Biology subjects

Johnson, G. R.

Publications and source records attributed to Johnson, G. R..

6 recordsLinked to original sources

bioRxiv2021

Characterization of the utility of three nebulizers in investigating infectivity of airborne viruses

Laboratory-generated bioaerosols are widely used in aerobiology studies of viruses, however few comparisons of alternative nebulizers exist. We compared aerosol production and virus survival for a Collison nebulizer, vibrating mesh nebulizer (VMN), and hydraulic spray atomizer (HAS). We also measured the dry size distribution of the aerosols produced, calculated the droplet sizes before evaporation and the dry size distribution from normal saline solution. Dry count median diameters of 0.25, 0.63 and 0.76 {micro}m were found for normal saline from the Collison nebulizer, VMN and HSA, respectively. The volume median diameters were 2.91, 3.2 and 2.43 {micro}m, respectively. The effect of nebulization on the viability of two influenza A viruses (IAVs) (H1N1, H3N2) and human rhinovirus (HRV)-16, was assessed by direct nebulization into an SKC Biosampler. The HSA had least impact on surviving fractions (SFs) of H1N1 and H3N2 (89{+/-}5%, 94{+/-}3%), followed by the Collison nebulizer (82{+/-}2%, 82{+/-}3%). The VMN yielded SFs of 78{+/-}2% and 76{+/-}2%, respectively. Conversely, for HRV-16, the VMN produced higher SFs (86{+/-}15%). Our findings indicate that although the VMN had the greatest impact on IAV survival, it produced higher aerosol concentrations within the airborne-size range making it more suitable where high aerosol mass production is required. ImportanceViral respiratory tract infections cause millions of lost days of work and physician visits globally, accounting for significant morbidity and mortality. Respiratory droplet and droplet nuclei from infected hosts are the substantial potential carriers of such viruses within indoor environments. Laboratory-generated bioaerosols are applied in understanding the transmission and infection of viruses, simulating the physiological aspects of bioaerosol generation in a controlled environment. However, little comparative characterization exists for nebulizers used in infectious disease aerobiology, including Collison nebulizer, Vibrating mesh nebulizer, and hydraulic spray atomizer. This study characterized the physical features of aerosols generated by laboratory nebulizers, and their performance in producing aerosols at a size relevant to airborne transmission used in infectious disease aerobiology. We also determined the impact of nebulization mechanisms of these nebulizers on the viability of human respiratory viruses, including IAV H1N1, IAV H3N2 and HRV-16.

microbiology↗

bioRxiv2020

Robust integrated intracellular organization of the human iPS cell: where, how much, and how variable?

Despite the intimate link between cell organization and function, the principles underlying intracellular organization and the relation between organization, gene expression and phenotype are not well understood. We address this by creating a benchmark for mean cell organization and the natural range of cell-to-cell variation. This benchmark can be used for comparison to other normal or abnormal cell states. To do this, we developed a reproducible microscope imaging pipeline to generate a high-quality dataset of 3D, high-resolution images of over 200,000 live cells from 25 isogenic human induced pluripotent stem cell (hiPSC) lines from the Allen Cell Collection. Each line contains one fluorescently tagged protein, created via endogenous CRISPR/Cas9 gene editing, representing a key cellular structure or organelle. We used these images to develop a new multi-part and generalizable analysis approach of the locations, amounts, and variation of these 25 cellular structures. Taking an integrated approach, we found that both the extent to which a structures individual location varied ("stereotypy") and the extent to which the structure localized relative to all the other cellular structures ("concordance") were robust to a wide range of cell shape variation, from flatter to taller, smaller to larger, or less to more polarized cells. We also found that these cellular structures varied greatly in how their volumes scaled with cell and nuclear size. These analyses create a data-driven set of quantitative rules for the locations, amounts, and variation of 25 cellular structures within the hiPSC as a normal baseline for cell organization.

cell biology↗

bioRxiv2020

Cell states beyond transcriptomics: integrating structural organization and gene expression in hiPSC-derived cardiomyocytes

We present a quantitative co-analysis of RNA abundance and sarcomere organization in single cells and an integrated framework to predict subcellular organization states from gene expression. We used human induced pluripotent stem cell (hiPSC)-derived cardiomyocytes expressing mEGFP-tagged alpha-actinin-2 to develop quantitative image analysis tools for systematic and automated classification of subcellular organization. This captured a wide range of sarcomeric organization states within cell populations that were previously difficult to quantify. We performed RNA FISH targeting genes identified by single cell RNA sequencing to simultaneously assess the relationship between transcript abundance and structural states in single cells. Co-analysis of gene expression and sarcomeric patterns in the same cells revealed biologically meaningful correlations that could be used to predict organizational states. This study establishes a framework for multi-dimensional analysis of single cells to study the relationships between gene expression and subcellular organization and to develop a more nuanced description of cell states. Graphical AbstractTranscriptional profiling and structural classification was performed on human induced pluripotent stem cell-derived cardiomyocytes to characterize the relationship between transcript abundance and subcellular organization. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=199 SRC="FIGDIR/small/081083v1_ufig1.gif" ALT="Figure 1"> View larger version (60K): org.highwire.dtl.DTLVardef@8f89c4org.highwire.dtl.DTLVardef@19dc813org.highwire.dtl.DTLVardef@1ba7c20org.highwire.dtl.DTLVardef@2b3daa_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2019

Image-derived Models of Cell Organization Changes During Differentiation of PC12 Cells

Cellular differentiation is a complex process requiring the coordination of many cellular components. PC12 cells are a popular model system to study changes driving and accompanying neuronal differentiation. While significant attention has been paid to changes in transcriptional regulation and protein signaling, much less is known about the changes in cell organization that accompany PC12 differentiation. Fluorescence microscopy can provide extensive information about this, although photobleaching and phototoxicity frequently limit the ability to continuously observe changes in single cells over the many days that differentiation occurs. Here we describe a generative model of differentiation-associated changes in cell and nuclear shape and their relationship to mitochondrial distribution constructed from images of different cells at discrete time points. We show that our spherical harmonic-based model can accurately represent cell and nuclear shapes by measuring reconstruction errors. We then learn a regression model that relates cell and nuclear shape and mitochondrial distribution and observe that the predictive accuracy generally increases during differentiation. Most importantly, we propose a method, based on cell matching and linear interpolation in the shape space, to model the dynamics of cell differentiation using only static images. Without any prior knowledge, the method produces a realistic shape evolution process.\n\nAuthor Summary\n\nCellular differentiation is an important process that is challenging to study due to the number of organizational changes it includes and the different time scales over which it occurs. Fluorescent microscopy is widely used to study cell dynamics and differentiation, but photobleaching and phototoxicity often make it infeasible to continuously observe a single cell undergoing differentiation for several days. In this work, we described a method to model aspects of the dynamics of PC12 cell differentiation without continuous imaging. We constructed accurate representations of cell and nuclear shapes and quantified the relationships between shapes and mitochondrial distributions. We used these to construct a generative model and combined it with a matching process to infer likely sequences of the changes in single cells undergoing differentiation.

cell biology↗

bioRxiv2017

Building a 3D Integrated Cell

We present a conditional generative model for learning variation in cell and nuclear morphology and predicting the location of subcellular structures from 3D microscopy images. The model generalizes well to a wide array of structures and allows for a probabilistic interpretation of cell and nuclear morphology and structure localization from fluorescence images. We demonstrate the effectiveness of the approach by producing and evaluating photo-realistic 3D cell images using the generative model, and show that the conditional nature of the model provides the ability to predict the localization of unobserved structures, given cell and nuclear morphology. We additionally explore the models utility in a number of applications, including cellular integration from multiple experiments and exploration of variation in structure localization. Finally, we discuss the model in the context of foundational and contemporary work and suggest forthcoming extensions.

cell biology↗

bioRxiv2017

Three dimensional cross-modal image inference: label-free methods for subcellular structure prediction

Fluorescence microscopy has enabled imaging of key subcellular structures in living cells; however, the use of fluorescent dyes and proteins is often expensive, time-consuming, and damaging to cells. Here, we present a tool for the prediction of fluorescently labeled structures in live cells solely from 3D brightfield microscopy images. We show the utility of this approach in predicting several structures of interest from the same static 3D brightfield image, and show that the same tool can prospectively be used to predict the spatiotemporal position of these structures from a bright-field time series. This approach could also be useful in a variety of application areas, such as cross-modal image registration, quantification of live cell imaging, and determination of cell state changes.

cell biology↗