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
- Humans MeSH
- Gene Expression Profiling * methods MeSH
- Transcriptome * MeSH
- Computational Biology methods MeSH
- Animals MeSH
- Check Tag
- Humans MeSH
- Animals MeSH
- Publication type
- Journal Article MeSH
- Review MeSH
Avian egg white is essential for protecting and nourishing bird embryos during their development. Being produced in the female magnum, variability in hen oviduct gene expression may affect egg white composition in domestic chickens. Since traditional poultry breeds may represent a source of variation, in the present study we describe the egg white proteome (mass spectrometry) and corresponding magnum transcriptome (high-throughput sequencing) for 20 hens from five domestic fowl breeds (large breeds: Araucana, Czech golden pencilled, Minorca; and small breeds: Booted bantam, Rosecomb bantam). In total, we identified 189 egg white proteins and 16391 magnum-expressed genes. The majority of egg white protein content comprised proteins with an antimicrobial function. Despite general similarity, Between-class Principal Component Analysis revealed significant breed-specific variability in protein abundances, differentiating especially small and large breeds. Though we found strong association between magnum mRNA expression and egg white protein abundance across genes, coinertia analysis revealed no transcriptome/proteome costructure at the individual level. Our study is the first to show variation in protein abundances in egg white across chicken breeds with potential effects on egg quality, biosafety, and chick development. The observed interindividual variation probably results from post-transcriptional regulation creating a discrepancy between proteomic and transcriptomic data.
- MeSH
- Animals, Domestic classification genetics metabolism MeSH
- Chickens classification genetics metabolism MeSH
- Proteome chemistry genetics metabolism MeSH
- Proteomics MeSH
- Gene Expression Profiling MeSH
- Egg Proteins chemistry genetics metabolism MeSH
- Animals MeSH
- Check Tag
- Female MeSH
- Animals MeSH
- Publication type
- Journal Article MeSH
Cardiovascular disease (CVD) remains the leading cause of death worldwide and, despite continuous advances, better diagnostic and prognostic tools, as well as therapy, are needed. The human transcriptome, which is the set of all RNA produced in a cell, is much more complex than previously thought and the lack of dialogue between researchers and industrials and consensus on guidelines to generate data make it harder to compare and reproduce results. This European Cooperation in Science and Technology (COST) Action aims to accelerate the understanding of transcriptomics in CVD and further the translation of experimental data into usable applications to improve personalized medicine in this field by creating an interdisciplinary network. It aims to provide opportunities for collaboration between stakeholders from complementary backgrounds, allowing the functions of different RNAs and their interactions to be more rapidly deciphered in the cardiovascular context for translation into the clinic, thus fostering personalized medicine and meeting a current public health challenge. Thus, this Action will advance studies on cardiovascular transcriptomics, generate innovative projects, and consolidate the leadership of European research groups in the field.COST (European Cooperation in Science and Technology) is a funding organization for research and innovation networks (www.cost.eu).
- Publication type
- Journal Article 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.
Far from being devoid of life, Antarctic waters are home to Cryonotothenioidea, which represent one of the fascinating cases of evolutionary adaptation to extreme environmental conditions in vertebrates. Thanks to a series of unique morphological and physiological peculiarities, which include the paradigmatic case of loss of hemoglobin in the family Channichthyidae, these fish survive and thrive at sub-zero temperatures. While some of the distinctive features of such adaptations have been known for decades, our knowledge of their genetic and molecular bases is still limited. We generated a reference de novo assembly of the icefish Chionodraco hamatus transcriptome and used this resource for a large-scale comparative analysis among five red-blooded Cryonotothenioidea, the sub-Antarctic notothenioid Eleginops maclovinus and seven temperate teleost species. Our investigation targeted the gills, a tissue of primary importance for gaseous exchange, osmoregulation, ammonia excretion, and its role in fish immunity. One hundred and twenty genes were identified as significantly up-regulated in Antarctic species and surprisingly shared by red- and white-blooded notothenioids, unveiling several previously unreported molecular players that might have contributed to the evolutionary success of Cryonotothenioidea in Antarctica. In particular, we detected cobalamin deficiency signatures and discussed the possible biological implications of this condition concerning hematological alterations and the heavy parasitic loads typically observed in all Cryonotothenioidea.
- MeSH
- Acclimatization * MeSH
- Vitamin B 12 Deficiency * genetics metabolism MeSH
- Fishes * genetics metabolism MeSH
- Transcriptome * MeSH
- Vitamin B 12 metabolism MeSH
- Gills metabolism MeSH
- Animals MeSH
- Check Tag
- Animals MeSH
- Publication type
- Journal Article MeSH
- Comparative Study MeSH
- Geographicals
- Antarctic Regions 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
- Humans MeSH
- Transcriptome * genetics MeSH
- Adipose Tissue MeSH
- Adipocytes * MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Comment MeSH
- Research Support, Non-U.S. Gov't MeSH
Genetic and transcriptional heterogeneity of Chronic lymphocytic leukaemia (CLL) limits prevention of disease progression. Longitudinal single-cell transcriptomics represents the state-of-the-art method to profile the disease heterogeneity at diagnosis and to inform about disease evolution. Here, we apply single-cell RNA-seq to a CLL case, sampled at diagnosis and relapse, that was treated with FCR (Fludarabine, Cyclophosphamide, Rituximab) and underwent a dramatic decrease in CD19 expression during disease progression. Computational analyses revealed a major switch in clones' dominance during treatment. The clone that expanded at relapse showed 17p and 3p chromosomal deletions, and up-regulation of pathways related to motility, cytokine signaling and antigen presentation. Single-cell RNA-seq uniquely revealed that this clone was already present at low frequency at diagnosis, and it displays feature of plasma cell differentiation, consistent with a more aggressive phenotype. This study shows the benefit of single-cell profiling of CLL heterogeneity at diagnosis, to identify clones that might otherwise not be recognized and to determine the best treatment options.
- Publication type
- Case Reports 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.
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.
- MeSH
- Chemokine CXCL10 genetics MeSH
- Fibroblasts MeSH
- Interleukin-8 * metabolism MeSH
- Humans MeSH
- Lymphocytes MeSH
- Gene Expression Profiling * MeSH
- Transcriptome * MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
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.
