Automated Nanoparticle Count via Modified BlendMask Instance Segmentation on SEM Images

Migrasomes are newly discovered cellular organelles, first described in 2015, that are formed by migrating cells. During migration, cells leave behind long membrane tethers called retraction fibers. Numerous micrometer-scale vesicles grow from the tips or junctions of these fibers, which we have named migrasomes. Migrasomes play important roles in various physiological processes, including releasing signaling factors to ensure organ morphogenesis during zebrafish embryo development, transferring mRNAs among cells, disposing of damaged mitochondria from the cell to maintain cell homeostasis, and secreting pro-angiogenic molecules to promote angiogenesis during chicken embryo development. Migrasomes are beginning to attract the attention of researchers in multiple fields. Here, we summarize the most commonly used protocols for migrasome detection using fluorescence microscopy imaging, purification through density-gradient centrifugation, characterization using electron microscopy (EM) imaging and biochemical analysis, and manipulation of migrasomes by targeting integrins and tetraspanins. © 2023 Wiley Periodicals LLC. Basic Protocol 1: Detection and observation of migrasomes by fluorescence microscopy imaging Basic Protocol 2: Purification of migrasomes from cultured cell lines and embryos by density-gradient centrifugation Basic Protocol 3: Characterization of migrasomes by electron microscopy imaging and biochemical analysis.

Tension wood of poplar (Populus nigra) branches was studied by lightand electron microscopy. The characteristic features of tension wood such as wider growth rings, reduced vessel density and higher gross density were confirmed by our results. Based on a novel combination of transmission electron microscopy (TEM) imaging and image analysis, involving Fourier transformation, the orientation of cellulose microfibrils in the S2- and G-layer was determined. Within the G-layer microfibril angle (MFA) was parallel to the growth axis (0°). However, in the S2 it was 13° in tension wood fibres and 4° in normal wood fibres. With the exception of the relatively low fibril angle in the S2 of tension wood fibres (13°) the results are in good agreement with those of the literature.

This is a response to the letter by Friedrich Thinnes (1). As you point out, it is important to clarify the role of store-operated calcium (SOC) channels in VRAC (volume-regulated anion channel), because calcium entry through SOC channels seems to inhibit Cl− efflux through VRAC and further induces apoptosis of cancer cell line LNCaP cells (2–4). Some TRP (transient receptor potential) channels involved in SOC entry have been picked up as candidates for the SOC channel in LNCaP cells (5–7), but interaction between these channels and VRAC has not been sufficiently clarified. Furthermore, because Orai1 and STIM1 distribute ubiquitously in our body, they are expected to have a regulatory role over VRAC in various cells. Indeed, the SOC current in LNCaP cells exhibits some CRAC (Ca2+ release-activated Ca2+)-like properties (5). Their involvement in VRAC, however, has not yet been reported. Therefore, the present discussion should provide new insights as to how we can integrate ongoing research into VRAC and store-operated channels. In addition to the present three-dimensional structure of Orai1, we have described some TRP channel structures using EM (electron microscopy) image analysis (8, 9). The swollen structures of both store-operated Orai1 and TRPC3 channels should be able to accommodate and/or associate with various components, like the VRAC complex. Single particle reconstruction from EM images is very promising for the analysis of such “super” complexes because it does not require crystallization. Since volume control of cells is universal to physiological functions, including apoptosis in our body, single particle reconstruction of an SOC channel-VRAC “super complex” should enhance the analysis of cell-volume control machinery, which may be general to its related physiology.

Biological electron microscopy (EM) of specimens prepared using chemical fixation, heavy metal staining, and resin embedding has advanced the biosciences for over half a century. These chemical preparation methods are applied every day to research and clinical transmission EM, the rapidly growing discipline of 3D volume EM, for correlative EM with light and X-ray microscopy, and for freeze-substitution cryogenic preparation EM. While EM imaging and image analysis are becoming increasingly automated, most research labs still perform their specimen preparation manually using commodity labware not designed for EM of biospecimens. This report shows how the purpose-built labware for microscopy, the mPrep™ System and the ASP™ family of automated specimen processors, improves preparation reliability, cuts reagent consumption and user exposure, and reduces specimen preparation time and effort by up to 10-fold. This report examines how the Multiscale Microscopy Core at Oregon Health & Science University, a core lab specializing in cancer and biomedical research, uses the mPrep™ ASP™-2000 automated specimen processor to streamline and speed specimen preparation workflows for volume EM (including serial block face, focused ion beam, and array tomography SEM) and for transmission EM.

A study of the fine scale microstructure of PVC was carried out using a combination of high resolution transmission electron microscopy and digital image analysis techniques. The images obtained contained a degree of order of the approximate size and distribution as predicted by the microdomain model of crystallinity in PVC. The microdomain model for crystallinity in PVC has been built up from previous studies using various analytical techniques including wide- and small-angle x-ray diffraction and differential scanning calorimetry. Earlier studies using transmission electron microscopy did not find any direct evidence supporting this model. Significant advances in both electron microscope and image processing technology had taken place since the earlier microscopic studies. The TEM imaging and image analysis procedures that have been utilized in this research may be applicable to the imaging of very fine scale ordering in other polymers. © 1996 John Wiley & Sons, Inc.

Instance segmentation of mitochondria in electron microscopy images with a generalist deep learning model trained on a diverse dataset

A robust transformer-based pipeline of 3D cell alignment, denoise and instance segmentation on electron microscopy sequence images

The mechanical cell environment is a key regulator of biological processes . In living tissues, cells are embedded into the 3D extracellular matrix and permanently exposed to mechanical forces. Quantification of the cellular strain state in a 3D matrix is therefore the first step towards understanding how physical cues determine single cell and multicellular behaviour. The majority of cell assays are, however, based on 2D cell cultures that lack many essential features of the in vivo cellular environment. Furthermore, nondestructive measurement of substrate and cellular mechanics requires appropriate computational tools for microscopic image analysis and interpretation. Here, we present an experimental and computational framework for generation and quantification of the cellular strain state in 3D cell cultures using a combination of 3D substrate stretcher, multichannel microscopic imaging and computational image analysis. The 3D substrate stretcher enables deformation of living cells embedded in bead-labelled 3D collagen hydrogels. Local substrate and cell deformations are determined by tracking displacement of fluorescent beads with subsequent finite element interpolation of cell strains over a tetrahedral tessellation. In this feasibility study, we debate diverse aspects of deformable 3D culture construction, quantification and evaluation, and present an example of its application for quantitative analysis of a cellular model system based on primary mouse hepatocytes undergoing transforming growth factor (TGF-β) induced epithelial-to-mesenchymal transition.

Statistical parameter estimation theory: principles and simulation studies

Deep learning-based synapse counting and synaptic ultrastructure analysis of electron microscopy images

Bayesian analysis of individual electron microscopy images: Towards structures of dynamic and heterogeneous biomolecular assemblies

Due to the increased number of applications of both microscopic imaging and image analysis including biomedical studies, the design of specialized algorithms and tools to facilitate quantitative assessment of objects in the image content is of urgent need. Recently, a number of approaches ranging from object counting by machine learning methods to statistical image analysis have been suggested and successfully implemented to resolve the cell quantification problem. Here, we revisit the above problem considering samples where objects presented in the same images have to be explicitly distinguished and quantified without involving any dedicated experimental setting like differential fluorescent staining. We consider several possible classification criteria and show explicitly how their combination in a single algorithm can be used to improve results in complex images where single criteria-based rules inevitably fail. Finally, we suggest a possible approach for the analysis of non-homogeneous images based on combining object selection results for different threshold values thus enhancing the algorithm from multi-criteria to multi-threshold analysis. To demonstrate the performance of the suggested solutions, we show several prominent examples of complex structures ranging from images containing both live and apoptotic cells as well as containing mixtures of globular and fibrous forms of heat-shock protein IbpA.

Sparsity techniques in medical imaging

Based on macro-scopic observations of outcrop, microscopic examination of thin sections, Field Emission Scanning Electron Microscopy(FESEM) imaging analysis and Energy Dispersive X-ray Spectrometry (EDS) analysis, nine units of microbial carbonates has been recognized in Lower Cretaceous Shipu Group and their thicknesses increase gradually upwards as the volcanism strength decreasing. Unit 7 is the thickest association of microbial carbonates-volcanics. These microbial carbonates consist of stromatolites, spherulites and laminated micorbialites with common recrystallization and local dolomization and analcitization. Thanks to intensive silicification, microorganisms have been preserved in crystal lattice so well that the original microstructure even the chamber of microorganism can be observed clearly through Field Emission Scanning Electron Microscopy(FESEM) imaging analysis. Macro structure of microbialites are massive, domal or laminated and micro structure of them are fanshaped, wavy, crenulate or spherical.

Analysis of microscope images is a tedious work which requires patience and time, usually done manually by the microscopist after data collection. The results obtained in such a way might be biased by the human who performed the analysis. Here we introduce an approach of automatic image analysis, which is based on locally applied Fourier Transform and Machine Learning methods. In this approach, a whole image is scanned by a local moving window with defined size and the 2D Fourier Transform is calculated for each window. Then, all the Local Fourier Transforms are fed into Machine Learning processing. Firstly, a number of components in the data is estimated from Principal Component Analysis (PCA) Scree Plot performed on the data. Secondly, the data are decomposed blindly by Non-Negative Matrix Factorization (NMF) into interpretable spatial maps (loadings) and corresponding Fourier Transforms (factors). As a result, the microscopic image is analyzed and the features on the image are automatically discovered, based on the local changes in Fourier Transform, without human bias. The user selects only a size and movement of the scanning local window which defines the final analysis resolution. This automatic approach was successfully applied to analysis of various microscopic images with and without local periodicity i.e. atomically resolved High Angle Annular Dark Field (HAADF) Scanning Transmission Electron Microscopy (STEM) image of Au nanoisland of fcc and Au hcp phases, Scanning Tunneling Microscopy (STM) image of Au-induced reconstruction on Ge(001) surface, Scanning Electron Microscopy (SEM) image of metallic nanoclusters grown on GaSb surface, and Fluorescence microscopy image of HeLa cell line of cervical cancer. The proposed approach could be used to automatically analyze the local structure of microscopic images within a time of about a minute for a single image on a modern desktop/notebook computer and it is freely available as a Python analysis notebook and Python program for batch processing.