scholarly journals RS-FISH: Precise, interactive, fast, and scalable FISH spot detection

2021 ◽  
Author(s):  
Stephan Preibisch ◽  
Ella Bahry ◽  
Laura Breimann ◽  
Marwan Zouinkhi ◽  
Leo Epstein ◽  
...  
Keyword(s):  
Single Molecule ◽  
Distributed Processing ◽  
Parameter Tuning ◽  
Radial Symmetry ◽  
Three Dimensions ◽  
Accurate Identification ◽  
Brain Scans ◽  
Large Sets ◽  
Spot Detection ◽  
Subcellular Resolution

Abstract Fluorescent in-situ hybridization (FISH)-based methods are powerful tools to study molecular processes with subcellular resolution, relying on accurate identification and localization of diffraction-limited spots in microscopy images. We developed the Radial Symmetry-FISH (RS-FISH) software that accurately, robustly, and quickly detects single-molecule spots in two and three dimensions, making it applicable to several key assays, including single-molecule FISH (smFISH), spatial transcriptomics, and spatial genomics. RS-FISH allows interactive parameter tuning and scales to large sets of images as well as tera-byte sized image volumes such as entire brain scans using straight-forward distributed processing on workstations, clusters, and in the cloud.

scholarly journals RS-FISH: Precise, interactive and scalable smFISH spot detection using Radial Symmetry

2021 ◽  
Author(s):  
Ella Bahry ◽  
Laura Breimann ◽  
Leo Epstein ◽  
Klim Kolyvanov ◽  
Kyle I. S. Harrington ◽  
...  
Keyword(s):  
Single Molecule ◽  
Parameter Tuning ◽  
Radial Symmetry ◽  
Three Dimensions ◽  
Powerful Method ◽  
Accurate Identification ◽  
Large Sets ◽  
Rna Fish ◽  
Spot Detection ◽  
Microscopy Images

AbstractStudying transcription using single-molecule RNA-FISH (smFISH) is a powerful method to gain insights into gene regulation on a single cell basis, which relies on accurate identification of sub-resolution fluorescent spots in microscopy images. Here we present Radial Symmetry-FISH (RS-FISH), which can robustly and quickly detect even close smFISH spots in two and three dimensions with high precision, allows interactive parameter tuning, and can easily be applied to large sets of images.Availability and implementationRS-FISH is an open-source implementation written in Java/ImgLib2 and provided as a macro-scriptable Fiji plugin. Source code, tutorial, documentation, and example images are available at: https://github.com/PreibischLab/RadialSymmetryLocalization

scholarly journals DeepSpot: a deep neural network for RNA spot enhancement in smFISH microscopy images

2021 ◽  
Author(s):  
Emmanuel Bouilhol ◽  
Edgar Lefevre ◽  
Benjamin Dartigues ◽  
Robyn Brackin ◽  
Anca F Savulescu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Single Molecule ◽  
Parameter Tuning ◽  
Small Object ◽  
Learning Approaches ◽  
Wound Healing Assay ◽  
Detection Thresholds ◽  
Spot Detection ◽  
Small Object Detection ◽  
Microscopy Images

Detection of RNA spots in single molecule FISH microscopy images remains a difficult task especially when applied to large volumes of data. The small size of RNA spots combined with high noise level of images often requires a manual adaptation of the spot detection thresholds for each image. In this work we introduce DeepSpot, a Deep Learning based tool specifically designed to enhance RNA spots which enables spot detection without need to resort to image per image parameter tuning. We show how our method can enable the downstream accurate detection of spots. The architecture of DeepSpot is inspired by small object detection approaches. It incorporates dilated convolutions into a module specifically designed for the Context Aggregation for Small Object (CASO) and uses Residual Convolutions to propagate this information along the network. This enables DeepSpot to enhance all RNA spots to the same intensity and thus circumvents the need for parameter tuning. We evaluated how easily spots can be detected in images enhanced by our method, by training DeepSpot on 20 simulated and 1 experimental datasets, and have shown that more than 97% accuracy is achieved. Moreover, comparison with alternative deep learning approaches for mRNA spot detection (deepBlink) indicated that DeepSpot allows more precise mRNA detection. In addition, we generated smFISH images from mouse fibroblasts in a wound healing assay to evaluate whether DeepSpot enhancement can enable seamless mRNA spot detection and thus streamline studies of localized mRNA expression in cells.

A Molecular Logic Gate Enables Super-Resolved Imaging of Intracellular Lipid Droplets

2019 ◽  
Author(s):  
Adam Eördögh ◽  
Carolina Paganini ◽  
Dorothea Pinotsi ◽  
Paolo Arosio ◽  
Pablo Rivera-Fuentes
Keyword(s):  
Single Molecule ◽  
Lipid Droplets ◽  
Neutral Lipids ◽  
Logic Gate ◽  
Living Cells ◽  
Three Dimensions ◽  
Single Molecule Imaging ◽  
Molecular Logic Gate ◽  
Molecular Logic ◽  
Cellular Compartments

<div>Photoactivatable dyes enable single-molecule imaging in biology. Despite progress in the development of new fluorophores and labeling strategies, many cellular compartments remain difficult to image beyond the limit of diffraction in living cells. For example, lipid droplets, which are organelles that contain mostly neutral lipids, have eluded single-molecule imaging. To visualize these challenging subcellular targets, it is necessary to develop new fluorescent molecular devices beyond simple on/off switches. Here, we report a fluorogenic molecular logic gate that can be used to image single molecules associated with lipid droplets with excellent specificity. This probe requires the subsequent action of light, a lipophilic environment and a competent nucleophile to produce a fluorescent product. The combination of these requirements results in a probe that can be used to image the boundary of lipid droplets in three dimensions with resolutions beyond the limit of diffraction. Moreover, this probe enables single-molecule tracking of lipids within and between droplets in living cells.</div>

scholarly journals Selective dendritic localization of mRNA in Drosophila mushroom body output neurons

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Jessica Mitchell ◽  
Carlas S Smith ◽  
Josh Titlow ◽  
Nils Otto ◽  
Pieter van Velde ◽  
...  
Keyword(s):  
Single Molecule ◽  
Nicotinic Acetylcholine Receptors ◽  
Neuronal Plasticity ◽  
Mushroom Body ◽  
Acetylcholine Receptors ◽  
Behavioural Change ◽  
Before And After ◽  
Specific Mrnas ◽  
Output Neurons ◽  
Subcellular Resolution

Memory-relevant neuronal plasticity is believed to require local translation of new proteins at synapses. Understanding this process requires the visualization of the relevant mRNAs within these neuronal compartments. Here we used single-molecule fluorescence in situ hybridization (smFISH) to localize mRNAs at subcellular resolution in the adult Drosophila brain. mRNAs for subunits of nicotinic acetylcholine receptors and kinases could be detected within the dendrites of co-labelled Mushroom Body Output Neurons (MBONs) and their relative abundance showed cell-specificity. Moreover, aversive olfactory learning produced a transient increase in the level of CaMKII mRNA within the dendritic compartments of the 52a MBONs. Localization of specific mRNAs in MBONs before and after learning represents a critical step towards deciphering the role of dendritic translation in the neuronal plasticity underlying behavioural change in Drosophila.

Enabling single-molecule localization microscopy in turbid food emulsions

2021 ◽  
Author(s):  
Abbas Jabermoradi ◽  
Suyeon Yang ◽  
Martijn Gobes ◽  
John P.M. van Duynhoven ◽  
Johannes Hohlbein
Keyword(s):  
Adaptive Optics ◽  
Single Molecule ◽  
Optical Resolution ◽  
Three Dimensions ◽  
Water Interface ◽  
Water Emulsion ◽  
Food Emulsions ◽  
Localization Microscopy ◽  
Oil Water ◽  
Single Molecule Localization Microscopy

Turbidity poses a major challenge for the microscopic characterization of many food systems. In these systems, local mismatches in refractive indices can cause reflection, absorption and scattering of incoming as well as outgoing light leading to significant image deterioration along sample depth. To mitigate the issue of turbidity and to increase the achievable optical resolution, we combined adaptive optics (AO) with single-molecule localization microscopy (SMLM). Building on our previously published open hardware microscopy framework, the miCube, we first added a deformable mirror to the detection path. This element enables both the compensation of aberrations directly from single-molecule data and, by further modulating the emission wavefront, the introduction of various point spread functions (PSFs) to enable SMLM in three dimensions. We further added a top hat beam shaper to the excitation path to obtain an even illumination profile across the field of view (FOV). As a model system for a non-transparent food colloid in which imaging in depth is challenging, we designed an oil-in-water emulsion in which phosvitin, a ferric ion binding protein present in from egg yolk, resides at the oil water interface. We targeted phosvitin with fluorescently labelled primary antibodies and used PSF engineering to obtain 2D and 3D images of phosvitin covered oil droplets with sub 100 nm resolution. Droplets with radii as low as 200 nm can be discerned, which is beyond the range of conventional confocal light microscopy. Our data indicated that in the model emulsion phosvitin is homogeneously distributed at the oil-water interface. With the possibility to obtain super-resolved images in depth of nontransparent colloids, our work paves the way for localizing biomacromolecules at colloidal interfaces in heterogeneous food emulsions.

Fast and precise algorithm based on maximum radial symmetry for single molecule localization

2012 ◽  
Vol 37 (13) ◽  
pp. 2481 ◽  
Author(s):  
Hongqiang Ma ◽  
Fan Long ◽  
Shaoqun Zeng ◽  
Zhen-Li Huang
Keyword(s):  
Single Molecule ◽  
Radial Symmetry

scholarly journals Surface kinematics of periglacial sorted circles using Structure-from-Motion technology

2013 ◽  
Vol 7 (6) ◽  
pp. 6043-6074 ◽  
Author(s):  
A. Kääb ◽  
L. Girod ◽  
I. Berthling
Keyword(s):  
Structure From Motion ◽  
Temporal Evolution ◽  
Horizontal Displacement ◽  
Radial Symmetry ◽  
Displacement Fields ◽  
Patterned Ground ◽  
Large Sets ◽  
Digital Elevation ◽  
Spatio Temporal ◽  
Changes Over Time

Abstract. Sorted soil circles are a conspicuous form of periglacial patterned ground. Numerical modelling suggests that these features develop from a convection-like circulation of material in the active layer of permafrost. The related iterative burying and resurfacing of material is believed to play an important role in the soil carbon cycle of high latitudes. The connection of sorted circles to permafrost conditions and its changes over time make these ground forms to a potential paleoclimatic indicator. In this study we apply the photogrammetric Structure-from-Motion technology (SfM) to large sets of overlapping terrestrial photos taken in Augusts 2007 and 2010 over three sorted circles at Kvadehuksletta, Western Spitsbergen. We retrieve repeat digital elevation models (DEMs) and orthoimages with millimetre-resolution and accuracy. Changes in microrelief over the three years are obtained from DEM-differencing and horizontal displacement fields from tracking features between the orthoimages. In the inner domains of the circles, consisting of fines, material moves radially outside with horizontal surface speeds of up to 2 cm yr−1. The outer circle ridges consist of coarse stones that displace towards the inner circle domain at similar rates. A number of substantial deviations from this overall radial symmetry, both in horizontal displacements and in microrelief, shed new light on the potential spatio-temporal evolution of sorted soil circles, and periglacial patterned ground in general.

scholarly journals Re-assembly, quality evaluation, and annotation of 678 microbial eukaryotic reference transcriptomes

2018 ◽  
Author(s):  
Lisa K. Johnson ◽  
Harriet Alexander ◽  
C. Titus Brown
Keyword(s):  
De Novo ◽  
Open Reading Frames ◽  
List Type ◽  
Accurate Identification ◽  
Sequencing Project ◽  
Large Sets ◽  
Rnaseq Data ◽  
Key Points ◽  
Computationally Intensive ◽  
Reading Frames

AbstractBackgroundDe novo transcriptome assemblies are required prior to analyzing RNAseq data from a species without an existing reference genome or transcriptome. Despite the prevalence of transcriptomic studies, the effects of using different workflows, or “pipelines”, on the resulting assemblies are poorly understood. Here, a pipeline was programmatically automated and used to assemble and annotate raw transcriptomic short read data collected by the Marine Microbial Eukaryotic Transcriptome Sequencing Project (MMETSP). The resulting transcriptome assemblies were evaluated and compared against assemblies that were previously generated with a different pipeline developed by the National Center for Genome Research (NCGR).ResultsNew transcriptome assemblies contained the majority of previous contigs as well as new content. On average, 7.8% of the annotated contigs in the new assemblies were novel gene names not found in the previous assemblies. Taxonomic trends were observed in the assembly metrics, with assemblies from the Dinoflagellata and Ciliophora phyla showing a higher percentage of open reading frames and number of contigs than transcriptomes from other phyla.ConclusionsGiven current bioinformatics approaches, there is no single ‘best’ reference transcriptome for a particular set of raw data. As the optimum transcriptome is a moving target, improving (or not) with new tools and approaches, automated and programmable pipelines are invaluable for managing the computationally-intensive tasks required for re-processing large sets of samples with revised pipelines and ensuring a common evaluation workflow is applied to all samples. Thus, re-assembling existing data with new tools using automated and programmable pipelines may yield more accurate identification of taxon-specific trends across samples in addition to novel and useful products for the community.Key PointsRe-assembly with new tools can yield new resultsAutomated and programmable pipelines can be used to process arbitrarily many samples.Analyzing many samples using a common pipeline identifies taxon-specific trends.

scholarly journals Improved single-molecule localization precision in astigmatism-based 3D superresolution imaging using weighted likelihood estimation

2018 ◽  
Author(s):  
Christopher H. Bohrer ◽  
Xinxing Yang ◽  
Zhixin Lyu ◽  
Shih-Chin Wang ◽  
Jie Xiao
Keyword(s):  
Single Molecule ◽  
Optical System ◽  
Gaussian Function ◽  
Gaussian Model ◽  
Likelihood Estimation ◽  
Three Dimensions ◽  
Weighted Likelihood ◽  
Superresolution Microscopy ◽  
Engineering Technique ◽  
Localization Precision

AbstractAstigmatism-based superresolution microscopy is widely used to determine the position of individual fluorescent emitters in three-dimensions (3D) with subdiffraction-limited resolutions. This point spread function (PSF) engineering technique utilizes a cylindrical lens to modify the shape of the PSF and break its symmetry above and below the focal plane. The resulting asymmetric PSFs at different z-positions for single emitters are fit with an elliptical 2D-Gaussian function to extract the widths along two principle x- and y-axes, which are then compared with a pre-measured calibration function to determine its z-position. While conceptually simple and easy to implement, in practice, distorted PSFs due to an imperfect optical system often compromise the localization precision; and it is laborious to optimize a multi-purpose optical system. Here we present a methodology that is independent of obtaining a perfect PSF and enhances the localization precision along the z-axis. By utilizing multiple calibration images of fluorescent beads at varying z-planes and characterizing experimentally measured background distributions, we numerically approximated the probability of observing a certain signal in a given pixel from a single emitter at a particular z-plane. We then used a weighted maximum likelihood estimator (WLE) to determine the 3D-position of the emitter. We demonstrate that this approach enhances z-axis localization precision in all conditions we tested, in particular when the PSFs deviate from a standard 2D Gaussian model.

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