(C) PLOS One
This story was originally published by PLOS One and is unaltered.
 . . . . . . . . . .



SpaMOAL is a deep learning method that enables accurate spatial domain identification from multi-omics data [1]

['Jinxia Wang', 'School Of Software Engineering', 'Beijing Jiaotong University', 'Beijing', 'Yuying Huo', 'Department Of Biochemistry', 'Molecular Biology', 'School Of Basic Medical Sciences', 'Peking University Health Science Center', 'Rui Zhao']

Date: 2026-09 

Recent advances in spatial multi-omics technologies have opened new avenues for characterizing tissue architecture and function in situ, by simultaneously providing multimodal and complementary information—such as spatially resolved transcriptomic, epigenomic, and proteomic features. Current computational approaches face substantial challenges, such as effective integration of multi-omics molecular information with spatial information and corresponding high-resolution histology images. To address this challenge, we proposed SpaMOAL ( Spa tially M ulti- O mics graph contr A stive L earning), a graph-based contrastive learning approach for spatial domain identification. SpaMOAL learns clustering-friendly representations from spatial multi-omics data by integrating spatial coordinates, histological image features, and molecular profiles, enabling accurate delineation of spatial tissue domains. Benchmarking across multiple recent paired spatial multi-omics datasets from mouse and human demonstrated that SpaMOAL consistently outperforms existing methods. By enabling accurate spatial domain delineation, SpaMOAL provides a powerful framework for interpreting tissue organization and cellular microenvironments.

Funding: This work was supported by the Fundamental Research Funds for the Central Universities (2026JBMC007 to X.L.), National Natural Science Foundation of China (62673041 to X.L.). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. No authors received a salary from any of the funders.

Data Availability: The code of SpaMOAL is available at Zenodo ( https://doi.org/10.5281/zenodo.22067852 ) and on Github at https://github.com/XiangyuLi-Lab/SpaMOAL . The simulated datasets are available at https://github.com/XiangyuLi-Lab/SpaMOAL . The MISAR-seq mouse brain dataset is accessible at the National Genomics Data Center with accession number OEP003285. The spatial ATAC-RNA-seq mouse brain dataset can be found at https://web.atlasxomics.com/visualization/Fan . Spatial ATAC-RNA-seq mouse embryonic day 13 (E13) data are reported in https://cells.ucsc.edu/?ds=brain-spatial-omics . 10x Visium human breast cancer gene and protein expression data can be found at https://www.10xgenomics.com/resources/datasets/gene-and-protein-expression-library-of-human-breast-cancer-cytassist-ffpe-2-standard .

Copyright: © 2026 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

To overcome all the challenges mentioned above, we present a spatially multi-omics graph contrastive learning method (SpaMOAL) to identify spatial domains. SpaMOAL integrates molecular, spatial, and morphological information in a unified embedding space. Specifically, it aligns cross-modal shared representations while preserving modality-specific components, thereby enabling coherent integration of heterogeneous modalities within a unified latent space. The concatenated embedding of the shared and private representations effectively combines complementary information, yielding a robust multimodal representation. Benchmarking on both simulated and real-world datasets demonstrates that SpaMOAL consistently achieves superior performance compared to state-of-the-art spatial multi-omics methods.

Effective integration of complementary data modalities provides a multidimensional view of tissue architecture and function, thereby enhancing our understanding of biological processes and disease mechanisms. Integrating heterogeneous modalities into unified low-dimensional representations is central to spatial multi-omics analysis, enabling downstream analysis such as domain detection. However, it is challenged by the distinct feature spaces of different modalities. Converting them into a common feature space based on prior knowledge can mitigate these differences, but often results in information loss. Moreover, most existing methods suffer from not fully leveraging spatial information modeling and matched high-resolution histological images. For example, traditional single-cell multi-omics integration methods such as Seurat WNN [ 13 ], MOFA+ [ 14 ], and totalVI [ 15 ] cannot incorporate spatial coordinate information. Some spatial transcriptomics clustering approaches, like MUSE [ 16 ], SpaGCN [ 17 ], and SpaDAC [ 18 ], can effectively integrate transcriptomics, spatial location, and morphological imaging data to identify spatial domains. However, these approaches are limited as they cannot incorporate additional omics modalities beyond transcriptomics. Most recent methods like SpatialGlue [ 19 ] and MISO [ 20 ] aim to learn unified low-dimensional representations from spatial multi-omics data. However, SpatialGlue does not support histology imaging as input, while MISO lacks explicit modeling of spatial coordinates. Additionally, MISO requires manual specification of low-quality modalities based on biological knowledge to mitigate their negative effects.

Spatial multi-omics technologies have emerged as a powerful approach for comprehensively dissecting complex tissues, providing insights beyond the reach of single-omics methods. Recent advances have enabled simultaneous profiling of multiple molecular features with spatial resolution. These technologies can be broadly categorized into sequencing-based and imaging-based strategies, each offering distinct advantages in resolution, throughput, and molecular coverage. Sequencing-based methods include DBiT-seq [ 1 ], spatial-CITE-seq [ 2 ], spatial ATAC-RNA-seq (assay for transposase-accessible chromatin and RNA using sequencing), CUT&Tag-RNA-seq [ 3 ], SPOTS [ 4 ], SM-Omics [ 5 ], Stereo-CITE-seq [ 6 ], spatial RNA-TCR-seq [ 7 ], and 10x Genomics Xenium Protein [ 8 ]. These methods rely on spatially barcoded molecular capture combined with next-generation sequencing to quantify multiple omics layers. In contrast, imaging-based platforms, including seqFISH+ [ 9 ], MERFISH [ 10 ], MERSCOPE for DNA [ 11 ], and NanoString CosMx [ 12 ], directly visualize molecular targets in situ through high-throughput multiplexed fluorescence imaging, providing subcellular spatial resolution. These technologies typically produce two or more spatially resolved omics layers from the same tissue section, often accompanied by corresponding high-resolution hematoxylin and eosin (H&E) stained images.

We benchmarked SpaMOAL against two recent representative methods—SpatialGlue and MISO—across 11 datasets comprising 17 samples ( S1 Table ). These datasets include: (i) seven simulated datasets generated using scMultiSim [ 31 ] or non-negative spatial factorization methods following the procedure by Townes and colleagues [ 32 ], spanning diverse biological scenarios. Each dataset comprises 500–12,965 cells and covers two to three omics modalities (e.g., RNA, ATAC, and protein). Three datasets also include synthetic H&E-like images with added noise to provide morphological modality; (ii) a mouse brain development dataset profiled by MISAR-seq [ 33 ], which jointly measures spatially resolved chromatin accessibility and transcriptome. Four tissue sections spanning different developmental stages (E11, E13.5, E15.5, and E18.5) were analyzed, each encompassing 1,263–2,129 spots with varying expert annotations; (iii) a juvenile mouse brain dataset comprising a postnatal day 22 (P22) coronal section, profiled for the active promoter mark H3K4me3, enhancer mark H3K27ac, histone modification mark H3K27me3, and ATAC [ 3 ], encompassing 9,215–9,752 pixels and annotated with 16 anatomical regions from the Allen Brain Atlas; (iv) an embryonic day 13 (E13) mouse brain dataset generated by spatial co-assay ATAC-RNA-seq, containing 2,187 pixels [ 3 ]; (v) a 10X Visium human breast cancer dataset covering spatial transcriptomics, spatial proteomics (with 35 protein markers), and histological imaging, containing 4,169 spots [ 34 ]. Clustering performance on the simulated and mouse brain development datasets (which have gold-standard annotations) was assessed through visualizations in addition to six quantitative metrics: the Adjusted Rand Index (ARI), Adjusted Mutual Information (AMI), Normalized Mutual Information (NMI), Homogeneity, Mutual Information (MI), and V-measure. For datasets lacking expert-annotated labels, the evaluations were based on marker-based and functional biological validation.

For each modality, the graph convolutional layer maps the input features (dimension F = 3,000) to a hidden representation of 256 dimensions. The subsequent MLP modules and ), each consist of a single linear layer that directly maps the GCN output to the common representation space (c_dim = 8 dimensions) and private representation space (p_dim = 2 dimensions), respectively. The model is trained using the Adam optimizer with an initial learning rate of and a weight decay of for 100 epochs.

where , and are non-negative weighting parameters. Based on empirical validation across diverse datasets, parameter is empirically recommended as , and . A parameter sensitivity analysis for the above parameters has been conducted, with relevant results provided in S1 Fig . All private representations are concatenated with the shared variable to form the final representations . The resulting representations are clustered using mclust [ 29 ], with Leiden and Louvain provided as alternative choices [ 30 ]. For all other parameters, we use the default values provided in the code.

where , , and indicate the private representations of node , , and , and , s is the number of sampled , is the cosine similarity operation and is a temperature parameter that adjusts the smoothness of similarity scores to control the model's ability to distinguish between positive and negative samples.

The contrastive objective encourages neighboring spots belonging to the same group to remain close (i.e., positive pairs), while pushing apart those from different groups (i.e., negative pairs). This spatially constrained optimization emphasizes microenvironment-level coherence and boundary discrimination, enabling each modality to capture locally discriminative patterns. Formally, the contrastive loss is defined as:

where denotes the reconstructed feature vector of node , is the original input feature and represents the average feature of node ’s k nearest neighbors in the spatial graph. The first term enforces accurate node feature reconstruction, and the second term preserves neighborhood consistency in the spatial graph.

where , and indicates the weight of self-connections. is the degree matrix of , is the activation function, and is the trainable weight matrix of . The encoded representations are passed through two MLPs with unshared parameters (i.e., and ) to derive common and private/modality-specific representations (i.e., and ), respectively.

The adjacency matrix , self-loops are added to each spot, and denotes the number of nodes. For each modality, the node features are defined as , where denotes the random dropout and denotes the original node features, F denotes the dimension of node features. To ensure dimensional consistency across modalities, is unified to 3,000 (the default target dimension), with lower-dimensional modalities zero-padded to match this size.

Based on the spatial coordinates, SpaMOAL constructs adjacency matrix and builds modality-specific spatial neighbor graphs , where is the number of modalities. Each graph corresponds to modality , with node set and edge set . Spatial relationships are defined by the Euclidean distances between spots’ spatial coordinates: two spots are connected if one lies among the (default , optional) nearest neighbors of the other.

For each spot, SpaMOAL segments a corresponding image tile centered on its spatial coordinates from the raw histology image. Morphological features are extracted from these tiles using ImageNet pretrained CNNs (Inception_v3 [ 27 ] or ResNet50 [ 28 ] via transfer learning. All layers except the classifier are retained, and the output of the final convolutional layer is used as the spot-level morphological representation.

SpaMOAL provides standardized preprocessing steps for RNA, protein, and chromatin accessibility, while allowing user-defined customization. For transcriptomic data, spots expressing fewer than 10 genes were filtered out. Gene expression counts were then log-transformed and normalized by library size using the SCANPY package, and the top 3,000 highly variable genes (HVGs) were selected for graph construction. For the ADT (antibody-derived tag) data, the counts were normalized using the centered log-ratio across spots, followed by principal component analysis (PCA) [ 23 ]. For the chromatin accessibility data, a GeneScore matrix was constructed from peak counts using Seurat, followed by read-count (RC) normalization and selection of the top 3,000 highly variable features through variance-stabilizing transformation. For imaging data, such as H&E-stained histology or immunofluorescence images, SpaMOAL directly processes raw high-resolution images and performs feature extraction using pretrained CNNs. Morphological features are extracted using specific CNN architectures (ResNet50 or Inception v3). The network weights are initialized using pretraining data from the ImageNet database. Regarding the CNN structure, we retain the full convolutional feature extractor while discarding the final classification head. For our pooling strategy, we apply Global Average Pooling (GAP) to the output of the last convolutional layer, compressing the spatial dimensions to yield a continuous, 1D spot-level morphological representation.

SpaMOAL is an efficient and scalable algorithm for integrative multi-omics analysis, capable of simultaneously processing spatial transcriptomics, spatial proteomics, spatial chromatin accessibility, and other omics data, together with spatial coordinates and morphological images. Unlike approaches that require manual specification of low-quality modalities, SpaMOAL does not explicitly assign modality-quality weights. Its disentangled design preserves consistent biological signals in common representations, while isolating modality-specific variations (including noise) within private representations. This strategy helps reduce the influence of noisy modality-specific signals on the global shared representation.

Materials from SciDraw [ 22 ] and BioIcons [ 23 , 24 ]. SpaMOAL integrates spatial coordinates and omics/image features using KNN to build modality-specific spatial neighbor graphs. Graph encoders and an MLP decouple these graphs into common and modality-specific (private) representations. The model is optimized via four joint losses: (1) reconstruction loss for graph and feature consistency; (2) correlation loss to ensure independence between common and private features; (3) matching loss for cross-modal alignment; and (4) contrastive loss for local spatial discrimination. The concatenated representations are output for downstream analyses.

The basic idea of SpaMOAL is to learn multimodal representations through a graph contrastive autoencoder framework that integrates multi-omics molecular profiles, spatial coordinates, and morphological information. It aims to capture both shared and modality-specific variation across modalities within a unified representation space [ 21 ]. As illustrated in Fig 1 , SpaMOAL first constructs an adjacency matrix based on spatial coordinates and augments it with omics- or image-derived features to build modality-specific spatial neighbor graphs, where each node represents an individual spot or cell. Modality-specific graph encoders are then employed to obtain low-dimensional representations that are further processed by a multilayer perceptron (MLP) module to separate shared and modality-specific components. To optimize these representations, SpaMOAL jointly leverages four complementary objectives to balance reconstruction-based structural consistency, cross-modal alignment, and modality-specific discrimination. A reconstruction term ensures accurate node-feature reconstruction and preserves neighborhood consistency in each modality’s spatial graph. A correlation loss encourages statistical independence between shared and modality-specific representations by minimizing the Pearson correlation coefficients between them. A matching objective further aligns shared representations across modalities, while a contrastive objective enhances within-modality discrimination by pulling similar spots/cells closer and pushing dissimilar ones apart within spatially local microenvironments in the private embedding space. The learned shared representations and private representations are concatenated into unified representations for downstream analyses, particularly for identifying spatial domains through clustering.

Results

SpaMOAL achieves state-of-the-art performance across diverse multi-omics simulated data To quantitatively assess SpaMOAL’s performance, we benchmarked it against SpatialGlue and MISO on seven simulated datasets with known ground-truth labels, including RNA-ATAC dual-omics (Simulations 1, 4, and 6), RNA-ATAC-protein triple-omics (Simulation 2), and image-integrated RNA-ATAC multi-omics (Simulations 3, 5, and 7), each representing distinct yet complementary facets of the ground truth. The RNA-ATAC dual-omics datasets were generated using scMultiSim [31]. To simulate morphological image data, we added random noise to the ground truth annotations to mimic the visual characteristics of H&E-staining. The RNA-ATAC-protein triple-omics dataset was generated following the approach implemented in SpatialGlue. In the RNA-ATAC dual-omics simulation (Simulation 1), SpaMOAL successfully identified all four simulated spatial domains with high structural continuity in the visualization results. However, SpatialGlue incorrectly identifies both domain1 and domain4. MISO produced fragmented points within domain1 and domain2. And it failed to distinguish the spatially continuous domain3 and domain4 (Fig 2A, 2B). In the RNA-ATAC-protein triple-omics simulation (Simulation 2), both SpaMOAL and SpatialGlue accurately resolved four spatial domains along with a technical noise background. In contrast, MISO incorrectly fragmented the spatially coherent distributed domain1 into scattered spots, which is likely because of the absence of explicit spatial constraints in its model architecture (Fig 2C, 2D). In the H&E-integrated dual-omics simulation (Simulation 3) (Fig 2E), SpaMOAL precisely identified all four spatial domains and their boundaries. SpatialGlue correctly delineated the narrow domain3 and its borders, but failed to fully separate domain4 and domain1. MISO only accurately identified domain2, while the remaining three domains showed substantial deviations in both localization and boundary definition compared to the ground truth. Quantitative metrics confirmed SpaMOAL outperformed MISO and SpatialGlue across all evaluation metrics (Fig 2F). PNG larger image

TIFF original image Download: Fig 2. SpaMOAL accurately identifies spatial domains in simulated multi-omics data. A, Spatial plots of Simulation1, which integrates RNA modality and ATAC modality. B, Quantitative evaluation results of methods based on six supervised metrics for Simulation1. C, Spatial plots of Simulation2, which integrates Protein modality, RNA modality, and ATAC modality. D, Quantitative evaluation results of methods for Simulation2. E, Spatial plots of Simulation3, which integrates RNA modality, ATAC modality, and H&E images. F, Quantitative evaluation results of methods for Simulation3. G, Information statistics (including modality composition, dataset size, feature number) of all seven simulated datasets. H, Box plots of six supervised metrics for three methods across seven simulated datasets. The asterisk (✳) indicates statistically significant improvement of SpaMOAL compared to the second-best performing method for each metric based on the Wilcoxon signed-rank test (p < 0.05). The underlying the data can be found in the Zenodo repository (https://doi.org/10.5281/zenodo.22067852). https://doi.org/10.1371/journal.pbio.3003690.g002 To further assess robustness, four additional simulated datasets with varied parameters were generated, the visualization results are in S2 Fig. The statistics of all seven simulated datasets were summarized in Fig 2G. Quantitative assessments using the same metrics demonstrated that SpaMOAL consistently achieved superior accuracy and stability (Fig 2H, S2 Table).

SpaMOAL accurately delineates spatial domains in the development of mouse brain tissues profiled by MISAR-seq To assess the ability of SpaMOAL to resolve fine-grained spatial domains within complex and dynamically developing tissues, we compared it with two representative methods on a multimodal spatial dataset of the developing mouse fetal brain generated by MISAR-seq. MISAR-seq is an advanced spatial multi-omics sequencing platform that jointly profiles high-quality spatial transcriptomics (RNA-seq) and chromatin accessibility landscapes (ATAC-seq) from the same tissue sections. We analyzed four embryonic stages, including E11.0, E13.5, E15.5, and E18.5, to capture a broad spectrum of neurodevelopmental progression. For each stage, we systematically evaluated clustering performance using individual modalities (RNA or ATAC), and further compared the performance of SpaMOAL against SpatialGlue and MISO. SpatialGlue, which does not support image input, was evaluated only without H&E images, whereas SpaMOAL and MISO were evaluated under two settings (with and without H&E images). Expert-curated spatial domain annotations from the original study were served as the gold standard for evaluation (Fig 3A, 3C and 3E, S4A). PNG larger image

TIFF original image Download: Fig 3. SpaMOAL accurately delineates spatial domains during mouse brain development using MISAR-seq. A, Anatomic annotation of major tissue regions based on the H&E images for E11.0 mouse brain. B, Spatial plots of the E11.0 mouse brain with unimodal clustering (left) and clustering results (right) from spatial multi-omics integration methods—SpatialGlue, MISO and SpaMOAL. C, Anatomic annotation of major tissue regions based on the H&E images for E13.5 mouse brain. D, Spatial plots of the E13.5 mouse brain. E, Anatomic annotation of major tissue regions based on the H&E images for E15.5 mouse brain. F, Spatial plots of the E15.5 mouse brain. https://doi.org/10.1371/journal.pbio.3003690.g003 Visual inspection of clustering outcomes indicated that single-omics analyses, based on either RNA or ATAC data alone, failed to capture the coherent spatial organization of the developing mouse brain, producing fragmented and disordered domains. At the E11.0 stage, both SpaMOAL (with image) and SpatialGlue accurately identified the Pallium region as a continuous ring-like structure (region 1), in alignment with known neuroanatomical features. Notably, only SpaMOAL (with and without image) was able to resolve the Cartilage domain as a smooth and spatially coherent structure (region 2). Whereas MISO misassigned the Cartilage domain, producing fragmented and discontinuous partitions (Fig 3B). At the E13.5 stage, SpaMOAL (with image) uniquely recovered the Tongue/muscle domain (region 6) as a distinct spatial domain, while all other methods failed to resolve this region (Fig 3D). Incorporation of image features enabled SpaMOAL to further refine the previously merged region (identified as region 4 without image) encompassing the hindbrain and midbrain into hindbrain (region 4) and midbrain (region 3), suggesting that high-resolution morphological cues provide additional spatial constraints that enhance the fidelity of domain delineation. In contrast, MISO—regardless of image integration—consistently produced fragmented and mosaic-like spurious domains, obscuring the underlying true anatomical structures. At the later E15.5 stage, SpaMOAL (with or without image) and SpatialGlue accurately recovered canonical neurodevelopmental structures, including the DPallm (region 1), DPallv (region 2), and Subpallium (region 10). Both SpaMOAL (with or without image) and MISO (with image) faithfully delineated the regularly arranged Muscle domain (region 6). Notably, at this stage, SpaMOAL, independent of image usage, was the only method capable of correctly identifying the Skull (region 7) and Thalamus (region 8) as discrete and spatially localized domains, whereas all other methods failed to reconstruct these regions (Fig 3F). Integration of image information, SpaMOAL further allowed to refine the boundaries of morphologically ambiguous tissues, such as Cartilage (region 5) and Mesenchyme (region 12). These observations indicated that such tissue structures might rely more heavily on morphological cues, thereby giving SpaMOAL’s triple-modal integration (RNA, ATAC, and H&E image) a clear advantage in resolving their spatial architecture. Results of E18.5 are shown in S4B, and quantitative metrics for each stage are provided in S4C–S4F. Taken together across developmental stages, the quantitative metrics further supported the visualization results, with SpaMOAL consistently outperforming MISO and SpatialGlue (S4G). SpaMOAL achieved the closest agreement with expert anatomical annotations, underscoring its superior performance in multimodal data integration and in resolving complex tissue architectures.

SpaMOAL accurately identifies spatial domains in mouse brain and embryonic tissue samples (spatial ATAC-RNA-seq) To further demonstrate SpaMOAL’s capability to resolve complex spatial domains in higher-resolution spatial multi-omics data, we applied it to mouse brain datasets jointly profiling transcriptomic and epigenomic features. Specifically, we analyzed a postnatal day 22 (P22) mouse brain coronal section profiled using spatial ATAC-RNA-seq, which simultaneously measures mRNA expression and chromatin accessibility within a single tissue slice. This dataset included four sample types: (1) RNA-seq combined with CUT&Tag-seq targeting the active promoter mark H3K4me3; (2) RNA-seq combined with CUT&Tag-seq targeting the enhancer mark H3K27ac; (3) RNA-seq combined with CUT&Tag-seq targeting the repressive mark H3K9me3; and (4) RNA-seq combined with ATAC-seq. To assess the correspondence between SpaMOAL-inferred spatial domains and true anatomical structures, we used high-resolution annotations from the Allen Brain Atlas as the reference. These annotations covered major brain regions, including cortex layers (ctx), genu of the corpus callosum (ccg), lateral septal nucleus (ls), nucleus accumbens (acb), ventrolateral thalamic nucleus (vl), anterior commissure (aco), and caudate putamen (cp) (Fig 4A). In this benchmark, we systematically evaluated the performance of SpaMOAL against unimodal baselines (RNA-only and ATAC-only) and existing multi-omics approaches (SpatialGlue and MISO). PNG larger image

TIFF original image Download: Fig 4. SpaMOAL accurately identified spatial domains in mouse brain samples (RNA-seq and CUT&Tag-seq) and mouse embryonic tissues (RNA-seq and ATAC-seq). A, Annotated reference of the mouse brain coronal section from the Allen Mouse Brain Atlas. B, Spatial plots of RNA and H3K4me3 with unimodal clustering and clustering results from spatial multi-omics integration methods—SpatialGlue, MISO and SpaMOAL. C, Spatial plots of RNA and H3K27ac dataset. D, Separate spatial plots of cluster 6 and cluster 12 identified by SpaMOAL in the mouse brain P22 sample (spatial-CUT&Tag-RNA-seq, H3K4me3). E, Spatial plots of cluster 15 and heatmaps of its marker genes Cldn11 and Ugt8a, along with spatial plots of cluster 17 and its marker genes Top2a and Ccnd2. F, H&E-stained histology image of an adjacent tissue section. G, Spatial plots of mouse embryo with unimodal clustering(left) and clustering results(right) from spatial multi-omics integration methods—SpatialGlue, MISO and SpaMOAL. H, The Ventricular zone spatial plots of clusters identified by SpaMOAL in the mouse embryo, and spatial gene expression plots of marker genes for the Ventricular zone. I, Separate spatial plots of cluster 3, cluster 10 and cluster 14 identified by SpaMOAL in the mouse embryo. https://doi.org/10.1371/journal.pbio.3003690.g004 Across the global visualizations of sample 1 (Fig 4B) and sample 2 (Fig 4C), we observed clear differences among methods in their ability to recover spatial organization. Unimodal approaches (RNA-only or ATAC-only), which lack complementary molecular signals and explicit spatial constraints, tend to produce more fragmented and spatially dispersed clusters that fail to delineate continuous anatomical structures. Although MISO leverages multi-omics signals and could recover certain regions (e.g., the ccg), it does not explicitly model spatial coordinates, resulting in fragmented output and numerous spurious micro-clusters. In contrast, SpaMOAL exhibits markedly improved spatial coherence, yielding smoother domain boundaries and more continuous structures. Specifically, in sample 1, all three multi-omics integration methods, SpaMOAL, MISO, and SpatialGlue, consistently identified the aco and the ccg (cluster 12), indicating that these regions exhibit robust molecular signatures across methods. Notably, SpaMOAL and SpatialGlue were able to accurately resolve and fully delineate the vl (cluster 6), a narrow laminar structure situated between the ccg, ls, and cp. This region functions as a critical relay station in the motor thalamo-cortical circuit, contributing to motor initiation, temporal coordination, and parkinsonian bradykinesia. By contrast, MISO offered an incomplete depiction of this fine-grained structure, underscoring their limitations in resolving regions with weak signals or diffuse boundaries (Fig 4B and 4D). In sample 2, consistent with the results from sample1, SpaMOAL, MISO, and SpatialGlue consistently detected the aco and ccg (cluster 15), whereas accurate resolution and complete delineation of the vl (cluster 17) were achieved only by SpaMOAL and SpatialGlue (Fig 4C). SpaMOAL uniquely identified a thin outer ctx (cluster 16) at the periphery of the ccg. This region exhibits weak signal intensity, sparse cellularity, and subtle laminar morphology, corresponding to an anatomically elusive marginal layer (Fig 4E). The other two methods failed to resolve this structure, indicating their limited sensitivity to weak chromatin accessibility or low-abundance transcriptional signals. Together, these results demonstrated that SpaMOAL achieves superior resolution and spatial coherence in dissecting complex brain architectures, enabling not only the identification of canonical anatomical domains but also the discovery of morphologically cryptic and transcriptionally subtle laminar structures. Results of sample 3 and sample 4 were presented in S7 Fig. We next extended our analysis to a spatial ATAC-RNA-seq dataset derived from embryonic day 13 (E13) mouse brain sections (Fig 4F). Visualization results revealed that SpaMOAL (with or without image integration) accurately delineated the fine substructure of the ventricular zone (cluster 3) within the telencephalon, demonstrating strong capacity for spatial multi-omics resolution (Fig 4G). Anatomically, the ventricular zone is primarily composed of neural stem and progenitor cells, with canonical markers such as MKI67 and TOP2A being highly enriched in neural progenitors [35]. Our spatial partitioning results validated by these molecular features: the high expression of MKI67 and TOP2A exhibited pronounced spatial colocalization with cluster 3, thereby confirming its correspondence to neural progenitor populations within the ventricular zone (Fig 4H). Notably, upon integration of histological images, SpaMOAL exhibited markedly improved in resolution and accuracy, clearly distinguishing the three major telencephalic subregions-ventricular zone (cluster 3), subpallium (cluster 14), and pallium (cluster 10) (Fig 4I). This observation underscores the advantage of SpaMOAL in image-guided integration and highlights its potential to achieve high-precision, multimodal spatial characterization in complex developing tissues.

[END]
---
[1] Url: https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3003690

Published and (C) by PLOS One
Content appears here under this condition or license: Creative Commons - Attribution BY 4.0.

via Magical.Fish Gopher News Feeds:
gopher://magical.fish/1/feeds/news/plosone/