Imaging and Analysis of Cellular Locations in Three-Dimensional Tissue Models

Spatial statistics is a comprehensive tool for quantifying cell neighbor relationships and biological processes via tissue image analysis

Automated microscopy and computational image analysis has transformed cell biology, providing quantitative, spatially resolved information on cells and their constituent molecules from the sub-micron to the whole-organ scale. Here we explore the application of spatial statistics to the cellular relationships within tissue microscopy data and discuss how spatial statistics offers cytometry a powerful yet underused mathematical tool set for which the required data are readily captured using standard protocols and microscopy equipment. We also highlight the often-overlooked need to carefully consider the structural heterogeneity of tissues in terms of the applicability of different statistical measures and their accuracy and demonstrate how spatial analyses offer a great deal more than just basic quantification of biological variance. Ultimately, we highlight how statistical modeling can help reveal the hierarchical spatial processes that connect the properties of individual cells to the establishment of biological function.

A Framework for Investigating Rules of Life by Establishing Zones of Influence

The incredible complexity of biological processes across temporal and spatial scales hampers defining common underlying mechanisms driving the patterns of life. However, recent advances in sequencing, big data analysis, machine learning, and molecular dynamics simulation have renewed the hope and urgency of finding potential hidden rules of life. There currently exists no framework to develop such synoptic investigations. Some efforts aim to identify unifying rules of life across hierarchical levels of time, space, and biological organization, but not all phenomena occur across all the levels of these hierarchies. Instead of identifying the same parameters and rules across levels, we posit that each level of a temporal and spatial scale and each level of biological organization has unique parameters and rules that may or may not predict outcomes in neighboring levels. We define this neighborhood, or the set of levels, across which a rule functions as the zone of influence. Here, we introduce the zone of influence framework and explain using three examples: (a) randomness in biology, where we use a Poisson process to describe processes from protein dynamics to DNA mutations to gene expressions, (b) island biogeography, and (c) animal coloration. The zone of influence framework may enable researchers to identify which levels are worth investigating for a particular phenomenon and reframe the narrative of searching for a unifying rule of life to the investigation of how, when, and where various rules of life operate.

The spatial distribution of focal stacks within the inner enamel layer of mandibular mouse incisors

Focal stacks are an alternative spatial arrangement of enamel rods within the inner enamel of mandibular mouse incisors where short rows comprised of 2–45 enamel rods are nestled at the side of much longer rows, both sharing the same rod tilt directed mesially or laterally. The significance of focal stacks to enamel function is unknown, but their high frequency in transverse sections (30% of all rows) suggests that they serve some purpose beyond representing an oddity of enamel development. In this study, we characterized the spatial distribution of focal stacks in random transverse sections relative to different regions of the inner enamel and to different locations across enamel thickness. The curving dentinoenamel junction (DEJ) in transverse sections complicated spatial distribution analyses, and a technique was developed to “unbend” the curving DEJ allowing for more linear quantitative analyses to be carried out. The data indicated that on average there were 36 ± 7 focal stacks located variably within the inner enamel in any given transverse section. Consistent with area distributions, focal stacks were four times more frequent in the lateral region (53%) and twice as frequent in the mesial region (33%) compared to the central region (14%). Focal stacks were equally split by tilt (52% mesial vs. 48% lateral, not significant), but those having a mesial tilt were more frequently encountered in the lateral and central regions (2:1) and those having a lateral tilt were more numerous in the mesial region (1:3). Focal stacks having a mesial tilt were longer on average compared to those having a lateral tilt (7.5 ± 5.6 vs. 5.9 ± 4.0 rods per row, p < 0.01). There was no relationship between the length of a focal stack and its location within the inner enamel. All results were consistent with the notion that focal stacks travel from the DEJ to the outer enamel the same as the longer and decussating companion rows to which they are paired. The spatial distribution of focal stacks within the inner enamel was not spatially random but best fit a null model based on a heterogenous Poisson point process dependent on regional location within the transverse plane of the enamel layer.

CytoMAP: A Spatial Analysis Toolbox Reveals Features of Myeloid Cell Organization in Lymphoid Tissues

Recently developed approaches for highly multiplexed imaging have revealed complex patterns of cellular positioning and cell-cell interactions with important roles in both cellular- and tissue-level physiology. However, tools to quantitatively study cellular patterning and tissue architecture are currently lacking. Here, we develop a spatial analysis toolbox, the histo-cytometric multidimensional analysis pipeline (CytoMAP), which incorporates data clustering, positional correlation, dimensionality reduction, and 2D/3D region reconstruction to identify localized cellular networks and reveal features of tissue organization. We apply CytoMAP to study the microanatomy of innate immune subsets in murine lymph nodes (LNs) and reveal mutually exclusive segregation of migratory dendritic cells (DCs), regionalized compartmentalization of SIRPα- dermal DCs, and preferential association of resident DCs with select LN vasculature. The findings provide insights into the organization of myeloid cells in LNs and demonstrate that CytoMAP is a comprehensive analytics toolbox for revealing features of tissue organization in imaging datasets.