Spatial Transcriptomics Research Articles

Single-Cell and Spatial Transcriptomics Identify Immediate Early Genes as Central Transcriptomic Factors during Renal Endothelial Regeneration In Vivo

Single-Cell and Spatial Transcriptomics Identify Immediate Early Genes as Central Transcriptomic Factors during Renal Endothelial Regeneration In Vivo

Spatial transcriptomic analysis deciphers adipocyte-to-fibroblast transformation in bleomycin-induced murine skin fibrosis.

Scleroderma is characterized by inflammation and fibrosis, predominantly occurring in the skin and extending to various parts of the body. The pathophysiology of scleroderma is multifaceted, with the current understanding including endothelial damage, inflammatory cell infiltration, and fibroblast activation in its progression. Nonetheless, the mechanism of cellular interactions and the precise spatial distribution of these cellular events within the fibrotic tissues remain elusive, highlighting a critical gap in our comprehensive understanding of scleroderma's pathogenesis. In this study, we administered bleomycin intradermally to the dorsal skin of four individual murine models. Subsequently, skin tissues were harvested at predetermined intervals for comprehensive spatial transcriptomic analysis to determine the spatial dynamics influencing scleroderma pathogenesis. To validate the possible results from bioinformatic analysis, further in vitro and in vivo experiments were conducted. Analysis of the spatial transcriptome revealed significant alterations in cell clusters during the progression of scleroderma. Gene Ontology analysis identified disruptions in lipid metabolism as the disease advanced. Pseudotime analysis provided evidence for a phenotypic transition from adipocytes to fibroblasts. In vitro studies demonstrated increased expression of Col1a1 and α-SMA as the disease progressed. These fibroblasts have been identified as key contributors to the increasing inflammation. Co-culturing TGF-β induced adipocytes with RAW264.7 cells resulted in overexpression of pro-inflammatory cytokines in the RAW264.7 cells. Both in vitro and in vivo experiments confirmed adipocyte loss and fibroblast formation, with transformed fibroblasts showing pronounced pro-inflammatory characteristics, highlighting their crucial role in the disease mechanism. Our study showed the spatial distribution and dynamic alterations of various cell types during scleroderma progression. Crucially, we identified the transformation of adipocytes into fibroblasts as a key factor promoting disease advancement. These emergent fibroblasts intensify inflammation, indicating that research on these cell clusters could reveal key scleroderma mechanisms and guide future therapies.

Damping excessive viral-induced IFN-γ rescues the impaired anti-Aspergillus host immune response in influenza-associated pulmonary aspergillosis

Influenza-associated pulmonary aspergillosis (IAPA) is a severe fungal superinfection in critically ill influenza patients that is of incompletely understood pathogenesis. Despite the use of contemporary therapies with antifungal and antivirals, mortality rates remain unacceptably high. We aimed to unravel the IAPA immunopathogenesis as a means to develop adjunctive immunomodulatory therapies. We used a murine model of IAPA to investigate how influenza predisposes to the development of invasive pulmonary aspergillosis. Immunocompetent mice were challenged with an intranasal instillation of influenza on day 0 followed by an orotracheal inoculation with Aspergillus 4 days later. Mice were monitored daily for overall health status, lung pathology with micro-computed tomography (μCT) and fungal burden with bioluminescence imaging (BLI). At endpoint, high parameter immunophenotyping, spatial transcriptomics, histopathology, dynamic phagosome biogenesis assays with live imaging, immunofluorescence staining, specialized functional phagocytosis and killing assays were performed. We uncovered an early exuberant influenza-induced interferon-gamma (IFN-γ) production as the major driver of immunopathology in IAPA and delineated the molecular mechanisms. Specifically, excessive IFN-γ production resulted in a defective Th17-immune response, depletion of macrophages, and impaired killing of Aspergillus conidia by macrophages due to the inhibition of NADPH oxidase-dependent activation of LC3-associated phagocytosis (LAP). Markedly, mice with partial or complete genetic ablation of IFN-γ had a restored Th17-immune response, LAP-dependent mechanism of killing and were fully protected from invasive fungal infection. Together, these results identify exuberant viral induced IFN-γ production as a major driver of immune dysfunction in IAPA, paving the way to explore the use of excessive viral-induced IFN-γ as a biomarker and new immunotherapeutic target in IAPA. This research was funded by the Research Foundation Flanders (FWO), project funding under Grant G053121N to JW, SHB and GVV; G057721N, G0G4820N to GVV; 1506114N to KL and GVV; KU Leuven internal funds (C24/17/061) to GVV, clinical research funding to JW, Research Foundation Flanders (FWO) aspirant mandate under Grant 1186121N/1186123N to LS, 11B5520N to FS, 1SF2222N to EV and 11M6922N/11M6924N to SF, travel grants V428023N, K103723N, K217722N to LS. FLvdV was supported by a Vidi grant of the Netherlands Association for Scientific Research. FLvdV, JW, AC and GC were supported by the Europeans Union's Horizon 2020 research and innovation program under grant agreement no 847507 HDM-FUN. AC was also supported by the Fundação para a Ciência e a Tecnologia (FCT), with the references UIDB/50026/2020, UIDP/50026/2020, PTDC/MED-OUT/1112/2021 (https://doi.org/10.54499/PTDC/MED-OUT/1112/2021), and 2022.06674.PTDC (http://doi.org/10.54499/2022.06674.PTDC); and the "la Caixa" Foundation under the agreement LCF/PR/HR22/52420003 (MICROFUN).

SPP1+ macrophages in HR+ breast cancer are associated with tumor-infiltrating lymphocytes.

Breast cancer categorized into hormone receptor-positive (HR+), HER2-positive (HER2+), and triple-negative (TNBC) subtypes, exhibits varied outcomes based on the number of tumor-infiltrating lymphocytes (TILs). To explore the divergent roles of TIL levels across different subtypes, we employed single-cell RNA sequencing on 31 patients with breast cancer. HR+ breast cancer with high TIL levels (TIL-high) revealed increased SPP1+ macrophages, increased SPP1 expression in other monocytes/macrophages (mono/macro) subgroups, and enriched pathways associated with extracellular matrix (ECM) remodeling in mono/macro. Moreover, cell-cell interaction analyses revealed enhanced SPP1, MIF, and FN1 signaling in the interaction between SPP1+ macrophages and T-cells in TIL-high HR+ breast cancer. Spatial transcriptomics data highlighted the close proximity of SPP1+ macrophages, CD8+ T-cells, and CD4+ T-cells in TIL-high HR+ breast cancer. Our findings unveil the novel influence of SPP1+ macrophages on T-cells in TIL-high HR+ breast cancer, potentially explaining the poor prognosis and offering insights for targeted interventions.

Hypoxia induces ROS-resistant memory upon reoxygenation in vivo promoting metastasis in part via MUC1-C

Hypoxia occurs in 90% of solid tumors and is associated with metastasis and mortality. Breast cancer cells that experience intratumoral hypoxia are 5x more likely to develop lung metastasis in animal models. Using spatial transcriptomics, we determine that hypoxic cells localized in more oxygenated tumor regions (termed ‘post-hypoxic’) retain expression of hypoxia-inducible and NF-kB-regulated genes, even in the oxygen-rich bloodstream. This cellular response is reproduced in vitro under chronic hypoxic conditions followed by reoxygenation. A subset of genes remains increased in reoxygenated cells. MUC1/MUC1-C is upregulated by both HIF-1α and NF-kB-p65 during chronic hypoxia. Abrogating MUC1 decreases the expression of superoxide dismutase enzymes, causing reactive oxygen species (ROS) production and cell death. A hypoxia-dependent genetic deletion of MUC1, or MUC1-C inhibition by GO-203, increases ROS levels in circulating tumor cells (CTCs), reducing the extent of metastasis. High MUC1 expression in tumor biopsies is associated with recurrence, and MUC1+ CTCs have lower ROS levels than MUC1- CTCs in patient-derived xenograft models. This study demonstrates that therapeutically targeting MUC1-C reduces hypoxia-driven metastasis.

Spatial proteomics and transcriptomics of the maternal-fetal interface in placenta accreta spectrum

In severe Placenta Accreta Spectrum (PAS), trophoblasts gain deep access in the myometrium (placenta increta). This study investigated alterations at the fetal-maternal interface in PAS cases using a systems biology approach consisting of immunohistochemistry, spatial transcriptomics and proteomics. We identified spatial variation in the distribution of CD4+, CD3+ and CD8+ T-cells at the maternal-interface in placenta increta cases. Spatial transcriptomics identified transcription factors involved in promotion of trophoblast invasion such as AP-1 subunits ATF-3 and JUN, and NFKB were upregulated in regions with deep myometrial invasion. Pathway analysis of differentially expressed genes demonstrated that degradation of extracellular matrix (ECM) and class 1 MHC protein were increased in increta regions, suggesting local tissue injury and immune suppression. Spatial proteomics demonstrated that increta regions were characterised by excessive trophoblastic proliferation in an immunosuppressive environment. Expression of inhibitors of apoptosis such as BCL-2 and fibronectin were increased, while CTLA-4 was decreased and increased expression of PD-L1, PD-L2 and CD14 macrophages. Additionally, CD44, which is a ligand of fibronectin that promotes trophoblast invasion and cell adhesion was also increased in increta regions. We subsequently examined ligand receptor interactions enriched in increta regions, with interactions with ITGβ1, including with fibronectin and ADAMS, emerging as central in increta. These ITGβ1 ligand interactions are involved in activation of epithelial–mesenchymal transition and remodelling of ECM suggesting a more invasive trophoblast phenotype. In PAS, we suggest this is driven by fibronectin via AP-1 signalling, likely as a secondary response to myometrial scarring.

Spatial transcriptomics reveal tumor microenvironment and SLCO2A1 correlated with tumor suppression in hypopharyngeal squamous cell carcinoma

BackgroundHypopharyngeal squamous cell carcinoma (HSCC) is a type of head and neck tumor with malignant behavior and poor prognosis. Spatial transcriptomics is a method that spatially analyzes gene expression patterns in tissues and has been used to discover tumor microenvironment and molecular markers in various tumors. However, there are no published reports on spatial transcriptomic analysis of HSCC. MethodsIn this study, spatial transcriptomic analysis was performed on tumor tissues in situ, peritumoral tissues, and lymphatic metastatic tissues of four patients with HSCC. Morphological markers, including panCK, SMA, and CD45, were used to identify epithelial, fibroblast, and immune cells, respectively. By analyzing the expression of more than 18, 000 genes within the transcriptome of all ROIs, differentially expressed genes of three cell types in different tissues were identified, and differentially expressed signaling pathways and immune infiltration were analyzed. ResultsThe spatial distribution of cells suggests that fibroblast cells in tumor tissues may be involved in the genesis and development of tumors, and the immune infiltration of lymphatic tumor metastasis is lower than that of tumors in situ. For epithelial cells, SLCO2A1, which is a favorable prognosis marker in head and neck squamous cell carcinoma (HNSCC), was significantly down-regulated in tumor tissues and lymphatic metastatic tissues compared with adjacent normal tissues. For immune cells, KANK3, which is a favorable prognosis markers in HNSCC, was significantly down-regulated in lymphatic metastatic tissues compared with adjacent normal tissues. For fibroblast cells, AQP1, CLEC3B and SLCO2A1, which are favorable prognosis markers in HNSCC, were significantly down-regulated in tumor tissues compared with adjacent normal tissues. ITGA8, which is a favorable prognosis markers in HNSCC, was significantly down-regulated in lymphatic metastatic tissues compared with normal lymphatic tissues. CSRP1, DES, and SLCO2A1 positively correlate with immune infiltration in HNSCC. Moreover, SLCO2A1 overexpression suppressed Fadu cells proliferation and metastasis and significantly correlated with favorable survival overcome in HSCC. ConclusionsWe investigated tumor and fibroblast heterogeneity, as well as the immune microenvironment in HSCC by using spatial transcriptomics. SLCO2A1 may be a tumor suppressor gene and correlates with immune infiltration for HSCC and could serve as a potential target for its diagnosis and treatment.

Spatial transcriptomics reveals modulation of transcriptional networks across brain regions after auditory threat conditioning.

Prior research has demonstrated genome-wide transcriptional changes related to fear and anxiety across species, often focusing on individual brain regions or cell types. However, the extent of gene expression differences across brain regions and how these changes interact at the level of transcriptional connectivity remains unclear. To address this, we performed spatial transcriptomics RNAseq analyses in an auditory threat conditioning paradigm in mice. We generated a spatial transcriptomic atlas of a coronal mouse brain section covering cortical and subcortical regions, corresponding to histologically defined regions. Our finding revealed widespread transcriptional responses across all brain regions examined, particularly in the medial and lateral habenula, and the choroid plexus. Network analyses highlighted altered transcriptional connectivity between cortical and subcortical regions, emphasizing the role of steroidogenic factor 1. These results provide new insights into the transcriptional networks involved in auditory threat conditioning, enhancing our understanding of molecular and neural mechanisms underlying fear and anxiety disorders.

Potential crosstalk between Naïve CD4+ T cells and SPP1+ Macrophages is associated with clinical outcome and therapeutic response in hepatocellular carcinoma

BackgroundThe highly heterogeneity of the tumor microenvironment (TME) in hepatocellular carcinoma (HCC) results in diverse clinical outcomes and therapeutic responses. This study aimed to investigate potential intercellular crosstalk and its impact on clinical outcomes and therapeutic responses. MethodsSingle-cell RNA sequencing (scRNA-seq), spatial transcriptomics (ST) and bulk RNA sequencing (RNA-seq) datasets were integrated to comprehensively analyze the intercellular interactions within the TME. Multiplex immunohistochemistry was conducted to validate the intercellular interactions. A machine learning-based integrative procedure was used in bulk RNA-seq datasets to generate a risk model to predict prognosis and therapeutic responses. ResultsSurvival analyses based on the bulk RNA-seq datasets revealed the negative impact of the naïve Cluster of Differentiation 4+ (CD4) T cells and Secreted Phosphoprotein 1+ (SPP1) macrophages on prognosis. Furthermore, their intricate intercellular crosstalk and spatial colocalization were also observed by scRNA-seq and ST analyses. Based on this crosstalk, a machine learning model, termed the naïve CD4+ T cell and SPP1+ macrophage prognostic score (TMPS), was established in the bulk-RNA seq datasets for prognostic prediction. The TMPS achieved C-index values of 0.785, 0.715, 0.692 and 0.857, respectively, across 4 independent cohorts. A low TMPS was associated with a significantly increased survival rates, improved response to immunotherapy and reduced infiltration of immunosuppressive cells, such as. regulatory T cells. Finally, 8 potential sensitive drugs and 6 potential targets were predicted for patients based on their TMPS. ConclusionThe crosstalk between naïve CD4+ T cells and SPP1+ macrophages play a crucial role in the TME. TMPS can reflect this crosstalk and serve as a valuable tool for prognostic stratification and guiding clinical decision-making.

ELLA: Modeling Subcellular Spatial Variation of Gene Expression within Cells in High-Resolution Spatial Transcriptomics.

Spatial transcriptomic technologies are becoming increasingly high-resolution, enabling precise measurement of gene expression at the subcellular level. Here, we introduce a computational method called subcellular expression localization analysis (ELLA), for modeling the subcellular localization of mRNAs and detecting genes that display spatial variation within cells in high-resolution spatial transcriptomics. ELLA creates a unified cellular coordinate system to anchor diverse cell shapes and morphologies, utilizes a nonhomogeneous Poisson process to model spatial count data, leverages an expression gradient function to characterize subcellular expression patterns, and produces effective control of type I error and high statistical power. We illustrate the benefits of ELLA through comprehensive simulations and applications to four spatial transcriptomics datasets from distinct technologies, where ELLA not only identifies genes with distinct subcellular localization patterns but also associates these patterns with unique mRNA characteristics. Specifically, ELLA shows that genes enriched in the nucleus exhibit an abundance of long noncoding RNAs or protein-coding mRNAs, often characterized by longer gene lengths. Conversely, genes containing signal recognition peptides, encoding ribosomal proteins, or involved in membrane related activities tend to enrich in the cytoplasm or near the cellular membrane. Furthermore, ELLA reveals dynamic subcellular localization patterns during the cell cycle, with certain genes showing decreased nuclear enrichment in the G1 phase while others maintain their enrichment patterns throughout the cell cycle. Overall, ELLA represents a calibrated, powerful, robust, scalable, and versatile tool for modeling subcellular spatial expression variation across diverse high-resolution spatial transcriptomic platforms.

INSPIRE: interpretable, flexible and spatially-aware integration of multiple spatial transcriptomics datasets from diverse sources.

Recent advances in spatial transcriptomics technologies have led to a growing number of diverse datasets, offering unprecedented opportunities to explore tissue organizations and functions within spatial contexts. However, it remains a significant challenge to effectively integrate and interpret these data, often originating from different samples, technologies, and developmental stages. In this paper, we present INSPIRE, a deep learning method for integrative analyses of multiple spatial transcriptomics datasets to address this challenge. With designs of graph neural networks and an adversarial learning mechanism, INSPIRE enables spatially informed and adaptable integration of data from varying sources. By incorporating non-negative matrix factorization, INSPIRE uncovers interpretable spatial factors with corresponding gene programs, revealing tissue architectures, cell type distributions and biological processes. We demonstrate the capabilities of INSPIRE by applying it to human cortex slices from different samples, mouse brain slices with complementary views, mouse hippocampus and embryo slices generated through different technologies, and spatiotemporal organogenesis atlases containing half a million spatial spots. INSPIRE shows superior performance in identifying detailed biological signals, effectively borrowing information across distinct profiling technologies, and elucidating dynamical changes during embryonic development. Furthermore, we utilize INSPIRE to build 3D models of tissues and whole organisms from multiple slices, demonstrating its power and versatility.

Single-cell multi-omics map of human fetal blood in Down syndrome

Down syndrome predisposes individuals to haematological abnormalities, such as increased number of erythrocytes and leukaemia in a process that is initiated before birth and is not entirely understood1–3. Here, to understand dysregulated haematopoiesis in Down syndrome, we integrated single-cell transcriptomics of over 1.1 million cells with chromatin accessibility and spatial transcriptomics datasets using human fetal liver and bone marrow samples from 3 fetuses with disomy and 15 fetuses with trisomy. We found that differences in gene expression in Down syndrome were dependent on both cell type and environment. Furthermore, we found multiple lines of evidence that haematopoietic stem cells (HSCs) in Down syndrome are ‘primed’ to differentiate. We subsequently established a Down syndrome-specific map linking non-coding elements to genes in disomic and trisomic HSCs using 10X multiome data. By integrating this map with genetic variants associated with blood cell counts, we discovered that trisomy restructured regulatory interactions to dysregulate enhancer activity and gene expression critical to erythroid lineage differentiation. Furthermore, as mutations in Down syndrome display a signature of oxidative stress4,5, we validated both increased mitochondrial mass and oxidative stress in Down syndrome, and observed that these mutations preferentially fell into regulatory regions of expressed genes in HSCs. Together, our single-cell, multi-omic resource provides a high-resolution molecular map of fetal haematopoiesis in Down syndrome and indicates significant regulatory restructuring giving rise to co-occurring haematological conditions.

Spatial transcriptomics of acute kidney injury.

Spatial transcriptomics of acute kidney injury.

Deconvolving organogenesis in space and time via spatial transcriptomics in thick tissues.

Organ development is guided by a space-time landscape that constraints cell behavior. This landscape is challenging to characterize for the hair follicle - the most abundant mini organ - due to its complex microscopic structure and asynchronous development. We developed 3DEEP, a tissue clearing and spatial transcriptomic strategy for characterizing tissue blocks up to 400 µm in thickness. We captured 371 hair follicles at different stages of organogenesis in 1 mm 3 of skin of a 12-hour-old mouse with 6 million transcripts from 81 genes. From this single time point, we deconvoluted follicles by age based on whole-organ molecular pseudotimes to animate a stop-motion 3D atlas of follicle development along its trajectory. We defined molecular stages for hair follicle organogenesis and characterized the order of emergence for its structures, differential signaling dynamics at its top and bottom, morphogen shifts preceding and accompanying structural changes, and series of structural changes leading to the formation of its canal and opening. We further found that hair follicle stem cells and their niche are established and stratified early in organogenesis, before the formation of the hair bulb. Overall, this work demonstrates the power of increased depth of spatial transcriptomics to provide a four-dimensional analysis of organogenesis.

BDNF augmentation reverses cranial radiation therapy-induced cognitive decline and neurodegenerative consequences.

Cranial radiation therapy (RT) for brain cancers is often associated with the development of radiation-induced cognitive dysfunction (RICD). RICD significantly impacts the quality of life for cancer survivors, highlighting an unmet medical need. Previous human studies revealed a marked reduction in plasma brain-derived neurotrophic factor (BDNF) post-chronic chemotherapy, linking this decline to a substantial cognitive dysfunction among cancer survivors. Moreover, riluzole (RZ)-mediated increased BDNF in vivo in the chemotherapy-exposed mice reversed cognitive decline. RZ is an FDA-approved medication for ALS known to increase BDNF in vivo . In an effort to mitigate the detrimental effects of RT-induced BDNF decline in RICD, we tested the efficacy of RZ in a cranially irradiated (9 Gy) adult mouse model. Notably, RT-exposed mice exhibited significantly reduced hippocampal BDNF, accompanied by increased neuroinflammation, loss of neuronal plasticity-related immediate early gene product, cFos, and synaptic density. Spatial transcriptomic profiling comparing the RT+Veh with the RT+RZ group showed gene expression signatures of neuroprotection of hippocampal excitatory neurons post-RZ. RT-exposed mice performed poorly on learning and memory, and memory consolidation tasks. However, irradiated mice receiving RZ (13 mg/kg, drinking water) for 6-7 weeks showed a significant improvement in cognitive function compared to RT-exposed mice receiving vehicle. Dual-immunofluorescence staining, spatial transcriptomics, and biochemical assessment of RZ-treated irradiated brains demonstrated preservation of synaptic integrity and neuronal plasticity but not neurogenesis and reduced neuroinflammation concurrent with elevated BDNF levels and transcripts compared to vehicle-treated irradiated brains. In summary, oral administration of RZ represents a viable and translationally feasible neuroprotective approach against RICD.

An initial game-theoretic assessment of enhanced tissue preparation and imaging protocols for improved deep learning inference of spatial transcriptomics from tissue morphology.

The application of deep learning to spatial transcriptomics (ST) can reveal relationships between gene expression and tissue architecture. Prior work has demonstrated that inferring gene expression from tissue histomorphology can discern these spatial molecular markers to enable population scale studies, reducing the fiscal barriers associated with large-scale spatial profiling. However, while most improvements in algorithmic performance have focused on improving model architectures, little is known about how the quality of tissue preparation and imaging can affect deep learning model training for spatial inference from morphology and its potential for widespread clinical adoption. Prior studies for ST inference from histology typically utilize manually stained frozen sections with imaging on non-clinical grade scanners. Training such models on ST cohorts is also costly. We hypothesize that adopting tissue processing and imaging practices that mirror standards for clinical implementation (permanent sections, automated tissue staining, and clinical grade scanning) can significantly improve model performance. An enhanced specimen processing and imaging protocol was developed for deep learning-based ST inference from morphology. This protocol featured the Visium CytAssist assay to permit automated hematoxylin and eosin staining (e.g. Leica Bond), 40×-resolution imaging, and joining of multiple patients' tissue sections per capture area prior to ST profiling. Using a cohort of 13 pathologic T Stage-III stage colorectal cancer patients, we compared the performance of models trained on slide prepared using enhanced versus traditional (i.e. manual staining and low-resolution imaging) protocols. Leveraging Inceptionv3 neural networks, we predicted gene expression across serial, histologically-matched tissue sections using whole slide images (WSI) from both protocols. The data Shapley was used to quantify and compare marginal performance gains on a patient-by-patient basis attributed to using the enhanced protocol versus the actual costs of spatial profiling. Findings indicate that training and validating on WSI acquired through the enhanced protocol as opposed to the traditional method resulted in improved performance at lower fiscal cost. In the realm of ST, the enhancement of deep learning architectures frequently captures the spotlight; however, the significance of specimen processing and imaging is often understated. This research, informed through a game-theoretic lens, underscores the substantial impact that specimen preparation/imaging can have on spatial transcriptomic inference from morphology. It is essential to integrate such optimized processing protocols to facilitate the identification of prognostic markers at a larger scale.

SELF-Former: multi-scale gene filtration transformer for single-cell spatial reconstruction.

The spatial reconstruction of single-cell RNA sequencing (scRNA-seq) data into spatial transcriptomics (ST) is a rapidly evolving field that addresses the significant challenge of aligning gene expression profiles to their spatial origins within tissues. This task is complicated by the inherent batch effects and the need for precise gene expression characterization to accurately reflect spatial information. To address these challenges, we developed SELF-Former, a transformer-based framework that utilizes multi-scale structures to learn gene representations, while designing spatial correlation constraints for the reconstruction of corresponding ST data. SELF-Former excels in recovering the spatial information of ST data and effectively mitigates batch effects between scRNA-seq and ST data. A novel aspect of SELF-Former is the introduction of a gene filtration module, which significantly enhances the spatial reconstruction task by selecting genes that are crucial for accurate spatial positioning and reconstruction. The superior performance and effectiveness of SELF-Former's modules have been validated across four benchmark datasets, establishing it as a robust and effective method for spatial reconstruction tasks. SELF-Former demonstrates its capability to extract meaningful gene expression information from scRNA-seq data and accurately map it to the spatial context of real ST data. Our method represents a significant advancement in the field, offering a reliable approach for spatial reconstruction.

Spatiotemporal Analysis of Mesenchymal Stem Cells Fate Determination by Inflammatory Niche Following Soft Tissue Injury at a Single‐Cell Level

AbstractHeterotopic ossification (HO), often arising in response to traumatic challenges, results from the aberrant osteochondral differentiation of mesenchymal stem cells (MSCs). Nevertheless, the impact of trauma‐induced inflammatory exposure on MSC fate determination remains ambiguous. In this study, the cellular diversity within inflammatory lesions is elucidated, comprising MSCs and several innate and adaptive immune cells. It is observed that quiescent MSCs transition into cycling MSCs, subsequently giving rise to chondrogenic (cMSC) and/or osteogenic (oMSC) lineages within the inflammatory microenvironment following muscle or tendon injuries, as revealed through single‐cell RNA sequencing (scRNA‐seq), spatial transcriptome and lineage tracing analysis. Moreover, these investigations demonstrate that neutrophils and natural killer (NK) cells enhance transition of quiescent MSCs into cycling MSCs, which is also controlled by M1 macrophages, a subpopulation of macrophages can also stimulate cMSC and oMSC production from cycling MSCs. Additionally, M2 macrophages, CD4+ and CD8+ T lymphocytes are found to promote chondrogenesis. Further analysis demonstrates that immune cells promotes the activation of signaling transducers and activators of transcription (STAT) pathway and phosphoinositide 3 (PI3K)/protein kinase B (AKT) pathway in MSC proliferation and osteochondral progenitors’ production, respectively. These findings highlight the dynamics of MSC fate within the inflammatory lesion and unveil the molecular landscape of osteoimmunological interactions, which holds promise for advancing HO treatment.

A robust statistical approach for finding informative spatially associated pathways.

Spatial transcriptomics offers deep insights into cellular functional localization and communication by mapping gene expression to spatial locations. Traditional approaches that focus on selecting spatially variable genes often overlook the complexity of biological pathways and the interactions among genes. Here, we introduce a novel framework that shifts the focus towards directly identifying functional pathways associated with spatial variability by adapting the Brownian distance covariance test in an innovative manner to explore the heterogeneity of biological functions over space. Unlike most other methods, this statistical testing approach is free of gene selection and parameter selection and allows nonlinear and complex dependencies. It allows for a deeper understanding of how cells coordinate their activities across different spatial domains through biological pathways. By analyzing real human and mouse datasets, the method found significant pathways that were associated with spatial variation, as well as different pathway patterns among inner- and edge-cancer regions. This innovative framework offers a new perspective on analyzing spatial transcriptomic data, contributing to our understanding of tissue architecture and disease pathology. The implementation is publicly available at https://github.com/tianlq-prog/STpathway.

Dysfunctional β-cell longevity in diabetes relies on energy conservation and positive epistasis.

Long-lived PFKFB3-expressing β-cells are dysfunctional partly because of prevailing glycolysis that compromises metabolic coupling of insulin secretion. Their accumulation in type 2 diabetes (T2D) appears to be related to the loss of apoptotic competency of cell fitness competition that maintains islet function by favoring constant selection of healthy "winner" cells. To investigate how PFKFB3 can disguise the competitive traits of dysfunctional "loser" β-cells, we analyzed the overlap between human β-cells with bona fide "loser signature" across diabetes pathologies using the HPAP scRNA-seq and spatial transcriptomics of PFKFB3-positive β-cells from nPOD T2D pancreata. The overlapping transcriptional profile of "loser" β-cells was represented by down-regulated ribosomal biosynthesis and genes encoding for mitochondrial respiration. PFKFB3-positive "loser" β-cells had the reduced expression of HLA class I and II genes. Gene-gene interaction analysis revealed that PFKFB3 rs1983890 can interact with the anti-apoptotic gene MAIP1 implicating positive epistasis as a mechanism for prolonged survival of "loser" β-cells in T2D. Inhibition of PFKFB3 resulted in the clearance of dysfunctional "loser" β-cells leading to restored glucose tolerance in the mouse model of T2D.