Imagining the future of optical microscopy: everything, everywhere, all at once

Fluorescent Reporters, Imaging, and Artificial Intelligence Toolkits to Monitor and Quantify Autophagy, Heterophagy, and Lysosomal Trafficking Fluxes

Lysosomal compartments control the clearance of cell-own material (autophagy) or of material that cells endocytose from the external environment (heterophagy) to warrant supply of nutrients, to eliminate macromolecules or parts of organelles present in excess, aged, or containing toxic material. Inherited or sporadic mutations in lysosomal proteins and enzymes may hamper their folding in the endoplasmic reticulum (ER) and their lysosomal transport via the Golgi compartment, resulting in lysosomal dysfunction and storage disorders. Defective cargo delivery to lysosomal compartments is harmful to cells and organs since it causes accumulation of toxic compounds and defective organellar homeostasis. Assessment of resident proteins and cargo fluxes to the lysosomal compartments is crucial for the mechanistic dissection of intracellular transport and catabolic events. It might be combined with high-throughput screenings to identify cellular, chemical, or pharmacological modulators of these events that may find therapeutic use for autophagy-related and lysosomal storage disorders. Here, discuss qualitative, quantitative and chronologic monitoring of autophagic, heterophagic and lysosomal protein trafficking in fixed and live cells, which relies on fluorescent single and tandem reporters used in combination with biochemical, flow cytometry, light and electron microscopy approaches implemented by artificial intelligence-based technology.

The best of both worlds: Chemigenetic fluorescent sensors for biological imaging

Synthetic-based fluorescent chemosensors and protein-based fluorescent biosensors are two well-established classes of tools for visualizing and monitoring biological processes in living tissues. Chemigenetic sensors, created using a combination of both synthetic parts and protein parts, are an emerging class of tools that aims to combine the strengths, and overcome the drawbacks, of traditional chemosensors and biosensors. This review will survey the landscape of strategies used for fluorescent chemigenetic sensor design. These strategies include: attachment of synthetic elements to proteins using in vitro protein conjugation; attachment of synthetic elements to proteins using autonomous protein labeling; and translational incorporation of unnatural amino acids.

Quantifying DNA damage following light sheet and confocal imaging of the mammalian embryo

Embryo quality assessment by optical imaging is increasing in popularity. Among available optical techniques, light sheet microscopy has emerged as a superior alternative to confocal microscopy due to its geometry, enabling faster image acquisition with reduced photodamage to the sample. However, previous assessments of photodamage induced by imaging may have failed to measure more subtle impacts. In this study, we employed DNA damage as a sensitive indicator of photodamage. We use light sheet microscopy with excitation at a wavelength of 405 nm for imaging embryo autofluorescence and compare its performance to laser scanning confocal microscopy. At an equivalent signal-to-noise ratio for images acquired with both modalities, light sheet microscopy reduced image acquisition time by ten-fold, and did not induce DNA damage when compared to non-imaged embryos. In contrast, imaging with confocal microscopy led to significantly higher levels of DNA damage within embryos and had a higher photobleaching rate. Light sheet imaging is also capable of inducing DNA damage within the embryo but requires multiple cycles of volumetric imaging. Collectively, this study confirms that light sheet microscopy is faster and safer than confocal microscopy for imaging live embryos, indicating its potential as a label-free diagnostic for embryo quality.

MicroBundlePillarTrack: A Python package for automated segmentation, tracking, and analysis of pillar deflection in cardiac microbundles

Movies of human induced pluripotent stem cell (hiPSC)-derived engineered cardiac tissue (microbundles) contain abundant information about structural and functional maturity. However, extracting these data in a reproducible and high-throughput manner remains a major challenge. Furthermore, it is not straightforward to make direct quantitative comparisons across the multiple in vitro experimental platforms employed to fabricate these tissues. Here, we present "MicroBundlePillarTrack," an open-source optical flow-based package developed in Python to track the deflection of pillars in cardiac microbundles grown on experimental platforms with two different pillar designs ("Type 1" and "Type 2" design). Our software is able to automatically segment the pillars, track their displacements, and output time-dependent metrics for contractility analysis, including beating amplitude and rate, contractile force, and tissue stress. Because this software is fully automated, it will allow for both faster and more reproducible analyses of larger datasets and it will enable more reliable cross-platform comparisons as compared to existing approaches that require manual steps and are tailored to a specific experimental platform. To complement this open-source software, we share a dataset of 1,540 brightfield example movies on which we have tested our software. Through sharing this data and software, our goal is to directly enable quantitative comparisons across labs, and facilitate future collective progress via the biomedical engineering open-source data and software ecosystem.

Capturing embryonic hematopoiesis in temporal and spatial dimensions

Hematopoietic stem cells (HSCs) possess the ability to sustain the continuous production of all blood cell types throughout an organism's lifespan. Although primarily located in the bone marrow of adults, HSCs originate during embryonic development. Visualization of the birth of HSCs, their developmental trajectory, and the specific interactions with their successive niches have significantly contributed to our understanding of the biology and mechanics governing HSC formation and expansion. Intravital techniques applied to live embryos or non-fixed samples have remarkably provided invaluable insights into the cellular and anatomical origins of HSCs. These imaging technologies have also shed light on the dynamic interactions between HSCs and neighboring cell types within the surrounding microenvironment or niche, such as endothelial cells or macrophages. This review delves into the advancements made in understanding the origin, production, and cellular interactions of HSCs, particularly during the embryonic development of mice and zebrafish, focusing on studies employing (live) imaging analysis.

LYNSU: automated 3D neuropil segmentation of fluorescent images for Drosophila brains

The brain atlas, which provides information about the distribution of genes, proteins, neurons, or anatomical regions, plays a crucial role in contemporary neuroscience research. To analyze the spatial distribution of those substances based on images from different brain samples, we often need to warp and register individual brain images to a standard brain template. However, the process of warping and registration may lead to spatial errors, thereby severely reducing the accuracy of the analysis. To address this issue, we develop an automated method for segmenting neuropils in the Drosophila brain for fluorescence images from the FlyCircuit database. This technique allows future brain atlas studies to be conducted accurately at the individual level without warping and aligning to a standard brain template. Our method, LYNSU (Locating by YOLO and Segmenting by U-Net), consists of two stages. In the first stage, we use the YOLOv7 model to quickly locate neuropils and rapidly extract small-scale 3D images as input for the second stage model. This stage achieves a 99.4% accuracy rate in neuropil localization. In the second stage, we employ the 3D U-Net model to segment neuropils. LYNSU can achieve high accuracy in segmentation using a small training set consisting of images from merely 16 brains. We demonstrate LYNSU on six distinct neuropils or structures, achieving a high segmentation accuracy comparable to professional manual annotations with a 3D Intersection-over-Union (IoU) reaching up to 0.869. Our method takes only about 7 s to segment a neuropil while achieving a similar level of performance as the human annotators. To demonstrate a use case of LYNSU, we applied it to all female Drosophila brains from the FlyCircuit database to investigate the asymmetry of the mushroom bodies (MBs), the learning center of fruit flies. We used LYNSU to segment bilateral MBs and compare the volumes between left and right for each individual. Notably, of 8,703 valid brain samples, 10.14% showed bilateral volume differences that exceeded 10%. The study demonstrated the potential of the proposed method in high-throughput anatomical analysis and connectomics construction of the Drosophila brain.

MicroBundlePillarTrack: A Python package for automated segmentation, tracking, and analysis of pillar deflection in cardiac microbundles

Movies of human induced pluripotent stem cell (hiPSC)-derived engineered cardiac tissue (microbundles) contain abundant information about structural and functional maturity. However, extracting these data in a reproducible and high-throughput manner remains a major challenge. Furthermore, it is not straightforward to make direct quantitative comparisons across the multiple in vitro experimental platforms employed to fabricate these tissues. Here, we present "MicroBundlePillarTrack," an open-source optical flow-based package developed in Python to track the deflection of pillars in cardiac microbundles grown on experimental platforms with two different pillar designs ("Type 1" and "Type 2" design). Our software is able to automatically segment the pillars, track their displacements, and output time-dependent metrics for contractility analysis, including beating amplitude and rate, contractile force, and tissue stress. Because this software is fully automated, it will allow for both faster and more reproducible analyses of larger datasets and it will enable more reliable cross-platform comparisons as compared to existing approaches that require manual steps and are tailored to a specific experimental platform. To complement this open-source software, we share a dataset of 1,540 brightfield example movies on which we have tested our software. Through sharing this data and software, our goal is to directly enable quantitative comparisons across labs, and facilitate future collective progress via the biomedical engineering open-source data and software ecosystem.

Unveiling nuclear chromatin distribution using IsoConcentraChromJ: A flourescence imaging plugin for IsoRegional and IsoVolumetric based ratios analysis

Chromatin exhibits non-random distribution within the nucleus being arranged into discrete domains that are spatially organized throughout the nuclear space. Both the spatial distribution and structural rearrangement of chromatin domains in the nucleus depend on epigenetic modifications of DNA and/or histones and structural elements such as the nuclear envelope. These components collectively contribute to the organization and rearrangement of chromatin domains, thereby influencing genome architecture and functional regulation. This study develops an innovative, user-friendly, ImageJ-based plugin, called IsoConcentraChromJ, aimed quantitatively delineating the spatial distribution of chromatin regions in concentric patterns. The IsoConcentraChromJ can be applied to quantitative chromatin analysis in both two- and three-dimensional spaces. After DNA and histone staining with fluorescent probes, high-resolution images of nuclei have been obtained using advanced fluorescence microscopy approaches, including confocal and stimulated emission depletion (STED) microscopy. IsoConcentraChromJ workflow comprises the following sequential steps: nucleus segmentation, thresholding, masking, normalization, and trisection with specified ratios for either 2D or 3D acquisitions. The effectiveness of the IsoConcentraChromJ has been validated and demonstrated using experimental datasets consisting in nuclei images of pre-adipocytes and mature adipocytes, encompassing both 2D and 3D imaging. The outcomes allow to characterize the nuclear architecture by calculating the ratios between specific concentric nuclear areas/volumes of acetylated chromatin with respect to total acetylated chromatin and/or total DNA. The novel IsoConcentrapChromJ plugin could represent a valuable resource for researchers investigating the rearrangement of chromatin architecture driven by epigenetic mechanisms using nuclear images obtained by different fluorescence microscopy methods.

Fiber optic-based integrated system for in vivo multiscale pharmacokinetic monitoring

This paper presents the development of a fiber-optic-based fluorescence detection system for multi-scale monitoring of drug distribution in living animals. The integrated system utilized dual laser sources at the wavelengths of 488 nm and 650 nm and three photomultiplier channels for multi-color fluorescence detection. The emission spectra of fluorescent substances were tracked using the time-resolved fluorescence spectroscopy module to continuously monitor their blood kinetics. The fiber bundle, consisting of 30,000 optic filaments, was designed for wide-field mesoscopic imaging of the drug's interactions within organs. The inclusion of a gradient refractive index (GRIN) lens within the setup enabled fluorescence confocal laser scanning microscopy to visualize the drug distribution at the cellular level. The system performance was verified by imaging hepatic and renal tissues in mice using cadmium telluride quantum dots (CdTe QDs) and R3. By acquiring multi-level images and real-time data, our integrated system underscores its potential as a potent tool for drug assessment, specifically within the realms of pharmacokinetic and pharmacodynamic investigations.

Quantitative phase imaging by gradient retardance optical microscopy

Quantitative phase imaging (QPI) has become a vital tool in bioimaging, offering precise measurements of wavefront distortion and, thus, of key cellular metabolism metrics, such as dry mass and density. However, only a few QPI applications have been demonstrated in optically thick specimens, where scattering increases background and reduces contrast. Building upon the concept of structured illumination interferometry, we introduce Gradient Retardance Optical Microscopy (GROM) for QPI of both thin and thick samples. GROM transforms any standard Differential Interference Contrast (DIC) microscope into a QPI platform by incorporating a liquid crystal retarder into the illumination path, enabling independent phase-shifting of the DIC microscope's sheared beams. GROM greatly simplifies related configurations, reduces costs, and eradicates energy losses in parallel imaging modalities, such as fluorescence. We successfully tested GROM on a diverse range of specimens, from microbes and red blood cells to optically thick (~ 300 μm) plant roots without fixation or clearing.

Believing is seeing - the deceptive influence of bias in quantitative microscopy

The visual allure of microscopy makes it an intuitively powerful research tool. Intuition, however, can easily obscure or distort the reality of the information contained in an image. Common cognitive biases, combined with institutional pressures that reward positive research results, can quickly skew a microscopy project towards upholding, rather than rigorously challenging, a hypothesis. The impact of these biases on a variety of research topics is well known. What might be less appreciated are the many forms in which bias can permeate a microscopy experiment. Even well-intentioned researchers are susceptible to bias, which must therefore be actively recognized to be mitigated. Importantly, although image quantification has increasingly become an expectation, ostensibly to confront subtle biases, it is not a guarantee against bias and cannot alone shield an experiment from cognitive distortions. Here, we provide illustrative examples of the insidiously pervasive nature of bias in microscopy experiments - from initial experimental design to image acquisition, analysis and data interpretation. We then provide suggestions that can serve as guard rails against bias.