This PDF file contains the front matter associated with SPIE Proceedings Volume 11650, including the Title Page, Copyright information, and Table of Contents.

Introduction to SPIE Photonics West BiOS conference 11650: Single Molecule Spectroscopy and Superresolution Imaging XIV

The functional dynamics of photosynthetic light-harvesting pigment-protein complexes play an essential physiological role. The dynamic switching between different states – associated with different spectroscopic signatures – allows these complexes to regulate the excitation energy flow in the photosynthetic apparatus as a direct response to changing environmental conditions. To a large extent, the robustness of the photosynthetic processes depends on this functional flexibility of the light-harvesting complexes. Single molecule spectroscopy (SMS) is a method of choice to investigate the spectroscopic properties of individual molecules or complexes and allows the direct observation of switches between emissive states. The fluorescence intensity dynamics of individual light-harvesting complexes are commonly analyzed using a two-state model, corresponding to an ON and OFF state, respectively. In this model, each switch across the intensity threshold that separates the two states is counted as an intensity switch. However, multichromophoric systems in general, and protein complexes binding multiple pigments in particular, are known to switch among various intensity levels, suggesting that the choice of the intensity threshold may significantly influence the two-state statistics. Here, we investigated the fluorescence data of a very large complex – the main light harvesting complex of cyanobacteria, the phycobilisome – and determined that the intensity threshold has only a limited impact on the binary switching analysis of the complex for an extended range of threshold positions.

We present fluorophore labels that transiently and repetitively bind to their targets as probes for various types of super-resolution fluorescence microscopy. Transient labels typically show a weak affinity to a target, and exchange constantly with the buffer that constitutes a reservoir with a large amount of intact probes, leading to repetitive binding events to the same target (we refer to these labels as “exchangeable labels”). This dynamic labeling approach is insensitive to common photobleaching and yields a constant fluorescence signal over time, which has been successfully exploited in SMLM, STED, single-particle tracking and super-resolution optical fluctuation imaging (SOFI). We discuss properties of suitable exchangeable labels and experimental parameters for optimal performance for the different super-resolution methods. In addition, we show how to combine different classes of exchangeable labels for high-quality multicolor super-resolution imaging.

Fluorescence-encoded Raman spectroscopy has become increasingly more popular by virtue of its high chemical specificity and sensitivity. However, current fluorescence-encoding methods are narrowband and lack sensitivity in the low wavenumber region which if addressed could further enhance these methods. To overcome these limitations, we propose and experimentally demonstrate a novel broadband method for fluorescence-encoded Raman spectroscopy, termed fluorescence-encoded time-domain coherent Raman spectroscopy (FLETCHERS), which is capable of probing molecular vibrations in the lower fingerprint region (200 – 750 cm-1 ) with sample concentrations as dilute as 100 nM and laser powers as low as 20 mW.

The field of view (FOV) of single-molecule localization microscopy (SMLM) is practically restricted to an area of approximately 50×50 μm^2 as only relatively perfect point spread functions (PSFs) in the center of the objective FOV can be used for accurate super-resolution image reconstruction. Here we present a systematical study to show that optical aberrations, such as spherical aberration, coma, field curvature, and chromatic aberration, commonly presented in the FOV periphery can significantly compromise the localization precision and accuracy and produce unreliable imaging results when using currently available localization algorithm for SMLM image reconstruction.

Fluorescence anisotropy is a powerful tool for observing molecular rotational speed, which is widely applied for molecular conjugation measurements (quantitative assay, membrane tagging, and protein-protein interaction). Fluorescence anisotropy (r) can be obtained by subtracting horizontal polarized fluorescence (H) from vertical polarized fluorescence (V), and dividing by non-polarized fluorescence (F). Since F equals to V+2H, V and H can be thought of as weighted sum and subtraction of F and r·F, respectively. Using phasor approach, which is graphical plotting technique based on fluorescence lifetime imaging microscopy, V and H are located on internal and external dividing points of F and r·F. Considering those four phasor points (V, H, F, and r·F) lie on a straight line, one can easily guess the value of the r. We first introduced phasor plot to fluorescence anisotropy, and confirmed that this method dramatically simplifies fluorescence anisotropy analysis with graphical intuition.

PSF engineering has been widely used over the past years to enable 3D localization in Single Molecule Localization Microscopy (SMLM) techniques, such as PALM/STORM superresolution methods. It can make use of a cylindrical lens, a phase mask, a Spatial Light Modulator, a Deformable Mirror (DM) to encode depth on a spatial variation of the PSF along the Z axis. Among these techniques, we demonstrate that a DM-based approach – such as implemented in our MicAO-3DSR system - provides the best versatility/performance combination

Measuring single molecules’ 3D orientations in addition to their 3D spatial localization is still today a challenge, due to the intrinsic coupling of both spatial and orientational parameters in the point spread function (PSF) image formation. We present polarized fluorescence microscopy methods able to report both orientational and spatial information from single molecules with high precision. These methods are applied to STORM and PALM nanoscale imaging of actin filaments organization and membrane proteins’ conformational changes in 3D.

We exploited, optimized, and developed various microscopy techniques for analyzing living matter at cellular and molecular levels. In particular, we developed a toolbox for single particle tracking (SPT) of membrane receptors and their ligands, suitable also for relatively fast single-pass membrane receptors; this is based on chemical tagging of recombinant proteins, TIRF microscopy, and automatized analysis of single particle trajectories. We are now extending it to simultaneous visualization and analysis of two moieties. The superresolved localization of this technique allowed analyzing functions, interactions and stoichiometry of (pro)neurotrophin receptors p75^NTR and TrkA and of their ligands in living cells, with particular attention on some of their existing or used mutants. The analysis also after treatments with ligands or drugs unraveled their mode of action in the first steps of sundry signaling pathways.

The organization and dynamics of the genome regulate DNA replication, DNA repair, and gene expression. Here, we developed a methodology for studies of 3D organization and dynamics of the nucleome over time scales ranging from milliseconds to hours, and length scales ranging from tens of nanometers to micrometers. We achieve unprecedented 3D track lengths throughout the mammalian nucleus by combining a novel labeling scheme using nanobody ArrayG/N fusions of dSpCas9 targeted to specific DNA loci; light sheet illumination for gentle imaging of live cells with high contrast; and point spread function engineering for parallel detection of multiple loci in 3D.

One of the main challenges in studying single biomolecules in a native or near-native environment is their constant diffusive motion. A method capable of keeping a single biomolecule in the detection volume without disturbing its surrounding environment is real-time single-particle tracking (SPT). There are a number of real-time SPT methods in use, and comparisons in literature are usually made based on experimental results, which, due to variations in experimental setups and specimens, are of limited use. In this study, we used statistical calculations to compare the theoretical performance of different localization methods, namely the orbital method, the Knight’s Tour (grid scan) method and MINFLUX, in the context of both fluorescence-based and interferometric scattering (iSCAT) approaches. While the Knight’s Tour method is able to track the fastest diffusion, MINFLUX is able to achieve the greatest precision, albeit only for slowly diffusing particles. For comparing iSCAT and fluorescence, we considered different biological examples, including both intrinsically fluorescent particles and particles with a fluorescent label. The relative success of iSCAT vs fluorescence is strongly dependent on the particle size, photophysical properties of the fluorophore, and the fluorophore density. This work should serve as a guideline for choosing a suitable method for experimental implementation of real-time SPT based on the sample of interest. The analysis used can easily be extended to other methods of interest that we have not considered.

Measuring the behavior of single molecules enables the discovery of states and dynamics obscured by bulk measurements. However, molecules in solution rapidly diffuse in three dimensions, precluding long-duration and high-temporal resolution measurement. To overcome this hurdle, we have developed 3D single-molecule active real-time tracking (3D-SMART) which enables active feedback tracking of rapidly diffusing (exceeding 10 μm2/sec) and lowly emitting fluorescent (rates of 10 kHz or less) particles. Here we demonstrate the application of 3D-SMART to a range targets, from single virus-like particles and quantum dots in water, all the way down to single fluorophores in viscous solution. This new single molecule tracking can be applied to continuously monitor single proteins and nucleic acids, including real-time measurement of transcription on a freely diffusing, single-dye labelled DNA strand. 3D-SMART represents a critical step towards the untethering of single molecule spectroscopy.

We propose and demonstrate the first analytical model of the spatial resolution of frequency-domain (FD) fluorescence lifetime imaging microscopy (FLIM) that explains how it is fundamentally different with the common resolution limit of the conventional fluorescence microscopy. Frequency modulation (FM) capture effect is also observed by the model, which results in distorted FLIM measurements. A super-resolution FLIM approach based on a localization-based technique, super-resolution radial fluctuations (SRRF), is presented. In this approach, we separately process the intensity and lifetime to generate a super-resolution FLIM composite image. The capability of the approach is validated both numerically and experimentally in fixed cells sample.

The use of photo-activated fluorescent molecules to create long sequences of low emitter-density diffraction-limited images enables high-precision emitter localization. However, this is achieved at the cost of lengthy imaging times, limiting temporal resolution. In recent years, a variety of approaches have been suggested to reduce imaging times, ranging from classical optimization and statistical algorithms to deep learning methods. Classical methods often rely on prior knowledge of the optical system and require heuristic adjustment of parameters or do not lead to good enough performance. Deep learning methods proposed to date tend to suffer from poor generalization ability outside the specific distribution they were trained on, and tend to lead to black-box solutions that are hard to interpret. In this paper, we suggest combining a recent high-performing classical method, SPARCOM, with model-based deep learning, using the algorithm unfolding approach which relies on an iterative algorithm to design a compact neural network considering domain knowledge. We show that the resulting network, Learned SPARCOM (LSPARCOM), requires far fewer layers and parameters, and can be trained on a single field of view. Nonetheless it yields comparable or superior results to those obtained by SPARCOM with no heuristic parameter determination or explicit knowledge of the point spread function, and is able to generalize better than standard deep learning techniques. Thus, we believe LSPARCOM will find broad use in single molecule localization microscopy of biological structures, and pave the way to interpretable, efficient live-cell imaging in a broad range of settings.

Fluorescence microscopy has enabled a dramatic development in modern biology by visualizing biological organ- isms with micrometer scale resolution. However, due to the diffraction limit, sub-micron/nanometer features are difficult to resolve. While various super-resolution techniques are developed to achieve nanometer-scale resolu- tion, they often either require expensive optical setup or specialized fluorophores. In recent years, deep learning has shown potentials to reduce the technical barrier and obtain super-resolution from diffraction-limited images. For accurate results, conventional deep learning techniques require thousands of images as a training dataset. Obtaining large datasets from biological samples is not often feasible due to photobleaching of fluorophores, phototoxicity, and dynamic processes occurring within the organism. Therefore, achieving deep learning-based super-resolution using small datasets is challenging. We address this limitation with a new convolutional neural network based approach that is successfully trained with small datasets and achieves super-resolution images. We captured 750 images in total from 15 different field-of-views as the training dataset to demonstrate the technique. In each FOV, a single target image is generated using the super-resolution radial fluctuation method. As expected, this small dataset failed to produce a usable model using traditional super-resolution architecture. However, using the new approach, a network can be trained to achieve super-resolution images from this small dataset. This deep learning model can be applied to other biomedical imaging modalities such as MRI and X-ray imaging, where obtaining large training datasets is challenging.

Localization based microscopy using self-interference digital holography (SIDH) provides three-dimensional (3D) positional information about point sources with nanometer scale precision. To understand the performance limits of SIDH, we calculate the Cram´er-Rao lower bound (CRLB) of the localization precision for SIDH. We further compare the calculated precision bounds to the 3D single molecule localization precision from different Point Spread Functions. SIDH results in almost constant localization precision in all three dimensions for a 20 µm axial range. For high signal-to-background ratio (SBR), SIDH on average achieves better localization precision. For lower SBR values, the large size of the hologram on the detector results in more overall noise, and PSF models perform better.

Abnormal deposition of amyloid-β (Aβ) causes the formation of senile plaques, which is one of the main pathological features of Alzheimer disease. To visualize the plaques at nanometer resolution, single-molecule localization microscopy (SMLM) is promising. It is also essential to develop a method for suppressing autofluorescence especially under high-magnifications used for detecting single molecules. Here, we report a novel method to reduce autofluorescence in mouse brains which is applicable to visualize the structure of Aβ plaques by SMLM. The super-resolution images of Aβ plaques showed fibrous structures that were not able to be discerned by conventional fluorescence imaging.

Photodynamic inactivation (PDI) is known to be effective for treatment of various viral and bacterial infections. In view of the current COVID-19 pandemic the search for therapeutic modalities efficient against this particular virus is of high demand. PDI with photosensitizer solution applied in the oral cavity and throat by flushing and gargling was already demonstrated to be promising for reduction of viral load at early stages of COVID- 19 infection. In this report we present experimental results on detection of singlet oxygen generated using Radachlorin photosensitizer in nebulizer aerosol jet and on different biological surfaces modeling, in particular, mucous membranes of the respiratory tract. The lifetimes of singlet oxygen and photosensitizer triplet state were shown to depend noticeably on the surface type. Moreover the surface type was found to be strongly affecting the photosensitizer photobleaching kinetics, with mucous samples providing much slower bleaching.