Parameter-free molecular super-structures quantification in single-molecule localization microscopy

Cryosectioning-enabled super-resolution microscopy for studying nuclear architecture at the single protein level

DNA-PAINT combined with total Internal Reflection Fluorescence (TIRF) microscopy enables the highest localization precisions, down to single nanometers in thin biological samples, due to TIRF's unique method for optical sectioning and attaining high contrast. However, most cellular targets elude the accessible TIRF range close to the cover glass and thus require alternative imaging conditions, affecting resolution and image quality. Here, we address this limitation by applying ultrathin physical cryosectioning in combination with DNA-PAINT. With "tomographic & kinetically-enhanced" DNA-PAINT (tokPAINT), we demonstrate the imaging of nuclear proteins with sub-3 nanometer localization precision, advancing the quantitative study of nuclear organization within fixed cells and mouse tissues at the level of single antibodies. We believe that ultrathin sectioning combined with the versatility and multiplexing capabilities of DNA-PAINT will be a powerful addition to the toolbox of quantitative DNA-based super-resolution microscopy in intracellular structural analyses of proteins, RNA and DNA in situ.

Restrictor synergizes with Symplekin and PNUTS to terminate extragenic transcription

Transcription termination pathways mitigate the detrimental consequences of unscheduled promiscuous initiation occurring at hundreds of thousands of genomic cis-regulatory elements. The Restrictor complex, composed of the Pol II-interacting protein WDR82 and the RNA-binding protein ZC3H4, suppresses processive transcription at thousands of extragenic sites in mammalian genomes. Restrictor-driven termination does not involve nascent RNA cleavage, and its interplay with other termination machineries is unclear. Here we show that efficient termination at Restrictor-controlled extragenic transcription units involves the recruitment of the protein phosphatase 1 (PP1) regulatory subunit PNUTS, a negative regulator of the SPT5 elongation factor, and Symplekin, a protein associated with RNA cleavage complexes but also involved in cleavage-independent and phosphatase-dependent termination of noncoding RNAs in yeast. PNUTS and Symplekin act synergistically with, but independently from, Restrictor to dampen processive extragenic transcription. Moreover, the presence of limiting nuclear levels of Symplekin imposes a competition for its recruitment among multiple transcription termination machineries, resulting in mutual regulatory interactions. Hence, by synergizing with Restrictor, Symplekin and PNUTS enable efficient termination of processive, long-range extragenic transcription.

HNRNPU's multi-tasking is essential for proper cortical development

Heterogeneous nuclear ribonucleoprotein U (HNRNPU) is a nuclear protein that plays a crucial role in various biological functions, such as RNA splicing and chromatin organization. HNRNPU/scaffold attachment factor A (SAF-A) activities are essential for regulating gene expression, DNA replication, genome integrity, and mitotic fidelity. These functions are critical to ensure the robustness of developmental processes, particularly those involved in shaping the human brain. As a result, HNRNPU is associated with various neurodevelopmental disorders (HNRNPU-related neurodevelopmental disorder, HNRNPU-NDD) characterized by developmental delay and intellectual disability. Our research demonstrates that the loss of HNRNPU function results in the death of both neural progenitor cells and post-mitotic neurons, with a higher sensitivity observed in the former. We reported that HNRNPU truncation leads to the dysregulation of gene expression and alternative splicing of genes that converge on several signaling pathways, some of which are likely to be involved in the pathology of HNRNPU-related NDD.

Unbiased choice of global clustering parameters for single-molecule localization microscopy

Single-molecule localization microscopy resolves objects below the diffraction limit of light via sparse, stochastic detection of target molecules. Single molecules appear as clustered detection events after image reconstruction. However, identification of clusters of localizations is often complicated by the spatial proximity of target molecules and by background noise. Clustering results of existing algorithms often depend on user-generated training data or user-selected parameters, which can lead to unintentional clustering errors. Here we suggest an unbiased algorithm (FINDER) based on adaptive global parameter selection and demonstrate that the algorithm is robust to noise inclusion and target molecule density. We benchmarked FINDER against the most common density based clustering algorithms in test scenarios based on experimental datasets. We show that FINDER can keep the number of false positive inclusions low while also maintaining a low number of false negative detections in densely populated regions.

Unbiased choice of global clustering parameters for single-molecule localization microscopy

Single-molecule localization microscopy resolves objects below the diffraction limit of light via sparse, stochastic detection of target molecules. Single molecules appear as clustered detection events after image reconstruction. However, identification of clusters of localizations is often complicated by the spatial proximity of target molecules and by background noise. Clustering results of existing algorithms often depend on user-generated training data or user-selected parameters, which can lead to unintentional clustering errors. Here we suggest an unbiased algorithm (FINDER) based on adaptive global parameter selection and demonstrate that the algorithm is robust to noise inclusion and target molecule density. We benchmarked FINDER against the most common density based clustering algorithms in test scenarios based on experimental datasets. We show that FINDER can keep the number of false positive inclusions low while also maintaining a low number of false negative detections in densely populated regions.

The role of SAF-A/hnRNP U in regulating chromatin structure

Scaffold attachment factor A (SAF-A) or hnRNP U is a nuclear RNA-binding protein with a well-documented role in processing newly transcribed RNA. Recent studies also indicate that SAF-A can oligomerise in an ATP-dependent manner and interact with RNA to form a dynamic nuclear mesh. This mesh is thought to regulate nuclear and chromatin architecture, yet a mechanistic understanding is lacking. Here, we review developments in the field to understand how the SAF-A/RNA mesh affects chromatin organisation in interphase and mitosis. As SAF-A has an intrinsically disordered domain we discuss how the chromatin mesh is related to nuclear phase-separated condensates, which in other situations have been shown to regulate transcription and cell functions. Finally, we infer possible links between diseases emerging from SAF-A mutations and its role in chromatin organisation and regulation.

Efficient Cross-Correlation Filtering of One- and Two-Color Single Molecule Localization Microscopy Data

Single molecule localization microscopy has become a prominent technique to quantitatively study biological processes below the optical diffraction limit. By fitting the intensity profile of single sparsely activated fluorophores, which are often attached to a specific biomolecule within a cell, the locations of all imaged fluorophores are obtained with ∼20 nm resolution in the form of a coordinate table. While rendered super-resolution images reveal structural features of intracellular structures below the optical diffraction limit, the ability to further analyze the molecular coordinates presents opportunities to gain additional quantitative insights into the spatial distribution of a biomolecule of interest. For instance, pair-correlation or radial distribution functions are employed as a measure of clustering, and cross-correlation analysis reveals the colocalization of two biomolecules in two-color SMLM data. Here, we present an efficient filtering method for SMLM data sets based on pair- or cross-correlation to isolate localizations that are clustered or appear in proximity to a second set of localizations in two-color SMLM data. In this way, clustered or colocalized localizations can be separately rendered and analyzed to compare other molecular properties to the remaining localizations, such as their oligomeric state or mobility in live cell experiments. Current matrix-based cross-correlation analyses of large data sets quickly reach the limitations of computer memory due to the space complexity of constructing the distance matrices. Our approach leverages k-dimensional trees to efficiently perform range searches, which dramatically reduces memory needs and the time for the analysis. We demonstrate the versatile applications of this method with simulated data sets as well as examples of two-color SMLM data. The provided MATLAB code and its description can be integrated into existing localization analysis packages and provides a useful resource to analyze SMLM data with new detail.

Tunable Wide-Field Illumination and Single-Molecule Photoswitching with a Single MEMS Mirror

Homogeneous illumination in single-molecule localization microscopy (SMLM) is key for the quantitative analysis of super-resolution images. Therefore, different approaches for flat-field illumination have been introduced as alternative to the conventional Gaussian illumination. Here, we introduce a single microelectromechanical systems (MEMS) mirror as a tunable and cost-effective device for adapting wide-field illumination in SMLM. In flat-field mode the MEMS allowed for consistent SMLM metrics across the entire field of view. Employing single-molecule photoswitching, we developed a simple yet powerful routine to benchmark different illumination schemes on the basis of local emitter brightness and ON-state lifetime. Moreover, we propose that tuning the MEMS beyond optimal flat-field conditions enables to study the kinetics of photoswitchable fluorophores within a single acquisition.

The hierarchical packing of euchromatin domains can be described as multiplicative cascades

The genome is packed into the cell nucleus in the form of chromatin. Biochemical approaches have revealed that chromatin is packed within domains, which group into larger domains, and so forth. Such hierarchical packing is equally visible in super-resolution microscopy images of large-scale chromatin organization. While previous work has suggested that chromatin is partitioned into distinct domains via microphase separation, it is unclear how these domains organize into this hierarchical packing. A particular challenge is to find an image analysis approach that fully incorporates such hierarchical packing, so that hypothetical governing mechanisms of euchromatin packing can be compared against the results of such an analysis. Here, we obtain 3D STED super-resolution images from pluripotent zebrafish embryos labeled with improved DNA fluorescence stains, and demonstrate how the hierarchical packing of euchromatin in these images can be described as multiplicative cascades. Multiplicative cascades are an established theoretical concept to describe the placement of ever-smaller structures within bigger structures. Importantly, these cascades can generate artificial image data by applying a single rule again and again, and can be fully specified using only four parameters. Here, we show how the typical patterns of euchromatin organization are reflected in the values of these four parameters. Specifically, we can pinpoint the values required to mimic a microphase-separated state of euchromatin. We suggest that the concept of multiplicative cascades can also be applied to images of other types of chromatin. Here, cascade parameters could serve as test quantities to assess whether microphase separation or other theoretical models accurately reproduce the hierarchical packing of chromatin.