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Transcriptional activation of Jun and Fos members of the AP-1 complex is a conserved signature of immune aging that contributes to inflammaging.
Diverse mouse strains have different health and life spans, mimicking the diversity among humans. To capture conserved aging signatures, we studied long-lived C57BL/6J and short-lived NZO/HILtJ mouse strains by profiling transcriptomes and epigenomes of immune cells from peripheral blood and the spleen from young and old mice. Transcriptional activation of the AP-1 transcription factor complex, particularly Fos, Junb, and Jun genes, was the most significant and conserved aging signature across tissues and strains. ATAC-seq data analyses showed that the chromatin around these genes was more accessible with age and there were significantly more binding sites for these TFs with age across all studied tissues, targeting pro-inflammatory molecules including Il6. Age-related increases in binding sites of JUN and FOS factors were also conserved in human peripheral blood ATAC-seq data. Single-cell RNA-seq data from the mouse aging cell atlas Tabula Muris Senis showed that the expression of these genes increased with age in B, T, NK cells, and macrophages, with macrophages from old mice expressing these molecules more abundantly than other cells. Functional data showed that upon myeloid cell activation via poly(I:C), the levels of JUN protein and its binding activity increased more significantly in spleen cells from old compared to young mice. In addition, upon activation, old cells produced more IL6 compared to young cells. In sum, we showed that the aging-related transcriptional activation of Jun and Fos family members in AP-1 complex is conserved across immune tissues and long- and short-living mouse strains, possibly contributing to increased inflammation with age.
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A spatial cell atlas of neuroblastoma reveals developmental, epigenetic and spatial axis of tumor heterogeneity.
Neuroblastoma is a pediatric cancer arising from the developing sympathoadrenal lineage with complex inter- and intra-tumoral heterogeneity. To chart this complexity, we generated a comprehensive cell atlas of 55 neuroblastoma patient tumors, collected from two pediatric cancer institutions, spanning a range of clinical, genetic, and histologic features. Our atlas combines single-cell/nucleus RNA-seq (sc/scRNA-seq), bulk RNA-seq, whole exome sequencing, DNA methylation profiling, spatial transcriptomics, and two spatial proteomic methods. Sc/snRNA-seq revealed three malignant cell states with features of sympathoadrenal lineage development. All of the neuroblastomas had malignant cells that resembled sympathoblasts and the more differentiated adrenergic cells. A subset of tumors had malignant cells in a mesenchymal cell state with molecular features of Schwann cell precursors. DNA methylation profiles defined four groupings of patients, which differ in the degree of malignant cell heterogeneity and clinical outcomes. Using spatial proteomics, we found that neuroblastomas are spatially compartmentalized, with malignant tumor cells sequestered away from immune cells. Finally, we identify spatially restricted signaling patterns in immune cells from spatial transcriptomics. To facilitate the visualization and analysis of our atlas as a resource for further research in neuroblastoma, single cell, and spatial-omics, all data are shared through the Human Tumor Atlas Network Data Commons at www.humantumoratlas.org.
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Advances and Opportunities in Single-Cell Transcriptomics for Plant Research.
Single-cell approaches are quickly changing our view on biological systems by increasing the spatiotemporal resolution of our analyses to the level of the individual cell. The field of plant biology has fully embraced single-cell transcriptomics and is rapidly expanding the portfolio of available technologies and applications. In this review, we give an overview of the main advances in plant single-cell transcriptomics over the past few years and provide the reader with an accessible guideline covering all steps, from sample preparation to data analysis. We end by offering a glimpse of how these technologies will shape and accelerate plant-specific research in the near future.
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BIOTIC: a Bayesian framework to integrate single-cell multi-omics for transcription factor activity inference and improve identity characterization of cells.
Understanding cell destiny requires unraveling the intricate mechanism of gene regulation, where transcription factors (TFs) play a pivotal role. However, the actual contribution of TFs, that is TF activity, is not only determined by TF expression, but also accessibility of corresponding chromatin regions. Therefore, we introduce BIOTIC, an advanced Bayesian model with a well-established gene regulation structure that harnesses the power of single-cell multi-omics data to model the gene expression process under the control of regulatory elements, thereby defining the regulatory activity of TFs with variational inference. We demonstrated that the TF activity inferred by BIOTIC can serve as a characterization of cell identity, and outperforms baseline methods for the tasks of cell typing, cell development tracking, and batch effect correction. Additionally, BIOTIC trained on multi-omics data can flexibly be applied to the scenario where merely single-cell transcriptome sequencing is available, to infer TF activity and annotate the cell type by mapping the query cell into the reference TF activity space, as an emerging application of cell atlases. The structure of BIOTIC has been determined to be adaptable for the inclusion of additional biological factors, allowing for flexible and more comprehensive gene regulation analysis. BIOTIC introduces a pioneering biological-mechanism-driven framework to infer TF activity and elucidate cell identity states at gene regulatory level, paving the way for a deeper understanding of the complex interplay between TFs and gene expression in living systems.
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SeuratIntegrate: an R package to facilitate the use of integration methods with Seurat.
MOTIVATION: Integrating multiple datasets has become an increasingly common task in scRNA-seq analysis. The advent of single-cell atlases adds further complexity, as they often involve combining data with nested batch effects. While common tools such as Seurat offer access to batch-correction methods, the diversity of available options remains limited. With growing evidence that integration method performance varies significantly between datasets, making an informed decision in selecting the most appropriate integration approach is not trivial. A broader range of accessible methods combined with a comprehensive toolbox for comparative integration analysis, would support more effective and flexible single-cell data integration workflows. RESULTS: Built on Seurat's foundations, we developed SeuratIntegrate, an open source R package that expands integration methods available to Seurat users, including Python-based approaches, while operating entirely within the R environment. The package enables integration benchmarking using well-established performance metrics, and provides automated Python environment management, cross-language object conversion, and tools for score handling and visualization. All features are designed for ease of use and extensibility. AVAILABILITY AND IMPLEMENTATION: The source code, installation process and vignettes demonstrating usage are freely available on GitHub: https://github.com/cbib/Seurat-Integrate. A Zenodo deposit contains a copy of the package code along with the data to reproduce the results presented above (accession 10.5281/zenodo.14288360). The package is released under the MIT License.
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A single-cell and spatially resolved atlas of human osteosarcomas.
Osteosarcomas are intricate cellular ecosystems, where heterotypic interactions significantly influence disease progression and therapeutic outcomes. Despite their importance, a detailed understanding of their cellular composition and organizational structure remains elusive. In this study, we provide a comprehensive single-cell and spatially resolved transcriptomics analysis of human osteosarcomas. We construct a cellular meta-map to dissect spatial transcriptomic data, unveiling a detailed atlas of osteosarcoma compositional subgroups. We meticulously characterize the unique gene signatures and functional states of each subgroup and investigate the impact of chemotherapy on these cellular subpopulations. Additionally, our spatial transcriptomics analysis identifies a distinct spatial niche, located at the forefront of tumor necrotic zones, potentially associated with chemotherapy resistance. We also delve into the crosstalk between different cellular subgroups. This study furnishes a comprehensive transcriptional atlas of osteosarcoma's cellular architecture, enriching our comprehension of its complexity and laying the groundwork for more targeted therapeutic approaches.
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Transcriptomics for Clinical and Experimental Biology Research: Hang on a Seq.
Sequencing the human genome empowers translational medicine, facilitating transcriptome-wide molecular diagnosis, pathway biology, and drug repositioning. Initially, microarrays are used to study the bulk transcriptome; but now short-read RNA sequencing (RNA-seq) predominates. Positioned as a superior technology, that makes the discovery of novel transcripts routine, most RNA-seq analyses are in fact modeled on the known transcriptome. Limitations of the RNA-seq methodology have emerged, while the design of, and the analysis strategies applied to, arrays have matured. An equitable comparison between these technologies is provided, highlighting advantages that modern arrays hold over RNA-seq. Array protocols more accurately quantify constitutively expressed protein coding genes across tissue replicates, and are more reliable for studying lower expressed genes. Arrays reveal long noncoding RNAs (lncRNA) are neither sparsely nor lower expressed than protein coding genes. Heterogeneous coverage of constitutively expressed genes observed with RNA-seq, undermines the validity and reproducibility of pathway analyses. The factors driving these observations, many of which are relevant to long-read or single-cell sequencing are discussed. As proposed herein, a reappreciation of bulk transcriptomic methods is required, including wider use of the modern high-density array data-to urgently revise existing anatomical RNA reference atlases and assist with more accurate study of lncRNAs.
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Cell atlas of trabecular meshwork in glaucomatous non-human primates and DEGs related to tissue contract based on single-cell transcriptomics.
As the major channel of aqueous humor outflow, dysfunction of trabecular meshwork (TM) can lead to intraocular pressure elevating, which can trigger primary open-angle glaucoma (POAG). In this study, we use single-cell RNA sequencing (scRNA-seq) technique to build an atlas and further explore the spontaneous POAG and healthy macaques cellular heterogeneity associated with the dysfunction of TM contraction. We built the TM atlas, which identified 14 different cell types. In Beam A, Beam B, Beam C, and smooth muscle cell (SMC) cell types, we first found multiple genes associated with TM contraction (e.g., TPM1, ACTC1, TNNT1), determining their differential expression in the POAG and healthy groups. In addition, the microstructural alterations in TM of POAG non-human primates were observed, which was compact and collapsed. Thus, our study indicated that TPM1 may be a key target for regulating TM structure, contraction function, and resistance of aqueous humor outflow.
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The Myeloid Cell Compartment-Cell by Cell.
Myeloid cells are a major cellular compartment of the immune system comprising monocytes, dendritic cells, tissue macrophages, and granulocytes. Models of cellular ontogeny, activation, differentiation, and tissue-specific functions of myeloid cells have been revisited during the last years with surprising results; for example, most tissue macrophages are yolk sac derived, monocytes and macrophages follow a multidimensional model of activation, and tissue signals have a significant impact on the functionality of all these cells. While these exciting results have brought these cells back to center stage, their enormous plasticity and heterogeneity, during both homeostasis and disease, are far from understood. At the same time, the ongoing revolution in single-cell genomics, with single-cell RNA sequencing (scRNA-seq) leading the way, promises to change this. Prevailing models of hematopoiesis with distinct intermediates are challenged by scRNA-seq data suggesting more continuous developmental trajectories in the myeloid cell compartment. Cell subset structures previously defined by protein marker expression need to be revised based on unbiased analyses of scRNA-seq data. Particularly in inflammatory conditions, myeloid cells exhibit substantially vaster heterogeneity than previously anticipated, and work performed within large international projects, such as the Human Cell Atlas, has already revealed novel tissue macrophage subsets. Based on these exciting developments, we propose the next steps to a full understanding of the myeloid cell compartment in health and diseases.
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Characterization of 2,2',4,4'-tetrabromodiphenyl ether (BDE47)-induced testicular toxicity via single-cell RNA-sequencing.
BACKGROUND: The growing male reproductive diseases have been linked to higher exposure to certain environmental compounds such as 2,2',4,4'-tetrabromodiphenyl ether (BDE47) that are widely distributed in the food chain. However, the specific underlying molecular mechanisms for BDE47-induced male reproductive toxicity are not completely understood. METHODS: Here, for the first time, advanced single-cell RNA sequencing (ScRNA-seq) was employed to dissect BDE47-induced prepubertal testicular toxicity in mice from a pool of 76 859 cells. RESULTS: Our ScRNA-seq results revealed shared and heterogeneous information of differentially expressed genes, signaling pathways, transcription factors, and ligands-receptors in major testicular cell types in mice upon BDE47 treatment. Apart from disruption of hormone homeostasis, BDE47 was discovered to downregulate multiple previously unappreciated pathways such as double-strand break repair and cytokinesis pathways, indicative of their potential roles involved in BDE47-induced testicular injury. Interestingly, transcription factors analysis of ScRNA-seq results revealed that Kdm5b (lysine-specific demethylase 5B), a key transcription factor required for spermatogenesis, was downregulated in all germ cells as well as in Sertoli and telocyte cells in BDE47-treated testes of mice, suggesting its contribution to BDE47-induced impairment of spermatogenesis. CONCLUSIONS: Overall, for the first time, we established the molecular cell atlas of mice testes to define BDE47-induced prepubertal testicular toxicity using the ScRNA-seq approach, providing novel insight into our understanding of the underlying mechanisms and pathways involved in BDE47-associated testicular injury at a single-cell resolution. Our results can serve as an important resource to further dissect the potential roles of BDE47, and other relevant endocrine-disrupting chemicals, in inducing male reproductive toxicity.