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Photobleaching Enables Super-resolution Imaging of the FtsZ Ring in the Cyanobacterium <em>Prochlorococcus</em>
摘要： Super-resolution microscopy has been widely used to study protein interactions and subcellular structures in many organisms. In photosynthetic organisms, however, the lateral resolution of super-resolution imaging is only ~100 nm. The low resolution is mainly due to the high autofluorescence background of photosynthetic cells caused by high-intensity lasers that are required for super-resolution imaging, such as stochastic optical reconstruction microscopy (STORM). Here, we describe a photobleaching-assisted STORM method which was developed recently for imaging the marine picocyanobacterium Prochlorococcus. After photobleaching, the autofluorescence of Prochlorococcus is effectively reduced so that STORM can be performed with a lateral resolution of ~10 nm. Using this method, we acquire the in vivo three-dimensional (3-D) organization of the FtsZ protein and characterize four different FtsZ ring morphologies during the cell cycle of Prochlorococcus. The method we describe here might be adopted for the super-resolution imaging of other photosynthetic organisms.
关键词： Prochlorococcus,photobleaching,FtsZ ring,Immunology and Infection,STORM,cell division,cyanobacterium,super-resolution imaging,three-dimensional,Issue 141
更新于2025-09-23 15:21:01
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Automated Behavioral Analysis of Large <em>C. elegans</em> Populations Using a Wide Field-of-view Tracking Platform
摘要： Caenorhabditis elegans is a well-established animal model in biomedical research, widely employed in functional genomics and ageing studies. To assess the health and fitness of the animals under study, one typically relies on motility readouts, such as the measurement of the number of body bends or the speed of movement. These measurements usually involve manual counting, making it challenging to obtain good statistical significance, as time and labor constraints often limit the number of animals in each experiment to 25 or less. Since high statistical power is necessary to obtain reproducible results and limit false positive and negative results when weak phenotypic effects are investigated, efforts have recently been made to develop automated protocols focused on increasing the sensitivity of motility detection and multi-parametric behavioral profiling. In order to extend the limit of detection to the level needed to capture the small phenotypic changes that are often crucial in genetic studies and drug discovery, we describe here a technological development that enables the study of up to 5,000 individual animals simultaneously, increasing the statistical power of the measurements by about 1,000-fold compared to manual assays and about 100-fold compared to other available automated methods.
关键词： nematode library,neurodegeneration,amyloid formation,Alzheimer's disease,Drug discovery,Issue 141,phenotype-based screening,high-throughput screening,Immunology and Infection,C. elegans,large population analysis
更新于2025-09-09 09:28:46
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Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
摘要： We describe here a protocol for the label-free identification of lymphocyte subtypes using quantitative phase imaging and machine learning. Identification of lymphocyte subtypes is important for the study of immunology as well as diagnosis and treatment of various diseases. Currently, standard methods for classifying lymphocyte types rely on labeling specific membrane proteins via antigen-antibody reactions. However, these labeling techniques carry the potential risks of altering cellular functions. The protocol described here overcomes these challenges by exploiting intrinsic optical contrasts measured by 3D quantitative phase imaging and a machine learning algorithm. Measurement of 3D refractive index (RI) tomograms of lymphocytes provides quantitative information about 3D morphology and phenotypes of individual cells. The biophysical parameters extracted from the measured 3D RI tomograms are then quantitatively analyzed with a machine learning algorithm, enabling label-free identification of lymphocyte types at a single-cell level. We measure the 3D RI tomograms of B, CD4+ T, and CD8+ T lymphocytes and identified their cell types with over 80% accuracy. In this protocol, we describe the detailed steps for lymphocyte isolation, 3D quantitative phase imaging, and machine learning for identifying lymphocyte types.
关键词： lymphocyte identification,machine learning,holotomography,immune cell,immunology,Immunology and Infection,Quantitative phase imaging,optical diffraction tomography,holographic microscopy,label-free imaging
更新于2025-09-04 15:30:14