Microscopy (Oxford, England)最新文献

Genome-wide profiling of gene expression levels in cells, such as transcriptomics and proteomics, is a powerful experimental approach in modern biology, allowing not only efficient exploration of the genetic elements responsible for biological phenomena of interest, but also characterization of the global constraints behind plastic phenotypic changes of cells that accompany large-scale remodeling of omics profiles. To understand how individual cells change their molecular profiles to achieve specific phenotypic changes in phenomena such as differentiation, cancer metastasis and adaptation, it is crucial to characterize the dynamics of cellular phenotypes and omics profiles simultaneously at the single-cell level. Especially in the last decade, significant technical progress has been made in the in situ identification of omics profiles of cells on the microscope. However, most approaches still remain destructive and cannot unravel the post-measurement dynamics. In recent years, Raman spectroscopy-based methods for omics inference have emerged, allowing the characterization of genome-wide molecular profile dynamics in living cells. In this review, we give a brief overview of the recent development of imaging-based omics profiling methods. We then present the approach to infer omics profiles from single-cell Raman spectra. Since Raman spectra can be obtained from living cells in a non-destructive and non-staining manner, this method may open the door to live-cell omics.

Electron microscopy (EM) is known to be the only research equipment able to resolve the ultrastructure of cells, including intracellular organelles and synapses. Researchers studying the brain connectome have re-evaluated the value of EM. The development of new EM techniques and tools has been active in these two decades. In this review, based on these trends, currently available EM tools and recently developing new techniques are introduced.

Structural observations are essential for the advancement of life science. Volume electron microscopy has recently realized remarkable progress in the three-dimensional analyses of biological specimens for elucidating complex ultrastructures in several fields of life science. The advancements in volume electron microscopy technologies have led to improvements, including higher resolution, more stability and the ability to handle larger volumes. Although human applications of volume electron microscopy remain limited, the reported applications in various organs have already provided previously unrecognized features of human tissues and also novel insights of human diseases. Simultaneously, the application of volume electron microscopy to human studies faces challenges, including ethical and clinical hurdles, costs of data storage and analysis, and efficient and automated imaging methods for larger volume. Solutions including the use of residual clinical specimens and data analysis based on artificial intelligence would address those issues and establish the role of volume electron microscopy in human structural research. Future advancements in volume electron microscopy are anticipated to lead to transformative discoveries in basic research and clinical practice, deepening our understanding of human health and diseases for better diagnostic and therapeutic strategies.

The three-dimensional (3D) anatomical structure of living organisms is intrinsically linked to their functions, yet modern life sciences have not fully explored this aspect. Recently, the combination of efficient tissue clearing techniques and light-sheet fluorescence microscopy for rapid 3D imaging has improved access to 3D spatial information in biological systems. This technology has found applications in various fields, including neuroscience, cancer research and clinical histopathology, leading to significant insights. It allows imaging of entire organs or even whole bodies of animals and humans at multiple scales. Moreover, it enables a form of spatial omics by capturing and analyzing cellome information, which represents the complete spatial organization of cells. While current 3D imaging of cleared tissues has limitations in obtaining sufficient molecular information, emerging technologies such as multi-round tissue staining and super-multicolor imaging are expected to address these constraints. 3D imaging using tissue clearing and light-sheet microscopy thus offers a valuable research tool in the current and future life sciences for acquiring and analyzing large-scale biological spatial information.

The brain is an intricate neuronal network that orchestrates our thoughts, emotions and actions through dynamic interactions between neurons. If we could record the activity of all neurons simultaneously in detail, it could revolutionize our understanding of brain function and lead to breakthroughs in treating neurological diseases. Recent technological innovations, particularly in large field-of-view two-photon microscopes, have made it possible to record the activity of tens of thousands of neurons simultaneously. However, the size and complexity of the datasets present significant challenges in extracting interpretable information. Conventional analysis methods are often insufficient, necessitating the development of new theoretical frameworks and computational efficiencies. In this review, we describe the characteristics of the data obtained from advanced imaging techniques and discuss analytical methods to facilitate mutual understanding between experimentalists and theorists. This interdisciplinary approach is crucial for effectively managing and interpreting large-scale neural activity datasets, ultimately advancing our understanding of brain function.

Large-scale reconstitution of neuronal circuits from volumetric electron microscopy images is a remarkable research goal in neuroanatomy. However, the large-scale reconstruction is a result of automatic segmentation using convolutional neural networks (CNNs), which is still challenging for general researchers to perform. This review focuses on two representative CNNs for dense neuronal segmentation: flood-filling networks (FFNs) and local shape descriptors (LSDs)-predicting U-Net (LSD network). It outlines their basic mechanisms, requirements, and output segmentation using the author's example segmentation. The FFN excels in segmenting long axons, and the LSD network is adept at segmenting myelinated axons. The choice between FFN and LSD depends on the target, as neither is universally superior. A common limitation of FFN and LSD is the easy detachment of thin spines from parent dendrites, which is fundamentally unavoidable. The author also introduces CNNs that were proposed to mitigate this issue. As CNN-based automated segmentation can take months, researchers need to be aware of the selection of an appropriate CNN, required computer resources and fundamental limitations. This review serves as a guide for such dense neuronal segmentation.

Recent advancements in imaging technologies have enabled the acquisition of high-quality, voluminous, multidimensional image data. Among these, light-sheet microscopy stands out for its ability to capture dynamic biological processes over extended periods and across large volumes, owing to its exceptional three-dimensional resolution and minimal invasiveness. However, handling and analyzing these vast datasets present significant challenges. Current computing environments struggle with high storage and computational demands, while traditional analysis methods relying heavily on human intervention are proving inadequate. Consequently, there is a growing shift toward automated solutions using artificial intelligence (AI), encompassing machine learning (ML) and other approaches. Although these technologies show promise, their application in extensive light-sheet imaging data analysis remains limited. This review explores the potential of light-sheet microscopy to revolutionize the life sciences through advanced imaging, addresses the primary challenges in data handling and analysis and discusses potential solutions, including the integration of AI and ML technologies.

Electron microscopy using an environmental cell is a powerful tool for observing catalysts and other nanomaterials in gases and liquids. An environmental cell must contain amorphous silicon-nitride membranes because they protect the sample environment from the vacuum of the electron microscope and enable the electron beam to pass through the cell. However, the membranes superimpose non-uniform contrast on the projected image, degrading image quality. We propose a method for removing the noise derived from the membranes using Noise2Noise, a deep-learning method, for a series of transmission-electron-microscope images with slight electron-beam tilt and evaluated its effectiveness. We succeeded in removing the membrane-derived noise while retaining the information of the sample in the cell. We also succeeded in efficiently removing Poisson noise. We believe this method will enable measurements requiring high signal-to-noise ratios, which could previously only be observed in a vacuum, to be conducted in an environmental cell.

Organosilica nanoparticles are considered one of the promising nanomaterials for biomedical imaging and clinical applications due to their tunable properties, biocompatibility, and multimodal imaging ability. In this review, we summarize the synthesis and functionalization of organosilica nanoparticles with a particular focus on their importance in biomedical imaging. By their high fluorescence intensity and unique photostability, organosilica nanoparticles provide capabilities for high-resolution and long-term imaging for in vivo, mesoscopic, and microscopic applications. In addition, surface modifications of organosilica nanoparticles control cellular interactions, facilitating the accurate monitoring of cellular uptake, mitochondrial activity, and endosomal sorting. Incorporating recent progress and experimental results, this review summarizes the multiformity and extensive prospects of organosilica nanoparticle-based imaging modalities and offers perspectives on future development in nanoparticle-driven biomedical imaging and therapeutic strategies.

Three-dimensional (3D) reconstruction is time-consuming owing to segmentation work. We evaluated the accuracy of the artificial intelligence (AI)-based segmentation and tracking model SAM-Track for segmentation of anatomical or histological structures and explored the potential of AI to enhance research efficiency. Images [obtained via computed tomography (CT) and magnetic resonance imaging (MRI)], anatomical sections from a Visible Korean Human open resource, and serial histological section images of cadavers were obtained. Six structures in the CT, MRI, and anatomical sections and seven in the histological sections were segmented using SAM-Track and compared with manual segmentation by calculating the Dice similarity coefficient. Segmented images were then reconstructed three dimensionally. The average Dice scores of CT and MRI results varied (0.13-0.83); anatomical sections showed mostly good accuracy (0.31-0.82). Clear-edged structures, such as the femur and liver, had high scores (0.69-0.83). In contrast, soft tissue structures, such as the rectus femoris and stomach, had variable accuracy (0.38-0.82). Histological sections showed high accuracy, especially for well-delineated tissues, such as the tibia and pancreas (0.95, 0.90). However, the tracking of branching structures, such as arteries and veins, was less successful (0.72, 0.52). In 3D reconstruction, high Dice scores were associated with accurate shapes, whereas low scores indicated discrepancies between the predicted and true shapes. AI-based automatic segmentation using SAM-Track provides moderate-to-good accuracy for anatomical and histological structures and is beneficial for conducting morphological studies involving 3D reconstruction.