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Parton, R. M.

Publications and source records attributed to Parton, R. M..

10 recordsLinked to original sources

Deep super-resolution imaging of thick tissue using structured illumination with adaptive optics

Three-dimensional structured illumination microscopy (3D-SIM) doubles the resolution of fluorescence imaging in all directions and increases the image contrast. However, 3D-SIM has not been widely applied to imaging deep in thick tissues due to its sensitivity to sample-induced aberrations, making the method difficult to apply beyond 10 {micro}m in depth. Furthermore, 3D-SIM has not been available in an upright configuration, limiting its use for live imaging while manipulating the specimen, for example with electrophysiology. Here, we have overcome these barriers by developing a novel upright 3D-SIM system (termed Deep3DSIM) that incorporates adaptive optics for aberration correction and remote focusing, reducing artefacts, improving contrast, restoring resolution, and removing the need to move the specimen or the objective lens in volume imaging. These advantages are equally applicable to inverted 3D-SIM systems. We demonstrate high-quality 3D-SIM imaging in various samples, including an example of imaging 130 {micro}m into Drosophila brain.

cell biology↗

Systematic analysis of YFP gene traps reveals common discordance between mRNA and protein across the nervous system

While post-transcriptional control is thought to be required at the periphery of neurons and glia, its extent is unclear. Here, we investigate systematically the spatial distribution and expression of mRNA at single molecule sensitivity and their corresponding proteins of 200 YFP trap protein trap lines across the intact Drosophila nervous system. 98% of the genes studied showed discordance between the distribution of mRNA and the proteins they encode in at least one region of the nervous system. These data suggest that post-transcriptional regulation is very common, helping to explain the complexity of the nervous system. We also discovered that 68.5% of these genes have transcripts present at the periphery of neurons, with 9.5% at the glial periphery. Peripheral transcripts include many potential new regulators of neurons, glia and their interactions. Our approach is applicable to most genes and tissues and includes powerful novel data annotation and visualisation tools for post-transcriptional regulation. Brief outlineA novel high resolution and sensitive approach to systematically co-visualise the distribution of mRNAs and proteins in the intact nervous system reveals that post-transcriptional regulation of gene expression is very common. The rich data landscape is provided as a browsable resource (link), using Zegami, a cloud-based data exploration platform (link). Our solution provides a paradigm for the characterisation of post-transcriptional regulation of most genes and model systems. HighlightsO_LI196/200 (98%) Drosophila genes show discordant RNA and protein expression in at least one nervous system region C_LIO_LI137/200 (68.5%) mRNAs are present in at least one synaptic compartment C_LIO_LINovel localised mRNA and protein discovered in periphery of glial processes C_LIO_LINew paradigm for analysis of post-transcriptional regulation and data exploration C_LI

cell biology↗

Absolute quantitation of individual SARS-CoV-2 RNA molecules: a new paradigm for infection dynamics and variant differences

Despite an unprecedented global research effort on SARS-CoV-2, early replication events remain poorly understood. Given the clinical importance of emergent viral variants with increased transmission, there is an urgent need to understand the early stages of viral replication and transcription. We used single molecule fluorescence in situ hybridisation (smFISH) to quantify positive sense RNA genomes with 95% detection efficiency, while simultaneously visualising negative sense genomes, sub-genomic RNAs and viral proteins. Our absolute quantification of viral RNAs and replication factories revealed that SARS-CoV-2 genomic RNA is long-lived after entry, suggesting that it avoids degradation by cellular nucleases. Moreover, we observed that SARS-CoV-2 replication is highly variable between cells, with only a small cell population displaying high burden of viral RNA. Unexpectedly, the B.1.1.7 variant, first identified in the UK, exhibits significantly slower replication kinetics than the Victoria strain, suggesting a novel mechanism contributing to its higher transmissibility with important clinical implications. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/450133v2_ufig1.gif" ALT="Figure 1"> View larger version (55K): org.highwire.dtl.DTLVardef@10f7bf1org.highwire.dtl.DTLVardef@192214dorg.highwire.dtl.DTLVardef@c84916org.highwire.dtl.DTLVardef@1366287_HPS_FORMAT_FIGEXP M_FIG C_FIG In briefBy detecting nearly all individual SARS-CoV-2 RNA molecules, we quantified viral replication and defined cell susceptibility to infection. We discovered that a minority of cells show significantly elevated viral RNA levels and observed slower replication kinetics for the Alpha variant relative to the Victoria strain. Highlights O_LISingle molecule quantification of SARS-CoV-2 replication uncovers early infection kinetics C_LIO_LIThere is substantial heterogeneity between cells in rates of SARS-CoV-2 replication C_LIO_LIGenomic RNA is stable and persistent during the initial stages of infection C_LIO_LIB.1.1.7 variant replicates more slowly than the Victoria strain C_LI

microbiology↗

Microscope-Cockpit: Python-based bespoke microscopy for bio-medical science

We have developed "Microscope-Cockpit" (Cockpit), a highly adaptable open source user-friendly Python-based GUI environment for precision control of both simple and elaborate bespoke microscope systems. The user environment allows next-generation near-instantaneous navigation of the entire slide landscape for efficient selection of specimens of interest and automated acquisition without the use of eyepieces. Cockpit uses "Python-Microscope" (Microscope) for high-performance coordinated control of a wide range of hardware devices using open source software. Microscope also controls complex hardware devices such as deformable mirrors for aberration correction and spatial light modulators for structured illumination via abstracted device models. We demonstrate the advantages of the Cockpit platform using several bespoke microscopes, including a simple widefield system and a complex system with adaptive optics and structured illumination. A key strength of Cockpit is its use of Python, which means that any microscope built with Cockpit is ready for future customisation by simply adding new libraries, for example machine learning algorithms to enable automated microscopy decision making while imaging. HighlightsO_LIUser-friendly setup and use for simple to complex bespoke microscopes. C_LIO_LIFacilitates collaborations between biomedical scientists and microscope technologists. C_LIO_LITouchscreen for near-instantaneous navigation of specimen landscape. C_LIO_LIUses Python-Microscope, for abstracted open source hardware device control. C_LIO_LIWell-suited for user training of AI-algorithms for automated microscopy. C_LI

biophysics↗

Wavefront-sensorless adaptive optics with a laser-free spinning disk confocal microscope

Adaptive optics is being applied widely to a range of microscopies in order to improve imaging quality in the presence of specimen-induced aberrations. We present here the first implementation of wavefront-sensorless adaptive optics for a laser-free, aperture correlation, spinning disk microscope. This widefield method provides confocal-like optical sectioning through use of a patterned disk in the illumination and detection paths. Like other high-resolution microscopes, its operation is compromised by aberrations due to refractive index mismatch and variations within the specimen. Correction of such aberrations shows improved signal level, contrast and resolution.

cell biology↗

Democratising "Microscopi": a 3D printed automated XYZT fluorescence imaging system for teaching, outreach and fieldwork

Commercial fluorescence microscope stands and fully automated XYZt fluorescence imaging systems are generally beyond the limited budgets available for teaching and outreach. We have addressed this problem by developing "Microscopi", an accessible, affordable, DIY automated imaging system that is built from 3D printed and commodity off-the-shelf hardware, including electro-mechanical, computer and optical components. Our design features automated sample navigation and image capture with a simple web-based graphical user interface, accessible with a tablet or other mobile device. The light path can easily be switched between different imaging modalities. The open source Python-based control software allows the hardware to be driven as an integrated imaging system. Furthermore, the microscope is fully customisable, which also enhances its value as a learning tool. Here, we describe the basic design and demonstrate imaging performance for a range of easily sourced specimens. HighlightsO_LIPortable, low cost, self-build from 3D printed and commodity components C_LIO_LIMultimodal imaging: bright field, dark field, pseudo-phase and fluorescence C_LIO_LIAutomated XYZt imaging from a tablet or smartphone via a simple GUI C_LIO_LIWide ranging applications in teaching, outreach and fieldwork C_LIO_LIOpen source hardware and software design, allowing user modification C_LI

scientific communication and education↗

CryoSIM: super resolution 3D structured illumination cryogenic fluorescence microscopy for correlated ultra-structural imaging

Rapid cryo-preservation of biological specimens is the gold standard for visualising cellular structures in their true structural context. However, current commercial cryo-fluorescence microscopes are limited to low resolutions. To fill this gap, we have developed cryoSIM, a microscope for 3D super-resolution fluorescence cryo-imaging for correlation with cryo electron microscopy or cryo soft X-ray tomography. We provide the full instructions for replicating the instrument mostly from off-the-shelf components and accessible, user-friendly open source Python control software. Therefore, cryoSIM democratises the ability to detect molecules using super-resolution fluorescence imaging of cryo-preserved specimens for correlation with their cellular ultrastructure.

cell biology↗

Testing models of mRNA localization reveals robustness regulated by reducing transport between cells

Robust control of gene expression in both space and time is of central importance in the regulation of cellular processes, and for multicellular development. However, the mechanisms by which robustness is achieved are generally not identified or well understood. For example, mRNA localization by molecular-motor-driven transport is crucial for cell polarization in numerous contexts, but the regulatory mechanisms that enable this process to take place in the face of noise or significant perturbations are not fully understood. Here we use a combined experimental-theoretical approach to characterize the robustness of gurken/TGF-alpha mRNA localization in Drosophila egg chambers, where the oocyte and 15 surrounding nurse cells are connected in a stereotypic network via intracellular bridges known as ring canals. We construct a mathematical model that encodes simplified descriptions of the range of steps involved in mRNA localization, including production and transport between and within cells until the final destination in the oocyte. Using Bayesian inference, we calibrate this model using quantitative single molecule fluorescence in situ hybridization data. By analyzing both the steady state and dynamic behaviours of the model, we provide estimates for the rates of different steps of the localization process, as well as the extent of directional bias in transport through the ring canals. The model predicts that mRNA synthesis and transport must be tightly balanced to maintain robustness, a prediction which we tested experimentally using an over-expression mutant. Surprisingly, the over-expression mutant fails to display the anticipated degree of overaccumulation of mRNA in the oocyte predicted by the model. Through careful model-based analysis of quantitative data from the over-expression mutant we show evidence of saturation of transport of mRNA through ring canals. We conclude that this saturation engenders robustness of the localization process, in the face of significant variation in the levels of mRNA synthesis. Statement of significanceFor development to function correctly and reliably across a population, gene expression must be controlled robustly in a repeatable manner. How this robustness is achieved is not well understood. We use modelling to better study the localization of polarity determining transcripts (RNA) in fruit fly development. By calibrating our model with quantitative imaging data we are able to make experimentally testable predictions, comparison of which with data from a genetic mutant, reveals evidence that saturation of RNA transport contributes to the robustness of RNA localization.

developmental biology↗

IsoSense: frequency enhanced sensorless adaptive optics through structured illumination

We present IsoSense, a wavefront sensing method that mitigates sample dependency in image based sensorless adaptive optics applications in microscopy. Our method employs structured illumination to create additional, high spatial frequencies in the image through custom illumination patterns. This improves the reliability of image quality metric calculations and enables sensorless wavefront measurement even in samples with sparse spatial frequency content. We demonstrate the feasibility of IsoSense for aberration correction in a deformable mirror based structured illumination superresolution fluorescence microscope.

bioengineering↗

Brain Development: Machine Learning Analysis Of Individual Stem Cells In Live 3D Tissue

A major challenge in cell and developmental biology is the automated identification and quantitation of cells in complex multilayered tissues. We developed CytoCensus: an easily deployed implementation of supervised machine learning that extends convenient 2D \"point- and-click\" user training to 3D detection of cells in challenging datasets with ill-defined cell boundaries. In tests on these datasets, CytoCensus outperforms other freely available image analysis software in accuracy and speed of cell detection. We used CytoCensus to count stem cells and their progeny, and to quantify individual cell divisions from time-lapse movies of explanted Drosophila larval brains, comparing wild-type and mutant phenotypes. We further illustrate the general utility and future potential of CytoCensus by analysing the 3D organisation of multiple cell classes in Zebrafish retinal organoids and cell distributions in mouse embryos. CytoCensus opens the possibility of straightforward and robust automated analysis of developmental phenotypes in complex tissues.\n\nSummaryHailstone et al. develop CytoCensus, a \"point-and-click\" supervised machine-learning image analysis software to quantitatively identify defined cell classes and divisions from large multidimensional data sets of complex tissues. They demonstrate its utility in analysing challenging developmental phenotypes in living explanted Drosophila larval brains, mammalian embryos and zebrafish organoids. They further show, in comparative tests, a significant improvement in performance over existing easy-to-use image analysis software.\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=198 HEIGHT=200 SRC=\"FIGDIR/small/137406v4_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (93K):\norg.highwire.dtl.DTLVardef@68912corg.highwire.dtl.DTLVardef@11300bdorg.highwire.dtl.DTLVardef@95810corg.highwire.dtl.DTLVardef@14b77a6_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LICytoCensus: machine learning quantitation of cell types in complex 3D tissues\nC_LIO_LISingle cell analysis of division rates from movies of living Drosophila brains in 3D\nC_LIO_LIDiverse applications in the analysis of developing vertebrate tissues and organoids\nC_LIO_LIOutperforms other image analysis software on challenging, low SNR datasets tested\nC_LI

developmental biology↗