Spatially resolved and multiplexed MicroRNA quantification from tissue using nanoliter well arrays

Potential diagnostic value of miRNAs in sexually transmitted infections

MiRNAs are small endogenous non-coding RNAs that have been demonstrated to be involved in post-transcriptional gene silencing, regulating a number of metabolic functions in the human body, including immune response, cellular physiology, organ development, angiogenesis, signaling, and other aspects. As popular molecules that have been studied in previous years, given their extensive regulatory functions, miRNAs hold considerable promise as non-invasive biomarkers. Sexually transmitted infections(STIs) are still widespread and have an adverse effect on individuals, communities, and society worldwide. miRNAs in the regulatory networks are generally involved in their molecular processes of formation and development. In this review, we discuss the value of miRNAs for the diagnosis of STIs.

Dual spatially resolved transcriptomics for human host-pathogen colocalization studies in FFPE tissue sections

Technologies to study localized host-pathogen interactions are urgently needed. Here, we present a spatial transcriptomics approach to simultaneously capture host and pathogen transcriptome-wide spatial gene expression information from human formalin-fixed paraffin-embedded (FFPE) tissue sections at a near single-cell resolution. We demonstrate this methodology in lung samples from COVID-19 patients and validate our spatial detection of SARS-CoV-2 against RNAScope and in situ sequencing. Host-pathogen colocalization analysis identified putative modulators of SARS-CoV-2 infection in human lung cells. Our approach provides new insights into host response to pathogen infection through the simultaneous, unbiased detection of two transcriptomes in FFPE samples.

Deep learning exploration of single-cell and spatially resolved cancer transcriptomics to unravel tumour heterogeneity

Tumour heterogeneity is one of the critical confounding aspects in decoding tumour growth. Malignant cells display variations in their gene transcription profiles and mutation spectra even when originating from a single progenitor cell. Single-cell and spatial transcriptomics sequencing have recently emerged as key technologies for unravelling tumour heterogeneity. Single-cell sequencing promotes individual cell-type identification through transcriptome-wide gene expression measurements of each cell. Spatial transcriptomics facilitates identification of cell-cell interactions and the structural organization of heterogeneous cells within a tumour tissue through associating spatial RNA abundance of cells at distinct spots in the tissue section. However, extracting features and analyzing single-cell and spatial transcriptomics data poses challenges. Single-cell transcriptome data is extremely noisy and its sparse nature and dropouts can lead to misinterpretation of gene expression and the misclassification of cell types. Deep learning predictive power can overcome data challenges, provide high-resolution analysis and enhance precision oncology applications that involve early cancer prognosis, diagnosis, patient survival estimation and anti-cancer therapy planning. In this paper, we provide a background to and review of the recent progress of deep learning frameworks to investigate tumour heterogeneity using both single-cell and spatial transcriptomics data types.

Spatial transcriptomics: recent developments and insights in respiratory research

The respiratory system's complex cellular heterogeneity presents unique challenges to researchers in this field. Although bulk RNA sequencing and single-cell RNA sequencing (scRNA-seq) have provided insights into cell types and heterogeneity in the respiratory system, the relevant specific spatial localization and cellular interactions have not been clearly elucidated. Spatial transcriptomics (ST) has filled this gap and has been widely used in respiratory studies. This review focuses on the latest iterative technology of ST in recent years, summarizing how ST can be applied to the physiological and pathological processes of the respiratory system, with emphasis on the lungs. Finally, the current challenges and potential development directions are proposed, including high-throughput full-length transcriptome, integration of multi-omics, temporal and spatial omics, bioinformatics analysis, etc. These viewpoints are expected to advance the study of systematic mechanisms, including respiratory studies.

Quantification of Multiplex miRNAs by Mass Spectrometry with Duplex-Specific Nuclease-Mediated Amplification

Early quantification of multiplex biomarkers such as microRNAs (miRNAs) is critical during disease pathologic development and therapy. To tackle challenges of low abundance and multiplexing, we herein report a mass-encoded biosensing approach with duplex-specific nuclease (DSN) mediated signal amplification. Magnetic Fe3O4 cores are coated with small gold nanoparticles (AuNPs), which are applied to achieve facile DNA immobilization subsequent separation. This biosensor integrates multiple mass reporters corresponding to different targets (five miRNAs as examples). Due to the excellent resolution of mass spectrometry, these targets can be successfully distinguished in a single spectrum. Wide detection ranges from 10 fM to 1 nM are achieved, and the limits of detection are estimated to be 10 fM. High selectivity is promised due to the enzyme activity of DSN, and practical application in human serum samples performs satisfactorily. The number of targets to be tested can be further expanded by designing different specific mass tags in theory. Therefore, the proposed method can be utilized as an important and valuable tool to quantify multiplex miRNAs for disease screening as well as biomedical investigations.

Light-Dependent Regulatory Interactions between the Redox System and miRNAs and Their Biochemical and Physiological Effects in Plants

Light intensity and spectrum play a major role in the regulation of the growth, development, and stress response of plants. Changes in the light conditions affect the formation of reactive oxygen species, the activity of the antioxidants, and, consequently, the redox environment in the plant tissues. Many metabolic processes, thus the biogenesis and function of miRNAs, are redox-responsive. The miRNAs, in turn, can modulate various components of the redox system, and this process is also associated with the alteration in the intensity and spectrum of the light. In this review, we would like to summarise the possible regulatory mechanisms by which the alterations in the light conditions can influence miRNAs in a redox-dependent manner. Daily and seasonal fluctuations in the intensity and spectral composition of the light can affect the expression of miRNAs, which can fine-tune the various physiological and biochemical processes due to their effect on their target genes. The interactions between the redox system and miRNAs may be modulated by light conditions, and the proposed function of this regulatory network and its effect on the various biochemical and physiological processes will be introduced in plants.

An intrinsic purine metabolite AICAR blocks lung tumour growth by targeting oncoprotein mucin 1

BACKGROUND

Lung cancer cells overexpress mucin 1 (MUC1) and active subunit MUC1-CT. Although a peptide blocks MUC1 signalling, metabolites targeting MUC1 are not well studied. AICAR is a purine biosynthesis intermediate.

METHODS

Cell viability and apoptosis were measured in AICAR-treated EGFR-mutant and wild-type lung cells. AICAR-binding proteins were evaluated by in silico and thermal stability assays. Protein-protein interactions were visualised by dual-immunofluorescence staining and proximity ligation assay. AICAR-induced whole transcriptomic profile was determined by RNA sequencing. EGFR-TL transgenic mice-derived lung tissues were analysed for MUC1 expression. Organoids and tumours from patients and transgenic mice were treated with AICAR alone or in combination with JAK and EGFR inhibitors to evaluate treatment effects.

RESULTS

AICAR reduced EGFR-mutant tumour cell growth by inducing DNA damage and apoptosis. MUC1 was one of the leading AICAR-binding and degrading proteins. AICAR negatively regulated JAK signalling and JAK1-MUC1-CT interaction. Activated EGFR upregulated MUC1-CT expression in EGFR-TL-induced lung tumour tissues. AICAR reduced EGFR-mutant cell line-derived tumour formation in vivo. Co-treating patient and transgenic mouse lung-tissue-derived tumour organoids with AICAR and JAK1 and EGFR inhibitors reduced their growth.

CONCLUSIONS

AICAR represses the MUC1 activity in EGFR-mutant lung cancer, disrupting protein-protein interactions between MUC1-CT and JAK1 and EGFR.

Recent advances in deciphering hippocampus complexity using single-cell transcriptomics

Single-cell and single-nucleus RNA sequencing (scRNA-seq and snRNA-seq) technologies have emerged as revolutionary and powerful tools, which have helped in achieving significant progress in biomedical research over the last decade. scRNA-seq and snRNA-seq resolve heterogeneous cell populations from different tissues and help reveal the function and dynamics at the single-cell level. The hippocampus is an essential component for cognitive functions, including learning, memory, and emotion regulation. However, the molecular mechanisms underlying the activity of hippocampus have not been fully elucidated. The development of scRNA-seq and snRNA-seq technologies provides strong support for attaining an in-depth understanding of hippocampal cell types and gene expression regulation from the single-cell transcriptome profiling perspective. This review summarizes the applications of scRNA-seq and snRNA-seq in the hippocampus to further expand our knowledge of the molecular mechanisms related to hippocampal development, health, and diseases.

Clinical and translational values of spatial transcriptomics

The combination of spatial transcriptomics (ST) and single cell RNA sequencing (scRNA-seq) acts as a pivotal component to bridge the pathological phenomes of human tissues with molecular alterations, defining in situ intercellular molecular communications and knowledge on spatiotemporal molecular medicine. The present article overviews the development of ST and aims to evaluate clinical and translational values for understanding molecular pathogenesis and uncovering disease-specific biomarkers. We compare the advantages and disadvantages of sequencing- and imaging-based technologies and highlight opportunities and challenges of ST. We also describe the bioinformatics tools necessary on dissecting spatial patterns of gene expression and cellular interactions and the potential applications of ST in human diseases for clinical practice as one of important issues in clinical and translational medicine, including neurology, embryo development, oncology, and inflammation. Thus, clear clinical objectives, designs, optimizations of sampling procedure and protocol, repeatability of ST, as well as simplifications of analysis and interpretation are the key to translate ST from bench to clinic.

Unravelling Prostate Cancer Heterogeneity Using Spatial Approaches to Lipidomics and Transcriptomics

Simple Summary

Prostate cancer is a heterogenous disease in terms of disease aggressiveness and therapy response, leading to dilemmas in treatment decisions. This heterogeneity reflects the multifocal nature of prostate cancer and its diversity in cellular and molecular composition, necessitating spatial molecular approaches. Here in view of the emerging importance of rewired lipid metabolism as a source of biomarkers and therapeutic targets for prostate cancer, we highlight recent advancements in technologies that enable the spatial mapping of lipids and related metabolic pathways associated with prostate cancer development and progression. We also evaluate their potential for future implementation in treatment decision-making in the clinical management of prostate cancer.

Abstract

Due to advances in the detection and management of prostate cancer over the past 20 years, most cases of localised disease are now potentially curable by surgery or radiotherapy, or amenable to active surveillance without treatment. However, this has given rise to a new dilemma for disease management; the inability to distinguish indolent from lethal, aggressive forms of prostate cancer, leading to substantial overtreatment of some patients and delayed intervention for others. Driving this uncertainty is the critical deficit of novel targets for systemic therapy and of validated biomarkers that can inform treatment decision-making and to select and monitor therapy. In part, this lack of progress reflects the inherent challenge of undertaking target and biomarker discovery in clinical prostate tumours, which are cellularly heterogeneous and multifocal, necessitating the use of spatial analytical approaches. In this review, the principles of mass spectrometry-based lipid imaging and complementary gene-based spatial omics technologies, their application to prostate cancer and recent advancements in these technologies are considered. We put in perspective studies that describe spatially-resolved lipid maps and metabolic genes that are associated with prostate tumours compared to benign tissue and increased risk of disease progression, with the aim of evaluating the future implementation of spatial lipidomics and complementary transcriptomics for prognostication, target identification and treatment decision-making for prostate cancer.

MicroRNA-21 guide and passenger strand regulation of adenylosuccinate lyase-mediated purine metabolism promotes transition to an EGFR-TKI-tolerant persister state

In EGFR-mutant lung cancer, drug-tolerant persister cells (DTPCs) show prolonged survival when receiving EGFR tyrosine kinase inhibitor (TKI) treatments. They are a likely source of drug resistance, but little is known about how these cells tolerate drugs. Ribonucleic acids (RNAs) molecules control cell growth and stress responses. Nucleic acid metabolism provides metabolites, such as purines, supporting RNA synthesis and downstream functions. Recently, noncoding RNAs (ncRNAs), such as microRNAs (miRNAs), have received attention due to their capacity to repress gene expression via inhibitory binding to downstream messenger RNAs (mRNAs). Here, our study links miRNA expression to purine metabolism and drug tolerance. MiR-21-5p (guide strand) is a commonly upregulated miRNA in disease states, including cancer and drug resistance. However, the expression and function of miR-21-3p (passenger strand) are not well understood. We found that upregulation of miR-21-5p and miR-21-3p tune purine metabolism leading to increased drug tolerance. Metabolomics data demonstrated that purine metabolism was the top pathway in the DTPCs compared with the parental cells. The changes in purine metabolites in the DTPCs were partially rescued by targeting miR-21. Analysis of protein levels in the DTPCs showed that reduced expression of adenylosuccinate lyase (ADSL) was reversed after the miR-21 knockdown. ADSL is an essential enzyme in the de novo purine biosynthesis pathway by converting succino-5-aminoimidazole-4-carboxamide riboside (succino-AICAR or SAICAR) to AICAR (or acadesine) as well as adenylosuccinate to adenosine monophosphate (AMP). In the DTPCs, miR-21-5p and miR-21-3p repress ADSL expression. The levels of top decreased metabolite in the DTPCs, AICAR was reversed when miR-21 was blocked. AICAR induced oxidative stress, evidenced by increased reactive oxygen species (ROS) and reduced expression of nuclear factor erythroid-2-related factor 2 (NRF2). Concurrently, miR-21 knockdown induced ROS generation. Therapeutically, a combination of AICAR and osimertinib increased ROS levels and decreased osimertinib-induced NRF2 expression. In a MIR21 knockout mouse model, MIR21 loss-of-function led to increased purine metabolites but reduced ROS scavenging capacity in lung tissues in physiological conditions. Our data has established a link between ncRNAs, purine metabolism, and the redox imbalance pathway. This discovery will increase knowledge of the complexity of the regulatory RNA network and potentially enable novel therapeutic options for drug-resistant patients.

microRNA-based diagnostic and therapeutic applications in cancer medicine

It has been almost two decades since the first link between microRNAs and cancer was established. In the ensuing years, this abundant class of short noncoding regulatory RNAs has been studied in virtually all cancer types. This tremendously large body of research has generated innovative technological advances for detection of microRNAs in tissue and bodily fluids, identified the diagnostic, prognostic, and/or predictive value of individual microRNAs or microRNA signatures as potential biomarkers for patient management, shed light on regulatory mechanisms of RNA-RNA interactions that modulate gene expression, uncovered cell-autonomous and cell-to-cell communication roles of specific microRNAs, and developed a battery of viral and nonviral delivery approaches for therapeutic intervention. Despite these intense and prolific research efforts in preclinical and clinical settings, there are a limited number of microRNA-based applications that have been incorporated into clinical practice. We review recent literature and ongoing clinical trials that highlight most promising approaches and standing challenges to translate these findings into viable microRNA-based clinical tools for cancer medicine. This article is categorized under: RNA in Disease and Development > RNA in Disease.

MicroRNA Interactome Multiomics Characterization for Cancer Research and Personalized Medicine: An Expert Review

MicroRNAs (miRNAs) that are mutually modulated by their interacting partners (interactome) are being increasingly noted for their significant role in pathogenesis and treatment of various human cancers. Recently, miRNA interactome dissected with multiomics approaches has been the subject of focus since individual tools or methods failed to provide the necessary comprehensive clues on the complete interactome. Even though single-omics technologies such as proteomics can uncover part of the interactome, the biological and clinical understanding still remain incomplete. In this study, we present an expert review of studies involving multiomics approaches to identification of miRNA interactome and its application in mechanistic characterization, classification, and therapeutic target identification in a variety of cancers, and with a focus on proteomics. We also discuss individual or multiple miRNA-based interactome identification in various pathological conditions of relevance to clinical medicine. Various new single-omics methods that can be integrated into multiomics cancer research and the computational approaches to analyze and predict miRNA interactome are also highlighted in this review. In all, we contextulize the power of multiomics approaches and the importance of the miRNA interactome to achieve the vision and practice of predictive, preventive, and personalized medicine in cancer research and clinical oncology.

Exploring tissue architecture using spatial transcriptomics

Deciphering the principles and mechanisms by which gene activity orchestrates complex cellular arrangements in multicellular organisms has far-reaching implications for research in the life sciences. Recent technological advances in next-generation sequencing- and imaging-based approaches have established the power of spatial transcriptomics to measure expression levels of all or most genes systematically throughout tissue space, and have been adopted to generate biological insights in neuroscience, development and plant biology as well as to investigate a range of disease contexts, including cancer. Similar to datasets made possible by genomic sequencing and population health surveys, the large-scale atlases generated by this technology lend themselves to exploratory data analysis for hypothesis generation. Here we review spatial transcriptomic technologies and describe the repertoire of operations available for paths of analysis of the resulting data. Spatial transcriptomics can also be deployed for hypothesis testing using experimental designs that compare time points or conditions-including genetic or environmental perturbations. Finally, spatial transcriptomic data are naturally amenable to integration with other data modalities, providing an expandable framework for insight into tissue organization.

Highly Magnetized Encoded Hydrogel Microparticles with Enhanced Rinsing Capabilities for Efficient microRNA Detection

Encoded hydrogel microparticles mounting DNA probes are powerful tools for high-performance microRNA (miRNA) detection in terms of sensitivity, specificity, and multiplex detection capability. However, several particle rinsing steps in the assay procedure present challenges for rapid and efficient detection. To overcome this limitation, we encapsulated dense magnetic nanoparticles to reduce the rinsing steps and duration via magnetic separation. A large number of magnetic nanoparticles were encapsulated into hydrogel microparticles based on a discontinuous dewetting technique combined with degassed micromolding lithography. In addition, we attached DNA probes targeting three types of miRNAs related to preeclampsia to magnetically encoded hydrogel microparticles by post-synthesis conjugation and achieved sensitivity comparable to that of conventional nonmagnetic encoded hydrogel microparticles. To demonstrate the multiplex capability of magnetically encoded hydrogel microparticles while maintaining the advantages of the simplified rinsing process when addressing multiple samples, we conducted a triplex detection of preeclampsia-related miRNAs. In conclusion, the introduction of magnetically encoded hydrogel microparticles not only allowed efficient miRNA detection but also provided comparable sensitivity and multiplexed detectability to conventional nonmagnetic encoded hydrogel microparticles.

Applicability of spatial transcriptional profiling to cancer research

Spatial transcriptional profiling provides gene expression information within the important anatomical context of tissue architecture. This approach is well suited to characterizing solid tumors, which develop within a complex landscape of malignant cells, immune cells, and stroma. In a single assay, spatial transcriptional profiling can interrogate the role of spatial relationships among these cell populations as well as reveal spatial patterns of relevant oncogenic genetic events. The broad utility of this approach is reflected in the array of strategies that have been developed for its implementation as well as in the recent commercial development of several profiling platforms. The flexibility to apply these technologies to both hypothesis-driven and discovery-driven studies allows widespread applicability in research settings. This review discusses available technologies for spatial transcriptional profiling and several applications for their use in cancer research.

High-Resolution Patterning of Hydrogel Sensing Motifs within Fibrous Substrates for Sensitive and Multiplexed Detection of Biomarkers

There has been an increasing and urgent demand to develop nucleic acid bioassays which not only offer high analytical performance but which are also amenable with point-of-care testing. Hydrogels present a versatile class of materials with biocompatible antifouling properties and the ability to be engineered for a range of advanced sensing applications. Fibrous substrates like nitrocellulose offer low-cost and durable platforms to run complex bioassays while enabling portability and ease of handling. We demonstrate herein the ability to synergistically combine these two materials into a portable biosensing platform by leveraging projection lithography. We demonstrate the direct polymerization of hydrogel sensing motifs within a range of fibrous substrates with precise control over their shape, size, location, and functionality. Spatial encoding of the hydrogel motifs enables the multiplex detection of multiple biomarkers on the same test. As a proof-of-concept, we apply the platform to the detection of microRNA, an emerging class of circulating biomarkers with promising potential for early diagnosis and monitoring of cancer. The assay offers a large dynamic range (over three orders of magnitude), high sensitivity (limit of detection of 2.5 amol), as well as versatility and ease of handling. Finally, the bioassay is validated using real biological samples, namely, total RNA extracted from the sera of late-stage breast cancer patients, demonstrating its utility and compatibility with clinical biosensing applications.

Encoding Method of Single-cell Spatial Transcriptomics Sequencing

Despite significant advances in parallel single-cell RNA sequencing revealing astonishing cellular heterogeneity in many tissue types, the spatial information in the tissue context remains missing. Spatial transcriptome sequencing technology is designed to distinguish the gene expression of individual cells in their original location. The technology is important for the identification of tissue function, tracking developmental processes, and pathological and molecular detection. Encoding the position information is the key to spatial transcriptomics because different methods have different encoding efficiencies and application scenarios. In this review, we focus on the latest technologies of single-cell spatial transcriptomics, including technologies based on microwell plates, barcoded bead arrays, microdissection, in situ hybridization, and barcode in situ targeting, as well as mixed separation-based technologies. Moreover, we compare these encoding methods for use as a reference when choosing the appropriate technology.