-resolution fluorescence microscopic imaging technologies have low time resolution, namely, the shooting speed is low, for example, the super-resolution imaging mode can improve the spatial resolution by ten times, but because thousands to tens of thousands of original data need to be collected for imaging, the time for shooting a super-resolution image can reach several minutes or even ten minutes; the spatial resolution of the stimulated microscopic imaging technology can reach dozens of nanometers, but the imaging speed is limited by a confocal-based scanning imaging mode; even if the structured light illumination microscopic imaging technology can reconstruct a super-resolution image through a plurality of original data, so that the structured light illumination microscopic imaging technology is regarded as a system most suitable for living cell imaging, if the imaging speed is pursued and the collection time of the original data is reduced, the lower signal-to-noise ratio of the original data can cause the reconstructed super-resolution image to have obvious pseudo-images, and the imaging result loses the authenticity. Secondly, the super-resolution fluorescence microscopic imaging technology generally needs stronger illumination light, such as the stimulated excitation microscopic imaging technology, high-intensity laser is focused to a small focus to irradiate a sample, certain damage to the sample is inevitably caused, fluorescent substances are bleached, and the number of times of imaging the sample is reduced. Finally, the super-resolution fluorescence microscopy imaging technology generally has a high limitation on the imaging field of view, and due to the reasons that a strong illumination intensity is required in the imaging process, and a certain time sequence operation needs to be performed on the illumination light, and the like, the imageable field of view of these imaging technologies for performing one-time shooting is generally limited to about several hundred to several tens of micrometers, and high-throughput imaging requires a very complex system design and is almost impossible to implement.
In order to realize super-resolution fluorescence microscopic live cell imaging, there are several schemes, one of which is a structured light illumination microscopic imaging technology, because super-resolution fluorescence imaging can be performed by only needing several pieces of original data, so the shooting speed is fast, but if the shooting speed is too fast, the signal-to-noise ratio of the original data is reduced, artifact problems can obviously occur, even though various algorithms are being developed and tried to overcome the artifact problems, because of the diversity of biological sample structures, the assumed conditions of the various algorithms also limit the applicable biological structures, and the improper application of the technology can possibly obtain wrong experimental results. In order to realize live cell imaging, a super-resolution imaging mode needs to label structures such as cell membranes by a fluorescent probe which can specifically label live cells and has a light switching property, and then hundreds or thousands of original data are acquired to perform reconstruction imaging, but the types of the fluorescent probes which meet the requirements are very limited, and high-quality live cell imaging is hardly realized. The implementation of the super-resolution high-throughput imaging technology is more limited, the super-resolution fluorescence microscopy imaging technology is feasible for imaging a field of view (tens or hundreds of nanometers), and once the field of view required to be continuously observed becomes large, the super-resolution microscopy technology becomes difficult to implement due to the three disadvantages mentioned above.
Disclosure of Invention
The invention aims to solve the technical problem in the prior art, and provides an ultrafast large-field-of-view super-resolution fluorescence microscopic imaging system and an imaging method with high imaging speed and high resolution by combining a fluorescence microscopic imaging system and a deep neural network model.
The technical scheme adopted by the invention for solving the technical problems is as follows:
an ultrafast large-view-field super-resolution fluorescence microscopic imaging system comprises a laser system, an illumination light path, a microscope body, a three-dimensional nanoscale sample locking module, a sample scanning module and an imaging module;
the laser system is used for emitting laser with different wavelengths;
the illumination light path is used for adjusting the laser with different wavelengths;
the microscope body comprises an objective lens, a first multicolor reflecting mirror, a front imaging lens and a distribution reflecting mirror;
the three-dimensional nanoscale sample locking module is positioned above the microscope main body and comprises a piezoelectric ceramic platform, the piezoelectric ceramic platform is used for placing a sample, and a bright field light path and a bright field light source are sequentially arranged above the piezoelectric ceramic platform;
the laser with different wavelengths emitted by the laser system is adjusted through the illumination light path, passes through the first multicolor reflector and irradiates on the sample through the objective lens; bright field light emitted by the bright field light source irradiates a sample through the bright field light path; after passing through the objective lens, the first multicolor reflector and the front imaging lens, the fluorescence and bright field light emitted by the sample are distributed by the distribution reflector, wherein one part of the fluorescence and bright field light is distributed into the sample scanning module, and the other part of the fluorescence and bright field light is distributed into the imaging module;
the imaging module comprises a fluorescence detector, and the fluorescence detector is used for performing fluorescence wide field imaging and super-resolution fluorescence imaging; the piezoelectric ceramic platform is used for locking the position of a sample during super-resolution fluorescence imaging;
the sample scanning module comprises a scanning camera, a driving module and an electric platform, wherein the scanning camera is used for scanning a sample during fluorescence wide field imaging and transmitting a scanning result to a computer, and the computer is used for controlling the electric platform through the driving module to change the position of the sample so as to perform fluorescence wide field imaging on a plurality of fields of view of the sample;
and the computer is used for training a deep neural network model according to the super-resolution fluorescence imaging result and obtaining a super-resolution image with a large field of view based on the trained deep neural network model, the fluorescence wide-field imaging result and the super-resolution fluorescence imaging result.
Preferably, the laser system emits laser wavelengths including, but not limited to, 405nm, 488nm, 561nm, and 656 nm.
Preferably, the sample is labeled with fluorescent molecules having different excitation wavelengths, and the fluorescent molecules have light switching properties corresponding to laser light having different wavelengths emitted by the laser.
Preferably, the illumination path includes, but is not limited to, a tirf illumination path.
Preferably, the imaging module further comprises a diaphragm and an imaging optical path, and the fluorescence and bright field light distributed to the imaging module sequentially enter the fluorescence detector through the diaphragm and the imaging optical path.
Preferably, the deep neural network model is a residual error network model.
An imaging method of an ultrafast large-field-of-view super-resolution fluorescence microscopic imaging system comprises the following steps:
(1) controlling a laser to generate laser with lower intensity, controlling the electric platform to change the position of the sample through the driving module, carrying out fluorescence wide field imaging on n field of view of the sample, and splicing the obtained n field of view images to obtain a fluorescence wide field imaging result with a large field of view;
(2) controlling a laser to generate laser with higher intensity, locking the position of a sample by using the piezoelectric ceramic platform, and performing super-resolution fluorescence imaging on m (m > -1) areas in n fields of view; after each shooting, training a deep neural network model by using the obtained super-resolution fluorescence imaging result as a training sample, and calculating the accuracy of the deep neural network model;
(3) and (3) when the accuracy reaches a preset value, reconstructing the large-field-of-view fluorescence wide-field imaging result obtained in the step (1) by using a trained deep neural network model, and fusing the reconstructed result with the super-resolution fluorescence imaging result obtained in the step (2) to obtain a large-field-of-view super-resolution image.
Preferably, in the step (1), the edges of the n fields overlap.
Preferably, in the step (2), when the deep neural network model is trained, data enhancement processing is performed on the training samples.
Compared with the prior art, the invention has the beneficial effects that:
according to the invention, an imaging mode combined with a deep learning algorithm is adopted, an imaging result of a large-field super-resolution can be obtained under the overall low-intensity illumination condition, and compared with a super-resolution imaging method which is only used, the imaging speed can be increased by n/m times; the defects of low shooting speed, limited imaging field of vision and high phototoxicity photobleaching of the super-resolution fluorescence microscopic imaging technology can be overcome, the super-resolution fluorescence microscopic imaging method has wide application prospect in the field of life science, and high-throughput living cell imaging can be realized.
Drawings
FIG. 1 is a schematic view of a super-resolution fluorescence microscopic imaging system with ultra-fast large field of view and an imaging method in embodiment 1 of the invention.
Fig. 2 is a schematic view of an imaging field of view of step (1) in embodiment 1 of the ultrafast large-field-of-view super-resolution fluorescence microscopy imaging system and the imaging method of the present invention.
Fig. 3 is a schematic view of an imaging field of view in step (2) of embodiment 1 of the ultrafast large-field-of-view super-resolution fluorescence microscopy imaging system and the imaging method of the present invention.
Reference numerals: 1. a laser system; 2. an illumination light path; 31. an objective lens; 32. a first polychromatic reflector; 33. a front imaging lens; 34. a distribution mirror; 41. an electric platform; 42. a piezoelectric ceramic platform; 43. a drive module; 5. a scanning camera; 6. an imaging module; 61. a diaphragm; 62. an imaging optical path; 63. a fluorescence detector; 7. a sample; 8. a computer; 9. a bright field light source; 10. a bright field optical path.
Detailed Description
The invention is described in further detail below with reference to the accompanying examples.
As shown in fig. 1, an ultrafast large-field-of-view super-resolution fluorescence microscopic imaging system comprises a laser system 1, an illumination light path 2, a microscope body, a three-dimensional nanoscale sample locking module, a sample scanning module and an imaging module 6;
the laser system 1 is used for emitting laser with different wavelengths; in this embodiment, the laser wavelength emitted by the laser system 1 includes 405nm, 488nm, 561nm, and 656 nm;
the illumination light path 2 is a tirf illumination light path; the illumination light path 2 is used for adjusting the laser with different wavelengths;
the microscope body comprises an objective lens 31, a first polychromatic mirror 32, a front imaging lens 33 and a distribution mirror 34;
the three-dimensional nanoscale sample locking module is positioned above the microscope body, the three-dimensional nanoscale sample locking module comprises a piezoelectric ceramic platform 42, the piezoelectric ceramic platform 42 is used for placing a sample 7, and the sample 7 is marked with fluorescent molecules which correspond to laser emitted by the laser system 1 and have light switching properties; the piezoelectric ceramic platform 42 is also used for locking the position of the sample 7 during super-resolution fluorescence imaging; a bright field light path 10 and a bright field light source 9 are sequentially arranged above the piezoelectric ceramic platform 42, and the bright field light source 9 is a light emitting diode;
after being adjusted by the illumination light path 2, the laser with different wavelengths emitted by the laser system 1 passes through the first multicolor reflecting mirror 32 and irradiates the sample 7 through the objective lens 31; the bright field light emitted by the bright field light source 9 is irradiated onto the sample 7 through the bright field light path 10; after passing through the objective lens 31, the first multicolor mirror 32 and the front imaging lens 33, the fluorescent light and the bright field light emitted by the sample 7 are distributed by the distribution mirror 34, wherein one part of the fluorescent light and the bright field light is distributed into the sample scanning module, and the other part of the fluorescent light and the bright field light is distributed into the imaging module 6;
the imaging module 6 comprises a rectangular diaphragm 61, an imaging light path 62 and a fluorescence detector 63, the fluorescence and bright field light distributed to the imaging module 6 sequentially enters the fluorescence detector 63 through the rectangular diaphragm 61 and the imaging light path 62, and the fluorescence detector 63 is used for performing fluorescence wide field imaging and super-resolution fluorescence imaging;
the sample scanning module comprises a scanning camera 5, a driving module 43 and an electric platform 41, wherein the scanning camera 5 is used for scanning the sample 7 during the fluorescence wide field imaging and transmitting the scanning result to a computer 8, and the computer 8 is used for controlling the electric platform 41 to change the position of the sample 7 through the driving module 43 so as to perform the fluorescence wide field imaging on a plurality of fields of view of the sample 7;
and the computer 8 is used for training a deep neural network model according to the super-resolution fluorescence imaging result and obtaining a super-resolution image of a large visual field based on the trained deep neural network model, the fluorescence wide field imaging result and the super-resolution fluorescence imaging result.
An imaging method of an ultrafast large-field-of-view super-resolution fluorescence microscopic imaging system comprises the following steps:
(1) controlling the laser 1 to generate laser with lower intensity, as shown in fig. 2, controlling the electric platform 41 to change the position of the sample 7 through the driving module 43, and performing wide-field fluorescence imaging on 24 fields of the sample 7 by using the fluorescence detector 63, wherein the edges of the 24 fields are overlapped; splicing the obtained 24 field images by using a computer 8 to obtain a large-field fluorescence wide-field imaging result;
(2) controlling the laser 1 to generate laser with higher intensity, as shown in fig. 3, locking the position of the sample 7 by using the piezoelectric ceramic platform 42, and performing super-resolution fluorescence imaging on 2 regions in 24 fields by using the fluorescence detector 63; after each shooting, using the obtained super-resolution fluorescence imaging result as a training sample, firstly performing data enhancement processing on the training sample, then training a deep neural network model by using the training sample after the data enhancement processing, wherein the deep neural network model is a residual network model, and calculating the accuracy of the deep neural network model;
(3) and (3) when the accuracy reaches a preset value, reconstructing the large-field-of-view fluorescence wide-field imaging result obtained in the step (1) by using a trained deep neural network model, and fusing the reconstructed result with the super-resolution fluorescence imaging result obtained in the step (2) to obtain a large-field-of-view super-resolution image.
Through observation, the image shot by the embodiment has high resolution, and the shooting effect of the traditional super-resolution microscope can be achieved; compared with the method of simply using the super-resolution imaging, the imaging speed is improved by 12 times.
According to the invention, an imaging mode combined with a deep learning algorithm is adopted, an imaging result of a large-field super-resolution can be obtained under the overall low-intensity illumination condition, and compared with a super-resolution imaging method which is only used, the imaging speed can be increased by n/m times; the defects of low shooting speed, limited imaging field of vision and high phototoxicity photobleaching of the super-resolution fluorescence microscopic imaging technology can be overcome, the super-resolution fluorescence microscopic imaging method has wide application prospect in the field of life science, and high-throughput living cell imaging can be realized.
Finally, it should be noted that: the above examples are merely illustrative of the technical solutions of the present invention, and not limitative thereof; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114778500A (en) * | 2022-04-01 | 2022-07-22 | 中国科学院苏州生物医学工程技术研究所 | A high-power large-field-of-view multi-band illumination fluorescence imaging system |
| CN115112621A (en) * | 2022-07-07 | 2022-09-27 | 浙江大学 | A machine learning-based method for near-infrared second-region fluorescence wide-field microscopy |
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| CN108181282A (en) * | 2018-01-03 | 2018-06-19 | 宁波纳美致生物科技有限公司 | A kind of triple channel fluorescence localization super-resolution biology microscope system and method |
| CN111024671A (en) * | 2019-12-31 | 2020-04-17 | 深圳大学 | A system and method for super-resolution imaging of structural changes in directional light stimulation |
| US20210239952A1 (en) * | 2018-05-30 | 2021-08-05 | Carl Zeiss Microscopy Gmbh | Method, computer program product, computer-readable medium and system for scanning partial regions of a sample using a scanning microscope |
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN108181282A (en) * | 2018-01-03 | 2018-06-19 | 宁波纳美致生物科技有限公司 | A kind of triple channel fluorescence localization super-resolution biology microscope system and method |
| US20210239952A1 (en) * | 2018-05-30 | 2021-08-05 | Carl Zeiss Microscopy Gmbh | Method, computer program product, computer-readable medium and system for scanning partial regions of a sample using a scanning microscope |
| CN111024671A (en) * | 2019-12-31 | 2020-04-17 | 深圳大学 | A system and method for super-resolution imaging of structural changes in directional light stimulation |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114778500A (en) * | 2022-04-01 | 2022-07-22 | 中国科学院苏州生物医学工程技术研究所 | A high-power large-field-of-view multi-band illumination fluorescence imaging system |
| CN115112621A (en) * | 2022-07-07 | 2022-09-27 | 浙江大学 | A machine learning-based method for near-infrared second-region fluorescence wide-field microscopy |
| CN115112621B (en) * | 2022-07-07 | 2023-02-03 | 浙江大学 | Near-infrared two-region fluorescence wide-field microscopy method based on machine learning |
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