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Summary Regeneration represents a fundamental biological process wherein an organism's tissues or organs repair and replace themselves following damage or environmental stress. In plant systems, injured tree branches can regenerate adventitious buds and develop new crowns through propagation techniques like cuttings and canopy pruning, while transgenic plants emerge via tissue culture in genetic engineering processes intimately connected to plant regeneration mechanisms. The advancement of plant regeneration technology is critical for addressing complex and dynamic climate challenges, ultimately ensuring global agricultural sustainability. This review comprehensively synthesizes the latest genetic transformation technologies, including transformation systems across woody, herbaceous and algal species, organellar genetic modifications, crucial regeneration factors facilitating Agrobacterium‐mediated transformations, the intricate hormonal networks regulating plant regeneration, comparative analyses of transient transformation approaches and marker gene dynamics throughout transformation processes. Ultimately, the review offers novel perspectives on current transformation bottlenecks and proposes future research trajectories.

Intracerebral hemorrhage (ICH) is a severe stroke subtype with limited therapeutic options. Programmed cell death (PCD) is crucial for immunological balance, and includes necroptosis, pyroptosis, apoptosis, ferroptosis, and necrosis. However, the distinctions between these programmed cell death modalities after ICH remain to be further investigated. We used single-cell transcriptome (single-cell RNA sequencing) and spatial transcriptome (spatial RNA sequencing) techniques to investigate PCD-related gene expression trends in the rat brain following hemorrhagic stroke. Ferroptosis was the main PCD process after ICH, and primarily affected mature oligodendrocytes. Its onset occurred as early as 1 hour post-ICH, peaking at 24 hours post-ICH. Additionally, ferroptosis-related genes were distributed in the hippocampus and choroid plexus. We also elucidated a specific interaction between lipocalin-2 (LCN2)-positive microglia and oligodendrocytes that was mediated by the colony stimulating factor 1 (CSF1)/CSF1 receptor pathway, leading to ferroptosis induction in oligodendrocytes and subsequent neurological deficits. In conclusion, our study highlights ferroptosis as the primary PCD mechanism, emerging as early as 1 hour post-ICH. Early therapeutic intervention via the suppression of microglial LCN2 expression may alleviate ferroptosis-induced damage in oligodendrocytes and associated neurological deficits, thus offering a promising neuroprotective strategy following ICH.

Clear cell renal cell carcinoma (ccRCC) is a highly heterogeneous tumor that lacks reliable biological markers for diagnosis and prognostic monitoring. Currently, the differentially expressed genes between paired adjacent normal tissues and ccRCC tumor tissues at single-cell resolution remained to be further discovered. To address this challenge, we performed an integrative analysis of multiple single-cell databases containing paired ccRCC samples. Using the “CopyKAT” algorithm, we accurately identified ccRCC tumor cells. Subsequently, various pseudotime algorithms were employed to identify malignant cells with tumor stem cell-like properties and high plasticity. This cell subgroup exhibited high expression of malignant features, including hypoxia, epithelial-mesenchymal transition (EMT), and proliferation/invasion phenotypes. We then performed differential analysis to identify genes highly expressed in this subgroup and constructed a reliable clinical diagnostic model for ccRCC using multiple machine learning algorithms. Furthermore, we identified AXL as a key gene with significant oncogenic activity, where high expression of AXL correlated with poor patient prognosis. Immune infiltration and spatial transcriptomics analyses further revealed that AXL promotes tumor progression interaction with M2 macrophages. Taken together, our analysis establishes a reliable 13-gene panel diagnostic model and AXL gene as reliable biological markers for ccRCC, providing valuable targets and a theoretical foundation for the development of precision-targeted therapies for ccRCC.

Pancreatic stellate cells (PSCs) contribute to pancreatic ductal adenocarcinoma (PDAC) progression and therapeutic resistance, yet their detailed functions remain unclear. This study combined RNA sequencing and assay for transposase‐accessible chromatin using sequencing (ATAC‐seq) on sorted PSCs from adjacent normal and PDAC tissues to investigate their transcriptional and epigenetic activation. PSCs heterogeneity and functions are characterized through bulk, single‐cell, and spatial transcriptomes, as well as in situ sequencing. The clinical relevance of PSCs in immunotherapy is assessed using an in‐house immune‐checkpoint blockade (ICB) treatment cohort. Findings showed that stress and hypoxia signaling activated PSCs in PDAC. Three common PSCs (CPSCs) and four tumor‐associated PSCs (TPSCs) are identified, each with distinct functions. CPSCs differentiated into CCL19+ TPSCs in immune‐enriched regions, MYH11+ TPSCs in the stromal region, and PLXDC1+ TPSCs, which exhibited cancer‐associated myofibroblasts (myCAFs) phenotype linked to poor prognosis. Notably, PLXDC1+ TPSCs, located near aggressive LRRC15+ myCAFs and SPP1+ macrophages, formed a desmoplastic and immunosuppressive niche around the tumor boundary, promoting CD8 T cell exhaustion. Single‐cell transcriptomics of PDAC patients treated with ICB revealed that PLXDC1+ TPSCs correlated with poor immunotherapy efficacy. Overall, this study provides key insights into PSCs in PDAC and potential therapeutic targets. This study uncovers the activation, heterogeneity, and regulatory roles of pancreatic stellate cells (PSCs) at single‐cell and spatial levels. It further identifies PLXDC1+ PSCs near aggressive LRRC15+ cancer‐associated myofibroblasts and SPP1+ macrophages, forming a desmoplastic and immunosuppressive tumor niche that promotes CD8+ T cell exhaustion, contributing to poor immunotherapy outcomes in pancreatic cancer.

Based on a deep generative model of variational graph autoencoder (VGAE), we develop a new method, DeepLinc (deep learning framework for Landscapes of Interacting Cells), for the de novo reconstruction of cell interaction networks from single-cell spatial transcriptomic data. DeepLinc demonstrates high efficiency in learning from imperfect and incomplete spatial transcriptome data, filtering false interactions, and imputing missing distal and proximal interactions. The latent representations learned by DeepLinc are also used for inferring the signature genes contributing to the cell interaction landscapes, and for reclustering the cells based on the spatially coded cell heterogeneity in complex tissues at single-cell resolution.

Spatial transcriptomics provides an unprecedented opportunity to study gene expression in its spatial tissue context, facilitating deeper insights into cellular organization and function. However, spatial transcriptomics data are often affected by high dropout noise, high dimensionality, and complex structure, posing significant challenges for downstream analysis. Here, we present Single‐cell Spatial Transcriptomics Analysis and Denoising Engine (scSTADE), an unsupervised deep learning framework for simultaneous denoising, clustering, and identifying functionally variable genes (FVGs). scSTADE employs a dual‐channel architecture that combines a linear denoising module with a graph convolutional network‐based nonlinear module, capturing both linear and nonlinear representation embeddings. It adaptively models gene distributions across spatial domains and leverages tissue‐level spatial context to identify FVGs at multiple spatial resolutions. Extensive benchmark analyses on diverse spatial transcriptomics datasets generated by different platforms demonstrate that scSTADE consistently outperforms eight popular methods across multiple clustering metrics. In brain tissue datasets, scSTADE achieves up to a 15% performance improvement and its inferred domains align closely with known neuroanatomical structures. In human breast cancer datasets, scSTADE reveals immune‐related tertiary lymphoid structures, with predicted FVGs supported by gene ontology enrichment, cell–cell communication analysis, and survival outcomes. scSTADE also exhibits high robustness against random initialization and varying levels of synthetic dropout noise.

Background Epitranscriptomics, the study of RNA modifications such as N 6 -methyladenosine (m 6 A), provides a novel layer of gene expression regulation with implications for numerous biological processes, including cellular adaptation to hypoxia. Hypoxia-inducible factor-1 (HIF-1), a master regulator of the cellular response to low oxygen, plays a critical role in adaptive and pathological processes, including cancer, ischemic heart disease, and metabolic disorders. Recent discoveries accent the dynamic interplay between m 6 A modifications and HIF-1 signaling, revealing a complex bidirectional regulatory network. While the roles of other RNA modifications in HIF-1 regulation remain largely unexplored, emerging evidence suggests their potential significance. Main body This review examines the reciprocal regulation between HIF-1 and epitranscriptomic machinery, including m 6 A writers, readers, and erasers. HIF-1 modulates the expression of key m 6 A components, while its own mRNA is regulated by m 6 A modifications, positioning HIF-1 as both a regulator and a target in this system. This interaction enhances our understanding of cellular hypoxic responses and opens avenues for clinical applications in treating conditions like cancer and ischemic heart disease. Promising progress has been made in developing selective inhibitors targeting the m 6 A-HIF-1 regulatory axis. However, challenges such as off-target effects and the complexity of RNA modification dynamics remain significant barriers to clinical translation. Conclusion The intricate interplay between m 6 A and HIF-1 highlights the critical role of epitranscriptomics in hypoxia-driven processes. Further research into these regulatory networks could drive therapeutic innovation in cancer, ischemic heart disease, and other hypoxia-related conditions. Overcoming challenges in specificity and off-target effects will be essential for realizing the potential of these emerging therapies.

Aims Atrial fibrillation (AF) has high mortality and morbidity rates. However, the intracellular molecular complexity of the atrial tissue of patients with AF has not been adequately assessed. Methods and results We investigated the cellular heterogeneity of human atrial tissue and changes in differentially expressed genes between cells using single-cell RNA sequencing, fluorescence in situ hybridization, intercellular communication, and cell trajectory analysis. Using genome-wide association studies (GWAS) and proteomics, we discovered cell types enriched for AF susceptibility genes. We discovered eight different cell types, which were further subdivided into 23 subpopulations. In AF, the communication strength between smooth muscle cells (SMCs) and fibroblast (FB) 3 cells increased and the relevant signaling pathways were quite similar. Subpopulations of endothelial cells (ECs) are mainly involved in fibrosis through TXNDC5 and POSTN . AF susceptibility genes revealed by GWAS were especially enriched in neuronal and epicardial cells, FB3, and lymphoid (Lys) cells, whereas proteomic sequencing differential proteins were concentrated in FB3 cells and SMCs. Conclusions This study provides a cellular landscape based on the atrial tissue of patients with AF and highlights intercellular changes and differentially expressed genes that occur during the disease process. A thorough description of the cellular populations involved in AF will facilitate the identification of new cell-based interventional targets with direct functional significance for the treatment of human disease.

Acute myeloid leukemia (AML) is caused by altered maturation and differentiation of myeloid blasts, as well as transcriptional/epigenetic alterations, all leading to excessive proliferation of malignant blood cells in the bone marrow. Tumor heterogeneity due to the acquisition of new somatic alterations leads to a high rate of resistance to current therapies or reduces the efficacy of hematopoietic stem cell transplantation (HSCT), thus increasing the risk of relapse and mortality. Single-cell RNA sequencing (scRNA-seq) will enable the classification of AML and guide treatment approaches by profiling patients with different facets of the same disease, stratifying risk, and identifying new potential therapeutic targets at the time of diagnosis or after treatment. ScRNA-seq allows the identification of quiescent stem-like cells, and leukemia stem cells responsible for resistance to therapeutic approaches and relapse after treatment. This method also introduces the factors and mechanisms that enhance the efficacy of the HSCT process. Generated data of the transcriptional profile of the AML could even allow the development of cancer vaccines and CAR T-cell therapies while saving valuable time and alleviating dangerous side effects of chemotherapy and HSCT in vivo. However, scRNA-seq applications face various challenges such as a large amount of data for high-dimensional analysis, technical noise, batch effects, and finding small biological patterns, which could be improved in combination with artificial intelligence models. Highlights Tumor heterogeneity and drug resistance reduce therapeutic efficacy in AML treatment. ScRNA-seq improves the accuracy and quality of HSCT, resulting in a durable antitumor response. ScRNA-seq introduces neoantigens, biomarkers and cell subtypes that may contribute to reversing resistance to standard therapies in AML. Combination approaches using scRNA-seq and AI may provide effective AML management in the future.

Background Esophageal Squamous Cell Carcinoma (ESCC) remains a predominant health concern in the world, characterized by high prevalence and mortality rates. Advances in single-cell transcriptomics have revolutionized cancer research by enabling a precise dissection of cellular and molecular diversity within tumors. Objective This study aims to elucidate the cellular dynamics and molecular mechanisms in ESCC, focusing on the transcriptional influence of STAT3 (Signal Transducer and Activator of Transcription 3) and its interaction with LHPP, thereby uncovering potential therapeutic targets. Methods Single-cell RNA sequencing was employed to analyze 44,206 cells from tumor and adjacent normal tissues of ESCC patients, identifying distinct cell types and their transcriptional shifts. We conducted differential gene expression analysis to assess changes within the tumor microenvironment (TME). Validation of key regulatory interactions was performed using qPCR in a cohort of 21 ESCC patients and further substantiated through experimental assays in ESCC cell lines. Results The study revealed critical alterations in cell composition and gene expression across identified cell populations, with a notable shift towards pro-tumorigenic states. A significant regulatory influence of STAT3 on LHPP was discovered, establishing a novel aspect of ESCC pathogenesis. Elevated levels of STAT3 and suppressed LHPP expression were validated in clinical samples. Functional assays confirmed that STAT3 directly represses LHPP at the promoter level, and disruption of this interaction by promoter mutations diminished STAT3's repressive effect. Conclusion This investigation underscores the central role of STAT3 as a regulator in ESCC, directly impacting LHPP expression and suggesting a regulatory loop crucial for tumor behavior. The insights gained from our comprehensive cellular and molecular analysis offer a deeper understanding of the dynamics within the ESCC microenvironment. These findings pave the way for targeted therapeutic interventions focusing on the STAT3-LHPP axis, providing a strategic approach to improve ESCC management and prognosis.