Super-Resolution Imaging Uncovers Nanoscale Tau Aggregate Hyperphosphorylation Patterns in Human Alzheimer's Disease Brain Tissue.

Tau aggregation plays a critical role in Alzheimer's Disease (AD), where tau neurofibrillary tangles (NFTs) are a key pathological hallmark. While much attention has been given to NFTs, emerging evidence underscores nano-sized pre-NFT tau aggregates as potentially toxic entities in AD. By leveraging DNA-PAINT super-resolution microscopy, we visualized and quantified nanoscale tau aggregates (nano-aggregates) in human postmortem brain tissues from intermediate and advanced AD, and Primary Age-Related Tauopathy (PART). Nano-aggregates were predominant across cases, with AD exhibiting a higher burden compared to PART. Hyperphosphorylated tau residues (p-T231, p-T181, and p-S202/T205) were present within nano-aggregates across all AD Braak stages and PART. Moreover, nano-aggregates displayed morphological differences between PART and AD, and exhibited distinct hyperphosphorylation patterns in advanced AD. These findings suggest that changes in nano-aggregate morphology and hyperphosphorylation patterns may exacerbate tau aggregation and AD progression. The ability to detect and profile nanoscale tau aggregates in human brain tissue opens new avenues for studying the molecular underpinnings of tauopathies.

Multiplexed DNA-PAINT Imaging of the Heterogeneity of Late Endosome/Lysosome Protein Composition.

Late endosomes/lysosomes (LELs) are crucial for numerous physiological processes and their dysfunction is linked to many diseases. Proteomic analyses have identified hundreds of LEL proteins, however, whether these proteins are uniformly present on each LEL, or if there are cell-type dependent LEL sub-populations with unique protein compositions is unclear. We employed a quantitative, multiplexed DNA-PAINT super-resolution approach to examine the distribution of six key LEL proteins (LAMP1, LAMP2, CD63, TMEM192, NPC1 and LAMTOR4) on individual LELs. While LAMP1 and LAMP2 were abundant across LELs, marking a common population, most analyzed proteins were associated with specific LEL subpopulations. Our multiplexed imaging approach identified up to eight different LEL subpopulations based on their unique membrane protein composition. Additionally, our analysis of the spatial relationships between these subpopulations and mitochondria revealed a cell-type specific tendency for NPC1-positive LELs to be closely positioned to mitochondria. Our approach will be broadly applicable to determining organelle heterogeneity with single organelle resolution in many biological contexts. Summary: This study develops a multiplexed and quantitative DNA-PAINT super-resolution imaging pipeline to investigate the distribution of late endosomal/lysosomal (LEL) proteins across individual LELs, revealing cell-type specific LEL sub-populations with unique protein compositions, offering insights into organelle heterogeneity at single-organelle resolution.

Interplay between stochastic enzyme activity and microtubule stability drives detyrosination enrichment on microtubule subsets.

Microtubules in cells consist of functionally diverse subpopulations carrying distinct post-translational modifications (PTMs). Akin to the histone code, the tubulin code regulates a myriad of microtubule functions, ranging from intracellular transport to chromosome segregation. However, how individual PTMs only occur on subsets of microtubules to contribute to microtubule specialization is not well understood. In particular, microtubule detyrosination, the removal of the C-terminal tyrosine on α-tubulin subunits, marks the stable population of microtubules and modifies how microtubules interact with other microtubule-associated proteins to regulate a wide range of cellular processes. Previously, we found that in certain cell types, only ∼30% of microtubules are highly enriched with the detyrosination mark and that detyrosination spans most of the length of a microtubule, often adjacent to a completely tyrosinated microtubule. How the activity of a cytosolic detyrosinase, vasohibin (VASH), leads to only a small subpopulation of highly detyrosinated microtubules is unclear. Here, using quantitative super-resolution microscopy, we visualized nascent microtubule detyrosination events in cells consisting of 1-3 detyrosinated α-tubulin subunits after nocodazole washout. Microtubule detyrosination accumulates slowly and in a dispersed pattern across the microtubule length. By visualizing single molecules of VASH in live cells, we found that VASH engages with microtubules stochastically on a short timescale, suggesting limited removal of tyrosine per interaction, consistent with the super-resolution results. Combining these quantitative imaging results with simulations incorporating parameters from our experiments, we provide evidence for a stochastic model for cells to establish a subset of detyrosinated microtubules via a detyrosination-stabilization feedback mechanism.

Quantitative Single-Molecule Localization Microscopy.

Super-resolution fluorescence microscopy allows the investigation of cellular structures at nanoscale resolution using light. Current developments in super-resolution microscopy have focused on reliable quantification of the underlying biological data. In this review, we first describe the basic principles of super-resolution microscopy techniques such as stimulated emission depletion (STED) microscopy and single-molecule localization microscopy (SMLM), and then give a broad overview of methodological developments to quantify super-resolution data, particularly those geared toward SMLM data. We cover commonly used techniques such as spatial point pattern analysis, colocalization, and protein copy number quantification but also describe more advanced techniques such as structural modeling, single-particle tracking, and biosensing. Finally, we provide an outlook on exciting new research directions to which quantitative super-resolution microscopy might be applied.

ECLiPSE: A Versatile Classification Technique for Structural and Morphological Analysis of Super-Resolution Microscopy Data.

We introduce a new automated machine learning analysis pipeline to precisely classify cellular structures captured through single molecule localization microscopy, which we call ECLiPSE (Enhanced Classification of Localized Pointclouds by Shape Extraction). ECLiPSE leverages 67 comprehensive shape descriptors encompassing geometric, boundary, skeleton and other properties, the majority of which are directly extracted from the localizations to accurately characterize individual structures. We validate ECLiPSE through unsupervised and supervised classification on a dataset featuring five distinct cellular structures, achieving exceptionally high classification accuracies nearing 100%. Moreover, we demonstrate the versatility of our approach by applying it to two novel biological applications: quantifying the clearance of tau protein aggregates, a critical marker for neurodegenerative diseases, and differentiating between two distinct morphological features (morphotypes) of TAR DNA-binding protein 43 proteinopathy, potentially associated to different TDP-43 strains, each exhibiting unique seeding and spreading properties. We anticipate that this versatile approach will significantly enhance the way we study cellular structures across various biological contexts, elucidating their roles in disease development and progression.

Actin polymerization promotes invagination of flat clathrin-coated lattices in mammalian cells by pushing at lattice edges.

Clathrin-mediated endocytosis (CME) requires energy input from actin polymerization in mechanically challenging conditions. The roles of actin in CME are poorly understood due to inadequate knowledge of actin organization at clathrin-coated structures (CCSs). Using platinum replica electron microscopy of mammalian cells, we show that Arp2/3 complex-dependent branched actin networks, which often emerge from microtubule tips, assemble along the CCS perimeter, lack interaction with the apical clathrin lattice, and have barbed ends oriented toward the CCS. This structure is hardly compatible with the widely held "apical pulling" model describing actin functions in CME. Arp2/3 complex inhibition or epsin knockout produce large flat non-dynamic CCSs, which split into invaginating subdomains upon recovery from Arp2/3 inhibition. Moreover, epsin localization to CCSs depends on Arp2/3 activity. We propose an "edge pushing" model for CME, wherein branched actin polymerization promotes severing and invagination of flat CCSs in an epsin-dependent manner by pushing at the CCS boundary, thus releasing forces opposing the intrinsic curvature of clathrin lattices.

Multilinear Slicing for curve resolution of fluorescence imaging with sequential illumination.

Fluorescence microscopy is an extremely powerful technique that allows to distinguish multiple labels based on their emission color or other properties, such as their photobleaching and fluorescence recovery kinetics. These kinetics are ideally assumed to be mono-exponential in nature, where the time constants intrinsic to each fluorophore can be used to quantify their presence in the sample. However, these time constants also depend on the specifics of the illumination and sample conditions, meaning that identifying the different contributions in a mixture using a single-channel detection may not be straightforward. In this work, we propose a factor analysis approach called Slicing to identify the different contributions in a multiplexed fluorescence microscopy image exploiting a single measurement channel. With Slicing, a two-way dataset is rearranged into a three-way dataset, which allows the application of a trilinear decomposition model to derive individual profiles for all the model components. We demonstrate this method on bleaching - recovery fluorescence microscopy imaging data of U2OS cells, allowing us to determine the spatial distribution of the dyes and their associated characteristic relaxation traces, without relying on a parametric fitting. By requiring little a priori knowledge and efficiently handling perturbation factors, our method represents a general approach for the recovery of multiple mono-exponential profiles from single-channel microscopy data.

Self-contained and modular structured illumination microscope.

We present a modular implementation of structured illumination microscopy (SIM) that is fast, largely self-contained and that can be added onto existing fluorescence microscopes. Our strategy, which we call HIT-SIM, can theoretically deliver well over 50 super-resolved images per second and is readily compatible with existing acquisition software packages. We provide a full technical package consisting of schematics, a list of components and an alignment scheme that provides detailed specifications and assembly instructions. We illustrate the performance of the instrument by imaging optically large samples containing sequence-specifically stained DNA fragments.

Design of experiments for the optimization of SOFI super-resolution microscopy imaging.

Super-resolution optical fluctuation imaging (SOFI) is a well-known super-resolution technique appreciated for its versatility and broad applicability. However, even though an extended theoretical description is available, it is still not fully understood how the interplay between different experimental parameters influences the quality of a SOFI image. We investigated the relationship between five experimental parameters (measurement time, on-time t on, off-time t off, probe brightness, and out of focus background) and the quality of the super-resolved images they yielded, expressed as Signal to Noise Ratio (SNR). Empirical relationships were modeled for second- and third-order SOFI using data simulated according to a D-Optimal design of experiments, which is an ad-hoc design built to reduce the experimental load when the total number of trials to be conducted becomes too high for practical applications. This approach proves to be more reliable and efficient for parameter optimization compared to the more classical parameter by parameter approach. Our results indicate that the best image quality is achieved for the fastest emitter blinking (lowest t on and t off), lowest background level, and the highest measurement duration, while the brightness variation does not affect the quality in a statistically significant way within the investigated range. However, when the ranges spanned by the parameters are constrained, a different set of optimal conditions may arise. For example, for second-order SOFI, we identified situations in which the increase of t off can be beneficial to SNR, such as when the measurement duration is long enough. In general, optimal values of t on and t off have been found to be highly dependent from each other and from the measurement duration.

Spectral imaging for characterization and detection of plastic substances in branded teabags.

The addition of plastic substances in teabags is of increasing concern for conscious consumers due to the harmful effects on the environment and the potential threats to human health. This work introduces an innovative and cost-effective approach to detect and quantify plastic substances in teabags by applying near infrared hyperspectral imaging (951-2496 nm) coupled with multivariate analysis. Teabags from 6 popular brands were investigated and categorized into three classes based on spectral unmixing and target detection results: 1) the plastic teabag primarily made of nylon 6/6; 2) those made of a composite with various polypropylene and cellulose ratios; 3) biodegradable teabags free from any plastic traces. Results demonstrated the presence of numerous plastic particles in the beverage obtained after steeping nylon teabags, but the release of particles was further amplified after microwave treatment. Nevertheless, target detection results obtained from Fourier transform infrared imaging (4000-675 cm-1) dataset evidenced that a considerable proportion of particle residues detected were the contaminants obtained from tea granules that adsorbed on the teabag. This work highlights the significant importance of performing rigorous spectral analysis for chemical characterization, which is lacking in most published microplastic studies.

Simultaneous readout of multiple FRET pairs using photochromism.

Förster resonant energy transfer (FRET) is a powerful mechanism to probe associations in situ. Simultaneously performing more than one FRET measurement can be challenging due to the spectral bandwidth required for the donor and acceptor fluorophores. We present an approach to distinguish overlapping FRET pairs based on the photochromism of the donor fluorophores, even if the involved fluorophores display essentially identical absorption and emission spectra. We develop the theory underlying this method and validate our approach using numerical simulations. To apply our system, we develop rsAKARev, a photochromic biosensor for cAMP-dependent protein kinase (PKA), and combine it with the spectrally-identical biosensor EKARev, a reporter for extracellular signal-regulated kinase (ERK) activity, to deliver simultaneous readout of both activities in the same cell. We further perform multiplexed PKA, ERK, and calcium measurements by including a third, spectrally-shifted biosensor. Our work demonstrates that exploiting donor photochromism in FRET can be a powerful approach to simultaneously read out multiple associations within living cells.

Smoothness correction for better SOFI imaging.

Sub-diffraction or super-resolution fluorescence imaging allows the visualization of the cellular morphology and interactions at the nanoscale. Statistical analysis methods such as super-resolution optical fluctuation imaging (SOFI) obtain an improved spatial resolution by analyzing fluorophore blinking but can be perturbed by the presence of non-stationary processes such as photodestruction or fluctuations in the illumination. In this work, we propose to use Whittaker smoothing to remove these smooth signal trends and retain only the information associated to independent blinking of the emitters, thus enhancing the SOFI signals. We find that our method works well to correct photodestruction, especially when it occurs quickly. The resulting images show a much higher contrast, strongly suppressed background and a more detailed visualization of cellular structures. Our method is parameter-free and computationally efficient, and can be readily applied on both two-dimensional and three-dimensional data.

Structure-Function Dataset Reveals Environment Effects within a Fluorescent Protein Model System

Anisotropic environments can drastically alter the spectroscopy and photochemistry of molecules, leading to complex structure-function relationships. We examined this using fluorescent proteins as easy-to-modify model systems. Starting from a single scaffold, we have developed a range of 27 photochromic fluorescent proteins that cover a broad range of spectroscopic properties, including the determination of 43 crystal structures. Correlation and principal component analysis confirmed the complex relationship between structure and spectroscopy, but also allowed us to identify consistent trends and to relate these to the spatial organization. We find that changes in spectroscopic properties can come about through multiple underlying mechanisms, of which polarity, hydrogen bonding and presence of water molecules are key modulators. We anticipate that our findings and rich structure/spectroscopy dataset can open opportunities for the development and evaluation of new and existing protein engineering methods.

Deep learning for classification of time series spectral images using combined multi-temporal and spectral features.

Time series spectral imaging facilitates a comprehensive understanding of the underlying dynamics of multi-component systems and processes. Most existing classification strategies focus exclusively on the spectral features and they tend to fail when spectra between classes closely resemble each other. This work proposes a hybrid approach of principal component analysis (PCA) and deep learning (i.e., long short-term memory (LSTM) model) for incorporating and utilizing the combined multi-temporal and spectral information from time series spectral imaging datasets. An example data, consisting of times series spectral images of casein-based biopolymers, was used to illustrate and evaluate the proposed hybrid approach. Compared to using partial least squares discriminant analysis (PLSDA), the proposed PCA-LSTM method applying the same spectral pretreatment achieved substantial improvement in the pixel-wise classification (i.e., accuracy increased from 59.97% of PLSDA to 85.73% of PCA-LSTM). When projecting the pixel-wise model to object-based classification, the PCA-LSTM approach produced an accuracy of 100%, correctly classifying the whole 21 film samples in the independent test set, while PLSDA only led to an accuracy of 80.95%. The proposed method is powerful and versatile in utilizing distinctive characteristics of time dependencies from multivariate time series dataset, which could be adapted to suit non-congruent images over time sequences as well as spectroscopic data.

Separation of spectrally overlapping fluorophores using intra-exposure excitation modulation.

Multicolor fluorescence imaging is an excellent method for the simultaneous visualization of multiple structures, although it is limited by the available spectral window. More labels can be measured by distinguishing these on properties, such as their fluorescence dynamics, but usually these dynamics must be directly resolvable by the instrument. We propose an approach to distinguish emitters over a much broader range of light-induced dynamics by combining fast modulation of the light source with the detection of the time-integrated fluorescence. We demonstrate our method by distinguishing four spectrally overlapping photochromic fluorophores within Escherichia coli bacteria, showing that we can accurately classify all four probes by acquiring just two to four fluorescence images. Our strategy expands the range of probes and processes that can be used for fluorescence multiplexing.

A spatial constraint to model and extract texture components in Multivariate Curve Resolution of near-infrared hyperspectral images.

This article highlights the importance of properly taking into account spatial structures and features to better resolve near-infrared (NIR) hyperspectral images by Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS), especially when highly mixed components (in terms of spatial and spectral overlap) underlying the systems under study are dealt with. As in the NIR domain these components can explain both chemical properties and physical phenomena, their improved unravelling can therefore represent an alternative or a complement to more standard approaches for, e.g., spectral data preprocessing. These points will be illustrated through the comprehensive analysis of a complex real-world forensic case-study where texture characterization is crucial for the sake of a more appropriate resolution.

Mechanistic investigation of mEos4b reveals a strategy to reduce track interruptions in sptPALM.

Green-to-red photoconvertible fluorescent proteins repeatedly enter dark states, causing interrupted tracks in single-particle-tracking localization microscopy (sptPALM). We identified a long-lived dark state in photoconverted mEos4b that results from isomerization of the chromophore and efficiently absorbs cyan light. Addition of weak 488-nm light swiftly reverts this dark state to the fluorescent state. This strategy largely eliminates slow blinking and enables the recording of longer tracks in sptPALM with minimum effort.

Effect of image processing constraints on the extent of rotational ambiguity in MCR-ALS of hyperspectral images.

Hyperspectral imaging is a way to explore the spatial and spectral information of the different compounds in chemical or biological samples. In addition, multivariate curve resolution - alternating least squares (MCR-ALS) can be used to extract this information based on the bilinearity assumption. However, it is well-known that using proper constraints can reduce the amount of uncertainty in the results of MCR, which is called rotational ambiguity. In MCR-ALS analysis of hyperspectral images, different image processing techniques, such as model fitting, image segmentation or sparse image recovery can be applied as spatial constraints. In this contribution, we aim to investigate how the use of these spatial constraints limits the extent of rotational ambiguity of MCR-ALS solutions. For this purpose, we evaluate the extent of rotational ambiguity and use Borgen plots to visualize it. We show on simulations and real hyperspectral imaging data that accuracy of the results is improved when spatial constraints are applied.

Application of a sparseness constraint in multivariate curve resolution - Alternating least squares.

The use of sparseness in chemometrics is a concept that has increased in popularity. The advantage is, above all, a better interpretability of the results obtained. In this work, sparseness is implemented as a constraint in multivariate curve resolution - alternating least squares (MCR-ALS), which aims at reproducing raw (mixed) data by a bilinear model of chemically meaningful profiles. In many cases, the mixed raw data analyzed are not sparse by nature, but their decomposition profiles can be, as it is the case in some instrumental responses, such as mass spectra, or in concentration profiles linked to scattered distribution maps of powdered samples in hyperspectral images. To induce sparseness in the constrained profiles, one-dimensional and/or two-dimensional numerical arrays can be fitted using a basis of Gaussian functions with a penalty on the coefficients. In this work, a least squares regression framework with L0-norm penalty is applied. This L0-norm penalty constrains the number of non-null coefficients in the fit of the array constrained without having an a priori on the number and their positions. It has been shown that the sparseness constraint induces the suppression of values linked to uninformative channels and noise in MS spectra and improves the location of scattered compounds in distribution maps, resulting in a better interpretability of the constrained profiles. An additional benefit of the sparseness constraint is a lower ambiguity in the bilinear model, since the major presence of null coefficients in the constrained profiles also helps to limit the solutions for the profiles in the counterpart matrix of the MCR bilinear model.

Edge-Preserving Image Smoothing Constraint in Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) of Hyperspectral Data.

This article explores smoothing with edge-preserving properties as a spatial constraint for the resolution of hyperspectral images with multivariate curve resolution-alternating least squares (MCR-ALS). For each constrained component image (distribution map), irrelevant spatial details and noise are smoothed applying an L1- or L0-norm penalized least squares regression, highlighting in this way big changes in intensity of adjacent pixels. The feasibility of the constraint is demonstrated on three different case studies, in which the objects under investigation are spatially clearly defined, but have significant spectral overlap. This spectral overlap is detrimental for obtaining a good resolution and additional spatial information should be provided. The final results show that the spatial constraint enables better image (map) abstraction, artifact removal, and better interpretation of the results obtained, compared to a classical MCR-ALS analysis of hyperspectral images.