Spatial Point Pattern Analysis Identifies Mechanisms Shaping the Skin Parasite Landscape in Leishmania donovani Infection

Increasing evidence suggests that in hosts infected with parasites of the Leishmania donovani complex, transmission of infection to the sand fly vector is linked to parasite repositories in the host skin. However, a detailed understanding of the dispersal (the mechanism of spread) and dispersion (the observed state of spread) of these obligatory-intracellular parasites and their host phagocytes in the skin is lacking. Using endogenously fluorescent parasites as a proxy, we apply image analysis combined with spatial point pattern models borrowed from ecology to characterize dispersion of parasitized myeloid cells (including ManR+ and CD11c+ cells) and predict dispersal mechanisms in a previously described immunodeficient model of L. donovani infection. Our results suggest that after initial seeding of infection in the skin, heavily parasite-infected myeloid cells are found in patches that resemble innate granulomas. Spread of parasites from these initial patches subsequently occurs through infection of recruited myeloid cells, ultimately leading to self-propagating networks of patch clusters. This combination of imaging and ecological pattern analysis to identify mechanisms driving the skin parasite landscape offers new perspectives on myeloid cell behavior following parasitism by L. donovani and may also be applicable to elucidating the behavior of other intracellular tissue-resident pathogens and their host cells.

Spatial point-pattern analysis as a powerful tool in identifying pattern-process relationships in plant ecology: an updated review

Background Ecological processes such as seedling establishment, biotic interactions, and mortality can leave footprints on species spatial structure that can be detectable through spatial point-pattern analysis (SPPA). Being widely used in plant ecology, SPPA is increasingly carried out to describe biotic interactions and interpret pattern-process relationships. However, some aspects are still subjected to a non-negligible debate such as required sample size (in terms of the number of points and plot area), the link between the low number of points and frequently observed random (or independent) patterns, and relating patterns to processes. In this paper, an overview of SPPA is given based on rich and updated literature providing guidance for ecologists (especially beginners) on summary statistics, uni-/bi-/multivariate analysis, unmarked/marked analysis, types of marks, etc. Some ambiguities in SPPA are also discussed. Results SPPA has a long history in plant ecology and is based on a large set of summary statistics aiming to describe species spatial patterns. Several mechanisms known to be responsible for species spatial patterns are actually investigated in different biomes and for different species. Natural processes, plant environmental conditions, and human intervention are interrelated and are key drivers of plant spatial distribution. In spite of being not recommended, small sample sizes are more common in SPPA. In some areas, periodic forest inventories and permanent plots are scarce although they are key tools for spatial data availability and plant dynamic monitoring. Conclusion The spatial position of plants is an interesting source of information that helps to make hypotheses about processes responsible for plant spatial structures. Despite the continuous progress of SPPA, some ambiguities require further clarifications.

The impact of strict measures as a result of the COVID-19 pandemic on the spatial pattern of the demand for police. Case study Antwerp (Belgium)

COVID-19 impacts the daily lives of millions of people. This radical change in our daily activities affected many aspects of life, but acted as well as a natural experiment for research into the spatial distribution of 911 calls. We analyse the impact of the COVID-19 measures on the spatial pattern of police interventions. Crime is not uniformly distributed across street segments, but how does COVID-19 affect these spatial patterns? To this end, a proportion differences spatial point pattern test is applied to compare the similarity of the patterns of incidents before, during, and after the first lockdown in Antwerp, Belgium. With only essential mobility being allowed, the emergency call pattern has not significantly changed before, during or after this lockdown, however, a qualitative shift in police officer’s daily work may have had an effect on the daily operation of the Antwerp police force.

Analysis of conditional colocalization relationships and hierarchies from three-color microscopy images

Colocalization is a cornerstone approach in cell biology for the analysis of multicolor microscopy images. It provides information on the localization of molecules within various subcellular compartments and allows the interrogation of molecular interactions in their spatiotemporal cellular context. However, the overwhelming majority of colocalization analyses are designed for two-color microscopy images, which limits their applicability and the type of information that they may reveal, leading to underutilization of multicolor microscopy images. Here we describe an approach for analyzing the colocalization relationships between three molecular entities, termed 'conditional colocalization analysis,' based on spatial point pattern analysis of detected objects in microscopy images. Going beyond the question of whether colocalization is present or not, it addresses the question of whether the colocalization between two molecular entities is influenced, positively or negatively, by their respective colocalization with a third entity. We showcase two applications of conditional colocalization analysis, one addressing the question of the compartmentalization of molecular interactions, and one investigating the hierarchy of molecular interactions in a multimolecular complex. The software for conditional colocalization analysis is freely accessible online at https://github.com/kjaqaman/conditionalColoc.

Spatial point pattern analysis identifies mechanisms shaping the skin parasite landscape in Leishmania donovani infection

Increasing evidence suggests that infectiousness of hosts carrying parasites of the Leishmania donovani complex, the causative agents of visceral leishmaniasis, is linked to parasite repositories in the host skin. This is particularly true for asymptomatic to moderately symptomatic hosts with no or minimally detectable parasitemia. However, a detailed description of the dispersal and dispersion of parasites and parasitized host phagocytes in the skin is still lacking. Here, we combined image analysis with spatial point pattern models borrowed from ecology, providing a new route to predicting modes of skin parasite dispersal and characterizing their dispersion. Our results suggest that, after initial parasite seeding in the skin, parasites form self-propagating networks of parasite patch clusters in the skin that may contribute to parasite outward transmission. This combination of imaging and ecological pattern analysis to identify mechanisms driving the skin parasite landscape offers new perspectives on parasitism by Leishmania donovani and may also be applicable to elucidating the behavior of other intracellular tissue-resident pathogens.

EXPLORATORY SPECTRAL ANALYSIS IN THREE-DIMENSIONAL SPATIAL POINT PATTERNS

A spatial point pattern is a collection of points irregularly located within a bounded area (2D) or space (3D) that have been generated by some form of stochastic mechanism. Examples of point patterns include locations of trees in a forest, of cases of a disease in a region, or of particles in a microscopic section of a composite material. Spatial Point pattern analysis is used mostly to determine the absence (completely spatial randomness) or presence (regularity and clustering) of spatial dependence structure of the locations. Methods based on the space domain are widely used for this purpose, while methods conducted in the frequency domain (spectral analysis) are still unknown to most researchers. Spectral analysis is a powerful tool to investigate spatial point patterns, since it does not assume any structural characteristics of the data (ex. isotropy), and uses only the autocovariance function, and its Fourier transform. There are some methods based on the spectral frameworks for analyzing 2D spatial point patterns. There is no such methods available for the 3D situation and, therefore, the aim of this work is to develop new methods based on spectral framework for the analysis of three-dimensional point patterns. The emphasis is on relating periodogram structure to the type of stochastic process which could have generated a 3D observed pattern. The results show that the methods based on spectral analysis developed in this work are able to identify patterns of three typical three-dimensional point processes, and can be used, concurrently, with analyzes in the space domain for a better characterization of spatial point patterns.