Single-cell transcriptomics
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BACKGROUND: Apicomplexa is a diverse phylum comprising unicellular endobiotic animal parasites and contains some of the most well-studied microbial eukaryotes including the devastating human pathogens Plasmodium falciparum and Cryptosporidium hominis. In contrast, data on the invertebrate-infecting gregarines remains sparse and their evolutionary relationship to other apicomplexans remains obscure. Most apicomplexans retain a highly modified plastid, while their mitochondria remain metabolically conserved. Cryptosporidium spp. inhabit an anaerobic host-gut environment and represent the known exception, having completely lost their plastid while retaining an extremely reduced mitochondrion that has lost its genome. Recent advances in single-cell sequencing have enabled the first broad genome-scale explorations of gregarines, providing evidence of differential plastid retention throughout the group. However, little is known about the retention and metabolic capacity of gregarine mitochondria. RESULTS: Here, we sequenced transcriptomes from five species of gregarines isolated from cockroaches. We combined these data with those from other apicomplexans, performed detailed phylogenomic analyses, and characterized their mitochondrial metabolism. Our results support the placement of Cryptosporidium as the earliest diverging lineage of apicomplexans, which impacts our interpretation of evolutionary events within the phylum. By mapping in silico predictions of core mitochondrial pathways onto our phylogeny, we identified convergently reduced mitochondria. These data show that the electron transport chain has been independently lost three times across the phylum, twice within gregarines. CONCLUSIONS: Apicomplexan lineages show variable functional restructuring of mitochondrial metabolism that appears to have been driven by adaptations to parasitism and anaerobiosis. Our findings indicate that apicomplexans are rife with convergent adaptations, with shared features including morphology, energy metabolism, and intracellularity.
High-Risk neuroblastoma (NB) survival rate is still <50%, despite treatments being more and more aggressive. The biggest hurdle liable to cancer therapy failure is the drug resistance by tumor cells that is likely due to the intra-tumor heterogeneity (ITH). To investigate the link between ITH and therapy resistance in NB, we performed a single cell RNA sequencing (scRNAseq) of etoposide and cisplatin resistant NB and their parental cells. Our analysis showed a clear separation of resistant and parental cells for both conditions by identifying 8 distinct tumor clusters in etoposide-resistant/parental and 7 in cisplatin-resistant/parental cells. We discovered that drug resistance can affect NB cell identities; highlighting the bi-directional ability of adrenergic-to-mesenchymal transition of NB cells. The biological processes driving the identified resistant cell subpopulations revealed genes such as (BARD1, BRCA1, PARP1, HISTH1 axis, members of RPL family), suggesting a potential drug resistance due to the acquisition of DNA repair mechanisms and to the modification of the drug targets. Deconvolution analysis of bulk RNAseq data from 498 tumors with cell subpopulation signatures showed that the transcriptional heterogeneity of our cellular models reflected the ITH of NB tumors and allowed the identification of clusters associated with worse/better survival. Our study demonstrates the distinct cell populations characterized by genes involved in different biological processes can have a role in NB drug treatment failure. These findings evidence the importance of ITH in NB drug resistance studies and the chance that scRNA-seq analysis offers in the identification of genes and pathways liable for drug resistance.
- Publikační typ
- časopisecké články MeSH
Reactive astrogliosis is a reaction of astrocytes to disturbed homeostasis in the central nervous system (CNS), accompanied by changes in astrocyte numbers, morphology, and function. Reactive astrocytes are important in the onset and progression of many neuropathologies, such as neurotrauma, stroke, and neurodegenerative diseases. Single-cell transcriptomics has revealed remarkable heterogeneity of reactive astrocytes, indicating their multifaceted functions in a whole spectrum of neuropathologies, with important temporal and spatial resolution, both in the brain and in the spinal cord. Interestingly, transcriptomic signatures of reactive astrocytes partially overlap between neurological diseases, suggesting shared and unique gene expression patterns in response to individual neuropathologies. In the era of single-cell transcriptomics, the number of new datasets steeply increases, and they often benefit from comparisons and integration with previously published work. Here, we provide an overview of reactive astrocyte populations defined by single-cell or single-nucleus transcriptomics across multiple neuropathologies, attempting to facilitate the search for relevant reference points and to improve the interpretability of new datasets containing cells with signatures of reactive astrocytes.
- Publikační typ
- časopisecké články MeSH
- přehledy MeSH
To understand the function of cortical circuits, it is necessary to catalog their cellular diversity. Past attempts to do so using anatomical, physiological or molecular features of cortical cells have not resulted in a unified taxonomy of neuronal or glial cell types, partly due to limited data. Single-cell transcriptomics is enabling, for the first time, systematic high-throughput measurements of cortical cells and generation of datasets that hold the promise of being complete, accurate and permanent. Statistical analyses of these data reveal clusters that often correspond to cell types previously defined by morphological or physiological criteria and that appear conserved across cortical areas and species. To capitalize on these new methods, we propose the adoption of a transcriptome-based taxonomy of cell types for mammalian neocortex. This classification should be hierarchical and use a standardized nomenclature. It should be based on a probabilistic definition of a cell type and incorporate data from different approaches, developmental stages and species. A community-based classification and data aggregation model, such as a knowledge graph, could provide a common foundation for the study of cortical circuits. This community-based classification, nomenclature and data aggregation could serve as an example for cell type atlases in other parts of the body.
- MeSH
- analýza jednotlivých buněk MeSH
- buňky klasifikace MeSH
- lidé MeSH
- neokortex cytologie MeSH
- neuroglie klasifikace MeSH
- neurony klasifikace MeSH
- terminologie jako téma MeSH
- transkriptom * MeSH
- výpočetní biologie MeSH
- zvířata MeSH
- Check Tag
- lidé MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- přehledy MeSH
Oligodendrocytes (OL) have been for decades considered a passive, homogenous population of cells that provide support to neurons, and show a limited response to pathological stimuli. This view has been dramatically changed by the introduction of powerful transcriptomic methods that have uncovered a broad spectrum of OL populations that co-exist within the healthy central nervous system (CNS) and also across a variety of diseases. Specifically, single-cell and single-nucleus RNA-sequencing (scRNA-seq, snRNA-seq) have been used to reveal OL variations in maturation, myelination and immune status. The newly discovered immunomodulatory role suggests that OL may serve as targets for future therapies. In this review, we summarize the current understanding of OL heterogeneity in mammalian CNS as revealed by scRNA-seq and snRNA-seq. We provide a list of key studies that identify consensus marker genes defining the currently known OL populations. This resource can be used to standardize analysis of OL related datasets and improve their interpretation, ultimately leading to a better understanding of OL functions in health and disease.
- Publikační typ
- časopisecké články MeSH
- přehledy MeSH
Extramedullary disease (EMD) is a high-risk feature of multiple myeloma (MM) and remains a poor prognostic factor, even in the era of novel immunotherapies. Here, we applied spatial transcriptomics (RNA tomography for spatially resolved transcriptomics [tomo-seq] [n = 2] and 10x Visium [n = 12]) and single-cell RNA sequencing (n = 3) to a set of 14 EMD biopsies to dissect the 3-dimensional architecture of tumor cells and their microenvironment. Overall, infiltrating immune and stromal cells showed both intrapatient and interpatient variations, with no uniform distribution over the lesion. We observed substantial heterogeneity at the copy number level within plasma cells, including the emergence of new subclones in circumscribed areas of the tumor, which is consistent with genomic instability. We further identified the spatial expression differences between GPRC5D and TNFRSF17, 2 important antigens for bispecific antibody therapy. EMD masses were infiltrated by various immune cells, including T cells. Notably, exhausted TIM3+/PD-1+ T cells diffusely colocalized with MM cells, whereas functional and activated CD8+ T cells showed a focal infiltration pattern along with M1 macrophages in tumor-free regions. This segregation of fit and exhausted T cells was resolved in the case of response to T-cell-engaging bispecific antibodies. MM and microenvironment cells were embedded in a complex network that influenced immune activation and angiogenesis, and oxidative phosphorylation represented the major metabolic program within EMD lesions. In summary, spatial transcriptomics has revealed a multicellular ecosystem in EMD with checkpoint inhibition and dual targeting as potential new therapeutic avenues.
The interplay among different cells in a tissue is essential for maintaining homeostasis. Although disease states have been traditionally attributed to individual cell types, increasing evidence and new therapeutic options have demonstrated the primary role of multicellular functions to understand health and disease, opening new avenues to understand pathogenesis and develop new treatment strategies. We recently described the cellular composition and dynamics of the human oral mucosa; however, the spatial arrangement of cells is needed to better understand a morphologically complex tissue. Here, we link single-cell RNA sequencing, spatial transcriptomics, and high-resolution multiplex fluorescence in situ hybridisation to characterise human oral mucosa in health and oral chronic inflammatory disease. We deconvolved expression for resolution enhancement of spatial transcriptomic data and defined highly specialised epithelial and stromal compartments describing location-specific immune programs. Furthermore, we spatially mapped a rare pathogenic fibroblast population localised in a highly immunogenic region, responsible for lymphocyte recruitment through CXCL8 and CXCL10 and with a possible role in pathological angiogenesis through ALOX5AP. Collectively, our study provides a comprehensive reference for the study of oral chronic disease pathogenesis.
Single-cell RNA sequencing (scRNA-seq) methods are widely used in life sciences, including immunology. Typical scRNA-seq analysis pipelines quantify the abundance of particular transcripts without accounting for alternative splicing. However, a well-established pan-leukocyte surface marker, CD45, encoded by the PTPRC gene, presents alternatively spliced variants that define different immune cell subsets. Information about some of the splicing patterns in particular cells in the scRNA-seq data can be obtained using isotype-specific DNA oligo-tagged anti-CD45 antibodies. However, this requires generation of an additional sequencing DNA library. Here, we present IDEIS, an easy-to-use software for CD45 isoform quantification that uses single-cell transcriptomic data as the input. We showed that IDEIS accurately identifies canonical human CD45 isoforms in datasets generated by 10× Genomics 5' sequencing assays. Moreover, we used IDEIS to determine the specificity of the Ptprc splicing pattern in mouse leukocyte subsets.
- MeSH
- alternativní sestřih MeSH
- analýza jednotlivých buněk metody MeSH
- antigeny CD45 * genetika metabolismus MeSH
- leukocyty metabolismus imunologie MeSH
- lidé MeSH
- myši MeSH
- protein - isoformy genetika MeSH
- sekvenční analýza RNA metody MeSH
- software * MeSH
- stanovení celkové genové exprese metody MeSH
- transkriptom MeSH
- zvířata MeSH
- Check Tag
- lidé MeSH
- myši MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
Single-cell transcriptomics has emerged as a powerful tool to investigate cells' biological landscape and focus on the expression profile of individual cells. Major advantage of this approach is an analysis of highly complex and heterogeneous cell populations, such as a specific subpopulation of T helper cells that are known to differentiate into distinct subpopulations. The need for distinguishing the specific expression profile is even more important considering the T cell plasticity. However, importantly, the universal pipelines for single-cell analysis are usually not sufficient for every cell type. Here, the aims are to analyze the diversity of T cell phenotypes employing classical in vitro cytokine-mediated differentiation of human T cells isolated from human peripheral blood by single-cell transcriptomic approach with support of labelled antibodies and a comprehensive bioinformatics analysis using combination of Seurat, Nebulosa, GGplot and others. The results showed high expression similarities between Th1 and Th17 phenotype and very distinct Th2 expression profile. In a case of Th2 highly specific marker genes SPINT2, TRIB3 and CST7 were expressed. Overall, our results demonstrate how donor difference, Th plasticity and cell cycle influence the expression profiles of distinct T cell populations. The results could help to better understand the importance of each step of the analysis when working with T cell single-cell data and observe the results in a more practical way by using our analyzed datasets.
