RASP: Optimal Single Puncta Detection in Complex Cellular Backgrounds.

Super-resolution and single-molecule microscopies have been increasingly applied to complex biological systems. A major challenge of these approaches is that fluorescent puncta must be detected in the low signal, high noise, heterogeneous background environments of cells and tissue. We present RASP, Radiality Analysis of Single Puncta, a bioimaging-segmentation method that solves this problem. RASP removes false-positive puncta that other analysis methods detect and detects features over a broad range of spatial scales: from single proteins to complex cell phenotypes. RASP outperforms the state-of-the-art methods in precision and speed using image gradients to separate Gaussian-shaped objects from the background. We demonstrate RASP's power by showing that it can extract spatial correlations between microglia, neurons, and α-synuclein oligomers in the human brain. This sensitive, computationally efficient approach enables fluorescent puncta and cellular features to be distinguished in cellular and tissue environments, with sensitivity down to the level of the single protein. Python and MATLAB codes, enabling users to perform this RASP analysis on their own data, are provided as Supporting Information and links to third-party repositories.

Single-molecule imaging in the primary cilium.

The primary cilium is an important signaling organelle critical for normal development and tissue homeostasis. Its small dimensions and complexity necessitate advanced imaging approaches to uncover the molecular mechanisms behind its function. Here, we outline how single-molecule fluorescence microscopy can be used for tracking molecular dynamics and interactions and for super-resolution imaging of nanoscale structures in the primary cilium. Specifically, we describe in detail how to capture and quantify the 2D dynamics of individual transmembrane proteins PTCH1 and SMO and how to map the 3D nanoscale distributions of the inversin compartment proteins INVS, ANKS6, and NPHP3. This protocol can, with minor modifications, be adapted for studies of other proteins and cell lines to further elucidate the structure and function of the primary cilium at the molecular level.

THY1-mediated mechanisms converge to drive YAP activation in skin homeostasis and repair.

Anchored cells of the basal epidermis constantly undergo proliferation in an overcrowded environment. An important regulator of epidermal proliferation is YAP, which can be controlled by both cell-matrix and cell-cell interactions. Here, we report that THY1, a GPI-anchored protein, inhibits epidermal YAP activity through converging molecular mechanisms. THY1 deficiency leads to increased adhesion by activating the integrin-ß1-SRC module. Notably, regardless of high cellular densities, the absence of THY1 leads to the dissociation of an adherens junction complex that enables the release and translocation of YAP. Due to increased YAP-dependent proliferation, Thy1-/- mice display enhanced wound repair and hair follicle regeneration. Taken together, our work reveals THY1 as a crucial regulator of cell-matrix and cell-cell interactions that controls YAP activity in skin homeostasis and regeneration.

Quantifying cell-cycle-dependent chromatin dynamics during interphase by live 3D tracking.

The study of cell cycle progression and regulation is important to our understanding of fundamental biophysics, aging, and disease mechanisms. Local chromatin movements are generally considered to be constrained and relatively consistent during all interphase stages, although recent advances in our understanding of genome organization challenge this claim. Here, we use high spatiotemporal resolution, 4D (x, y, z and time) localization microscopy by point-spread-function (PSF) engineering and deep learning-based image analysis, for live imaging of mouse embryonic fibroblast (MEF 3T3) and MEF 3T3 double Lamin A Knockout (LmnaKO) cell lines, to characterize telomere diffusion during the interphase. We detected varying constraint levels imposed on chromatin, which are prominently decreased during G0/G1. Our 4D measurements of telomere diffusion offer an effective method to investigate chromatin dynamics and reveal cell-cycle-dependent motion constraints, which may be caused by various cellular processes.

Synthetic cells with self-activating optogenetic proteins communicate with natural cells.

Development of regulated cellular processes and signaling methods in synthetic cells is essential for their integration with living materials. Light is an attractive tool to achieve this, but the limited penetration depth into tissue of visible light restricts its usability for in-vivo applications. Here, we describe the design and implementation of bioluminescent intercellular and intracellular signaling mechanisms in synthetic cells, dismissing the need for an external light source. First, we engineer light generating SCs with an optimized lipid membrane and internal composition, to maximize luciferase expression levels and enable high-intensity emission. Next, we show these cells' capacity to trigger bioprocesses in natural cells by initiating asexual sporulation of dark-grown mycelial cells of the fungus Trichoderma atroviride. Finally, we demonstrate regulated transcription and membrane recruitment in synthetic cells using bioluminescent intracellular signaling with self-activating fusion proteins. These functionalities pave the way for deploying synthetic cells as embeddable microscale light sources that are capable of controlling engineered processes inside tissues.

High-Throughput Imaging of CRISPR- and Recombinant Adeno-Associated Virus-Induced DNA Damage Response in Human Hematopoietic Stem and Progenitor Cells.

CRISPR-Cas technology has revolutionized gene editing, but concerns remain due to its propensity for off-target interactions. This, combined with genotoxicity related to both CRISPR-Cas9-induced double-strand breaks and transgene delivery, poses a significant liability for clinical genome-editing applications. Current best practice is to optimize genome-editing parameters in preclinical studies. However, quantitative tools that measure off-target interactions and genotoxicity are costly and time-consuming, limiting the practicality of screening large numbers of potential genome-editing reagents and conditions. Here, we show that flow-based imaging facilitates DNA damage characterization of hundreds of human hematopoietic stem and progenitor cells per minute after treatment with CRISPR-Cas9 and recombinant adeno-associated virus serotype 6. With our web-based platform that leverages deep learning for image analysis, we find that greater DNA damage response is observed for guide RNAs with higher genome-editing activity, differentiating even single on-target guide RNAs with different levels of off-target interactions. This work simplifies the characterization and screening process of genome-editing parameters toward enabling safer and more effective gene-therapy applications.

Multiplexed PSF Engineering for Three-Dimensional Multicolor Particle Tracking.

Three-dimensional spatiotemporal tracking of microscopic particles in multiple colors is a challenging optical imaging task. Existing approaches require a trade-off between photon efficiency, field of view, mechanical complexity, spectral specificity, and speed. Here, we introduce multiplexed point-spread-function engineering that achieves photon-efficient, 3D multicolor particle tracking over a large field of view. This is accomplished by first chromatically splitting the emission path of a microscope to different channels, engineering the point-spread function of each, and then recombining them onto the same region of the camera. We demonstrate our technique for simultaneously tracking five types of emitters in vitro as well as colocalization of DNA loci in live yeast cells.

Automated Analysis of Fluorescence Kinetics in Single-Molecule Localization Microscopy Data Reveals Protein Stoichiometry.

Understanding the function of protein complexes requires information on their molecular organization, specifically, their oligomerization level. Optical super-resolution microscopy can localize single protein complexes in cells with high precision, however, the quantification of their oligomerization level, remains a challenge. Here, we present a Quantitative Algorithm for Fluorescent Kinetics Analysis (QAFKA), that serves as a fully automated workflow for quantitative analysis of single-molecule localization microscopy (SMLM) data by extracting fluorophore "blinking" events. QAFKA includes an automated localization algorithm, the extraction of emission features per localization cluster, and a deep neural network-based estimator that reports the ratios of cluster types within the population. We demonstrate molecular quantification of protein monomers and dimers on simulated and experimental SMLM data. We further demonstrate that QAFKA accurately reports quantitative information on the monomer/dimer equilibrium of membrane receptors in single immobilized cells, opening the door to single-cell single-protein analysis.

3D printable diffractive optical elements by liquid immersion.

Diffractive optical elements (DOEs) are used to shape the wavefront of incident light. This can be used to generate practically any pattern of interest, albeit with varying efficiency. A fundamental challenge associated with DOEs comes from the nanoscale-precision requirements for their fabrication. Here we demonstrate a method to controllably scale up the relevant feature dimensions of a device from tens-of-nanometers to tens-of-microns by immersing the DOEs in a near-index-matched solution. This makes it possible to utilize modern 3D-printing technologies for fabrication, thereby significantly simplifying the production of DOEs and decreasing costs by orders of magnitude, without hindering performance. We demonstrate the tunability of our design for varying experimental conditions, and the suitability of this approach to ultrasensitive applications by localizing the 3D positions of single molecules in cells using our microscale fabricated optical element to modify the point-spread-function (PSF) of a microscope.

Learning Optimal Wavefront Shaping for Multi-Channel Imaging.

Fast acquisition of depth information is crucial for accurate 3D tracking of moving objects. Snapshot depth sensing can be achieved by wavefront coding, in which the point-spread function (PSF) is engineered to vary distinctively with scene depth by altering the detection optics. In low-light applications, such as 3D localization microscopy, the prevailing approach is to condense signal photons into a single imaging channel with phase-only wavefront modulation to achieve a high pixel-wise signal to noise ratio. Here we show that this paradigm is generally suboptimal and can be significantly improved upon by employing multi-channel wavefront coding, even in low-light applications. We demonstrate our multi-channel optimization scheme on 3D localization microscopy in densely labelled live cells where detectability is limited by overlap of modulated PSFs. At extreme densities, we show that a split-signal system, with end-to-end learned phase masks, doubles the detection rate and reaches improved precision compared to the current state-of-the-art, single-channel design. We implement our method using a bifurcated optical system, experimentally validating our approach by snapshot volumetric imaging and 3D tracking of fluorescently labelled subcellular elements in dense environments.

Microscopic scan-free surface profiling over extended axial ranges by point-spread-function engineering.

The shape of a surface, i.e., its topography, influences many functional properties of a material; hence, characterization is critical in a wide variety of applications. Two notable challenges are profiling temporally changing structures, which requires high-speed acquisition, and capturing geometries with large axial steps. Here, we leverage point-spread-function engineering for scan-free, dynamic, microsurface profiling. The presented method is robust to axial steps and acquires full fields of view at camera-limited framerates. We present two approaches for implementation: fluorescence-based and label-free surface profiling, demonstrating the applicability to a variety of sample geometries and surface types.

Publisher Correction: DeepSTORM3D: dense 3D localization microscopy and PSF design by deep learning.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

DeepSTORM3D: dense 3D localization microscopy and PSF design by deep learning.

An outstanding challenge in single-molecule localization microscopy is the accurate and precise localization of individual point emitters in three dimensions in densely labeled samples. One established approach for three-dimensional single-molecule localization is point-spread-function (PSF) engineering, in which the PSF is engineered to vary distinctively with emitter depth using additional optical elements. However, images of dense emitters, which are desirable for improving temporal resolution, pose a challenge for algorithmic localization of engineered PSFs, due to lateral overlap of the emitter PSFs. Here we train a neural network to localize multiple emitters with densely overlapping Tetrapod PSFs over a large axial range. We then use the network to design the optimal PSF for the multi-emitter case. We demonstrate our approach experimentally with super-resolution reconstructions of mitochondria and volumetric imaging of fluorescently labeled telomeres in cells. Our approach, DeepSTORM3D, enables the study of biological processes in whole cells at timescales that are rarely explored in localization microscopy.

VIPR: vectorial implementation of phase retrieval for fast and accurate microscopic pixel-wise pupil estimation.

In microscopy, proper modeling of the image formation has a substantial effect on the precision and accuracy in localization experiments and facilitates the correction of aberrations in adaptive optics experiments. The observed images are subject to polarization effects, refractive index variations, and system specific constraints. Previously reported techniques have addressed these challenges by using complicated calibration samples, computationally heavy numerical algorithms, and various mathematical simplifications. In this work, we present a phase retrieval approach based on an analytical derivation of the vectorial diffraction model. Our method produces an accurate estimate of the system's phase information, without any prior knowledge about the aberrations, in under a minute.

Three-dimensional localization microscopy in live flowing cells.

Capturing the dynamics of live cell populations with nanoscale resolution poses a significant challenge, primarily owing to the speed-resolution trade-off of existing microscopy techniques. Flow cytometry would offer sufficient throughput, but lacks subsample detail. Here we show that imaging flow cytometry, in which the point detectors of flow cytometry are replaced with a camera to record 2D images, is compatible with 3D localization microscopy through point-spread-function engineering, which encodes the depth of the emitter into the emission pattern captured by the camera. The extraction of 3D positions from sub-cellular objects of interest is achieved by calibrating the depth-dependent response of the imaging system using fluorescent beads mixed with the sample buffer. This approach enables 4D imaging of up to tens of thousands of objects per minute and can be applied to characterize chromatin dynamics and the uptake and spatial distribution of nanoparticles in live cancer cells.

Single-Particle Diffusion Characterization by Deep Learning.

Diffusion plays a crucial role in many biological processes including signaling, cellular organization, transport mechanisms, and more. Direct observation of molecular movement by single-particle-tracking experiments has contributed to a growing body of evidence that many cellular systems do not exhibit classical Brownian motion but rather anomalous diffusion. Despite this evidence, characterization of the physical process underlying anomalous diffusion remains a challenging problem for several reasons. First, different physical processes can exist simultaneously in a system. Second, commonly used tools for distinguishing between these processes are based on asymptotic behavior, which is experimentally inaccessible in most cases. Finally, an accurate analysis of the diffusion model requires the calculation of many observables because different transport modes can result in the same diffusion power-law α, which is typically obtained from the mean-square displacements (MSDs). The outstanding challenge in the field is to develop a method to extract an accurate assessment of the diffusion process using many short trajectories with a simple scheme that is applicable at the nonexpert level. Here, we use deep learning to infer the underlying process resulting in anomalous diffusion. We implement a neural network to classify single-particle trajectories by diffusion type: Brownian motion, fractional Brownian motion and continuous time random walk. Further, we demonstrate the applicability of our network architecture for estimating the Hurst exponent for fractional Brownian motion and the diffusion coefficient for Brownian motion on both simulated and experimental data. These networks achieve greater accuracy than time-averaged MSD analysis on simulated trajectories while only requiring as few as 25 steps. When tested on experimental data, both net and ensemble MSD analysis converge to similar values; however, the net needs only half the number of trajectories required for ensemble MSD to achieve the same confidence interval. Finally, we extract diffusion parameters from multiple extremely short trajectories (10 steps) using our approach.

Motional dynamics of single Patched1 molecules in cilia are controlled by Hedgehog and cholesterol.

The Hedgehog-signaling pathway is an important target in cancer research and regenerative medicine; yet, on the cellular level, many steps are still poorly understood. Extensive studies of the bulk behavior of the key proteins in the pathway established that during signal transduction they dynamically localize in primary cilia, antenna-like solitary organelles present on most cells. The secreted Hedgehog ligand Sonic Hedgehog (SHH) binds to its receptor Patched1 (PTCH1) in primary cilia, causing its inactivation and delocalization from cilia. At the same time, the transmembrane protein Smoothened (SMO) is released of its inhibition by PTCH1 and accumulates in cilia. We used advanced, single molecule-based microscopy to investigate these processes in live cells. As previously observed for SMO, PTCH1 molecules in cilia predominantly move by diffusion and less frequently by directional transport, and spend a fraction of time confined. After treatment with SHH we observed two major changes in the motional dynamics of PTCH1 in cilia. First, PTCH1 molecules spend more time as confined, and less time freely diffusing. This result could be mimicked by a depletion of cholesterol from cells. Second, after treatment with SHH, but not after cholesterol depletion, the molecules that remain in the diffusive state showed a significant increase in the diffusion coefficient. Therefore, PTCH1 inactivation by SHH changes the diffusive motion of PTCH1, possibly by modifying the membrane microenvironment in which PTCH1 resides.

Multicolor localization microscopy and point-spread-function engineering by deep learning.

Deep learning has become an extremely effective tool for image classification and image restoration problems. Here, we apply deep learning to microscopy and demonstrate how neural networks can exploit the chromatic dependence of the point-spread function to classify the colors of single emitters imaged on a grayscale camera. While existing localization microscopy methods for spectral classification require additional optical elements in the emission path, e.g., spectral filters, prisms, or phase masks, our neural net correctly identifies static and mobile emitters with high efficiency using a standard, unmodified single-channel configuration. Furthermore, we show how deep learning can be used to design new phase-modulating elements that, when implemented into the imaging path, result in further improved color differentiation between species, including simultaneously differentiating four species in a single image.

Revealing Nanoscale Morphology of the Primary Cilium Using Super-Resolution Fluorescence Microscopy.

Super-resolution (SR) microscopy has been used to observe structural details beyond the diffraction limit of ∼250 nm in a variety of biological and materials systems. By combining this imaging technique with both computer-vision algorithms and topological methods, we reveal and quantify the nanoscale morphology of the primary cilium, a tiny tubular cellular structure (∼2-6 µm long and 200-300 nm in diameter). The cilium in mammalian cells protrudes out of the plasma membrane and is important in many signaling processes related to cellular differentiation and disease. After tagging individual ciliary transmembrane proteins, specifically Smoothened, with single fluorescent labels in fixed cells, we use three-dimensional (3D) single-molecule SR microscopy to determine their positions with a precision of 10-25 nm. We gain a dense, pointillistic reconstruction of the surfaces of many cilia, revealing large heterogeneity in membrane shape. A Poisson surface reconstruction algorithm generates a fine surface mesh, allowing us to characterize the presence of deformations by quantifying the surface curvature. Upon impairment of intracellular cargo transport machinery by genetic knockout or small-molecule treatment of cells, our quantitative curvature analysis shows significant morphological differences not visible by conventional fluorescence microscopy techniques. Furthermore, using a complementary SR technique, two-color, two-dimensional stimulated emission depletion microscopy, we find that the cytoskeleton in the cilium, the axoneme, also exhibits abnormal morphology in the mutant cells, similar to our 3D results on the Smoothened-measured ciliary surface. Our work combines 3D SR microscopy and computational tools to quantitatively characterize morphological changes of the primary cilium under different treatments and uses stimulated emission depletion to discover correlated changes in the underlying structure. This approach can be useful for studying other biological or nanoscale structures of interest.

Ultrasensitive Refractometry via Supercritical Angle Fluorescence.

Refractometry, namely, the measurement of refractive index (RI), provides information about various sample properties such as concentrations and molecular structure. One physical phenomenon which enables precise determination of a sample's RI in a microscope is the supercritical-angle fluorescence. This effect is observed when the fluorescence from an emitter near a glass-medium interface is captured by an objective lens with a high numerical aperture. The materials' index mismatch creates a distinguishable transition in the intensity pattern at the back focal plane of the objective that changes proportionally to the RI of the media. Here, we present a refractometry approach in which the fluorophores are preattached to the bottom surface of a microfluidic channel, enabling highly sensitive determination of the RI using tiny amounts of liquid (picoliters). With this method, we attained a standard deviation of 3.1 × 10-5 and a repeatability of 2.7 × 10-5 RI units. We first determine the capabilities of the device for glycerol-water solutions and then demonstrate the relevance of our system for monitoring changes in biological systems. As a model system, we show that we can detect single bacteria ( Escherichia coli) and measure population growth.