Co-location Patterns Research Articles

The TCONS_00046732/chi-miR-135b-5p/PRLR regulatory axis promotes endometrial epithelial cells growth through the PI3K-Akt signaling pathway

Non-coding RNAs are considered key regulatory factors in biological and reproductive physiological processes in mammals. However, the molecular functions of long noncoding RNAs (lncRNAs) in regulating dynamic uterine development and reproductive capacity during sexual maturation remain unclear. This study analyzed the expression characteristics and molecular functions of lncRNAs in uterine tissues from 20 goats at four specific time points during sexual maturation: day 1 after birth (D1), 2 months (M2), 4 months (M4), and 6 months (M6), finding that stage-specific DE lncRNAs may regulate cell proliferation, apoptosis, hormone secretion, metabolism, and immune response through multiple action modes. Within the lncRNA-miRNA-mRNA network, a novel lncRNA, TCONS_00046732, associated with uterine development, exhibited significantly higher expression during sexual maturity compared to the prepubertal stage, correlating positively with PRLR and negatively with chi-miR-135b-5p. FISH and IF analyses revealed significant co-localization and distinct expression patterns of TCONS_00046732, chi-miR-135b-5p, and PRLR in the endometrial epithelium. Further experiments confirmed that TCONS_00046732 competitively binds to chi-miR-135b-5p to upregulate PRLR, thereby activating the PI3K-Akt signaling pathway, promoting primary endometrial epithelial cell proliferation, G1-to-S phase transition, and inhibiting apoptosis. These findings enhance our understanding of uterine molecular regulation and provide key insights into the molecular basis of goat sexual development.

Integrative spatial and genomic analysis of tumor heterogeneity with Tumoroscope

Spatial and genomic heterogeneity of tumors are crucial factors influencing cancer progression, treatment, and survival. However, a technology for direct mapping the clones in the tumor tissue based on somatic point mutations is lacking. Here, we propose Tumoroscope, the first probabilistic model that accurately infers cancer clones and their localization in close to single-cell resolution by integrating pathological images, whole exome sequencing, and spatial transcriptomics data. In contrast to previous methods, Tumoroscope explicitly addresses the problem of deconvoluting the proportions of clones in spatial transcriptomics spots. Applied to a reference prostate cancer dataset and a newly generated breast cancer dataset, Tumoroscope reveals spatial patterns of clone colocalization and mutual exclusion in sub-areas of the tumor tissue. We further infer clone-specific gene expression levels and the most highly expressed genes for each clone. In summary, Tumoroscope enables an integrated study of the spatial, genomic, and phenotypic organization of tumors.

8650 Co-Localization Pattern of Snhg14 Long Noncoding RNA and Nhlh2 mRNA in Hypothalamus Coincides with Obesity and Endocrine Impairment Phenotypes in Prader-Willi Syndrome

Abstract Disclosure: S. Ariyanfar: None. C.K. Thompson: None. S. Tobet: None. D.J. Good: None. Prader–Willi syndrome (PWS) is a multisystemic neurodevelopmental disorder, characterized by biphasic symptoms, including diminished weight gain during development, along with neonatal hypotonia and failure to thrive, with later onset hyperphagia, severe weight gain, and morbid obesity. PWS arises from the deletion or inactivation of paternally inherited genes, with the smallest region encompassing a group of 30 small nucleolar RNAs ‘snoRNAs’ known as the SNORD116@ locus. SNORD116@ snoRNAs are cleaved from a larger long non-coding gene, SNHG14. Whole-body deletion of Nhlh2 in mice results in PWS-like phenotypes, such as later onset weight gain, and delayed puberty. We previously have shown that overexpression of Snord116-3 in a mouse hypothalamic cell line improves the stability of Nhlh2 mRNA, likely through a motif in the 3’ untranslated region of the Nhlh2 mRNA. This motif can be disrupted by the presence of a single nucleotide polymorphism, confirming the mechanism of action. Nevertheless, to date, no published studies are showing the in vivo co-localization between Nhlh2 and Snord116 (or its host gene, Snhg14), and the question of where and when Snord116 snoRNAs interact with target mRNA Nhlh2 remains unanswered. To fill this gap, we mapped simultaneous expression of Snhg14 and Nhlh2 throughout the adult mouse brain, using quantitative RNA multi-plex in situ hybridization “RNAScope”. In our initial analysis, we focused on Pomc neurons of the hypothalamus as targeted deletion of Nhlh2 in these neurons results in obesity levels similar to that of the whole body deletion of Nhlh2, a present phenotype in PWS. Our data for the first time showed the co-expression of Nhlh2 and Snhg14 in Pomc neurons of Arcuate nucleus. In the analysis of 54 Pomc neurons, 72% and 39% were positive for Nhlh2 and Snhg14 expression respectively, with 39% of detected signals co-expressed in the same cell (correlation coefficient 0.78, P < 0.0001). Interestingly, in neurons that only expressed Nhlh2 (33) there were nearly evenly distributed signals in the cytoplasm (9 cells) and nucleus (10 cells). When both Nhlh2 and Snhg14 messages were coexpressed, 17 of the 21 cells expressed Nhlh2 in the nucleus only (P < 0.0003, P< 0.0007 for the Likelihood ratio test and Chi-square test, respectively). Thus, based on the high magnification cell analysis, acquired 3D images, and probe location, we can conclude that the Snhg14 host transcript and Nhlh2 interact spatially. This new in vivo data supports the previously shown regulatory role for Snord 116 snoRNAs towards Nhlh2 mRNA stability and suggests that regulation may involve nuclear sequestration of Nhlh2. We are currently extending our studies to examine expression in the brains from patients with PWS and determine if energy balance signals affect co-localization patterns within the hypothalamus. Presentation: 6/3/2024

Super-resolved analysis of colocalization between replication and transcription along the cell cycle in a model of oncogene activation

To understand how oncogenes affect genome organization, it is essential to visualize fundamental processes such as DNA replication and transcription at high resolution in intact cells. At the same time, it is important to determine the progression of the cell along the cell cycle, as cell cycle regulation is crucial for the control of cell proliferation and oncogenesis. Here, we present a super-resolution imaging-based method to analyze single cell nuclei sorted according to specific phases of the cell cycle. The sorting is based on the evaluation of the number and the intensity of pixels in the replication foci image and the colocalization analysis is based on image cross-correlation spectroscopy (ICCS). We evaluate the colocalization between replication and transcription, at different cell cycle phases, in a model of PML-RARα oncogene activation. We find that colocalization between replication and transcription is higher in cells in early S phase compared to cells in middle and late S phase. When we turn on the PML-RARα oncogene, this colocalization pattern is preserved but we detect an increase of colocalization between replication and transcription in the early S phase which points to an effect of the PML-RARα oncogene on the coordination between replication and transcription.

The spinal muscular atrophy gene product regulates actin dynamics.

Spinal Muscular Atrophy (SMA) is a neuromuscular disease caused by low levels of the Survival of Motoneuron (SMN) protein. SMN interacts with and regulates the actin-binding protein profilin2a, thereby influencing actin dynamics. Dysfunctional actin dynamics caused by SMN loss disrupts neurite outgrowth, axonal pathfinding, and formation of functional synapses in neurons. Whether the SMN protein directly interacts with and regulates filamentous (F-) and monomeric globular (G-) actin is still elusive. In a quantitative single cell approach, we show that SMN loss leads to dysregulated F-/G-actin fractions. Furthermore, quantitative assessment of cell morphology suggests an F-actin organizational defect. Interestingly, this is mediated by an interaction of SMN with G- and F-actin. In co-immunoprecipitation, in-vitro pulldown and co-localization assays, we elucidated that this interaction is independent of the SMN-profilin2a interaction. Therefore, we suggest two populations being relevant for functional actin dynamics in healthy neurons: SMN-profilin2a-actin and SMN-actin. Additionally, those two populations may influence each other and therefore regulate binding of SMN to actin. In SMA, we showed a dysregulated co-localization pattern of SMN-actin which could only partially rescued by SMN restoration. However, dysregulation of F-/G-actin fractions was reduced by SMN restoration. Taken together, our results suggest a novel molecular function of SMN in binding to actin independent from SMN-profilin2a interaction.

Intracellular spatial transcriptomic analysis toolkit (InSTAnT)

Imaging-based spatial transcriptomics technologies such as Multiplexed error-robust fluorescence in situ hybridization (MERFISH) can capture cellular processes in unparalleled detail. However, rigorous and robust analytical tools are needed to unlock their full potential for discovering subcellular biological patterns. We present Intracellular Spatial Transcriptomic Analysis Toolkit (InSTAnT), a computational toolkit for extracting molecular relationships from spatial transcriptomics data at single molecule resolution. InSTAnT employs specialized statistical tests and algorithms to detect gene pairs and modules exhibiting intriguing patterns of co-localization, both within individual cells and across the cellular landscape. We showcase the toolkit on five different datasets representing two different cell lines, two brain structures, two species, and three different technologies. We perform rigorous statistical assessment of discovered co-localization patterns, find supporting evidence from databases and RNA interactions, and identify associated subcellular domains. We uncover several cell type and region-specific gene co-localizations within the brain. Intra-cellular spatial patterns discovered by InSTAnT mirror diverse molecular relationships, including RNA interactions and shared sub-cellular localization or function, providing a rich compendium of testable hypotheses regarding molecular functions.

A Novel Algorithm for Efficiently Mining Spatial Multi-Level Co-Location Patterns

A Novel Algorithm for Efficiently Mining Spatial Multi-Level Co-Location Patterns

Re-analysis of single cell and spatial transcriptomics data reveals B cell landscape in gastric cancer microenvironment and its potential crosstalk with tumor cells for clinical prognosis

BackgroundAt present, immunotherapy has become a powerful treatment for advanced gastric cancer (AGC), but not all patients can benefit from it. According to the latest research, the impact of B cell subpopulations on the immune microenvironment of gastric cancer (GC) is unknown. Exploring whether the interaction between B cells and tumor cells in GC affects the effectiveness of immunotherapy has attracted our interest.MethodsThis study involved the re-analysis of single-cell RNA (scRNA) and spatial transcriptomics (ST) data from publicly available datasets. The focus was on investigating the subpopulations and differentiation trajectories of B cells in the gastric cancer (GC) tumor immune microenvironment (TIME). Spatial transcriptomics (ST) and multiple immunofluorescence (mIF) revealed a clear co-localization pattern between B cells and tumor cells. Multiple immunotherapy datasets were collected to identify unique immunotherapy biomarkers. The unique immunotherapeutic potential of targeting CCL28 was validated through a mouse gastric cancer model. In addition, flow cytometry revealed changes in the tumor immune microenvironment targeting CCL28.ResultsThe re-analysis of ST data from multiple cancer types revealed a co-localization pattern between B cells and tumor cells. A significant number of IgA plasma cells were identified in the GC TIME. Five different tumor-infiltrating B cell subpopulations and two unique B cell differentiation trajectories were characterized, along with seven GC-related states. By analyzing the communication between GC cells and B cells, it was further discovered that tumor cells can influence and recruit plasma cells through CCL28-CCR10 signaling. Additionally, there was a crosstalk between GC cells and B cells. Finally, we identified the LAMA/CD44 signaling axis as a potential prognostic marker for immunotherapy through a large amount of immunotherapy data. We also validated through various animal tumor models that targeting CCL28 can significantly promote CD8+T cell infiltration and function in the TME by regulating B cell and plasma cell functions, and has the ability to synergize immunotherapy.ConclusionThe co-localization and crosstalk between GC cells and B cells significantly affect the efficacy of immunotherapy, and inhibiting the CCL28-CCR10 signal axis is a potential immunotherapy target for GC. Meanwhile, LAMA/CD44 pair may be a potential adverse indicator for immunotherapy and tumor prognosis.

Mining Interpretable Regional Co-location Patterns Based on Urban Functional Region Division

Mining Interpretable Regional Co-location Patterns Based on Urban Functional Region Division

Dendritically localized RNAs are packaged as diversely composed ribonucleoprotein particles with heterogeneous copy number states.

Localization of mRNAs to dendrites is a fundamental mechanism by which neurons achieve spatiotemporal control of gene expression. Translationally repressed neuronal mRNA transport granules, also referred to as ribonucleoprotein particles (RNPs), have been shown to be trafficked as single or low copy number RNPs and as larger complexes with multiple copies and/or species of mRNAs. However, there is little evidence of either population in intact neuronal circuits. Using single molecule fluorescence in situ hybridization studies in the dendrites of adult rat and mouse hippocampus, we provide evidence that supports the existence of multi-transcript RNPs with the constituents varying in amounts for each RNA species. By competing-off fluorescently labeled probe with serial increases of unlabeled probe, we detected stepwise decreases in Arc RNP number and fluorescence intensity, suggesting Arc RNAs localize to dendrites in both low- and multiple-copy number RNPs. When probing for multiple mRNAs, we find that localized RNPs are heterogeneous in size and colocalization patterns that vary per RNA. Further, localized RNAs that are targeted by the same trans-acting element (FMRP) display greater levels of colocalization compared to an RNA not targeted by FMRP. Simultaneous visualization of a dozen FMRP-targeted mRNA species using highly multiplexed imaging demonstrates that dendritic RNAs are mostly trafficked as heteromeric cargoes of multiple types of RNAs (at least one or more RNAs). Moreover, the composition of these RNA cargoes, as assessed by colocalization, correlates with the abundance of the transcripts even after accounting for the expected differences in colocalization based on expression. Collectively, these results suggest that dendritic RNPs are packaged as heterogeneous co-assemblies of different mRNAs and that RNP contents may be driven, at least partially, by highly abundant dendritic RNAs; a model that favors efficiency over fine-tuned control for sustaining long-distance trafficking of thousands of messenger molecules.

Graph-based spatial co-location pattern mining: integrate geospatial analysis and logical reasoning

ABSTRACT Spatial co-location patterns reflect the inherent correlations among geographical elements. Mining co-location patterns of POIs can provide valuable insights for urban planning and resource management. Generally, co-location mining comprises two steps: proximity relationship determination (geospatial analysis) and frequent pattern recursion (logical reasoning). Previous methods often separate these two steps: serializing proximity relationships to enumerate frequent sequences. However, this approach suffers from limited flexibility and intuitiveness: as continuous spatial contexts are segmented into numerous small parts, it fails to adequately represent geographic correlations and hinders the effective visualization of logical reasoning. Facing these challenges, this study proposes a novel graph-based spatial co-location mining method (GSCM), which leverages graphs to integrate geospatial analysis and logical reasoning. Initially, to establish adjacency relationships, GSCM constructs the adaptive neighborhood graph, which dynamically adjusts proximity thresholds to accommodate geographic heterogeneity. Subsequently, the Apriori logical recursive process is realized on the graph structure. By leveraging graph searching, pruning, and growing, the potential growth directions of co-location patterns are identified, enhancing both the efficiency and intuition of frequent pattern recursion. Through experiments conducted on large-scale POI datasets from Wuhan, GSCM is compared with existing baseline methods, verifying its potential to uncover co-location patterns in complex spatial contexts.

Representative co-location pattern post-mining based on maximal row instances representation model

The mining result set of spatial prevalent co-location patterns(SPCPs) is often large and redundant, especially when the prevalence threshold is set to low or long SPCPs are present. Meanwhile, the distribution of SPCPs in continuous space and complex spatial relationships with each other of spatial data make the compression and reorganization of SPCPs a challenging problem. To solve this problem, in this paper, a representative co-location pattern mining framework based on the maximal row instance(MRI) representation model is proposed. First, the MRI representation model is proposed to effectively preserve the pattern distribution information of prevalent co-location patterns. To establish the MRI representation model, the basic algorithm and geometric algorithm are proposed in this paper. Two materialization methods based on the MRI representation model, 0-1 vector and key–value vector, are presented. Secondly, the similarity measure of SPCPs under the context of the MRI representation model is proposed, which calculates the similarity between any two co-location patterns without adding additional information such as domain background. Furthermore, the mining framework based on the k nearest neighbors density peak clustering algorithm is presented to extract representative co-location patterns. Finally, the efficiency and scalability of the proposed method are verified on the synthetic data and real data. Compared with the existing methods, the representative co-location patterns of the proposed method have well compression performances.

Investigating clinical links in edge-labeled citation networks of biomedical research: A translational science perspective

While clinical citations have been widely used as the preeminent measure of the clinical impact of biomedical paper, there has been a scarcity of in-depth studies exploring their temporal and structural characteristics, as well as its influence on the clinical translation. To fill this gap, we categorized biomedical papers and their citations into four groups from the translational science perspective: basic, clinical, mixed, and human-related. Subsequently, we constructed an edge-labeled citation network and four clinical translation networks. Our analysis encompassed 114,342 papers in the field of Alzheimer's Disease, accompanied by 5,161,626 citations, of which 2.77 % were clinical citations, 18.77 % basic citations, 41.85 % mixed citations, and 36.61 % human-related citations. First, utilizing time- and structure-randomized networks, we conducted a quantitative analysis of clinical citations' incidence patterns, impact assortativity, temporal occurrence patterns, and temporal co-location patterns throughout the lifecycles of biomedical research. Second, in comparison to control groups, we evaluated the short- and long-term impacts of different types of citations on the academic influence and clinical translation of biomedical research. Our findings reveal that clinical citations effectively bolster the academic influence of biomedical papers, and this positive effect appears to amplify over time. Conversely, while basic, mixed, and human-related citations may initially aid in the clinical translation of biomedical research, over 70 % of them exhibit an inhibitory effect on clinical translation in the long run. These findings afford us a deep and specific understanding of how clinical citations operate within the context of biomedical papers, thereby serving as a crucial guide for effectively promoting the clinical translation of biomedical research.

Integrating single-nucleus RNA sequencing and spatial transcriptomics to elucidate a specialized subpopulation of astrocytes, microglia and vascular cells in brains of mouse model of lipopolysaccharide-induced sepsis-associated encephalopathy

BackgroundUnderstanding the mechanism behind sepsis-associated encephalopathy (SAE) remains a formidable task. This study endeavors to shed light on the complex cellular and molecular alterations that occur in the brains of a mouse model with SAE, ultimately unraveling the underlying mechanisms of this condition.MethodsWe established a murine model using intraperitoneal injection of lipopolysaccharide (LPS) in wild type and Anxa1−/− mice and collected brain tissues for analysis at 0-hour, 12-hour, 24-hour, and 72-hour post-injection. Utilizing advanced techniques such as single-nucleus RNA sequencing (snRNA-seq) and Stereo-seq, we conducted a comprehensive characterization of the cellular responses and molecular patterns within the brain.ResultsOur study uncovered notable temporal differences in the response to LPS challenge between Anxa1−/− (annexin A1 knockout) and wild type mice, specifically at the 12-hour and 24-hour time points following injection. We observed a significant increase in the proportion of Astro-2 and Micro-2 cells in these mice. These cells exhibited a colocalization pattern with the vascular subtype Vas-1, forming a distinct region known as V1A2M2, where Astro-2 and Micro-2 cells surrounded Vas-1. Moreover, through further analysis, we discovered significant upregulation of ligands and receptors such as Timp1-Cd63, Timp1-Itgb1, Timp1-Lrp1, as well as Ccl2-Ackr1 and Cxcl2-Ackr1 within this region. In addition, we observed a notable increase in the expression of Cd14-Itgb1, Cd14-Tlr2, and Cd14-C3ar1 in regions enriched with Micro-2 cells. Additionally, Cxcl10-Sdc4 showed broad upregulation in brain regions containing both Micro-2 and Astro-2 cells. Notably, upon LPS challenge, there was an observed increase in Anxa1 expression in the mouse brain. Furthermore, our study revealed a noteworthy increase in mortality rates following Anxa1 knockdown. However, we did not observe substantial differences in the types, numbers, or distribution of other brain cells between Anxa1−/− and wildtype mice over time. Nevertheless, when comparing the 24-hour post LPS injection time point, we observed a significant decrease in the proportion and distribution of Micro-2 and Astro-2 cells in the vicinity of blood vessels in Anxa1−/− mice. Additionally, we noted reduced expression levels of several ligand-receptor pairs including Cd14-Tlr2, Cd14-C3ar1, Cd14-Itgb1, Cxcl10-Sdc4, Ccl2-Ackr1, and Cxcl2-Ackr1.ConclusionsBy combining snRNA-seq and Stereo-seq techniques, our study successfully identified a distinctive cellular colocalization, referred to as a special pathological niche, comprising Astro-2, Micro-2, and Vas-1 cells. Furthermore, we observed an upregulation of ligand-receptor pairs within this niche. These findings suggest a potential association between this cellular arrangement and the underlying mechanisms contributing to SAE or the increased mortality observed in Anxa1 knockdown mice.

Discovering spatial co‐location patterns of urban facilities and their asymmetric characteristics

AbstractSpatial co‐location pattern (CP) mining can discover sets of geographical features frequently appearing in adjacent locations, which is valuable for comprehending the co‐occurrence relationship between features. However, due to the quantitative differences and heterogeneous distribution of features, the probabilities that features appear in each other's neighborhood are unequal, resulting in an asymmetric spatial pattern. Current studies have paid little attention to the asymmetric characteristics of CPs. Therefore, this study explores the CPs and their asymmetric relationships. Firstly, we adopt the weighted participation index to evaluate the frequency of global candidate CPs. Secondly, we employ an asymmetry index we developed and the local co‐location quotient to quantify the asymmetry intensity of CPs. The results indicate that the frequent CPs mainly comprise facilities related to the residents' daily lives. Investigating the asymmetric relationships and spatial associations among features in the CPs is significant for identifying resource shortages and rationally planning urban resources.

Knowledge-based discovery of multi-level co-location patterns using ontology

Knowledge-based discovery of multi-level co-location patterns using ontology

ProteinCoLoc streamlines Bayesian analysis of colocalization in microscopic images

Colocalization, the spatial overlap of molecular entities, is often key to support their involvement in common functions. Existing colocalization tools, however, face limitations, particularly because of their basic statistical analysis and their low-throughput manual entry processes making them unsuitable for automation and potentially introducing bias. These shortcomings underscore the need for user-friendly tools streamlining colocalization assessments and enabling their robust and automated quantitative analyses. We have developed ProteinCoLoc, an innovative software designed for automated high-throughput colocalization analyses and incorporating advanced statistical features such as Bayesian modelling, automatic background detection and localised correlation analysis. ProteinCoLoc rationalises colocalization assessments without manual input, comes with a user-friendly graphical user interface and provides various analytics allowing to study and locally quantify colocalization. This easy-to-use application presents numerous advantages, including a direct comparison with controls employing a Bayesian model and the analysis of local correlation patterns, while reducing hands-on time through automatic background detection. The software was validated while studying the colocalization pattern of two proteins forming a stable complex: the huntingtin protein (HTT) and its partner huntingtin-associated protein 40 (HAP40). Our results showcase the software’s capacity to quantitatively assess colocalizations. ProteinCoLoc is available both as a Julia package and as a compiled software (https://github.com/ma-seefelder/ProteinCoLoc).

Evidence of RNA polymerase III recruitment and transcription at protein-coding gene promoters.

RNA polymerase (Pol) I, II, and III are most commonly described as having distinct roles in synthesizing ribosomal RNA (rRNA), messenger RNA (mRNA), and specific small noncoding (nc)RNAs, respectively. This delineation of transcriptional responsibilities is not definitive, however, as evidenced by instances of Pol II recruitment to genes conventionally transcribed by Pol III, including the co-transcription of RPPH1 - the catalytic RNA component of RNase P. A comprehensive understanding of the interplay between RNA polymerase complexes remains lacking, however, due to limited comparative analyses for all three enzymes. To address this gap, we applied a uniform framework for quantifying global Pol I, II, and III occupancies that integrates currently available human RNA polymerase ChIP-seq datasets. Occupancy maps are combined with a comprehensive multi-class promoter set that includes protein-coding genes, noncoding genes, and repetitive elements. While our genomic survey appropriately identifies recruitment of Pol I, II, and III to canonical target genes, we unexpectedly discover widespread recruitment of the Pol III machinery to promoters of specific protein-coding genes, supported by colocalization patterns observed for several Pol III-specific subunits. We show that Pol III-occupied Pol II promoters are enriched for small, nascent RNA reads terminating in a run of 4 Ts, a unique hallmark of Pol III transcription termination and evidence of active Pol III activity at these sites. Pol III disruption differentially modulates the expression of Pol III-occupied coding genes, which are functionally enriched for ribosomal proteins and genes broadly linked to unfavorable outcomes in cancer. Our map also identifies additional, currently unannotated genomic elements occupied by Pol III with clear signatures of nascent RNA species that are sensitive to disruption of La (SSB) - a Pol III-related RNA chaperone protein. These findings reshape our current understanding of the interplay between Pols II and III and identify potentially novel small ncRNAs with broad implications for gene regulatory paradigms and RNA biology.

Mining Spatial Co-Location Patterns With a Mixed Prevalence Measure.

A co-location pattern indicates a subset of spatial features whose instances are frequently located together in proximate geographical space. Most previous studies of spatial co-location pattern mining concern what percentage of instances per feature are involved in the table instance of a pattern, but neglect the heterogeneity in the number of feature instances and the distribution of instances. As a result, the deviation may be occurred in the interest measure of co-locations. In this article, we propose a novel mixed prevalence index (MPI) incorporating the effect of feature-level and instance-level heterogeneity on the prevalence measure, which can address some dilemmas in existing interest measures. Luckily, MPI possesses the partial antimonotone property. In virtue of this property, a branch-based search algorithm equipped with some optimizing strategies of MPI calculation is proposed, namely, Branch-Opt-MPI. Comprehensive experiments are conducted on both real and synthetic spatial datasets. Experimental results reveal the superiority of MPI compared to other interest measures and also validate the efficiency and scalability of the Branch-Opt-MPI. Particularly, the Branch-Opt-MPI performs more efficiently than baselines for several times or even orders of magnitude in dense data.

Systematic dissection of tumor-normal single-cell ecosystems across a thousand tumors of 30 cancer types

The complexity of the tumor microenvironment poses significant challenges in cancer therapy. Here, to comprehensively investigate the tumor-normal ecosystems, we perform an integrative analysis of 4.9 million single-cell transcriptomes from 1070 tumor and 493 normal samples in combination with pan-cancer 137 spatial transcriptomics, 8887 TCGA, and 1261 checkpoint inhibitor-treated bulk tumors. We define a myriad of cell states constituting the tumor-normal ecosystems and also identify hallmark gene signatures across different cell types and organs. Our atlas characterizes distinctions between inflammatory fibroblasts marked by AKR1C1 or WNT5A in terms of cellular interactions and spatial co-localization patterns. Co-occurrence analysis reveals interferon-enriched community states including tertiary lymphoid structure (TLS) components, which exhibit differential rewiring between tumor, adjacent normal, and healthy normal tissues. The favorable response of interferon-enriched community states to immunotherapy is validated using immunotherapy-treated cancers (n = 1261) including our lung cancer cohort (n = 497). Deconvolution of spatial transcriptomes discriminates TLS-enriched from non-enriched cell types among immunotherapy-favorable components. Our systematic dissection of tumor-normal ecosystems provides a deeper understanding of inter- and intra-tumoral heterogeneity.