Biology subjects

Johns, E.

Publications and source records attributed to Johns, E..

3 recordsLinked to original sources

bioRxiv2020

An in vivo reporter for tracking lipid droplet dynamics in transparent zebrafish

Lipid droplets are lipid storage organelles found in nearly all cell types from adipocytes to cancer cells. Although increasingly implicated in disease, current methods to study lipid droplets require fixation or static imaging which limits investigation of their rapid in vivo dynamics. To address this, we created a lipid droplet transgenic reporter in whole animals and cell culture by fusing tdTOMATO to Perilipin-2 (PLIN2), a lipid droplet structural protein. Expression of this transgene in transparent casper zebrafish enabled in vivo imaging of adipose depots responsive to nutrient deprivation and high-fat diet. Using this system, we tested novel regulators of lipolysis, revealing an unexpected role for nitric oxide in modulating adipocyte lipid droplets. Similarly, we expressed the PLIN2-tdTOMATO transgene in melanoma cells and found that the nitric oxide pathway also regulated lipid droplets in cancer. This model offers a tractable imaging platform to study lipid droplets across cell types and disease contexts.

cell biology↗

bioRxiv2020

A dynamic and spatially periodic micro-pattern of HES5 expression underlies the probability of neuronal differentiation in the mouse spinal cord

Ultradian oscillations of HES Transcription Factors (TFs) at the single cell level, enable cell state transitions. However, the tissue level organisation of HES5 dynamics in neurogenesis is unknown. Here, we analyse the expression of HES5 ex-vivo in the developing mouse ventral spinal cord and identify microclusters of 4-6 cells with positively correlated HES5 level and ultradian dynamics. These microclusters are spatially periodic along the dorsoventral axis and temporally dynamic, alternating between high and low expression with a supra-ultradian persistence time. We show that Notch signaling is required for temporal dynamics but not the spatial periodicity of HES5. Few Neurogenin-2 cells are observed per cluster, irrespective of high or low state, suggesting that the microcluster organization of HES5 enables the stable selection of differentiating cells. Computational modelling predicts that different cell coupling strengths underlie the HES5 spatial patterns and rate of differentiation, which is consistent with comparison between the motoneuron and interneuron progenitor domains. Our work shows a previously unrecognised spatiotemporal organisation of neurogenesis, emergent at the tissue level from the synthesis of single cell dynamics. SynopsisLive imaging of HES5 expression in the ventral mouse spinal cord together with computational modelling is used to identify and analyse spatially periodic HES5 micro-patterns that emerge from the synthesis of single cell dynamics. O_LIHES5 is expressed in spatially periodic microclusters along the dorsal-ventral axis in spinal cord that are dynamically maintained by Notch signalling. C_LIO_LIMicroclusters can arise, in part, from single cell oscillators that are synchronous and weakly coupled via Notch. C_LIO_LISpatial patterns are different between motorneuron and interneuron progenitor domains and the probability for progenitor differentiation is regulated by the coupling strength between cells. C_LIO_LINGN2 is also spatially periodic along the dorso-ventral axis and microclusters of HES5 may act to pick a single NGN2 high cell for differentiation. C_LI

developmental biology↗

bioRxiv2020

Learning to count: determining the stoichiometry of bio-molecular complexes using fluorescence microscopy and statistical modelling

Cellular biology occurs through myriad interactions between diverse molecular components, many of which assemble in to specific complexes. Various techniques can provide a qualitative survey of which components are found in a given complex. However, quantitative analysis of the absolute number of molecules within a complex (known as stoichiometry) remains challenging. Here we provide a novel method that combines fluorescence microscopy and statistical modelling to derive accurate molecular counts. We have devised a system in which a given biomolecule is differentially labelled with spectrally distinct fluorescent dyes (label A or B), which are then mixed such that B-labelled molecules are vastly outnumbered by those with label A. Complexes, containing this component, are then simply scored as either being positive or negative for label B. The frequency of positive complexes is directly related to the stoichiometry of interaction and molecular counts can be inferred by statistical modelling. We demonstrate this method using complexes of Adenovirus particles and monoclonal antibodies, achieving counts that are in excellent agreement with previous estimates. Beyond virology, this approach is readily transferable to other experimental systems and, therefore, provides a powerful tool for quantitative molecular biology. The statistical models used in our analysis are available here: https://github.com/sophiamersmann/molecular-counting, the raw data used for molecular counting can be found here: 10.5281/zenodo.3955142.

bioinformatics↗