Multicellular factor analysis of single-cell data for a tissue-centric understanding of disease

Biomedical single-cell atlases describe disease at the cellular level. However, analysis of this data commonly focuses on cell-type-centric pairwise cross-condition comparisons, disregarding the multicellular nature of disease processes. Here, we propose multicellular factor analysis for the unsupervised analysis of samples from cross-condition single-cell atlases and the identification of multicellular programs associated with disease. Our strategy, which repurposes group factor analysis as implemented in multi-omics factor analysis, incorporates the variation of patient samples across cell-types or other tissue-centric features, such as cell compositions or spatial relationships, and enables the joint analysis of multiple patient cohorts, facilitating the integration of atlases. We applied our framework to a collection of acute and chronic human heart failure atlases and described multicellular processes of cardiac remodeling, independent to cellular compositions and their local organization, that were conserved in independent spatial and bulk transcriptomics datasets. In sum, our framework serves as an exploratory tool for unsupervised analysis of cross-condition single-cell atlases and allows for the integration of the measurements of patient cohorts across distinct data modalities.

Calibration methods to fit parameters within complex biological models

Mathematical and computational models of biological systems are increasingly complex, typically comprised of hybrid multi-scale methods such as ordinary differential equations, partial differential equations, agent-based and rule-based models, etc. These mechanistic models concurrently simulate detail at resolutions of whole host, multi-organ, organ, tissue, cellular, molecular, and genomic dynamics. Lacking analytical and numerical methods, solving complex biological models requires iterative parameter sampling-based approaches to establish appropriate ranges of model parameters that capture corresponding experimental datasets. However, these models typically comprise large numbers of parameters and therefore large degrees of freedom. Thus, fitting these models to multiple experimental datasets over time and space presents significant challenges. In this work we undertake the task of reviewing, testing, and advancing calibration practices across models and dataset types to compare methodologies for model calibration. Evaluating the process of calibrating models includes weighing strengths and applicability of each approach as well as standardizing calibration methods. Our work compares the performance of our model agnostic Calibration Protocol (CaliPro) with approximate Bayesian computing (ABC) to highlight strengths, weaknesses, synergies, and differences among these methods. We also present next-generation updates to CaliPro. We explore several model implementations and suggest a decision tree for selecting calibration approaches to match dataset types and modeling constraints.

Advancing Understanding of Non-Small Cell Lung Cancer with Multiplexed Antibody-Based Spatial Imaging Technologies

Non-small cell lung cancer (NSCLC) remains a cause of significant morbidity and mortality, despite significant advances made in its treatment using immune checkpoint inhibitors (ICIs) over the last decade; while a minority experience prolonged responses with ICIs, benefit is limited for most patients. The development of multiplexed antibody-based (MAB) spatial tissue imaging technologies has revolutionised analysis of the tumour microenvironment (TME), enabling identification of a wide range of cell types and subtypes, and analysis of the spatial relationships and interactions between them. Such study has the potential to translate into a greater understanding of treatment susceptibility and resistance, factors influencing prognosis and recurrence risk, and identification of novel therapeutic approaches and rational treatment combinations to improve patient outcomes in the clinic. Herein we review studies that have leveraged MAB technologies to deliver novel insights into the TME of NSCLC.

Design and Behavior of Lightweight Flexible Structure with Spatial Pattern Reducing Contact Surface Fraction

Flexible structures are increasingly important in biomedical applications, where they can be used to achieve adaptable designs. This paper presents a study of the design and behavior of 3D-printed lightweight flexible structures. In this work, we focus on the design principles and numerical modelling of spatial patterns, as well as their mechanical properties and behavior under various loads. Contact surface fraction was determined as the ratio of the surface area of the printed pattern to the surface area of the entire curved surface. The objective of this work is to design a spatial pattern reducing contact surface fraction and develop a non-linear numerical model evaluating the structure's stiffness; in addition, we aimed to identify the best design pattern with respect to its stiffness:mass ratio. The experimental verification of the numerical model is performed on 3D-printed prototypes prepared using the Selective Laser Sintering (SLS) method and made of Nylon-Polyamide 12. The obtained results provide insights into designing and optimizing lightweight external biomedical applications such as prostheses, orthoses, helmets, or adaptive cushions.

Enhancing Handwriting Performance of Children with Developmental Coordination Disorder (DCD) Using Computerized Visual Feedback

Recent studies have analyzed the writing metrics of children with developmental coordination disorder (DCD) using computerized systems. To date, the use of computerized visual feedback to improve handwriting has not been investigated. This study aimed to examine the effects of computerized visual feedback on handwriting performance in time, spatial orientation, and pressure indices for children with DCD. Twenty-seven children aged 7 to 12 years with DCD assessed by the Movement Assessment Battery for Children and the Developmental Coordination Disorder Questionnaire received one weekly intervention session for 8 weeks, during which they twice copied an excerpt onto a tablet. Once, they received visual feedback where the writing color corresponded to the degree of pressure on the writing surface, and once they received no visual feedback. The two conditions were counterbalanced throughout the sessions. Pre-intervention sessions were compared with post-intervention sessions and with new texts for time, spatial orientation, and pressure measures. The findings revealed significantly decreased total and mean letter writing, in-air, and writing time and increased capacity in the visual feedback condition. In the spatial variables, a significant decrease in letter height variance was found. Pressure increased significantly throughout the intervention with visual feedback, whereas it decreased post-test in the writing task in both conditions and was maintained in the new text. Visual feedback intervention can increase the kinesthetic-haptic feedback required to regulate pressure during writing, promoting more efficient feedforward processes and improving output quality and capacity. The training effectiveness was transferable, and the intervention accessibility could increase student autonomy.

LYVE-1+ macrophages form a collaborative CCR5-dependent perivascular niche that influences chemotherapy responses in murine breast cancer

Tumor-associated macrophages (TAMs) are a heterogeneous population of cells that facilitate cancer progression. However, our knowledge of the niches of individual TAM subsets and their development and function remain incomplete. Here, we describe a population of lymphatic vessel endothelial hyaluronan receptor-1 (LYVE-1)-expressing TAMs, which form coordinated multi-cellular "nest" structures that are heterogeneously distributed proximal to vasculature in tumors of a spontaneous murine model of breast cancer. We demonstrate that LYVE-1+ TAMs develop in response to IL-6, which induces their expression of the immune-suppressive enzyme heme oxygenase-1 and promotes a CCR5-dependent signaling axis, which guides their nest formation. Blocking the development of LYVE-1+ TAMs or their nest structures, using gene-targeted mice, results in an increase in CD8+ T cell recruitment to the tumor and enhanced response to chemotherapy. This study highlights an unappreciated collaboration of a TAM subset to form a coordinated niche linked to immune exclusion and resistance to anti-cancer therapy.

Spatial distribution and associated factors of poor tetanus toxoid immunization among pregnant women in Ethiopia: spatial and multilevel analysis

Background

Neonatal mortality from tetanus can be reduced by 94% when pregnant women receive at least two doses of tetanus toxoid. In Ethiopia, immunization programs are suboptimal despite their importance. Therefore, the aim of this study was to examine the geographic distribution and associated factors of poor tetanus toxoid (TT) immunization among pregnant women in Ethiopia.

Methods

Secondary data analysis was used using the 2016 Ethiopian Demographic and Health Survey (EDHS). ArcGIS version 10.8 statistical software was used to explore the spatial distribution of poor TT immunization and SaTScan version 9.6 software was used to identify significant hotspot areas of poor TT immunization. For associated factors, a multilevel binary logistic regression model was fitted using STATA version 14 software. In the multivariable multilevel analysis, adjusted OR (AOR) with 95% CI was reported to reveal significantly associated factors of poor TT immunization.

Result

In Ethiopia, the spatial distribution of poor tetanus toxoid immunization was clustered with Global Moran's I = 0.59 at p-value of <0.0001. The highest poor TT immunization clusters were observed in the East and South Tigray, the central part of Amhara, West Afar, East Somali, and West Gambella. Pregnant women with no Antenatal care (ANC) visits [Adjusted Odds Ratio (aOR) = 10.46, 95% CI: (8.82, 12.41))], pregnant women with 1-3 ANC visits [aOR = 1.51, 95% CI: (1.31, 1.73)], media exposure [aOR = 1.45, 95% CI: (1.26, 1.67)], poor wealth index [aOR = 1.22; 95% CI: (1.03, 1.45)], middle wealth index [aOR = 1.23; 95% CI: (1.03, 1.47)], family planning use [aOR = 1.28; 95% CI: (1.11, 1.57)] and community level education [aOR = 1.43, 95% CI: (1.14, 1.80)] were significantly associated with poor tetanus toxoid immunization.

Conclusion

Poor tetanus toxoid immunization among pregnant women varies in Ethiopia. It was highest in East and South Tigray, the central part of Amhara, West Afar, East Somali, and West Gambella. Therefore, public health programs should design targeted interventions in identified hot spots to improve tetanus toxoid immunization. Health programmers should be promoting optimal ANC visits, women's education, and family planning use.

Bull Shark (Carcharhinus leucas) Occurrence along Beaches of South-Eastern Australia: Understanding Where, When and Why

Unprovoked shark bites have increased over the last three decades, yet they are still relatively rare. Bull sharks are globally distributed throughout rivers, estuaries, nearshore areas and continental shelf waters, and are capable of making long distance movements between tropical and temperate regions. As this species is implicated in shark bites throughout their range, knowledge of the environmental drivers of bull shark movements are important for better predicting the likelihood of their occurrence at ocean beaches and potentially assist in reducing shark bites. Using the largest dataset of acoustically tagged bull sharks in the world, we examined the spatial ecology of 233 juvenile and large (including sub-adult and adult) bull sharks acoustically tagged and monitored over a 5.5-year period (2017-2023) using an array of real-time acoustic listening stations off 21 beaches along the coast of New South Wales, Australia. Bull sharks were detected more in coastal areas of northern NSW (<32° S) but they travelled southwards during the austral summer and autumn. Juveniles were not detected on shark listening stations until they reached 157 cm and stayed north of 31.98° S (Old Bar). Intra-specific diel patterns of occurrence were observed, with juveniles exhibiting higher nearshore presence between 20:00 and 03:00, whilst the presence of large sharks was greatest from midday through to 04:00. The results of generalised additive models revealed that large sharks were more often found when water temperatures were higher than 20 °C, after >45 mm of rain and when swell heights were between 1.8 and 2.8 m. Understanding the influence that environmental variables have on the occurrence of bull sharks in the coastal areas of NSW will facilitate better education and could drive shark smart behaviour amongst coastal water users.

Improving Surveillance of Human Tick-Borne Disease Risks: Spatial Analysis Using Multimodal Databases

BACKGROUND

The extent of tick-borne disease (TBD) risk in the United States is generally unknown. Active surveillance using entomological measures, such as presence and density of infected nymphal Ixodes scapularis ticks, have served as indicators for assessing human risk, but results have been inconsistent and passive surveillance via public health systems suggests TBDs are underreported.

OBJECTIVE

Research using various data sources and collection methods (eg, Google Trends, apps, and tick bite encounters [TBEs] reports) has shown promise for assessing human TBD risk. In that vein, and engaging a One Health perspective, this study used multimodal databases, geographically overlaying patient survey data on TBEs and concomitant reports of TBDs with data drawn from other sources, such as canine serological reports, to glean insights and to determine and assess the use of various indicators as proxies for human TBD risk.

METHODS

This study used a mixed methods research strategy, relying on triangulation techniques and drawing on multiple data sources to provide insights into various aspects of human disease risk from TBEs and TBDs in the United States. A web-based survey was conducted over a 15-month period beginning in December 2020 to collect data on TBEs. To maximize the value of the covariate data, related analyses included TBE reports that occurred in the United States between January 1, 2000, and March 31, 2021. TBEs among patients diagnosed with Lyme disease were analyzed at the county level and compared to I scapularis and I pacificus tick presence, human cases identified by the Centers for Disease Control and Prevention (CDC), and canine serological data. Spatial analyses employed multilayer thematic mapping and other techniques.

RESULTS

After cleaning, survey results showed a total of 249 (75.7%) TBEs spread across 148 respondents (61.9% of all respondents, 81.7% of TBE-positive respondents); 144 (4.7%) counties in 30 states (60%) remained eligible for analysis, with an average of 1.68 (SD 1.00) and median of 1 (IQR 1) TBEs per respondent. Analysis revealed significant spatial matching at the county level among patient survey reports of TBEs and disease risk indicators from the CDC and other official sources. Thematic mapping results included one-for-one county-level matching of reported TBEs with at least 1 designated source of human disease risk (ie, positive canine serological tests, CDC-reported Lyme disease, or known tick presence).

CONCLUSIONS

Use of triangulation methods to integrate patient data on TBE recall with established canine serological reports, tick presence, and official human TBD information offers more granular, county-level information regarding TBD risk to inform clinicians and public health officials. Such data may supplement public health sources to offer improved surveillance and provide bases for developing robust proxies for TBD risk among humans.

Exploratory study of macrophage polarization and spatial distribution in colorectal cancer liver metastasis: a pilot study

Background

The liver is the most typical site of metastatic disease for patients with colorectal cancer (CRC), and up to half the patients with CRC will develop colorectal liver metastasis (CLM). Studying the tumor microenvironment, particularly macrophages and their spatial distribution, can give us critical insight into treatment.

Methods

Ten CLMs (five treatment-naïve and five post-neoadjuvant chemotherapy) were stained with multiplex immunofluorescence panels against cytokeratins, CD68, Arg1, CD206, CD86, CD163, PD-L1, and MRP8-14. Densities of cell phenotypes and their spatial distribution in the tumor center and the normal liver-tumor interface were correlated with clinicopathological variables.

Results

M2 macrophages were the predominant subtype in both the tumor center and the periphery, with a relatively higher density at the periphery. The larger tumors, more than 3.9 cm, were associated with higher densities of total CD68+ macrophages and CD68+CD163+ CD206neg and CD68+CD206+ CD163neg M2 macrophage subtypes. Total macrophages in the tumor periphery demonstrated significantly greater proximity to malignant cells than did those in the tumor center (p=0.0371). The presence of higher than median CD68+MRP8-14+CD86neg M1 macrophages in the tumor center was associated with poor overall survival (median 2.34 years) compared to cases with lower than median M1 macrophages at the tumor center (median 6.41 years) in univariate analysis.

Conclusion

The dominant polarization of the M2 macrophage subtype could drive new therapeutic approaches in CLM patients.

An Application of Spatio-temporal Modeling to Finite Population Abundance Prediction

Spatio-temporal models can be used to analyze data collected at various spatial locations throughout multiple time points. However, even with a finite number of spatial locations, there may be a lack of resources to collect data from every spatial location at every time point. We develop a spatio-temporal finite-population block kriging (ST-FPBK) method to predict a quantity of interest, such as a mean or total, across a finite number of spatial locations. This ST-FPBK predictor incorporates an appropriate variance reduction for sampling from a finite population. Through an application to moose surveys in the east-central region of Alaska, we show that the predictor has a substantially smaller standard error compared to a predictor from the purely spatial model that is currently used to analyze moose surveys in the region. We also show how the model can be used to forecast a prediction for abundance in a time point for which spatial locations have not yet been surveyed. A separate simulation study shows that the spatio-temporal predictor is unbiased and that prediction intervals from the ST-FPBK predictor attain appropriate coverage. For ecological monitoring surveys completed with some regularity through time, use of ST-FPBK could improve precision. We also give an R package that ecologists and resource managers could use to incorporate data from past surveys in predicting a quantity from a current survey.

Social and spatial inequalities in healthcare use among people living with dementia in England (2002-2016)

OBJECTIVES

Healthcare services for people living with dementia (PLWD) are stretched, and government promises of increased funding remain undelivered. With the UK dementia population to surpass 1 million by 2024, and dementia care costs predicted to almost treble by 2040, it is essential we understand differences in healthcare use among PLWD. This study aimed to explore social and spatial variations in healthcare use among people diagnosed with dementia (2002-2016).

METHODS

Data were derived from Electronic Health Records of Clinical Practice Research Datalink GP patients in England (n = 142,302). To standardise healthcare contacts, rates of healthcare contacts per year were calculated for three primary (GP observations and medications) and three secondary healthcare types [Accident & Emergency (A&E) attendances and, emergency and elective hospital admissions]. Fully-adjusted generalised linear regression models were used to identify healthcare use variation by social and spatial groups. Twelve models were generated, one for each healthcare type in early- and late-onset populations separately.

RESULTS

This study highlights numerous social and spatial variations in healthcare use among PLWD. Among PLWD, several groups tended to have healthcare service use more closely associated with negative outcomes, including a greater likelihood of A&E attendances and emergency and elective hospital admissions. These groups include: men, people from White ethnicity groups and people from more deprived and rural areas.

CONCLUSIONS

Systemic and social measures are needed to reduce variations in healthcare use inequalities in PWLD. These include greater healthcare continuity, health checks and medicines reviews, culturally appropriate services, better and more accessible treatment and improved infrastructure.

Neighborhood Places for Preschool Children's Physical Activity: A Mixed-Methods Study Using Global Positioning System, Geographic Information Systems, and Accelerometry Data

BACKGROUND

This study adds to the current literature by using a novel device-based method to explore where preschool children are physically active outside of home and childcare settings. This study combined accelerometry with geospatial data to explore the influence of the environment on preschool children's physical activity by objectively identifying the locations where preschool children engage in moderate to vigorous physical activity (MVPA) within and outside of their neighborhood.

METHODS

Accelerometry, Global Positioning System, and Geographic Information Systems data from 168 preschool children aged 2-5 years were processed in ArcGIS Pro to identify locations (per 25 × 25-m fishnet cell) with high MVPA counts. Locations with high MVPA counts were defined as those with the top 20% of MVPA counts per fishnet cell. The land use for high MVPA count location was determined for 3 domains: <500 m from home, 500 to 1600 m from home, and >1600 m from home.

RESULTS

Locations with high MVPA counts <500 m from home were playgrounds (66.6%), schools (16.7%), and parks (16.7%). Locations with high MVPA counts 500 to 1600 m from home included playgrounds (33.3%), nonhome residential (29.6%), childcare centers (11.1%), and parks (3.7%). Locations with high MVPA counts >1600 m from home included nonhome residential (54.7%), sports and recreation centers (11.1%), playgrounds (6.8%), and parks (5.3%).

CONCLUSIONS

Our findings highlight that local parks and playgrounds provide physical activity opportunities for preschool children, yet beyond the local neighborhood, the homes of others are important locations for preschool children to accumulate MVPA. These findings can be used to inform the design of current and future neighborhood places to better accommodate preschool children's MVPA.

Spatial patterns of white matter hyperintensities: a systematic review

Background

White matter hyperintensities are an important marker of cerebral small vessel disease. This disease burden is commonly described as hyperintense areas in the cerebral white matter, as seen on T2-weighted fluid attenuated inversion recovery magnetic resonance imaging data. Studies have demonstrated associations with various cognitive impairments, neurological diseases, and neuropathologies, as well as clinical and risk factors, such as age, sex, and hypertension. Due to their heterogeneous appearance in location and size, studies have started to investigate spatial distributions and patterns, beyond summarizing this cerebrovascular disease burden in a single metric-its volume. Here, we review the evidence of association of white matter hyperintensity spatial patterns with its risk factors and clinical diagnoses.

Design/methods

We performed a systematic review in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) Statement. We used the standards for reporting vascular changes on neuroimaging criteria to construct a search string for literature search on PubMed. Studies written in English from the earliest records available until January 31st, 2023, were eligible for inclusion if they reported on spatial patterns of white matter hyperintensities of presumed vascular origin.

Results

A total of 380 studies were identified by the initial literature search, of which 41 studies satisfied the inclusion criteria. These studies included cohorts based on mild cognitive impairment (15/41), Alzheimer's disease (14/41), Dementia (5/41), Parkinson's disease (3/41), and subjective cognitive decline (2/41). Additionally, 6 of 41 studies investigated cognitively normal, older cohorts, two of which were population-based, or other clinical findings such as acute ischemic stroke or reduced cardiac output. Cohorts ranged from 32 to 882 patients/participants [median cohort size 191.5 and 51.6% female (range: 17.9-81.3%)]. The studies included in this review have identified spatial heterogeneity of WMHs with various impairments, diseases, and pathologies as well as with sex and (cerebro)vascular risk factors.

Conclusion

The results show that studying white matter hyperintensities on a more granular level might give a deeper understanding of the underlying neuropathology and their effects. This motivates further studies examining the spatial patterns of white matter hyperintensities.

Spatial dimensions of water quality value in New England river networks

Households' willingness to pay (WTP) for water quality improvements-representing their economic value-depends on where improvements occur. Households often hold higher values for improvements close to their homes or iconic areas. Are there other areas where improvements might hold high value to individual households, do effects on WTP vary by type of improvement, and can these areas be identified even if they are not anticipated by researchers? To answer these questions, we integrated a water quality model and map-based, interactive choice experiment to estimate households' WTP for water quality improvements throughout a river network covering six New England states. The choice experiment was implemented using a push-to-web survey over a sample of New England households. Voting scenarios used to elicit WTP included interactive geographic information system (GIS) maps that illustrated three water quality measures at various zoom levels across the study domain. We captured data on how respondents maneuvered through these maps prior to answering the value-eliciting questions. Results show that WTP was influenced by regionwide quality improvements and improvements surrounding each respondent's home, as anticipated, but also by improvements in individualized locations identifiable via each respondent's map interactions. These spatial WTP variations only appear for low-quality rivers and are focused around particular areas of New England. The study shows that dynamic map interactions can convey salient information for WTP estimation and that predicting spatial WTP heterogeneity based primarily on home or iconic locations, as typically done, may overlook areas where water quality has high value.

A Synthetic Review of Various Dimensions of Non-Destructive Plant Stress Phenotyping

Non-destructive plant stress phenotyping begins with traditional one-dimensional (1D) spectroscopy, followed by two-dimensional (2D) imaging, three-dimensional (3D) or even temporal-three-dimensional (T-3D), spectral-three-dimensional (S-3D), and temporal-spectral-three-dimensional (TS-3D) phenotyping, all of which are aimed at observing subtle changes in plants under stress. However, a comprehensive review that covers all these dimensional types of phenotyping, ordered in a spatial arrangement from 1D to 3D, as well as temporal and spectral dimensions, is lacking. In this review, we look back to the development of data-acquiring techniques for various dimensions of plant stress phenotyping (1D spectroscopy, 2D imaging, 3D phenotyping), as well as their corresponding data-analyzing pipelines (mathematical analysis, machine learning, or deep learning), and look forward to the trends and challenges of high-performance multi-dimension (integrated spatial, temporal, and spectral) phenotyping demands. We hope this article can serve as a reference for implementing various dimensions of non-destructive plant stress phenotyping.

Tissue and Time Optimization for Real-Time Detection of Apple Mosaic Virus and Apple Necrotic Mosaic Virus Associated with Mosaic Disease of Apple (Malus domestica)

Besides apple mosaic virus (ApMV), apple necrotic mosaic virus (ApNMV) has also been found to be associated with apple mosaic disease. Both viruses are unevenly distributed throughout the plant and their titer decreases variably with high temperatures, hence requiring proper tissue and time for early and real-time detection within plants. The present study was carried out to understand the distribution and titer of ApMV and ApNMV in apple trees from different plant parts (spatial) during different seasons (temporal) for the optimization of tissue and time for their timely detection. The Reverse Transcription-Polymerase Chain Reaction (RT-PCR) and Reverse Transcription-quantitative Polymerase Chain Reaction (RT-qPCR) was carried out to detect and quantify both viruses in the various plant parts of apple trees during different seasons. Depending on the availability of tissue, both ApMV and ApNMV were detected in all the plant parts during the spring season using RT-PCR. During the summer, both viruses were detected only in seeds and fruits, whereas they were detected in leaves and pedicel during the autumn season. The RT-qPCR results showed that during the spring, the ApMV and ApNMV expression was higher in leaves, whereas in the summer and autumn, the titer was mostly detected in seeds and leaves, respectively. The leaves in the spring and autumn seasons and the seeds in the summer season can be used as detection tissues through RT-PCR for early and rapid detection of ApMV and ApNMV. This study was validated on 7 cultivars of apples infected with both viruses. This will help to accurately sample and index the planting material well ahead of time, which will aid in the production of virus-free, quality planting material.

Spatial cancer systems biology resolves heterotypic interactions and identifies disruption of spatial hierarchy as a pathological driver event

Spatially annotated single-cell datasets provide unprecedented opportunities to dissect cell-cell communication in development and disease. Heterotypic signaling includes interactions between different cell types and is well established in tissue development and spatial organization. Epithelial organization requires several different programs that are tightly regulated. Planar cell polarity (PCP) is the organization of epithelial cells along the planar axis, orthogonal to the apical-basal axis. Here, we investigate PCP factors and explore the implications of developmental regulators as malignant drivers. Utilizing cancer systems biology analysis, we derive a gene expression network for WNT-ligands (WNT) and their cognate frizzled (FZD) receptors in skin cutaneous melanoma. The profiles supported by unsupervised clustering of multiple-sequence alignments identify ligand-independent signaling and implications for metastatic progression based on the underpinning developmental spatial program. Omics studies and spatial biology connect developmental programs with oncological events and explain key spatial features of metastatic aggressiveness. Dysregulation of prominent PCP factors such as specific representatives of the WNT and FZD families in malignant melanoma recapitulates the development program of normal melanocytes but in an uncontrolled and disorganized fashion.

Mapping the Protein Kinome: Current Strategy and Future Direction

The kinome includes over 500 different protein kinases, which form an integrated kinase network that regulates cellular phosphorylation signals. The kinome plays a central role in almost every cellular process and has strong linkages with many diseases. Thus, the evaluation of the cellular kinome in the physiological environment is essential to understand biological processes, disease development, and to target therapy. Currently, a number of strategies for kinome analysis have been developed, which are based on monitoring the phosphorylation of kinases or substrates. They have enabled researchers to tackle increasingly complex biological problems and pathological processes, and have promoted the development of kinase inhibitors. Additionally, with the increasing interest in how kinases participate in biological processes at spatial scales, it has become urgent to develop tools to estimate spatial kinome activity. With multidisciplinary efforts, a growing number of novel approaches have the potential to be applied to spatial kinome analysis. In this paper, we review the widely used methods used for kinome analysis and the challenges encountered in their applications. Meanwhile, potential approaches that may be of benefit to spatial kinome study are explored.

Habitat complexity affects microbial growth in fractal maze

The great variety of earth's microorganisms and their functions are attributed to the heterogeneity of their habitats, but our understanding of the impact of this heterogeneity on microbes is limited at the microscale. In this study, we tested how a gradient of spatial habitat complexity in the form of fractal mazes influenced the growth, substrate degradation, and interactions of the bacterial strain Pseudomonas putida and the fungal strain Coprinopsis cinerea. These strains responded in opposite ways: complex habitats strongly reduced fungal growth but, in contrast, increased the abundance of bacteria. Fungal hyphae did not reach far into the mazes and forced bacteria to grow in deeper regions. Bacterial substrate degradation strongly increased with habitat complexity, even more than bacterial biomass, up to an optimal depth, while the most remote parts of the mazes showed both decreased biomass and substrate degradation. These results suggest an increase in enzymatic activity in confined spaces, where areas may experience enhanced microbial activity and resource use efficiency. Very remote spaces showing a slower turnover of substrates illustrate a mechanism which may contribute to the long-term storage of organic matter in soils. We demonstrate here that the sole effect of spatial microstructures affects microbial growth and substrate degradation, leading to differences in local microscale spatial availability. These differences might add up to considerable changes in nutrient cycling at the macroscale, such as contributing to soil organic carbon storage.

Spatial structure and benefits to hosts allow plasmids with and without post-segregational killing systems to coexist

To persist, a plasmid relies on being passed on to a daughter cell, but this does not always occur. Plasmids with post-segregational killing (PSK) systems kill a daughter cell if the plasmid has not been passed on. By killing the host, it also kills competing plasmids in the same host, something competing plasmids without a similar system cannot do. Accordingly, plasmids with PSK systems can displace other plasmids. In nature, plasmids with and without PSK systems coexist and prior theory has suggested this is expected to be very rare or unstable, such that one or the other type of plasmid eventually takes over. Here, we show that if there is spatial structure and plasmids confer benefits to hosts, coexistence of plasmids occurs broadly. Often plasmids confer benefits (even ones with a PSK system) and bacteria are often spatially structured. So, our results may be generally applicable.

Spatiotemporal Variations and Determinants of Under-Five Stunting in Ethiopia

BACKGROUND

Stunting has been a major concern in sub-Saharan Africa. However, little evidence exists on the spatiotemporal variations in under-five stunting within a national context.

OBJECTIVE

This paper examines the spatiotemporal variations in under-five stunting and determinants using data from the Ethiopia Demographic and Health Surveys (2000-2016).

METHODS

Spatial autocorrelation and multilevel logistic regression models were used to conduct the analyses.

RESULTS

The stunting prevalence has decreased from 51% to 37%, while the prevalence of severe stunting has decreased by more than half (from 28% to 12%). Wide regional variations in stunting have been consistently observed over the years, which exhibited a higher level of stunting in Tigray (48%), Afar (42%), and Amhara (42%). The results show considerable local and regional variations in under-five stunting levels with diverse patterns of improvements in regional stunting levels over time. Stunting levels were associated with child-level factors such as the sex of a child, birth size, age of a child, birth order, preceding birth interval, and place of birth. Maternal educational attainment, nutritional status, household wealth, toilet facility type, and place of residence were linked to under-five stunting. The regional-level infant mortality rate was associated with under-five stunting.

CONCLUSIONS

Specially tailored policies and interventions should be devised to address persistent spatial inequalities in stunting by focusing on higher risk populations.

Monitoring Spatiotemporal Vegetation Response to Drought Using Remote Sensing Data

Environmental factors such as drought significantly influence vegetation growth, coverage, and ecosystem functions. Hence, monitoring the spatiotemporal vegetation responses to drought in a high temporal and adequate spatial resolution is essential, mainly at the local scale. This study was conducted to investigate the aspatial and spatial relationships between vegetation growth status and drought in the southeastern South Dakota, USA. For this purpose, Landsat 8 OLI images from the months of April through September for the years 2016-2021, with cloud cover of less than 10%, were acquired. After that, radiometric calibration and atmospheric correction were performed on all of the images. Some spectral indices were calculated using the Band Math toolbox in ENVI 5.3 (Environment for Visualizing Images v. 5.3). In the present study, the extracted spectral indices from Landsat 8 OLI images were the Normalized Difference Vegetation Index (NDVI) and the Normalized Multiband Drought Index (NMDI). The results showed that the NDVI values for the month of July in different years were at maximum value at mostly pixels. Based on the statistical criteria, the best regression models for explaining the relationship between NDVI and NMDI_Soil were polynomial order 2 for 2016 to 2019 and linear for 2021. The developed regression models accounted for 96.7, 95.7, 96.2, 88.4, and 32.2% of vegetation changes for 2016, 2017, 2018, 2019, and 2021, respectively. However, there was no defined trend between NDVI and NMDI_Soil observed in 2020. In addition, pixel-by-pixel analyses showed that drought significantly impacted vegetation coverage, and 69.6% of the pixels were negatively correlated with the NDVI. It was concluded that the Landsat satellite images have potential information for studying the relationships between vegetation growth status and drought, which is the primary step in site-specific management.

The human protein atlas-Integrated omics for single cell mapping of the human proteome

Studying the spatial distribution of proteins provides the basis for understanding the biology, molecular repertoire, and architecture of every human cell. The Human Protein Atlas (HPA) has grown into one of the world's largest biological databases, and in the most recent version, a major update of the structure of the database was performed. The data has now been organized into 10 different comprehensive sections, each summarizing different aspects of the human proteome and the protein-coding genes. In particular, large datasets with information on the single cell type level have been integrated, refining the tissue and cell type specificity and detailing the expression in cell states with an increased resolution. The multi-modal data constitute an important resource for both basic and translational science, and hold promise for integration with novel emerging technologies at the protein and RNA level.

Spatial transcriptomics add a new dimension to our understanding of the gut

The profound complexity of the intestinal mucosa demands a spatial approach to the study of gut transcriptomics. Although single-cell RNA sequencing has revolutionized our ability to survey the diverse cell types of the intestine, knowledge of cell type alone cannot fully describe the cells that make up the intestinal mucosa. During homeostasis and disease, dramatic gradients of oxygen, nutrients, extracellular matrix proteins, morphogens, and microbiota collectively dictate intestinal cell state, and only spatial techniques can articulate differences in cellular transcriptomes at this level. Spatial transcriptomic techniques assign transcriptomic data to precise regions in a tissue of interest. In recent years, these protocols have become increasingly accessible, and their application in the intestinal mucosa has exploded in popularity. In the gut, spatial transcriptomics typically involve the application of tissue sections to spatially barcoded RNA sequencing or laser capture microdissection followed by RNA sequencing. In combination with single-cell RNA sequencing, these spatial sequencing approaches allow for the construction of spatial transcriptional maps at pseudosingle-cell resolution. In this review, we describe the spatial transcriptomic technologies recently applied in the gut and the previously unattainable discoveries that they have produced.