Analysis of the spatial organization of molecules with robust statistics

Integrating AI-Powered Digital Pathology and Imaging Mass Cytometry Identifies Key Classifiers of Tumor Cells, Stroma, and Immune Cells in Non-Small Cell Lung Cancer

Artificial intelligence (AI)-powered approaches are becoming increasingly used as histopathologic tools to extract subvisual features and improve diagnostic workflows. On the other hand, hi-plex approaches are widely adopted to analyze the immune ecosystem in tumor specimens. Here, we aimed at combining AI-aided histopathology and imaging mass cytometry (IMC) to analyze the ecosystem of non-small cell lung cancer (NSCLC). An AI-based approach was used on hematoxylin and eosin (H&E) sections from 158 NSCLC specimens to accurately identify tumor cells, both adenocarcinoma and squamous carcinoma cells, and to generate a classifier of tumor cell spatial clustering. Consecutive tissue sections were stained with metal-labeled antibodies and processed through the IMC workflow, allowing quantitative detection of 24 markers related to tumor cells, tissue architecture, CD45+ myeloid and lymphoid cells, and immune activation. IMC identified 11 macrophage clusters that mainly localized in the stroma, except for S100A8+ cells, which infiltrated tumor nests. T cells were preferentially localized in peritumor areas or in tumor nests, the latter being associated with better prognosis, and they were more abundant in highly clustered tumors. Integrated tumor and immune classifiers were validated as prognostic on whole slides. In conclusion, integration of AI-powered H&E and multiparametric IMC allows investigation of spatial patterns and reveals tissue relevant features with clinical relevance.

SIGNIFICANCE

Leveraging artificial intelligence-powered H&E analysis integrated with hi-plex imaging mass cytometry provides insights into the tumor ecosystem and can translate tumor features into classifiers to predict prognosis, genotype, and therapy response.

Co-cultures of cerebellar slices from mice with different reelin genetic backgrounds as a model to study cortical lamination

Background: Reelin has fundamental functions in the developing and mature brain. Its absence gives rise to the Reeler phenotype in mice, the first described cerebellar mutation. In homozygous mutants missing the Reelin gene ( reln -/-), neurons are incapable of correctly positioning themselves in layered brain areas such as the cerebral and cerebellar cortices. We here demonstrate that by employing ex vivo cultured cerebellar slices one can reduce the number of animals and use a non-recovery procedure to analyze the effects of Reelin on the migration of Purkinje neurons (PNs). Methods: We generated mouse hybrids (L7-GFP relnF1/) with green fluorescent protein (GFP)-tagged PNs, directly visible under fluorescence microscopy. We then cultured the slices obtained from mice with different reln genotypes and demonstrated that when the slices from reln -/- mutants were co-cultured with those from reln +/- mice, the Reelin produced by the latter induced migration of the PNs to partially rescue the normal layered cortical histology. We have confirmed this observation with Voronoi tessellation to analyze PN dispersion. Results: In images of the co-cultured slices from reln -/- mice, Voronoi polygons were larger than in single-cultured slices of the same genetic background but smaller than those generated from slices of reln +/- animals. The mean roundness factor, area disorder, and roundness factor homogeneity were different when slices from reln -/- mice were cultivated singularly or co-cultivated, supporting mathematically the transition from the clustered organization of the PNs in the absence of Reelin to a layered structure when the protein is supplied ex vivo. Conclusions: Neurobiologists are the primary target users of this 3Rs approach. They should adopt it for the possibility to study and manipulate ex vivo the activity of a brain-secreted or genetically engineered protein (scientific perspective), the potential reduction (up to 20%) of the animals used, and the total avoidance of severe surgery (3Rs perspective).

Modeling the effect of acquired resistance on cancer therapy outcomes

Adaptive therapy (AT) is an evolution-based treatment strategy that exploits cell-cell competition. Acquired resistance can change the competitive nature of cancer cells in a tumor, impacting AT outcomes. We aimed to determine if adaptive therapy can still be effective with cell's acquiring resistance. We developed an agent-based model for spatial tumor growth considering three different types of acquired resistance: random genetic mutations during cell division, drug-induced reversible (plastic) phenotypic changes, and drug-induced irreversible phenotypic changes. These three resistance mechanisms lead to different spatial distributions of resistant cells. To quantify the spatial distribution, we propose an extension of Ripley's K-function, Sampled Ripley's K-function (SRKF), which calculates the non-randomness of the resistance distribution over the tumor domain. Our model predicts that the emergent spatial distribution of resistance can determine the time to progression under both adaptive and continuous therapy (CT). Notably, a high rate of random genetic mutations leads to quicker progression under AT than CT due to the emergence of many small clumps of resistant cells. Drug-induced phenotypic changes accelerate tumor progression irrespective of the treatment strategy. Low-rate switching to a sensitive state reduces the benefits of AT compared to CT. Furthermore, we also demonstrated that drug-induced resistance necessitates aggressive treatment under CT, regardless of the presence of cancer-associated fibroblasts. However, there is an optimal dose that can most effectively delay tumor relapse under AT by suppressing resistance. In conclusion, this study demonstrates that diverse resistance mechanisms can shape the distribution of resistance and thus determine the efficacy of adaptive therapy.

Introducing Diinamic, a flexible and robust method for clustering analysis in single-molecule localization microscopy

Super-resolution microscopy allowed major improvements in our capacity to describe and explain biological organization at the nanoscale. Single-molecule localization microscopy (SMLM) uses the positions of molecules to create super-resolved images, but it can also provide new insights into the organization of molecules through appropriate pointillistic analyses that fully exploit the sparse nature of SMLM data. However, the main drawback of SMLM is the lack of analytical tools easily applicable to the diverse types of data that can arise from biological samples. Typically, a cloud of detections may be a cluster of molecules or not depending on the local density of detections, but also on the size of molecules themselves, the labeling technique, the photo-physics of the fluorophore, and the imaging conditions. We aimed to set an easy-to-use clustering analysis protocol adaptable to different types of data. Here, we introduce Diinamic, which combines different density-based analyses and optional thresholding to facilitate the detection of clusters. On simulated or real SMLM data, Diinamic correctly identified clusters of different sizes and densities, being performant even in noisy datasets with multiple detections per fluorophore. It also detected subdomains ("nanodomains") in clusters with non-homogeneous distribution of detections.

Advanced image-free analysis of the nano-organization of chromatin and other biomolecules by Single Molecule Localization Microscopy (SMLM)

The cell as a system of many components, governed by the laws of physics and chemistry drives molecular functions having an impact on the spatial organization of these systems and vice versa. Since the relationship between structure and function is an almost universal rule not only in biology, appropriate methods are required to parameterize the relationship between the structure and function of biomolecules and their networks, the mechanisms of the processes in which they are involved, and the mechanisms of regulation of these processes. Single molecule localization microscopy (SMLM), which we focus on here, offers a significant advantage for the quantitative parametrization of molecular organization: it provides matrices of coordinates of fluorescently labeled biomolecules that can be directly subjected to advanced mathematical analytical procedures without the need for laborious and sometimes misleading image processing. Here, we propose mathematical tools for comprehensive quantitative computer data analysis of SMLM point patterns that include Ripley distance frequency analysis, persistent homology analysis, persistent 'imaging', principal component analysis and co-localization analysis. The application of these methods is explained using artificial datasets simulating different, potentially possible and interpretatively important situations. Illustrative analyses of real complex biological SMLM data are presented to emphasize the applicability of the proposed algorithms. This manuscript demonstrated the extraction of features and parameters quantifying the influence of chromatin (re)organization on genome function, offering a novel approach to study chromatin architecture at the nanoscale. However, the ability to adapt the proposed algorithms to analyze essentially any molecular organizations, e.g., membrane receptors or protein trafficking in the cytosol, offers broad flexibility of use.

Quantitative Analysis of PcG-Associated Condensates by Stochastic Optical Reconstruction Microscopy (STORM)

The polycomb group (PcG) proteins play a central role in the maintenance of a repressive state of gene expression. Recent findings demonstrate that PcG components are organized into nuclear condensates, contributing to the reshaping of chromatin architecture in physiological and pathological conditions, thus affecting the nuclear mechanics. In this context, direct stochastic optical reconstruction microscopy (dSTORM) provides an effective tool to achieve a detailed characterization of PcG condensates by visualizing them at a nanometric level. Furthermore, by analyzing dSTORM datasets with cluster analysis algorithms, quantitative information can be yielded regarding protein numbers, grouping, and spatial organization. Here, we describe how to set up a dSTORM experiment and perform the data analysis to study PcG complexes' components in adhesion cells quantitatively.

Correlated STORM-homoFRET imaging reveals highly heterogeneous membrane receptor structures

Mapping the self-organization and spatial distribution of membrane proteins is key to understanding their function. Developing methods that can provide insight into correlations between membrane protein co-localization and interactions is challenging. We report here on a correlated stochastic optical reconstruction microscopy (STORM)/homoFRET imaging approach for resolving the nanoscale distribution and oligomeric state of membrane proteins. Using live cell homoFRET imaging of carcinoembryonic antigen-related cellular adhesion molecule (CEACAM1), a cell-surface receptor known to exist in a complex equilibrium between monomer and dimer/oligomer states, we revealed highly heterogeneous diffraction-limited structures on the surface of HeLa cells. Furthermore, correlated super-resolved STORM imaging showed that these structures comprised a complex mixture and spatial distribution of self-associated CEACAM1 molecules. In conclusion, this correlated approach provides a compelling strategy for addressing challenging questions about the interplay between membrane protein concentration, distribution, interaction, clustering, and function.

Identifying a spatial scale for the analysis of residential burglary: An empirical framework based on point pattern analysis

A key issue in the spatial and temporal analysis of residential burglary is the choice of scale: spatial patterns might differ appreciably for different time periods and vary across geographic units of analysis. Based on point pattern analysis of burglary incidents in Columbus, Ohio during a 9-year period, this study develops an empirical framework to identify a useful spatial scale and its dependence on temporal aggregation. Our analysis reveals that residential burglary in Columbus clusters at a characteristic scale of 2.2 km. An ANOVA test shows no significant impact of temporal aggregation on spatial scale of clustering. This study demonstrates the value of point pattern analysis in identifying a scale for the analysis of crime patterns. Furthermore, the characteristic scale of clustering determined using our method has great potential applications: (1) it can reflect the spatial environment of criminogenic processes and thus be used to define the spatial boundary for place-based policing; (2) it can serve as a candidate for the bandwidth (search radius) for hot spot policing; (3) its independence of temporal aggregation implies that police officials need not be concerned about the shifting sizes of risk-areas depending on the time of the year.

Probabilistic spatial analysis in quantitative microscopy with uncertainty-aware cell detection using deep Bayesian regression

The investigation of biological systems with three-dimensional microscopy demands automatic cell identification methods that not only are accurate but also can imply the uncertainty in their predictions. The use of deep learning to regress density maps is a popular successful approach for extracting cell coordinates from local peaks in a postprocessing step, which then, however, hinders any meaningful probabilistic output. We propose a framework that can operate on large microscopy images and output probabilistic predictions (i) by integrating deep Bayesian learning for the regression of uncertainty-aware density maps, where peak detection algorithms generate cell proposals, and (ii) by learning a mapping from prediction proposals to a probabilistic space that accurately represents the chances of a successful prediction. Using these calibrated predictions, we propose a probabilistic spatial analysis with Monte Carlo sampling. We demonstrate this in a bone marrow dataset, where our proposed methods reveal spatial patterns that are otherwise undetectable.

Methods to Determine and Analyze the Cellular Spatial Distribution Extracted From Multiplex Immunofluorescence Data to Understand the Tumor Microenvironment

Image analysis using multiplex immunofluorescence (mIF) to detect different proteins in a single tissue section has revolutionized immunohistochemical methods in recent years. With mIF, individual cell phenotypes, as well as different cell subpopulations and even rare cell populations, can be identified with extraordinary fidelity according to the expression of antibodies in an mIF panel. This technology therefore has an important role in translational oncology studies and probably will be incorporated in the clinic. The expression of different biomarkers of interest can be examined at the tissue or individual cell level using mIF, providing information about cell phenotypes, distribution of cells, and cell biological processes in tumor samples. At present, the main challenge in spatial analysis is choosing the most appropriate method for extracting meaningful information about cell distribution from mIF images for analysis. Thus, knowing how the spatial interaction between cells in the tumor encodes clinical information is important. Exploratory analysis of the location of the cell phenotypes using point patterns of distribution is used to calculate metrics summarizing the distances at which cells are processed and the interpretation of those distances. Various methods can be used to analyze cellular distribution in an mIF image, and several mathematical functions can be applied to identify the most elemental relationships between the spatial analysis of cells in the image and established patterns of cellular distribution in tumor samples. The aim of this review is to describe the characteristics of mIF image analysis at different levels, including spatial distribution of cell populations and cellular distribution patterns, that can increase understanding of the tumor microenvironment.

Testing for complete spatial randomness on three dimensional bounded convex shapes

There is currently a gap in theory for point patterns that lie on the surface of objects, with researchers focusing on patterns that lie in a Euclidean space, typically planar and spatial data. Methodology for planar and spatial data thus relies on Euclidean geometry and is therefore inappropriate for analysis of point patterns observed in non-Euclidean spaces. Recently, there has been extensions to the analysis of point patterns on a sphere, however, many other shapes are left unexplored. This is in part due to the challenge of defining the notion of stationarity for a point process existing on such a space due to the lack of rotational and translational isometries. Here, we construct functional summary statistics for Poisson processes defined on convex shapes in three dimensions. Using the Mapping Theorem, a Poisson process can be transformed from any convex shape to a Poisson process on the unit sphere which has rotational symmetries that allow for functional summary statistics to be constructed. We present the first and second order properties of such summary statistics and demonstrate how they can be used to construct a test statistics to determine whether an observed pattern exhibits complete spatial randomness or spatial preference on the original convex space. We compare this test statistic with one constructed from an analogue L -function for inhomogeneous point processes on the sphere. A study of the Type I and II errors of our test statistics are explored through simulations on ellipsoids of varying dimensions.

Detecting Differences of Fluorescent Markers Distribution in Single Cell Microscopy: Textural or Pointillist Feature Space?

We consider the detection of change in spatial distribution of fluorescent markers inside cells imaged by single cell microscopy. Such problems are important in bioimaging since the density of these markers can reflect the healthy or pathological state of cells, the spatial organization of DNA, or cell cycle stage. With the new super-resolved microscopes and associated microfluidic devices, bio-markers can be detected in single cells individually or collectively as a texture depending on the quality of the microscope impulse response. In this work, we propose, via numerical simulations, to address detection of changes in spatial density or in spatial clustering with an individual (pointillist) or collective (textural) approach by comparing their performances according to the size of the impulse response of the microscope. Pointillist approaches show good performances for small impulse response sizes only, while all textural approaches are found to overcome pointillist approaches with small as well as with large impulse response sizes. These results are validated with real fluorescence microscopy images with conventional resolution. This, a priori non-intuitive result in the perspective of the quest of super-resolution, demonstrates that, for difference detection tasks in single cell microscopy, super-resolved microscopes may not be mandatory and that lower cost, sub-resolved, microscopes can be sufficient.

In vivo nanoscale analysis of the dynamic synergistic interaction of Bacillus thuringiensis Cry11Aa and Cyt1Aa toxins in Aedes aegypti

The insecticidal Cry11Aa and Cyt1Aa proteins are produced by Bacillus thuringiensis as crystal inclusions. They work synergistically inducing high toxicity against mosquito larvae. It was proposed that these crystal inclusions are rapidly solubilized and activated in the gut lumen, followed by pore formation in midgut cells killing the larvae. In addition, Cyt1Aa functions as a Cry11Aa binding receptor, inducing Cry11Aa oligomerization and membrane insertion. Here, we used fluorescent labeled crystals, protoxins or activated toxins for in vivo localization at nano-scale resolution. We show that after larvae were fed solubilized proteins, these proteins were not accumulated inside the gut and larvae were not killed. In contrast, if larvae were fed soluble non-toxic mutant proteins, these proteins were found inside the gut bound to gut-microvilli. Only feeding with crystal inclusions resulted in high larval mortality, suggesting that they have a role for an optimal intoxication process. At the macroscopic level, Cry11Aa completely degraded the gastric caeca structure and, in the presence of Cyt1Aa, this effect was observed at lower toxin-concentrations and at shorter periods. The labeled Cry11Aa crystal protein, after midgut processing, binds to the gastric caeca and posterior midgut regions, and also to anterior and medium regions where it is internalized in ordered "net like" structures, leading finally to cell break down. During synergism both Cry11Aa and Cyt1Aa toxins showed a dynamic layered array at the surface of apical microvilli, where Cry11Aa is localized in the lower layer closer to the cell cytoplasm, and Cyt1Aa is layered over Cry11Aa. This array depends on the pore formation activity of Cry11Aa, since the non-toxic mutant Cry11Aa-E97A, which is unable to oligomerize, inverted this array. Internalization of Cry11Aa was also observed during synergism. These data indicate that the mechanism of action of Cry11Aa is more complex than previously anticipated, and may involve additional steps besides pore-formation activity.

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.

Prognostic significance of spatial immune profiles in human solid cancers

Immune-based tumor characteristics in the context of tumor heterogeneity are associated with suppression as well as promotion of cancer progression in various tumor types. As immunity typically functions based on intercellular contacts and short-distance cytokine communications, the location and spatial relationships of the tumor immune microenvironment can provide a framework to understand the biology and potential predictive biomarkers related to disease outcomes. Immune spatial analysis is a newly emerging form of cancer research based on recent methodological advances in in situ single-cell analysis, where cell-cell interaction and the tissue architecture can be analyzed in relation to phenotyping the tumor immune heterogeneity. Spatial characteristics of tumors can be stratified into the tissue architecture level and the single-cell level. At the tissue architecture level, the prognostic significance of the density of immune cell lineages, particularly T cells, is leveraged by understanding longitudinal changes in cell distribution in the tissue architecture such as intra-tumoral and peri-tumoral regions, and invasive margins. At the single-cell level, the proximity of the tumor to the immune cells correlates with disease aggressiveness and therapeutic resistance, providing evidence to understand biological interactions and characteristics of the tumor immune microenvironment. In this review, we summarize recent findings regarding spatial information of the tumor immune microenvironment and review advances and challenges in spatial single-cell analysis toward developing tissue-based biomarkers rooted in the immune spatial landscape.

Two HIRA-dependent pathways mediate H3.3 de novo deposition and recycling during transcription

Nucleosomes represent a challenge in regard to transcription. Histone eviction enables RNA polymerase II (RNAPII) progression through DNA, but compromises chromatin integrity. Here, we used the SNAP-tag system to distinguish new and old histones and monitor chromatin reassembly coupled to transcription in human cells. We uncovered a transcription-dependent loss of old histone variants H3.1 and H3.3. At transcriptionally active domains, H3.3 enrichment reflected both old H3.3 retention and new deposition. Mechanistically, we found that the histone regulator A (HIRA) chaperone is critical to processing both new and old H3.3 via different pathways. De novo H3.3 deposition is totally dependent on HIRA trimerization as well as on its partner ubinuclein 1 (UBN1), while antisilencing function 1 (ASF1) interaction with HIRA can be bypassed. By contrast, recycling of H3.3 requires HIRA but proceeds independently of UBN1 or HIRA trimerization and shows absolute dependency on ASF1-HIRA interaction. We propose a model whereby HIRA coordinates these distinct pathways during transcription to fine-tune chromatin states.

The Decade of Super-Resolution Microscopy of the Presynapse

The presynaptic compartment of the chemical synapse is a small, yet extremely complex structure. Considering its size, most methods of optical microscopy are not able to resolve its nanoarchitecture and dynamics. Thus, its ultrastructure could only be studied by electron microscopy. In the last decade, new methods of optical superresolution microscopy have emerged allowing the study of cellular structures and processes at the nanometer scale. While this is a welcome addition to the experimental arsenal, it has necessitated careful analysis and interpretation to ensure the data obtained remains artifact-free. In this article we review the application of nanoscopic techniques to the study of the synapse and the progress made over the last decade with a particular focus on the presynapse. We find to our surprise that progress has been limited, calling for imaging techniques and probes that allow dense labeling, multiplexing, longer imaging times, higher temporal resolution, while at least maintaining the spatial resolution achieved thus far.

Assembling nuclear domains: Lessons from DNA repair

Schrank and Gautier discuss the generation and function of nuclear domains during DNA repair with a special focus on nuclear actin polymerization.

Eukaryotic nuclei are organized into nuclear domains that unite loci sharing a common function. These domains are essential for diverse processes including (1) the formation of topologically associated domains (TADs) that coordinate replication and transcription, (2) the formation of specialized transcription and splicing factories, and (3) the clustering of DNA double-strand breaks (DSBs), which concentrates damaged DNA for repair. The generation of nuclear domains requires forces that are beginning to be identified. In the case of DNA DSBs, DNA movement and clustering are driven by actin filament nucleators. Furthermore, RNAs and low-complexity protein domains such as RNA-binding proteins also accumulate around sites of transcription and repair. The link between liquid–liquid phase separation and actin nucleation in the formation of nuclear domains is still unknown. This review discusses DSB repair domain formation as a model for functional nuclear domains in other genomic contexts.

A tessellation-based colocalization analysis approach for single-molecule localization microscopy

Multicolor single-molecule localization microscopy (λSMLM) is a powerful technique to reveal the relative nanoscale organization and potential colocalization between different molecular species. While several standard analysis methods exist for pixel-based images, λSMLM still lacks such a standard. Moreover, existing methods only work on 2D data and are usually sensitive to the relative molecular organization, a very important parameter to consider in quantitative SMLM. Here, we present an efficient, parameter-free colocalization analysis method for 2D and 3D λSMLM using tessellation analysis. We demonstrate that our method allows for the efficient computation of several popular colocalization estimators directly from molecular coordinates and illustrate its capability to analyze multicolor SMLM data in a robust and efficient manner.

Multicolour single-molecule localization microscopy lacks a standard analysis method. Here Levet et al. introduce Coloc-Tesseler, a parameter-free colocalisation analysis method based on tessellation analysis for the efficient analysis of multicolour SMLM data.

Resolution limit of image analysis algorithms

The resolution of an imaging system is a key property that, despite many advances in optical imaging methods, remains difficult to define and apply. Rayleigh’s and Abbe’s resolution criteria were developed for observations with the human eye. However, modern imaging data is typically acquired on highly sensitive cameras and often requires complex image processing algorithms to analyze. Currently, no approaches are available for evaluating the resolving capability of such image processing algorithms that are now central to the analysis of imaging data, particularly location-based imaging data. Using methods of spatial statistics, we develop a novel algorithmic resolution limit to evaluate the resolving capabilities of location-based image processing algorithms. We show how insufficient algorithmic resolution can impact the outcome of location-based image analysis and present an approach to account for algorithmic resolution in the analysis of spatial location patterns.

The resolution limitations when using the ubiquitous algorithms that process images obtained using modern techniques are not straightforward to define. Here, the authors examine the performance of localization algorithms and use spatial statistics to provide a metric for assessing the resolution limit of such algorithms.

Which Elements to Build Co-localization Workflows? From Metrology to Analysis

Co-localization analysis is one of the main interests of users entering a facility with slides in hands and nice analysis perspectives in mind. While being available through most, if not all, analysis software, co-localization tools are mainly perceived as black boxes, fed with images, that will, hopefully, return (the expected) numbers.In this chapter, we will aim at deconstructing existing generic co-localization workflows, extracting elementary tools that may be reused and recombined to generate new workflows. By differentiating work cases, identifying co-localization reporters and the metrics others have been using, we aim at providing the audience with the elementary bricks and methods to build their really own co-localization workflows. A special emphasis is given on the preparatory phase where the acquisition system is assessed, using basic metrological tests.

The Rényi divergence enables accurate and precise cluster analysis for localization microscopy

Motivation

Clustering analysis is a key technique for quantitatively characterizing structures in localization microscopy images. To build up accurate information about biological structures, it is critical that the quantification is both accurate (close to the ground truth) and precise (has small scatter and is reproducible).

Results

Here, we describe how the Rényi divergence can be used for cluster radius measurements in localization microscopy data. We demonstrate that the Rényi divergence can operate with high levels of background and provides results which are more accurate than Ripley’s functions, Voronoi tesselation or DBSCAN.

Availability and implementation

The data supporting this research and the software described are accessible at the following site: https://dx.doi.org/10.18742/RDM01-316. Correspondence and requests for materials should be addressed to the corresponding author.

Supplementary information

Supplementary data are available at Bioinformatics online.

Nuclear ARP2/3 drives DNA break clustering for homology-directed repair

DNA double-strand breaks repaired by non-homologous end joining display limited DNA end-processing and chromosomal mobility. By contrast, double-strand breaks undergoing homology-directed repair exhibit extensive processing and enhanced motion. The molecular basis of this movement is unknown. Here, using Xenopus laevis cell-free extracts and mammalian cells, we establish that nuclear actin, WASP, and the actin-nucleating ARP2/3 complex are recruited to damaged chromatin undergoing homology-directed repair. We demonstrate that nuclear actin polymerization is required for the migration of a subset of double-strand breaks into discrete sub-nuclear clusters. Actin-driven movements specifically affect double-strand breaks repaired by homology-directed repair in G2 cell cycle phase; inhibition of actin nucleation impairs DNA end-processing and homology-directed repair. By contrast, ARP2/3 is not enriched at double-strand breaks repaired by non-homologous end joining and does not regulate non-homologous end joining. Our findings establish that nuclear actin-based mobility shapes chromatin organization by generating repair domains that are essential for homology-directed repair in eukaryotic cells.

Quantitative spatial analysis of haematopoiesis-regulating stromal cells in the bone marrow microenvironment by 3D microscopy

Sinusoidal endothelial cells and mesenchymal CXCL12-abundant reticular cells are principal bone marrow stromal components, which critically modulate haematopoiesis at various levels, including haematopoietic stem cell maintenance. These stromal subsets are thought to be scarce and function via highly specific interactions in anatomically confined niches. Yet, knowledge on their abundance, global distribution and spatial associations remains limited. Using three-dimensional quantitative microscopy we show that sinusoidal endothelial and mesenchymal reticular subsets are remarkably more abundant than estimated by conventional flow cytometry. Moreover, both cell types assemble in topologically complex networks, associate to extracellular matrix and pervade marrow tissues. Through spatial statistical methods we challenge previous models and demonstrate that even in the absence of major specific interaction forces, virtually all tissue-resident cells are invariably in physical contact with, or close proximity to, mesenchymal reticular and sinusoidal endothelial cells. We further show that basic structural features of these stromal components are preserved during ageing.

The bone marrow microenvironment modulates haematopoiesis, stem cell maintenance and differentiation. Here, the authors use 3D microscopy to map the topography of haematopoietic stem cell niche stromal components.

How Computational Models Enable Mechanistic Insights into Virus Infection

An implicit aim in cellular infection biology is to understand the mechanisms how viruses, microbes, eukaryotic parasites, and fungi usurp the functions of host cells and cause disease. Mechanistic insight is a deep understanding of the biophysical and biochemical processes that give rise to an observable phenomenon. It is typically subject to falsification, that is, it is accessible to experimentation and empirical data acquisition. This is different from logic and mathematics, which are not empirical, but built on systems of inherently consistent axioms. Here, we argue that modeling and computer simulation, combined with mechanistic insights, yields unprecedented deep understanding of phenomena in biology and especially in virus infections by providing a way of showing sufficiency of a hypothetical mechanism. This ideally complements the necessity statements accessible to empirical falsification by additional positive evidence. We discuss how computational implementations of mathematical models can assist and enhance the quantitative measurements of infection dynamics of enveloped and non-enveloped viruses and thereby help generating causal insights into virus infection biology.

Spatiotemporal dynamics of HSV genome nuclear entry and compaction state transitions using bioorthogonal chemistry and super-resolution microscopy

We investigated the spatiotemporal dynamics of HSV genome transport during the initiation of infection using viruses containing bioorthogonal traceable precursors incorporated into their genomes (HSVEdC). In vitro assays revealed a structural alteration in the capsid induced upon HSVEdC binding to solid supports that allowed coupling to external capture agents and demonstrated that the vast majority of individual virions contained bioorthogonally-tagged genomes. Using HSVEdC in vivo we reveal novel aspects of the kinetics, localisation, mechanistic entry requirements and morphological transitions of infecting genomes. Uncoating and nuclear import was observed within 30 min, with genomes in a defined compaction state (ca. 3-fold volume increase from capsids). Free cytosolic uncoated genomes were infrequent (7-10% of the total uncoated genomes), likely a consequence of subpopulations of cells receiving high particle numbers. Uncoated nuclear genomes underwent temporal transitions in condensation state and while ICP4 efficiently associated with condensed foci of initial infecting genomes, this relationship switched away from residual longer lived condensed foci to increasingly decondensed genomes as infection progressed. Inhibition of transcription had no effect on nuclear entry but in the absence of transcription, genomes persisted as tightly condensed foci. Ongoing transcription, in the absence of protein synthesis, revealed a distinct spatial clustering of genomes, which we have termed genome congregation, not seen with non-transcribing genomes. Genomes expanded to more decondensed forms in the absence of DNA replication indicating additional transitional steps. During full progression of infection, genomes decondensed further, with a diffuse low intensity signal dissipated within replication compartments, but frequently with tight foci remaining peripherally, representing unreplicated genomes or condensed parental strands of replicated DNA. Uncoating and nuclear entry was independent of proteasome function and resistant to inhibitors of nuclear export. Together with additional data our results reveal new insight into the spatiotemporal dynamics of HSV genome uncoating, transport and organisation.

Robust nonparametric quantification of clustering density of molecules in single-molecule localization microscopy

We report a robust nonparametric descriptor, J'(r), for quantifying the density of clustering molecules in single-molecule localization microscopy. J'(r), based on nearest neighbor distribution functions, does not require any parameter as an input for analyzing point patterns. We show that J'(r) displays a valley shape in the presence of clusters of molecules, and the characteristics of the valley reliably report the clustering features in the data. Most importantly, the position of the J'(r) valley ([Formula: see text]) depends exclusively on the density of clustering molecules (ρc). Therefore, it is ideal for direct estimation of the clustering density of molecules in single-molecule localization microscopy. As an example, this descriptor was applied to estimate the clustering density of ptsG mRNA in E. coli bacteria.

A passive mutualistic interaction promotes the evolution of spatial structure within microbial populations

Background

While mutualistic interactions between different genotypes are pervasive in nature, their evolutionary origin is not clear. The dilemma is that, for mutualistic interactions to emerge and persist, an investment into the partner genotype must pay off: individuals of a first genotype that invest resources to promote the growth of a second genotype must receive a benefit that is not equally accessible to individuals that do not invest. One way for exclusive benefits to emerge is through spatial structure (i.e., physical barriers to the movement of individuals and resources).

Results

Here we propose that organisms can evolve their own spatial structure based on physical attachment between individuals, and we hypothesize that attachment evolves when spatial proximity to members of another species is advantageous. We tested this hypothesis using experimental evolution with combinations of E. coli strains that depend on each other to grow. We found that attachment between cells repeatedly evolved within 8 weeks of evolution and observed that many different types of mutations potentially contributed to increased attachment.

Conclusions

We postulate a general principle by which passive beneficial interactions between organisms select for attachment, and attachment then provides spatial structure that could be conducive for the evolution of active mutualistic interactions.

Electronic supplementary material

The online version of this article (doi:10.1186/s12862-017-0950-y) contains supplementary material, which is available to authorized users.

A leucine aminopeptidase is involved in kinetoplast DNA segregation in Trypanosoma brucei

The kinetoplast (k), the uniquely packaged mitochondrial DNA of trypanosomatid protists is formed by a catenated network of minicircles and maxicircles that divide and segregate once each cell cycle. Although many proteins involved in kDNA replication and segregation are now known, several key steps in the replication mechanism remain uncharacterized at the molecular level, one of which is the nabelschnur or umbilicus, a prominent structure which in the mammalian parasite Trypanosoma brucei connects the daughter kDNA networks prior to their segregation. Here we characterize an M17 family leucyl aminopeptidase metalloprotease, termed TbLAP1, which specifically localizes to the kDNA disk and the nabelschur and represents the first described protein found in this structure. We show that TbLAP1 is required for correct segregation of kDNA, with knockdown resulting in delayed cytokinesis and ectopic expression leading to kDNA loss and decreased cell proliferation. We propose that TbLAP1 is required for efficient kDNA division and specifically participates in the separation of daughter kDNA networks.

A Bayesian cluster analysis method for single-molecule localization microscopy data

Cell function is regulated by the spatiotemporal organization of the signaling machinery, and a key facet of this is molecular clustering. Here, we present a protocol for the analysis of clustering in data generated by 2D single-molecule localization microscopy (SMLM)-for example, photoactivated localization microscopy (PALM) or stochastic optical reconstruction microscopy (STORM). Three features of such data can cause standard cluster analysis approaches to be ineffective: (i) the data take the form of a list of points rather than a pixel array; (ii) there is a non-negligible unclustered background density of points that must be accounted for; and (iii) each localization has an associated uncertainty in regard to its position. These issues are overcome using a Bayesian, model-based approach. Many possible cluster configurations are proposed and scored against a generative model, which assumes Gaussian clusters overlaid on a completely spatially random (CSR) background, before every point is scrambled by its localization precision. We present the process of generating simulated and experimental data that are suitable to our algorithm, the analysis itself, and the extraction and interpretation of key cluster descriptors such as the number of clusters, cluster radii and the number of localizations per cluster. Variations in these descriptors can be interpreted as arising from changes in the organization of the cellular nanoarchitecture. The protocol requires no specific programming ability, and the processing time for one data set, typically containing 30 regions of interest, is ∼18 h; user input takes ∼1 h.

Inhomogeneity Based Characterization of Distribution Patterns on the Plasma Membrane

Cell surface protein and lipid molecules are organized in various patterns: randomly, along gradients, or clustered when segregated into discrete micro- and nano-domains. Their distribution is tightly coupled to events such as polarization, endocytosis, and intracellular signaling, but challenging to quantify using traditional techniques. Here we present a novel approach to quantify the distribution of plasma membrane proteins and lipids. This approach describes spatial patterns in degrees of inhomogeneity and incorporates an intensity-based correction to analyze images with a wide range of resolutions; we have termed it Quantitative Analysis of the Spatial distributions in Images using Mosaic segmentation and Dual parameter Optimization in Histograms (QuASIMoDOH). We tested its applicability using simulated microscopy images and images acquired by widefield microscopy, total internal reflection microscopy, structured illumination microscopy, and photoactivated localization microscopy. We validated QuASIMoDOH, successfully quantifying the distribution of protein and lipid molecules detected with several labeling techniques, in different cell model systems. We also used this method to characterize the reorganization of cell surface lipids in response to disrupted endosomal trafficking and to detect dynamic changes in the global and local organization of epidermal growth factor receptors across the cell surface. Our findings demonstrate that QuASIMoDOH can be used to assess protein and lipid patterns, quantifying distribution changes and spatial reorganization at the cell surface. An ImageJ/Fiji plugin of this analysis tool is provided.

Plasma membrane organization is fundamental to cellular signaling, transport of molecules, and cell adhesion. To achieve this, plasma membrane proteins and lipids are spatially organized: they form clusters, aggregate in signaling platforms, distribute into gradients on polarized cells, or randomly distribute across the membrane. It is also clear that these organizations can be affected in various contexts. For example, in aging or neurodegenerative diseases, the composition of the plasma membrane is altered and, consequently, the protein and lipid distributions in the membrane fluctuate. In addition, cancer progression is characterized by changes in cellular polarity, lipid content, and the redistribution of cell surface receptors and adhesion molecules. Here we have developed a method to quantify such alterations that, unlike current tools, is compatible with diverse types of cellular organization, including polarity. Our tool can be employed to screen for changes in a straightforward manner and to elucidate distributions of cell surface components in different disciplines, ranging from neurobiology to cancer research.

On characterizing protein spatial clusters with correlation approaches

Spatial aggregation of proteins might have functional importance, e.g., in signaling, and nano-imaging can be used to study them. Such studies require accurate characterization of clusters based on noisy data. A set of spatial correlation approaches free of underlying cluster processes and input parameters have been widely used for this purpose. They include the radius of maximal aggregation r_a obtained from Ripley’s L(r) − r function as an estimator of cluster size, and the estimation of various cluster parameters based on an exponential model of the Pair Correlation Function(PCF). While convenient, the accuracy of these methods is not clear: e.g., does it depend on how the molecules are distributed within the clusters, or on cluster parameters? We analyze these methods for a variety of cluster models. We find that r_a relates to true cluster size by a factor that is nonlinearly dependent on parameters and that can be arbitrarily large. For the PCF method, for the models analyzed, we obtain linear relationships between the estimators and true parameters, and the estimators were found to be within ±100% of true parameters, depending on the model. Our results, based on an extendable general framework, point to the need for caution in applying these methods.

MIiSR: Molecular Interactions in Super-Resolution Imaging Enables the Analysis of Protein Interactions, Dynamics and Formation of Multi-protein Structures

Our current understanding of the molecular mechanisms which regulate cellular processes such as vesicular trafficking has been enabled by conventional biochemical and microscopy techniques. However, these methods often obscure the heterogeneity of the cellular environment, thus precluding a quantitative assessment of the molecular interactions regulating these processes. Herein, we present Molecular Interactions in Super Resolution (MIiSR) software which provides quantitative analysis tools for use with super-resolution images. MIiSR combines multiple tools for analyzing intermolecular interactions, molecular clustering and image segmentation. These tools enable quantification, in the native environment of the cell, of molecular interactions and the formation of higher-order molecular complexes. The capabilities and limitations of these analytical tools are demonstrated using both modeled data and examples derived from the vesicular trafficking system, thereby providing an established and validated experimental workflow capable of quantitatively assessing molecular interactions and molecular complex formation within the heterogeneous environment of the cell.

In this paper we present the software package Molecular Interactions in Super Resolution (MIiSR), which provides a series of quantitative analytical tools for measuring molecular interactions and the formation of higher-order molecular complexes in super-resolution microscopy images.

Extending Ripley's K-Function to Quantify Aggregation in 2-D Grayscale Images

In this work, we describe the extension of Ripley's K-function to allow for overlapping events at very high event densities. We show that problematic edge effects introduce significant bias to the function at very high densities and small radii, and propose a simple correction method that successfully restores the function's centralization. Using simulations of homogeneous Poisson distributions of events, as well as simulations of event clustering under different conditions, we investigate various aspects of the function, including its shape-dependence and correspondence between true cluster radius and radius at which the K-function is maximized. Furthermore, we validate the utility of the function in quantifying clustering in 2-D grayscale images using three modalities: (i) Simulations of particle clustering; (ii) Experimental co-expression of soluble and diffuse protein at varying ratios; (iii) Quantifying chromatin clustering in the nuclei of wt and crwn1 crwn2 mutant Arabidopsis plant cells, using a previously-published image dataset. Overall, our work shows that Ripley's K-function is a valid abstract statistical measure whose utility extends beyond the quantification of clustering of non-overlapping events. Potential benefits of this work include the quantification of protein and chromatin aggregation in fluorescent microscopic images. Furthermore, this function has the potential to become one of various abstract texture descriptors that are utilized in computer-assisted diagnostics in anatomic pathology and diagnostic radiology.

Non-redundant Functions of ATM and DNA-PKcs in Response to DNA Double-Strand Breaks

DNA double-strand breaks (DSBs) elicit the so-called DNA damage response (DDR), largely relying on ataxia telangiectasia mutated (ATM) and DNA-dependent protein kinase (DNA-PKcs), two members of the PI3K-like kinase family, whose respective functions during the sequential steps of the DDR remains controversial. Using the DIvA system (DSB inducible via AsiSI) combined with high-resolution mapping and advanced microscopy, we uncovered that both ATM and DNA-PKcs spread in cis on a confined region surrounding DSBs, independently of the pathway used for repair. However, once recruited, these kinases exhibit non-overlapping functions on end joining and γH2AX domain establishment. More specifically, we found that ATM is required to ensure the association of multiple DSBs within "repair foci." Our results suggest that ATM acts not only on chromatin marks but also on higher-order chromatin organization to ensure repair accuracy and survival.

Measuring Aggregation of Events about a Mass Using Spatial Point Pattern Methods

We present a methodology that detects event aggregation about a mass surface using 3-dimensional study regions with a point pattern and a mass present. The Aggregation about a Mass function determines aggregation, randomness, or repulsion of events with respect to the mass surface. Our method closely resembles Ripley's K function but is modified to discern the pattern about the mass surface. We briefly state the definition and derivation of Ripley's K function and explain how the Aggregation about a Mass function is different. We develop the novel function according to the definition: the Aggregation about a Mass function times the intensity is the expected number of events within a distance h of a mass. Special consideration of edge effects is taken in order to make the function invariant to the location of the mass within the study region. Significance of aggregation or repulsion is determined using simulation envelopes. A simulation study is performed to inform researchers how the Aggregation about a Mass function performs under different types of aggregation. Finally, we apply the Aggregation about a Mass function to neuroscience as a novel analysis tool by examining the spatial pattern of neurotransmitter release sites as events about a neuron.

Quantitative image analysis of cell colocalization in murine bone marrow

Long-term antibody production is a key property of humoral immunity and is accomplished by long-lived plasma cells. They mainly reside in the bone marrow, whose importance as an organ hosting immunological memory is becoming increasingly evident. Signals provided by stromal cells and eosinophils may play an important role for plasma cell maintenance, constituting a survival microenvironment. In this joint study of experiment and theory, we investigated the spatial colocalization of plasma cells, eosinophils and B cells by applying an image-based systems biology approach. To this end, we generated confocal fluorescence microscopy images of histological sections from murine bone marrow that were subsequently analyzed in an automated fashion. This quantitative analysis was combined with computer simulations of the experimental system for hypothesis testing. In particular, we tested the observed spatial colocalization of cells in the bone marrow against the hypothesis that cells are found within available areas at positions that were drawn from a uniform random number distribution. We find that B cells and plasma cells highly colocalize with stromal cells, to an extent larger than in the simulated random situation. While B cells are preferentially in contact with each other, i.e., form clusters among themselves, plasma cells seem to be solitary or organized in aggregates, i.e., loosely defined groups of cells that are not necessarily in direct contact. Our data suggest that the plasma cell bone marrow survival niche facilitates colocalization of plasma cells with stromal cells and eosinophils, respectively, promoting plasma cell longevity.

Quantitative analysis of single-molecule superresolution images

This review highlights the quantitative capabilities of single-molecule localization-based superresolution imaging methods. In addition to revealing fine structural details, the molecule coordinate lists generated by these methods provide the critical ability to quantify the number, clustering, and colocalization of molecules with 10-50 nm resolution. Here we describe typical workflows and precautions for quantitative analysis of single-molecule superresolution images. These guidelines include potential pitfalls and essential control experiments, allowing critical assessment and interpretation of superresolution images.

A phosphotyrosine switch for cargo sequestration at clathrin-coated buds

The AP-2 clathrin adaptor complex oversees endocytic cargo selection in two parallel but independent manners. First, by physically engaging peptide-based endocytic sorting signals, a subset of clathrin-dependent transmembrane cargo is directly collected into assembling buds. Synchronously, by interacting with an assortment of clathrin-associated sorting proteins (CLASPs) that independently select different integral membrane cargo for inclusion within the incipient bud, AP-2 handles additional cargo capture indirectly. The distal platform subdomain of the AP-2 β2 subunit appendage is a privileged CLASP-binding surface that recognizes a cognate, short α-helical interaction motif. This signal, found in the CLASPs β-arrestin and the autosomal recessive hypercholesterolemia (ARH) protein, docks into an elongated groove on the β2 appendage platform. Tyr-888 is a critical constituent of this spatially confined β2 appendage contact interface and is phosphorylated in numerous high-throughput proteomic studies. We find that a phosphomimetic Y888E substitution does not interfere with incorporation of expressed β2-YFP subunit into AP-2 or alter AP-2 deposition at surface clathrin-coated structures. The Y888E mutation does not affect interactions involving the sandwich subdomain of the β2 appendage, indicating that the mutated appendage is folded and operational. However, the Y888E, but not Y888F, switch selectively uncouples interactions with ARH and β-arrestin. Phyogenetic conservation of Tyr-888 suggests that this residue can reversibly control occupancy of the β2 platform-binding site and, hence, cargo sorting.