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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.

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.

Diffractive optical elements, used to shape the wavefront of incident light, are ubiquitous in optics thanks to the high flexibility in design and compactness that they offer [1]. However, the nanoscale-precision requirements typically necessitates a complicated fabrication process, e.g. nanolithography. Here we present the liquid immersed DOE. By immersing the DOE in liquid, i.e. decreasing the difference between the refractive indices of the DOE and the surrounding media, we can scale up the dimensions of the DOE from the nano-scale to the micro-scale (Fig. 1 ab λ is the wavelength; h is the height of the DOE; and n refers to the refractive index of the DOE material and surrounding media, respectively.

Real-time microscopic tracking of multicolor fluorescent probes in 3D is of key importance for understanding a variety of biological processes. Point-Spread-Function (PSF) engineering is an effective tool for such tasks – by modulating the PSF of the optical system, emitters can be tracked with nm-scale resolution in 3D by means of computational analysis [1] . Existing approaches that make use of PSF engineering for high spatiotemporal-resolution tracking, while aiming to provide spectral information, typically separate the light to multiple channels, thus have a reduced Field of View (FOVs) [2] . Alternatively, multicolor phase masks are used, which suffer from low efficiency due to strong optical constraints and high sensitivity to manufacturing errors [3] .

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.

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 synthesis and application of blue-light-generating synthetic cells using bioluminescence, dismissing the need for an external light source. First, the lipid membrane and internal composition of light-producing synthetic cells were optimized to enable high-intensity emission. Next, we show these cells' capacity for triggering bioprocesses in natural cells by initiating asexual sporulation of dark-grown mycelial cells of the fungus Trichoderma atroviride in a quorum-sensing like mechanism. Finally, we demonstrate regulated transcription and membrane recruitment in synthetic cells using bioluminescent self-activating fusion proteins. These functionalities pave the way for deploying synthetic cells as embeddable microscale light sources that are capable of activating engineered processes inside tissues.

Attaining three-dimensional data at high throughput is a grand challenge in microscopy. I will discuss two recent contributions to 3D microscopy utilizing point-spread-function (PSF) engineering: 1.a new method that extends the capabilities of flow-based imaging to 3D co-localization microscopy, and 2. efficient and fast localization of dense molecules in volumetric single-molecule-localization microscopy (SMLM) using deep learning, termed DeepSTORM3D. Together, these methods enable the study of sub-cellular biological processes at unprecedented timescales and throughputs.

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-β

In this talk I will describe how joint optimization of the microscope’s point-spread-function alongside the image processing algorithm, both using neural nets, enables dense emitter fitting for super-resolution microscopy and other challenging volumetric microscopy applications.

We demonstrate photon-efficient, multicolor, large FOV 3D localization microscopy with high spatiotemporal resolution by multiplexing spectrally-defined, single-channel PSFs. This is accomplished by encoding spectral and 3D information into the PSFs.

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.

Illuminated points reveal 3D microscopic surface topography., 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.

Significance Primary cilia are antenna-like sensory organelles critical for many signal transduction pathways. One such pathway, called Hedgehog signaling, is essential for normal embryonic development and linked to tissue homeostasis and tumorigenesis. While the tightly regulated spatial and temporal localization of proteins in cilia controls activity, the mechanistic details are not fully understood. To characterize the movements of the Hedgehog receptor Patched1 in the cilium, we used single-molecule fluorescence microscopy, a tool that separates the behavior of individual proteins from the ensemble average, unveiling the underlying hidden physical behaviors. Our study reveals Hedgehog-induced changes in the motion of individual Patched1 molecules, which precede the exodus of Patched1 from cilia. These changes constitute one of the earliest measurable steps of Hedgehog-signal transduction., 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.

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 phase information (without any prior knowledge) in under a minute.

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 ...

The structural organization and dynamics of DNA are known to be of paramount importance in countless cellular processes, but capturing these events poses a unique challenge. Fluorescence microscopy is well suited for these live-cell investigations, but requires attaching fluorescent labels to the species under investigation. Over the past several decades, a suite of techniques have been developed for labeling and imaging DNA, each with various advantages and drawbacks. Here, we provide an overview of the labeling and imaging tools currently available for visualizing DNA in live cells, and discuss their suitability for various applications.

Point-spread-function engineering enables the extraction of depth information from fluorescence microscopy images by sacrificing lateral resolution and labeling density. Here, we introduce a bifurcated multimodal approach that facilitates 3D super-resolution in densely labeled samples.

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 subcellular 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.

Imaging flow cytometry replaces the canonical point-source detector of flow cytometry with a camera, unveiling subsample details in 2D images while maintaining high-throughput. Here we show that the technique is inherently compatible with 3D localization microscopy by point-spread-function engineering, namely the encoding of emitter depth in the emission pattern captured by a camera. By exploiting the laminar-flow profile in microfluidics, 3D positions can be extracted from cells or other objects of interest by calibrating the depth-dependent response of the imaging system using fluorescent microspheres mixed with the sample buffer. We demonstrate this approach for measuring fluorescently-labeled DNAin vitroand the chromosomal compaction state in large populations of live cells, collecting thousands of samples each minute. Furthermore, our approach is fully compatible with existing commercial apparatus, and can extend the imaging volume of the device, enabling faster flowrates thereby increasing throughput.

Deep learning has become an extremely effective tool for image classification and image restoration problems. Here, we address two fundamental problems of localization microscopy using machine learning: emitter density, and color determination. Modern microscopy can produce images of biological specimen at very high (super) resolution, by precisely determining the positions of numerous blinking light emitting molecules over time. To achieve fast acquisition time, a high density of molecules is required, which poses a significant challenge in terms of image processing. Existing approaches use elaborate algorithms with many parameters that require tuning and a long computation time. Here, we report an ultra-fast, precise, and parameter-free method for super-resolution microscopy that utilizes deep-learning: by feeding the computer images of dense molecules along with their correct positions, it is trained to automatically produce super-resolution images from blinking data. Next, we 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 single-molecule 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 as well as mobile emitters with high efficiency using a standard, unmodified single-channel configuration – based on inherent chromatic aberrations in a standard microscope. Finally, we demonstrate how deep learning can be used to design phase-modulating elements that, when implemented into the imaging path, result in further improved color differentiation between species. While point-spread-function engineering for spectral differentiation has been implemented in various applications in recent years, the optimal way to design such a PSF remains unclear. Here, we use a neural net to perform such design automatically, directly optimizing the desired cost function, namely, simultaneous localization and color detection of point emitters.

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 to distinguish between these processes are based on asymptotic behavior, which is inaccessible experimentally in most cases. Finally, an accurate analysis of the diffusion model requires the calculation of many observables, since different transport modes can result in the same diffusion power-law α, that is obtained from the commonly used mean-squared-displacement (MSD) in its various forms. 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 non-expert 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 according to diffusion type – Brownian motion, fractional Brownian motion (FBM) and Continuous Time Random Walk (CTRW). We further use the net to estimate the Hurst exponent for FBM, and the diffusion coefficient for Brownian motion, demonstrating its applicability on simulated and experimental data. The networks outperform time averaged MSD analysis on simulated trajectories while requiring as few as 25 time-steps. Furthermore, when tested on experimental data, both network and ensemble MSD analysis converge to similar values, with the network requiring half the trajectories required for ensemble MSD. Finally, we use the nets to extract diffusion parameters from multiple extremely short trajectories (10 steps).

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.

By imaging the back focal plane of a fluorescence microscope, we demonstrate precise determination of a sample’s refractive index in a fluorophore-laden microfluidic device. We apply the technique to biosensing by detecting unlabeled bacteria.

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 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 (SMO), 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 (PSR) 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, 2-color, 2D STimulated Emission Depletion (STED) 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 SMO-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 STED to discover correlated changes in the underlying structure. This approach can be useful for studying other biological or nanoscale structures of interest.

We present an ultra-fast, precise, parameter-free method, which we term Deep-STORM, for obtaining super-resolution images from stochastically-blinking emitters, such as fluorescent molecules used for localization microscopy. Deep-STORM uses a deep convolutional neural network that can be trained on simulated data or experimental measurements, both of which are demonstrated. The method achieves state-of-the-art resolution under challenging signal-to-noise conditions and high emitter densities, and is significantly faster than existing approaches. Additionally, no prior information on the shape of the underlying structure is required, making the method applicable to any blinking data-set. We validate our approach by super-resolution image reconstruction of simulated and experimentally obtained data., 7 pages, added code download reference and DOI for the journal version

We present, in experiments and simulations, a novel technique facilitating subwavelength resolution in a single-shot fluorescence imaging without capturing multiple frames, thereby enabling video-rate super-resolution imaging within living cells.

The repertoire of functional outputs interfaced with the LuxI/LuxR quorum sensing system in engineered Escherichia coli has been expanded to include motility via inducible expression of motB. Appropriate choice of ribosome binding site controlling MotB translation was crucial to achieving control over motility.

The Moerner Laboratory has been engineering the point spread function (PSF) of a wide-field fluorescence microscope in order to obtain simultaneous X, Y, and Z spatial information from single molecules over a large Z-range. The new PSFs (e.g., the double-helix, tetrapod, and corkscrew PSFs) change their shapes or positions as the single-molecule emitter moves along the optical (Z) axis—thus conveying rich information about the Z position of the molecule. These approaches are relevant both for tracking individual biomolecules at low concentration as well as reconstructing super-resolution images by single-molecule localization microscopy. The corkscrew PSF is special because it behaves like a single Gaussian spot of light that moves along a U-shaped path as the emitter moves along the optical axis. Hence, the corkscrew PSF occupies less space on the camera as compared to our other 3D PSFs and allows us to image a sample more quickly at a higher density of localizations per camera frame.In this work, we use the corkscrew PSF to image a rectilinear array of nanoholes filled with fluorescent molecules to calibrate the three-dimensional response of our microscope. This involves mapping the 3D object space of molecules within a sample to the 2D image space on our camera. By sampling the microscope's response throughout the field of view, we account for field-dependent aberrations that would otherwise cause errors in the position measurements of single molecules. Recent progress in imaging biomolecules with the corkscrew PSF will be summarized.

Super-resolution microscopy has revolutionized cellular imaging in recent years1-4. Methods relying on sequential localization of single point emitters enable spatial tracking at ~10-40 nm resolution. Moreover, tracking and imaging in three dimensions is made possible by various techniques, including point-spread-function (PSF) engineering5-9 -namely, encoding the axial (z) position of a point source in the shape that it creates in the image plane. However, a remaining challenge for localization-microscopy is efficient multicolour imaging - a task of the utmost importance for contextualizing biological data. Normally, multicolour imaging requires sequential imaging10, 11, multiple cameras12, or segmented dedicated fields of view13, 14. Here, we demonstrate an alternate strategy, the encoding of spectral information (colour), in addition to 3D position, directly in the image. By exploiting chromatic dispersion, we design a new class of optical phase masks that simultaneously yield controllably different PSFs for different wavelengths, enabling simultaneous multicolour tracking or super-resolution imaging in a single optical path.

Aberrant aggregation of improperly folded proteins is the hallmark of several human neurodegenerative disorders, including Huntington's Disease (HD) with autosomal-dominant inheritance. In HD, expansion of the CAG-repeat-encoded polyglutamine (polyQ) stretch beyond ~40 glutamines in huntingtin (Htt) and its N-terminal fragments leads to the formation of large (up to several μm) globular neuronal inclusion bodies (IBs) over time. We report direct observations of aggregating Htt exon 1 in living and fixed cells at enhanced spatial resolution by stimulated emission depletion (STED) microscopy and single-molecule super-resolution optical imaging. Fibrils of Htt exon 1 arise abundantly across the cytosolic compartment and also in neuritic processes only after nucleation and aggregation into a fairly advanced stage of growth of the prominent IB have taken place. Structural characterizations of fibrils by STED show a distinct length cutoff at ~1·5 µm and reveal subsequent coalescence (bundling/piling). Cytosolic fibrils are observed even at late stages in the process, side-by-side with the mature IB. Htt sequestration into the IB, which in neurons has been argued to be a cell-protective phenomenon, thus appears to saturate and over-power the cellular degradation systems and leaves cells vulnerable to further aggregation producing much smaller, potentially toxic, conformational protein species of which the fibrils may be comprised. We further found that exogenous delivery of the apical domain of the chaperonin subunit CCT1 to the cells via the cell medium reduced the aggregation propensity of mutant Htt exon 1 in general, and strongly reduced the occurrence of such late-stage fibrils in particular.

Significance In vertebrates, the transmembrane protein Smoothened (Smo) accumulates in the ciliary membrane when cells receive the Hedgehog (Hh) signal. The presence of Smo in primary cilia at baseline conditions has been postulated, but not directly observed. We used highly sensitive single-molecule imaging in live cells to track and analyze the dynamics of individual Smo molecules in cilia, not only after treatment with pathway agonists but also at low, baseline levels. In both conditions, Smo molecules bind at distinct sites at the bases of cilia, but with different dissociation constants. The results provide mechanistic insight into the Hh signal transduction and highlight the distinct compartmentalization of Smo behavior within cilia, which is normally masked by the bulk distribution in ensemble measurements.

We employ a novel framework for information-optimal microscopy to design a family of point spread functions (PSFs), the Tetrapod PSFs, which enable high-precision localization of nanoscale emitters in three dimensions over customizable axial (z) ranges of up to 20 μm with a high numerical aperture objective lens. To illustrate, we perform flow profiling in a microfluidic channel and show scan-free tracking of single quantum-dot-labeled phospholipid molecules on the surface of living, thick mammalian cells.

We generate an information-optimal point spread function (PSF) for localization-based 3D imaging. Such a PSF exhibits excellent localization precision by design, as we demonstrate theoretically and experimentally, and can be tailored for specific imaging parameters.

Pennsylvania State University is well known for the breadth and depth of its athletic programs. But, until last year, the Penn State Athletic Department had focused on tuition-paying and scholarship-funded eukaryotes, while bacteria had languished in neglect. To reconcile this apparent inequity, we designed a bacterial relay race. We eventually hope to have a multi-leg relay race with laps, but for the initial iGEM competition the project was simplified to a ‘hand-off’ where a group of motile bacteria would encounter an immotile group, transferring a signal to turn on the latter's motility.

The identities of toxic aggregate species in Huntington's disease (HD) pathogenesis remain ambiguous. While polyQ-expanded mutant huntingtin (Htt) is known to accumulate in compact inclusion bodies inside neurons, this is widely thought to be a protective coping response that sequesters misfolded conformations or aggregated states of the mutated protein. To define the spatial distributions of fluorescently-labeled Htt-exon1 species in the cell model PC12m (terminally differentiated into sympathetic-neuron-like cells with nerve growth factor), we employed highly sensitive single-molecule-based and stimulated emission depletion (STED) super-resolution fluorescence imaging modalities. In addition to inclusion bodies and the diffuse pool of monomers and oligomers, fibrillar aggregates ∼100 nm in diameter and up to ∼1-2 μm in length were observed for pathogenic polyQ tracts (expression experiments with 46 and 97 repeats) after targeted photo-bleaching of the inclusion bodies. These short structures bear a striking resemblance to fibers described in vitro. We identified a sharp cut-off behavior of maximum fibril length and documented the ensuing bundling of these fibers into denser arrangements of varying complexity, both in the cytosolic space and inside the neuritic processes.View Large Image | View Hi-Res Image | Download PowerPoint Slide

The identities of toxic aggregate species in Huntington's disease pathogenesis remain ambiguous. While polyQ-expanded huntingtin (Htt) is known to accumulate in compact inclusion bodies inside neurons, this is widely thought to be a protective coping response that sequesters misfolded conformations or aggregated states of the mutated protein. To define the spatial distributions of fluorescently-labeled Htt-exon1 species in the cell model PC12m, we employed highly sensitive single-molecule super-resolution fluorescence imaging. In addition to inclusion bodies and the diffuse pool of monomers and oligomers, fibrillar aggregates ~100 nm in diameter and up to ~1–2 µm in length were observed for pathogenic polyQ tracts (46 and 97 repeats) after targeted photo-bleaching of the inclusion bodies. These short structures bear a striking resemblance to fibers described in vitro. Definition of the diverse Htt structures in cells will provide an avenue to link the impact of therapeutic agents to aggregate populations and morphologies.

Semiconductor nanowires (NWs) have unique electronic properties and sizes comparable with biological structures involved in cellular communication, thus making them promising nanostructures for establishing active interfaces with biological systems. We report a flexible approach to interface NW field-effect transistors (NWFETs) with cells and demonstrate this for silicon NWFET arrays coupled to embryonic chicken cardiomyocytes. Cardiomyocyte cells were cultured on thin, optically transparent polydimethylsiloxane (PDMS) sheets and then brought into contact with Si-NWFET arrays fabricated on standard substrates. NWFET conductance signals recorded from cardiomyocytes exhibited excellent signal-to-noise ratios with values routinely >5 and signal amplitudes that were tuned by varying device sensitivity through changes in water gate–voltage potential, V g . Signals recorded from cardiomyocytes for V g from −0.5 to +0.1 V exhibited amplitude variations from 31 to 7 nS whereas the calibrated voltage remained constant, indicating a robust NWFET/cell interface. In addition, signals recorded as a function of increasing/decreasing displacement of the PDMS/cell support to the device chip showed a reversible >2× increase in signal amplitude (calibrated voltage) from 31 nS (1.0 mV) to 72 nS (2.3 mV). Studies with the displacement close to but below the point of cell disruption yielded calibrated signal amplitudes as large as 10.5 ± 0.2 mV. Last, multiplexed recording of signals from NWFET arrays interfaced to cardiomyocyte monolayers enabled temporal shifts and signal propagation to be determined with good spatial and temporal resolution. Our modular approach simplifies the process of interfacing cardiomyocytes and other cells to high-performance Si-NWFETs, thus increasing the experimental versatility of NWFET arrays and enabling device registration at the subcellular level.

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.

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.

The Sonic Hedgehog (SHH) signaling pathway plays a key role in cell division and differentiation in embryonic development and adult stem cells. Malfunction in the pathway not only leads to deformities in developing embryos, but has been implicated in a variety of cancer types. The pathway is activated by the binding of SHH ligand to a transmembrane receptor protein, Patched, causing a cascade of translocation events involving the primary cilium, a small microtubule-structured organelle on the cell's surface whose spatial dimensions are on the order of the diffraction limit (∼400 nm in diameter and 2-5 μm long). One pathway intermediary, the transmembrane protein Smoothened (Smo) which accumulates in the ciliary membrane upon pathway activation, has proven to be susceptible to a number of small molecules that activate or disrupt the pathway; however, the detailed mechanisms underlying these effects remain unclear. By labeling Smo with an organic dye, we have performed single-molecule tracking experiments to elucidate its dynamics and interactions with high spatial resolution (∼30 nm) and high temporal resolution (5-10 ms). We have observed that the primary mechanism for movement of Smo is diffusion with transient lingering in parts of the primary cilium consistent with binding events.