On characterizing protein spatial clusters with correlation approaches

Membrane topography and the overestimation of protein clustering in single molecule localisation microscopy - identification and correction

According to single-molecule localisation microscopy almost all plasma membrane proteins are clustered. We demonstrate that clusters can arise from variations in membrane topography where the local density of a randomly distributed membrane molecule to a degree matches the variations in the local amount of membrane. Further, we demonstrate that this false clustering can be differentiated from genuine clustering by using a membrane marker to report on local variations in the amount of membrane. In dual colour live cell single molecule localisation microscopy using the membrane probe DiI alongside either the transferrin receptor or the GPI-anchored protein CD59, we found that pair correlation analysis reported both proteins and DiI as being clustered, as did its derivative pair correlation-photoactivation localisation microscopy and nearest neighbour analyses. After converting the localisations into images and using the DiI image to factor out topography variations, no CD59 clusters were visible, suggesting that the clustering reported by the other methods is an artefact. However, the TfR clusters persisted after topography variations were factored out. We demonstrate that membrane topography variations can make membrane molecules appear clustered and present a straightforward remedy suitable as the first step in the cluster analysis pipeline.

Sticky, Adaptable, and Many-sided: SAM protein versatility in normal and pathological hematopoietic states

With decades of research seeking to generalize sterile alpha motif (SAM) biology, many outstanding questions remain regarding this multi-tool protein module. Recent data from structural and molecular/cell biology has begun to reveal new SAM modes of action in cell signaling cascades and biomolecular condensation. SAM-dependent mechanisms underlie blood-related (hematologic) diseases, including myelodysplastic syndromes and leukemias, prompting our focus on hematopoiesis for this review. With the increasing coverage of SAM-dependent interactomes, a hypothesis emerges that SAM interaction partners and binding affinities work to fine tune cell signaling cascades in developmental and disease contexts, including hematopoiesis and hematologic disease. This review discusses what is known and remains unknown about the standard mechanisms and neoplastic properties of SAM domains and what the future might hold for developing SAM-targeted therapies.

K-Neighbourhood Analysis: A Method for Understanding SMLM Images as Compositions of Local Neighbourhoods

Single molecule localisation microscopy (SMLM) is a powerful tool that has revealed the spatial arrangement of cell surface signalling proteins, producing data of enormous complexity. The complexity is partly driven by the convolution of technical and biological signal components, and partly by the challenge of pooling information across many distinct cells. To address these two particular challenges, we have devised a novel algorithm called K-neighbourhood analysis (KNA), which emphasises the fact that each image can also be viewed as a composition of local neighbourhoods. KNA is based on a novel transformation, spatial neighbourhood principal component analysis (SNPCA), which is defined by the PCA of the normalised K-nearest neighbour vectors of a spatially random point pattern. Here, we use KNA to define a novel visualisation of individual images, to compare within and between groups of images and to investigate the preferential patterns of phosphorylation. This methodology is also highly flexible and can be used to augment existing clustering methods by providing clustering diagnostics as well as revealing substructure within microclusters. In summary, we have presented a highly flexible analysis tool that presents new conceptual possibilities in the analysis of SMLM images.

Protocol for multicolor three-dimensional dSTORM data analysis using MATLAB-based script package Grafeo

This protocol describes the step-by-step analysis of the multicolor (one-, two-, or three-color) two- and three-dimensional dSTORM (direct STochastic Optical Reconstruction Microscopy) data using MATLAB-based script package Grafeo. Grafeo primarily uses pointillist data visualization and analysis frameworks, including the nearest neighbors, approach, Voronoi tessellation, Delaunay triangulation, Ripley’s (K, L) and two-point correlation functions, and graph-based clustering.

For complete details on the use and execution of this protocol, please refer to Peaucelle et al. (2020), Haas et al., 2018, Haas et al. (2020b).

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Multicolor 3D dSTORM data visualization using scatter plots and Voronoi Diagrams

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Procedure for data filtering

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Spatial statistics, cluster, and colocalization analysis

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Graph-based cluster analysis with Delaunay triangulation

Transcription-mediated supercoiling regulates genome folding and loop formation

The chromatin fiber folds into loops, but the mechanisms controlling loop extrusion are still poorly understood. Using super-resolution microscopy, we visualize that loops in intact nuclei are formed by a scaffold of cohesin complexes from which the DNA protrudes. RNA polymerase II decorates the top of the loops and is physically segregated from cohesin. Augmented looping upon increased loading of cohesin on chromosomes causes disruption of Lamin at the nuclear rim and chromatin blending, a homogeneous distribution of chromatin within the nucleus. Altering supercoiling via either transcription or topoisomerase inhibition counteracts chromatin blending, increases chromatin condensation, disrupts loop formation, and leads to altered cohesin distribution and mobility on chromatin. Overall, negative supercoiling generated by transcription is an important regulator of loop formation in vivo.

Multicolor 3D-dSTORM Reveals Native-State Ultrastructure of Polysaccharides' Network during Plant Cell Wall Assembly

The plant cell wall, a form of the extracellular matrix, is a complex and dynamic network of polymers mediating a plethora of physiological functions. How polysaccharides assemble into a coherent and heterogeneous matrix remains mostly undefined. Further progress requires improved molecular-level visualization methods that would gain a deeper understanding of the cell wall nanoarchitecture. dSTORM, a type of super-resolution microscopy, permits quantitative nanoimaging of the cell wall. However, due to the lack of single-cell model systems and the requirement of tissue-level imaging, its use in plant science is almost absent. Here we overcome these limitations; we compare two methods to achieve three-dimensional dSTORM and identify optimal photoswitching dyes for tissue-level multicolor nanoscopy. Combining dSTORM with spatial statistics, we reveal and characterize the ultrastructure of three major polysaccharides, callose, mannan, and cellulose, in the plant cell wall precursor and provide evidence for cellulose structural re-organization related to callose content.

Three dimensional cluster analysis for atom probe tomography using Ripley's K-function and machine learning

The size and structure of spatial molecular and atomic clustering can significantly impact material properties and is therefore important to accurately quantify. Ripley's K-function (K(r)), a measure of spatial correlation, can be used to perform such quantification when the material system of interest can be represented as a marked point pattern. This work demonstrates how machine learning models based on K(r)-derived metrics can accurately estimate cluster size and intra-cluster density in simulated three dimensional (3D) point patterns containing spherical clusters of varying size; over 90% of model estimates for cluster size and intra-cluster density fall within 11% and 18% error of the true values, respectively. These K(r)-based size and density estimates are then applied to an experimental APT reconstruction to characterize MgZn clusters in a 7000 series aluminum alloy. We find that the estimates are more accurate, consistent, and robust to user interaction than estimates from the popular maximum separation algorithm. Using K(r) and machine learning to measure clustering is an accurate and repeatable way to quantify this important material attribute.

Quantitative Data Analysis in Single-Molecule Localization Microscopy

Super-resolution microscopy, and specifically single-molecule localization microscopy (SMLM), is becoming a transformative technology for cell biology, as it allows the study of cellular structures with nanometer resolution. Here, we review a wide range of data analyses approaches for SMLM that extract quantitative information about the distribution, size, shape, spatial organization, and stoichiometry of macromolecular complexes to guide biological interpretation. We present a case study using the nuclear pore complex as an example that highlights the power of combining complementary approaches by identifying its symmetry, ringlike structure, and protein copy number. In face of recent technical and computational advances, this review serves as a guideline for selecting appropriate analysis tools and controls to exploit the potential of SMLM for a wide range of biological questions.

Eleven grand challenges in single-cell data science

The recent boom in microfluidics and combinatorial indexing strategies, combined with low sequencing costs, has empowered single-cell sequencing technology. Thousands-or even millions-of cells analyzed in a single experiment amount to a data revolution in single-cell biology and pose unique data science problems. Here, we outline eleven challenges that will be central to bringing this emerging field of single-cell data science forward. For each challenge, we highlight motivating research questions, review prior work, and formulate open problems. This compendium is for established researchers, newcomers, and students alike, highlighting interesting and rewarding problems for the coming years.