Most animal genomes are partitioned into topologically associating domains (TADs), created by cohesin-mediated loop extrusion and defined by convergently oriented CCCTC-binding factor (CTCF) sites. The dynamics of loop extrusion and its regulation remain poorly characterized in vivo. Here we tracked the motion of TAD anchors in living human cells to visualize and quantify cohesin-dependent loop extrusion across multiple endogenous genomic regions. We show that TADs are dynamic structures whose anchors are brought in proximity about once per hour and for 6-19 min (~16% of the time). Moreover, TADs are continuously extruded by multiple cohesin complexes. Remarkably, despite strong differences in Hi-C patterns across chromatin regions, their dynamics is consistent with the same density, residence time and speed of cohesin. Our results suggest that TAD dynamics is primarily governed by the location and affinity of CTCF sites, enabling genome-wide predictive models of cohesin-dependent chromatin interactions.

Genome-wide chromosome conformation capture (Hi-C) has revealed the organization of chromatin into topologically associating domains (TADs) and loops, which are thought to help regulate genome functions. TADs and loops are understood as the result of DNA extrusion mediated by the cohesin complex. However, despite recent efforts, direct visualization and quantification of this process in single cells remains an open challenge. Here, we use polymer simulations and dedicated analysis methods to explore if, and under which conditions, DNA loop extrusion can be detected and quantitatively characterized by imaging pairs of fluorescently labeled loci located near loop or TAD anchors in fixed or living cells. We find that under realistic conditions, extrusion can be detected and the frequency of loop formation can be quantified from fixed cell images alone, while the lifetime of loops and the speed of extrusion can be estimated from dynamic live-cell data. Our delineation of appropriate imaging conditions and the proposed analytical methods lay the groundwork for a systematic quantitative characterization of loop extrusion in fixed or living cells.

The current COVID-19 pandemic is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The positive-sense single-stranded RNA virus contains a single linear RNA segment that serves as a template for transcription and replication, leading to the synthesis of positive and negative-stranded viral RNA (vRNA) in infected cells. Tools to visualize vRNA directly in infected cells are critical to analyze the viral replication cycle, screen for therapeutic molecules, or study infections in human tissue. Here, we report the design, validation, and initial application of FISH probes to visualize positive or negative RNA of SARS-CoV-2 (CoronaFISH). We demonstrate sensitive visualization of vRNA in African green monkey and several human cell lines, in patient samples and human tissue. We further demonstrate the adaptation of CoronaFISH probes to electron microscopy. We provide all required oligonucleotide sequences, source code to design the probes, and a detailed protocol. We hope that CoronaFISH will complement existing techniques for research on SARS-CoV-2 biology and COVID-19 pathophysiology, drug screening, and diagnostics.

The 3D organization of the chromatin fiber in cell nuclei plays a key role in the regulation of gene expression. Genome-wide techniques to score DNA-DNA contacts, such as Hi-C, reveal the partitioning of chromosomes into epigenetically defined active and repressed compartments and smaller "topologically associated" domains. These domains are often associated with chromatin loops, which largely disappear upon removal of cohesin. Because most Hi-C implementations average contact frequencies over millions of cells and do not provide direct spatial information, it remains unclear whether and how frequently chromatin domains and loops exist in single cells.

The speed of super-resolution microscopy methods based on single-molecule localization, for example, PALM and STORM, is limited by the need to record many thousands of frames with a small number of observed molecules in each. Here, we present ANNA-PALM, a computational strategy that uses artificial neural networks to reconstruct super-resolution views from sparse, rapidly acquired localization images and/or widefield images. Simulations and experimental imaging of microtubules, nuclear pores, and mitochondria show that high-quality, super-resolution images can be reconstructed from up to two orders of magnitude fewer frames than usually needed, without compromising spatial resolution. Super-resolution reconstructions are even possible from widefield images alone, though adding localization data improves image quality. We demonstrate super-resolution imaging of >1,000 fields of view containing >1,000 cells in ∼3 h, yielding an image spanning spatial scales from ∼20 nm to ∼2 mm. The drastic reduction in acquisition time and sample irradiation afforded by ANNA-PALM enables faster and gentler high-throughput and live-cell super-resolution imaging.