Co-location Patterns Research Articles

Rethinking the null hypothesis in significant colocation pattern mining of spatial flows

Rethinking the null hypothesis in significant colocation pattern mining of spatial flows

Molecular Pixelation: Spatial proteomics of single-cells by next generation sequencing

Abstract Molecular Pixelation (MPX) is a novel single cell spatial proteomics panel able to simultaneously quantify protein abundance, the spatial distribution, and colocalization of targeted cell surface proteins on up to 1,000 individual cells. MPX creates 3D spatial maps of cells by imprinting spatial information on antibody oligonucleotide conjugates using a DNA reagent we call DNA-pixels. These DNA-pixels form over 1,000 connected spatial zones per single cell producing graphs that reconstruct the cell surface in silico; forming a single cell surface map of 80 proteins. By applying spatial statistics on these cell surface graph representations, we uncover both known and novel patterns of protein spatial polarization and co-localization associated with vital immune processes such as intercellular communication and mobility. In our recent paper we show how MPX can be used to monitor the cell surface effects of treatment or stimulation, such as the spatial restructuring of the T cell surface proteins upon the formation of the uropod, the hind part of a migrating T cell. With high throughput and multiplex, MPX is a powerful tool for investigating the intricate spatial organization of proteins in single cells, paving the way for new discoveries in immune system research.

Identifying a good business location using prescriptive analytics: Restaurant location recommendation based on spatial data mining

This study proposes a new prescriptive analytics method that aims to provide decision-makers with a systematic and objective approach to identify suitable locations, considering the spatial distribution of different types of restaurants. The method comprises of two main components: spatial co-location pattern mining and locationGCN, where locationGCN is based on graph convolutional network (GCN). The spatial co-location pattern mining is utilized to capture the spatial correlation of specific restaurant to determine the candidate location selection range. The locationGCN is designed to further screen out final suitable location ranges for the specific restaurant type. A case study using restaurant data from Xiamen Island collected from Dianping.com is conducted. The empirical results demonstrate that the algorithm achieves an accuracy of 74.88%, precision of 63.59%, and recall of 77.48%. Results indicate that the proposed approach can provide suitable location recommendations for specific types of restaurants based on existing restaurant distribution information.

Resting-State Changes in Aging and Parkinson’s Disease Are Shaped by Underlying Neurotransmission: A Normative Modeling Study

BackgroundHuman healthy and pathological aging is linked to a steady decline in brain resting-state activity and connectivity measures. The neurophysiological mechanisms that underlie these changes remain poorly understood. MethodsMaking use of recent developments in normative modeling and availability of in vivo maps for various neurochemical systems, we tested in the UK Biobank cohort (n = 25,917) whether and how age- and Parkinson’s disease–related resting-state changes in commonly applied local and global activity and connectivity measures colocalize with underlying neurotransmitter systems. ResultsWe found that the distributions of several major neurotransmitter systems including serotonergic, dopaminergic, noradrenergic, and glutamatergic neurotransmission correlated with age-related changes across functional activity and connectivity measures. Colocalization patterns in Parkinson’s disease deviated from normative aging trajectories for these, as well as for cholinergic and GABAergic (gamma-aminobutyric acidergic) neurotransmission. The deviation from normal colocalization of brain function and GABAA correlated with disease duration. ConclusionsThese findings provide new insights into molecular mechanisms underlying age- and Parkinson’s-related brain functional changes by extending the existing evidence elucidating the vulnerability of specific neurochemical attributes to normal aging and Parkinson’s disease. The results particularly indicate that alongside dopamine and serotonin, increased vulnerability of glutamatergic, cholinergic, and GABAergic systems may also contribute to Parkinson’s disease–related functional alterations. Combining normative modeling and neurotransmitter mapping may aid future research and drug development through deeper understanding of neurophysiological mechanisms that underlie specific clinical conditions.

A Novel and Efficient Spatio-Temporal Co-location Pattern Mining Algorithm

Colocation pattern mining approaches aim at discovering neighboring relationships of distinct spatial features in geographic and temporal space.With big spatio-temporal dataset, there is large number of patterns often discovered. Then it is of importance to discover meaningful and patterns of interest which come as an aid in applications in use for humans and commercial use. To discover interesting patterns, we present in this article a co-location pattern mining algorithmic framework by considering neighbourhood, clustering, hashing, and mining methods. Neighbourhood relationship describes the closeness between the entities. The results of neighbourhood could be varied by varying the distance threshold. These objects exhibiting neighbouring entities are grouped using clustering technique. Clustering is a classical research approach that produces grouping of the entities. A clustering technique has been presented in the paper for spatio-temporal dimension that offers the advantage of faster grouping based on the neighbourhood relationship. Finally, a hash structure is utilized for faster access and retrieval of patterns. The proposed mining algorithm along with the distance and time threshold efficiently discovers the interesting spatio-temporal patterns and validates the patterns. The experiment conducted shows that the proposed co-location algorithm method yields effective and efficient outcome.

Molecular robotic agents that survey molecular landscapes for information retrieval

DNA-based artificial motors have allowed the recapitulation of biological functions and the creation of new features. Here, we present a molecular robotic system that surveys molecular environments and reports spatial information in an autonomous and repeated manner. A group of molecular agents, termed ‘crawlers’, roam around and copy information from DNA-labeled targets, generating records that reflect their trajectories. Based on a mechanism that allows random crawling, we show that our system is capable of counting the number of subunits in example molecular complexes. Our system can also detect multivalent proximities by generating concatenated records from multiple local interactions. We demonstrate this capability by distinguishing colocalization patterns of three proteins inside fixed cells under different conditions. These mechanisms for examining molecular landscapes may serve as a basis towards creating large-scale detailed molecular interaction maps inside the cell with nanoscale resolution.

An experimental framework to assess biomolecular condensates in bacteria

High-resolution imaging of biomolecular condensates in living cells is essential for correlating their properties to those observed through in vitro assays. However, such experiments are limited in bacteria due to resolution limitations. Here we present an experimental framework that probes the formation, reversibility, and dynamics of condensate-forming proteins in Escherichia coli as a means to determine the nature of biomolecular condensates in bacteria. We demonstrate that condensates form after passing a threshold concentration, maintain a soluble fraction, dissolve upon shifts in temperature and concentration, and exhibit dynamics consistent with internal rearrangement and exchange between condensed and soluble fractions. We also discover that an established marker for insoluble protein aggregates, IbpA, has different colocalization patterns with bacterial condensates and aggregates, demonstrating its potential applicability as a reporter to differentiate the two in vivo. Overall, this framework provides a generalizable, accessible, and rigorous set of experiments to probe the nature of biomolecular condensates on the sub-micron scale in bacterial cells.

Fraction-Score: A Generalized Support Measure for Weighted and Maximal Co-Location Pattern Mining

Fraction-Score: A Generalized Support Measure for Weighted and Maximal Co-Location Pattern Mining

A fast spatial high utility co-location pattern mining approach based on branch-and-depth-extension

Mining co-location patterns hidden in spatial data is crucial for spatial association discovery, and it has broad prospects in many applications. High Utility Co-location Pattern Mining (HUCPM) further takes the utility factor of spatial features into consideration, so it is more realistic compared with the traditional co-location pattern mining. However, HUCPM is more difficult, since the Apriori-like pruning technique does not apply. To address this problem, we firstly suggest two novel pruning strategies to trim the pattern search space. Then, a series of optimizing techniques are presented to speed up the pattern utility ratio calculation of each candidate. Based on above techniques, a fast HUCPM algorithm is proposed, which searches for high utility co-locations involved in each pattern branch via a depth-extending manner and equips with a heuristic strategy to enhance the effect of pruning techniques. Moreover, we theoretically prove the completeness and correctness of the proposed algorithm, and discuss its algorithmic complexity. On multiple spatial datasets, we conduct substantial experiments to reveal the superiority of our algorithm in efficiency and scalability, as well as the effectiveness of the proposed technique. Particularly, the proposed algorithm in this paper runs faster than other baselines for several times to several orders of magnitude.

Fuzzy Regional Co-Location Pattern Mining Based on Efficient Density Peak Clustering and Maximal Fuzzy Grid Cliques

Due to the heterogeneity of data distribution in real life and the spatial autocorrelation among spatial instances, traditional spatial co-location pattern mining methods tend to ignore valuable information specific to local regions. To address the limitation, regional co-location pattern mining has been proposed to find patterns that may be hidden within local regions. In this paper, a fuzzy regional co-location pattern mining framework based on efficient density peak clustering and maximal fuzzy grid cliques is presented. By incorporating a grid-splitting method and fuzzy theory, an efficient density peak clustering algorithm is proposed to divide the global area into distinct local regions. Furthermore, we propose a method to materialize the neighbor relationships between instances based on the maximal fuzzy grid cliques, and parallelize the clustering process to improve the algorithm efficiency. Experimental results show that the proposed algorithm can not only reduce the time consumption by about 40%, but also mine meaningful patterns with tighter instance distributions.

Investigation of exosomal tetraspanin profile in sepsis patients as a promising diagnostic biomarker

Introduction Sepsis, a leading cause of mortality globally, has a complex and multifaceted pathophysiology which still requires elucidation. Therefore, this study aimed to analyze and quantify the number of exosomes in sepsis patients from a South African cohort using the ExoView (NanoView Biosciences, Boston, MA) platform. Methods Blood samples were collected from black South African patients attending the local Intensive Care Unit (ICU) hospital. Exosomes were isolated and characterize via TEM and CD63 ELISA kits. ExoView was used to determine particle count, particle size distribution and colocalization of different tetraspanin markers. Results Exosomal levels in sepsis patients were significantly higher compared to the control group (p < 0.05). Sepsis exosomes showed a homogenous size distribution ranging from 55 to 70 nm. Tetraspanin colocalization analysis revealed that sepsis exosomes have significantly higher CD63/CD9, CD63/CD81 and CD63/CD9/CD81 colocalization percentages than the control group. Conclusion This unique tetraspanin colocalization pattern of sepsis exosomes could serve as a potential sepsis biomarker. Further investigations are required to identify sepsis exosomal cargo signatures for further understanding of sepsis pathophysiology in order to develop effective diagnostics and treatments.

An approach based on maximal cliques and multi-density clustering for regional co-location pattern mining

Spatial co-location pattern (SCP) mining aims to mine the implicit relationships between different spatial features. These features often have certain connections and co-occur in close geographical proximity. Regional co-location pattern (RCP) mining is a branch of SCP mining, which is usually used to discover some sets of spatial features that do not often co-occur in large spatial scales but co-occur in local regions. Discovering RCPs is still very challenging, because different RCPs will be obtained under different region partitions. However, existing region division methods still suffer from ignoring the influence of the density of individual feature instances, low recognition rate of regions with low density distribution but containing RCPs, and lack of semantic information in the delineated regions. To this end, first, we propose a novel multi-density clustering method based on maximal cliques (MCs) during the partitioning phase of RCP mining. Second, we design a two-stage mining algorithm based on MCs in the mining phase, which fully exploits the advantages of the MC to improve the mining efficiency, and the algorithm can quickly obtain new mining results when changing the prevalence threshold. Third, regional similarity is defined based on RCPs over regions to merge similar sub-regions. Finally, the proposed method is compared with a state-of-the-art method on both synthetic and real datasets. The experimental results show that the proposed method can not only effectively solve the issues of existing methods to make the divided sub-regions more closely matched with the real spatial distribution, but also quickly obtain new mining results when changing the prevalence threshold.

Novel mechanism of drug resistance triggered by tumor-associated macrophages through Heat Shock Factor-1 activation

Macrophages constitute a major part of tumor microenvironment, and most of existing data demonstrate their ruling role in the development of anti-drug resistance of cancer cell. One of the most powerful protection system is based on heat shock proteins whose synthesis is triggered by activated Heat Shock Factor-1 (HSF1); the inhibition of the HSF1 with CL-43 sensitized A549 lung cancer cells to the anti-cancer effect of etoposide. Notably, analyzing A549 tumor xenografts in mice we observed nest-like pattern of co-localization of A549 cells demonstrating enhanced expression of HSF1 with macrophages, and decided to check whether the above arrangement has a functional value for both cell types. It was found that the incubation of A549 or DLD1 colon cancer cells with either human monocytes or THP1 monocyte-like cells activated HSF1 and increased resistance to etoposide. Importantly, the same effect was shown when primary cultures of colon tumors were incubated with THP1 cells or with human monocytes. To prove that HSF1 is implicated in enhanced resistance caused by monocytic cells, we generated an A549 cell subline devoid of HSF1 which did not respond to incubation with THP1 cells. The pharmacological inhibition of HSF1 with CL-43 also abolished the effect of THP1 cells on primary tumor cells, highlighting a new target of tumor-associated macrophages in a cell proteostasis mechanism.

Urban Economic Structures as Multidimensional Networks: A Complex Systems Framework for Analyzing Economic Development Trajectories

Urban and regional systems often face the difficulty and necessity of structural transitions. These transitions, which can be imposed by external circumstances or initiated by a city itself, include energy transitions, transitions to a circular economy, transitions following a pandemic or natural disaster, or intentional policies meant to “move” an urban economy toward a desired state. However, what does economic structure mean in these cases? Traditional notions of economic structure are ambiguous and simplistic and typically consist of simple distributions, such as number of workers per industry. Yet to better understand, guide, or respond to system transitions, planners must move beyond these nebulous notions toward a theoretically grounded, quantifiable definition of economic structure. A recent trend emerging from the nexus of complexity science and urban science has been to operationalize urban economic structures as networks of interacting economic components. Typically based on colocation patterns of some type of entity, these networks have previously been constructed using economic entities such as products, occupations, or labor skills. Yet different types of entities also exhibit colocation patterns with each other, such as patent technology classes and industries. Here, those cross-entity colocation patterns are used to merge multiple types of entities into a single network representation of urban economies, offering a granularity not possible using a single node type. Occupations, industries, college degrees, and patent technology codes are merged into one multidimensional or multinodal network. As in previous studies, a dense core of highly connected entities emerges in this network. The network locations of individual cities are contrasted, and community detection algorithms are used to identify clusters of highly connected economic entities, showing that the densely connected network core is associated with science, technology, and business-related economic entities. Proximities between individual cities within the network are also measured revealing that many cities that are close to each other in the network are also close to each other in physical space. This framework offers potential applications including the ability to quantify structural change over time in response to a shock or to assess the relative difficulty of future desirable trajectories. More broadly, this framework might be applied to the study of structural change in other complex adaptive systems from human institutions to ecosystems.

Profiles of Wnt pathway gene expression during tooth morphogenesis.

Mouse and human genetic studies indicate key roles of the Wnt10a ligand in odontogenesis. Previous studies have identified effectors and regulators of the Wnt signaling pathway actively expressed during key stages of tooth morphogenesis. However, limitations in multiplexing and spatial resolution hindered a more comprehensive analysis of these signaling molecules. Here, profiling of transcriptomes using fluorescent multiplex in situ hybridization and single-cell RNA-sequencing (scRNA-seq) provide robust insight into the synchronized expression patterns of Wnt10a, Dkk1, and Sost simultaneously during tooth development. First, we identified Wnt10a transcripts restricted to the epithelium at the stage of tooth bud morphogenesis, contrasting that of Sost and Dkk1 localization to the dental mesenchyme. By embryonic day 15.5 (E15.5), a marked shift of Wnt10a expression from dental epithelium to mesenchyme was noted, while Sost and Dkk1 expression remained enriched in the mesenchyme. By postnatal day 0 (P0), co-localization patterns of Wnt10a, Dkk1, and Sost were observed in both terminally differentiating and secreting odontoblasts of molars and incisors. Interestingly, Wnt10a exhibited robust expression in fully differentiated ameloblasts at the developing cusp tip of both molars and incisors, an observation not previously noted in prior studies. At P7 and 14, after the mineralization of dentin and enamel, Wnt10a expression was limited to odontoblasts. Meanwhile, Wnt modulators showed reduced or absent signals in molars. In contrast, strong signals persisted in ameloblasts (for Wnt10a) and odontoblasts (for Wnt10a, Sost, and Dkk1) towards the proximal end of incisors, near the cervical loop. Our scRNA-seq analysis used CellChat to further contextualize Wnt pathway-mediated communication between cells by examining ligand-receptor interactions among different clusters. The co-localization pattern of Wnt10a, Dkk1, and Sost in both terminally differentiating and secreting odontoblasts of molars and incisors potentially signifies the crucial ligand-modulator interaction along the gradient of cytodifferentiation starting from each cusp tip towards the apical region. These data provide cell type-specific insight into the role of Wnt ligands and mediators during epithelial-mesenchymal interactions in odontogenesis.

Spatial co-location patterns between early COVID-19 risk and urban facilities: a case study of Wuhan, China.

COVID-19, being a new type of infectious disease, holds significant implications for scientific prevention and control to understand its spatiotemporal transmission process. This study examines the diverse spatial patterns of COVID-19 within Wuhan by analyzing early case data alongside urban infrastructure information. Through co-location analysis, we assess both local and global spatial risks linked to the epidemic. In addition, we use the Geodetector, identifying facilities displaying unique spatial risk characteristics, revealing factors contributing to heightened risk. Our findings unveil a noticeable spatial distribution of COVID-19 in the city, notably influenced by road networks and functional zones. Higher risk levels are observed in the central city compared to its outskirts. Specific facilities such as parking, residence, ATM, bank, entertainment, and hospital consistently exhibit connections with COVID-19 case sites. Conversely, facilities like subway station, dessert restaurant, and movie theater display a stronger association with case sites as distance increases, hinting at their potential as outbreak focal points. Despite our success in containing the recent COVID-19 outbreak, uncertainties persist regarding its origin and initial spread. Some experts caution that with increased human activity, similar outbreaks might become more frequent. This research provides a comprehensive analytical framework centered on urban facilities, contributing quantitatively to understanding their impact on the spatial risks linked with COVID-19 outbreaks. It enriches our understanding of the interconnectedness between urban facility distribution and transportation flow, affirming and refining the distance decay law governing infectious disease risks. Furthermore, the study offers practical guidance for post-epidemic urban planning, promoting the development of safer urban environments resilient to epidemics. It equips government bodies with a reliable quantitative analysis method for more accurately predicting and assessing infectious disease risks. In conclusion, this study furnishes both theoretical and empirical support for tailoring distinct strategies to prevent and control COVID-19 epidemics.

Mining co-location patterns of manufacturing firms using Q statistic and additive color mixing.

The agglomeration effect significantly influences firms' site selection. Manufacturing firms often exhibit intricate spatial co-location patterns that are indicative of agglomerations due to their reliance on material input and product output across various subdivisions of manufacture. In this study, we present an analytical approach employing the Q statistic and additive color mixing visualization to assess co-location patterns of manufacturing firms. We identified frequent pairs and triplets of manufacturing divisions, mapping them to reveal distinct categories: labor-intensive clusters, upstream/downstream industrial chains, and technology-spillover clusters. These agglomeration categories concentrate in different regions of the city. Policy implications are proposed to promote the upgrade of labor-intensive divisions, enhance the operational efficiency of upstream/downstream industrial chains, and reinforce the spillover effects of technology-intensive divisions.

Identifying Anomalous Regions of Vegetation Change from 2000 to 2020, China: Driving Forces, Probability, and Colocation Patterns

Identifying Anomalous Regions of Vegetation Change from 2000 to 2020, China: Driving Forces, Probability, and Colocation Patterns

Checkpoint Inhibitor PD-1H/VISTA Affects Myeloma Bone Disease By Osteoclast Cytoskeleton Remodeling

Introduction We have recently described the critical role of checkpoint inhibitor PD-1H/VISTA in osteoclast activation and multiple myeloma bone disease. Highly expressed in osteoclast precursors and mature osteoclasts, PD-1H functions as the receptor for osteoclastogenic factor MMP-13 and mediates MMP-13 induced osteoclast activation and myeloma bone disease ( Nat Commun14, 4271 (2023)). Despite the extensive functional study of PD-1H in immune disease models, the PD-1H interacting cellular complex is yet to be defined. Here we report how PD-1H interacts with cytoskeleton proteins contributing to osteoclast remodeling in multiple myeloma bone disease. Methods and Results To identify interacting proteins of PD-1H, PD-1H-His 6 recombinant protein was overexpressed in mouse mononuclear BMCs, and PD-1H-His 6 binding protein complex was pulled down by Ni-NTA agarose beads followed by mass spectrum analysis (Figure A). LIM and SH3 domain protein 1 (LASP1) was one of the most abundant cytoskeleton proteins identified in the PD-1H complex. We therefore first confirmed its binding to PD-1H by two-way co-IP assays (Figure B and C). Co-localization of LASP1 and PD-1H were further confirmed by immunofluorescence assay when co-expressed in HEK293 cells (Figure D). LASP1 is an F-actin-binding protein that stabilizes F-actin bundles and is involved in podosome regulation in macrophages (PLoS ONE 7(4): e35340). It has four domains: LIM, two nebulin-like repeats (N1 and N2) and SH3 domains. While nebulin-like repeats mediate binding to F-actin, SH3 domain is the major protein-protein binding site (PLoS ONE 7(4): e35340). In co-IP assay using PD-1H and domain deleted LASP1, deletion of LIM (△LIM) or either one or both nebulin-like repeats (△N1, △N2 and △N1/2) did not affect the binding to PD-1H. Instead, deletion of SH3 (△SH3) in LASP1 lead to loss of PD-1H binding, demonstrating that PD-1H binds to LASP1 at the SH3 domain (Figure E). Next, the association of LASP1 and PD-1H in the osteoclast cytoskeleton was demonstrated by confocal immunofluorescence microscopy. LASP1 co-localized with F-actin (Figure F) and PD-1H (Figure G) at the sealing ring in mature osteoclasts. Interestingly, although MMP-13 stimulation increased the size of osteoclasts and the sealing ring, MMP-13 did not change the co-localization pattern of LASP1 with PD-1H or F-actin. Conclusions Taken together, this study reveals the novel mechanism of PD-1H regulation of osteoclasts cytoskeleton reorganization, which is critical for osteoclast bone resorption activity. PD-1H directly associates with LASP1 at its C-terminal SH3 domain. Although LASP1 was reported to regulates podosomes function in macrophages, its role in osteoclast cytoskeletons remain largely obscure. The future study will be focused on delineating the role of LASP1 in osteoclast function, and its role in MMP-13/PD-1H signaling regulated myeloma bone disease.

The dynamic recruitment of LAB proteins senses meiotic chromosome axis differentiation in C. elegans.

During meiosis, cohesin and meiosis-specific proteins organize chromatin into an axis-loop architecture, coordinating homologous synapsis, recombination, and ordered chromosome segregation. However, how the meiotic chromosome axis is assembled and differentiated with meiotic progression remains elusive. Here, we explore the dynamic recruitment of two long arms of the bivalent proteins, LAB-1 and LAB-2, in Caenorhabditis elegans. LAB proteins directly interact with the axis core HORMA complexes and weak interactions contribute to their recruitment. LAB proteins phase separate in vitro, and this capacity is promoted by HORMA complexes. During early prophase, synapsis oppositely regulates the axis enrichment of LAB proteins. After the pachytene exit, LAB proteins switch from a reciprocal localization pattern to a colocalization pattern, and the normal dynamic pattern of LAB proteins is altered in meiotic mutants. We propose that LAB recruitment senses axis differentiation, and phase separation of meiotic structures helps subdomain establishment and accurate segregation of the chromosomes.