The MINimal emission FLUXes (MINFLUX) technique in optical microscopy, widely recognized as the next innovative fluorescence microscopy method, claims a spatial resolution of 1–3nm in both dead and living cells. To make use of the full resolution of the MINFLUX microscope, it is important to select appropriate fluorescence probes and labeling strategies, especially in living-cell imaging. This paper mainly focuses on recent applications and developments of fluorescence probes and the relevant labeling strategy for MINFLUX microscopy. Moreover, we discuss the deficiencies that need to be addressed in the future and a plan for the possible progression of MINFLUX to help investigators who have been involved in or are just starting in the field of super-resolution imaging microscopy with theoretical support.The MINimal emission FLUXes (MINFLUX) technique in optical microscopy, widely recognized as the next innovative fluorescence microscopy method, claims a spatial resolution of 1–3nm in both dead and living cells. To make use of the full resolution of the MINFLUX microscope, it is important to select appropriate fluorescence probes and labeling strategies, especially in living-cell imaging. This paper mainly focuses on recent applications and developments of fluorescence probes and the relevant labeling strategy for MINFLUX microscopy. Moreover, we discuss the deficiencies that need to be addressed in the future and a plan for the possible progression of MINFLUX to help investigators who have been involved in or are just starting in the field of super-resolution imaging microscopy with theoretical support.

Light field microscopy (LFM), featured for high three-dimensional imaging speed and low phototoxicity, has emerged as a technique of choice for instantaneous volumetric imaging. In contrast with other scanning-based three-dimensional (3D) imaging approaches, LFM enables to encode 3D spatial information in a snapshot manner, permitting high-speed 3D imaging that is only limited by the frame rate of the camera. In this review, we first introduce the fundamental theory of LFM and current corresponding advanced approaches. Then, we summarize various applications of LFM in biological imaging.Light field microscopy (LFM), featured for high three-dimensional imaging speed and low phototoxicity, has emerged as a technique of choice for instantaneous volumetric imaging. In contrast with other scanning-based three-dimensional (3D) imaging approaches, LFM enables to encode 3D spatial information in a snapshot manner, permitting high-speed 3D imaging that is only limited by the frame rate of the camera. In this review, we first introduce the fundamental theory of LFM and current corresponding advanced approaches. Then, we summarize various applications of LFM in biological imaging.

Multi-photon microscopy (MPM) and coherent anti-Stokes Raman scattering (CARS) are two advanced nonlinear optical imaging techniques, which provide complementary information and have great potential in combination for noninvasive in vivo biomedical applications. This paper provides a detailed discussion of the basics, development and applications of these technologies for in vivo skin research, covering the following topics: The principle and advantage of MPM and CARS, instrumentation development for in vivo applications, MPM and CARS of normal skin, application of MPM and CARS in skin cancer and disease diagnosis; application of MPM in skin disease intervention, i.e., imaging guided two-photon photothermolysis.Multi-photon microscopy (MPM) and coherent anti-Stokes Raman scattering (CARS) are two advanced nonlinear optical imaging techniques, which provide complementary information and have great potential in combination for noninvasive in vivo biomedical applications. This paper provides a detailed discussion of the basics, development and applications of these technologies for in vivo skin research, covering the following topics: The principle and advantage of MPM and CARS, instrumentation development for in vivo applications, MPM and CARS of normal skin, application of MPM and CARS in skin cancer and disease diagnosis; application of MPM in skin disease intervention, i.e., imaging guided two-photon photothermolysis.

Nonlinear optical imaging is a versatile tool that has been proven to be exceptionally useful in various research fields. However, due to the use of photomultiplier tubes (PMTs), the wide application of nonlinear optical imaging is limited by the incapability of imaging under ambient light. In this paper, we propose and demonstrate a new optical imaging detection method based on optical parametric amplification (OPA). As a nonlinear optical process, OPA intrinsically rejects ambient light photons by coherence gating. Periodical poled lithium niobate (PPLN) crystals are used in this study as the media for OPA. Compared to bulk nonlinear optical crystals, PPLN crystals support the generation of OPA signal with lower pump power. Therefore, this characteristic of PPLN crystals is particularly beneficial when using high-repetition-rate lasers, which facilitate high-speed optical signal detection, such as in spectroscopy and imaging. A PPLN-based OPA system was built to amplify the emitted imaging signal from second harmonic generation (SHG) and coherent anti-Stokes Raman scattering (CARS) microscopy imaging, and the amplified optical signal was strong enough to be detected by a biased photodiode under ordinary room light conditions. With OPA detection, ambient-light-on SHG and CARS imaging becomes possible, and achieves a similar result as PMT detection under strictly dark environments. These results demonstrate that OPA can be used as a substitute for PMTs in nonlinear optical imaging to adapt it to various applications with complex lighting conditions.Nonlinear optical imaging is a versatile tool that has been proven to be exceptionally useful in various research fields. However, due to the use of photomultiplier tubes (PMTs), the wide application of nonlinear optical imaging is limited by the incapability of imaging under ambient light. In this paper, we propose and demonstrate a new optical imaging detection method based on optical parametric amplification (OPA). As a nonlinear optical process, OPA intrinsically rejects ambient light photons by coherence gating. Periodical poled lithium niobate (PPLN) crystals are used in this study as the media for OPA. Compared to bulk nonlinear optical crystals, PPLN crystals support the generation of OPA signal with lower pump power. Therefore, this characteristic of PPLN crystals is particularly beneficial when using high-repetition-rate lasers, which facilitate high-speed optical signal detection, such as in spectroscopy and imaging. A PPLN-based OPA system was built to amplify the emitted imaging signal from second harmonic generation (SHG) and coherent anti-Stokes Raman scattering (CARS) microscopy imaging, and the amplified optical signal was strong enough to be detected by a biased photodiode under ordinary room light conditions. With OPA detection, ambient-light-on SHG and CARS imaging becomes possible, and achieves a similar result as PMT detection under strictly dark environments. These results demonstrate that OPA can be used as a substitute for PMTs in nonlinear optical imaging to adapt it to various applications with complex lighting conditions.

Cerenkov Luminescence Tomography (CLT) is a novel and potential imaging modality which can display the three-dimensional distribution of radioactive probes. However, due to severe ill-posed inverse problem, obtaining accurate reconstruction results is still a challenge for traditional model-based methods. The recently emerged deep learning-based methods can directly learn the mapping relation between the surface photon intensity and the distribution of the radioactive source, which effectively improves the performance of CLT reconstruction. However, the previously proposed deep learning-based methods cannot work well when the order of input is disarranged. In this paper, a novel 3D graph convolution-based residual network, GCR-Net, is proposed, which can obtain a robust and accurate reconstruction result from the photon intensity of the surface. Additionally, it is proved that the network is insensitive to the order of input. The performance of this method was evaluated with numerical simulations and in vivo experiments. The results demonstrated that compared with the existing methods, the proposed method can achieve efficient and accurate reconstruction in localization and shape recovery by utilizing three-dimensional information.Cerenkov Luminescence Tomography (CLT) is a novel and potential imaging modality which can display the three-dimensional distribution of radioactive probes. However, due to severe ill-posed inverse problem, obtaining accurate reconstruction results is still a challenge for traditional model-based methods. The recently emerged deep learning-based methods can directly learn the mapping relation between the surface photon intensity and the distribution of the radioactive source, which effectively improves the performance of CLT reconstruction. However, the previously proposed deep learning-based methods cannot work well when the order of input is disarranged. In this paper, a novel 3D graph convolution-based residual network, GCR-Net, is proposed, which can obtain a robust and accurate reconstruction result from the photon intensity of the surface. Additionally, it is proved that the network is insensitive to the order of input. The performance of this method was evaluated with numerical simulations and in vivo experiments. The results demonstrated that compared with the existing methods, the proposed method can achieve efficient and accurate reconstruction in localization and shape recovery by utilizing three-dimensional information.

Bioluminescence tomography (BLT) is a promising imaging modality that can provide noninvasive three-dimensional visualization information on tumor distribution. In BLT reconstruction, the widely used methods based on regularization or greedy strategy face problems such as over-sparsity, over-smoothing, spatial discontinuity, poor robustness, and poor multi-target resolution. To deal with these problems, combining the advantages of the greedy strategies as well as regularization methods, we propose a hybrid reconstruction framework for model-based multispectral BLT using the support set of a greedy strategy as a feasible region and the Alpha-divergence to combine the weighted solutions obtained by L1-norm and L2-norm regularization methods. In numerical simulations with digital mouse and in vivo experiments, the results show that the proposed framework has better localization accuracy, spatial resolution, and multi-target resolution.Bioluminescence tomography (BLT) is a promising imaging modality that can provide noninvasive three-dimensional visualization information on tumor distribution. In BLT reconstruction, the widely used methods based on regularization or greedy strategy face problems such as over-sparsity, over-smoothing, spatial discontinuity, poor robustness, and poor multi-target resolution. To deal with these problems, combining the advantages of the greedy strategies as well as regularization methods, we propose a hybrid reconstruction framework for model-based multispectral BLT using the support set of a greedy strategy as a feasible region and the Alpha-divergence to combine the weighted solutions obtained by L1-norm and L2-norm regularization methods. In numerical simulations with digital mouse and in vivo experiments, the results show that the proposed framework has better localization accuracy, spatial resolution, and multi-target resolution.

Radiation-induced acoustic computed tomography (RACT) is an evolving biomedical imaging modality that aims to reconstruct the radiation energy deposition in tissues. Traditional back-projection (BP) reconstructions carry noisy and limited-view artifacts. Model-based algorithms have been demonstrated to overcome the drawbacks of BPs. However, model-based algorithms are relatively more complex to develop and computationally demanding. Furthermore, while a plethora of novel algorithms has been developed over the past decade, most of these algorithms are either not accessible, readily available, or hard to implement for researchers who are not well versed in programming. We developed a user-friendly MATLAB-based graphical user interface (GUI; RACT2D) that facilitates back-projection and model-based image reconstructions for two-dimensional RACT problems. We included numerical and experimental X-ray-induced acoustic datasets to demonstrate the capabilities of the GUI. The developed algorithms support parallel computing for evaluating reconstructions using the cores of the computer, thus further accelerating the reconstruction speed. We also share the MATLAB-based codes for evaluating RACT reconstructions, which users with MATLAB programming expertise can further modify to suit their needs. The shared GUI and codes can be of interest to researchers across the globe and assist them in efficient evaluation of improved RACT reconstructions.Radiation-induced acoustic computed tomography (RACT) is an evolving biomedical imaging modality that aims to reconstruct the radiation energy deposition in tissues. Traditional back-projection (BP) reconstructions carry noisy and limited-view artifacts. Model-based algorithms have been demonstrated to overcome the drawbacks of BPs. However, model-based algorithms are relatively more complex to develop and computationally demanding. Furthermore, while a plethora of novel algorithms has been developed over the past decade, most of these algorithms are either not accessible, readily available, or hard to implement for researchers who are not well versed in programming. We developed a user-friendly MATLAB-based graphical user interface (GUI; RACT2D) that facilitates back-projection and model-based image reconstructions for two-dimensional RACT problems. We included numerical and experimental X-ray-induced acoustic datasets to demonstrate the capabilities of the GUI. The developed algorithms support parallel computing for evaluating reconstructions using the cores of the computer, thus further accelerating the reconstruction speed. We also share the MATLAB-based codes for evaluating RACT reconstructions, which users with MATLAB programming expertise can further modify to suit their needs. The shared GUI and codes can be of interest to researchers across the globe and assist them in efficient evaluation of improved RACT reconstructions.

Glioma is the most malignant brain cancer. The neurons, macrophages, T cells and other immune cells constitute the glioma immunosuppressive microenvironment. The accurate spatial distribution of these cells in the glioma microenvironment and its relationship with glioma metastasis is unknown. We constructed a mouse glioma cell line stably expressing the large Stokes-shifted yellow fluorescent protein and applied it to the multicolor immunofluorescence imaging. The imaging data revealed that the neurons were sparsely distributed in the glioma core and the number of neurons decreased by 90% compared with normal brain site. The spatial distribution of monocyte-macrophages and microglia is heterogeneous. The monocyte-macrophages and T cells were heavily recruited into the glioma core and metastasis. There was no significant difference in the distribution of microglia among glioma core, margin, and normal brain site. Our results provided new perspectives for targeting immune regulation cells and developing new immunotherapy strategies for glioma.Glioma is the most malignant brain cancer. The neurons, macrophages, T cells and other immune cells constitute the glioma immunosuppressive microenvironment. The accurate spatial distribution of these cells in the glioma microenvironment and its relationship with glioma metastasis is unknown. We constructed a mouse glioma cell line stably expressing the large Stokes-shifted yellow fluorescent protein and applied it to the multicolor immunofluorescence imaging. The imaging data revealed that the neurons were sparsely distributed in the glioma core and the number of neurons decreased by 90% compared with normal brain site. The spatial distribution of monocyte-macrophages and microglia is heterogeneous. The monocyte-macrophages and T cells were heavily recruited into the glioma core and metastasis. There was no significant difference in the distribution of microglia among glioma core, margin, and normal brain site. Our results provided new perspectives for targeting immune regulation cells and developing new immunotherapy strategies for glioma.

Cells are the basic unit of human organs that are not fully understood. The revolutionary advancements of optical imaging allowed us to observe single cells in whole organs, revealing the complicated composition of cells with spatial information. Therefore, in this review, we revisit the principles of optical contrast related to those biomolecules and the optical techniques that transform optical contrast into detectable optical signals. Then, we describe optical imaging to achieve three-dimensional spatial discrimination for biological tissues. Due to the milky appearance of tissues, the spatial information blurred deep in the whole organ. Fortunately, strategies developed in the last decade could circumvent this issue and lead us into a new era of investigation of the cells with their original spatial information.Cells are the basic unit of human organs that are not fully understood. The revolutionary advancements of optical imaging allowed us to observe single cells in whole organs, revealing the complicated composition of cells with spatial information. Therefore, in this review, we revisit the principles of optical contrast related to those biomolecules and the optical techniques that transform optical contrast into detectable optical signals. Then, we describe optical imaging to achieve three-dimensional spatial discrimination for biological tissues. Due to the milky appearance of tissues, the spatial information blurred deep in the whole organ. Fortunately, strategies developed in the last decade could circumvent this issue and lead us into a new era of investigation of the cells with their original spatial information.