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. We mitigated this limitation using deep learning to segment cells and subcellular ultrastructure in 3D FIB-SEM images of tumor biopsies obtained from patients with metastatic breast and pancreatic cancers. The ultrastructures, such as nuclei, nucleoli, mitochondria, endosomes, and lysosomes, are relatively better defined than their surroundings and can be segmented with high accuracy using a neural network trained with sparse manual labels. Cell segmentation, on the other hand, is much more challenging due to the lack of clear boundaries separating cells in the tissue. We adopted a multi-pronged approach combining detection, boundary propagation, and tracking for cell segmentation. Specifically, a neural network was employed to detect the intracellular space; optical flow was used to propagate cell boundaries across the z-stack from the nearest ground truth image in order to facilitate the separation of individual cells; finally, the filopodium-like protrusions were tracked to the main cells by calculating the intersection over union measure for all regions detected in consecutive images along z-stack and connecting regions with maximum overlap. The proposed cell segmentation methodology resulted in an average Dice score of 0.93. For nuclei, nucleoli, and mitochondria, the segmentation achieved Dice scores of 0.99, 0.98, and 0.86, respectively. The segmentation of FIB-SEM images will enable interpretative rendering and provide quantitative image features to be associated with relevant clinical variables.

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. The protocols and mounting strategies described in this workflow are compatible with 2D large format EM mapping, 3D focused ion beam-SEM and serial block face-SEM. The flexibility to use diverse imaging technologies available at most academic institutions makes this workflow useful and applicable for most life science samples. Volumetric analysis of the biopsies studied here revealed morphological, organizational and ultrastructural aspects of the tumor cells and surrounding environment that cannot be revealed by conventional 2D EM imaging. Our results indicate that although 2D EM is still an important tool in many areas of diagnostic pathology, 3D images of ultrastructural relationships between both normal and cancerous cells, in combination with their extracellular matrix, enables cancer researchers and pathologists to better understand the progression of the disease and identify potential therapeutic targets.

Effective reduction of non-specific binding of blood cells in a microfluidic chip for isolation of rare cancer cells.

The high purity of target cells enriched from blood samples plays an important role in the clinical detection of diseases. However, non-specific binding of blood cells in the isolated cell samples can complicate downstream molecular and genetic analysis. In this work, we report a simple solution to non-specific binding of blood cells by modifying the surface of microchips with a multilayer nanofilm, with the outmost layer containing both PEG brushes for reducing blood cell adhesion and antibodies for enriching target cells. This layer-by-layer (LbL) polysaccharide nanofilm was modified with neutravindin and then conjugated with a mixture of biotinylated PEG molecules and biotinylated antibodies. Using EpCAM-expressing and HER2-expressing cancer cells in blood as model platforms, we were able to dramatically reduce the non-specific binding of blood cells to approximately 1 cell per mm2 without sacrificing the high capture efficiency of the microchip. To support the rational extension of this approach to other applications for cell isolation and blood cell resistance, we conducted extensive characterization on the nanofilm formation and degradation, antifouling with PEG brushes and introducing functional antibodies. This simple, yet effective, approach can be applied to a variety of microchip applications that require high purity of sample cells containing minimal contamination from blood cells.