Biology subjects

Shebelut, C. W.

Publications and source records attributed to Shebelut, C. W..

2 recordsLinked to original sources

bioRxiv2022

An Automated Pipeline for Differential Cell Counts on Whole-Slide Bone Marrow Aspirate Smears

Pathologic diagnosis of bone marrow disorders relies in part on microscopic analysis of bone marrow aspirate (BMA) smears and manual counting of marrow nucleated cells to obtain a differential cell count (DCC). This manual process has significant limitations, including analysis of only a small subset of optimal slide areas and nucleated cells, and inter-observer variability due to differences in cell selection and classification. To address these shortcomings, we developed an automated machine learning-based pipeline for obtaining 11-component DCCs on whole-slide BMAs. This pipeline utilizes a sequential process of identifying optimal BMA regions with high proportions of marrow nucleated cells, detecting individual cells within these optimal areas, and classifying these cells into one of 11 DCC components. Convolutional neural network models were trained on 396,048 BMA region, 28,914 cell boundary, and 1,510,976 cell class images from manual annotations. The resulting automated pipeline produces 11-component DCCs that demonstrate high statistical and diagnostic concordance with manual DCCs among a heterogeneous group of testing BMA slides with varying pathologies and cellularities. Additionally, we show that automated analysis can reduce intra-slide variance in DCCs by analyzing the whole slide and marrow nucleated cells within optimal regions. Finally, pipeline outputs of region classification, cell detection, and cell classification can be visualized using whole-slide image analysis software. This study demonstrates the feasibility of a fully-automated pipeline for generating DCCs on scanned whole-slide BMA images, with the potential for improving the current standard of practice for utilizing BMA smears in the laboratory analysis of hematologic disorders.

pathology↗

bioRxiv2020

Flagellar structures from the bacterium Caulobacter crescentus and implications for phage {Phi}CbK predation of multi-flagellin bacteria

Caulobacter crescentus is a gram-negative alpha-proteobacterium that commonly lives in oligotrophic fresh and salt-water environments. C. crescentus is a host to many bacteriophages, including {phi}CbK and {phi}CbK-like bacteriophages, which first adsorb to cells by interaction with the bacterial flagellum. It is commonly thought that the six paralogs of the flagellin gene present in C. crescentus are important for bacteriophage evasion. Here, we show that deletion of specific flagellins in C. crescentus can indeed attenuate {phi}CbK adsorption efficiency, although no single deletion completely ablates {phi}CbK adsorption. Thus, bacteriophage {phi}CbK likely recognizes a common motif amongst the six known flagellins in C. crescentus with varying degrees of efficiency. Interestingly, we observe that most deletion strains still generate flagellar filaments, with the exception of a strain that contains only the most divergent flagellin, FljJ, or a strain that contains only FljN and FljO. To visualize the surface residues that are likely recognized by {phi}CbK, we determined two high-resolution structures of the FljK filament, with and without an amino acid substitution that induces straightening of the filament. We observe post-translational modifications on conserved surface threonine residues of FljK that are likely O-linked glycans. The possibility of interplay between these modifications and {phi}CbK adsorption is discussed. We also determined the structure of a filament composed of a heterogeneous mixture of FljK and FljL, the final resolution of which was limited to approximately 4.6 [A]. Altogether, this work builds a platform for future investigation of how phage {phi}CbK infects C. crescentus at the molecular level.

microbiology↗