SpaceANOVA:Spatial Co-occurrence Analysis of CellTypes in Multiplex Imaging Data Using Point Process and FunctionalANOVA

<div>Abstract<p>The spatial organization of different types of cells in tumor tissues reveals important information about the tumor microenvironment (TME). To facilitate the study of cellular spatial organization and interactions, we developed Histology-based Digital-Staining, a deep learning-based computation model, to segment the nuclei of tumor, stroma, lymphocyte, macrophage, karyorrhexis, and red blood cells from standard hematoxylin and eosin–stained pathology images in lung adenocarcinoma. Using this tool, we identified and classified cell nuclei and extracted 48 cell spatial organization-related features that characterize the TME. Using these features, we developed a prognostic model from the National Lung Screening Trial dataset, and independently validated the model in The Cancer Genome Atlas lung adenocarcinoma dataset, in which the predicted high-risk group showed significantly worse survival than the low-risk group (<i>P</i> = 0.001), with a HR of 2.23 (1.37–3.65) after adjusting for clinical variables. Furthermore, the image-derived TME features significantly correlated with the gene expression of biological pathways. For example, transcriptional activation of both the T-cell receptor and programmed cell death protein 1 pathways positively correlated with the density of detected lymphocytes in tumor tissues, while expression of the extracellular matrix organization pathway positively correlated with the density of stromal cells. In summary, we demonstrate that the spatial organization of different cell types is predictive of patient survival and associated with the gene expression of biological pathways.</p>Significance:<p>These findings present a deep learning-based analysis tool to study the TME in pathology images and demonstrate that the cell spatial organization is predictive of patient survival and is associated with gene expression.</p><p><i>See related commentary by Rodriguez-Antolin, p. 1912</i></p></div>

BackgroundIdentifying biomarkers that predict patient response to immunotherapy is critical for optimizing treatment strategies and improving clinical outcomes. Despite the success of immunotherapy, a significant proportion of patients do not...

This contribution demonstrates that quantitative ultrasound (QUS) capabilities are platform independent, using an in vivo model. Frequency-dependent attenuation estimates, backscatter coefficient, and effective scatterer diameter estimates are shown to be comparable across four different ultrasound imaging systems with varied processing techniques. The backscatter coefficient (BSC) is a fundamental material property from which several QUS parameters are estimated; therefore, consistent BSC estimates among different systems must be demonstrated. This study is an intercomparison of BSC estimates acquired by three research groups (UIUC, UW, ISU) from four in vivo spontaneous rat mammary fibroadenomas using three clinical array systems and a single-element laboratory scanner system. Because of their highly variable backscatter properties, fibroadenomas provided an extreme test case for BSC analysis, and the comparison is across systems for each tumor, not across the highly heterogeneous tumors. RF echo data spanning the 1 to 12 MHz frequency range were acquired in three dimensions from all animals using each system. Each research group processed their RF data independently, and the resulting attenuation, BSC, and effective scatterer diameter (ESD) estimates were compared. The attenuation estimates across all systems showed the same trends and consistently fit the power-law dependence on frequency. BSCs varied among the multiple slices of data acquired by each transducer, with variations between transducers being of a similar magnitude as those from slice to slice. Variation between BSC estimates was assessed via functional signal-to-noise ratios derived from backscatter data. These functional signal-to-noise ratios indicated that BSC versus frequency variations between systems ranged from negligible compared with the noise level to roughly twice the noise level. The corresponding functional analysis of variance (fANOVA) indicated statistically significant differences between BSC curves from different systems. However, root mean squared difference errors of the BSC values (in decibels) between different transducers and imaging platforms were less than half of the BSC magnitudes in most cases. Statistical comparison of the effective scatterer diameter (ESD) estimates resulted in no significant differences in estimates from three of the four transducers used for those estimates, demonstrating agreement among estimates based on the BSC. This technical advance demonstrates that these in vivo measurements can be made in a system-independent manner; the necessary step toward clinical implementation of the technology.

BackgroundMultiplex immunofluorescence (mIF) is a powerful tool for tumor-microenvironment (TME) characterization. However, the adoption of high-plex panels in the clinical setting is limited due to cost and technical complexity. Hematoxylin...

Protein-based systems for light directed migration of cells have been demonstrated up to distances of several hundred microns, but larger distances in the centimeter scale would allow new possible applications. Light activated migration in mammalian cells can be achieved by cells expressing channelrhodopsin-2 and an engineered Ca2+ sensitive Rac1 protein called RACer. In this study, light was used to induce wound healing, localize cells into a region of interest, and move cells over centimeter scale distances. Given the spatially complex organization of different types of cells in real tissue, light directed migration over the centimeter scale could potentially organize cell type arrangement to help develop more realistic tissues for transplantation.

Overlapping Roles and Collective Requirement for the Coreceptors GAS1, CDO, and BOC in SHH Pathway Function

A point process is a collection of positional variables in a given space, while a marked point process (MPP) is basically a point process that measures some additional feature or value at each point. Existing applications such as financial market models and hospitalization models have demonstrated that MPPS are suitable for modeling random events with meaningful values. A big challenge is to study the relationship between different types of events, or how different types of markers affect each other. While Hawkes procedures are an effective and efficient way to model point processes with internal influences, inferential methods of intensity functions can be expensive, especially for multi-dimensional processes. Graph convolutional Network (GCN) is a powerful neural network designed to directly process graphs. A GCN-based model is presented to study potential patterns in the marking process and to predict the location of future events without estimating the Hawkes intensity function. A real data set of water pipe leakage and rupture records over the last 50 years was used in the experiment, modeled as a marker point process, where leakage and rupture are two marker forms. Experimental results on synthetic data sets and real water pipe data sets show that the proposed model is superior to recent state-of-the-art methods.

With the development of modern information and the increase of space-time sensor and resolution, the volume of temporal and spatial data continues to be a significant grow. Therefore, mining valuable information from spatial and temporal data sets becomes more and more meaningful. The mining of periodic spatial and temporal co-occurrence pattern has been the central issue of recent research. The traditional algorithms for mining spatial temporal co-occurrence patterns are very time-consuming and having some redundant computation. On the other hand, these algorithms are based on threshold method. As we all know, the selection of threshold is suffering and lack of scientific basis. Therefore, T-PPCOP miner is proposed, which integrated TOP-K% method into the above algorithms to replace the confidence threshold method. Experimental results by real data sets show that the proposed T-PPCOP miner is feasible, and can effectively dig up partially periodic spatial temporal co-occurrence pattern (PPCOP) from spatial temporal data sets.

BackgroundTranscription factors (TFs) are proteins that bind to DNA and regulate gene expression. To understand details of gene regulation, characterizing TF binding sites in different cell types, diseases and among individuals is essential. However, sometimes TF binding can only be measured from biological samples that contain multiple cell or tissue types. Sample heterogeneity can have a considerable effect on TF binding site detection. While manual separation techniques can be used to isolate a cell type of interest from heterogeneous samples, such techniques are challenging and can change intra-cellular interactions, including protein-DNA binding. Computational deconvolution methods have emerged as an alternative strategy to study heterogeneous samples and numerous methods have been proposed to analyze gene expression. However, no computational method exists to deconvolve cell type specific TF binding from heterogeneous samples.ResultsWe present a probabilistic method, MixChIP, to identify cell type specific TF binding sites from heterogeneous chromatin immunoprecipitation sequencing (ChIP-seq) data. Our method simultaneously estimates the binding strength in different cell types as well as the proportions of different cell types in each sample when only partial prior information about cell type composition is available. We demonstrate the utility of MixChIP by analyzing ChIP-seq data from two cell lines which we artificially mix to generate (simulated) heterogeneous samples and by analyzing ChIP-seq data from breast cancer patients measuring oestrogen receptor (ER) binding in primary breast cancer tissues. We show that MixChIP is more accurate in detecting TF binding sites from multiple heterogeneous ChIP-seq samples than the standard methods which do not account for sample heterogeneity.ConclusionsOur results show that MixChIP can estimate cell-type proportions and identify cell type specific TF binding sites from heterogeneous ChIP-seq samples. Thus, MixChIP can be an invaluable tool in analyzing heterogeneous ChIP-seq samples, such as those originating from cancer studies. R implementation is available at http://research.ics.aalto.fi/csb/software/mixchip/.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0834-3) contains supplementary material, which is available to authorized users.

Automated segmentation and quantification of cellular and subcellular components in multiplexed images has allowed for a combination of both spatial and protein expression information to become available for analysis. However, performing analyses across multiple patients and tissue types continues to be a challenge, as well as the greater challenge of tissue classification itself. We propose a model of tissues as interconnected networks of epithelial cells whose connectivity is determined by their size, specific expression levels, and proximity to other cells. These Biomarker Enhanced Tissue Networks (BETN) reflect both the individual nature of the cells and the complex cell to cell relationships within the tissue. Performing a simple analysis of such tissue networks managed to successfully classify epithelial cells from stromal cells across multiple patients and tissue types. Further experiments show that significant information about the structure and nature of tissues can also be extracted through analysis of the networks, which will hopefully move towards the eventual goal of true tissue classification.

Enzyme replacement therapy (ERT) has been shown to be effective at reducing the accumulation of undegraded substrates in lysosomal storage diseases. Most ERT studies have been performed with recombinant proteins that are mixtures of phosphorylated and non-phosphorylated enzyme. Because different cell types use different receptors to take up phosphorylated or non-phosphorylated enzyme, it is difficult to determine which form of enzyme contributed to the clinical response. Here we compare the uptake, distribution, and efficacy of highly phosphorylated and non-phosphorylated beta-glucuronidase (GUSB) in the MPS VII mouse. Highly phosphorylated murine GUSB was efficiently taken up by a wide range of tissues. In contrast, non-phosphorylated murine GUSB was taken up primarily by tissues of the reticuloendothelial (RE) system. Although the tissue distribution was different, the half-lives of both enzymes in any particular tissue were similar. Both preparations of enzyme were capable of preventing the accumulation of lysosomal storage in cell types they targeted. An important difference in clinical efficacy emerged in that phosphorylated GUSB was more efficient than non-phosphorylated enzyme at preventing the hearing loss associated with this disease. These data suggest that both forms of enzyme contribute to the clinical responses of ERT in MPS VII mice but that enzyme preparations containing phosphorylated GUSB are more broadly effective than non-phosphorylated enzyme.

Study question Can we map the key maturation stages of human spermatogonial stem cells (SSCs) within their niche during infancy and puberty? Summary answer Using highplex spatial proteomics, we describe the key cellular events taking place during human testis maturation. What is known already The human testis is a complex “ecosystem” that matures in response to endocrine and exocrine signals and interactions between its diverse cellular components. Unlike animal models, in humans, testicular maturation is a slow process that takes place over more than a decade. In contrast to ovaries, the human testis retains its immature state until the onset of puberty. Single-cell RNA sequencing data has revealed significant developmental changes for germ cell and the somatic niche during infancy and puberty. Study design, size, duration Using the hyperplex imaging technique (CellDIVE) and a panel of 30 protein markers - selected to label key components of the developing human testis - we have systematically analyzed a time series of ∼75 testicular biopsies from boys aged 8 months to adulthood. Following sequential staining and capture of high-resolution images, our analysis pipeline involves the conversion of image into single-cell data, which allows a systematic description of the spatial SSC/niche microenvironment interactions across development. Participants/materials, setting, methods The panel of antibody markers was defined using integrated scRNAseq data and represents all key cell types, including germ cell markers (e.g., MAGEA4), Sertoli cell markers (e.g., SOX9), Leydig cell markers (e.g., INSL3), myoid cell markers (e.g., ACTA2) ... Main results and the role of chance To describe the events occurring during human testicular maturation, we build a Tissue Microarray (TMA) containing ∼75 biopsies of boys of different ages (0-16yrs old) and using an in-house panel of 30 markers for high-dimensional multiplex CELL DIVE images. Our analytical strategy uses Uniform Manifold Approximation and Projection (UMAP) maps to quantify and statistically compare single-cell data. Our marker panel allows us to phenotype testicular cells at different stages of development, including SSCs (state 0 to state 4), Sertoli cells, Leydig cells, and so on. Moreover, employing spatial-latent Dirichlet allocation (LDA), we identified recurrent cellular neighbourhoods (RCNs), revealing distinct cell clusters and their corresponding RCNs. These RCNs reflect recurring spatial arrangements of cells within testicular tissues, allowing us to infer the spatial organization and interplay among different cell types. Additionally, we explored spatial distances and neighbourhood frequencies across different tissues and conducted spatial co-occurrence analysis to assess the likelihood of finding adjacent cell types. The identification of RCNs offers valuable insights into the microenvironmental dynamics and cellular relationships taking place within the developing testis. Looking ahead, we aim to delve into the functional significance of RCNs in shaping the developmental trajectories and physiological processes within human testicular tissues. Limitations, reasons for caution Due to limitations in proteomics techniques, we could only include ∼30 protein markers on our panel. Although the biopsy samples used in our study are all from chemo-naïve patients, it possible that the conditions affecting these paediatric patients may impact the architecture or development of their testicular tissues. Wider implications of the findings Our study provides a systematic approach to study the development and organization of human testicular structures, which will allow to enumerate the cellular content of individual testicular biopsies and infer their stage of maturation, therefore providing a personalised baseline assessment for each patient at the time of cryopreservation. Trial registration number not applicable

The rapid accumulation of single-cell chromatin accessibility data offers a unique opportunity to investigate common and specific regulatory mechanisms across different cell types. However, existing methods for cis-regulatory network reconstruction using single-cell chromatin accessibility data were only designed for cells belonging to one cell type, and resulting networks may be incomparable directly due to diverse cell numbers of different cell types. Here, we adopt a computational method to jointly reconstruct cis-regulatory interaction maps (JRIM) of multiple cell populations based on patterns of co-accessibility in single-cell data. We applied JRIM to explore common and specific regulatory interactions across multiple tissues from single-cell ATAC-seq dataset containing ~80 000 cells across 13 mouse tissues. Reconstructed common interactions among 13 tissues indeed relate to basic biological functions, and individual cis-regulatory networks show strong tissue specificity and functional relevance. More importantly, tissue-specific regulatory interactions are mediated by coordination of histone modifications and tissue-related TFs, and many of them may reveal novel regulatory mechanisms.

Background: In the past few years, animal models and cultured cells have shown that smooth muscle cells (SMCs) and inflammatory cytokines are important new players in atherosclerotic disease. Validating the roles of these new players in human atherogenesis is crucial for the development of therapeutics targeting them. Multiplex imaging is a powerful tool for mapping cell phenotypes and microenvironment in tissue sections, enabling proof-of-concept studies using biobanked human specimens. However, this technology has seldom been applied to human atherosclerotic lesions. This study is a trial to benchmark the application of the multiplex imaging platform PhenoCycler to biobanked human coronary lesions. We hypothesize that this technology is a powerful tool to prove theories raised by previous studies: SMCs initiate foam cell formation and inflammation is associated with phenotypic switching in SMCs. Methods: We created an 8-plex imaging panel to map lesion cells previously identified as key players in atherogenesis, including foam cells from SMC and macrophage origins, CD68 + SMCs, apoptotic cells, and cells expressing inflammatory cytokines interleukin-1β and tumor necrosis factor α. They were imaged simultaneously on each section of early human lesions (n=9) to test theories related to foam cell formation, phenotypic transition, and inflammatory cytokines. Results: Foam cells of SMC origin greatly outnumbered those of macrophage origin and were enriched in the deep intima, where lipids accumulate in early atherogenesis, distinct from macrophage foam cells in the superficial intima. A higher presence of CD68 + SMCs was observed among lesion SMCs highly expressing interleukin-1β, but not TNF-α. Highly inflamed SMCs exhibited a trend of increased apoptosis compared to macrophages with similar cytokine levels. Conclusions: Our multiplex imaging results confirmed that SMCs form the majority of foam cells in early atherogenesis, and their phenotypic transition to macrophage-like is related to inflammation. Applying multiplex imaging to biobanked human lesions unlocks opportunities to prove that theories based on animal models and cultured cells apply to human atherogenesis.

Despite a vast application of temporal point processes in infectious disease diffusion forecasting, ecommerce, traffic prediction, preventive maintenance, etc, there is no significant development in improving the simulation and prediction of temporal point processes in real-world environments. With this problem at hand, we propose a novel methodology for learning temporal point processes based on one-dimensional numerical integration techniques. These techniques are used for linearising the negative maximum likelihood (neML) function and enabling backpropagation of the neML derivatives. Our approach is tested on two real-life datasets. Firstly, on high frequency point process data, (prediction of highway traffic) and secondly, on a very low frequency point processes dataset, (prediction of ski injuries in ski resorts). Four different point process baseline models were compared: second-order Polynomial inhomogeneous process, Hawkes process with exponential kernel, Gaussian process, and Poisson process. The results show the ability of the proposed methodology to generalize on different datasets and illustrate how different numerical integration techniques and mathematical models influence the quality of the obtained models. The presented methodology is not limited to these datasets and can be further used to optimize and predict other processes that are based on temporal point processes.