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Post-Hoc Long-Read Sequencing Links Leukemic Mutation Status to Single-Cell Transcriptomes.

Single-cell RNA-sequencing-based characterization of cells that belong to the neoplastic clone is a major challenge in hematologic neoplasms, where malignant and normal cells coexist. Confident molecular profiling requires simultaneous analysis of gene expression and genetic mutations in individual cells, an ability that is not supported by the standard 10X Genomics workflow. Here, we systematically evaluated the potential and limitations of repurposing amplified cDNA generated during the 10X Genomics 3' workflow for post hoc genotyping of individual cells. We first established a mixed leukemic cell line system comprising one cell line with KIT point mutations and another with the BCR::ABL1 fusion gene. Targeted long-read PacBio sequencing enabled post hoc assignment of mutation data to transcriptionally profiled cells, but recovery differed between targets. Consistent with ambient RNA in microfluidics-based single-cell workflows, mutation-associated transcripts were detected in cells not expected to carry the corresponding mutations, illustrating how transcript recovery complicates cell-level genotype assignment. Target-specific thresholds mitigated this source of misclassification. In primary chronic myeloid leukemia samples, the post hoc approach detected BCR::ABL1-positive cells at diagnosis, but not during imatinib treatment. Together, we present a framework for adding mutation status to cells already profiled using the 10X Genomics workflow and highlight broader considerations for transcript-based single-cell genotyping.

BCR::ABL1

VINE-seq and MultiVINE-seq for single-nucleus and multiome profiling of the brain vasculature.

The human cerebrovasculature is a critical yet historically understudied component of neurological health. Dysfunction of the diverse endothelial, mural, and perivascular cells that comprise cerebral vessels is central to diseases ranging from stroke to Alzheimer's disease. However, characterizing these cell populations at a molecular level has proven exceptionally challenging. Encased within a robust basement membrane, vascular cells resist standard dissociation methods, leading to their systematic depletion and underrepresentation in existing single-nucleus genomic atlases. This has created a major blind spot in neuroscience. To overcome this barrier, we developed vessel isolation and nucleus extraction for sequencing (VINE-seq) and its advanced iteration, MultiVINE-seq. The protocol provides a robust, reproducible workflow for the enrichment and high-resolution profiling of vascular, perivascular, and immune cells from fresh or frozen human and mouse brain tissue. First, intact vessels (predominantly capillaries and small arterioles/venules, 100 µm in diameter) are isolated from homogenized brain tissue via dextran-based density-gradient centrifugation, separating the vascular pellet from myelin and the parenchymal fraction. Second, the collected vessels are rigorously washed over a cell strainer to remove trapped contaminants. A critical innovation lies in the third stage: the optimized extraction of nuclei from purified vessels using enzymatic digestion. After extraction, the protocol uses fluorescence-activated cell sorting (FACS) to ensure collection of high-purity nuclei suitable for widely used droplet-based sequencing platforms (e.g., 10x Genomics single cell 3' or multiome). This protocol requires 4-5 h to complete and can be carried out by researchers with single-cell and flow cytometry training.

Journal Article

Exploratory single-nucleus multiomics analysis of myeloid cell states associated with neoadjuvant chemotherapy response in pancreatic ductal adenocarcinoma.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) continues to be one of the most lethal human malignancies, with the vast majority of patients ineligible for immunotherapy. Tumour-associated macrophages (TAMs) are key regulators of the PDAC tumour microenvironment (TME), yet their transcriptional and epigenomic heterogeneity in the context of chemotherapy response is poorly understood. Therefore, we performed an exploratory single nucleus multiomics analysis of PDAC tumors stratified by histopathologic response to neoadjuvant chemotherapy. METHODS: Surgical resection specimens from PDAC patients were classified as responders or non-responders using the American College of Pathologists (CAP) histopathologic criteria. Frozen tissue underwent simultaneous snRNA-seq and snATAC-seq on the 10x Genomics Chromium Single Cell Multiome platform, followed by downstream analyses such as differential gene expression, GO and hallmark pathway enrichment, pseudotime trajectory inference and ChromVAR transcription factor motif analysis. RESULTS: Multiomics profiling of 30 840 high-quality nuclei revealed a myeloid compartment that differed in composition and transcriptional state between CAP-defined responders and non-responders in this small cohort. We observed a trend toward higher LAM-like state proportions in the responders than non-responders (38.4% vs. 26.7%), although this disparity did not achieve statistical significance. The transcriptional programs of the responder myeloid cells are associated with phagocytosis and lipid handling. Chromatin accessibility analysis further suggested candidate response-associated transcription factor motif accessibility patterns. CONCLUSIONS: Neoadjuvant-treated PDAC tumours from CAP-defined responders in this cohort myeloid landscape with apparent enrichment of LAM-like states and immune-activating transcriptional/epigenetic programs. However, these findings are preliminary and hypothesis-generating because of the small cohort size, heterogeneous treatment regimens, absence of matched pre-treatment biopsies, and lack of knockout validation. Larger treatment cohorts and functional/mechanistic studies are needed to determine whether LAM-like myeloid programs contribute to chemotherapy response or reflect a consequence of chemotherapy treatment.

Humans

scBaseCount: An AI agent-curated, standardized, auto-updated single-cell data repository.

Single-cell RNA sequencing has transformed cell biology by enabling precise transcriptomic measurements of individual cells. The Sequence Read Archive (SRA) is the largest public repository of sequencing reads, yet much of it remains underutilized due to unstandardized metadata. Here, we introduce scBaseCount, a database that leverages an AI agent to automate discovery and metadata extraction and standardize data processing. Built by mining all 10x Genomics datasets, scBaseCount is the largest public repository of single-cell gene expression data, comprising over 502 million cells across 27 organisms and 75 tissues. It offers an unbiased view of the data landscape within the SRA and enables the training of more performant computational models through access to broader phenotypic diversity. Uniform processing enables measurement of both intronic and exonic reads and non-coding gene expression and improves alignment across experiments. Moreover, scBaseCount provides a blueprint for how AI can be leveraged to autonomously curate biological data repositories.

Single-Cell Analysis

Synovial short-lived plasma cells mediate adalimumab resistance in rheumatoid arthritis via MIF-CD74 axis-driven, partially TNF-α-independent inflammation.

OBJECTIVE: Synovial plasma cell infiltration predicts inadequate response to adalimumab in patients with rheumatoid arthritis (RA), yet the cellular and molecular mechanisms underlying this association remain unclear. This study aimed to dissect the functional heterogeneity of synovial plasma cells between adalimumab responders and non-responders at single-cell resolution, and to identify the molecular pathways driving treatment resistance. METHODS: This study was based on a prospective clinical cohort of 101 RA patients receiving adalimumab, from which synovial tissues of 8 patients (4 ACR20 responders and 4 non-responders) were profiled by 10x Genomics single-cell RNA sequencing (66,539 high-quality cells). A systematic ligand-receptor screening was performed to identify candidate signaling axes. Core findings were validated at four levels: an independent single-cell validation cohort (n = 4), external bulk RNA-seq cohorts (GSE15602, GSE47726), multiplex immunofluorescence on synovial tissues (n = 9 per group), and in vitro functional experiments using patient-derived peripheral blood monocyte-derived macrophages stimulated with recombinant human MIF under pharmacological intervention with adalimumab, the MIF inhibitor ISO-1, and an anti-CD74 neutralizing antibody. RESULTS: Plasma cells were significantly enriched in non-responder synovium, with a heterogeneous pattern characterized by quantitative accumulation of long-lived plasma cells (LLPCs) and functional dominance of short-lived plasma cells (SLPCs): SLPCs contributed 58.15% of total ribosomal module activity and preferentially overexpressed MIF. Systematic screening of 145 candidate ligand-receptor pairs identified MIF-CD74 as the only axis satisfying all four independent evidence layers. Tissue-level immunofluorescence confirmed that approximately 95% of synovial CD138+ plasma cells in non-responders co-expressed MIF, compared with approximately 45% in responders. In vitro, rh-MIF upregulated macrophage activation markers (CD74, CD80, CD86, HLA-DR) and induced IL-6 and TNF-α secretion. Adalimumab neutralized supernatant TNF-α but failed to suppress MIF-driven IL-6 and IL-1β activation, whereas ISO-1 and anti-CD74 effectively blocked MIF-induced effects at all levels examined. These findings were replicated in patient-derived PBMC macrophages. CONCLUSION: In adalimumab-resistant RA, a functionally active SLPC subset drives partially TNF-α-independent macrophage inflammation through the MIF-CD74 axis, representing a resistance pathway not fully addressed by anti-TNF therapy. Targeting MIF or CD74 blocked this axis in vitro, supporting MIF-CD74-directed precision intervention.

Adalimumab

Integrating histology and spatial transcriptomics via multimodal transformers and contrastive representation learning for accurate gene expression prediction.

Predicting spatial gene expression from Histological images is a fundamental task in understanding tissue organization and molecular phenotypes. However, existing methods often rely on single-model representations or lack effective alignment between image and transcriptomic features. To address these limitations, we propose a unified multimodal learning framework that integrates histological imaging and spatial transcriptomics through a shared latent representation space. Specifically, histological H&E images are encoded by a ResNet50-based convolutional stem and a MobileViT Transformer backbone to extract hierarchical visual representations. Both modalities are projected into a shared latent space via linear-GELU-dropout transformation blocks, enabling cross-modal alignment through a contrastive learning objective that maximizes agreement between the corresponding image and the spot embeddings. Experimental results on the 10x Genomics Visium dataset of human liver tissue demonstrate that MViTGene achieves significantly higher prediction accuracy than existing methods across multiple gene subsets, with improvements of 20%, 33%, and 12% in predicting marker genes, highly expressed genes, and highly variable genes, respectively. The significant improvement in relevance indicates that the model can more accurately capture the true correspondence between tissue morphology and gene expression, therefore enabling more reliable biological interpretation. It provides a computational tool for high-throughput spatial gene expression prediction that balances performance and interpretability.

Humans

Single-cell transcriptomics on FFPE placenta: A novel method for comprehensive exploration of an entire placental section.

INTRODUCTION: The placenta's complex cellular diversity challenges traditional transcriptomic analyses. Single-cell RNA sequencing (scRNA-seq) offers breakthrough capabilities by enabling transcriptome profiling at the single-cell level. However, traditional scRNA-seq relies on fresh or frozen samples, which present practical storage and quality challenges. Applying scRNA-seq to Formalin-Fixed, Paraffin-Embedded (FFPE) placentas could harness archived samples for clinical insights. METHODS: We used 10x Genomics Flex technology to analyze 8 non-pathological placentas ranging from 21 + 6 weeks of gestation (WoG) to 39 + 4 WoG. RESULTS: Our approach identifies diverse cell populations and allows us to discern maternal from fetal cells. Despite sample size limitations, the method yields comparable data to prior fresh/frozen tissue studies and we complete these data by integrating new molecular markers. The potential to correlate single-cell results with histopathology enables us to conduct an in-depth analysis across entire placental sections by concurrently addressing both fetal and maternal cells. We could thus confirm molecular markers like KRT5/6 using immunohistochemistry by revisiting the slide. DISCUSSION: This innovation could aid in understanding focal anomalies observed on standard histology slides, thereby enhancing traditional histopathological assessments. Given its practicality, integrating our method into routine practice is both feasible and promising.

Differentially expressed genes (DEG)

Gene expression profiles of endothelium, microglia and oligodendrocytes in hippocampus of post-stroke depression rat at single cell resolution.

Post-stroke depression (PSD) is a common but severe mental complication after stroke. However, the cellular and molecular understanding of PSD is still yet to be illustrated. In current study, we prepared PSD rat model (MD) via unilateral middle cerebral artery occlusion (MCAO) and chronic stress stimulation (DEPR), and isolated hippocampal tissues for single cell sequencing of 10x Genomics Chromium. First, we determined the presence of the increased cell population of endothelium and microglia and the compromised oligodendrocytes in MD compared to NC, MCAO and DEPR. The enriched functions of highly variable genes (HVGs) of endothelium and microglia suggested a reinforced blood-brain barrier in MD. Next, cell clusters of endothelium, microglia and oligodendrocytes were individually analyzed, and the subtypes with distinct functions were identified. The presence of expression profiles, intercellular communications and signaling pathways of these three cell populations of PSD displayed a similar but more aggressive appearance with DEPR compared to MCAO and NC. Taken together, this study characterized the specific gene profile of endothelium, microglia and oligodendrocytes of hippocampal PSD by single cell sequencing, emphasizing the crosstalk among them to provide theoretical basis for the in-depth mechanism research and drug therapy of PSD.

Animals

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

CERTOMICS: trusted single-cell multiomics pipeline for high-resolution profiling of adoptive cellular immunotherapies.

SUMMARY: Adoptive cellular immunontherapies, such as chimeric antigen receptor (CAR) T cell therapy, have transformed cancer treatment, yet challenges such as resistance, relapse, and high costs limit their efficacy and accessibility. A comprehensive understanding of cellular heterogeneity and molecular profiles is essential to improve these therapies. Advanced single-cell multiomics technologies have the power to analyze the complex interactions between CAR-engineered cells, immune cells, and tumor cells. However, standardized single-cell multiomics computational pipelines specifically tailored to CAR-engineered cell products are lacking. Due to the synthetic nature of CAR transgenes, additional steps for reliable identification and characterization of CAR-positive cells are required but not included in existing data-processing workflows. To address this, we present CERTOMICS, a Nextflow-based, CAR-aware pipeline offering enhanced CERTainty in immunophenotyping and data interpretation, tailored for single-cell multiOMICSprofiling of adoptive cellular immunotherapies. The pipeline standardizes processing 10x Genomics single-cell multiomics data and integrates CAR-specific identification and quality control. Additionally, a curated repository of CAR construct sequences and annotation data is provided, serving as an extensible resource to support the analysis and development of CAR T cell therapies. AVAILABILITY AND IMPLEMENTATION: Detailed documentation of this pipeline, along with a resource on latest FDA-approved CAR therapies is available on our website: https://fraunhofer-izi.github.io/Living-Drugs-Wiki/. The data underlying this article are available on GitHub at https://github.com/fraunhofer-izi/CERTOMICS. The code is also published on Zenodo at https://doi.org/10.5281/zenodo.18709693.

Multiomics

Striping artifact removal in VisiumHD data through nuclear counts modeling.

MOTIVATION: 10x Genomics VisiumHD enables spatial transcriptomics at 2 µm × 2 µm resolution but exhibits slide-specific, non-periodic striping artifacts due to lane-width variability. These multiplicative row/column effects distort bin total counts and can bias downstream analyses. The state-of-the-art destriping approach is the normalization procedure used as a preprocessing step in bin2cell; it applies sequential high-quantile row- then column-wise normalization, which is asymmetric and can introduce edge effects/macro-stripes and distortions of large-scale total-count structure. RESULTS: We propose a statistical destriping approach that leverages nuclei segmentation from the co-registered H&E image. Assuming transcript abundance is constant within each nucleus, we model bin counts with a negative binomial distribution whose mean is a product of a nucleus-specific concentration and row- and column-specific stripe-factors reflecting lane-width variation. We fit all parameters in a generalized linear modeling framework with cross-validated regularization on stripe-factors and iterative dispersion estimation, and use the fitted parameters to correct the observed counts into a destriped image. On synthetic data with known ground truth, our method improves stripe-factor estimation accuracy and reduces error in corrected counts relative to bin2cell and bin2cell-derived baselines. Across four public VisiumHD slides, it consistently lowers striping intensity while substantially better preserving biological signal present in the large-scale global count structure and avoiding the artifacts introduced by other methods. AVAILABILITY AND IMPLEMENTATION: All source code and links to publicly available data used for this study are available at https://github.com/paolamalsot/destriping-GLM.

Artifacts

Representation learning for multi-modal spatially resolved transcriptomics data.

MOTIVATION: Spatial transcriptomics enables in-depth molecular characterization of samples on a morphology and RNA level while preserving spatial location. Integrating the resulting multi-modal data is an unsolved problem, and developing new solutions in precision medicine depends on improved methodologies. RESULTS: We introduce AESTETIK, a convolutional deep learning model that jointly integrates spatial, transcriptomics, and morphology information to learn accurate spot representations. AESTETIK yielded substantially improved cluster assignments on widely adopted technology platforms (e.g. 10x Genomics™, NanoString™) across multiple datasets. We achieved performance enhancement on structured tissues (e.g. brain) with a 21% increase in median ARI over previous state-of-the-art methods. Notably, AESTETIK also demonstrated superior performance on cancer tissues with heterogeneous cell populations, showing a 2-fold increase in breast cancer, 79% in melanoma, and 21% in liver cancer. We expect that these advances will enable a multi-modal understanding of key biological processes. AVAILABILITY AND IMPLEMENTATION: AESTETIK is implemented in Python 3 and is available as open source software at http://www.github.com/ratschlab/aestetik. The Snakemake pipeline for reproducing the results is available at http://www.github.com/ratschlab/st-rep.

Spatial Transcriptomics

A Molecularly Anchored Spatial Transcriptomic Framework for Precise CA1-Subiculum Parcellation and Region-Resolved Analysis in Alzheimer's Disease.

BACKGROUND: The precise molecular delineation of the interface between the Subiculum (Sub) and cornu ammonis 1 (CA1) is a challenge in hippocampal research, as conventional cytoarchitectural boundaries are often ambiguous and limit reproducible regional annotation. Here, we developed a molecularly anchored spatial transcriptomic framework to define CA1-Sub regional identities using high-definition spatial transcriptomics (Stereo-seq) and single-nucleus RNA sequencing (snRNA-seq) references. FINDINGS: Using a human hippocampal Stereo-seq dataset from 12 donors, we established a data-driven parcellation framework that defines reproducible molecular features distinguishing CA1 and Sub while capturing the transition between these regions. FN1 was identified as a Sub-enriched marker in a subset of EX_Sub and, together with ETV1 and additional regional markers, enabled molecular assignment of CA1 and Sub identities across datasets. The Sub association of FN1 and ETV1 was further supported by human 10X Genomics spatial transcriptomics, mouse in situ hybridization data, and a mouse spatial transcriptomic dataset. Applying this framework to Alzheimer's disease (AD) tissues revealed region-specific transcriptional alterations across CA1 and Sub, including enrichment of mitochondrial energy metabolism-related transcripts in the Sub, suggesting exploratory transcriptional associations of altered metabolic function. CONCLUSIONS: This study provides a molecularly anchored framework for human CA1-Sub parcellation that complements conventional annotation. By defining regional molecular states while preserving the biological continuum across CA1-Sub interface, this approach enables more consistent regional analysis of human hippocampus tissue across donors, datasets, and disease conditions.

Journal Article

Spatial transcriptomic analysis of mouse parathyroid gland cells expressing an activating variant of Gcm2.

Glial cells missing 2 (GCM2) is an essential transcription factor for the development of parathyroid glands. Germline GCM2 variants that repress or enhance transcriptional activity predispose a subset of patients to hypoparathyroidism or hyperparathyroidism, respectively. A recurrent germline heterozygous activating missense variant of GCM2, p.Y394S has been identified in some patients with primary hyperparathyroidism. A genetically engineered knock-in mouse model of this variant corresponding to p.Y392S in the mouse Gcm2 gene (Gcm2 +/Y392S) did not show obvious parathyroid tumors. However, in GCM2-binding site mediated luciferase reporter assays in HEK293 cells, the mouse and the human variant both exhibited enhanced transcriptional activity. Therefore, we assessed the effect of this variant on gene expression in vivo in parathyroid glands from Gcm2 +/Y392S and WT mice. Using the 10x Genomics Visium platform, spatially resolved transcriptomic analysis was performed on formalin-fixed and paraffin-embedded (FFPE) tracheal tissue sections of Gcm2 +/Y392S and WT mice to capture RNA from parathyroid glands together with other cell types in the tissue sections. Transcriptome sequence data analysis detected 8 different clusters in the tissue sections based on similarity of gene expression profiles. Cluster-1, which contained parathyroid gland cells expressing Pth and Gcm2, was further evaluated for transcripts that were differentially expressed more than 2-fold in Gcm2 +/Y392S compared to WT. Increased transcript level of Lgals3 (galectin-3) was seen in Gcm2 +/Y392S parathyroid gland cells which is among markers of parathyroid carcinoma. Galectin-3 protein was detected in available FFPE human parathyroid samples of patients with germline heterozygous activating GCM2 variants, p.Y394S (n = 4/10) or p.L379Q (n = 2/2). These results indicate a potential for growth and malignancy of parathyroid glands expressing GCM2 variants. The transcriptomic data of mouse parathyroid gland cells generated in this study can serve as a valuable resource for investigating genes and pathways in normal or abnormal parathyroid gland growth and physiology.

GCM2, gene

PoweREST: Statistical Power Estimation for Spatial Transcriptomics Experiments to Detect Differentially Expressed Genes Between Two Conditions.

Recent advancements in Spatial Transcriptomics (ST) have significantly enhanced biological research in various domains. However, the high cost of current ST data generation techniques restricts its application in large-scale population studies. Consequently, there is a pressing need to maximize the use of available resources to achieve robust statistical power. One fundamental question in ST analysis is to detect differentially expressed genes (DEGs) among different conditions using ST data. Such DEG analysis is often performed but the associated power calculation is rarely discussed in the literature. To address this gap, we introduce, PoweREST (https://github.com/lanshui98/PoweREST), a power estimation tool designed to support power calculation of DEG detection with 10X Genomics Visium data. PoweREST enables power estimation both before any ST experiments or after preliminary data are collected, making it suitable for a wide variety of power analyses in ST studies. We also provide a user-friendly, program-free web application (https://lanshui.shinyapps.io/PoweREST/), allowing users to interactively calculate and visualize the study power along with relevant the parameters.

Differentially expressed genes

A single-cell transcriptomic atlas of the pigtail macaque placenta in late gestation.

The placenta is a complex organ with multiple immune and non-immune cell types that promote fetal tolerance and facilitate the transfer of nutrients and oxygen. The nonhuman primate (NHP) is a key experimental model for studying human pregnancy complications, in part due to similarities in placental structure, which makes it essential to understand how single-cell populations compare across the human and NHP maternal-fetal interface. We constructed a single-cell RNA-Seq (scRNA-Seq) atlas of the placenta from the pigtail macaque ( Macaca nemestrina ) in the third trimester, comprising three different tissues at the maternal-fetal interface: the chorionic villi (placental disc), chorioamniotic membranes, and the maternal decidua. Each tissue was separately dissociated into single cells and processed through the 10X Genomics and Seurat pipeline, followed by aggregation, unsupervised clustering, and cluster annotation. Next, we determined the maternal-fetal origins of cell populations and analyzed single-cell RNA trajectory, Gene Ontology enrichment, and cell-cell communication. Single-cell populations in the pigtail macaque were strikingly similar in their identity and frequency to those found in the human placenta, including cells from trophoblast, stromal cell, immune, and macrophage lineages. An advantage of our approach was the deep sequencing of three tissues at the maternal-fetal interface, which yielded a rich diversity of common and rare single-cell populations. The third-trimester pigtail macaque single-cell atlas enables the identification of cellular subclusters analogous to those in humans and provides a powerful resource for understanding experimental perturbations on the NHP placenta.

Journal Article

Single-cell-scale spatial transcriptome of the developing and adult mouse ovary.

Mammalian ovary development is essential for female fertility, involving the complex spatial patterning of diverse cell types to establish the finite reserve of ovarian follicles. While single-cell transcriptome analyses have provided important insights into the mechanisms driving specification and developmental trajectories of ovarian cells, they disrupt this crucial spatial context. To overcome this limitation, we used 10X Genomics Visium HD spatial transcriptomics to analyze the developing mouse ovary while maintaining its native cellular architecture. We captured all ovarian cell types at eight key fetal and postnatal timepoints, generating a near single cell resolution library of spatial gene expression across ovarian development. This comprehensive dataset allows analysis of dynamic transcriptional signatures associated with unique spatial patterning throughout development, including the establishment of cortex and medulla and assembly of ovarian follicles in each region. This dataset represents a fundamental resource for the investigation of regulatory mechanisms driving spatial patterning of the ovary and opens new avenues to explore the spatial determinants of female fertility and reproductive longevity.

Journal Article

Unifying multimodal single-cell data with a mixture-of-experts β-variational autoencoder framework.

Multimodal single-cell assays profile complementary layers of cell state, but integration is complicated by modality mismatch, sparsity, and uneven cohort coverage. Here, we present Unified Variational Inference (UniVI), a scalable mixture-of-experts β-variational autoencoder that learns a shared latent space while preserving modality-specific structure. UniVI couples modality-specific encoders/decoders with a shared latent prior and a symmetric cross-modal alignment objective, enabling consistent integration of paired measurements without curated feature-link graphs or preannotated reference atlases; optional supervised heads can be added when labels are available. Across paired RNA-protein (CITE-seq) and RNA-chromatin (10x Genomics Multiome, SHARE-seq) data spanning human PBMCs and mouse back skin-a nonhematopoietic tissue with continuous differentiation hierarchies-UniVI produces coherent embeddings, improves label transfer, and enables cross-modal reconstruction and denoising. Extending to trimodal measurements, UniVI maintains robust three-way alignment among RNA, chromatin accessibility, and surface proteins (TEA-seq), and accommodates DNA methylation in a paired scNMT-seq mouse gastrulation proof-of-concept under beta-binomial likelihoods. Performance degrades gracefully under severe cell type imbalance and in the presence of modality-exclusive populations. In an acute myeloid leukemia mosaic design, a paired RNA-protein bridge anchors independent RNA-only and protein+genotype cohorts, revealing genotype-associated neighborhoods that sharpen with mutation-aware fine-tuning. UniVI thus provides a flexible, interpretable framework for multimodal integration across paired, trimodal, and mosaic study designs and supports practical reference-to-query projection in partially observed studies.

Journal Article