Recovery and Analysis of Bacterial Membrane Vesicle Nanoparticles from Human Plasma Using Dielectrophoresis.

Bacterial membrane vesicle (BMV) nanoparticles are secreted naturally by bacteria throughout their lifecycle and are a rich source of biomarkers from the parent bacteria, but they are currently underutilized for clinical diagnostic applications, such as pathogen identification, due to the time-consuming and low-yield nature of traditional recovery methods required for analysis. The recovery of BMVs is particularly difficult from complex biological fluids. Here, we demonstrate a recovery method that uses dielectrophoretic (DEP) forces generated on electrokinetic microfluidic chips to isolate and analyze BMVs from human plasma.

Segmentation of cellular ultrastructures on sparsely labeled 3D electron microscopy images using deep learning.

Focused ion beam-scanning electron microscopy (FIB-SEM) images can provide a detailed view of the cellular ultrastructure of tumor cells. A deeper understanding of their organization and interactions can shed light on cancer mechanisms and progression. However, the bottleneck in the analysis is the delineation of the cellular structures to enable quantitative measurements and analysis.

Efficient semi-supervised semantic segmentation of electron microscopy cancer images with sparse annotations.

Electron microscopy (EM) enables imaging at a resolution of nanometers and can shed light on how cancer evolves to develop resistance to therapy. Acquiring these images has become a routine task.However, analyzing them is now a bottleneck, as manual structure identification is very time-consuming and can take up to several months for a single sample.

Towards Generalizability and Robustness in Biological Object Detection in Electron Microscopy Images.

Machine learning approaches have the potential for meaningful impact in the biomedical field. However, there are often challenges unique to biomedical data that prohibits the adoption of these innovations. For example, limited data, data volatility, and data shifts all compromise model robustness and generalizability.

Efficient semi-supervised semantic segmentation of electron microscopy cancer images with sparse annotations.

Electron microscopy (EM) enables imaging at nanometer resolution and can shed light on how cancer evolves to develop resistance to therapy. Acquiring these images has become a routine task; however, analyzing them is now the bottleneck, as manual structure identification is very time-consuming and can take up to several months for a single sample. Deep learning approaches offer a suitable solution to speed up the analysis.

Automated large volume sample preparation for vEM.

New developments in electron microscopy technology, improved efficiency of detectors, and artificial intelligence applications for data analysis over the past decade have increased the use of volume electron microscopy (vEM) in the life sciences field. Moreover, sample preparation methods are continuously being modified by investigators to improve final sample quality, increase electron density, combine imaging technologies, and minimize the introduction of artifacts into specimens under study. There are a variety of conventional bench protocols that a researcher can utilize, though most of these protocols require several days.

An omic and multidimensional spatial atlas from serial biopsies of an evolving metastatic breast cancer.

Mechanisms of therapeutic resistance and vulnerability evolve in metastatic cancers as tumor cells and extrinsic microenvironmental influences change during treatment. To support the development of methods for identifying these mechanisms in individual people, here we present an omic and multidimensional spatial (OMS) atlas generated from four serial biopsies of an individual with metastatic breast cancer during 3.5 years of therapy.

Multiscale cardiac imaging spanning the whole heart and its internal cellular architecture in a small animal model.

Cardiac pumping depends on the morphological structure of the heart, but also on its subcellular (ultrastructural) architecture, which enables cardiac contraction. In cases of congenital heart defects, localized ultrastructural disruptions that increase the risk of heart failure are only starting to be discovered. This is in part due to a lack of technologies that can image the three-dimensional (3D) heart structure, to assess malformations; and its ultrastructure, to assess organelle disruptions.

A workflow for visualizing human cancer biopsies using large-format electron microscopy.

Recent developments in large format electron microscopy have enabled generation of images that provide detailed ultrastructural information on normal and diseased cells and tissues. Analyses of these images increase our understanding of cellular organization and interactions and disease-related changes therein. In this manuscript, we describe a workflow for two-dimensional (2D) and three-dimensional (3D) imaging, including both optical and scanning electron microscopy (SEM) methods, that allow pathologists and cancer biology researchers to identify areas of interest from human cancer biopsies.

A fully integrated, three-dimensional fluorescence to electron microscopy correlative workflow.

While fluorescence microscopy provides tools for highly specific labeling and sensitive detection, its resolution limit and lack of general contrast has hindered studies of cellular structure and protein localization. Recent advances in correlative light and electron microscopy (CLEM), including the fully integrated CLEM workflow instrument, the FEI CorrSight with MAPS, have allowed for a more reliable, reproducible, and quicker approach to correlate three-dimensional time-lapse confocal fluorescence data, with three-dimensional focused ion beam-scanning electron microscopy data. Here we demonstrate the entire integrated CLEM workflow using fluorescently tagged MCF7 breast cancer cells.