Pub Date : 2025-01-01Epub Date: 2025-01-31DOI: 10.1038/s44303-024-00066-8
Ayush Saurabh, Peter T Brown, J Shepard Bryan Iv, Zachary R Fox, Rory Kruithoff, Cristopher Thompson, Comert Kural, Douglas P Shepherd, Steve Pressé
Biological images captured by microscopes are characterized by heterogeneous signal-to-noise ratios (SNRs) due to spatially varying photon emission across the field of view convoluted with camera noise. State-of-the-art unsupervised structured illumination microscopy (SIM) reconstruction methods, commonly implemented in the Fourier domain, often do not accurately model this noise. Such methods therefore suffer from high-frequency artifacts, user-dependent choices of smoothness constraints making assumptions on biological features, and unphysical negative values in the recovered fluorescence intensity map. On the other hand, supervised algorithms rely on large datasets for training, and often require retraining for new sample structures. Consequently, achieving high contrast near the maximum theoretical resolution in an unsupervised, physically principled manner remains an open problem. Here, we propose Bayesian-SIM (B-SIM), a Bayesian framework to quantitatively reconstruct SIM data, rectifying these shortcomings by accurately incorporating known noise sources in the spatial domain. To accelerate the reconstruction process, we use the finite extent of the point-spread-function to devise a parallelized Monte Carlo strategy involving chunking and restitching of the inferred fluorescence intensity. We benchmark our framework on both simulated and experimental images, and demonstrate improved contrast permitting feature recovery at up to 25% shorter length scales over state-of-the-art methods at both high- and low SNR. B-SIM enables unsupervised, quantitative, physically accurate reconstruction without the need for labeled training data, democratizing high-quality SIM reconstruction and expands the capabilities of live-cell SIM to lower SNR, potentially revealing biological features in previously inaccessible regimes.
{"title":"Approaching maximum resolution in structured illumination microscopy via accurate noise modeling.","authors":"Ayush Saurabh, Peter T Brown, J Shepard Bryan Iv, Zachary R Fox, Rory Kruithoff, Cristopher Thompson, Comert Kural, Douglas P Shepherd, Steve Pressé","doi":"10.1038/s44303-024-00066-8","DOIUrl":"10.1038/s44303-024-00066-8","url":null,"abstract":"<p><p>Biological images captured by microscopes are characterized by heterogeneous signal-to-noise ratios (SNRs) due to spatially varying photon emission across the field of view convoluted with camera noise. State-of-the-art unsupervised structured illumination microscopy (SIM) reconstruction methods, commonly implemented in the Fourier domain, often do not accurately model this noise. Such methods therefore suffer from high-frequency artifacts, user-dependent choices of smoothness constraints making assumptions on biological features, and unphysical negative values in the recovered fluorescence intensity map. On the other hand, supervised algorithms rely on large datasets for training, and often require retraining for new sample structures. Consequently, achieving high contrast near the maximum theoretical resolution in an unsupervised, physically principled manner remains an open problem. Here, we propose Bayesian-SIM (B-SIM), a Bayesian framework to quantitatively reconstruct SIM data, rectifying these shortcomings by accurately incorporating known noise sources in the spatial domain. To accelerate the reconstruction process, we use the finite extent of the point-spread-function to devise a parallelized Monte Carlo strategy involving chunking and restitching of the inferred fluorescence intensity. We benchmark our framework on both simulated and experimental images, and demonstrate improved contrast permitting feature recovery at up to 25% shorter length scales over state-of-the-art methods at both high- and low SNR. B-SIM enables unsupervised, quantitative, physically accurate reconstruction without the need for labeled training data, democratizing high-quality SIM reconstruction and expands the capabilities of live-cell SIM to lower SNR, potentially revealing biological features in previously inaccessible regimes.</p>","PeriodicalId":501709,"journal":{"name":"npj Imaging","volume":"3 1","pages":"5"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11785531/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143082692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-12DOI: 10.1038/s44303-024-00062-y
Hongje Jang, Shuang Wu, Yajuan Li, Zhi Li, Lingyan Shi
The advances in microscopy techniques have led to new findings in biology. The recent advances in super-resolution microscopy technologies revealed precise molecular distribution. The techniques to visualize the distributions of multiple molecules in biological samples from experimental techniques to computational approaches are reviewed. By summarizing the techniques, the future direction of collaborative research of the techniques is highlighted to show the nanoscopic chemical details of biological samples.
{"title":"Metabolic nanoscopy enhanced by experimental and computational approaches","authors":"Hongje Jang, Shuang Wu, Yajuan Li, Zhi Li, Lingyan Shi","doi":"10.1038/s44303-024-00062-y","DOIUrl":"10.1038/s44303-024-00062-y","url":null,"abstract":"The advances in microscopy techniques have led to new findings in biology. The recent advances in super-resolution microscopy technologies revealed precise molecular distribution. The techniques to visualize the distributions of multiple molecules in biological samples from experimental techniques to computational approaches are reviewed. By summarizing the techniques, the future direction of collaborative research of the techniques is highlighted to show the nanoscopic chemical details of biological samples.","PeriodicalId":501709,"journal":{"name":"npj Imaging","volume":" ","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44303-024-00062-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142811336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-11DOI: 10.1038/s44303-024-00059-7
B. H. Peter Duinkerken, Ahmad M. J. Alsahaf, Jacob P. Hoogenboom, Ben N. G. Giepmans
Microscopy is a key technique to visualize and understand biology. Electron microscopy (EM) facilitates the investigation of cellular ultrastructure at biomolecular resolution. Cellular EM was recently revolutionized by automation and digitalisation allowing routine capture of large areas and volumes at nanoscale resolution. Analysis, however, is hampered by the greyscale nature of electron images and their large data volume, often requiring laborious manual annotation. Here we demonstrate unsupervised and automated extraction of biomolecular assemblies in conventionally processed tissues using large-scale hyperspectral energy-dispersive X-ray (EDX) imaging. First, we discriminated biological features in the context of tissue based on selected elemental maps. Next, we designed a data-driven workflow based on dimensionality reduction and spectral mixture analysis, allowing the visualization and isolation of subcellular features with minimal manual intervention. Broad implementations of the presented methodology will accelerate the understanding of biological ultrastructure.
{"title":"Automated analysis of ultrastructure through large-scale hyperspectral electron microscopy","authors":"B. H. Peter Duinkerken, Ahmad M. J. Alsahaf, Jacob P. Hoogenboom, Ben N. G. Giepmans","doi":"10.1038/s44303-024-00059-7","DOIUrl":"10.1038/s44303-024-00059-7","url":null,"abstract":"Microscopy is a key technique to visualize and understand biology. Electron microscopy (EM) facilitates the investigation of cellular ultrastructure at biomolecular resolution. Cellular EM was recently revolutionized by automation and digitalisation allowing routine capture of large areas and volumes at nanoscale resolution. Analysis, however, is hampered by the greyscale nature of electron images and their large data volume, often requiring laborious manual annotation. Here we demonstrate unsupervised and automated extraction of biomolecular assemblies in conventionally processed tissues using large-scale hyperspectral energy-dispersive X-ray (EDX) imaging. First, we discriminated biological features in the context of tissue based on selected elemental maps. Next, we designed a data-driven workflow based on dimensionality reduction and spectral mixture analysis, allowing the visualization and isolation of subcellular features with minimal manual intervention. Broad implementations of the presented methodology will accelerate the understanding of biological ultrastructure.","PeriodicalId":501709,"journal":{"name":"npj Imaging","volume":" ","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44303-024-00059-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142798608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Animal magnetic resonance imaging (MRI) systems typically deliver superior imaging performance over conventional human MRI systems, making them a prevailing instrument in preclinical research. It is challenging to achieve the high performance of these animal MRI systems, due to the multifaceted nature of the various system components and the complexity of integration debugging. This work described the design, fabrication, measurement and integration of a 7 T animal MRI system, which exhibits several performance highlights. Both the magnet and gradient assembly adopted an ultra-shielding strategy, facilitating ease of system installation, maintenance and debugging. The main magnetic field exhibits acceptable homogeneity and stability, and the gradient coil is mechanically reliable thanks to zero-force control. The animal MRI system underwent debugging using proprietary imaging software to generate images of phantoms, fruits and organisms. Further research investigation will be performed to promote more scientific outputs with enhanced functional capabilities.
{"title":"Ultrahigh-field animal MRI system with advanced technological update","authors":"Yaohui Wang, Guyue Zhou, Haoran Chen, Pengfei Wu, Wenhui Yang, Feng Liu, Qiuliang Wang","doi":"10.1038/s44303-024-00060-0","DOIUrl":"10.1038/s44303-024-00060-0","url":null,"abstract":"Animal magnetic resonance imaging (MRI) systems typically deliver superior imaging performance over conventional human MRI systems, making them a prevailing instrument in preclinical research. It is challenging to achieve the high performance of these animal MRI systems, due to the multifaceted nature of the various system components and the complexity of integration debugging. This work described the design, fabrication, measurement and integration of a 7 T animal MRI system, which exhibits several performance highlights. Both the magnet and gradient assembly adopted an ultra-shielding strategy, facilitating ease of system installation, maintenance and debugging. The main magnetic field exhibits acceptable homogeneity and stability, and the gradient coil is mechanically reliable thanks to zero-force control. The animal MRI system underwent debugging using proprietary imaging software to generate images of phantoms, fruits and organisms. Further research investigation will be performed to promote more scientific outputs with enhanced functional capabilities.","PeriodicalId":501709,"journal":{"name":"npj Imaging","volume":" ","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44303-024-00060-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142798576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Duchenne muscular dystrophy (DMD) is a genetic muscular disease and is the most common type of muscular dystrophy in Japan. Noninvasive magnetic resonance imaging (MRI) can be used for follow-up evaluation of myositis and muscular dystrophy, including DMD and inflammation is evaluated based on the increased muscle water as evaluated by T2-weighted MR images. However, in MDM, the redox status has not been evaluated non-invasively during the disease progression. We assessed the inflammation via the redox status in experimental animal disease models using in vivo dynamic nuclear polarization MRI (DNP-MRI) with a redox probe. The current study aimed to evaluate the skeletal muscle of mdx mice, a DMD model, in which muscle fiber necrosis, inflammation, and muscle regeneration were chronically repeated. Results showed that the reduction rate of Carbamoyl-PROXYL (CmP), one of the redox probes, radicals in mdx mice increased compared with that in normal mice. In vitro, more mitochondria or macrophages enhanced the radical form decay reaction by reducing CmP. Due to muscle fiber damage, the mdx mice had a lower mitochondrial concentration in the gastrocnemius muscle than the normal mice. However, the in vivo DNP-MRI results strongly reflected the increased reduction of CmP radicals by macrophages. In conclusion, in vivo DNP-MRI, a noninvasive imaging method is useful for locally evaluating skeletal muscle inflammation.
{"title":"Evaluation of the redox alteration in Duchenne muscular dystrophy model mice using in vivo DNP-MRI","authors":"Hinako Eto, Masaharu Murata, Takahito Kawano, Yoko Tachibana, Abdelazim Elsayed Elhelaly, Yoshifumi Noda, Hiroki Kato, Masayuki Matsuo, Fuminori Hyodo","doi":"10.1038/s44303-024-00058-8","DOIUrl":"10.1038/s44303-024-00058-8","url":null,"abstract":"Duchenne muscular dystrophy (DMD) is a genetic muscular disease and is the most common type of muscular dystrophy in Japan. Noninvasive magnetic resonance imaging (MRI) can be used for follow-up evaluation of myositis and muscular dystrophy, including DMD and inflammation is evaluated based on the increased muscle water as evaluated by T2-weighted MR images. However, in MDM, the redox status has not been evaluated non-invasively during the disease progression. We assessed the inflammation via the redox status in experimental animal disease models using in vivo dynamic nuclear polarization MRI (DNP-MRI) with a redox probe. The current study aimed to evaluate the skeletal muscle of mdx mice, a DMD model, in which muscle fiber necrosis, inflammation, and muscle regeneration were chronically repeated. Results showed that the reduction rate of Carbamoyl-PROXYL (CmP), one of the redox probes, radicals in mdx mice increased compared with that in normal mice. In vitro, more mitochondria or macrophages enhanced the radical form decay reaction by reducing CmP. Due to muscle fiber damage, the mdx mice had a lower mitochondrial concentration in the gastrocnemius muscle than the normal mice. However, the in vivo DNP-MRI results strongly reflected the increased reduction of CmP radicals by macrophages. In conclusion, in vivo DNP-MRI, a noninvasive imaging method is useful for locally evaluating skeletal muscle inflammation.","PeriodicalId":501709,"journal":{"name":"npj Imaging","volume":" ","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44303-024-00058-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142789391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-04DOI: 10.1038/s44303-024-00054-y
Ciarán Butler-Hallissey, Christophe Leterrier
The complexity of the brain organization and the unique architecture of neurons have motivated neuroscientists to stay at the forefront of cellular microscopy and rapidly take advantage of technical developments in this field. Among these developments, super-resolution microscopy has transformed our understanding of neurobiology by allowing us to image identified macromolecular scaffolds and complexes directly in cells. Super-resolution microscopy approaches have thus provided key insights into the organization and functions of the neuronal cytoskeleton and its unique nanostructures. These insights are the focus of our review, where we attempt to provide a panorama of super-resolution microscopy applications to the study of the neuronal cytoskeleton, delineating the progress they have made possible and the current challenges they meet.
{"title":"Super-resolution imaging of the neuronal cytoskeleton","authors":"Ciarán Butler-Hallissey, Christophe Leterrier","doi":"10.1038/s44303-024-00054-y","DOIUrl":"10.1038/s44303-024-00054-y","url":null,"abstract":"The complexity of the brain organization and the unique architecture of neurons have motivated neuroscientists to stay at the forefront of cellular microscopy and rapidly take advantage of technical developments in this field. Among these developments, super-resolution microscopy has transformed our understanding of neurobiology by allowing us to image identified macromolecular scaffolds and complexes directly in cells. Super-resolution microscopy approaches have thus provided key insights into the organization and functions of the neuronal cytoskeleton and its unique nanostructures. These insights are the focus of our review, where we attempt to provide a panorama of super-resolution microscopy applications to the study of the neuronal cytoskeleton, delineating the progress they have made possible and the current challenges they meet.","PeriodicalId":501709,"journal":{"name":"npj Imaging","volume":" ","pages":"1-15"},"PeriodicalIF":0.0,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44303-024-00054-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142789403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-04DOI: 10.1038/s44303-024-00055-x
Brice Wang, Tianle Ma, Theresa Chen, Trinh Nguyen, Ethan Crouse, Stephen J. Fleming, Alison S. Walker, Vera Valakh, Ralda Nehme, Evan W. Miller, Samouil L. Farhi, Mehrtash Babadi
Voltage imaging is a powerful technique for studying neuronal activity, but its effectiveness is often constrained by low signal-to-noise ratios (SNR). Traditional denoising methods, such as matrix factorization, impose rigid assumptions about noise and signal structures, while existing deep learning approaches fail to fully capture the rapid dynamics and complex dependencies inherent in voltage imaging data. Here, we introduce CellMincer, a novel self-supervised deep learning method specifically developed for denoising voltage imaging datasets. CellMincer operates by masking and predicting sparse pixel sets across short temporal windows and conditions the denoiser on precomputed spatiotemporal auto-correlations to effectively model long-range dependencies without large temporal contexts. We developed and utilized a physics-based simulation framework to generate realistic synthetic datasets, enabling rigorous hyperparameter optimization and ablation studies. This approach highlighted the critical role of conditioning on spatiotemporal auto-correlations, resulting in an additional 3-fold SNR gain. Comprehensive benchmarking on both simulated and real datasets, including those validated with patch-clamp electrophysiology (EP), demonstrates CellMincer’s state-of-the-art performance, with substantial noise reduction across the frequency spectrum, enhanced subthreshold event detection, and high-fidelity recovery of EP signals. CellMincer consistently outperforms existing methods in SNR gain (0.5–2.9 dB) and reduces SNR variability by 17–55%. Incorporating CellMincer into standard workflows significantly improves neuronal segmentation, peak detection, and functional phenotype identification, consistently surpassing current methods in both SNR gain and consistency.
{"title":"Robust self-supervised denoising of voltage imaging data using CellMincer","authors":"Brice Wang, Tianle Ma, Theresa Chen, Trinh Nguyen, Ethan Crouse, Stephen J. Fleming, Alison S. Walker, Vera Valakh, Ralda Nehme, Evan W. Miller, Samouil L. Farhi, Mehrtash Babadi","doi":"10.1038/s44303-024-00055-x","DOIUrl":"10.1038/s44303-024-00055-x","url":null,"abstract":"Voltage imaging is a powerful technique for studying neuronal activity, but its effectiveness is often constrained by low signal-to-noise ratios (SNR). Traditional denoising methods, such as matrix factorization, impose rigid assumptions about noise and signal structures, while existing deep learning approaches fail to fully capture the rapid dynamics and complex dependencies inherent in voltage imaging data. Here, we introduce CellMincer, a novel self-supervised deep learning method specifically developed for denoising voltage imaging datasets. CellMincer operates by masking and predicting sparse pixel sets across short temporal windows and conditions the denoiser on precomputed spatiotemporal auto-correlations to effectively model long-range dependencies without large temporal contexts. We developed and utilized a physics-based simulation framework to generate realistic synthetic datasets, enabling rigorous hyperparameter optimization and ablation studies. This approach highlighted the critical role of conditioning on spatiotemporal auto-correlations, resulting in an additional 3-fold SNR gain. Comprehensive benchmarking on both simulated and real datasets, including those validated with patch-clamp electrophysiology (EP), demonstrates CellMincer’s state-of-the-art performance, with substantial noise reduction across the frequency spectrum, enhanced subthreshold event detection, and high-fidelity recovery of EP signals. CellMincer consistently outperforms existing methods in SNR gain (0.5–2.9 dB) and reduces SNR variability by 17–55%. Incorporating CellMincer into standard workflows significantly improves neuronal segmentation, peak detection, and functional phenotype identification, consistently surpassing current methods in both SNR gain and consistency.","PeriodicalId":501709,"journal":{"name":"npj Imaging","volume":" ","pages":"1-21"},"PeriodicalIF":0.0,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44303-024-00055-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142789399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-21DOI: 10.1038/s44303-024-00057-9
Siyang Cheng, Yuya Nakatani, Gabriella Gagliano, Nahima Saliba, Anna-Karin Gustavsson
Single-molecule localization microscopy has revealed cellular architectures and molecular dynamics beyond the diffraction limit of light. However, imaging thick samples presents challenges from increased fluorescence background. Light sheet illumination, which utilizes a plane of light for optical sectioning, is effective in reducing fluorescence background, photobleaching, and photodamage. Here, we present the principles of single-molecule localization microscopy and light sheet illumination, followed by an introduction to light sheet microscopy geometries and their imaging applications. Finally, we discuss light sheet illumination approaches for high- and super-resolution imaging of biological structures and dynamics.
{"title":"Light sheet illumination in single-molecule localization microscopy for imaging of cellular architectures and molecular dynamics","authors":"Siyang Cheng, Yuya Nakatani, Gabriella Gagliano, Nahima Saliba, Anna-Karin Gustavsson","doi":"10.1038/s44303-024-00057-9","DOIUrl":"10.1038/s44303-024-00057-9","url":null,"abstract":"Single-molecule localization microscopy has revealed cellular architectures and molecular dynamics beyond the diffraction limit of light. However, imaging thick samples presents challenges from increased fluorescence background. Light sheet illumination, which utilizes a plane of light for optical sectioning, is effective in reducing fluorescence background, photobleaching, and photodamage. Here, we present the principles of single-molecule localization microscopy and light sheet illumination, followed by an introduction to light sheet microscopy geometries and their imaging applications. Finally, we discuss light sheet illumination approaches for high- and super-resolution imaging of biological structures and dynamics.","PeriodicalId":501709,"journal":{"name":"npj Imaging","volume":" ","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44303-024-00057-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142679997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-13DOI: 10.1038/s44303-024-00053-z
Yuriko Mori, Emil Novruzov, Dominik Schmitt, Jens Cardinale, Tadashi Watabe, Peter L. Choyke, Abass Alavi, Uwe Haberkorn, Frederik L. Giesel
The discovery of fibroblast activation protein inhibitor positron emission tomography (FAPI-PET) has paved the way for a new class of PET tracers that target the tumor microenvironment (TME) rather than the tumor itself. Although 18F-fluorodeoxyglucose (FDG) is the most common PET tracer used in clinical imaging of cancer, multiple studies have now shown that the family of FAP ligands commonly outperform FDG in detecting cancers, especially those known to have lower uptake on FDG-PET. Moreover, FAPI-PET will have applications in benign fibrotic or inflammatory conditions. Thus, even while new FAPI-PET tracers are in development and applications are yet to enter clinical guidelines, a significant body of literature has emerged on FAPI-PET, suggesting it will have important clinical roles. This article summarizes the current state of clinical FAPI-PET imaging as well as potential uses as a theranostic agent.
成纤维细胞活化蛋白抑制剂正电子发射断层扫描（FAPI-PET）的发现，为针对肿瘤微环境（TME）而非肿瘤本身的新型 PET 示踪剂铺平了道路。虽然 18F-氟脱氧葡萄糖（FDG）是癌症临床成像中最常用的 PET 示踪剂，但多项研究表明，FAP 配体家族在检测癌症方面通常优于 FDG，尤其是那些已知在 FDG-PET 上摄取较低的癌症。此外，FAPI-PET 还可应用于良性纤维化或炎症。因此，尽管新的 FAPI-PET 示踪剂正在开发中，其应用也尚未进入临床指南，但关于 FAPI-PET 的大量文献已经出现，这表明它将发挥重要的临床作用。本文总结了 FAPI-PET 临床成像的现状以及作为治疗剂的潜在用途。
{"title":"Clinical applications of fibroblast activation protein inhibitor positron emission tomography (FAPI-PET)","authors":"Yuriko Mori, Emil Novruzov, Dominik Schmitt, Jens Cardinale, Tadashi Watabe, Peter L. Choyke, Abass Alavi, Uwe Haberkorn, Frederik L. Giesel","doi":"10.1038/s44303-024-00053-z","DOIUrl":"10.1038/s44303-024-00053-z","url":null,"abstract":"The discovery of fibroblast activation protein inhibitor positron emission tomography (FAPI-PET) has paved the way for a new class of PET tracers that target the tumor microenvironment (TME) rather than the tumor itself. Although 18F-fluorodeoxyglucose (FDG) is the most common PET tracer used in clinical imaging of cancer, multiple studies have now shown that the family of FAP ligands commonly outperform FDG in detecting cancers, especially those known to have lower uptake on FDG-PET. Moreover, FAPI-PET will have applications in benign fibrotic or inflammatory conditions. Thus, even while new FAPI-PET tracers are in development and applications are yet to enter clinical guidelines, a significant body of literature has emerged on FAPI-PET, suggesting it will have important clinical roles. This article summarizes the current state of clinical FAPI-PET imaging as well as potential uses as a theranostic agent.","PeriodicalId":501709,"journal":{"name":"npj Imaging","volume":" ","pages":"1-21"},"PeriodicalIF":0.0,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44303-024-00053-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142637014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-06DOI: 10.1038/s44303-024-00052-0
Xiaoyi Zhu, Luca Menozzi, Soon-Woo Cho, Junjie Yao
Photoacoustic microscopy (PAM) is a key implementation of photoacoustic imaging (PAI). PAM merges rich optical contrast with deep acoustic detection, allowing for broad biomedical research and diverse clinical applications. Recent advancements in PAM technology have dramatically improved its imaging speed, enabling real-time observation of dynamic biological processes in vivo and motion-sensitive targets in situ, such as brain activities and placental development. This review introduces the engineering principles of high-speed PAM, focusing on various excitation and detection methods, each presenting unique benefits and challenges. Driven by these technological innovations, high-speed PAM has expanded its applications across fundamental, preclinical, and clinical fields. We highlight these notable applications, discuss ongoing technical challenges, and outline future directions for the development of high-speed PAM.
{"title":"High speed innovations in photoacoustic microscopy","authors":"Xiaoyi Zhu, Luca Menozzi, Soon-Woo Cho, Junjie Yao","doi":"10.1038/s44303-024-00052-0","DOIUrl":"10.1038/s44303-024-00052-0","url":null,"abstract":"Photoacoustic microscopy (PAM) is a key implementation of photoacoustic imaging (PAI). PAM merges rich optical contrast with deep acoustic detection, allowing for broad biomedical research and diverse clinical applications. Recent advancements in PAM technology have dramatically improved its imaging speed, enabling real-time observation of dynamic biological processes in vivo and motion-sensitive targets in situ, such as brain activities and placental development. This review introduces the engineering principles of high-speed PAM, focusing on various excitation and detection methods, each presenting unique benefits and challenges. Driven by these technological innovations, high-speed PAM has expanded its applications across fundamental, preclinical, and clinical fields. We highlight these notable applications, discuss ongoing technical challenges, and outline future directions for the development of high-speed PAM.","PeriodicalId":501709,"journal":{"name":"npj Imaging","volume":" ","pages":"1-16"},"PeriodicalIF":0.0,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44303-024-00052-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142588296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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