transcriptomics
Dotaz
Zobrazit nápovědu
Spatial transcriptomics is revolutionizing modern biology, offering researchers an unprecedented ability to unravel intricate gene expression patterns within tissues. From pioneering techniques to newly commercialized platforms, the field of spatial transcriptomics has evolved rapidly, ushering in a new era of understanding across various disciplines, from developmental biology to disease research. This dynamic expansion is reflected in the rapidly growing number of technologies and data analysis techniques developed and introduced. However, the expanding landscape presents a considerable challenge for researchers, especially newcomers to the field, as staying informed about these advancements becomes increasingly complex. To address this challenge, we have prepared an updated review with a particular focus on technologies that have reached commercialization and are, therefore, accessible to a broad spectrum of potential new users. In this review, we present the fundamental principles of spatial transcriptomic methods, discuss the challenges in data analysis, provide insights into experimental considerations, offer information about available resources for spatial transcriptomics, and conclude with a guide for method selection and a forward-looking perspective. Our aim is to serve as a guiding resource for both experienced users and newcomers navigating the complex realm of spatial transcriptomics in this era of rapid development. We intend to equip researchers with the necessary knowledge to make informed decisions and contribute to the cutting-edge research that spatial transcriptomics offers.
- MeSH
- lidé MeSH
- stanovení celkové genové exprese * metody MeSH
- transkriptom * MeSH
- výpočetní biologie metody MeSH
- zvířata MeSH
- Check Tag
- lidé MeSH
- zvířata MeSH
- 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.
'Omics' technologies have facilitated the identification of hundreds to thousands of tick molecules that mediate tick feeding and play a role in the transmission of tick-borne diseases. Deep sequencing methodologies have played a key role in this knowledge accumulation, profoundly facilitating the study of the biology of disease vectors lacking reference genomes. For example, the nucleotide sequences of the entire set of tick salivary effectors, the so-called tick 'sialome', now contain at least one order of magnitude more transcript sequences compared to similar projects based on Sanger sequencing. Tick feeding is a complex and dynamic process, and while the dynamic 'sialome' is thought to mediate tick feeding success, exactly how transcriptome dynamics relate to tick-host-pathogen interactions is still largely unknown. The identification and, importantly, the functional analysis of the tick 'sialome' is expected to shed light on this 'black box'. This information will be crucial for developing strategies to block pathogen transmission, not only for anti-tick vaccine development but also the discovery and development of new, pharmacologically active compounds for human diseases.
- MeSH
- genom fyziologie MeSH
- interakce hostitele a patogenu MeSH
- klíšťata genetika fyziologie MeSH
- lidé MeSH
- proteomika * MeSH
- slinné žlázy fyziologie MeSH
- transkriptom fyziologie MeSH
- zvířata MeSH
- Check Tag
- lidé MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- přehledy MeSH
This review defines limits of currently used techniques to assess developmental capacity of human embryos in assisted reproduction and provides an overview of techniques assessing embryo's physiology on levels of genomics, transcriptomics, proteomics and metabolomics. Basic principles of respective techniques are included. Discovered biomarkers are discussed with respect to biochemical functions and their prognostic values of embryonal development.
- Klíčová slova
- vývojový potenciál embrya,
- MeSH
- 2D gelová elektroforéza MeSH
- aneuploidie MeSH
- asistovaná reprodukce MeSH
- biologické markery analýza metabolismus MeSH
- biopsie MeSH
- embryo savčí * MeSH
- embryonální vývoj * genetika MeSH
- fertilizace in vitro MeSH
- hybridizace in situ fluorescenční metody MeSH
- lidé MeSH
- messenger RNA analýza MeSH
- metabolom MeSH
- metabolomika klasifikace metody MeSH
- polymerázová řetězová reakce metody MeSH
- preimplantační diagnóza metody MeSH
- proteom analýza MeSH
- sekvenční analýza DNA metody MeSH
- srovnávací genomová hybridizace metody MeSH
- transkriptom MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- práce podpořená grantem MeSH
- přehledy MeSH
Adipose tissue is composed of adipocytes and cells from the stromal vascular fraction. In this issue of Cell Metabolism, Bäckdahl et al. (2021) use spatial transcriptomics to provide a first glimpse at the architecture of human adipose tissue. The authors identify distinct adipocyte subpopulations with specific metabolic features.
- MeSH
- lidé MeSH
- transkriptom * genetika MeSH
- tuková tkáň MeSH
- tukové buňky * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- komentáře MeSH
- práce podpořená grantem MeSH
Rare diseases may affect the quality of life of patients and be life-threatening. Therapeutic opportunities are often limited, in part because of the lack of understanding of the molecular mechanisms underlying these diseases. This can be ascribed to the low prevalence of rare diseases and therefore the lower sample sizes available for research. A way to overcome this is to integrate experimental rare disease data with prior knowledge using network-based methods. Taking this one step further, we hypothesized that combining and analyzing the results from multiple network-based methods could provide data-driven hypotheses of pathogenic mechanisms from multiple perspectives.We analyzed a Huntington's disease transcriptomics dataset using six network-based methods in a collaborative way. These methods either inherently reported enriched annotation terms or their results were fed into enrichment analyses. The resulting significantly enriched Reactome pathways were then summarized using the ontological hierarchy which allowed the integration and interpretation of outputs from multiple methods. Among the resulting enriched pathways, there are pathways that have been shown previously to be involved in Huntington's disease and pathways whose direct contribution to disease pathogenesis remains unclear and requires further investigation.In summary, our study shows that collaborative network analysis approaches are well-suited to study rare diseases, as they provide hypotheses for pathogenic mechanisms from multiple perspectives. Applying different methods to the same case study can uncover different disease mechanisms that would not be apparent with the application of a single method.
Molecular methods for the analysis of biomolecules have undergone rapid technological development in the last decade. The advent of next-generation sequencing methods and improvements in instrumental resolution enabled the analysis of complex transcriptome, proteome and metabolome data, as well as a detailed annotation of microbial genomes. The mechanisms of decomposition by model fungi have been described in unprecedented detail by the combination of genome sequencing, transcriptomics and proteomics. The increasing number of available genomes for fungi and bacteria shows that the genetic potential for decomposition of organic matter is widespread among taxonomically diverse microbial taxa, while expression studies document the importance of the regulation of expression in decomposition efficiency. Importantly, high-throughput methods of nucleic acid analysis used for the analysis of metagenomes and metatranscriptomes indicate the high diversity of decomposer communities in natural habitats and their taxonomic composition. Today, the metaproteomics of natural habitats is of interest. In combination with advanced analytical techniques to explore the products of decomposition and the accumulation of information on the genomes of environmentally relevant microorganisms, advanced methods in microbial ecophysiology should increase our understanding of the complex processes of organic matter transformation.
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.
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
During pregnancy, two fetomaternal interfaces, the placenta-decidua basalis and the fetal membrane-decidua parietals, allow for fetal growth and maturation and fetal-maternal crosstalk, and protect the fetus from infectious and inflammatory signaling that could lead to adverse pregnancy outcomes. While the placenta has been studied extensively, the fetal membranes have been understudied, even though they play critical roles in pregnancy maintenance and the initiation of term or preterm parturition. Fetal membrane dysfunction has been associated with spontaneous preterm birth (PTB, < 37 weeks gestation) and preterm prelabor rupture of the membranes (PPROM), which is a disease of the fetal membranes. However, it is unknown how the individual layers of the fetal membrane decidual interface (the amnion epithelium [AEC], the amnion mesenchyme [AMC], the chorion [CTC], and the decidua [DEC]) contribute to these pregnancy outcomes. In this study, we used a single-cell transcriptomics approach to unravel the transcriptomics network at spatial levels to discern the contributions of each layer of the fetal membranes and the adjoining maternal decidua during the following conditions: scheduled caesarian section (term not in labor [TNIL]; n = 4), vaginal term in labor (TIL; n = 3), preterm labor with and without rupture of membranes (PPROM; n = 3; and PTB; n = 3). The data included 18,815 genes from 13 patients (including TIL, PTB, PPROM, and TNIL) expressed across the four layers. After quality control, there were 11,921 genes and 44 samples. The data were processed by two pipelines: one by hierarchical clustering the combined cases and the other to evaluate heterogeneity within the cases. Our visual analytical approach revealed spatially recognized differentially expressed genes that aligned with four gene clusters. Cluster 1 genes were present predominantly in DECs and Cluster 3 centered around CTC genes in all labor phenotypes. Cluster 2 genes were predominantly found in AECs in PPROM and PTB, while Cluster 4 contained AMC and CTC genes identified in term labor cases. We identified the top 10 differentially expressed genes and their connected pathways (kinase activation, NF-κB, inflammation, cytoskeletal remodeling, and hormone regulation) per cluster in each tissue layer. An in-depth understanding of the involvement of each system and cell layer may help provide targeted and tailored interventions to reduce the risk of PTB.
- MeSH
- amnion metabolismus cytologie MeSH
- chorion metabolismus MeSH
- decidua * metabolismus MeSH
- dospělí MeSH
- extraembryonální obaly * metabolismus MeSH
- lidé MeSH
- porod v termínu genetika MeSH
- předčasný odtok plodové vody genetika metabolismus MeSH
- předčasný porod * genetika MeSH
- stanovení celkové genové exprese MeSH
- těhotenství MeSH
- transkriptom * MeSH
- Check Tag
- dospělí MeSH
- lidé MeSH
- těhotenství MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
