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.
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
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
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 cells are basic physiological and biological units that can function individually as well as in groups in tissues and organs. It is central to identify, characterize and profile single cells at molecular level to be able to distinguish different kinds, to understand their functions and determine how they interact with each other. During the last decade several technologies for single-cell profiling have been developed and used in various applications, revealing many novel findings. Quantitative PCR (qPCR) is one of the most developed methods for single-cell profiling that can be used to interrogate several analytes, including DNA, RNA and protein. Single-cell qPCR has the potential to become routine methodology but the technique is still challenging, as it involves several experimental steps and few molecules are handled. Here, we discuss technical aspects and provide recommendation for single-cell qPCR analysis. The workflow includes experimental design, sample preparation, single-cell collection, direct lysis, reverse transcription, preamplification, qPCR and data analysis. Detailed reporting and sharing of experimental details and data will promote further development and make validation studies possible. Efforts aiming to standardize single-cell qPCR open up means to move single-cell analysis from specialized research settings to standard research laboratories.
To date, single-cell studies of human white adipose tissue (WAT) have been based on small cohort sizes and no cellular consensus nomenclature exists. Herein, we performed a comprehensive meta-analysis of publicly available and newly generated single-cell, single-nucleus, and spatial transcriptomic results from human subcutaneous, omental, and perivascular WAT. Our high-resolution map is built on data from ten studies and allowed us to robustly identify >60 subpopulations of adipocytes, fibroblast and adipogenic progenitors, vascular, and immune cells. Using these results, we deconvolved spatial and bulk transcriptomic data from nine additional cohorts to provide spatial and clinical dimensions to the map. This identified cell-cell interactions as well as relationships between specific cell subtypes and insulin resistance, dyslipidemia, adipocyte volume, and lipolysis upon long-term weight changes. Altogether, our meta-map provides a rich resource defining the cellular and microarchitectural landscape of human WAT and describes the associations between specific cell types and metabolic states.
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
Single-cell RNA-seq methods can be used to delineate cell types and states at unprecedented resolution but do little to explain why certain genes are expressed. Single-cell ATAC-seq and multiome (ATAC + RNA) have emerged to give a complementary view of the cell state. It is however unclear what additional information can be extracted from ATAC-seq data besides transcription factor binding sites. Here, we show that ATAC-seq telomere-like reads counter-inituively cannot be used to infer telomere length, as they mostly originate from the subtelomere, but can be used as a biomarker for chromatin condensation. Using long-read sequencing, we further show that modern hyperactive Tn5 does not duplicate 9 bp of its target sequence, contrary to common belief. We provide a new tool, Telomemore, which can quantify nonaligning subtelomeric reads. By analyzing several public datasets and generating new multiome fibroblast and B-cell atlases, we show how this new readout can aid single-cell data interpretation. We show how drivers of condensation processes can be inferred, and how it complements common RNA-seq-based cell cycle inference, which fails for monocytes. Telomemore-based analysis of the condensation state is thus a valuable complement to the single-cell analysis toolbox.
- MeSH
- analýza jednotlivých buněk * metody MeSH
- B-lymfocyty metabolismus cytologie MeSH
- buněčný cyklus * genetika MeSH
- ChiP sekvenování metody MeSH
- chromatin * metabolismus chemie genetika MeSH
- fibroblasty metabolismus cytologie MeSH
- lidé MeSH
- sekvenování transkriptomu metody MeSH
- telomery * genetika MeSH
- vazebná místa MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články 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.
