Joint registration of multiple point clouds for fast particle fusion in localization microscopy.

SUMMARY: We present a fast particle fusion method for particles imaged with single-molecule localization microscopy. The state-of-the-art approach based on all-to-all registration has proven to work well but its computational cost scales unfavorably with the number of particles N, namely as N2. Our method overcomes this problem and achieves a linear scaling of computational cost with N by making use of the Joint Registration of Multiple Point Clouds (JRMPC) method. Straightforward application of JRMPC fails as mostly locally optimal solutions are found. These usually contain several overlapping clusters that each consist of well-aligned particles, but that have different poses. We solve this issue by repeated runs of JRMPC for different initial conditions, followed by a classification step to identify the clusters, and a connection step to link the different clusters obtained for different initializations. In this way a single well-aligned structure is obtained containing the majority of the particles. RESULTS: We achieve reconstructions of experimental DNA-origami datasets consisting of close to 400 particles within only 10 min on a CPU, with an image resolution of 3.2 nm. In addition, we show artifact-free reconstructions of symmetric structures without making any use of the symmetry. We also demonstrate that the method works well for poor data with a low density of labeling and for 3D data. AVAILABILITY AND IMPLEMENTATION: The code is available for download from https://github.com/wexw/Joint-Registration-of-Multiple-Point-Clouds-for-Fast-Particle-Fusion-in-Localization-Microscopy. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Mechanics of epithelial tissue formation.

A key process in the life of any multicellular organism is its development from a single egg into a full grown adult. The first step in this process often consists of forming a tissue layer out of randomly placed cells on the surface of the egg. We present a model for generating such a tissue, based on mechanical interactions between the cells, and find that the resulting cellular pattern corresponds to the Voronoi tessellation of the nuclei of the cells. Experimentally, we obtain the same result in both fruit flies and flour beetles, with a distribution of cell shapes that matches that of the model, without any adjustable parameters. Finally, we show that this pattern is broken when the cells grow at different rates.

Ultrack: pushing the limits of cell tracking across biological scales.

Tracking live cells across 2D, 3D, and multi-channel time-lapse recordings is crucial for understanding tissue-scale biological processes. Despite advancements in imaging technology, achieving accurate cell tracking remains challenging, particularly in complex and crowded tissues where cell segmentation is often ambiguous. We present Ultrack, a versatile and scalable cell-tracking method that tackles this challenge by considering candidate segmentations derived from multiple algorithms and parameter sets. Ultrack employs temporal consistency to select optimal segments, ensuring robust performance even under segmentation uncertainty. We validate our method on diverse datasets, including terabyte-scale developmental time-lapses of zebrafish, fruit fly, and nematode embryos, as well as multi-color and label-free cellular imaging. We show that Ultrack achieves state-of-the-art performance on the Cell Tracking Challenge and demonstrates superior accuracy in tracking densely packed embryonic cells over extended periods. Moreover, we propose an approach to tracking validation via dual-channel sparse labeling that enables high-fidelity ground truth generation, pushing the boundaries of long-term cell tracking assessment. Our method is freely available as a Python package with Fiji and napari plugins and can be deployed in a high-performance computing environment, facilitating widespread adoption by the research community.

DNA Self-Assembly of Single Molecules with Deterministic Position and Orientation.

An ideal nanofabrication method should allow the organization of nanoparticles and molecules with nanometric positional precision, stoichiometric control, and well-defined orientation. The DNA origami technique has evolved into a highly versatile bottom-up nanofabrication methodology that fulfils almost all of these features. It enables the nanometric positioning of molecules and nanoparticles with stoichiometric control, and even the orientation of asymmetrical nanoparticles along predefined directions. However, orienting individual molecules has been a standing challenge. Here, we show how single molecules, namely, Cy5 and Cy3 fluorophores, can be incorporated in a DNA origami with controlled orientation by doubly linking them to oligonucleotide strands that are hybridized while leaving unpaired bases in the scaffold. Increasing the number of bases unpaired induces a stretching of the fluorophore linkers, reducing its mobility freedom, and leaves more space for the fluorophore to accommodate and find different sites for interaction with the DNA. Particularly, we explore the effects of leaving 0, 2, 4, 6, and 8 bases unpaired and find extreme orientations for 0 and 8 unpaired bases, corresponding to the molecules being perpendicular and parallel to the DNA double-helix, respectively. We foresee that these results will expand the application field of DNA origami toward the fabrication of nanodevices involving a wide range of orientation-dependent molecular interactions, such as energy transfer, intermolecular electron transport, catalysis, exciton delocalization, or the electromagnetic coupling of a molecule to specific resonant nanoantenna modes.

Detecting structural heterogeneity in single-molecule localization microscopy data.

Particle fusion for single molecule localization microscopy improves signal-to-noise ratio and overcomes underlabeling, but ignores structural heterogeneity or conformational variability. We present a-priori knowledge-free unsupervised classification of structurally different particles employing the Bhattacharya cost function as dissimilarity metric. We achieve 96% classification accuracy on mixtures of up to four different DNA-origami structures, detect rare classes of origami occuring at 2% rate, and capture variation in ellipticity of nuclear pore complexes.