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NBAtlas: A harmonized single-cell transcriptomic reference atlas of human neuroblastoma tumors.
Neuroblastoma, a rare embryonic tumor arising from neural crest development, is responsible for 15% of pediatric cancer-related deaths. Recently, several single-cell transcriptome studies were performed on neuroblastoma patient samples to investigate the cell of origin and tumor heterogeneity. However, these individual studies involved a small number of tumors and cells, limiting the conclusions that could be drawn. To overcome this limitation, we integrated seven single-cell or single-nucleus datasets into a harmonized cell atlas covering 362,991 cells across 61 patients. We use this atlas to decipher the transcriptional landscape of neuroblastoma at single-cell resolution, revealing associations between transcriptomic profiles and clinical outcomes within the tumor compartment. In addition, we characterize the complex immune-cell landscape and uncover considerable heterogeneity among tumor-associated macrophages. Finally, we showcase the utility of our atlas as a resource by expanding it with additional data and using it as a reference for data-driven cell-type annotation.
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Systemic and single cell level responses to 1 nm size biomaterials demonstrate distinct biological effects revealed by multi-omics atlas.
Although ultra-small nanoclusters (USNCs, < 2 nm) have immense application capabilities in biomedicine, the investigation on body-wide organ responses towards USNCs is scant. Here, applying a novel strategy of single-cell mass cytometry combined with Nano Genome Atlas of multi-tissues, we systematically evaluate the interactions between the host and calcium phosphate (CaP) USNCs at the organism level. Combining single-cell mass cytometry, and magnetic luminex assay results, we identify dynamic immune responses to CaP USNCs at the single cell resolution. The innate immune is initially activated and followed by adaptive immune activation, as evidenced by dynamic immune cells proportions. Furthermore, using Nano Genome Atlas of multi-tissues, we uncover CaP USNCs induce stronger activation of the immune responses in the cartilage and subchondral bone among the five local tissues while promote metabolic activities in the liver and kidney. Moreover, based on the immunological response profiles, histological evaluation of major organs and local tissue, and a body-wide transcriptomics, we demonstrate that CaP USNCs are not more hazardous than the Food and Drug Administration-approved CaP nanoparticles after 14 days of injection. Our findings provide valuable information on the future clinical applications of USNCs and introduce an innovative strategy to decipher the whole body response to implants.
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Invasion status stratifies the composition of the human pancreatic cancer perineural niche.
UNLABELLED: The peripheral nervous system innervates the pancreatic ductal adenocarcinoma (PDAC) microenvironment, and perineural invasion (PNI), the invasion of cancer cells in and around nerves, correlates with metastatic burden and poor outcomes. Though PDAC innervation is near universal and the majority of PDAC patients harbor PNI, the cellular and molecular composition of the heterocellular perineural niche is largely unknown, obscuring functional significance. Here we provide a deeply phenotyped spatial and single-cell atlas of the human PDAC perineural niche, enabling a high-resolution comparison of invaded versus non-invaded nerve neighborhoods. This atlas leverages a novel vision-centric artificial intelligence model for imaging-based spatial transcriptomics, coupled with pathology-guided single-nucleus RNA-seq. These analyses revealed that invaded nerve neighborhoods harbor cancer cells of the classical subtype, together with myofibroblastic cancer-associated fibroblasts (CAFs) and lipid-associated macrophages. Non-invaded nerve neighborhoods, in contrast, harbor inflammatory CAFs and infiltration of B and T lymphocytes. These findings raise the possibility that PNI and not innervation itself is immune-suppressive in this setting, motivating functional studies. STATEMENT OF SIGNIFICANCE: Human PDAC is innervated and almost invariably harbors PNI, but the composition and functions of perineural niches remain unclear. Here we provide a spatial and single-cell atlas of the human PDAC perineural niche, defining cancer and stromal cell states in the context of PNI and nominating this process as potentially immune-suppressive.
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Adversarial domain translation networks for integrating large-scale atlas-level single-cell datasets.
The rapid emergence of large-scale atlas-level single-cell RNA-seq datasets presents remarkable opportunities for broad and deep biological investigations through integrative analyses. However, harmonizing such datasets requires integration approaches to be not only computationally scalable, but also capable of preserving a wide range of fine-grained cell populations. We have created Portal, a unified framework of adversarial domain translation to learn harmonized representations of datasets. When compared to other state-of-the-art methods, Portal achieves better performance for preserving biological variation during integration, while achieving the integration of millions of cells, in minutes, with low memory consumption. We show that Portal is widely applicable to integrating datasets across different samples, platforms and data types. We also apply Portal to the integration of cross-species datasets with limited shared information among them, elucidating biological insights into the similarities and divergences in the spermatogenesis process among mouse, macaque and human.
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Single-cell chemokine receptor profiles delineate the immune contexture of tertiary lymphoid structures in head and neck squamous cell carcinoma.
Tertiary lymphoid structures (TLSs) are organized aggregates of immune cells associated with favourable prognosis and response to immunotherapy in cancer, but the immune architecture of TLSs remains poorly elucidated. Here, we hypothesize that the spatial architecture of leukocytes in TLSs can be reconstructed de novo, at least partially, by cell-inherent chemokine receptors profiles. Single-cell RNA-sequencing (scRNA-seq) revealed 47 subpopulations of leukocytes in head and neck squamous cell carcinoma (HNSC). Combined with bulk RNA-seq, we observed that CXCR3, CCR7, CCR6, CXCR5, and CCR1 are TLS-associated chemokine receptors. According to the spatial reference, the cellular atlas with TLS-associated chemokine receptors in HNSC TLSs was elaborately portrayed by multiplex immunohistochemistry (mIHC). Subsequently, we explored the functions and evolutionary trajectory of cells distributed in TLSs. Our investigation presents an approach to reconstructing the immune architecture of TLSs, which would help boost the antitumor immune response by inducing neogenesis TLSs in HNSC.
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Single-Cell RNA Sequencing Reveals Dynamic Transcriptional Landscape of Testicular Maturation in Dezhou Donkeys.
Testicular development and spermatogenesis are critical for male reproduction, but their molecular mechanisms in Dezhou donkeys remain understudied. This study used single-cell RNA sequencing (scRNA-seq) to analyze testicular tissues from Dezhou donkeys at juvenile (2 months), pre-pubertal (12 months), and mature (24 months) stages. A total of 24,606 high-quality cells were profiled, constructing a comprehensive single-cell transcriptional atlas. Unsupervised clustering identified nine major cell types: three germ cell subtypes (spermatogonia, spermatocytes, spermatids) and six somatic cell subtypes (Leydig cells, Sertoli cells, peritubular muscle cells, macrophages, endothelial cells, T cells). Key marker genes (AMH, TNP1, UTF1, ZMYND10) were validated by immunofluorescence. Pseudotemporal trajectory analysis revealed sequential germ cell differentiation (spermatogonia → spermatocytes → spermatids) and Sertoli cell maturation (immature → mature), while Leydig cells and peritubular muscle cells shared common progenitors. CellChat analysis identified critical ligand-receptor pairs in BMP, IGF, WNT, and FSH pathways, which regulate testicular development. This study provides the comprehensive single-cell transcriptional map of Dezhou donkey testicular development, elucidating key molecular mechanisms of germ and somatic cell maturation. The findings offer valuable insights into donkey reproductive biology, supporting breeding improvement and male infertility research.
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Single-Cell Transcriptome Atlas and Regulatory Dynamics in Developing Cotton Anthers.
Plant anthers are composed of different specialized cell types with distinct roles in plant reproduction. High temperature (HT) stress causes male sterility, resulting in crop yield reduction. However, the spatial expression atlas and regulatory dynamics during anther development and in response to HT remain largely unknown. Here, the first single-cell transcriptome atlas and chromatin accessibility survey in cotton anther are established, depicting the specific expression and epigenetic landscape of each type of cell in anthers. The reconstruction of meiotic cells, tapetal cells, and middle layer cell developmental trajectories not only identifies novel expressed genes, but also elucidates the precise degradation period of middle layer and reveals a rapid function transition of tapetal cells during the tetrad stage. By applying HT, heterogeneity in HT response is shown among cells of anthers, with tapetal cells responsible for pollen wall synthesis are most sensitive to HT. Specifically, HT shuts down the chromatin accessibility of genes specifically expressed in the tapetal cells responsible for pollen wall synthesis, such as QUARTET 3 (QRT3) and CYTOCHROME P450 703A2 (CYP703A2), resulting in a silent expression of these genes, ultimately leading to abnormal pollen wall and male sterility. Collectively, this study provides substantial information on anthers and provides clues for heat-tolerant crop creation.
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Single-Cell Transcriptomic Analysis of Cardiac Progenitor Differentiation.
PURPOSE OF REVIEW: Emerging single-cell RNA sequencing technologies hold great promises to boost our understanding of the heterogeneity and molecular regulation of diverse cell phenotypes during organ development. In this review, we aimed at summarizing recent advances in employing single-cell transcriptomic analysis to depict the landscape of embryonic heart development, in particular, focusing on cardiac progenitor (CP) differentiation. RECENT FINDINGS: Recent studies unbiasedly cataloged and characterized cardiac cell types in the spatial and temporal resolution during early heart development. Pseudo-time analysis revealed a temporal continuum of the differentiation progress from embryonic day (E) 6.5 to E9.5, implicating early cardiac lineage restriction during mouse gastrulation. First and second heart field (FHF and SHF) CPs adopted different differentiation strategies and underwent distinct transcriptional regulation. Collectively, the comprehensive molecular atlases yield a rich resource for identification of the key cardiac regulators and signaling molecules within the key cardiac gene regulatory network (GRN) governing cardiac cell fate determinations. This review offers insights into the exquisite process and its regulation of CP differentiation at single-cell resolution. As single-cell technologies continuously grow and evolve, computational integration of multimodal single-cell data with well-designed experimental validation promises to further delineate molecular basis in deploying cardiac progenitors of distinct sources with anatomical information.
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Exploring Cellular Heterogeneity: Single-Cell and Spatial Transcriptomics of Alzheimer Disease Brains and iPSC-Derived Microglia.
BACKGROUND: Substantial evidence has established the critical role of microglia, the brain's resident immune cells, in the pathogenesis of Alzheimer's disease (AD). Microglia exhibit diverse transcriptional states in response to neuroinflammatory stimuli, and understanding these states is crucial for elucidating the underlying mechanisms of AD. METHODS: In this work, we integrated single-cell and spatially resolved transcriptomics data from multiple cohorts and brain regions, including microglia from experimental and human brains. RESULTS: This comprehensive atlas revealed a great heterogeneity of microglial states, with a significant enrichment of specific states, including activated microglia, in AD brains compared to controls. Further integration of spatial transcriptomics and immunohistochemistry showed that activated microglia are predominantly located in the external cortical layers near amyloid plaques, while homeostatic microglia are more prevalent in the internal cortical layers and further away from the plaques. These spatial patterns were further validated using P2RY12 immunostaining, which confirmed the reliability of the transcriptomic data. CONCLUSION: By integrating single-cell and spatial transcriptomics, we have provided a detailed atlas of microglial diversity, revealing the regional and pathological specificity of microglial states.
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L2,1-Extreme Learning Machine: An Efficient Robust Classifier for Tumor Classification.
With the development of cancer research, various gene expression datasets containing cancer information show an explosive growth trend. In addition, due to the continuous maturity of single-cell RNA sequencing (scRNA-seq) technology, the protein information and pedigree information of a single cell are also continuously mined. It is a technical problem of how to classify these high-dimensional data correctly. In recent years, Extreme Learning Machine (ELM) has been widely used in the field of supervised learning and unsupervised learning. However, the traditional ELM does not consider the robustness of the method. To improve the robustness of ELM, in this paper, a novel ELM method based on L2,1-norm named L2,1-Extreme Learning Machine (L2,1 -ELM) has been proposed. The method introduces L2,1-norm on loss function to improve the robustness, and minimizes the influence of noise and outliers. Firstly, we evaluate the new method on five UCI datasets. The experiment results prove that our method can achieve competitive results. Next, the novel method is applied to the problem of classification of cancer samples and single-cell RNA sequencing datasets. The experimental results on The Cancer Genome Atlas (TCGA) datasets and scRNA-seq datasets prove that ELM and its variants has great potential in the classification of cancer samples.