Low power super resolution fluorescence microscopy by lifetime modification and image reconstruction

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.

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Advanced Static and Dynamic Fluorescence Microscopy Techniques to Investigate Drug Delivery Systems

Pharmaceutics ◽

10.3390/pharmaceutics13060861 ◽

2021 ◽

Vol 13 (6) ◽

pp. 861

Author(s):

Jacopo Cardellini ◽

Arianna Balestri ◽

Costanza Montis ◽

Debora Berti

Keyword(s):

Drug Delivery ◽

Fluorescence Microscopy ◽

Drug Delivery Systems ◽

Laser Scanning ◽

Super Resolution ◽

Laser Scanning Confocal Microscopy ◽

Delivery Systems ◽

Scanning Confocal Microscopy ◽

Biological Phenomena

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.

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