Learning to Become an Expert: Deep Networks Applied To Super-Resolution Microscopy

With super-resolution optical microscopy, it is now possible to observe molecular interactions in living cells. The obtained images have a very high spatial precision but their overall quality can vary a lot depending on the structure of interest and the imaging parameters. Moreover, evaluating this quality is often difficult for non-expert users. In this work, we tackle the problem of learning the quality function of super- resolution images from scores provided by experts. More specifically, we are proposing a system based on a deep neural network that can provide a quantitative quality measure of a STED image of neuronal structures given as input. We conduct a user study in order to evaluate the quality of the predictions of the neural network against those of a human expert. Results show the potential while highlighting some of the limits of the proposed approach.Comment: Accepted to the Thirtieth Innovative Applications of Artificial Intelligence Conference (IAAI), 201

Filtering Pixel Latent Variables for Unmixing Noisy and Undersampled Volumetric Images

The development of robust signal unmixing algorithms is essential for leveraging multimodal datasets acquired through a wide array of scientific imaging technologies, including hyperspectral or time-resolved acquisitions. In experimental physics, enhancing the spatio-temporal resolution or expanding the number of detection channels often leads to diminished sampling rate and signal-to-noise ratio, significantly affecting the efficacy of signal unmixing algorithms. We propose applying band-pass filters to the latent space of a multi-dimensional convolutional neural network to disentangle overlapping signal components, enabling the isolation and quantification of their individual contributions. Using multi-dimensional convolution kernels to process all dimensions simultaneously enhances the network's ability to extract information from adjacent pixels, time- or spectral-bins. This approach enables more effective separation of components in cases where individual pixels do not provide clear, well-resolved information. We showcase the method's practical use in experimental physics through two test cases that highlight the versatility of our approach: fluorescence lifetime microscopy and mode decomposition in optical fibers. The latent unmixing method extracts valuable information from complex signals that cannot be resolved by standard methods. Application of latent unmixing to real FLIM experiments will increase the number of distinguishable fluorescent markers. It will also open new possibilities in optics and photonics for multichannel separations at increased sampling rate.Comment: 16 pages, 8 figures (main paper) + 18 pages, 9 figures (supplementary material

Gold nanoparticle-assisted all optical localized stimulation and monitoring of Ca2+ signaling in neurons

Light-assisted manipulation of cells to control membrane activity or intracellular signaling has become a major avenue in life sciences. However, the ability to perform subcellular light stimulation to investigate localized signaling has been limited. Here, we introduce an all optical method for the stimulation and the monitoring of localized Ca2+ signaling in neurons that takes advantage of plasmonic excitation of gold nanoparticles (AuNPs). We show with confocal microscopy that 800 nm laser pulse application onto a neuron decorated with a few AuNPs triggers a transient increase in free Ca2+, measured optically with GCaMP6s. We show that action potentials, measured electrophysiologically, can be induced with this approach. We demonstrate activation of local Ca2+ transients and Ca2+ signaling via CaMKII in dendritic domains, by illuminating a single or few functionalized AuNPs specifically targeting genetically-modified neurons. This NP-Assisted Localized Optical Stimulation (NALOS) provides a new complement to light-dependent methods for controlling neuronal activity and cell signaling

Understanding the nervous system: Lessons from Frontiers in Neurophotonics

The Frontiers in Neurophotonics Symposium is a biennial event that brings together neurobiologists and physicists/engineers who share interest in the development of leading-edge photonics-based approaches to understand and manipulate the nervous system, from its individual molecular components to complex networks in the intact brain. In this Community paper, we highlight several topics that have been featured at the symposium that took place in October 2022 in Québec City, Canada

Annotation Cost-Sensitive Deep Active Learning with Limited Data (Student Abstract)

Deep learning is a promising avenue to automate tedious analysis tasks in biomedical imaging. However, its application in such a context is limited by the large amount of labeled data required to train deep learning models. While active learning may be used to reduce the amount of labeling data, many approaches do not consider the cost of annotating, which is often significant in a biomedical imaging setting. In this work we show how annotation cost can be considered and learned during active learning on a classification task on the MNIST dataset

Nature of Localized Excitons in CsMgX3 ( X = Cl , Br, I) and Their Interactions with Eu2+ Ions

International audienceIn this paper, the luminescence properties of self-trapped excitons (STEs) of undoped and Eu2+-doped perovskite-type materials CsMgX3 (X=Cl, Br, I) are presented. The three compounds crystallize isostructurally in a hexagonal crystal system that exhibits an intrinsic pseudo-one-dimensionality. This feature has a highly stabilizing effect on the localization of excitons. The similarities to the properties of STEs in alkali halides are drawn that are justified by the band-structure and density-of-states calculations. The luminescence spectra of all three halides are characterized and interpreted despite their high complexity with many emissive transitions. It is illustrated that both STEs and impurity-localized self-trapped excitons (IL STEs) are responsible for the features in the spectra. The impurity localization of the STEs is proven by doping the hosts with Sr2+ ions instead of Eu2+ ions. The decay times in the microsecond range indicate that emission predominantly occurs from a triplet state of the STEs with a prominent afterglow component for the IL STEs that ideally suits a trapping model along the one-dimensional chains of the halides. Moreover, by thermal activation, the excitons tend to annihilate at the Eu2+ traps, thereby inducing an energy transfer to the Eu2+ ions. Because of this action, an extreme increase of the intensity of the Eu2+-based 4f65d−4f7 emission at room temperature is observed, which might be a general explanation for unusual temperature-dependent emission intensities of Eu2+ ions. In general, an understanding of the basic optical properties of the STEs may give some insights into the mechanism of currently used x-ray storage phosphors as well as scintillators

Rating Super-Resolution Microscopy Images With Deep Learning

With super-resolution optical microscopy, it is now possible to observe molecular mechanisms. The quality of the obtained images vary a lot depending on the samples and the imaging parameters. Moreover, evaluating this quality is a difficult task. In this work, we want to learn the quality function from scores provided by experts. We propose the use of a deep network that output a quality score for a given image. A user study evaluate the quality of the predictions against human expert scores

Nano-positioning and tubulin conformation contribute to axonal transport regulation of mitochondria along microtubules

Correct spatiotemporal distribution of organelles and vesicles is crucial for healthy cell functioning and is regulated by intracellular transport mechanisms. Controlled transport of bulky mitochondria is especially important in polarized cells such as neurons that rely on these organelles to locally produce energy and buffer calcium. Mitochondrial transport requires and depends on microtubules that fill much of the available axonal space. How mitochondrial transport is affected by their position within the microtubule bundles is not known. Here, we found that anterograde transport, driven by kinesin motors, is susceptible to the molecular conformation of tubulin in neurons both in vitro and in vivo. Anterograde velocities negatively correlate with the density of elongated tubulin dimers like guanosine triphosphate (GTP)-tubulin. The impact of the tubulin conformation depends primarily on where a mitochondrion is positioned, either within or at the rim of microtubule bundle. Increasing elongated tubulin levels lowers the number of motile anterograde mitochondria within the microtubule bundle and increases anterograde transport speed at the microtubule bundle rim. We demonstrate that the increased kinesin velocity and density on microtubules consisting of elongated dimers add to the increased mitochondrial dynamics. Our work indicates that the molecular conformation of tubulin contributes to the regulation of mitochondrial motility and as such to the local distribution of mitochondria along axons