splitSMLM, a spectral demixing method for high-precision multi-color localization microscopy applied to nuclear pore complexes

MultiMatch: geometry-informed colocalization in multi-color super-resolution microscopy

With recent advances in multi-color super-resolution light microscopy, it is possible to simultaneously visualize multiple subunits within biological structures at nanometer resolution. To optimally evaluate and interpret spatial proximity of stainings on such an image, colocalization analysis tools have to be able to integrate prior knowledge on the local geometry of the recorded biological complex. We present MultiMatch to analyze the abundance and location of chain-like particle arrangements in multi-color microscopy based on multi-marginal optimal unbalanced transport methodology. Our object-based colocalization model statistically addresses the effect of incomplete labeling efficiencies enabling inference on existent, but not fully observable particle chains. We showcase that MultiMatch is able to consistently recover existing chain structures in three-color STED images of DNA origami nanorulers and outperforms geometry-uninformed triplet colocalization methods in this task. MultiMatch generalizes to an arbitrary number of color channels and is provided as a user-friendly Python package comprising colocalization visualizations.

Real-time data processing in colorimetry camera-based single-molecule localization microscopy via CPU-GPU-FPGA heterogeneous computation

Because conventional low-light cameras used in single-molecule localization microscopy (SMLM) do not have the ability to distinguish colors, it is often necessary to employ a dedicated optical system and/or a complicated image analysis procedure to realize multi-color SMLM. Recently, researchers explored the potential of a new kind of low-light camera called colorimetry camera as an alternative detector in multi-color SMLM, and achieved two-color SMLM under a simple optical system, with a comparable cross-talk to the best reported values. However, extracting images from all color channels is a necessary but lengthy process in colorimetry camera-based SMLM (called CC-STORM), because this process requires the sequential traversal of a massive number of pixels. By taking advantage of the parallelism and pipeline characteristics of FPGA, in this paper, we report an updated multi-color SMLM method called HCC-STORM, which integrated the data processing tasks in CC-STORM into a home-built CPU-GPU-FPGA heterogeneous computing platform. We show that, without scarifying the original performance of CC-STORM, the execution speed of HCC-STORM was increased by approximately three times. Actually, in HCC-STORM, the total data processing time for each raw image with 1024 × 1024 pixels was 26.9 ms. This improvement enabled real-time data processing for a field of view of 1024 × 1024 pixels and an exposure time of 30 ms (a typical exposure time in CC-STORM). Furthermore, to reduce the difficulty of deploying algorithms into the heterogeneous computing platform, we also report the necessary interfaces for four commonly used high-level programming languages, including C/C++, Python, Java, and Matlab. This study not only pushes forward the mature of CC-STORM, but also presents a powerful computing platform for tasks with heavy computation load.

UBAP2L ensures homeostasis of nuclear pore complexes at the intact nuclear envelope

Assembly of macromolecular complexes at correct cellular sites is crucial for cell function. Nuclear pore complexes (NPCs) are large cylindrical assemblies with eightfold rotational symmetry, built through hierarchical binding of nucleoporins (Nups) forming distinct subcomplexes. Here, we uncover a role of ubiquitin-associated protein 2-like (UBAP2L) in the assembly and stability of properly organized and functional NPCs at the intact nuclear envelope (NE) in human cells. UBAP2L localizes to the nuclear pores and facilitates the formation of the Y-complex, an essential scaffold component of the NPC, and its localization to the NE. UBAP2L promotes the interaction of the Y-complex with POM121 and Nup153, the critical upstream factors in a well-defined sequential order of Nups assembly onto NE during interphase. Timely localization of the cytoplasmic Nup transport factor fragile X-related protein 1 (FXR1) to the NE and its interaction with the Y-complex are likewise dependent on UBAP2L. Thus, this NPC biogenesis mechanism integrates the cytoplasmic and the nuclear NPC assembly signals and ensures efficient nuclear transport, adaptation to nutrient stress, and cellular proliferative capacity, highlighting the importance of NPC homeostasis at the intact NE.

Interstitial macrophages are a focus of viral takeover and inflammation in COVID-19 initiation in human lung

Early stages of deadly respiratory diseases including COVID-19 are challenging to elucidate in humans. Here, we define cellular tropism and transcriptomic effects of SARS-CoV-2 virus by productively infecting healthy human lung tissue and using scRNA-seq to reconstruct the transcriptional program in "infection pseudotime" for individual lung cell types. SARS-CoV-2 predominantly infected activated interstitial macrophages (IMs), which can accumulate thousands of viral RNA molecules, taking over 60% of the cell transcriptome and forming dense viral RNA bodies while inducing host profibrotic (TGFB1, SPP1) and inflammatory (early interferon response, CCL2/7/8/13, CXCL10, and IL6/10) programs and destroying host cell architecture. Infected alveolar macrophages (AMs) showed none of these extreme responses. Spike-dependent viral entry into AMs used ACE2 and Sialoadhesin/CD169, whereas IM entry used DC-SIGN/CD209. These results identify activated IMs as a prominent site of viral takeover, the focus of inflammation and fibrosis, and suggest targeting CD209 to prevent early pathology in COVID-19 pneumonia. This approach can be generalized to any human lung infection and to evaluate therapeutics.

Deep learning-based spectroscopic single-molecule localization microscopy

Significance

Spectroscopic single-molecule localization microscopy (sSMLM) takes advantage of nanoscopy and spectroscopy, enabling sub-10 nm resolution as well as simultaneous multicolor imaging of multi-labeled samples. Reconstruction of raw sSMLM data using deep learning is a promising approach for visualizing the subcellular structures at the nanoscale.

Aim

Develop a novel computational approach leveraging deep learning to reconstruct both label-free and fluorescence-labeled sSMLM imaging data.

Approach

We developed a two-network-model based deep learning algorithm, termed DsSMLM, to reconstruct sSMLM data. The effectiveness of DsSMLM was assessed by conducting imaging experiments on diverse samples, including label-free single-stranded DNA (ssDNA) fiber, fluorescence-labeled histone markers on COS-7 and U2OS cells, and simultaneous multicolor imaging of synthetic DNA origami nanoruler.

Results

For label-free imaging, a spatial resolution of 6.22 nm was achieved on ssDNA fiber; for fluorescence-labeled imaging, DsSMLM revealed the distribution of chromatin-rich and chromatin-poor regions defined by histone markers on the cell nucleus and also offered simultaneous multicolor imaging of nanoruler samples, distinguishing two dyes labeled in three emitting points with a separation distance of 40 nm. With DsSMLM, we observed enhanced spectral profiles with 8.8% higher localization detection for single-color imaging and up to 5.05% higher localization detection for simultaneous two-color imaging.

Conclusions

We demonstrate the feasibility of deep learning-based reconstruction for sSMLM imaging applicable to label-free and fluorescence-labeled sSMLM imaging data. We anticipate our technique will be a valuable tool for high-quality super-resolution imaging for a deeper understanding of DNA molecules' photophysics and will facilitate the investigation of multiple nanoscopic cellular structures and their interactions.

Nanoscale cellular organization of viral RNA and proteins in SARS-CoV-2 replication organelles

The SARS-CoV-2 viral infection transforms host cells and produces special organelles in many ways, and we focus on the replication organelles, the sites of replication of viral genomic RNA (vgRNA). To date, the precise cellular localization of key RNA molecules and replication intermediates has been elusive in electron microscopy studies. We use super-resolution fluorescence microscopy and specific labeling to reveal the nanoscopic organization of replication organelles that contain numerous vgRNA molecules along with the replication enzymes and clusters of viral double-stranded RNA (dsRNA). We show that the replication organelles are organized differently at early and late stages of infection. Surprisingly, vgRNA accumulates into distinct globular clusters in the cytoplasmic perinuclear region, which grow and accommodate more vgRNA molecules as infection time increases. The localization of endoplasmic reticulum (ER) markers and nsp3 (a component of the double-membrane vesicle, DMV) at the periphery of the vgRNA clusters suggests that replication organelles are encapsulated into DMVs, which have membranes derived from the host ER. These organelles merge into larger vesicle packets as infection advances. Precise co-imaging of the nanoscale cellular organization of vgRNA, dsRNA, and viral proteins in replication organelles of SARS-CoV-2 may inform therapeutic approaches that target viral replication and associated processes.

MINFLUX nanoscopy: Visualising biological matter at the nanoscale level

Since its introduction in 2017, MINFLUX nanoscopy has shown that it can visualise fluorescent molecules with an exceptional localisation precision of a few nanometres. In this overview, we provide a brief insight into technical implementations, fluorescent marker developments and biological studies that have been conducted in connection with MINFLUX imaging and tracking. We also formulate ideas on how MINFLUX nanoscopy and derived technologies could influence bioimaging in the future. This insight is intended as a general starting point for an audience looking for a brief overview of MINFLUX nanoscopy from theory to application.

Nanoscale cellular organization of viral RNA and proteins in SARS-CoV-2 replication organelles

The SARS-CoV-2 viral infection transforms host cells and produces special organelles in many ways, and we focus on the replication organelle where the replication of viral genomic RNA (vgRNA) occurs. To date, the precise cellular localization of key RNA molecules and replication intermediates has been elusive in electron microscopy studies. We use super-resolution fluorescence microscopy and specific labeling to reveal the nanoscopic organization of replication organelles that contain vgRNA clusters along with viral double-stranded RNA (dsRNA) clusters and the replication enzyme, encapsulated by membranes derived from the host endoplasmic reticulum (ER). We show that the replication organelles are organized differently at early and late stages of infection. Surprisingly, vgRNA accumulates into distinct globular clusters in the cytoplasmic perinuclear region, which grow and accommodate more vgRNA molecules as infection time increases. The localization of ER labels and nsp3 (a component of the double-membrane vesicle, DMV) at the periphery of the vgRNA clusters suggests that replication organelles are enclosed by DMVs at early infection stages which then merge into vesicle packets as infection progresses. Precise co-imaging of the nanoscale cellular organization of vgRNA, dsRNA, and viral proteins in replication organelles of SARS-CoV-2 may inform therapeutic approaches that target viral replication and associated processes.

DUAL: deep unsupervised simultaneous simulation and denoising for cryo-electron tomography

Recent biotechnological developments in cryo-electron tomography allow direct visualization of native sub-cellular structures with unprecedented details and provide essential information on protein functions/dysfunctions. Denoising can enhance the visualization of protein structures and distributions. Automatic annotation via data simulation can ameliorate the time-consuming manual labeling of large-scale datasets. Here, we combine the two major cryo-ET tasks together in DUAL, by a specific cyclic generative adversarial network with novel noise disentanglement. This enables end-to-end unsupervised learning that requires no labeled data for training. The denoising branch outperforms existing works and substantially improves downstream particle picking accuracy on benchmark datasets. The simulation branch provides learning-based cryo-ET simulation for the first time and generates synthetic tomograms indistinguishable from experimental ones. Through comprehensive evaluations, we showcase the effectiveness of DUAL in detecting macromolecular complexes across a wide range of molecular weights in experimental datasets. The versatility of DUAL is expected to empower cryo-ET researchers by improving visual interpretability, enhancing structural detection accuracy, expediting annotation processes, facilitating cross-domain model adaptability, and compensating for missing wedge artifacts. Our work represents a significant advancement in the unsupervised mining of protein structures in cryo-ET, offering a multifaceted tool that facilitates cryo-ET research.

Protein nanobarcodes enable single-step multiplexed fluorescence imaging

Multiplexed cellular imaging typically relies on the sequential application of detection probes, as antibodies or DNA barcodes, which is complex and time-consuming. To address this, we developed here protein nanobarcodes, composed of combinations of epitopes recognized by specific sets of nanobodies. The nanobarcodes are read in a single imaging step, relying on nanobodies conjugated to distinct fluorophores, which enables a precise analysis of large numbers of protein combinations. Fluorescence images from nanobarcodes were used as input images for a deep neural network, which was able to identify proteins with high precision. We thus present an efficient and straightforward protein identification method, which is applicable to relatively complex biological assays. We demonstrate this by a multicell competition assay, in which we successfully used our nanobarcoded proteins together with neurexin and neuroligin isoforms, thereby testing the preferred binding combinations of multiple isoforms, in parallel.

Unveiling the complexity: assessing models describing the structure and function of the nuclear pore complex

The nuclear pore complex (NPC) serves as a pivotal subcellular structure, acting as a gateway that orchestrates nucleocytoplasmic transport through a selectively permeable barrier. Nucleoporins (Nups), particularly those containing phenylalanine-glycine (FG) motifs, play indispensable roles within this barrier. Recent advancements in technology have significantly deepened our understanding of the NPC's architecture and operational intricacies, owing to comprehensive investigations. Nevertheless, the conspicuous presence of intrinsically disordered regions within FG-Nups continues to present a formidable challenge to conventional static characterization techniques. Historically, a multitude of strategies have been employed to unravel the intricate organization and behavior of FG-Nups within the NPC. These endeavors have given rise to multiple models that strive to elucidate the structural layout and functional significance of FG-Nups. Within this exhaustive review, we present a comprehensive overview of these prominent models, underscoring their proposed dynamic and structural attributes, supported by pertinent research. Through a comparative analysis, we endeavor to shed light on the distinct characteristics and contributions inherent in each model. Simultaneously, it remains crucial to acknowledge the scarcity of unequivocal validation for any of these models, as substantiated by empirical evidence.

Assessing crosstalk in simultaneous multicolor single-molecule localization microscopy

Single-molecule localization microscopy (SMLM) can reach sub-50 nm resolution using techniques such as stochastic optical reconstruction microscopy (STORM) or DNA-point accumulation for imaging in nanoscale topography (PAINT). Here we implement two approaches for faster multicolor SMLM by splitting the emitted fluorescence toward two cameras: simultaneous two-color DNA-PAINT (S2C-DNA-PAINT) that images spectrally separated red and far-red imager strands on each camera, and spectral demixing dSTORM (SD-dSTORM) where spectrally close far-red fluorophores appear on both cameras before being identified by demixing. Using S2C-DNA-PAINT as a reference for low crosstalk, we evaluate SD-dSTORM crosstalk using three types of samples: DNA origami nanorulers of different sizes, single-target labeled cells, or cells labeled for multiple targets. We then assess if crosstalk can affect the detection of biologically relevant subdiffraction patterns. Extending these approaches to three-dimensional acquisition and SD-dSTORM to three-color imaging, we show that spectral demixing is an attractive option for robust and versatile multicolor SMLM investigations.

Advancing Spectrally-Resolved Single Molecule Localization Microscopy with Deep Learning

Spectrally-resolved single molecule localization microscopy (srSMLM) is a recent technique enriching single molecule localization microscopy with the simultaneous recording of spectra of the single emitters. srSMLM resolution is limited by the number of photons collected per emitters. Sharing a photon budget to record the localization and the spectroscopic information results in a loss of spatial and spectral resolution-or forces the sacrifice of one at the expense of the other. Here, srUnet-a deep-learning Unet-based image processing routine trained to increase the spectral and spatial signals to compensate for the resolution loss inherent in additionally recording the spectral component is reported. Both localization and spectral precision are improved by srUnet-particularly for the low-emitting species. srUnet increases the fraction of localization whose signal can be both spatially and spectrally characterized. It preserves spectral shifts and the linearity of the dispersion of light. It strongly facilitates wavelength assignment in multicolor experiments. srUnet is a simple post-processing add-on boosting srSMLM performance close to conventional SMLM with the potential to turn srSMLM into the new standard for multicolor single molecule imaging.