Superresolution imaging with optical fluctuation using speckle patterns illumination

PySOFI: an open source Python package for SOFI

Super-resolution optical fluctuation imaging (SOFI) is a highly democratizable technique that provides optical super-resolution without requirement of sophisticated imaging instruments. Easy-to-use open-source packages for SOFI are important to support the utilization and community adoption of the SOFI method, they also encourage the participation and further development of SOFI by new investigators. In this work, we developed PySOFI, an open-source Python package for SOFI analysis that offers the flexibility to inspect, test, modify, improve, and extend the algorithm. We provide complete documentation for the package and a collection of Jupyter Notebooks to demonstrate the usage of the package. We discuss the architecture of PySOFI and illustrate how to use each functional module. A demonstration on how to extend the PySOFI package with additional modules is also included in the PySOFI package. We expect PySOFI to facilitate efficient adoption, testing, modification, dissemination, and prototyping of new SOFI-relevant algorithms.

Wide-Field Super-Resolution Optical Fluctuation Imaging through Dynamic Near-Field Speckle Illumination

Stochastic optical fluctuation imaging (SOFI) generates super-resolution fluorescence images by emphasizing the positions of fluorescent emitters via statistical analysis of their on-and-off blinking dynamics. In SOFI with speckle illumination (S-SOFI), the diffraction-limited grain size of the far-field speckles prevents independent blinking of closely located emitters, becoming a hurdle to realize the full super-resolution granted by SOFI processing. Here, we present a surface-sensitive super-resolution technique exploiting dynamic near-field speckle illumination to bring forth the full super-resolving power of SOFI without blinking fluorophores. With our near-field S-SOFI technique, up to 2.8- and 2.3-fold enhancements in lateral spatial resolution are demonstrated with computational and experimental fluorescent test targets labeled with conventional fluorophores, respectively. Fluorescent beads separated by 175 nm are also super-resolved by near-field speckles of 150 nm grain size, promising sub-100 nm resolution with speckle patterns of much smaller grain size.

Orientation-selective sub-Rayleigh imaging with spatial coherence lattices

The Rayleigh resolution criterion sets the minimum separation for two-point objects to be distinguishable in a classical optical imaging system. We demonstrate that the sub-Rayleigh resolution can be achieved in a telecentric imaging system with the help of a partially coherent illumination whose spatial coherence has lattice-like distribution. We show that the orientation-selective sub-Rayleigh imaging can be realized by controlling the spatial distribution of the coherence lattice into different symmetries. We carry out a proof-of-principle experiment to demonstrate the orientation-selective sub-Rayleigh imaging for a 1951 USAF resolution target. Our results indicate a flexible orientation-selective high-resolution imaging with spatial coherence engineering of the partially coherent light.

High-Throughput, Label-Free and Slide-Free Histological Imaging by Computational Microscopy and Unsupervised Learning

Rapid and high-resolution histological imaging with minimal tissue preparation has long been a challenging and yet captivating medical pursuit. Here, the authors propose a promising and transformative histological imaging method, termed computational high-throughput autofluorescence microscopy by pattern illumination (CHAMP). With the assistance of computational microscopy, CHAMP enables high-throughput and label-free imaging of thick and unprocessed tissues with large surface irregularity at an acquisition speed of 10 mm2 /10 s with 1.1-µm lateral resolution. Moreover, the CHAMP image can be transformed into a virtually stained histological image (Deep-CHAMP) through unsupervised learning within 15 s, where significant cellular features are quantitatively extracted with high accuracy. The versatility of CHAMP is experimentally demonstrated using mouse brain/kidney and human lung tissues prepared with various clinical protocols, which enables a rapid and accurate intraoperative/postoperative pathological examination without tissue processing or staining, demonstrating its great potential as an assistive imaging platform for surgeons and pathologists to provide optimal adjuvant treatment.

Metamaterial assisted illumination nanoscopy via random super-resolution speckles

Structured illumination microscopy (SIM) is one of the most powerful and versatile optical super-resolution techniques. Compared with other super-resolution methods, SIM has shown its unique advantages in wide-field imaging with high temporal resolution and low photon damage. However, traditional SIM only has about 2 times spatial resolution improvement compared to the diffraction limit. In this work, we propose and experimentally demonstrate an easily-implemented, low-cost method to extend the resolution of SIM, named speckle metamaterial-assisted illumination nanoscopy (speckle-MAIN). A metamaterial structure is introduced to generate speckle-like sub-diffraction-limit illumination patterns in the near field with improved spatial frequency. Such patterns, similar to traditional SIM, are then used to excite objects on top of the surface. We demonstrate that speckle-MAIN can bring the resolution down to 40 nm and beyond. Speckle-MAIN represents a new route for super-resolution, which may lead to important applications in bio-imaging and surface characterization.

Structured illumination microscopy is usually limited to 2 times spatial resolution improvement over the diffraction limit. Here, the authors introduce a metamaterial structure to generate speckle-like sub-diffraction limit illumination patterns in the near field, and achieve a 7-fold resolution improvement down to 40 nm.

Resolution enhanced photothermal imaging by high-order correlation

Laser scanning photothermal imaging offers a powerful non-destructive testing tool to visualize subsurface structures of opaque materials, but it suffers the resolution limit imposed by thermal diffusion. To overcome this physical obstacle, a tightly focused excitation beam with a high repetition rate is usually used to improve the spatial resolution. Here, we demonstrate that the resolution of photothermal imaging could be enhanced using the high-order correlation imaging method inspired by correlated optical imaging. By carefully designing the laser scanning and modulation behavior, we can individually control the statistical properties of isolated hotspots induced by lasers. Imaging reconstructions of subsurface structures are performed afterward by reading out time-fluctuated thermal images. Moreover, the resolution can be further enhanced by using the high-order correlation, which enables a new way for highly resolved thermal imaging and metrology applications.

Computational optical imaging with a photonic lantern

The thin and flexible nature of optical fibres often makes them the ideal technology to view biological processes in-vivo, but current microendoscopic approaches are limited in spatial resolution. Here, we demonstrate a route to high resolution microendoscopy using a multicore fibre (MCF) with an adiabatic multimode-to-single-mode “photonic lantern” transition formed at the distal end by tapering. We show that distinct multimode patterns of light can be projected from the output of the lantern by individually exciting the single-mode MCF cores, and that these patterns are highly stable to fibre movement. This capability is then exploited to demonstrate a form of single-pixel imaging, where a single pixel detector is used to detect the fraction of light transmitted through the object for each multimode pattern. A custom computational imaging algorithm we call SARA-COIL is used to reconstruct the object using only the pre-measured multimode patterns themselves and the detector signals.

Here, the authors demonstrate a route to high resolution microendoscopy using a multicore fibre with a photonic lantern. They show that distinct multimode patterns of light can be projected from the output of the lantern by individually exciting the single-mode MCF cores, whose patterns are highly stable to fibre movement.

Using fiber-bending-generated speckles for improved working distance and background rejection in lensless micro-endoscopy

Lensless flexible fiber-bundle-based endoscopes allow imaging at depths beyond the reach of conventional microscopes with a minimal footprint. These multicore fibers provide a simple solution for wide-field fluorescent imaging when the target is adjacent to the fiber facet. However, they suffer from a very limited working distance and out-of-focus background. Here, we carefully study the dynamic speckle illumination patterns generated by bending a commercial fiber bundle and show that they can be exploited to allow extended working distance and background rejection, using a super-resolution fluctuations imaging analysis of multiple frames, without the addition of any optical elements.

Moments reconstruction and local dynamic range compression of high order superresolution optical fluctuation imaging

Super-resolution optical fluctuation imaging (SOFI) offers a simple and affordable alternative to other super-resolution (SR) imaging techniques. The theoretical resolution enhancement of SOFI scales linearly with the order of cumulants, while the imaging conditions exhibit less photo-toxicity to the living samples as compared to other SR methods. High order SOFI could, therefore, be a method of choice for dynamic live cell imaging. However, due to the cusp-artifacts and dynamic range expansion of pixel intensities, this promise has not been materialized as of yet. Here we investigated and compared high order moments vs. high order cumulant SOFI reconstructions. We demonstrate that even-order moments reconstructions are intrinsically free of cusp artifacts, allowing for a subsequent deconvolution operation to be performed, hence enhancing the resolution even further. High order moments reconstruction performance was examined for various (simulated) conditions and applied to (experimental) imaging of QD labeled microtubules in fixed cells, and actin stress fiber dynamics in live cells.

Resolution-enhanced imaging through scattering media by high-order correlation

Super-resolution optical imaging is a rapidly emerging technology enabling many applications. Recently, correlation imaging has shown its capability in imaging beyond the diffraction limit, relying on quantum and statistical properties of light. High-order correlation imaging can further enhance resolution, however, at the expense of complicated algorithms. Here, we experimentally demonstrate a resolution-enhanced method of imaging through scattering media by exploiting high-order correlation of fluorescence light. Based on this method, individual fluorophores' temporal fluctuations are recorded and computed for their distinguished high-order correlations that enable super-resolution. Special designed time sequences are chosen to reduce computation time and memory. Such high-order correlation imaging exhibits reliable performance through scattering media with significant resolution enhancement and background noise reduction. This efficient imaging method paves the way for new biomedical applications.

Scattering Assisted Imaging

Standard imaging systems provide a spatial resolution that is ultimately dictated by the numerical aperture (NA) of the illumination and collection optics. In biological tissues, the resolution is strongly affected by scattering, which limits the penetration depth to a few tenths of microns. Here, we exploit the properties of speckle patterns embedded into a strongly scattering matrix to illuminate the sample at high spatial frequency content. Combining adaptive optics with a custom deconvolution algorithm, we obtain an increase in the transverse spatial resolution by a factor of 2.5 with respect to the natural diffraction limit. Our Scattering Assisted Imaging (SAI) provides an effective solution to increase the resolution when long working distance optics are needed, potentially paving the way to bulk imaging in turbid tissues.

State-of-the-Art Fluorescence Fluctuation-Based Spectroscopic Techniques for the Study of Protein Aggregation

Neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS), Alzheimer’s disease, Parkinson’s disease, and Huntington’s disease, are devastating proteinopathies with misfolded protein aggregates accumulating in neuronal cells. Inclusion bodies of protein aggregates are frequently observed in the neuronal cells of patients. Investigation of the underlying causes of neurodegeneration requires the establishment and selection of appropriate methodologies for detailed investigation of the state and conformation of protein aggregates. In the current review, we present an overview of the principles and application of several methodologies used for the elucidation of protein aggregation, specifically ones based on determination of fluctuations of fluorescence. The discussed methods include fluorescence correlation spectroscopy (FCS), imaging FCS, image correlation spectroscopy (ICS), photobleaching ICS (pbICS), number and brightness (N&B) analysis, super-resolution optical fluctuation imaging (SOFI), and transient state (TRAST) monitoring spectroscopy. Some of these methodologies are classical protein aggregation analyses, while others are not yet widely used. Collectively, the methods presented here should help the future development of research not only into protein aggregation but also neurodegenerative diseases.

Photoacoustic imaging beyond the acoustic diffraction-limit with dynamic speckle illumination and sparse joint support recovery

In deep tissue photoacoustic imaging the spatial resolution is inherently limited by the acoustic wavelength. Recently, it was demonstrated that it is possible to surpass the acoustic diffraction limit by analyzing fluctuations in a set of photoacoustic images obtained under unknown speckle illumination patterns. Here, we purpose an approach to boost reconstruction fidelity and resolution, while reducing the number of acquired images by utilizing a compressed sensing computational reconstruction framework. The approach takes into account prior knowledge of the system response and sparsity of the target structure. We provide proof of principle experiments of the approach and demonstrate that improved performance is obtained when both speckle fluctuations and object priors are used. We numerically study the expected performance as a function of the measurement's signal to noise ratio and sample spatial-sparsity. The presented reconstruction framework can be applied to analyze existing photoacoustic experimental data sets containing dynamic fluctuations.

Structured illumination microscopy with unknown patterns and a statistical prior

Structured illumination microscopy (SIM) improves resolution by down-modulating high-frequency information of an object to fit within the passband of the optical system. Generally, the reconstruction process requires prior knowledge of the illumination patterns, which implies a well-calibrated and aberration-free system. Here, we propose a new algorithmic self-calibration strategy for SIM that does not need to know the exact patterns a priori, but only their covariance. The algorithm, termed PE-SIMS, includes a pattern-estimation (PE) step requiring the uniformity of the sum of the illumination patterns and a SIM reconstruction procedure using a statistical prior (SIMS). Additionally, we perform a pixel reassignment process (SIMS-PR) to enhance the reconstruction quality. We achieve 2× better resolution than a conventional widefield microscope, while remaining insensitive to aberration-induced pattern distortion and robust against parameter tuning.

Improving the axial and lateral resolution of three-dimensional fluorescence microscopy using random speckle illuminations

We consider a fluorescence microscope in which several three-dimensional images of a sample are recorded for different speckle illuminations. We show, on synthetic data, that by summing the positive deconvolution of each speckle image, one obtains a sample reconstruction with axial and transverse resolutions that compare favorably to that of an ideal confocal microscope.

Superior performance with sCMOS over EMCCD in super-resolution optical fluctuation imaging

Super-resolution optical fluctuation imaging (SOFI) is a fast and low-cost live-cell optical nanoscopy for extracting subdiffraction information from the statistics of fluorescence intensity fluctuation. As SOFI is based on the fluctuation statistics, rather than the detection of single molecules, it poses unique requirements for imaging detectors, which still lack a systematic evaluation. Here, we analyze the influences of pixel sizes, frame rates, noise levels, and different gains in SOFI with simulations and experimental tests. Our analysis shows that the smaller pixel size and faster readout speed of scientific-grade complementary metal oxide semiconductor (sCMOS) enables SOFI to achieve high spatiotemporal resolution with a large field-of-view, which is especially beneficial for live-cell super-resolution imaging. Overall, as the performance of SOFI is relatively insensitive to the signal-to-noise ratio (SNR), the gain in pixel size and readout speed exceeds the loss in SNR, indicating sCMOS is superior to electron multiplying charge coupled device in context to SOFI in many cases. Super-resolution imaging of cellular microtubule structures with high-order SOFI is experimentally demonstrated at large field-of-view, taking advantage of the large pixel number and fast frame rate of sCMOS cameras.