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At least 19 recordsLinked to original sources
PubMed2026

Dual-contrastive learning for spatial domain identification in spatial transcriptomics with STAMGC.

Spatial transcriptomics (STs) have become a valuable approach for understanding the growth and development of organisms. Despite the recent emergence of numerous ST models, accurately identifying spatial domains remains challenging owing to the trade-off between preserving local details and reducing noise. Here, we introduce STAMGC, which is a dual-contrastive learning framework built upon graph convolutional networks. This model leverages regional and topological contrastive learning to jointly optimize the model, effectively reducing the noise in spatial domain identification and enhancing the extraction of detailed features. In this study, Gaussian smoothing, originally developed in the image processing field, is introduced to process ST data, providing a foundation for region-level contrastive learning by mitigating spatial discontinuities of gene expression signals. Experimental results indicate that STAMGC outperforms existing methods across multiple data sets according to comprehensive evaluations. Furthermore, STAMGC not only identifies finer structures in the mouse brain but also brings new discoveries for human breast cancer research.

Journal Article↗
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↗
PubMed2025

soFusion: facilitating tissue structure identification via spatial multi-omics data fusion.

The rapid advancement of spatial multi-omics technologies has opened new avenues for dissecting tissue architecture with unprecedented resolution. However, inherent disparities across omics modalities, such as differences in biological hierarchy and resolution, pose significant challenges for integrative analysis. To address this, we present soFusion, a method for representation learning on spatial multi-omics data that enables automated identification of tissue compartmentalization. soFusion employs a graph convolutional network (GCN) to extract latent embeddings from spatial omics profiles. To simultaneously capture both cross-modality relationships and modality-specific features, we introduce a novel strategy for intra- and inter-omics feature learning. Moreover, modality-specific decoders are designed to preserve the unique information embedded in each omics type. We evaluated soFusion on multiple datasets including gene expression, protein expression, and epigenetic features. Across all benchmarks, soFusion consistently outperformed existing methods in delineating anatomical structures and identifying spatial domains with improved continuity and reduced noise. Collectively, soFusion offers an effective solution for spatial multi-omics integration, substantially enhancing the robustness of spatial domain identification.

Humans↗
PubMed2025

BISON: bi-clustering of spatial omics data with feature selection.

MOTIVATION: The advent of next-generation sequencing-based spatially resolved transcriptomics (SRT) techniques has reshaped genomic studies by enabling high-throughput gene expression profiling while preserving spatial and morphological context. Understanding gene functions and interactions in different spatial domains is crucial, as it can enhance our comprehension of biological mechanisms, such as cancer-immune interactions and cell differentiation in various regions. It is necessary to cluster tissue regions into distinct spatial domains and identify discriminating genes (DGs) that elucidate the clustering result, referred to as spatial domain-specific DGs. Existing methods for identifying these genes typically rely on a two-stage approach, which can lead to the phenomenon known as double-dipping. RESULTS: To address the challenge, we propose a unified Bayesian latent block model that simultaneously detects a list of DGs contributing to spatial domain identification while clustering these DGs and spatial locations. The efficacy of our proposed method is validated through a series of simulation experiments, and its capability to identify DGs is demonstrated through applications to benchmark SRT datasets. AVAILABILITY AND IMPLEMENTATION: The R/C++ implementation of BISON is available at https://github.com/new-zbc/BISON.

Software↗
PubMed2026

CAGNet: a structure-aware clustering-alternated graph network for cell-cell interaction inference in spatial transcriptomics.

MOTIVATION: Understanding cell-cell interactions (CCIs) in spatial transcriptomics is crucial for uncovering the spatial organization and functional heterogeneity of tissues. However, existing graph-based models typically rely on static clustering or fixed adjacency structures, which limits their ability to capture dynamic cellular relationships. RESULTS: We propose CAGNet, a two-stage framework for CCI inference from spatial transcriptomics data. In Stage 1, a Graph Attention Network encoder with joint feature and graph reconstruction learns structure-aware node embeddings from spatial gene expression profiles. In Stage 2, an alternating optimization mechanism iteratively updates cluster centers via KL-guided soft assignment and refines node embeddings through spatial graph reconstruction, establishing a closed-loop between representation learning and clustering. Experiments on three 10x Genomics Visium datasets demonstrate that CAGNet consistently outperforms six CCI inference baselines across ACC, AUC, AP, Precision, Recall, and F1. CAGNet also achieves the highest Adjusted Rand Index on all three datasets against six spatial domain identification methods, confirming that the learned embeddings capture biologically relevant spatial organization. Information-theoretic analysis further shows that CAGNet retains the highest mutual information between input features and learned embeddings among all compared methods. Ablation studies and 5-fold cross-validation confirm the contribution of each component and the reproducibility of the results. AVAILABILITY: The proposed method is implemented in the CAGNet package available at http://github.com/mahan1233333-maker/CAGNet .

Spatial Transcriptomics↗
PubMed2026

GenOT: generative optimal transport enables spatiotemporal interpolation and generation in cross-platform spatial transcriptomics.

Spatial transcriptomics technologies have revolutionized the analysis of spatial gene expression, yet integrating spatial information and generating data across heterogeneous samples remain challenging. We present GenOT, a generative framework combining multi-scale graph self-supervised contrastive learning with optimal transport barycenter theory for efficient cross-slice and cross-platform spatiotemporal interpolation. The core innovation of GenOT lies in introducing an optimal transport barycenter-based interpolation algorithm, which mathematically models spatial distribution differences across heterogeneous samples to reconstruct spatiotemporal gene expression dynamics. Extensive evaluations demonstrate that GenOT consistently outperforms existing approaches in spatial domain identification, cross-platform interpolation, and developmental trajectory reconstruction.

Spatial Transcriptomics↗
PubMed2026

ARISE: RNA-anchored shared-edge topology and hierarchical fusion for spatial multi-omics integration.

MOTIVATION: Spatial multi-omics technologies jointly profile transcriptomes, proteins and chromatin accessibility in situ, enabling integrative analysis of tissue organization across molecular layers. However, most existing graph-based integration methods rely on independently constructed modality-specific k-nearest-neighbor graphs. When auxiliary modalities are sparse or noisy, these graphs can become topologically discordant, propagate spurious edges, weaken cross-modal alignment, and reduce spatial domain resolution. RESULTS: We present Anchored RNA for Integrated Spatial Embedding (ARISE), an RNA expression anchored framework for spatial multi-omics integration. ARISE defines a shared-edge topology by intersecting RNA feature-similarity and spatial-proximity graphs, encodes auxiliary modalities on this common scaffold, and integrates them through inside-out hierarchical fusion. We further show theoretically that graph intersection minimizes false-positive edges within a broad class of k-of-r graph fusion rules, providing a principled basis for topology anchoring. Across various spatial multi-omics benchmarks spanning simulated and real datasets in bi-modal and tri-modal settings, ARISE improves spatial domain identification, cross-modal consistency, and preservation of tissue structure relative to existing methods. Furthermore, the learned representation supports biologically meaningful downstream analyses, including marker-based domain annotation, pathway enrichment, and cis-regulatory inference, indicating that ARISE yields a robust and interpretable framework for spatial multi-omics integration. AVAILABILITY AND IMPLEMENTATION: The source code is available at https://github.com/XiangxiangWang-code/ARISE. The archived version used in this study is available at https://doi.org/10.6084/m9.figshare.32686137.v2.

Multiomics↗
PubMed2025

Enhancing and accelerating cell type deconvolution of large-scale spatial transcriptomics slices with dual network model.

MOTIVATION: Cell type deconvolution deciphers spatial distribution of mRNA transcripts at single cell level by integrating single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics data to infer mixture of cell types of spots in slices. Current algorithms are criticized for neglecting connection between scRNA-seq and spatial transcriptomics data, as well as time-consuming, hampering their application to large-scale datasets. RESULTS: In this study, we propose a joint learning nonnegative matrix factorization algorithm for fast cell type deconvolution (aka jMF2D), which integrates scRNA-seq and spatial transcriptomics data with network models. To bridge scRNA-seq and spatial transcriptomics data, jMF2D jointly learns cell type similarity network to enhance quality of signatures of cell types, thereby promoting accuracy and efficiency of deconvolution. Experiments demonstrate that jMF2D outperforms state-of-the-art baselines in terms of accuracy by saving about 90% running time on various datasets generated by different platforms. Furthermore, it can also facilitates the identification of spatial domains and bio-marker genes, providing an efficient and effective model for analyzing spatial transcriptomics data. AVAILABILITY AND IMPLEMENTATION: The software is coded using python, and is free available for academic https://github.com/xkmaxidian/jMF2D.

Algorithms↗
PubMed1991

Spatial and temporal control of nonmuscle myosin localization: identification of a domain that is necessary for myosin filament disassembly in vivo.

Myosin null mutants of Dictyostelium are defective for cytokinesis, multicellular development, and capping of surface proteins. We have used these cells as transformation recipients for an altered myosin heavy chain gene that encodes a protein bearing a carboxy-terminal 34-kD truncation. This truncation eliminates threonine phosphorylation sites previously shown to control filament assembly in vitro. Despite restoration of growth in suspension, development, and ability to cap cell surface proteins, these delta C34-truncated myosin transformants display severe cytoskeletal abnormalities, including excessive localization of the truncated myosin to the cortical cytoskeleton, impaired cell shaped dynamics, and a temporal defect in myosin dissociation from beneath capped surface proteins. These data demonstrate that the carboxy-terminal domain of myosin plays a critical role in regulating the disassembly of the protein from contractile structures in vivo.

Cell Compartmentation↗
PubMed1994

Image alignment by integrated rotational and translational transformation matrix.

Presents an algorithm to align 2D images with similar densitometric patterns in the spatial domain. It performs automatic identification of control points through local maxima in normalized image cross correlations computed through projections of an image function on 2D orthonormal bases over a predefined circular domain. The image is subsequently subjected to translational and rotational corrections through an integrated transformation matrix obtained by the least-squares minimization technique. The algorithm restricts the corrections required for alignment to rotation and translation only, and care is taken to see that the image does not go through scaling (isotropic or anisotropic) or nonlinear distortions. The least-squares method results in implicit equations in three variables, theta , h, upsilon , representing rotation, and translations along x and y axes. The present algorithm, on the other hand, is based on an approximation that results in a set of explicit equations that are easy to solve. The mathematical validity of this approximation is proved and the results obtained show that the algorithm performs well with similar biological (histological, autoradiographic, and tomographic) images. The algorithm is iterative, and its computer implementation is discussed.

Algorithms↗
PubMed2006

Orienting attention based on long-term memory experience.

Attentional orienting and memory are intrinsically bound, but their interaction has rarely been investigated. Here we introduce an experimental paradigm using naturalistic scenes to investigate how long-term memory can guide spatial attention and thereby enhance identification of events in the perceptual domain. In the task, stable memories of objects embedded within complex scenes guide spatial orienting. We compared the behavioral effects and neural systems of memory-guided orienting with those in a more traditional attention-orienting task in which transient spatial cues guide attention. Memory-guided attention operated within surprisingly short intervals and conferred reliable and sizeable advantages for detection of objects embedded in scenes. Event-related functional magnetic resonance imaging showed that memory-guided attention involves the interaction between brain areas participating in retrieval of memories for spatial context with the parietal-frontal network for visual spatial orienting. Activity in the hippocampus was specifically engaged in memory-guided spatial attention and correlated with the ensuing behavioral advantage.

Adult↗
PubMed2004

Simultaneous storage of medical images in the spatial and frequency domain: a comparative study.

BACKGROUND: Digital watermarking is a technique of hiding specific identification data for copyright authentication. This technique is adapted here for interleaving patient information with medical images, to reduce storage and transmission overheads. METHODS: The patient information is encrypted before interleaving with images to ensure greater security. The bio-signals are compressed and subsequently interleaved with the image. This interleaving is carried out in the spatial domain and Frequency domain. The performance of interleaving in the spatial, Discrete Fourier Transform (DFT), Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) coefficients is studied. Differential pulse code modulation (DPCM) is employed for data compression as well as encryption and results are tabulated for a specific example. RESULTS: It can be seen from results, the process does not affect the picture quality. This is attributed to the fact that the change in LSB of a pixel changes its brightness by 1 part in 256. Spatial and DFT domain interleaving gave very less %NRMSE as compared to DCT and DWT domain. CONCLUSION: The Results show that spatial domain the interleaving, the %NRMSE was less than 0.25% for 8-bit encoded pixel intensity. Among the frequency domain interleaving methods, DFT was found to be very efficient.

Algorithms↗
PubMed2004

Identification of an antigenic domain near the C terminus of human granulocyte-macrophage colony-stimulating factor and its spatial localization.

The goal of this study was to map an epitope on the human granulocyte-macrophage colony-stimulating factor (hGM-CSF) at its C terminus, a region whose integrity is fundamental in maintaining the normal function of this molecule. Residues including the fourth alpha-helix (D, 103-116) were analyzed for their role in the interaction with antibodies (Abs) raised against the protein. Five peptides homologous to different segments of the C terminus of hGM-CSF were synthesized. Peptide-(102-121) included the same residues of the alpha-helix D and the next five amino acids toward the C terminus; peptide-[E108A]-(102-121) introduced the mutation E108A in order to verify the role of acidic residues; peptide-[C96A](93-110) encompassed the beta-sheet 2 and half of the alpha-helix D; peptide-[C121A]-(110-127) included the second half of the alpha-helix D and the C terminus of hGMCSF; peptide-(13-31)-Gly-Pro-Gly-(103-116) included both the alpha-helices A and D connected by the tripeptide Gly-Pro-Gly, which allows the original antiparallel orientation of the two alpha-helices to be maintained. Both anti-protein and anti-peptide-(102-121) antibodies, capable of neutralizing the stimulatory activity of hGMCSF in the bone marrow colony-forming assays, recognized a specific epitope in the C terminus of hGM-CSF. Molecular modeling estimated the surface accessibility of hGM-CSF and the stability of the synthetic peptides in aqueous solution. Altogether, our results showed that the immunogenic region includes part of the alpha-helix D and the residues 116-120, which are external to this helix and particularly exposed on the protein surface, confirming the feasible participation of this region in antibody binding.

Antigens↗
PubMed2005

Hierarchical clustering of the correlation patterns: new method of domain identification in proteins.

New method of identification of dynamical domains in proteins - Hierarchical Clustering of the Correlation Patterns (HCCP) is proposed. HCCP allows to identify the domains using single three-dimensional structure of the studied proteins and does not require any adjustable parameters that can influence the results. The method is based on hierarchical clustering performed on the matrices of correlation patterns, which are obtained by the transformation of ordinary pairwise correlation matrices. This approach allows to extract additional information from the correlation matrices, which increases reliability of domain identification. It is shown that HCCP is insensitive to small variations of the pairwise correlation matrices. Particularly it produces identical results if the data obtained for the same protein crystallized with different spatial positions of domains are used for analysis. HCCP can utilize correlation matrices obtained by any method such as normal mode or essential dynamics analysis, Gaussian network or anisotropic network models, etc. These features make HCCP an attractive method for domain identification in proteins.

Algorithms↗
PubMed2000

Identification of two pairs of spatially approximated residues within the carboxyl terminus of secretin and its receptor.

The carboxyl-terminal domains of secretin family peptides have been shown to contain key determinants for high affinity binding to their receptors. In this work, we have examined the interaction between carboxyl-terminal residues within secretin and the prototypic secretin receptor. We previously utilized photoaffinity labeling to demonstrate spatial approximation between secretin residue 22 and the receptor domain that includes the first 30 residues of the amino terminus (Dong, M., Wang, Y., Pinon, D. I., Hadac, E. M., and Miller, L. J. (1999) J. Biol. Chem. 274, 903-909). Here, we further refined the site of labeling with the p-benzoyl-phenylalanine (Bpa(22)) probe to receptor residue Leu(17) using progressive cleavage of wild type and mutant secretin receptors (V13M and V16M) and sequence analysis. We also developed a new probe incorporating a photolabile Bpa at position 26 of secretin, closer to its carboxyl terminus. This analogue was also a potent agonist (EC(50) = 72 +/- 6 pm) and bound to the secretin receptor specifically and with high affinity (K(i) = 10.3 +/- 2.4 nm). It covalently labeled the secretin receptor at a single site saturably and specifically. This was localized to the segment between residues Gly(34) and Ala(41) using chemical and enzymatic cleavage of labeled wild type and A41M mutant receptor constructs and immunoprecipitation of epitope-tagged receptor fragments. Radiochemical sequencing identified the site of covalent attachment as residue Leu(36). These new insights, along with our recent report of contact between residue 6 within the amino-terminal half of secretin and this same amino-terminal region of this receptor (Dong, M., Wang, Y., Hadac, E. M., Pinon, D. I., Holicky, E. L., and Miller, L. J. (1999) J. Biol. Chem. 274, 19161-19167), support a key role for this region, making the molecular details of this interaction of major interest.

Alanine↗
PubMed2000

Development of a computer software for analysis of SDS-PAGE protein fingerprints of bacterial isolates.

Protein fingerprinting is a widely used technique in epidemiological studies for typing bacterial strains. This study reports the development of a computer based gel analysis system. The system has the capability to analyse SDS-PAGE whole-cell protein profiles using digital image processing techniques. The software incorporates spatial and frequency domain operators for image enhancement, support for geometric correction of images and new algorithms for identification of strain tracks and protein bands. The system also provides facilities for correcting imaging defects for inter-gel comparison, similarity analysis, clustering and pictorial representation of results as a dendrogram. The software is highly interactive, user-friendly and can produce accurate results for differentiation of bacterial strains with minimal overhead of time.

Algorithms↗
PubMed2004

Compound library development guided by protein structure similarity clustering and natural product structure.

To identify biologically relevant and drug-like protein ligands for medicinal chemistry and chemical biology research the grouping of proteins according to evolutionary relationships and conservation of molecular recognition is an established method. We propose to employ structure similarity clustering of the ligand-sensing cores of protein domains (PSSC) in conjunction with natural product guided compound library development as a synergistic approach for the identification of biologically prevalidated ligands with high fidelity. This is supported by the concepts that (i) in nature spatial structure is more conserved than amino acid sequence, (ii) the number of fold types characteristic for all protein domains is limited, and (iii) the underlying frameworks of natural product classes with multiple biological activities provide evolutionarily selected starting points in structural space. On the basis of domain core similarity considerations and irrespective of sequence similarity, Cdc25A phosphatase, acetylcholinesterase, and 11beta-hydroxysteroid dehydrogenases type 1 and type 2 were grouped into a similarity cluster. A 147-member compound collection derived from the naturally occurring Cdc25A inhibitor dysidiolide yielded potent and selective inhibitors of the other members of the similarity cluster with a hit rate of 2-3%. Protein structure similarity clustering may provide an experimental opportunity to identify supersites in proteins.

11-beta-Hydroxysteroid Dehydrogenases↗
PubMed2000

Nuclear topology of murine, cerebellar Purkinje neurons: changes as a function of development.

The interphase nucleus is a structurally ordered, three-dimensional structure, in which specific chromatin domains occupy distinct spatial positions that can, in turn, be modified with changes in cell function. A fundamental goal in developmental neurobiology is the identification of mechanisms that dictate the orderly expression of genes in a cell-specific manner. Given that different neuronal populations feature a characteristic spatial topology of centromeric sequences, the positioning of specific DNA sequences may constitute such a mechanism. We tested the hypothesis that the cell-specific nuclear topology in fully differentiated neurons is acquired before or during that stage at which neuron-specific sequences are first expressed. For this, we assessed the number and spatial distribution of centromeric domains in the murine, cerebellar Purkinje neuron as a function of postnatal development. Centromeric domains were localized by immunofluorescence of centromere-associated kinetochore proteins and visualized by confocal microscopy. Kinetochores are known to cluster in Purkinje neurons. Thus, the number of signals discerned is always less than the chromosome complement of the species. The number of signals observed in adults (10.8 +/- 0.46) (mean +/- SEM) is established by postnatal day 15 (P15), after a transient decrease from 11.44 +/- 0.44 at P0 to 8.78 +/- 0.24 at P3. The distribution of signals characteristic of the adult, with the majority located at the nucleolus, is established by P5 and is associated with a decrease in the fraction of signals at the nuclear periphery. These changes are temporally associated with the onset of processes such as dendritic differentiation and synaptic maturation and might serve the process of differentiation by placing specific sequences into transcriptionally competent, nuclear sites.

Aging↗