Robust blind spectral unmixing for fluorescence microscopy using unsupervised learning

Two-site microendoscopic imaging probe for simultaneous three-dimensional imaging at two anatomic locations in tissues

Systems that can image in three dimensions at cellular resolution and across different locations within an organism may enable insights into complex biological processes, such as immune responses, for which a single location measurement may be insufficient. In this Letter, we describe an in vivo two-site imaging probe (TIP) that can simultaneously image two anatomic sites with a maximum separation of a few centimeters. The TIP consists of two identical bendable graded index (GRIN) lenses and is demonstrated by a two-photon two-color fluorescence imaging system. Each GRIN lens has a field of view of 162 × 162 × 170 µm3, a nominal numerical aperture of 0.5, a magnification of 0.7, and working distances of 0.2 mm in air for both ends. A blind linear unmixing algorithm is applied to suppress bleedthrough between channels. We use this system to successfully demonstrate two-site two-photon two-color imaging of two biomedically relevant samples, i.e., (1) a mixture of two autofluorescent anti-cancer drugs and (2) a live hybrid tumor consisting of two spectrally distinct fluorescent cell lines.

Live-cell imaging powered by computation

The proliferation of microscopy methods for live-cell imaging offers many new possibilities for users but can also be challenging to navigate. The prevailing challenge in live-cell fluorescence microscopy is capturing intra-cellular dynamics while preserving cell viability. Computational methods can help to address this challenge and are now shifting the boundaries of what is possible to capture in living systems. In this Review, we discuss these computational methods focusing on artificial intelligence-based approaches that can be layered on top of commonly used existing microscopies as well as hybrid methods that integrate computation and microscope hardware. We specifically discuss how computational approaches can improve the signal-to-noise ratio, spatial resolution, temporal resolution and multi-colour capacity of live-cell imaging.

BINGO: a blind unmixing algorithm for ultra-multiplexing fluorescence images

MOTIVATION

Spectral imaging is often used to observe different objects with multiple fluorescent labels to reveal the development of the biological event. As the number of observed objects increases, the spectral overlap between fluorophores becomes more serious, and obtaining a "pure" picture of each fluorophore becomes a major challenge. Here, we propose a blind spectral unmixing algorithm called BINGO (Blind unmixing via SVD-based Initialization Nmf with project Gradient descent and spare cOnstrain), which can extract all kinds of fluorophores more accurately from highly overlapping multichannel data, even if the spectra of the fluorophores are extremely similar or their fluorescence intensity varies greatly.

RESULTS

BINGO can isolate up to 10 fluorophores from spectral imaging data for a single excitation. nine-color living HeLa cells were visualized distinctly with BINGO. It provides an important algorithmic tool for multiplex imaging studies, especially in intravital imaging. BINGO shows great potential in multicolor imaging for biomedical sciences.

AVAILABILITY AND IMPLEMENTATION

The source code used for this paper is available with the test data at https://github.com/Xinyuan555/BINGO_unmixing.

Harnessing artificial intelligence to reduce phototoxicity in live imaging

Fluorescence microscopy is essential for studying living cells, tissues and organisms. However, the fluorescent light that switches on fluorescent molecules also harms the samples, jeopardizing the validity of results - particularly in techniques such as super-resolution microscopy, which demands extended illumination. Artificial intelligence (AI)-enabled software capable of denoising, image restoration, temporal interpolation or cross-modal style transfer has great potential to rescue live imaging data and limit photodamage. Yet we believe the focus should be on maintaining light-induced damage at levels that preserve natural cell behaviour. In this Opinion piece, we argue that a shift in role for AIs is needed - AI should be used to extract rich insights from gentle imaging rather than recover compromised data from harsh illumination. Although AI can enhance imaging, our ultimate goal should be to uncover biological truths, not just retrieve data. It is essential to prioritize minimizing photodamage over merely pushing technical limits. Our approach is aimed towards gentle acquisition and observation of undisturbed living systems, aligning with the essence of live-cell fluorescence microscopy.

Mapping of individual sensory nerve axons from digits to spinal cord with the transparent embedding solvent system

Achieving uniform optical resolution for a large tissue sample is a major challenge for deep imaging. For conventional tissue clearing methods, loss of resolution and quality in deep regions is inevitable due to limited transparency. Here we describe the Transparent Embedding Solvent System (TESOS) method, which combines tissue clearing, transparent embedding, sectioning and block-face imaging. We used TESOS to acquire volumetric images of uniform resolution for an adult mouse whole-body sample. The TESOS method is highly versatile and can be combined with different microscopy systems to achieve uniformly high resolution. With a light sheet microscope, we imaged the whole body of an adult mouse, including skin, at a uniform 0.8 × 0.8 × 3.5 μm3 voxel resolution within 120 h. With a confocal microscope and a 40×/1.3 numerical aperture objective, we achieved a uniform sub-micron resolution in the whole sample to reveal a complete projection of individual nerve axons within the central or peripheral nervous system. Furthermore, TESOS allowed the first mesoscale connectome mapping of individual sensory neuron axons spanning 5 cm from adult mouse digits to the spinal cord at a uniform sub-micron resolution.

AutoUnmix: an autoencoder-based spectral unmixing method for multi-color fluorescence microscopy imaging

Multiplexed fluorescence microscopy imaging is widely used in biomedical applications. However, simultaneous imaging of multiple fluorophores can result in spectral leaks and overlapping, which greatly degrades image quality and subsequent analysis. Existing popular spectral unmixing methods are mainly based on computational intensive linear models, and the performance is heavily dependent on the reference spectra, which may greatly preclude its further applications. In this paper, we propose a deep learning-based blindly spectral unmixing method, termed AutoUnmix, to imitate the physical spectral mixing process. A transfer learning framework is further devised to allow our AutoUnmix to adapt to a variety of imaging systems without retraining the network. Our proposed method has demonstrated real-time unmixing capabilities, surpassing existing methods by up to 100-fold in terms of unmixing speed. We further validate the reconstruction performance on both synthetic datasets and biological samples. The unmixing results of AutoUnmix achieve the highest SSIM of 0.99 in both three- and four-color imaging, with nearly up to 20% higher than other popular unmixing methods. For experiments where spectral profiles and morphology are akin to simulated data, our method realizes the quantitative performance demonstrated above. Due to the desirable property of data independency and superior blind unmixing performance, we believe AutoUnmix is a powerful tool for studying the interaction process of different organelles labeled by multiple fluorophores.

Multiplex Immunohistochemistry and Immunofluorescence: A Practical Update for Pathologists

Our understanding of the biology and management of human disease has undergone a remarkable evolution in recent decades. Improved understanding of the roles of complex immune populations in the tumor microenvironment has advanced our knowledge of antitumor immunity, and immunotherapy has radically improved outcomes for many advanced cancers. Digital pathology has unlocked new possibilities for the assessment and discovery of the tumor microenvironment, such as quantitative and spatial image analysis. Despite these advances, tissue-based evaluations for diagnosis and prognosis continue to rely on traditional practices, such as hematoxylin and eosin staining, supplemented by the assessment of single biomarkers largely using chromogenic immunohistochemistry (IHC). Such approaches are poorly suited to complex quantitative analyses and the simultaneous evaluation of multiple biomarkers. Thus, multiplex staining techniques have significant potential to improve diagnostic practice and immuno-oncology research. The different approaches to achieve multiplexed IHC and immunofluorescence are described in this study. Alternatives to multiplex immunofluorescence/IHC include epitope-based tissue mass spectrometry and digital spatial profiling (DSP), which require specialized platforms not available to most clinical laboratories. Virtual multiplexing, which involves digitally coregistering singleplex IHC stains performed on serial sections, is another alternative to multiplex staining. Regardless of the approach, analysis of multiplexed stains sequentially or simultaneously will benefit from standardized protocols and digital pathology workflows. Although this is a complex and rapidly advancing field, multiplex staining is now technically feasible for most clinical laboratories and may soon be leveraged for routine diagnostic use. This review provides an update on the current state of the art for tissue multiplexing, including the capabilities and limitations of different techniques, with an emphasis on potential relevance to clinical diagnostic practice.

Semantic Segmentation of Cell Painted Organelles using DeepLabv3plus Model

Cell painting based high content fluorescence imaging technique offers deep insight into the functional and biological changes in subcellular structures. However, advanced instrumentation and the limited availability of suitable fluorescent dyes restricts the tool to comprehensively characterize the cell morphology. Therefore, generating fluorescent specific organelle images using transmitted light microscopy provides an alternative solution for clinical applications. In this work, the utility of semantic segmentation deep network for predicting the Endoplasmic Reticulum (ER), cytoplasm and nuclei from a composite image is investigated. To perform this study, a public dataset consisting of 3456 composite images are considered from Broad Bioimage Benchmark collection. The pixel wise labeling is carried out with the generated binary masks for ER, cytoplasm and nuclei. DeepLabv3plus architecture with Atrous Spatial Pyramid Pooling (ASPP) and depth wise separable convolution is used as a learning model to perform semantic segmentation. The accuracy and loss function at different learning rates are analyzed and the segmentation results are validated using Jaccard index, mean Boundary F (BF) score and dice index. The trained model achieved 97.86% accuracy with a loss of 0.07 at the learning rate of 0.01. Mean BF score, dice index and Jaccard index for nuclei, ER and cytoplasm are (0.98, 0.94, 0.88), (0.97, 0.82, 0.7) and (0.95, 0.88, 0.66) respectively. The obtained results indicate that the adopted methodology could delineate the subcellular structures by accurately detecting sharp object boundaries. Therefore, this study could be useful for predicting the cell painted images from transmitted light microscopy without the requirement of fluorescent labeling.

High-plex immunofluorescence imaging and traditional histology of the same tissue section for discovering image-based biomarkers

Precision medicine is critically dependent on better methods for diagnosing and staging disease and predicting drug response. Histopathology using hematoxylin and eosin (H&E)-stained tissue (not genomics) remains the primary diagnostic method in cancer. Recently developed highly multiplexed tissue imaging methods promise to enhance research studies and clinical practice with precise, spatially resolved single-cell data. Here, we describe the 'Orion' platform for collecting H&E and high-plex immunofluorescence images from the same cells in a whole-slide format suitable for diagnosis. Using a retrospective cohort of 74 colorectal cancer resections, we show that immunofluorescence and H&E images provide human experts and machine learning algorithms with complementary information that can be used to generate interpretable, multiplexed image-based models predictive of progression-free survival. Combining models of immune infiltration and tumor-intrinsic features achieves a 10- to 20-fold discrimination between rapid and slow (or no) progression, demonstrating the ability of multimodal tissue imaging to generate high-performance biomarkers.

Changes in the microstructure of the human aortic adventitia under biaxial loading investigated by multi-photon microscopy

Among the three layers of the aortic wall, the media is primarily responsible for its mechanical properties, but the adventitia prevents the aorta from overstretching and rupturing. The role of the adventitia is therefore crucial with regard to aortic wall failure, and understanding the load-induced changes in tissue microstructure is of high importance. Specifically, the focus of this study is on the changes in collagen and elastin microstructure in response to macroscopic equibiaxial loading applied to the aortic adventitia. To observe these changes, multi-photon microscopy imaging and biaxial extension tests were performed simultaneously. In particular, microscopy images were recorded at 0.02 stretch intervals. The microstructural changes of collagen fiber bundles and elastin fibers were quantified with the parameters of orientation, dispersion, diameter, and waviness. The results showed that the adventitial collagen was divided from one into two fiber families under equibiaxial loading conditions. The almost diagonal orientation of the adventitial collagen fiber bundles remained unchanged, but the dispersion was substantially reduced. No clear orientation of the adventitial elastin fibers was observed at any stretch level. The waviness of the adventitial collagen fiber bundles decreased under stretch, but the adventitial elastin fibers showed no change. These original findings highlight differences between the medial and adventitial layers and provide insight into the stretching process of the aortic wall. STATEMENT OF SIGNIFICANCE: To provide accurate and reliable material models, it is essential to understand the mechanical behavior of the material and its microstructure. Such understanding can be enhanced with tracking of the microstructural changes caused by mechanical loading of the tissue. This study provides therefore a unique dataset of structural parameters of the human aortic adventitia obtained under equibiaxial loading. The structural parameters describe orientation, dispersion, diameter, and waviness of collagen fiber bundles and elastin fibers. Eventually, the microstructural changes in the human aortic adventitia are compared with the microstructural changes in the human aortic media from a previous study. This comparison reveals the cutting-edge findings on the differences in the response to the loading between these two human aortic layers.

SUFI: an automated approach to spectral unmixing of fluorescent multiplex images captured in mouse and post-mortem human brain tissues

BACKGROUND

Multispectral fluorescence imaging coupled with linear unmixing is a form of image data collection and analysis that allows for measuring multiple molecular signals in a single biological sample. Multiple fluorescent dyes, each measuring a unique molecule, are simultaneously measured and subsequently "unmixed" to provide a read-out for each molecular signal. This strategy allows for measuring highly multiplexed signals in a single data capture session, such as multiple proteins or RNAs in tissue slices or cultured cells, but can often result in mixed signals and bleed-through problems across dyes. Existing spectral unmixing algorithms are not optimized for challenging biological specimens such as post-mortem human brain tissue, and often require manual intervention to extract spectral signatures. We therefore developed an intuitive, automated, and flexible package called SUFI: spectral unmixing of fluorescent images.

RESULTS

This package unmixes multispectral fluorescence images by automating the extraction of spectral signatures using vertex component analysis, and then performs one of three unmixing algorithms derived from remote sensing. We evaluate these remote sensing algorithms' performances on four unique biological datasets and compare the results to unmixing results obtained using ZEN Black software (Zeiss). We lastly integrate our unmixing pipeline into the computational tool dotdotdot, which is used to quantify individual RNA transcripts at single cell resolution in intact tissues and perform differential expression analysis, and thereby provide an end-to-end solution for multispectral fluorescence image analysis and quantification.

CONCLUSIONS

In summary, we provide a robust, automated pipeline to assist biologists with improved spectral unmixing of multispectral fluorescence images.

Quantifying multiple stain distributions in bioimaging by hyperspectral X-ray tomography

Chemical staining of biological specimens is commonly utilised to boost contrast in soft tissue structures, but unambiguous identification of staining location and distribution is difficult without confirmation of the elemental signature, especially for chemicals of similar density contrast. Hyperspectral X-ray computed tomography (XCT) enables the non-destructive identification, segmentation and mapping of elemental composition within a sample. With the availability of hundreds of narrow, high resolution (~ 1 keV) energy channels, the technique allows the simultaneous detection of multiple contrast agents across different tissue structures. Here we describe a hyperspectral imaging routine for distinguishing multiple chemical agents, regardless of contrast similarity. Using a set of elemental calibration phantoms, we perform a first instance of direct stain concentration measurement using spectral absorption edge markers. Applied to a set of double- and triple-stained biological specimens, the study analyses the extent of stain overlap and uptake regions for commonly used contrast markers. An improved understanding of stain concentration as a function of position, and the interaction between multiple stains, would help inform future studies on multi-staining procedures, as well as enable future exploration of heavy metal uptake across medical, agricultural and ecological fields.

Image segmentation and separation of spectrally similar dyes in fluorescence microscopy by dynamic mode decomposition of photobleaching kinetics

Background

Image segmentation in fluorescence microscopy is often based on spectral separation of fluorescent probes (color-based segmentation) or on significant intensity differences in individual image regions (intensity-based segmentation). These approaches fail, if dye fluorescence shows large spectral overlap with other employed probes or with strong cellular autofluorescence.

Results

Here, a novel model-free approach is presented which determines bleaching characteristics based on dynamic mode decomposition (DMD) and uses the inferred photobleaching kinetics to distinguish different probes or dye molecules from autofluorescence. DMD is a data-driven computational method for detecting and quantifying dynamic events in complex spatiotemporal data. Here, DMD is first used on synthetic image data and thereafter used to determine photobleaching characteristics of a fluorescent sterol probe, dehydroergosterol (DHE), compared to that of cellular autofluorescence in the nematode Caenorhabditis elegans. It is shown that decomposition of those dynamic modes allows for separating probe from autofluorescence without invoking a particular model for the bleaching process. In a second application, DMD of dye-specific photobleaching is used to separate two green-fluorescent dyes, an NBD-tagged sphingolipid and Alexa488-transferrin, thereby assigning them to different cellular compartments.

Conclusions

Data-based decomposition of dynamic modes can be employed to analyze spatially varying photobleaching of fluorescent probes in cells and tissues for spatial and temporal image segmentation, discrimination of probe from autofluorescence and image denoising. The new method should find wide application in analysis of dynamic fluorescence imaging data.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04881-x.

SAIBR: a simple, platform-independent method for spectral autofluorescence correction

Biological systems are increasingly viewed through a quantitative lens that demands accurate measures of gene expression and local protein concentrations. CRISPR/Cas9 gene tagging has enabled increased use of fluorescence to monitor proteins at or near endogenous levels under native regulatory control. However, owing to typically lower expression levels, experiments using endogenously tagged genes run into limits imposed by autofluorescence (AF). AF is often a particular challenge in wavelengths occupied by commonly used fluorescent proteins (GFP, mNeonGreen). Stimulated by our work in C. elegans, we describe and validate Spectral Autofluorescence Image Correction By Regression (SAIBR), a simple platform-independent protocol and FIJI plug-in to correct for autofluorescence using standard filter sets and illumination conditions. Validated for use in C. elegans embryos, starfish oocytes and fission yeast, SAIBR is ideal for samples with a single dominant AF source; it achieves accurate quantitation of fluorophore signal, and enables reliable detection and quantification of even weakly expressed proteins. Thus, SAIBR provides a highly accessible low-barrier way to incorporate AF correction as standard for researchers working on a broad variety of cell and developmental systems.

Label-Free Imaging of Lipid Droplets in Prostate Cells Using Stimulated Raman Scattering Microscopy and Multivariate Analysis

Hyperspectral stimulated Raman scattering (SRS) microscopy is a powerful imaging modality for the analysis of biological systems. Here, we report the application of k-means cluster analysis (KMCA) of multi-wavelength SRS images in the high-wavenumber region of the Raman spectrum as a robust and reliable method for the segmentation of cellular organelles based on the intrinsic SRS spectrum. KMCA has been applied to the study of the endogenous lipid biochemistry of prostate cancer and prostate healthy cell models, while the corresponding SRS spectrum of the lipid droplet (LD) cluster enabled direct comparison of their composition. The application of KMCA in visualizing the LD content of prostate cell models following the inhibition of de novo lipid synthesis (DNL) using the acetyl-coA carboxylase inhibitor, 5-(tetradecyloxy)-2-furoic acid (TOFA), is demonstrated. This method identified a reliance of prostate cancer cell models upon DNL for metabolic requirements, with a significant reduction in the cellular LD content after treatment with TOFA, which was not observed in normal prostate cell models. SRS imaging combined with KMCA is a robust method for investigating drug–cell interactions in a label-free manner.

Unsupervised Hyperspectral Microscopic Image Segmentation Using Deep Embedded Clustering Algorithm

Hyperspectral microscopy in biology and minerals, unsupervised deep learning neural network denoising SRS photos: hyperspectral resolution enhancement and denoising one hyperspectral picture is enough to teach unsupervised method. An intuitive chemical species map for a lithium ore sample is produced using k-means clustering. Many researchers are now interested in biosignals. Uncertainty limits the algorithms' capacity to evaluate these signals for further information. Even while AI systems can answer puzzles, they remain limited. Deep learning is used when machine learning is inefficient. Supervised learning needs a lot of data. Deep learning is vital in modern AI. Supervised learning requires a large labeled dataset. The selection of parameters prevents over- or underfitting. Unsupervised learning is used to overcome the challenges outlined above (performed by the clustering algorithm). To accomplish this, two processing processes were used: (1) utilizing nonlinear deep learning networks to turn data into a latent feature space (Z). The Kullback-Leibler divergence is used to test the objective function convergence. This article explores a novel research on hyperspectral microscopic picture using deep learning and effective unsupervised learning.

PICASSO allows ultra-multiplexed fluorescence imaging of spatially overlapping proteins without reference spectra measurements

Ultra-multiplexed fluorescence imaging requires the use of spectrally overlapping fluorophores to label proteins and then to unmix the images of the fluorophores. However, doing this remains a challenge, especially in highly heterogeneous specimens, such as the brain, owing to the high degree of variation in the emission spectra of fluorophores in such specimens. Here, we propose PICASSO, which enables more than 15-color imaging of spatially overlapping proteins in a single imaging round without using any reference emission spectra. PICASSO requires an equal number of images and fluorophores, which enables such advanced multiplexed imaging, even with bandpass filter-based microscopy. We show that PICASSO can be used to achieve strong multiplexing capability in diverse applications. By combining PICASSO with cyclic immunofluorescence staining, we achieve 45-color imaging of the mouse brain in three cycles. PICASSO provides a tool for multiplexed imaging with high accessibility and accuracy for a broad range of researchers.

Ultra-multiplexed fluorescence imaging is currently difficult. Here the authors report PICASSO which enables 15-colour imaging of spatially overlapping proteins in a single-round of imaging; they combine it with cyclic immunofluorescence to achieve 45-colour imaging of the mouse brain in 3 cycles.

High-Resolution Digital Panorama of Multiple Structures in Whole Brain of Alzheimer's Disease Mice

Simultaneously visualizing Amyloid-β (Aβ) plaque with its surrounding brain structures at the subcellular level in the intact brain is essential for understanding the complex pathology of Alzheimer's disease, but is still rarely achieved due to the technical limitations. Combining the micro-optical sectioning tomography (MOST) system, whole-brain Nissl staining, and customized image processing workflow, we generated a whole-brain panorama of Alzheimer's disease mice without specific labeling. The workflow employed the steps that include virtual channel splitting, feature enhancement, iso-surface rendering, direct volume rendering, and feature fusion to extract and reconstruct the different signals with distinct gray values and morphologies. Taking advantage of this workflow, we found that the denser-distribution areas of Aβ plaques appeared with relatively more somata and smaller vessels, but show a dissimilar distributing pattern with nerve tracts. In addition, the entorhinal cortex and adjacent subiculum regions present the highest density and biggest diameter of plaques. The neuronal processes in the vicinity of these Aβ plaques showed significant structural alternation such as bending or abrupt branch ending. The capillaries inside or adjacent to the plaques were observed with abundant distorted micro-vessels and abrupt ending. Depicting Aβ plaques, somata, nerve processes and tracts, and blood vessels simultaneously, this panorama enables us for the first time, to analyze how the Aβ plaques interact with capillaries, somata, and processes at a submicron resolution of 3D whole-brain scale, which reveals potential pathological effects of Aβ plaques from a new cross-scale view. Our approach opens a door to routine systematic studies of complex interactions among brain components in mouse models of Alzheimer's disease.

Live-cell fluorescence spectral imaging as a data science challenge

Live-cell fluorescence spectral imaging is an evolving modality of microscopy that uses specific properties of fluorophores, such as excitation or emission spectra, to detect multiple molecules and structures in intact cells. The main challenge of analyzing live-cell fluorescence spectral imaging data is the precise quantification of fluorescent molecules despite the weak signals and high noise found when imaging living cells under non-phototoxic conditions. Beyond the optimization of fluorophores and microscopy setups, quantifying multiple fluorophores requires algorithms that separate or unmix the contributions of the numerous fluorescent signals recorded at the single pixel level. This review aims to provide both the experimental scientist and the data analyst with a straightforward description of the evolution of spectral unmixing algorithms for fluorescence live-cell imaging. We show how the initial systems of linear equations used to determine the concentration of fluorophores in a pixel progressively evolved into matrix factorization, clustering, and deep learning approaches. We outline potential future trends on combining fluorescence spectral imaging with label-free detection methods, fluorescence lifetime imaging, and deep learning image analysis.

When light meets biology - how the specimen affects quantitative microscopy

Fluorescence microscopy images should not be treated as perfect representations of biology. Many factors within the biospecimen itself can drastically affect quantitative microscopy data. Whereas some sample-specific considerations, such as photobleaching and autofluorescence, are more commonly discussed, a holistic discussion of sample-related issues (which includes less-routine topics such as quenching, scattering and biological anisotropy) is required to appropriately guide life scientists through the subtleties inherent to bioimaging. Here, we consider how the interplay between light and a sample can cause common experimental pitfalls and unanticipated errors when drawing biological conclusions. Although some of these discrepancies can be minimized or controlled for, others require more pragmatic considerations when interpreting image data. Ultimately, the power lies in the hands of the experimenter. The goal of this Review is therefore to survey how biological samples can skew quantification and interpretation of microscopy data. Furthermore, we offer a perspective on how to manage many of these potential pitfalls.

Structural basis of adenylyl cyclase 9 activation

Adenylyl cyclase 9 (AC9) is a membrane-bound enzyme that converts ATP into cAMP. The enzyme is weakly activated by forskolin, fully activated by the G protein Gαs subunit and is autoinhibited by the AC9 C-terminus. Although our recent structural studies of the AC9-Gαs complex provided the framework for understanding AC9 autoinhibition, the conformational changes that AC9 undergoes in response to activator binding remains poorly understood. Here, we present the cryo-EM structures of AC9 in several distinct states: (i) AC9 bound to a nucleotide inhibitor MANT-GTP, (ii) bound to an artificial activator (DARPin C4) and MANT-GTP, (iii) bound to DARPin C4 and a nucleotide analogue ATPαS, (iv) bound to Gαs and MANT-GTP. The artificial activator DARPin C4 partially activates AC9 by binding at a site that overlaps with the Gαs binding site. Together with the previously observed occluded and forskolin-bound conformations, structural comparisons of AC9 in the four conformations described here show that secondary structure rearrangements in the region surrounding the forskolin binding site are essential for AC9 activation.

Adenylyl cyclases (ACs) generate the second messenger cAMP and play an important role in cellular signaling. Here, the authors use cryo-EM to trace the conformational changes resulting from binding to partial and full activators to one of these enzymes, AC9.

Microskeletal stiffness promotes aortic aneurysm by sustaining pathological vascular smooth muscle cell mechanosensation via Piezo1

Mechanical overload of the vascular wall is a pathological hallmark of life-threatening abdominal aortic aneurysms (AAA). However, how this mechanical stress resonates at the unicellular level of vascular smooth muscle cells (VSMC) is undefined. Here we show defective mechano-phenotype signatures of VSMC in AAA measured with ultrasound tweezers-based micromechanical system and single-cell RNA sequencing technique. Theoretical modelling predicts that cytoskeleton alterations fuel cell membrane tension of VSMC, thereby modulating their mechanoallostatic responses which are validated by live micromechanical measurements. Mechanistically, VSMC gradually adopt a mechanically solid-like state by upregulating cytoskeleton crosslinker, α-actinin2, in the presence of AAA-promoting signal, Netrin-1, thereby directly powering the activity of mechanosensory ion channel Piezo1. Inhibition of Piezo1 prevents mice from developing AAA by alleviating pathological vascular remodeling. Our findings demonstrate that deviations of mechanosensation behaviors of VSMC is detrimental for AAA and identifies Piezo1 as a novel culprit of mechanically fatigued aorta in AAA.

Low-invasive 5D visualization of mitotic progression by two-photon excitation spinning-disk confocal microscopy

Non-linear microscopy, such as multi-photon excitation microscopy, offers spatial localities of excitations, thereby achieving 3D cross-sectional imaging with low phototoxicity even in thick biological specimens. We had developed a multi-point scanning two-photon excitation microscopy system using a spinning-disk confocal scanning unit. However, its severe color cross-talk has precluded multi-color simultaneous imaging. Therefore, in this study, we introduced a mechanical switching system to select either of two NIR laser light pulses and an image-splitting detection system for 3- or 4-color imaging. As a proof of concept, we performed multi-color fluorescent imaging of actively dividing human HeLa cells and tobacco BY-2 cells. We found that the proposed microscopy system enabled time-lapse multi-color 3D imaging of cell divisions while avoiding photodamage. Moreover, the application of a linear unmixing method to the 5D dataset enabled the precise separation of individual intracellular components in multi-color images. We thus demonstrated the versatility of our new microscopy system in capturing the dynamic processes of cellular components that could have multitudes of application.

IBEX: an iterative immunolabeling and chemical bleaching method for high-content imaging of diverse tissues

High-content imaging is needed to catalog the variety of cellular phenotypes and multicellular ecosystems present in metazoan tissues. We recently developed iterative bleaching extends multiplexity (IBEX), an iterative immunolabeling and chemical bleaching method that enables multiplexed imaging (>65 parameters) in diverse tissues, including human organs relevant for international consortia efforts. IBEX is compatible with >250 commercially available antibodies and 16 unique fluorophores, and can be easily adopted to different imaging platforms using slides and nonproprietary imaging chambers. The overall protocol consists of iterative cycles of antibody labeling, imaging and chemical bleaching that can be completed at relatively low cost in 2-5 d by biologists with basic laboratory skills. To support widespread adoption, we provide extensive details on tissue processing, curated lists of validated antibodies and tissue-specific panels for multiplex imaging. Furthermore, instructions are included on how to automate the method using competitively priced instruments and reagents. Finally, we present a software solution for image alignment that can be executed by individuals without programming experience using open-source software and freeware. In summary, IBEX is a noncommercial method that can be readily implemented by academic laboratories and scaled to achieve high-content mapping of diverse tissues in support of a Human Reference Atlas or other such applications.

Fluorescent visualization of oil displacement in a microfluidic device for enhanced oil recovery applications

A microfluidic device was developed to mimic the reservoir pore-scale and track the oil/water phases during air flooding. The chip was generated by combining soft-lithography and NOA81 replication. A unique feature of this approach is the inclusion of fluorescent dyes into the oil/water phases, allowing for real-time visualization of oil recovery without altering the phases' surface properties. As a proof of concept, the air was injected into the water/oil-flooded device for enhanced oil recovery applications.

Multiplexed Single-Cell Plasmonic Immunoassay of Intracellular Signaling Proteins Enables Non-Destructive Monitoring of Cell Fate

It is of significant importance in cancer biology to identify signaling pathways that play key roles in cell fate determination. Dissecting cellular signaling pathways requires the measurement of a large number of signaling proteins. However, tools for simultaneously monitoring multiple signaling pathway components in single living cells remain limited at present. Herein, we describe an approach, termed multiplexed single-cell plasmonic immunosandwich assay (mxscPISA), for simultaneous detection of multiple signaling proteins in individual living cells. This approach enabled simultaneous non-destructive monitoring of multiple (up to five, currently the highest multiplexing capacity in living cells) cytoplasmic and nucleus signaling proteins in individual cells with ultrahigh detection sensitivity. As a proof of principle, the epidermal growth factor receptor (EGFR) pathway, which plays a central role in cell fate determination, was investigated using this approach in this study. We found that there were differential attenuation rate of pro-survival and accumulation rate of pro-death signaling protein of the EGFR pathway in response to EGFR inactivation. These findings implicate that, after EGFR inactivation, a transient imbalance between survival and apoptotic signaling outputs contributed to the final cell fate of death. The mxscPISA approach can be a promising tool to reveal a signaling dynamic pattern at the single-cell level and to identify key components of signaling pathways that contribute to the final cell fate using only a limited number of cells.

Enhanced Multiplexing of Immunofluorescence Microscopy Using a Long-Stokes-Shift Fluorophore

Immunofluorescence labeling and microscopy offer a highly specific means to visualize proteins or other molecular species in a sample by labeling target antigens with fluorescent probes. These fluorescent probes can then be visualized using a fluorescence microscope, allowing their relative spatial relationships to be determined. Due to spectral overlap of common fluorophores, however, it can be challenging to analyze more than three antigens in a single sample with standard imaging approaches. This article describes multiplexed labeling and imaging of four target antigens through the use of a long-Stokes-shift fluorophore-a fluorophore with an unusually large gap between its excitation and emission maxima-in tandem with three conventional fluorophores. This combination allows for multiplexed imaging of four antigens in a single sample with excellent spectral discrimination suitable for sensitive analyses using standard imaging hardware. Particular advantages of this approach are its flexibility in terms of target antigens and the lack of any specialized procedures, reagents, or equipment beyond the commercially available labeling reagent coupled to the long-Stokes-shift fluorophore. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Four-probe immunofluorescence labeling Basic Protocol 2: Four-probe immunofluorescence imaging.

Phasor S-FLIM: a new paradigm for fast and robust spectral fluorescence lifetime imaging

Fluorescence lifetime imaging microscopy (FLIM) and spectral imaging are two broadly applied methods for increasing dimensionality in microscopy. However, their combination is typically inefficient and slow in terms of acquisition and processing. By integrating technological and computational advances, we developed a robust and unbiased spectral FLIM (S-FLIM) system. Our method, Phasor S-FLIM, combines true parallel multichannel digital frequency domain electronics with a multidimensional phasor approach to extract detailed and precise information about the photophysics of fluorescent specimens at optical resolution. To show the flexibility of the Phasor S-FLIM technology and its applications to the biological and biomedical field, we address four common, yet challenging, problems: the blind unmixing of spectral and lifetime signatures from multiple unknown species, the unbiased bleedthrough- and background-free Förster resonance energy transfer analysis of biosensors, the photophysical characterization of environment-sensitive probes in living cells and parallel four-color FLIM imaging in tumor spheroids.

Fluorophore unmixing based on bleaching and recovery kinetics using MCR-ALS

Fluorescence microscopy is a key technology in the life sciences, though its performance is constrained by the number of labels that can be recorded. We propose to use the kinetics of fluorophore photodestruction and subsequent fluorescence recovery to distinguish multiple spectrally-overlapping emitters in fixed cells, thus enhancing the information that can be obtained from a single measurement. We show that the data can be directly processed using multivariate curve resolution - alternating least squares (MCR-ALS) to deliver distinct images for each fluorophore in their local environment, and apply this methodology to membrane imaging using DiBAC₄(3) and concanavalin A - Alexa Fluor 488 as the fluorophores. We find that the DiBAC₄(3) displays two distinct degradation/recovery kinetics that correspond to two different label distributions, allowing us to simultaneously distinguish three different fluorescence distributions from two spectrally overlapping fluorophores. We expect that our approach will scale to other dynamically-binding dyes, leading to similarly increased multiplexing capability.

Current and future advances in fluorescence-based visualization of plant cell wall components and cell wall biosynthetic machineries

Plant cell wall-derived biomass serves as a renewable source of energy and materials with increasing importance. The cell walls are biomacromolecular assemblies defined by a fine arrangement of different classes of polysaccharides, proteoglycans, and aromatic polymers and are one of the most complex structures in Nature. One of the most challenging tasks of cell biology and biomass biotechnology research is to image the structure and organization of this complex matrix, as well as to visualize the compartmentalized, multiplayer biosynthetic machineries that build the elaborate cell wall architecture. Better knowledge of the plant cells, cell walls, and whole tissue is essential for bioengineering efforts and for designing efficient strategies of industrial deconstruction of the cell wall-derived biomass and its saccharification. Cell wall-directed molecular probes and analysis by light microscopy, which is capable of imaging with a high level of specificity, little sample processing, and often in real time, are important tools to understand cell wall assemblies. This review provides a comprehensive overview about the possibilities for fluorescence label-based imaging techniques and a variety of probing methods, discussing both well-established and emerging tools. Examples of applications of these tools are provided. We also list and discuss the advantages and limitations of the methods. Specifically, we elaborate on what are the most important considerations when applying a particular technique for plants, the potential for future development, and how the plant cell wall field might be inspired by advances in the biomedical and general cell biology fields.

Challenges facing quantitative large-scale optical super-resolution, and some simple solutions

Optical super-resolution microscopy (SRM) has enabled biologists to visualize cellular structures with near-molecular resolution, giving unprecedented access to details about the amounts, sizes, and spatial distributions of macromolecules in the cell. Precisely quantifying these molecular details requires large datasets of high-quality, reproducible SRM images. In this review, we discuss the unique set of challenges facing quantitative SRM, giving particular attention to the shortcomings of conventional specimen preparation techniques and the necessity for optimal labeling of molecular targets. We further discuss the obstacles to scaling SRM methods, such as lengthy image acquisition and complex SRM data analysis. For each of these challenges, we review the recent advances in the field that circumvent these pitfalls and provide practical advice to biologists for optimizing SRM experiments.

Measurement Techniques to Resolve and Control Population Dynamics of Mixed-Culture Processes

Microbial mixed cultures are gaining increasing attention as biotechnological production systems, since they offer a large but untapped potential for future bioprocesses. Effects of secondary metabolite induction and advantages of labor division for the degradation of complex substrates offer new possibilities for process intensification. However, mixed cultures are highly complex, and, consequently, many biotic and abiotic parameters are required to be identified, characterized, and ideally controlled to establish a stable bioprocess. In this review, we discuss the advantages and disadvantages of existing measurement techniques for identifying, characterizing, monitoring, and controlling mixed cultures and highlight promising examples. Moreover, existing challenges and emerging technologies are discussed, which lay the foundation for novel analytical workflows to monitor mixed-culture bioprocesses.

Kilohertz frame rate snapshot hyperspectral imaging of metal reactive materials

We demonstrate a kilohertz frame rate snapshot hyperspectral imaging system suitable for high-speed imaging, which we name snapshot hyperspectral imager for emission and reactions (SHEAR). This system splits the sensor of a single high-speed camera to simultaneously capture a conventional image and a spectrally sheared response of the scene under study. Given the small, point-source-like nature of burning metal micro-particles, the spectral response of the species is captured without the need for a slit, as is needed in conventional imaging spectrometers. We pair robust image registration techniques with sparse reconstruction algorithms to computationally disentangle overlapping spectra associated with many burning particles over the course of a combustion experiment. As a proof-of-concept experiment, representative physical vapor deposited Al:Zr composite particles are ignited, and their burn evolution is recorded at a frame rate of 2 kHz using this method. We demonstrate operation over two distinct wavelength ranges spanning hundreds of nanometers in wavelength and with sub-nanometer resolution. We are able to track hundreds of individual Al:Zr particles in a single high-speed video, providing ample statistics of burn time, temperature, and AlO emission timing in a high-throughput method. The demonstrated technology is high-throughput, flexible in wavelength, inexpensive, and relatively easy to implement, and provides a much needed tool for in situ composite metal fuel diagnostics.

Simultaneous Monitoring of Multi-Enzyme Activity and Concentration in Tumor Using a Triply Labeled Fluorescent In Vivo Imaging Probe

The use of fluorescent imaging probes that monitor the activity of proteases that experience an increase in expression and activity in tumors is well established. These probes can be conjugated to nanoparticles of iron oxide, creating a multimodal probe serving as both a magnetic resonance imaging (MRI) agent and an indicator of local protease activity. Previous works describe probes for cathepsin D (CatD) and metalloproteinase-2 (MMP2) protease activity grafted to cross-linked iron oxide nanoparticles (CLIO). Herein, we have synthesized a triply labeled fluorescent iron oxide nanoparticle molecular imaging (MI) probe, including an AF750 substrate concentration reporter along with probes for cathepsin B (CatB) sand MMP2 protease activity. The reporter provides a baseline signal from which to compare the activity of the two proteases. The activity of the MI probe was verified through incubation with the proteases and tested in vitro using the human HT29 tumor cell line and in vivo using female nude mice injected with HT29 cells. We found the MI probe had the appropriate specificity to the activity of their respective proteases, and the reporter dye did not activate when incubated in the presence of only MMP2 and CatB. Probe fluorescent activity was confirmed in vitro, and reporter signal activation was also noted. The fluorescent activity was also visible in vivo, with injected HT29 cells exhibiting fluorescence, distinguishing them from the rest of the animal. The reporter signal was also observable in vivo, which allowed the signal intensities of the protease probes to be corrected; this is a unique feature of this MI probe design.

Who is where in the Plastisphere, and why does it matter?

To fully understand how plastic is affecting the ocean, we need to understand how marine life interacts directly with it. Besides their ecological relevance, microbes can affect the distribution, degradation and transfer of plastics to the rest of the marine food web. From amplicon sequencing and scanning electron microscopy, we know that a diverse array of microorganisms rapidly associate with plastic marine debris in the form of biofouling and biofilms, also known as the "Plastisphere." However, observation of multiple microbial interactions in situ, at small spatial scales in the Plastisphere, has been a challenge. In this issue of Molecular Ecology Resources, Schlundt et al. apply the combination labelling and spectral imaging - fluorescence in situ hybridization to study microbial communities on plastic marine debris. The images demonstrate the colocalization of abundant bacterial groups on plastic marine debris at a relatively high taxonomic and spatial resolution while also visualizing biofouling of eukaryotes, such as diatoms and bryozoans. This modern imaging technology provides new possibilities to address questions regarding the ecology of marine microbes on plastic marine debris and describe more specific impacts of plastic pollution in the marine food webs.