The in vitro micronucleus assay is a globally significant method for DNA damage quantification used for regulatory compound safety testing in addition to inter-individual monitoring of environmental, ...lifestyle and occupational factors. However, it relies on time-consuming and user-subjective manual scoring. Here we show that imaging flow cytometry and deep learning image classification represents a capable platform for automated, inter-laboratory operation. Images were captured for the cytokinesis-block micronucleus (CBMN) assay across three laboratories using methyl methanesulphonate (1.25–5.0 μg/mL) and/or carbendazim (0.8–1.6 μg/mL) exposures to TK6 cells. Human-scored image sets were assembled and used to train and test the classification abilities of the “DeepFlow” neural network in both intra- and inter-laboratory contexts. Harnessing image diversity across laboratories yielded a network able to score unseen data from an entirely new laboratory without any user configuration. Image classification accuracies of 98%, 95%, 82% and 85% were achieved for ‘mononucleates’, ‘binucleates’, ‘mononucleates with MN’ and ‘binucleates with MN’, respectively. Successful classifications of ‘trinucleates’ (90%) and ‘tetranucleates’ (88%) in addition to ‘other or unscorable’ phenotypes (96%) were also achieved. Attempts to classify extremely rare, tri- and tetranucleated cells with micronuclei into their own categories were less successful (≤ 57%). Benchmark dose analyses of human or automatically scored micronucleus frequency data yielded quantitation of the same equipotent concentration regardless of scoring method. We conclude that this automated approach offers significant potential to broaden the practical utility of the CBMN method across industry, research and clinical domains. We share our strategy using openly-accessible frameworks.
The 380-to-393-amino-acid glycoprotein I (gI) encoded by herpes simplex virus 1 (HSV-1) is a critical mediator for viral cell-to-cell spread and syncytium formation. Here we report a previously ...unrecognized aberrant form of gI in HSV-1-infected cells. Production of this molecule is independent of cell type and viral strains. It had an unexpected gel migration size of approximately 23 kDa, was packaged into viral particles, and could be coimmunoprecipitated by antibodies to both N and C termini of gI. Deep sequencing failed to detect alternative RNA splicing, and the
transcribed full-length mRNA gave rise to the 23 kDa protein in transfected cells. Combined mass spectrometry and antibody probing analyses detected peptide information across different regions of gI, suggesting the possibility of a full-length gI but with abnormal migration behavior. In line with this notion, the HA insertion mutagenesis revealed a stable fold in the gI extracellular region aa.38-196 resistant to denaturing conditions, whereas small deletions within this region failed the antibodies to detect the fast, but not the slow-moving species of gI. It is also intriguing that the structure could be perturbed to some extent by a gBsyn mutation, leading to exposure or shielding of the gI epitopes. Thus, the HSV-1 gI apparently adopts a very stable fold in its natural form, rendering it an unusual biophysical property. Our findings provide novel insight into the biological properties of HSV gI and have important implications in understanding the viral spread and pathogenesis.
The HSV-1 gI is required for viral cell-to-cell spread within the host, but its behavior during infection has remained poorly defined. Along with the classic 66 kDa product, here we report a previously unrecognized, approximately 23 kDa form of gI. Biochemical and genetics analyses revealed that this molecule represents the full-length form of gI but adopts a stable fold in its extracellular domain that is resistant to denatured conditions, thus contributing to the aberrant migration rate. Our results revealed a novel property of HSV-1 gI and have important implications in understanding viral pathogenesis.
Unlocking and quantifying fundamental biological processes through tissue microscopy requires accurate, in situ segmentation of all cells imaged. Currently, achieving this is complex and requires ...exogenous fluorescent labels that occupy significant spectral bandwidth, increasing the duration and complexity of imaging experiments while limiting the number of channels remaining to address the study’s objectives. We demonstrate that the excitation light reflected during routine confocal microscopy contains sufficient information to achieve accurate, label-free cell segmentation in 2D and 3D. This is achieved using a simple convolutional neural network trained to predict the probability that reflected light pixels belong to either nucleus, cytoskeleton, or background classifications. We demonstrate the approach across diverse lymphoid tissues and provide video tutorials demonstrating deployment in Python and MATLAB or via standalone software for Windows.
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•Cell segmentation of tissues can be achieved from reflected laser excitation light•Single-cell information is freely established for many tissue microscopy studies•Windows software is provided alongside extensive video tutorials and data
Across the biomedical sciences, there is an urgent need to move beyond qualitative imaging to quantitative, cell-based reporting of tissue microscopy data. Typically, cell segmentation requires fluorescent labeling of nucleus and cytoplasm, which limits the spectral bandwidth available for other reporter molecules. However, recent advances in deep-learning algorithms have transformed automated image classification, and this raises the possibility of proceeding with reduced image information. Here, we show that 2D and 3D cell segmentation of lymphoid tissues can be freely established from the reflected laser excitation light always present during routine confocal microscopy using entirely standard equipment.
Cell segmentation is an essential step in quantitative tissue microscopy. Wills et al. show this can be achieved simply using the reflected laser light always present during routine imaging by confocal microscopy. This frees up microscope channels and establishes single-cell information as an attainable start point for many tissue microscopy experiments.
The application of flow cytometry as a scoring platform for both in vivo and in vitro micronucleus (MN) studies has enabled the efficient generation of high quality datasets suitable for ...comprehensive assessment of dose-response. Using this information, it is possible to obtain precise estimates of the clastogenic potency of chemicals. We illustrate this by estimating the in vivo and the in vitro potencies of seven model clastogenic agents (melphalan, chlorambucil, thiotepa, 1,3-propane sultone, hydroxyurea, azathioprine and methyl methanesulfonate) by deriving BMDs using freely available BMD software (PROAST). After exposing male rats for 3 days with up to nine dose levels of each individual chemical, peripheral blood samples were collected on Day 4. These chemicals were also evaluated for in vitro MN induction by treating TK6 cells with up to 20 concentrations in quadruplicate. In vitro MN frequencies were determined via flow cytometry using a 96-well plate autosampler. The estimated in vitro and in vivo BMDs were found to correlate to each other. The correlation showed considerable scatter, as may be expected given the complexity of the whole animal model versus the simplicity of the cell culture system. Even so, the existence of the correlation suggests that information on the clastogenic potency of a compound can be derived from either whole animal studies or cell culture-based models of chromosomal damage. We also show that the choice of the benchmark response, i.e. the effect size associated with the BMD, is not essential in establishing the correlation between both systems. Our results support the concept that datasets derived from comprehensive genotoxicity studies can provide quantitative dose-response metrics. Such investigational studies, when supported by additional data, might then contribute directly to product safety investigations, regulatory decision-making and human risk assessment.
Purpose Transforming growth factor-β is a potent stimulator of extracellular matrix production. Several studies show that loss of transforming growth factor-β signaling decreases kidney, liver and ...lung fibrosis. However, the role of transforming growth factor-β signaling in bladder fibrosis is not entirely understood. We investigated the effect of stromal loss of such signaling in mice after partial bladder outlet obstruction. Materials and Methods We performed partial bladder outlet obstruction by urethral ligation in 5-week-old female Tgfbr2colTKO mice. These mice were compared to WT mice with partial bladder outlet obstruction and to WT nonobstructed controls. After 4 weeks and before sacrifice urodynamics were performed. Bladder tissue was harvested, and p-Smad2 and collagen (Masson's trichrome) staining were performed. Results Bladder compliance was increased in partially obstructed Tgfbr2colTKO mice and decreased in partially obstructed WT mice. The latter had increased smooth muscle hypertrophy and increased collagen deposition between smooth muscle bundles compared to those in Tgfbr2colTKO mice and nonobstructed controls. Transforming growth factor-β responsive collagen promoter activity was significantly decreased in Tgfbr2 knockout bladder stromal cells vs WT stromal cells. Conclusions Stromal loss of transforming growth factor-β signaling decreased collagen deposition after partial bladder outlet obstruction. In contrast to collagen production by recruited macrophages, stromal transforming growth factor-β signaling appears to be the primary source of fibrosis after partial bladder outlet obstruction. These findings further support the hypothesis that manipulating transforming growth factor-β signaling in bladder stromal cells would provide a future avenue for neuropathic bladder and bladder fibrosis treatment.
Many applications in biomedical research require the long-term identification and tracking of cells over time. In previous work we have demonstrated that by sequentially dosing a cell population with ...different emission wavelength nanoparticles it is possible to use the random number of nanoparticle loaded vesicles generated by the cells as a barcode for individual cells within the population. In this paper we develop a simple model to describe the number of codes that can be generated using this sequential loading protocol. The methodology is validated by comparison with experiment and subsequently used to predict the effect of varying the number of colors used to encode the cells and also to assess the effect of misreading the cellular code due to errors in imaging the vesicles.
Abstract
Use of imaging flow cytometry to assess induced DNA damage via the cytokinesis block micronucleus (CBMN) assay has thus far been limited to radiation dosimetry in human lymphocytes using ...high end, ‘ImageStream X’ series imaging cytometers. Its potential to enumerate chemically induced DNA damage using in vitro cell lines remains unexplored. In the present manuscript, we investigate the more affordable FlowSight® imaging cytometry platform to assess in vitro micronucleus (MN) induction in the human lymphoblastoid TK6 and metabolically competent MCL-5 cells treated with Methyl Methane Sulfonate (MMS) (0–5 µg/ml), Carbendazim (0–1.6 µg/ml), and BenzoaPyrene (BaP) (0–6.3 µg/ml) for a period of 1.5–2 cell-cycles. Cells were fixed, and nuclei and MN were stained using the fluorescent nuclear dye DRAQ5™. Image acquisition was carried out using a 20X objective on a FlowSight® imaging cytometer (Amnis, part of Merck Millipore) equipped with a 488 nm laser. Populations of ∼20000 brightfield cell images, alongside DRAQ5™ stained nuclei/MN were rapidly collected (≤10 min). Single, in-focus cells suitable for scoring were then isolated using the IDEAS® software. An overlay of the brightfield cell outlines and the DRAQ5 nuclear fluorescence was used to facilitate scoring of mono-, bi-, tri-, and tetra-nucleated cells with or without MN events and in context of the cytoplasmic boundary of the parent cell.
To establish the potential of the FlowSight® platform, and to establish ‘ground truth’ cell classification for the supervised machine learning based scoring algorithm that represents the next stage of our project, the captured images were scored manually. Alongside, MN frequencies were also derived using the ‘gold standard’ light microscopy and manual scoring. A minimum of 3000 bi-nucleated cells were assessed using both approaches. Using the benchmark dose approach, the comparability of genotoxic potency estimations for the different compounds and cell lines was assessed across the two scoring platforms as highly similar. This study therefore provides essential proof-of-concept that FlowSight® imaging cytometry is capable of reproducing the results of ‘gold standard’ manual scoring by light microscopy. We conclude that, with the right automated scoring algorithm, imaging flow cytometry could revolutionise the reportability and scoring throughput of the CBMN assay.
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