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Results for “spatial”

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At least 19 recordsLinked to original sources

PubMed1991

Spatial interactions in apparent contrast: inhibitory effects among grating patterns of different spatial frequencies, spatial positions and orientations.

Suppression of the apparent contrast of a small 4 cycle wide suprathreshold sine wave grating patch by a high contrast sine wave grating surround pattern was studied as a function of the spatial frequency, orientation and spatial extent of the surround. The data are consistent with the existence of a complex network of inhibitory interconnections among mechanisms that mediate contrast perception. These connections must extend over spatial distances equivalent to more than 12 cycles of the central grating patch.

Adaptation, Physiological↗

PubMed1979

Stimulus-response spatial contiguity vs. S-R spatial discontiguity in auditory spatial tasks. I. Acquisition by normal dogs.

Twelve dogs were trained in spatial tasks with auditory location cues. One group, tested on delayed response with stimuli and responses spatially contiguous, solved the task at once, whereas the other group, trained with actual stimuli and responses spatially discontiguous, attained criterion after errors. The differences in behavior of these groups suggest that two learning strategies may be involved. In the first group - approaching a specific (directly determined by auditory targeting reflex) feeder by an unspecific directional response. In the other group - approaching a non-specific feeder by a specific directional response, established in the differentiation learning.

Animals↗

PubMed2026

jsPCA: fast, scalable, and interpretable identification of spatial domains and variable genes across multi-slice and multi-sample spatial transcriptomics data.

MOTIVATION: Spatial transcriptomics technologies record genome-wide measurements of gene expression with high spatial resolution. These technologies generate large and high-dimensional datasets requiring efficient automated methods for their analysis. We introduce joint spatial PCA (jsPCA), a novel, fast, scalable and interpretable method for the automatic identification of spatial domains and variable genes in multi-slice and multi-sample spatial transcriptomics data. RESULTS: jsPCA relies on a simple mathematical formulation of a spatial covariance defined as the product of the gene expression covariance with the spatial autocorrelation. The principal components of this spatial covariance yield a biologically meaningful low-dimensional representation. From this representation, spatial domains are derived by simple clustering and spatially variable genes are identified directly from the principal component coefficients. A joint representation of multiple slices and samples without spatial alignment is obtained by computing common principal components via joint diagonalization. By leveraging data sparsity and non-convex manifold optimization, jsPCA leads to computing time in the order of seconds to minutes, substantially outperforming state-of-the-art approaches. We benchmarked jsPCA against 10 state-of-the-art methods on two reference databases. Our approach demonstrated excellent performance, comparable or better than state-of-the-art methods, while being much faster, interpretable, and scalable to very large datasets.

Journal Article↗

PubMed2026

Dissecting spatial patterning and signaling with directional diffusion in spatial multi-omics.

Spatial multi-omics sequencing enables the simultaneous profiling of transcriptomics, proteomics, and epigenomics at a spatial resolution, offering insights into complex tissue organization and molecular regulation. However, the effective integration of multiple omics modalities in a spatial context remains a major challenge. Here, we present SpaDDM, a spatial multi-omics integration framework based on directional diffusion models (DDMs), which supports spatial pattern identification, cross-omics alignment, and inter-and intracellular signaling flow analysis. SpaDDM employs DDM-based graph networks to learn omics-specific representations by jointly incorporating spatial coordinates and molecular measurements within each modality, followed by an attention mechanism to align features across modalities. We benchmarked SpaDDM on diverse spatial multi-omics datasets, including transcriptomics-epigenomics and transcriptomics-proteomics combinations across multiple tissues and species. SpaDDM consistently outperformed existing methods by more accurately deciphering spatial tissue patterns and effectively reducing the boundary noise between spatial regions. Moreover, the learned low-dimensional coembedded representations of individual cells serve as integral mediators for inferring the signaling flows that underlie spatial patterning. Finally, we demonstrated that SpaDDM alignment of complementary information across multi-omics layers facilitates cross-omics translation and significantly improves the prediction of cell state alignments.

Multiomics↗

PubMed2026

DPAS-Graph: adaptive spatial-feature relation learning for spatial RNA-to-protein prediction and virtual protein profiling.

Paired spatial multi-omics provides a supervised basis for learning RNA-protein correspondence in situ, but predicting protein abundance from spatial transcriptomic data alone remains challenging across tissue contexts and protein panels. Here, we present DPAS-Graph, an adaptive relation-learning framework for spatial RNA-to-protein prediction. Rather than directly merging spatial proximity and transcriptomic similarity as fixed graph priors, DPAS-Graph represents them as two relation channels on a shared edge support and updates their contributions during representation learning for protein prediction. Its Niche-Coupled Field Encoder combines layer-wise edge-relation modeling, intra-branch relation refinement, and cross-branch residual correction to learn spot representations for protein abundance prediction. In a leave-one-dataset-out benchmark across seven paired spatial multi-omics datasets, DPAS-Graph achieved lower aggregate prediction errors and improved spot-level agreement of protein expression profiles, with gains mainly reflected in error-based metrics and PCC-Spot. Spatial autocorrelation and protein-derived domain agreement analyses were further used to characterize the spatial behavior of the predicted protein maps. When applied to external RNA-only spatial sections, DPAS-Graph generated qualitatively interpretable marker-level virtual protein maps, illustrating its use as a complementary tool for protein-level interpretation of transcriptomics-only spatial data.

RNA↗

PubMed1992

Contrast sensitivity as a function of spatial frequency, viewing distance and eccentricity with and without spatial noise.

Using computer graphics and a two-alternative forced-choice method we measured threshold contrast as a function of viewing distance, spatial frequency, and eccentricity for gratings with and without added, white two-dimensional spatial noise. Our experiments showed that in spatial noise contrast sensitivity was independent of viewing distance as long as contrast sensitivity was lower with noise than without. With increasing spatial frequency (f) the grating area (A) was reduced in order to keep the relative grating size (Af2) constant. At all spatial frequencies the test gratings thus had the same amount of detail and contour. Noise spectral density was reduced in direct proportion to grating area in order to keep the physical signal-to-noise ratio constant. An increase in spatial frequency was thus accompanied with reductions in grating area and noise spectral density similar to those produced by a corresponding increase in viewing distance. In agreement, contrast detection in spatial noise was found to be independent of spatial frequency as long as contrast sensitivity was lower with noise than without. The effect of increasing eccentricity on visual performance can be compensated for by reducing the viewing distance (M-scaling). Hence, without M-scaling the effect of increasing eccentricity is similar to that of increasing viewing distance. In agreement, we found that contrast sensitivity in spatial noise was independent of eccentricity as long as contrast sensitivity was lower with noise than without.

Adult↗

PubMed2025

scBSP: a fast and accurate tool for identifying spatially variable features from high-resolution spatial omics data.

MOTIVATION: Emerging spatial omics technologies empower comprehensive exploration of biological systems from multi-omics perspectives in their native tissue location in 2D and 3D space. However, the limited sequencing depth, increasing spatial resolution, and growing spatial spots in spatial omics technologies present significant computational challenges in identifying biologically meaningful molecules with variable spatial distributions across various omics modalities. RESULTS: We introduce scBSP, an open-source, versatile, and user-friendly package for identifying spatially variable features in large-scale spatial omics data. scBSP demonstrates significantly enhanced computational efficiency, processing high-resolution spatial omics data within seconds, and exhibits robust cross-platform performance by consistently identifying spatially variable features with high reproducibility across various sequencing platforms. AVAILABILITY AND IMPLEMENTATION: scBSP is available for download from R CRAN at https://cran.r-project.org/web/packages/scBSP/index.html and PyPI at https://pypi.org/project/scbsp/.

Software↗

PubMed2026

Spatial clustering and transmission networks of multidrug-resistant tuberculosis in Rwanda: a national retrospective genomic and spatial epidemiological study.

BACKGROUND: Approximately 96% of rifampicin resistance/multidrug-resistant tuberculosis (RR/MDR-TB) cases in Rwanda result from direct transmission rather than acquired resistance. However, the nationwide spatial distribution and transmission dynamics of RR/MDR-TB remain poorly characterised. This study aims to analyse spatial patterns of RR/MDR-TB in Rwanda and explore relationships between spatial proximity and RR/MDR-TB strains' genetic relatedness. METHODS: We conducted a retrospective analysis of 249 confirmed RR-TB cases across Rwanda from 2017 to 2024, using the known geolocations of patients' residences. Spatial and space-time clustering was assessed using Kulldorff's scan statistics. Demographic and socioeconomic determinants were evaluated using multivariable regression. For 201 cases with whole-genome sequencing data, we performed transmission analysis using a 5-SNP threshold to define recent transmission clusters and investigated spatial relationships within genetically related strains. RESULTS: Significant spatial clustering of RR/MDR-TB was identified in 21 sectors, mainly in Nyarugenge, southern Gasabo and western Kicukiro (relative risk: 10.06; p<0.001). Our multivariable analysis showed that population density is positively associated with case notification rates. Molecular analysis revealed 88.5% of cases belonged to genotype clusters defined using a 12-SNP threshold, with 73.6% forming clusters at a strict 5-SNP threshold. Spatial K-function analysis of the six major clusters revealed heterogeneous transmission patterns, characterised by both tightly clustered outbreaks and regional transmission networks that spanned administrative boundaries. Most clusters (5/6) extended beyond Kigali, indicating that transmission networks operate across administrative divides. CONCLUSION: RR/MDR-TB in Rwanda shows significant spatial clustering with transmission occurring through both localised and regional networks. Integrating genomic and spatial data reveals transmission patterns that extend beyond household contacts and administrative boundaries. These findings underscore the need to implement geographically targeted interventions that address community-level transmission to control RR/MDR-TB in Rwanda effectively.

Rwanda↗

PubMed2026

Spatial isoform sequencing at single-cell resolution reveals cell-type-specific spatial isoform variability in multiple brain cell types.

Spatial long-read technologies are increasingly common but usually lack single-cell resolution. This leaves unanswered whether spatially variable isoforms reflect variability within one cell type or differences in region-specific cell-type composition. Here, we developed Spl-ISO-Seq2 (500-nm resolution) and accompanying software, Spl-IsoQuant-2 and Spl-IsoFind, enabling long-read sequencing of >450 million barcodes versus 80,000 previously. Applying this to the adult mouse brain, we compared differential isoform abundance between known regions and spatial isoform patterns independent of predefined regions. Both identified overlapping hits, for example, Rps24 in oligodendrocytes. For known Snap25 spatial isoform variation, we show that it occurs in excitatory neurons. The region-agnostic approach also uncovered patterns missed by region-based comparisons, for example, for Ighm. Notably, many spatial isoform signals are not driven by cell-type composition alone. Finally, our software is applicable to many spatial and single-cell protocols, demonstrating reproducibility between platforms (for example, Visium HD/Stereo-seq). Overall, our experimental/analytical methods enable a submicron-resolution-isoform view and open avenues for spatial isoform disease research.

Animals↗

PubMed2025

Spatial mutual nearest neighbors for spatial transcriptomics data.

MOTIVATION: Mutual nearest neighbors (MNN) is a widely used computational tool to perform batch correction for single-cell RNA-sequencing data. However, in applications such as spatial transcriptomics, it fails to take into account the 2D spatial information. RESULTS: Here, we present spatialMNN, an algorithm that integrates multiple spatial transcriptomic samples and identifies spatial domains. Our approach begins by building a k-nearest neighbors (kNN) graph based on the spatial coordinates, prunes noisy edges, and identifies niches to act as anchor points for each sample. Next, we construct a MNN graph across the samples to identify similar niches. Finally, the spatialMNN graph can be partitioned using existing algorithms, such as the Louvain algorithm to predict spatial domains across the tissue samples. We demonstrate the performance of spatialMNN using large datasets, including one with N = 31 10x Genomics Visium samples. We also evaluate the computing performance of spatialMNN to other popular spatial clustering methods. AVAILABILITY AND IMPLEMENTATION: Our software package is available on GitHub (https://github.com/Pixel-Dream/spatialMNN). The code is available on Zenodo (https://doi.org/10.5281/zenodo.15073963).

Algorithms↗

PubMed2026

SIVA: diagonal integration of spatial multi-omics data via spatially informed variational autoencoders and anchor guidance.

MOTIVATION: Understanding cellular states and regulatory programs requires integrative analysis of multiple omics layers. Although recent spatial sequencing technologies allow molecular profiling of cells within their tissue context, paired spatial multi-omics assays are still limited by technical complexity and cost. This creates a pressing need for diagonal integration methods that enable joint analysis of unpaired spatial omics datasets. RESULTS: We propose SIVA, a deep generative framework based on Spatially-Informed Variational Autoencoders with Anchor Guidance, for diagonal integration of spatial multi-modal data. SIVA employs modality-specific variational autoencoders (VAEs) with a hybrid latent embedding that integrates Gaussian process and standard Gaussian priors, enabling joint modeling of spatially structured variation and dominant underlying data distributions across modalities. To facilitate cross-modal alignment in the absence of one-to-one cell correspondence, SIVA adopts a dual integration strategy combining global distribution alignment via Maximum Mean Discrepancy and local correspondence guidance using mutual nearest neighbor anchors. Extensive experiments across multiple cross-slice integration scenarios demonstrate that SIVA achieves robust and accurate integration of unpaired spatial omics datasets, consistently outperforming existing methods. AVAILABILITY AND IMPLEMENTATION: The source codes are available at https://github.com/PelenJiang/SIVA.

Autoencoder↗

PubMed1992

Spatial recognition and spatial order memory in patients with dementia of the Alzheimer's type.

Patients diagnosed as having mild or moderate primary degenerative dementia of the Alzheimer's type (PDDAT), and normal elderly subjects were tested for spatial order and a spatial recognition memory. Results for spatial order memory indicated that compared to normal elderly subjects, patients with mild PDDAT showed an impaired memory only for the last serial positions. In contrast, with respect to spatial recognition memory, patients with mild PDDAT showed an impaired memory only for the early serial positions. Patients with moderate PDDAT were impaired on all serial positions for both spatial order and spatial recognition memory. Based on comparable deficit patterns seen in animals and patients with hippocampal and parietal cortex lesions, it is suggested that memory deficits displayed by PDDAT patients might be a function of underlying pathology in the hippocampus and parietal cortex.

Aged↗

PubMed1992

A new one-trial test for neurobiological studies of memory in rats. III. Spatial vs. non-spatial working memory.

Rats were submitted to object and spatial recognition tests (both based on the same paradigm) and to the radial-arm maze. The results are as follows: (1) rats could discriminate between a new and a familiar object when the retention delay was 1 min, 15 min or 60 min but not 24 h. The relationship between the level of discrimination and intertrial delays is quadratic with a maximum for 15 min. (2) Exposure to distractive stimuli during the retention delay may impair object recognition. (3) Rats discriminated between a new and a familiar space. (4) There is no correlation between the three tests which argues for a multiple form of working memory, especially a spatial and a non-spatial one. (5) Medial septal lesion did not impair object and spatial recognition memory, but the level of discrimination in the spatial recognition test was significantly reduced compared to that of control.

Aging↗

PubMed2026

ARCADIA reveals spatially dependent transcriptional programs through integration of scRNA-seq and spatial proteomics.

MOTIVATION: Cellular states are strongly influenced by spatial context, but single-cell RNA sequencing (scRNA-seq) loses information about local tissue organization, while spatial proteomic assays capture limited marker panels that constrain transcriptomic inference. Integrating these modalities can elucidate how spatial niches shape transcriptional programs, yet existing approaches depend on either feature-level correspondence such as gene-protein linkage or cell-level barcode pairing, which is often unavailable. RESULTS: We present ARCADIA (ARchetype-based Clustering and Alignment with Dual Integrative Autoencoders), a generative framework for cross-modal integration that operates without cell barcode pairing and does not assume direct feature-to-feature correspondence. ARCADIA identifies modality-specific archetypes, that is, convex combinations of cells representing extreme phenotypic states, and aligns these anchors across modalities by minimizing the discrepancy between their cell-type composition profiles. The aligned archetypes define a shared coordinate system that anchors dual variational autoencoders (VAEs) trained with cross-modal geometric regularization, preserving archetype structure and spatial neighborhood information while enabling bidirectional translation between modalities. On semi-synthetic CITE-seq data, ARCADIA outperforms existing weak-linkage methods. Applied to independent human tonsil scRNA-seq and CODEX data, ARCADIA reconstructs known tissue architecture and reveals spatially dependent transcriptional programs linking B-cell maturation and T-cell activation or exhaustion to microenvironmental niches. AVAILABILITY AND IMPLEMENTATION: Source code is accessible at https://github.com/azizilab/ARCADIA_public. Reproducibility scripts and data are available at https://github.com/azizilab/arcadia_reproducibility.

Proteomics↗

PubMed1976

Usefulness of the group-comparison method to demonstrate sex differences in spatial orientation and spatial visualization in older men and women.

This paper reports an analysis of sex differences in cognitive test scores covering the dimensions of spatial orientation and spatial visualization in groups of 6 older men and 6 women matched for speed of performance on a maze test and level of performance on a spatial relations task. Older men were more proficient solving spatial problems using the body as a referent, whereas there was no significant difference between the sexes in imagining spatial displacement. Matched comparisons appear a useful adjunct to population research to understand the type(s) of cognitive processes where differential performance by the sexes is observed.

Age Factors↗

PubMed2026

Integrated single-cell and spatial transcriptomic analyses reveal malignant epithelial glycolytic heterogeneity and spatial niche remodeling during colorectal cancer progression.

Colorectal cancer (CRC) progression is shaped by metabolic reprogramming and complex interactions within the tumor microenvironment. However, the cellular heterogeneity, spatial organization, and clinical relevance of glycolytic activity in CRC remain incompletely understood. In this study, we integrated single-cell RNA sequencing, bulk transcriptomics, and spatial transcriptomics data to systematically characterize glycolytic heterogeneity in CRC. Glycolytic activity was quantified using five independent scoring methods, consistently showing that epithelial cells exhibited the highest glycolytic activity across the two single-cell cohorts. Stratification of CopyKAT-verified aneuploid malignant epithelial cells into high-glycolysis (HG) and low-glycolysis (LG) subgroups by glycolysis scores revealed that HG cells exhibited higher stemness scores and chromosomal copy number variations. Cell-cell communication analysis revealed that, compared with LG cells, HG cells exhibited increased interaction frequency and strength with immune and stromal populations, indicating enhanced malignant epithelial-microenvironment crosstalk. Spatial transcriptomics analyses further revealed that glycolytic activity varied across normal colorectal tissue, primary CRC, and colorectal liver metastases, accompanied by progressive remodeling of epithelial-associated spatial niches and MIF-mediated intercellular communication. Bulk transcriptomic analysis identified a glycolysis-related prognostic signature with robust predictive performance, which served as an independent prognostic factor for overall survival in CRC cohorts. Collectively, these findings indicate that glycolytic heterogeneity is a key feature of CRC malignant epithelial cells and is closely associated with tumor progression, microenvironmental remodeling, and clinical outcomes.

Humans↗

PubMed2026

Full-length single-cell spatial transcriptomics reveals spatial and cell-type-specific transcript isoforms in the primate brain.

The primate brain exhibits complex RNA alternative splicing heterogeneity crucial for functional complexity, yet systematic spatial isoform characterization has been lacking. We developed Fullscope-seq, a full-length single-molecule large field-of-view spatial transcriptomics sequencing method at single-cell resolution, based on programmed concatenation cDNA for multiple long-read sequencing platforms. Applying Fullscope-seq to the macaque brain, we uncovered thousands of genes exhibiting differential transcript usage (DTU) across cortical layers, cell types and brain regions. Fullscope-seq resolved hundreds of major isoform switches across distinct brain regions and identified DTUs between superficial and deep cortical layers. Cortical layer-specific DTUs showed cell-composition dependence, whereas regional DTUs were regulated according to both cellular composition and spatial contexts. These isoform variations showed substantial enrichment for neuropsychiatric disorder-associated genes and were conserved across platforms and species. Our study establishes a scalable framework for spatial isoform analysis and provides a resource for understanding transcriptomic diversity in complex tissues.

Animals↗

PubMed1991

A model for perceived spatial frequency and spatial frequency discrimination.

The responses of labelled spatial frequency channels are combined to generate an index of perceived spatial frequency. Spatial frequency discrimination thresholds are shown to be inversely related to the slope of the function of the index vs spatial frequency, when it is plotted on log-log co-ordinates.

Discrimination, Psychological↗