Spatial Transcriptomic Technologies

Rediscovering the human thymus through cutting-edge technologies

Recent technological advances have transformed our understanding of the human thymus. Innovations such as high-resolution imaging, single-cell omics, and organoid cultures, including thymic epithelial cell (TEC) differentiation and culture, and improvements in biomaterials, have further elucidated the thymus architecture, cellular dynamics, and molecular mechanisms underlying T cell development, and have unraveled previously unrecognized levels of stromal cell heterogeneity. These advancements offer unprecedented insights into thymic biology and hold promise for the development of novel therapeutic strategies for immune-related disorders.

Multiplexed spatial transcriptomics methods and the application of expansion microscopy

While spatial transcriptomics has undeniably revolutionized our ability to study cellular organization, it has driven the development of a great number of innovative transcriptomics methods, which can be classified into in situ sequencing (ISS) methods, in situ hybridization (ISH) techniques, and next-generation sequencing (NGS)-based sequencing with region capture. These technologies not only refine our understanding of cellular processes, but also open up new possibilities for breakthroughs in various research domains. One challenge of spatial transcriptomics experiments is the limitation of RNA detection due to optical crowding of RNA in the cells. Expansion microscopy (ExM), characterized by the controlled enlargement of biological specimens, offers a means to achieve super-resolution imaging, overcoming the diffraction limit inherent in conventional microscopy and enabling precise visualization of RNA in spatial transcriptomics methods. In this review, we elaborate on ISS, ISH and NGS-based spatial transcriptomic protocols and on how performance of these techniques can be extended by the combination of these protocols with ExM. Moving beyond the techniques and procedures, we highlight the broader implications of transcriptomics in biology and medicine. These include valuable insight into the spatial organization of gene expression in cells within tissues, aid in the identification and the distinction of cell types and subpopulations and understanding of molecular mechanisms and intercellular changes driving disease development.

Transcriptomic Approaches to Cardiomyocyte-Biomaterial Interactions: A Review

Biomaterials, essential for supporting, enhancing, and repairing damaged tissues, play a critical role in various medical applications. This Review focuses on the interaction of biomaterials and cardiomyocytes, emphasizing the unique significance of transcriptomic approaches in understanding their interactions, which are pivotal in cardiac bioengineering and regenerative medicine. Transcriptomic approaches serve as powerful tools to investigate how cardiomyocytes respond to biomaterials, shedding light on the gene expression patterns, regulatory pathways, and cellular processes involved in these interactions. Emerging technologies such as bulk RNA-seq, single-cell RNA-seq, single-nucleus RNA-seq, and spatial transcriptomics offer promising avenues for more precise and in-depth investigations. Longitudinal studies, pathway analyses, and machine learning techniques further improve the ability to explore the complex regulatory mechanisms involved. This review also discusses the challenges and opportunities of utilizing transcriptomic techniques in cardiomyocyte-biomaterial research. Although there are ongoing challenges such as costs, cell size limitation, sample differences, and complex analytical process, there exist exciting prospects in comprehensive gene expression analyses, biomaterial design, cardiac disease treatment, and drug testing. These multimodal methodologies have the capacity to deepen our understanding of the intricate interaction network between cardiomyocytes and biomaterials, potentially revolutionizing cardiac research with the aim of promoting heart health, and they are also promising for studying interactions between biomaterials and other cell types.

Graphene oxide quantum dots-loaded sinomenine hydrochloride nanocomplexes for effective treatment of rheumatoid arthritis via inducing macrophage repolarization and arresting abnormal proliferation of fibroblast-like synoviocytes

The characteristic features of the rheumatoid arthritis (RA) microenvironment are synovial inflammation and hyperplasia. Therefore, there is a growing interest in developing a suitable therapeutic strategy for RA that targets the synovial macrophages and fibroblast-like synoviocytes (FLSs). In this study, we used graphene oxide quantum dots (GOQDs) for loading anti-arthritic sinomenine hydrochloride (SIN). By combining with hyaluronic acid (HA)-inserted hybrid membrane (RFM), we successfully constructed a new nanodrug system named HA@RFM@GP@SIN NPs for target therapy of inflammatory articular lesions. Mechanistic studies showed that this nanomedicine system was effective against RA by facilitating the transition of M1 to M2 macrophages and inhibiting the abnormal proliferation of FLSs in vitro. In vivo therapeutic potential investigation demonstrated its effects on macrophage polarization and synovial hyperplasia, ultimately preventing cartilage destruction and bone erosion in the preclinical models of adjuvant-induced arthritis and collagen-induced arthritis in rats. Metabolomics indicated that the anti-arthritic effects of HA@RFM@GP@SIN NPs were mainly associated with the regulation of steroid hormone biosynthesis, ovarian steroidogenesis, tryptophan metabolism, and tyrosine metabolism. More notably, transcriptomic analyses revealed that HA@RFM@GP@SIN NPs suppressed the cell cycle pathway while inducing the cell apoptosis pathway. Furthermore, protein validation revealed that HA@RFM@GP@SIN NPs disrupted the excessive growth of RAFLS by interfering with the PI3K/Akt/SGK/FoxO signaling cascade, resulting in a decline in cyclin B1 expression and the arrest of the G2 phase. Additionally, considering the favorable biocompatibility and biosafety, these multifunctional nanoparticles offer a promising therapeutic approach for patients with RA.

Progress of single-cell RNA sequencing combined with spatial transcriptomics in tumour microenvironment and treatment of pancreatic cancer

In recent years, single-cell analyses have revealed the heterogeneity of the tumour microenvironment (TME) at the genomic, transcriptomic, and proteomic levels, further improving our understanding of the mechanisms of tumour development. Single-cell RNA sequencing (scRNA-seq) technology allow analysis of the transcriptome at the single-cell level and have unprecedented potential for exploration of the characteristics involved in tumour development and progression. These techniques allow analysis of transcript sequences at higher resolution, thereby increasing our understanding of the diversity of cells found in the tumour microenvironment and how these cells interact in complex tumour tissue. Although scRNA-seq has emerged as an important tool for studying the tumour microenvironment in recent years, it cannot be used to analyse spatial information for cells. In this regard, spatial transcriptomics (ST) approaches allow researchers to understand the functions of individual cells in complex multicellular organisms by understanding their physical location in tissue sections. In particular, in related research on tumour heterogeneity, ST is an excellent complementary approach to scRNA-seq, constituting a new method for further exploration of tumour heterogeneity, and this approach can also provide unprecedented insight into the development of treatments for pancreatic cancer (PC). In this review, based on the methods of scRNA-seq and ST analyses, research progress on the tumour microenvironment and treatment of pancreatic cancer is further explained.

Multi Omics Applications in Biological Systems

Traditional methodologies often fall short in addressing the complexity of biological systems. In this regard, system biology omics have brought invaluable tools for conducting comprehensive analysis. Current sequencing capabilities have revolutionized genetics and genomics studies, as well as the characterization of transcriptional profiling and dynamics of several species and sample types. Biological systems experience complex biochemical processes involving thousands of molecules. These processes occur at different levels that can be studied using mass spectrometry-based (MS-based) analysis, enabling high-throughput proteomics, glycoproteomics, glycomics, metabolomics, and lipidomics analysis. Here, we present the most up-to-date techniques utilized in the completion of omics analysis. Additionally, we include some interesting examples of the applicability of multi omics to a variety of biological systems.

Spatiotemporal multi-omics: exploring molecular landscapes in aging and regenerative medicine

Aging and regeneration represent complex biological phenomena that have long captivated the scientific community. To fully comprehend these processes, it is essential to investigate molecular dynamics through a lens that encompasses both spatial and temporal dimensions. Conventional omics methodologies, such as genomics and transcriptomics, have been instrumental in identifying critical molecular facets of aging and regeneration. However, these methods are somewhat limited, constrained by their spatial resolution and their lack of capacity to dynamically represent tissue alterations. The advent of emerging spatiotemporal multi-omics approaches, encompassing transcriptomics, proteomics, metabolomics, and epigenomics, furnishes comprehensive insights into these intricate molecular dynamics. These sophisticated techniques facilitate accurate delineation of molecular patterns across an array of cells, tissues, and organs, thereby offering an in-depth understanding of the fundamental mechanisms at play. This review meticulously examines the significance of spatiotemporal multi-omics in the realms of aging and regeneration research. It underscores how these methodologies augment our comprehension of molecular dynamics, cellular interactions, and signaling pathways. Initially, the review delineates the foundational principles underpinning these methods, followed by an evaluation of their recent applications within the field. The review ultimately concludes by addressing the prevailing challenges and projecting future advancements in the field. Indubitably, spatiotemporal multi-omics are instrumental in deciphering the complexities inherent in aging and regeneration, thus charting a course toward potential therapeutic innovations.

A review of the role of epigenetic studies for intramuscular fat deposition in beef cattle

Intramuscular fat (IMF) deposition profoundly influences meat quality and economic value in beef cattle production. Meanwhile, contemporary developments in epigenetics have opened new outlooks for understanding the molecular basics of IMF regulation, and it has become a key area of research for world scholars. Therefore, the aim of this paper was to provide insight and synthesis into the intricate relationship between epigenetic mechanisms and IMF deposition in beef cattle. The methodology involves a thorough analysis of existing literature, including pertinent books, academic journals, and online resources, to provide a comprehensive overview of the role of epigenetic studies in IMF deposition in beef cattle. This review summarizes the contemporary studies in epigenetic mechanisms in IMF regulation, high-resolution epigenomic mapping, single-cell epigenomics, multi-omics integration, epigenome editing approaches, longitudinal studies in cattle growth, environmental epigenetics, machine learning in epigenetics, ethical and regulatory considerations, and translation to industry practices from perspectives of IMF deposition in beef cattle. Moreover, this paper highlights DNA methylation, histone modifications, acetylation, phosphorylation, ubiquitylation, non-coding RNAs, DNA hydroxymethylation, epigenetic readers, writers, and erasers, chromatin immunoprecipitation followed by sequencing, whole genome bisulfite sequencing, epigenome-wide association studies, and their profound impact on the expression of crucial genes governing adipogenesis and lipid metabolism. Nutrition and stress also have significant influences on epigenetic modifications and IMF deposition. The key findings underscore the pivotal role of epigenetic studies in understanding and enhancing IMF deposition in beef cattle, with implications for precision livestock farming and ethical livestock management. In conclusion, this review highlights the crucial significance of epigenetic pathways and environmental factors in affecting IMF deposition in beef cattle, providing insightful information for improving the economics and meat quality of cattle production.

Spatial Transcriptomic Profiling of Tetraspanins in Stage 4 Colon Cancer from Primary Tumor and Liver Metastasis

Stage 4 colon cancer (CC) presents a significant global health challenge due to its poor prognosis and limited treatment options. Tetraspanins, the transmembrane proteins involved in crucial cancer processes, have recently gained attention as diagnostic markers and therapeutic targets. However, their spatial expression and potential roles in stage 4 CC tissues remain unknown. Using the GeoMx digital spatial profiler, we profiled all 33 human tetraspanin genes in 48 areas within stage 4 CC tissues, segmented into immune, fibroblast, and tumor compartments. Our results unveiled diverse gene expression patterns across different primary tumor sub-regions. CD53 exhibited distinct overexpression in the immune compartment, hinting at a potential role in immune modulation. TSPAN9 was specifically overexpressed in the fibroblast compartment, suggesting involvement in tumor invasion and metastasis. CD9, CD151, TSPAN1, TSPAN3, TSPAN8, and TSPAN13 displayed specific overexpression in the tumor compartment, indicating potential roles in tumor growth. Furthermore, our differential analysis revealed significant spatial changes in tetraspanin expression between patient-matched stage 4 primary CC and metastatic liver tissues. These findings provide spatially resolved insights into the expression and potential roles of tetraspanins in stage 4 CC progression, proposing their utility as diagnostic markers and therapeutic targets. Understanding this landscape is beneficial for tailoring therapeutic strategies to specific sub-tumor regions in the context of stage 4 CC and liver metastasis.

Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges

Over the past two decades, Next-Generation Sequencing (NGS) has revolutionized the approach to cancer research. Applications of NGS include the identification of tumor specific alterations that can influence tumor pathobiology and also impact diagnosis, prognosis and therapeutic options. Pharmacogenomics (PGx) studies the role of inheritance of individual genetic patterns in drug response and has taken advantage of NGS technology as it provides access to high-throughput data that can, however, be difficult to manage. Machine learning (ML) has recently been used in the life sciences to discover hidden patterns from complex NGS data and to solve various PGx problems. In this review, we provide a comprehensive overview of the NGS approaches that can be employed and the different PGx studies implicating the use of NGS data. We also provide an excursus of the ML algorithms that can exert a role as fundamental strategies in the PGx field to improve personalized medicine in cancer.