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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.

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.

Introduction Gliomas remain aggressive despite current therapies, highlighting the urgent need for new biomarkers and targets. Transmembrane protein 106A (TMEM106A), implicated as a tumor suppressor in various cancers, has an unclear role in gliomas. We hypothesized that TMEM106A expression associates with tumor aggressiveness and may serve as a prognostic, microenvironmental biomarker. Methods We integrated TCGA and CGGA bulk RNA‐seq, single‐cell (GSE131928, GSE89567), spatial (Ivy Atlas, Visium), and immunohistochemistry (n = 79) to evaluate TMEM106A. Differential expression used limma. Survival used Kaplan–Meier and multivariable Cox models. Immune contexture used a 12‐cell‐state deconvolution and CIBERSORT. GSEA assessed hallmark pathways. Drug sensitivity was inferred using pRRophetic. Immunotherapy modeling combined TCGA expression with TCIA immunophenoscore and PD‐L1. Results (1) TMEM106A mRNA is significantly upregulated in high‐grade gliomas compared to lower‐grade gliomas and normal brain. (2) In IDH‐wildtype tumors, differential analyses highlight roles of TMEM106A and TMEM106C; high expression links to poorer prognosis. (3) TMEM106A is an independent prognostic factor associated with aggressive behavior, especially in IDH‐wildtype astrocytomas. (4) Upregulation is confirmed by immunohistochemistry. (5) High TMEM106A associates with pro‐inflammatory signatures and higher inferred fractions of myeloid cells and granulocytes. (6) Single‐cell RNA‐seq shows enrichment in myeloid lineages, and (7) CIBERSORT shows modest positive correlations with polarized macrophage signatures. (8) Spatial transcriptomics shows higher TMEM106A in myeloid‐rich regions, consistent with a microenvironmental readout. (9) In IDH‐wildtype tumors, pRRophetic predicts lower IC50 for multiple targeted agents in TMEM106A‐high tumors. (10) TMEM106A‐high IDH‐wildtype tumors show higher IPS only when PD‐1 is “on,” suggesting a context‐dependent, inflamed‐but‐suppressed state. Conclusion TMEM106A independently predicts survival and correlates with myeloid‐enriched transcriptional states in gliomas. Given its high expression in myeloid lineages in single‐cell data, bulk upregulation is potentially driven by myeloid infiltration rather than tumor‐cell intrinsic mechanisms. All findings are correlative; prospective studies are needed before any clinical use is considered.

The tumor microenvironment (TME) play important roles in progression of endometrial carcinoma (EC). We aimed to assess the cell populations in TME of EC. We downloaded datasets of single-cell RNA-seq (scRNA-seq) and spatial transcriptome (ST) for EC from GEO, and downloaded RNA-Seq (FPKM) and clinical data of TCGA-UCEC project from TCGA. The datasets were analyzed using R software. We obtained 5 datasets of scRNA-seq, 1 of ST and 569 samples of RNA-seq. Totally, 0.2 billion transcripts and 33,408 genes were detected in 33,162 cells from scRNA-seq. The cells were classified into 9 clusters, and EC cells were originated from epithelial cells and ciliated cells. Gene set variation analysis (GSVA) indicated that the pathways enriched in the subclusters of epithelial cells and endothelial cells were significantly different, indicating great heterogeneity in EC. Cell-cell communication analyses showed that EC cells emitted the strongest signals, and endothelial cells received more signals than other cells. Further analysis found that subclusters of 1 and 2 of epithelial cells were showed a more malignant phenotype, which may confer malignant phenotype to subcluster of 0 of endothelial cells through MK pathway by MDL-NCL signal. We also analyzed communications between spatial neighbors with ST data and confirmed the findings on MDL-NCL in cell-cell communication. TCGA and GEO analyses indicated that the expression levels of NCL was inversely correlated with ImmuneScore. Our study revealed EC cells can confer malignant phenotype to endothelial cells by MDK-NCL signal, and NCL is associated with suppressed immune activity. EC cells may shape TME by inhibiting immune cells and \"educating\" stromal cells MDK-NCL signal.

The ovary is indispensable for female reproduction, and its age-dependent functional decline is the primary cause of infertility. However, the molecular basis of ovarian aging in higher vertebrates remains poorly understood. Herein, we apply spatiotemporal transcriptomics to benchmark architecture organization as well as cellular and molecular determinants in young primate ovaries and compare these to aged primate ovaries. From a global view, somatic cells within the non-follicle region undergo more pronounced transcriptional fluctuation relative to those in the follicle region, likely constituting a hostile microenvironment that facilitates ovarian aging. Further, we uncovered that inflammation, the senescent-associated secretory phenotype, senescence, and fibrosis are the likely primary contributors to ovarian aging (PCOA). Of note, we identified spatial co-localization between a PCOA-featured spot and an unappreciated MT2 (Metallothionein 2) highly expressing spot (MT2 high) characterized by high levels of inflammation, potentially serving as an aging hotspot in the primate ovary. Moreover, with advanced age, a subpopulation of MT2 high accumulates, likely disseminating and amplifying the senescent signal outward. Our study establishes the first primate spatiotemporal transcriptomic atlas, advancing our understanding of mechanistic determinants underpinning primate ovarian aging and unraveling potential biomarkers and therapeutic targets for aging and age-associated human ovarian disorders.

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.

Non-small cell lung cancer (NSCLC) represents the most common pathological type of lung cancer, and the combination of neoadjuvant immunotherapy with chemotherapy has emerged as the first-line treatment for NSCLC. Nevertheless, the efficacy of this therapeutic approach remains variable. The present study aims to examine the impact of chemoimmunotherapy in NSCLC patients, with a view to identifying key molecules, critical cell subpopulations, communication patterns and spatial distributions that potentially correlate with therapeutic sensitivity. A total of 16 lung cancer tissue samples were collected from a cohort of 12 NSCLC patients and subjected to single-cell RNA and spatial transcriptome sequencing. Our data demonstrated that the distribution of CD4 + Treg T cells and mCAFs indicated an immunosuppressive tumor microenvironment, while the accumulation of CD4 + Th17 T cells and iCAFs could act as a positive marker for the sensitivity to chemoimmunotherapy. Furthermore, a significant high level of SELENOP-macrophages was observed in tissues from positive responders, and a strong co-localization between SELENOP-macrophages and antigen-presenting cancer associated fibroblasts (CAFs) in the tumor boundaries was identified, indicating the cooperative roles of these two cell types in response to combined therapy. Moreover, SELENOP-macrophages were observed to be accumulated in tertiary lymphoid structures, which further suggested its critical role in recruiting lymphocytes. Furthermore, analysis of cell–cell communication, based on spatial transcriptomics, suggests that the interactions between SELENOP-macrophages, apCAFs, CD4 + and CD8 + T cells were significantly enhanced in responders. In addition, SELENOP-macrophages recruited CD4 + Naïve, Helper and CD8 + Naïve T cells through pathways such as the cholesterol, interleukin, chemokine and HLA when responding to combined therapy. The present study further unveils the dynamic spatial and transcriptional changes in the tumor microenvironment of non-small cell lung cancer in response to combination therapy. Graphical Abstract

Brain aging is associated with cognitive decline, memory loss and many neurodegenerative disorders. The mammalian brain has distinct structural regions that perform specific functions. However, our understanding in gene expression and cell types within the context of the spatial organization of the mammalian aging brain is limited. Here we generated spatial transcriptomic maps of young and old mouse brains. We identified 27 distinguished brain spatial domains, including layer‐specific subregions that are difficult to dissect individually. We comprehensively characterized spatial‐specific changes in gene expression in the aging brain, particularly for isocortex, the hippocampal formation, brainstem and fiber tracts, and validated some gene expression differences by qPCR and immunohistochemistry. We identified aging‐related genes and pathways that vary in a coordinated manner across spatial regions and parsed the spatial features of aging‐related signals, providing important clues to understand genes with specific functions in different brain regions during aging. Combined with single‐cell transcriptomics data, we characterized the spatial distribution of brain cell types. The proportion of immature neurons decreased in the DG region with aging, indicating that the formation of new neurons is blocked. Finally, we detected changes in information interactions between regions and found specific pathways were deregulated with aging, including classic signaling WNT and layer‐specific signaling COLLAGEN. In summary, we established a spatial molecular atlas of the aging mouse brain (http://sysbio.gzzoc.com/Mouse‐Brain‐Aging/), which provides important resources and novel insights into the molecular mechanism of brain aging. We generated spatial maps of young and old mouse brains and identified brain spatial domains. We analyzed genes and functional changes in different spatial regions with aging, and revealed changes in cell types in different regions by combining single‐cell data. We detected changes in information interactions between regions. In addition, qPCR and immunohistochemistry were used to verify some genes. This study provides important resources for the mouse brain aging. To facilitate data sharing, we provided an online website for display and data mining.

Understanding the function of a tissue requires knowing the spatial organization of its constituent cell types. In the cerebral cortex, single-cell RNA sequencing (scRNA-seq) has revealed the genome-wide expression patterns that define its many, closely related neuronal types, but cannot reveal their spatial arrangement. Here we introduce probabilistic cell typing by in situ sequencing (pciSeq), an approach that leverages previous scRNA-seq classification to identify cell types using multiplexed in situ RNA detection. We applied this method by mapping the inhibitory neurons of mouse hippocampal area CA1, for which ground truth is available from extensive previous work identifying their laminar organization. Our method identified these neuronal classes in a spatial arrangement matching ground truth, and further identified multiple classes of isocortical pyramidal cell in a pattern matching their known organization. This method will allow identifying the spatial organization of closely related cell types across the brain and other tissues. Probabilistic cell typing by in situ sequencing (pciSeq), leverages previous single-cell RNA sequencing classification and multiplexed in situ RNA detection to spatially map cell types accurately in the mouse hippocampus and isocortex.