SHADE: A multilevel Bayesian framework for modeling directional spatial interactions in tissue microenvironments.

Motivation: Understanding how different cell types interact spatially within tissue microenvironments is critical for deciphering immune dynamics, tumor progression, and tissue organization. Many current spatial analysis methods assume symmetric associations or compute image-level summaries separately without sharing information across patients and cohorts, limiting biological interpretability and statistical power.Results: We present SHADE (Spatial Hierarchical Asymmetry via Directional Estimation), a multilevel Bayesian framework for modeling asymmetric spatial interactions across scales. SHADE quantifies direction-specific cell-cell associations using smooth spatial interaction curves (SICs) and integrates data across tissue sections, patients, and cohorts. Through simulation studies, SHADE demonstrates improved accuracy, robustness, and interpretability over existing methods. Application to colorectal cancer multiplexed imaging data demonstrates SHADE’s ability to quantify directional spatial patterns while controlling for tissue architecture confounders and capturing substantial patient-level heterogeneity. The framework successfully identifies biologically interpretable spatial organization patterns, revealing that local microenvironmental structure varies considerably across patients within molecular subtypes.

Motivation:Spatial dependencies in tissue microenvironments, particularly asymmetric interactions between cell types, are central to understanding immune dynamics, tumor behavior, and tissue organization. Existing spatial statistical methods often assume symmetric associations or analyze images independently, limiting biological interpretability and inference quality.Results:We introduce SHADE (Spatial Hierarchical Asymmetry via Directional Estimation), a Bayesian hierarchical framework that models asymmetric spatial associations and multilevel structure in multiplexed imaging data. SHADE captures directional relationships via smooth spatial interaction curves (SICs), provides interpretable distance-resolved summaries of cell-cell interactions, and supports multiscale inference across tissue sections, patients, and cohorts. Simulation studies demonstrate improved inference quality and robustness, and application to colorectal cancer imaging data reveals biologically meaningful differences in immune and stromal organization.Availability and Implementation:Source code and analysis scripts are freely available at http://github.com/jeliason/SHADE and http://github.com/jeliason/shade_paper_code, implemented in R and Stan.

Identification of spatial and functional interactions in Beijing based on trajectory data

Background: As the tumor microenvironment (TME) consists of various cell types with complex spatial interaction, its spatial organization patterns affect response to immune-oncology treatment. Therefore, describing the spatial composition and interaction of cells in the tumor microenvironment (TME) is necessary. Here, we developed a tool, STopover, which adopts topological analysis in spatial transcriptomics to reveal cell-cell colocalization patterns in TME and capture the key components and niche of intercellular communication, and applied it to human lung cancer data. Methods: The spatial colocalization pattern of pairs of features was defined using topological data analysis with Morse filtration. The spatial network of spatial gene expression data was generated for each sample and then the patterns were summarized as connected components (CCs) based on the spatial distance between unit tissue regions and the persistence of each CC. The global and local extent of spatial overlap of a feature pair was calculated as Jaccard indices between extracted CC pairs. We applied STopover to 11 barcode-based spatial transcriptomic data of human lung adenocarcinoma with different PD-L1 expressions. Spatial mapping of cell types in TME was performed by CellDART. In addition, image-based spatial transcriptomic data of lung cancer were also used to find key spatial molecular interactions in TME using STopover. Results: First, STopover disclosed the distinct immune and stromal infiltration patterns in lung cancer tissues. Spatial colocalization of cancer cells and T-cells was heterogeneous and correlated with the immune-related markers such as MHC class I signature. The cancer types were clustered according to spatial colocalization patterns of various immune cells with cancer cells, which showed different infiltration patterns of immune cells. Moreover, STopover could estimate the top cell-cell interaction and emphasize the key locations based on the literature-supported ligand-receptor database. Conclusion: STopover is expected to account for significant spatial cell interactions in tumor-immune and tumor-stromal components and could be utilized as a platform to decipher the mechanisms underlying immune-oncology treatment response. Citation Format: Sungwoo Bae, Hyekyoung Lee, Kwon Joong Na, Dong Soo Lee, Hongyoon Choi, Young Tae Kim. Topological analysis of spatial transcriptomics reveals different spatial interaction patterns in tumor microenvironment of lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3138.

Breast cancer is one of the most prominent cancers in women with approximately 40,000 women dying each year of the disease in the United States alone. Although cancer has a strong genetic basis, the tissue microenvironment also plays a significant role in tumor formation and progression. To explore this process in greater detail, we analyzed tissue structure, cellular biochemistry and gene expression change during tumor progression in a previously developed FVB C3(1)-SV40Tag transgenic mouse model in which the females spontaneously develop mammary tumors that undergo changes in cell tissue morphology that closely mimic those observed during human breast cancer progression. Histological characterization of breast tissues isolated from 8, 12, 16 and 20 week old female mice confirmed that this model is extremely robust with normal glandular morphology being observed at 8 weeks, ductal carcinoma in situ (DCIS) morphology appearing at 12 weeks and 16 weeks and large tumors with metastatic lesions by 20 weeks of age. More careful analysis revealed that tumor formation did not scale directly with expression of SV40 large T antigen that is the drive gene in this transgenic model, and instead appeared to be more closely related to variations in the tissue microenvironment. This was supported by the observation that the number of myoepithelial cells and expression levels of known breast cancer differentiation markers, including estrogen and progesterone receptors, varied among ducts within tissues at each time point even though the majority of epithelial cells in these ducts expressed T antigen. Gene microarray analysis of mammary tissues from these time points revealed consistent changes in genome-wide expression profiles, and several interesting candidates are currently being analyzed regarding their potential involvement in early microenvironmental changes that might drive the tumor progression. In summary, our preliminary results showed that despite expression of constitutively active large T antigen in the breast epithelial cells of these transgenic mice, we observe heterogeneous tumor formation within mammary tissues. These local variations in tumor progression may be related to regional changes in stromal and extracellular matrix components that are currently being analyzed by immunohistochemical analysis. Our long-term goal is to leverage the information we uncover through this effort to design differentiation therapies that suppress tumor cell growth, induce differentiation and hence, reverse breast cancer progression. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 520. doi:10.1158/1538-7445.AM2011-520

Taking the station realm of Xi'an Metro Line 2 as the research object, this book focuses on solving the scientific problems of spatial development imbalance of urban rail transit station realms and unclear interaction mechanisms between population activities and functional facilities, against the background of rapid expansion of urban rail transit and the strategic positioning of Xi'an as the starting point of China's "The Belt and Road Initiative". By integrating multi-source big data and geographic information technology, the book systematically explores the spatial distribution pattern and interaction mechanism within the rail transit station realm. Starting with a comprehensive literature review and theoretical foundation construction, the book clarifies core concepts such as urban rail transit, station realm, and spatial interaction, and sorts out theoretical frameworks including Central Place Theory and Urban Catalyst Theory. On this basis, a research methodology system is established: using pedestrian walking scale combined with urban road and parcel boundaries to define the scope of 16 key station realms of Xi'an Metro Line 2; adopting cluster analysis to classify these station realms into five types, namely Under Development Type, Urban Center Type, High Population Density Hub Type, Interchange Type, and General Residential Type; using spatial analysis methods to identify the spatiotemporal distribution characteristics of population activities and functional facilities; applying Spearman correlation analysis to screen functional facilities with significant positive correlation with population activities; and constructing Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models to reveal the interaction mechanism between population activities and functional facilities.

The rapid development of the economy promotes the increasing of interactions between cities and forms complex networks. Many scholars have explored the structural characteristics of urban spatial interaction networks in China and have conducted spatio-temporal analyzes. However, scholars have mainly focused on the perspective of static networks and have not understood the dynamic spatial interaction patterns of Chinese cities. Therefore, this paper proposes a research framework to explore the urban dynamic spatial interaction patterns. Firstly, we establish a dynamic urban spatial interaction network according to monthly migration data. Then, the dynamic community detection algorithm, combined with the Louvain and Jaccard matching method, is used to obtain urban communities and their dynamic events. We construct event vectors for each urban community and use hierarchical clustering to cluster event vectors to obtain different types of spatial interaction patterns. Finally, we divide the urban dynamic interaction into three urban spatial interaction modes: fixed spatial interaction pattern, long-term spatial interaction pattern, and short-term spatial interaction pattern. According to the results, we find that the cities in well-developed areas (eastern China) and under-developed areas (northwestern China) mostly show fixed spatial interaction patterns and long-term spatial interaction patterns, while the cities in moderately developed areas (central and western China) often show short-term spatial interaction patterns. The research results and conclusions of this paper reveal the inter-monthly urban spatial interaction patterns in China, provide theoretical support for the policy making and development planning of urban agglomeration construction, and contribute to the coordinated development of national and regional cities.

Identifying spatial interaction patterns of vehicle movements on urban road networks by topic modelling

Seed dispersal by frugivores plays a key role in structuring and maintaining tree diversity in forests. However, little is known about how the spatial legacy of seed dispersal and early recruitment shapes spatial patterns and the spatial interaction network of plant species in mature forest communities. We analysed two fully mapped mixed Pine–Oak forest communities using spatial point pattern analysis to determine (a) the detailed structure of the intraspecific spatial patterns of saplings and adults, (b) the intra‐ and interspecific spatial interaction of saplings, adults and saplings relative to adults, (c) the spatial patterns of species richness at the community level and (d) whether seed dispersal mechanisms affect the plant–plant interaction networks and the ratio of adult to sapling neighbourhood densities used as surrogate for spatial self‐thinning. The intraspecific spatial patterns of saplings and adults showed in general complex nested cluster structures that were similar for sapling and adult stages, despite substantial self‐thinning in some dry‐fruited species. The spatial network of saplings was characterized by positive spatial interactions. Adults of several tree species facilitated saplings in their proximity; however, adults of dry‐fruited species, but not those of fleshy‐fruited ones, lost almost all positive interactions that occurred at the sapling stage. Besides, interaction strength between adults was positive and often significantly stronger if both species were fleshy‐fruited. At the community level, the forests were structured into multispecies clumps across all life stages. Synthesis. Our analyses highlight the importance of the spatial legacy of seed dispersal and early recruitment in the assembly of plant communities. Particularly, animal seed dispersal can lead to multispecies clusters and positive spatial associations across life stages in Mediterranean forests, with surprisingly little signatures of negative interactions. Our analysis suggests that changes of the spatial structure across plant life stages are driven by seed dispersal mechanisms and subsequent spatial self‐thinning, generating a spatial footprint at the sapling stage that conditions the long‐term interactions between adult plants. Combining spatial point pattern analysis with network analysis and species traits is a promising way to disentangle the processes underlying observed patterns of local diversity.

Conventional gravity-model estimates of spatial interactions assume that spatial interactions or flows from all locations to all other locations are independent of other flows and, therefore, overlook the latent multilateral influences in play. This research developed a novel application of Self-Organizing Map (SOM) to retrospectively examine historical spatial interactions among multiple locations for new insights into the spatial and temporal dependence of flows across locations. We built a SOM with units of spatial interaction patterns and traced changes in spatial interactions patterns for each location over time. By tracing the changes, we created new trajectories of spatial interactions on the SOM to contextualize spatial interactions at individual locations and among all locations over time. We used international trade data among 207 countries from 1900 to 2014 to demonstrate the proposed data-driven approach for retrospective analysis of spatial interactions. We created a SOM of international trade patterns, mapped each country’s trading trajectory, and compared the trajectories among all 207 countries. We showed that the SOM application could answer questions about multilateral spatial interactions over time. Our findings extended earlier network analyses of the global system with an integrated space-time view of spatial interactions. The SOM approach is adaptable to other domains of spatial interactions (e.g., urban transportation, immigrations) to characterize spatial interactions among locations change over time individually or contextually among all locations.

Urban development in Indonesia significantly incerasing in line with rapid development of infrastructure, utility, and transportation network. Recently, people live depend on lights at night and social media and these two aspects can depicted urban spatial pattern and interaction. This research used nighttime remote sensing data with the VIIRS (Visible Infrared Imaging Radiometer Suite) day-night band detects lights, gas flares, auroras, and wildfires. Geo-social media information derived from twitter data gave big picture on spatial interaction from the geospatial footprint. Combined both data produced comprehensive urban spatial pattern and interaction in general for Indonesian territory. The result is shown as a preliminary study of integrating nighttime remote sensing data and geospatial footprint from twitter data.

Introduction. Tissue Microarrays (TMAs), widely utilized in the field of pathology, have now found a powerful ally in Image-based Spatial Transcriptomics (ST). By analyzing various gene expression data with high resolution, image-based ST data on TMA can provide the heterogeneous patterns of tumor microenvironment in multiple samples. However, an efficient method for processing multiple samples post-data acquisition is still under development. A streamlined process would expedite the discovery of spatial gene expression patterns, thereby enhancing our understanding of the tumor microenvironment and its implications for cancer diagnostics and treatment strategies. Methods. Our automated pipeline is initiated by automatic segmentation of every core in the whole TMA, derived from the processed output of the MERSCOPE or Xenium platform. Then, it subsequently removes QC failed cells unrelated to any core. Each core receives sequential naming and undergoes automated cell type mapping utilizing a reference single-cell RNAseq data. Additionally, the pipeline computes neighborhood enrichment between cell types, providing a nuanced comprehension of spatial relationships and interactions among diverse cell populations within the tissue microenvironment from multiple samples on the TMA. Results. The automated pipeline we've developed provides several key advantages. It enables the simultaneous analysis of multiple tissue cores, effectively minimizing batch effects and ensuring the reliability of results across diverse tissue samples. Furthermore, by automating core separation, labeling, and cell type mapping, our pipeline significantly streamlines the time and effort required for TMA data analysis. This cost-effective approach allows researchers to optimize resource allocation, making our automated pipeline a valuable tool in cancer research. It facilitates the exploration of gene expression patterns within specific tissue regions, ultimately contributing to the advancement of our understanding of cancer biology. Conclusion. By seamlessly integrating TMA data with image-based ST technologies such as Xenium and MERSCOPE, we can perform comprehensive spatial transcriptomics analyses and provide detailed statistical information on cell type proportions within the tumor microenvironment. This automated pipeline not only ensures robust and reliable results across diverse tissue samples but also optimizes resource allocation by minimizing batch effects and reducing analysis time. This cost-effective solution empowers researchers to delve deeper into spatial gene expression patterns, enhancing our comprehension of the tumor microenvironment. Citation Format: Dongjoo Lee, Seungho Cook, Yeonjae Jung, Myunghyun Lim, Jae Eun Lee, Hyung-Jun Im, Daeseung Lee, Hongyoon Choi. Automated tumor microenvironment analysis for multiple samples by image-based spatial transcriptomics on tissue microarray [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4948.

Knowledge of spatial patterns and interactions of tree species allows for understanding the ecological processes of spatiotemporal structures of tropical forests, becoming essential for the establishment of strategies for the conservation and management of their resources in long term. The aim of this study was to investigate spatial patterns and interactions of Astronium lecointei, Dinizia excelsa and Peltogyne paniculata, three dominant timber tree species in Jamari National Forest, Brazilian Amazon. Kernel estimator was used aiming to verify the possible influence of first-order factors on species distributions and Inhomogeneous K-function was applied to analyze spatial patterns and interactions of the species by means of second-order factors. Univariate analyses revealed different scale-dependent spatial patterns for the species. Aggregation related to ecological characteristics, such as preferential habitat and dispersal limitation, was verified for A. lecointei and P. paniculata. D. excelsa presented a random spatial pattern, explained by specific features of its establishment, such as the need for clearings due to light requirement. Interspecific associations were evidenced by bivariate analyses, in which the spatial attraction of the species resulted from the same preference for microhabitats and the repulsion was a result of niche segregation.

The increasing interest in the concept of ecosystem services (ES) for decision-making requires a profound understanding of ecological processes, social values and spatial patterns to mitigate the effects of global change on human well-being. Although great progress has been made in the assessment and valuation of ES, scientists are still facing challenges due to a frequent emphasis on ES potential and individual ecosystems as well as disciplinary thinking. This post-doctoral thesis addresses these challenges by (1) contributing to novel mapping approaches with a focus on cultural ES, (2) examining impacts of global change on ES at the ecosystem and landscape level and (3) analysing spatial patterns and interactions between ES supply and demand for ES across multiple spatial scales. This work focuses on the European Alps, as mountain regions are highly important for providing ES while beeing particularly vulnerable to global change. The findings clearly confirm the relevance of mountain landscapes not only to local populations, but indicates spatial interactions that go far beyond the regional level with great implications for decision- and policy-making. The findings also indicate how the concept of ES may promote biodiversity conservation and the maintainance of multiple ES supported by a sustainable use of natural resources. This work also suggests how interdisciplinary approaches can help to integrate ES supply and demand across different temporal and spatial scales for decision-making in planning and management, taking into account ecological processes in response to climate change. Finally, this work reveals research gaps that need to be addressed in future research to deepen the understanding of socio-ecological systems and underlying mechanisms, as well as to enhance interdisciplinary research.

A Molecular Pathologic Framework for Risk Stratification of Stage T1 Urothelial Carcinoma