Mapping Pixel Dissimilarity in Wide-Field Super-Resolution Fluorescence Microscopy

2015 ◽  
Vol 87 (9) ◽  
pp. 4675-4682 ◽  
Author(s):  
Cyril Ruckebusch ◽  
Romain Bernex ◽  
Franco Allegrini ◽  
Michel Sliwa ◽  
Johan Hofkens ◽  
...  
2018 ◽  
Author(s):  
Hongda Wang ◽  
Yair Rivenson ◽  
Yiyin Jin ◽  
Zhensong Wei ◽  
Ronald Gao ◽  
...  

AbtsractWe present a deep learning-based method for achieving super-resolution in fluorescence microscopy. This data-driven approach does not require any numerical models of the imaging process or the estimation of a point spread function, and is solely based on training a generative adversarial network, which statistically learns to transform low resolution input images into super-resolved ones. Using this method, we super-resolve wide-field images acquired with low numerical aperture objective lenses, matching the resolution that is acquired using high numerical aperture objectives. We also demonstrate that diffraction-limited confocal microscopy images can be transformed by the same framework into super-resolved fluorescence images, matching the image resolution acquired with a stimulated emission depletion (STED) microscope. The deep network rapidly outputs these super-resolution images, without any iterations or parameter search, and even works for types of samples that it was not trained for.


2021 ◽  
Vol 22 (4) ◽  
pp. 1903
Author(s):  
Ivona Kubalová ◽  
Alžběta Němečková ◽  
Klaus Weisshart ◽  
Eva Hřibová ◽  
Veit Schubert

The importance of fluorescence light microscopy for understanding cellular and sub-cellular structures and functions is undeniable. However, the resolution is limited by light diffraction (~200–250 nm laterally, ~500–700 nm axially). Meanwhile, super-resolution microscopy, such as structured illumination microscopy (SIM), is being applied more and more to overcome this restriction. Instead, super-resolution by stimulated emission depletion (STED) microscopy achieving a resolution of ~50 nm laterally and ~130 nm axially has not yet frequently been applied in plant cell research due to the required specific sample preparation and stable dye staining. Single-molecule localization microscopy (SMLM) including photoactivated localization microscopy (PALM) has not yet been widely used, although this nanoscopic technique allows even the detection of single molecules. In this study, we compared protein imaging within metaphase chromosomes of barley via conventional wide-field and confocal microscopy, and the sub-diffraction methods SIM, STED, and SMLM. The chromosomes were labeled by DAPI (4′,6-diamidino-2-phenylindol), a DNA-specific dye, and with antibodies against topoisomerase IIα (Topo II), a protein important for correct chromatin condensation. Compared to the diffraction-limited methods, the combination of the three different super-resolution imaging techniques delivered tremendous additional insights into the plant chromosome architecture through the achieved increased resolution.


2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Luzhe Huang ◽  
Hanlong Chen ◽  
Yilin Luo ◽  
Yair Rivenson ◽  
Aydogan Ozcan

AbstractVolumetric imaging of samples using fluorescence microscopy plays an important role in various fields including physical, medical and life sciences. Here we report a deep learning-based volumetric image inference framework that uses 2D images that are sparsely captured by a standard wide-field fluorescence microscope at arbitrary axial positions within the sample volume. Through a recurrent convolutional neural network, which we term as Recurrent-MZ, 2D fluorescence information from a few axial planes within the sample is explicitly incorporated to digitally reconstruct the sample volume over an extended depth-of-field. Using experiments on C. elegans and nanobead samples, Recurrent-MZ is demonstrated to significantly increase the depth-of-field of a 63×/1.4NA objective lens, also providing a 30-fold reduction in the number of axial scans required to image the same sample volume. We further illustrated the generalization of this recurrent network for 3D imaging by showing its resilience to varying imaging conditions, including e.g., different sequences of input images, covering various axial permutations and unknown axial positioning errors. We also demonstrated wide-field to confocal cross-modality image transformations using Recurrent-MZ framework and performed 3D image reconstruction of a sample using a few wide-field 2D fluorescence images as input, matching confocal microscopy images of the same sample volume. Recurrent-MZ demonstrates the first application of recurrent neural networks in microscopic image reconstruction and provides a flexible and rapid volumetric imaging framework, overcoming the limitations of current 3D scanning microscopy tools.


2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Martin Schmidt ◽  
Adam C. Hundahl ◽  
Henrik Flyvbjerg ◽  
Rodolphe Marie ◽  
Kim I. Mortensen

AbstractUntil very recently, super-resolution localization and tracking of fluorescent particles used camera-based wide-field imaging with uniform illumination. Then it was demonstrated that structured illuminations encode additional localization information in images. The first demonstration of this uses scanning and hence suffers from limited throughput. This limitation was mitigated by fusing camera-based localization with wide-field structured illumination. Current implementations, however, use effectively only half the localization information that they encode in images. Here we demonstrate how all of this information may be exploited by careful calibration of the structured illumination. Our approach achieves maximal resolution for given structured illumination, has a simple data analysis, and applies to any structured illumination in principle. We demonstrate this with an only slightly modified wide-field microscope. Our protocol should boost the emerging field of high-precision localization with structured illumination.


Pharmaceutics ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 861
Author(s):  
Jacopo Cardellini ◽  
Arianna Balestri ◽  
Costanza Montis ◽  
Debora Berti

In the past decade(s), fluorescence microscopy and laser scanning confocal microscopy (LSCM) have been widely employed to investigate biological and biomimetic systems for pharmaceutical applications, to determine the localization of drugs in tissues or entire organisms or the extent of their cellular uptake (in vitro). However, the diffraction limit of light, which limits the resolution to hundreds of nanometers, has for long time restricted the extent and quality of information and insight achievable through these techniques. The advent of super-resolution microscopic techniques, recognized with the 2014 Nobel prize in Chemistry, revolutionized the field thanks to the possibility to achieve nanometric resolution, i.e., the typical scale length of chemical and biological phenomena. Since then, fluorescence microscopy-related techniques have acquired renewed interest for the scientific community, both from the perspective of instrument/techniques development and from the perspective of the advanced scientific applications. In this contribution we will review the application of these techniques to the field of drug delivery, discussing how the latest advancements of static and dynamic methodologies have tremendously expanded the experimental opportunities for the characterization of drug delivery systems and for the understanding of their behaviour in biologically relevant environments.


2021 ◽  
Vol 11 (6) ◽  
pp. 2773
Author(s):  
Hiroaki Yokota ◽  
Atsuhito Fukasawa ◽  
Minako Hirano ◽  
Toru Ide

Over the years, fluorescence microscopy has evolved and has become a necessary element of life science studies. Microscopy has elucidated biological processes in live cells and organisms, and also enabled tracking of biomolecules in real time. Development of highly sensitive photodetectors and light sources, in addition to the evolution of various illumination methods and fluorophores, has helped microscopy acquire single-molecule fluorescence sensitivity, enabling single-molecule fluorescence imaging and detection. Low-light photodetectors used in microscopy are classified into two categories: point photodetectors and wide-field photodetectors. Although point photodetectors, notably photomultiplier tubes (PMTs), have been commonly used in laser scanning microscopy (LSM) with a confocal illumination setup, wide-field photodetectors, such as electron-multiplying charge-coupled devices (EMCCDs) and scientific complementary metal-oxide-semiconductor (sCMOS) cameras have been used in fluorescence imaging. This review focuses on the former low-light point photodetectors and presents their fluorescence microscopy applications and recent progress. These photodetectors include conventional PMTs, single photon avalanche diodes (SPADs), hybrid photodetectors (HPDs), in addition to newly emerging photodetectors, such as silicon photomultipliers (SiPMs) (also known as multi-pixel photon counters (MPPCs)) and superconducting nanowire single photon detectors (SSPDs). In particular, this review shows distinctive features of HPD and application of HPD to wide-field single-molecule fluorescence detection.


The Analyst ◽  
2014 ◽  
Vol 139 (12) ◽  
pp. 3174-3178 ◽  
Author(s):  
Ian L. Gunsolus ◽  
Dehong Hu ◽  
Cosmin Mihai ◽  
Samuel E. Lohse ◽  
Chang-soo Lee ◽  
...  

Nanoscale ◽  
2021 ◽  
Author(s):  
Fernando D Stefani ◽  
Alan M. M. Szalai ◽  
Cecilia Zaza

Super-resolution fluorescence microscopy and Förster Resonance Energy Transfer (FRET) form a well-established family of techniques that has provided unique tools to study the dynamic architecture and functionality of biological systems,...


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