Single-cell quantification of a broad RNA spectrum reveals unique noncoding patterns associated with cell types and states

Revealing microRNA regulation in single cells

MicroRNAs (miRNAs) are key regulators of gene expression and control cellular functions in physiological and pathophysiological states. miRNAs play important roles in disease, stress, and development, and are now being investigated for therapeutic approaches. Alternative processing of miRNAs during biogenesis results in the generation of miRNA isoforms (isomiRs) which further diversify miRNA gene regulation. Single-cell RNA-sequencing (scsRNA-seq) technologies, together with computational strategies, enable exploration of miRNAs, isomiRs, and interacting RNAs at the cellular level. By integration with other miRNA-associated single-cell modalities, miRNA roles can be resolved at different stages of processing and regulation. In this review we discuss (i) single-cell experimental assays that measure miRNA and isomiR abundances, and (ii) computational methods for their analysis to investigate the mechanisms of miRNA biogenesis and post-transcriptional regulation.

Sequencing technologies to measure translation in single cells

Translation is one of the most energy-intensive processes in a cell and, accordingly, is tightly regulated. Genome-wide methods to measure translation and the translatome and to study the complex regulation of protein synthesis have enabled unprecedented characterization of this crucial step of gene expression. However, technological limitations have hampered our understanding of translation control in multicellular tissues, rare cell types and dynamic cellular processes. Recent optimizations, adaptations and new techniques have enabled these measurements to be made at single-cell resolution. In this Progress, we discuss single-cell sequencing technologies to measure translation, including ribosome profiling, ribosome affinity purification and spatial translatome methods.

Inferring single-cell and spatial microRNA activity from transcriptomics data

The activity of miRNA varies across different cell populations and systems, as part of the mechanisms that distinguish cell types and roles in living organisms and in human health and disease. Typically, miRNA regulation drives changes in the composition and levels of protein-coding RNA and of lncRNA, with targets being down-regulated when miRNAs are active. The term "miRNA activity" is used to refer to this transcriptional effect of miRNAs. This study introduces miTEA-HiRes, a method designed to facilitate the evaluation of miRNA activity at high resolution. The method applies to single-cell transcriptomics, type-specific single-cell populations, and spatial transcriptomics data. By comparing different conditions, differential miRNA activity is inferred. For instance, miTEA-HiRes analysis of peripheral blood mononuclear cells comparing Multiple Sclerosis patients to control groups revealed differential activity of miR-20a-5p and others, consistent with the literature on miRNA underexpression in Multiple Sclerosis. We also show miR-519a-3p differential activity in specific cell populations.

Integrated analysis of miRNAs and mRNAs in thousands of single cells

The simultaneous sequencing of multiple types of biomolecules can facilitate understanding various forms of regulation occurring in cells. Cosequencing of miRNA and mRNA at single-cell resolution is challenging, and to date, only a few such studies (examining a quite limited number of cells) have been reported. Here, we developed a parallel single-cell small RNA and mRNA coprofiling method (PSCSR-seq V2) that enables miRNA and mRNA coexpression analysis in many cells. The PSCSR-seq V2 method is highly sensitive for miRNA analysis, and it also provides rich mRNA information about the examined cells at the same time. We employed PSCSR-seq V2 to profile miRNA and mRNA in 2310 cultured cells, and detected an average of 181 miRNA species and 7354 mRNA species per cell. An integrated analysis of miRNA and mRNA profiles linked miRNA functions with the negative regulation of tumor suppressor and reprogramming of cellular metabolism. We coprofiled miRNA and mRNA in 9403 lung cells and generated a coexpression atlas for known cell populations in mouse lungs, and detected conserved expression patterns of miRNAs among lineage-related cells. Based on this information, we identified informative age-associated miRNAs in mouse and human lung cells including miR-29, which can be understood as a conserved marker for immunosenescence. PSCSR-seq V2 offers unique functionality to users conducting functional studies of miRNAs in clinical and basic biological research.

Detecting gene expression in Caenorhabditis elegans

Reliable methods for detecting and analyzing gene expression are necessary tools for understanding development and investigating biological responses to genetic and environmental perturbation. With its fully sequenced genome, invariant cell lineage, transparent body, wiring diagram, detailed anatomy, and wide array of genetic tools, Caenorhabditis elegans is an exceptionally useful model organism for linking gene expression to cellular phenotypes. The development of new techniques in recent years has greatly expanded our ability to detect gene expression at high resolution. Here, we provide an overview of gene expression methods for C. elegans, including techniques for detecting transcripts and proteins in situ, bulk RNA sequencing of whole worms and specific tissues and cells, single-cell RNA sequencing, and high-throughput proteomics. We discuss important considerations for choosing among these techniques and provide an overview of publicly available online resources for gene expression data.

Statistical modeling of single-cell epitranscriptomics enabled trajectory and regulatory inference of RNA methylation

As a fundamental mechanism for gene expression regulation, post-transcriptional RNA methylation plays versatile roles in various biological processes and disease mechanisms. Recent advances in single-cell technology have enabled simultaneous profiling of transcriptome-wide RNA methylation in thousands of cells, holding the promise to provide deeper insights into the dynamics, functions, and regulation of RNA methylation. However, it remains a major challenge to determine how to best analyze single-cell epitranscriptomics data. In this study, we developed SigRM, a computational framework for effectively mining single-cell epitranscriptomics datasets with a large cell number, such as those produced by the scDART-seq technique from the SMART-seq2 platform. SigRM not only outperforms state-of-the-art models in RNA methylation site detection on both simulated and real datasets but also provides rigorous quantification metrics of RNA methylation levels. This facilitates various downstream analyses, including trajectory inference and regulatory network reconstruction concerning the dynamics of RNA methylation.

Single-cell RNA sequencing in ovarian cancer: Current progress and future prospects

Ovarian cancer is one of the most prevalent gynaecological malignancies. The rapid development of single-cell RNA sequencing (scRNA-seq) has allowed scientists to use this technique to study ovarian cancer development, heterogeneity, and tumour environment. Although multiple original research articles have reported the use of scRNA-seq in understanding ovarian cancer and how therapy resistance occurs, there is a lack of a comprehensive review that could summarize the findings from multiple studies. Therefore, this review aimed to fill this gap by comparing and summarizing the results from different studies that have used scRNA-seq in understanding ovarian cancer development, heterogeneity, tumour microenvironment, and treatment resistance. This review will begin with an overview of scRNA-seq workflow, followed by a discussion of various applications of scRNA-seq in studying ovarian cancer. Next, the limitations and future directions of scRNA-seq in ovarian cancer research will be presented.

Single-cell omics: experimental workflow, data analyses and applications

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

A Current Perspective of Medical Informatics Developments for a Clinical Translation of (Non-coding)RNAs and Single-Cell Technologies

The journey from laboratory research to clinical practice is marked by significant advancements in the fields of single-cell technologies and non-coding RNA (ncRNA) research. This convergence may reshape our approach to personalized medicine, offering groundbreaking insights and treatments in various clinical settings. This chapter discusses advancements in (nc)RNAs in the clinics, innovations in single-cell technologies and algorithms, and the impact on actual precision medicine, showing the integration of single-cell and ncRNA research can have a tangible impact on precision medicine. Case studies in Oncology, Immunology, and other fields demonstrate how these technologies can guide treatment decisions, tailor therapies to individual patients, and improve outcomes. This approach is particularly potent in addressing diseases with high inter- and intra-tumor heterogeneity. The final sections address standardization, data integration, and analysis challenges because the complexity and volume of data generated by single-cell and ncRNA research poses significant challenges. Medical Informatics is not just a support tool but could be seen as a pivotal component in advancing clinical applications of single-cell and ncRNA research by bridging the gap between bench and bedside. The future of personalized medicine depends on our ability to harness the power of these technologies, and Medical Informatics in combination with ncRNA and single-cell technologies may stand at the forefront of this endeavor.

scTrends: A living review of commercial single-cell and spatial 'omic technologies

Understanding the rapidly evolving landscape of single-cell and spatial omic technologies is crucial for advancing biomedical research and drug development. We provide a living review of both mature and emerging commercial platforms, highlighting key methodologies and trends shaping the field. This review spans from foundational single-cell technologies such as microfluidics and plate-based methods to newer approaches like combinatorial indexing; on the spatial side, we consider next-generation sequencing and imaging-based spatial transcriptomics. Finally, we highlight emerging methodologies that may fundamentally expand the scope for data generation within pharmaceutical research, creating opportunities to discover and validate novel drug mechanisms. Overall, this review serves as a critical resource for navigating the commercialization and application of single-cell and spatial omic technologies in pharmaceutical and academic research.

RAG-seq: NSR-primed and Transposase Tagmentation-mediated Strand-specific Total RNA Sequencing in Single Cells

Single-cell RNA sequencing (scRNA-seq) has transformed our understanding of cellular diversity with unprecedented resolution. However, many current methods are limited in capturing full-length transcripts and discerning strand orientation. Here, we present RAG-seq, an innovative strand-specific total RNA sequencing technique that combines not-so-random (NSR) primers with Tn5 transposase-mediated tagmentation. RAG-seq overcomes previous limitations by delivering comprehensive transcript coverage and maintaining strand orientation, which are essential for accurate quantification of overlapping genes and detection of antisense transcripts. Through optimized reverse transcription with oligo-dT primers, rRNA depletion via Depletion of Abundant Sequences by Hybridization (DASH), and linear amplification, RAG-seq enhances sensitivity and reproducibility, especially for low-input samples and single cells. Application to mouse oocytes and early embryos highlights RAG-seq's superior performance in identifying stage-specific antisense transcripts, shedding light on their regulatory roles during early development. This advancement represents a significant leap in transcriptome analysis within complex biological contexts.

Transcriptome and Temporal Transcriptome Analyses in Single Cells

Transcriptome analysis in single cells, enabled by single-cell RNA sequencing, has become a prevalent approach in biomedical research, ranging from investigations of gene regulation to the characterization of tissue organization. Over the past decade, advances in single-cell RNA sequencing technology, including its underlying chemistry, have significantly enhanced its performance, marking notable improvements in methodology. A recent development in the field, which integrates RNA metabolic labeling with single-cell RNA sequencing, has enabled the profiling of temporal transcriptomes in individual cells, offering new insights into dynamic biological processes involving RNA kinetics and cell fate determination. In this review, we explore the chemical principles and design improvements that have enhanced single-molecule capture efficiency, improved RNA quantification accuracy, and increased cellular throughput in single-cell transcriptome analysis. We also illustrate the concept of RNA metabolic labeling for detecting newly synthesized transcripts and summarize recent advancements that enable single-cell temporal transcriptome analysis. Additionally, we examine data analysis strategies for the precise quantification of newly synthesized transcripts and highlight key applications of transcriptome and temporal transcriptome analyses in single cells.

ADM: adaptive graph diffusion for meta-dimension reduction

Dimension reduction is essential for analyzing high-dimensional data, with various techniques developed to address diverse data characteristics. However, individual methods often struggle to capture all intricate patterns and complex structures simultaneously. To overcome this limitation, we introduce ADM (Adaptive graph Diffusion for Meta-dimension reduction), a novel meta-dimension reduction method grounded in graph diffusion theory. ADM integrates results from multiple dimension reduction techniques, leveraging their individual strengths while mitigating their specific weaknesses.ADM utilizes dynamic Markov processes to transform Euclidean space results into an information space, revealing intrinsic nonlinear manifold structures that are hard to capture by conventional methods. A critical advancement in ADM is its adaptive diffusion mechanism, which dynamically selects optimal diffusion time scales for each sample, enabling effective representation of multi-scale structures. This approach generates robust, high-quality low-dimensional representations that capture both local and global data structures while reducing noise and technique-specific distortions. We demonstrate ADM's efficacy on simulated and real-world datasets, including various omics data types. Results show that ADM provides clearer separation between biological groups and reveals more meaningful patterns compared to existing methods, advancing the analysis and visualization of complex biological data.

snPATHO-seq, a versatile FFPE single-nucleus RNA sequencing method to unlock pathology archives

Formalin-fixed paraffin-embedded (FFPE) samples are valuable but underutilized in single-cell omics research due to their low RNA quality. In this study, leveraging a recent advance in single-cell genomic technology, we introduce snPATHO-seq, a versatile method to derive high-quality single-nucleus transcriptomic data from FFPE samples. We benchmarked the performance of the snPATHO-seq workflow against existing 10x 3' and Flex assays designed for frozen or fresh samples and highlighted the consistency in snRNA-seq data produced by all workflows. The snPATHO-seq workflow also demonstrated high robustness when tested across a wide range of healthy and diseased FFPE tissue samples. When combined with FFPE spatial transcriptomic technologies such as FFPE Visium, the snPATHO-seq provides a multi-modal sampling approach for FFPE samples, allowing more comprehensive transcriptomic characterization.

A ligation-independent sequencing method reveals tRNA-derived RNAs with blocked 3' termini

Despite the numerous sequencing methods available, the diversity in RNA size and chemical modification makes it difficult to capture all RNAs in a cell. We developed a method that combines quasi-random priming with template switching to construct sequencing libraries from RNA molecules of any length and with any type of 3' modifications, allowing for the sequencing of virtually all RNA species. Our ligation-independent detection of all types of RNA (LIDAR) is a simple, effective tool to identify and quantify all classes of coding and non-coding RNAs. With LIDAR, we comprehensively characterized the transcriptomes of mouse embryonic stem cells, neural progenitor cells, mouse tissues, and sperm. LIDAR detected a much larger variety of tRNA-derived RNAs (tDRs) compared with traditional ligation-dependent sequencing methods and uncovered tDRs with blocked 3' ends that had previously escaped detection. Therefore, LIDAR can capture all RNAs in a sample and uncover RNA species with potential regulatory functions.

Spatial genomics: mapping human steatotic liver disease

Metabolic dysfunction-associated steatotic liver disease (MASLD, formerly known as non-alcoholic fatty liver disease) is a leading cause of chronic liver disease worldwide. MASLD can progress to metabolic dysfunction-associated steatohepatitis (MASH, formerly known as non-alcoholic steatohepatitis) with subsequent liver cirrhosis and hepatocellular carcinoma formation. The advent of current technologies such as single-cell and single-nuclei RNA sequencing have transformed our understanding of the liver in homeostasis and disease. The next frontier is contextualizing this single-cell information in its native spatial orientation. This understanding will markedly accelerate discovery science in hepatology, resulting in a further step-change in our knowledge of liver biology and pathobiology. In this Review, we discuss up-to-date knowledge of MASLD development and progression and how the burgeoning field of spatial genomics is driving exciting new developments in our understanding of human liver disease pathogenesis and therapeutic target identification.

Unraveling the intricacies of host-pathogen interaction through single-cell genomics

Single-cell genomics provide researchers with tools to assess host-pathogen interactions at a resolution previously inaccessible. Transcriptome analysis, epigenome analysis, and immune profiling techniques allow for a better comprehension of the heterogeneity underlying both the host response and infectious agents. Here, we highlight technological advancements and data analysis workflows that increase our understanding of host-pathogen interactions at the single-cell level. We review various studies that have used these tools to better understand host-pathogen dynamics in a variety of infectious disease contexts, including viral, bacterial, and parasitic diseases. We conclude by discussing how single-cell genomics can advance our understanding of host-pathogen interactions.

Single-cell RNA sequencing of nc886, a non-coding RNA transcribed by RNA polymerase III, with a primer spike-in strategy

Single-cell RNA sequencing (scRNA-seq) has emerged as a versatile tool in biology, enabling comprehensive genomic-level characterization of individual cells. Currently, most scRNA-seq methods generate barcoded cDNAs by capturing the polyA tails of mRNAs, which exclude many non-coding RNAs (ncRNAs), especially those transcribed by RNA polymerase III (Pol III). Although previously thought to be expressed constitutively, Pol III-transcribed ncRNAs are expressed variably in healthy and disease states and play important roles therein, necessitating their profiling at the single-cell level. In this study, we developed a measurement protocol for nc886 as a model case and initial step for scRNA-seq for Pol III-transcribed ncRNAs. Specifically, we spiked in an oligo-tagged nc886-specific primer during the polyA tail capture process for the 5'scRNA-seq. We then produced sequencing libraries for standard 5' gene expression and oligo-tagged nc886 separately, to accommodate different cDNA sizes and ensure undisturbed transcriptome analysis. We applied this protocol in three cell lines that express high, low, and zero levels of nc886. Our results show that the identification of oligo tags exhibited limited target specificity, and sequencing reads of nc886 enabled the correction of non-specific priming. These findings suggest that gene-specific primers (GSPs) can be employed to capture RNAs lacking a polyA tail, with subsequent sequence verification ensuring accurate gene expression counting. Moreover, we embarked on an analysis of differentially expressed genes in cell line sub-clusters with differential nc886 expression, demonstrating variations in gene expression phenotypes. Collectively, the primer spike-in strategy allows combined analysis of ncRNAs and gene expression phenotype.

Single-cell total-RNA profiling unveils regulatory hubs of transcription factors

Recent development of RNA velocity uses master equations to establish the kinetics of the life cycle of RNAs from unspliced RNA to spliced RNA (i.e., mature RNA) to degradation. To feed this kinetic analysis, simultaneous measurement of unspliced RNA and spliced RNA in single cells is greatly desired. However, the majority of single-cell RNA-seq chemistry primarily captures mature RNA species to measure gene expressions. Here, we develop a one-step total-RNA chemistry-based single-cell RNA-seq method: snapTotal-seq. We benchmark this method with multiple single-cell RNA-seq assays in their performance in kinetic analysis of cell cycle by RNA velocity. Next, with LASSO regression between transcription factors, we identify the critical regulatory hubs mediating the cell cycle dynamics. We also apply snapTotal-seq to profile the oncogene-induced senescence and identify the key regulatory hubs governing the entry of senescence. Furthermore, from the comparative analysis of unspliced RNA and spliced RNA, we identify a significant portion of genes whose expression changes occur in spliced RNA but not to the same degree in unspliced RNA, indicating these gene expression changes are mainly controlled by post-transcriptional regulation. Overall, we demonstrate that snapTotal-seq can provide enriched information about gene regulation, especially during the transition between cell states.

Depletion-assisted multiplexed cell-free RNA sequencing reveals distinct human and microbial signatures in plasma versus extracellular vesicles

BACKGROUND

Cell-free long RNAs in human plasma and extracellular vesicles (EVs) have shown promise as biomarkers in liquid biopsy, despite their fragmented nature.

METHODS

To investigate these fragmented cell-free RNAs (cfRNAs), we developed a cost-effective cfRNA sequencing method called DETECTOR-seq (depletion-assisted multiplexed cell-free total RNA sequencing). DETECTOR-seq utilised a meticulously tailored set of customised guide RNAs to remove large amounts of unwanted RNAs (i.e., fragmented ribosomal and mitochondrial RNAs) in human plasma. Early barcoding strategy was implemented to reduce costs and minimise plasma requirements.

RESULTS

Using DETECTOR-seq, we conducted a comprehensive analysis of cell-free transcriptomes in both whole human plasma and EVs. Our analysis revealed discernible distributions of RNA types in plasma and EVs. Plasma exhibited pronounced enrichment in structured circular RNAs, tRNAs, Y RNAs and viral RNAs, while EVs showed enrichment in messenger RNAs (mRNAs) and signal recognition particle RNAs (srpRNAs). Functional pathway analysis highlighted RNA splicing-related ribonucleoproteins (RNPs) and antimicrobial humoral response genes in plasma, while EVs demonstrated enrichment in transcriptional activity, cell migration and antigen receptor-mediated immune signals. Our study indicates the comparable potential of cfRNAs from whole plasma and EVs in distinguishing cancer patients (i.e., colorectal and lung cancer) from healthy donors. And microbial cfRNAs in plasma showed potential in classifying specific cancer types.

CONCLUSIONS

Our comprehensive analysis of total and EV cfRNAs in paired plasma samples provides valuable insights for determining the need for EV purification in cfRNA-based studies. We envision the cost effectiveness and efficiency of DETECTOR-seq will empower transcriptome-wide investigations in the fields of cfRNAs and liquid biopsy.

KEYPOINTS

DETECTOR-seq (depletion-assisted multiplexed cell-free total RNA sequencing) enabled efficient and specific depletion of sequences derived from fragmented ribosomal and mitochondrial RNAs in plasma. Distinct human and microbial cell-free RNA (cfRNA) signatures in whole Plasma versus extracellular vesicles (EVs) were revealed. Both Plasma and EV cfRNAs were capable of distinguishing cancer patients from normal individuals, while microbial RNAs in Plasma cfRNAs enabled better classification of cancer types than EV cfRNAs.

Next-Generation Sequencing-Based Spatial Transcriptomics: A Perspective from Barcoding Chemistry

Gene expression profiling of tissue cells with spatial context is in high demand to reveal cell types, locations, and intercellular or molecular interactions for physiological and pathological studies. With rapid advances in barcoding chemistry and sequencing chemistry, spatially resolved transcriptome (SRT) techniques have emerged to quantify spatial gene expression in tissue samples by correlating transcripts with their spatial locations using diverse strategies. These techniques provide both physical tissue structure and molecular characteristics and are poised to revolutionize many fields, such as developmental biology, neuroscience, oncology, and histopathology. In this context, this Perspective focuses on next-generation sequencing-based SRT methods, particularly highlighting spatial barcoding chemistry. It delves into optically manipulated spatial indexing methods and DNA array-barcoded spatial indexing methods by exploring current advances, challenges, and future development directions in this nascent field.

Untacking small RNA profiling and RNA fragment footprinting: Approaches and challenges in library construction

Small RNAs (sRNAs) with sizes ranging from 15 to 50 nucleotides (nt) are critical regulators of gene expression control. Prior studies have shown that sRNAs are involved in a broad range of biological processes, such as organ development, tumorigenesis, and epigenomic regulation; however, emerging evidence unveils a hidden layer of diversity and complexity of endogenously encoded sRNAs profile in eukaryotic organisms, including novel types of sRNAs and the previously unknown post-transcriptional RNA modifications. This underscores the importance for accurate, unbiased detection of sRNAs in various cellular contexts. A multitude of high-throughput methods based on next-generation sequencing (NGS) are developed to decipher the sRNA expression and their modifications. Nonetheless, distinct from mRNA sequencing, the data from sRNA sequencing suffer frequent inconsistencies and high variations emanating from the adapter contaminations and RNA modifications, which overall skew the sRNA libraries. Here, we summarize the sRNA-sequencing approaches, and discuss the considerations and challenges for the strategies and methods of sRNA library construction. The pros and cons of sRNA sequencing have significant implications for implementing RNA fragment footprinting approaches, including CLIP-seq and Ribo-seq. We envision that this review can inspire novel improvements in small RNA sequencing and RNA fragment footprinting in future. This article is categorized under: RNA Evolution and Genomics > Computational Analyses of RNA RNA Processing > Processing of Small RNAs Regulatory RNAs/RNAi/Riboswitches > Biogenesis of Effector Small RNAs.

Single-Cell RNA Sequencing in Organ and Cell Transplantation

Single-cell RNA sequencing is a high-throughput novel method that provides transcriptional profiling of individual cells within biological samples. This method typically uses microfluidics systems to uncover the complex intercellular communication networks and biological pathways buried within highly heterogeneous cell populations in tissues. One important application of this technology sits in the fields of organ and stem cell transplantation, where complications such as graft rejection and other post-transplantation life-threatening issues may occur. In this review, we first focus on research in which single-cell RNA sequencing is used to study the transcriptional profile of transplanted tissues. This technology enables the analysis of the donor and recipient cells and identifies cell types and states associated with transplant complications and pathologies. We also review the use of single-cell RNA sequencing in stem cell implantation. This method enables studying the heterogeneity of normal and pathological stem cells and the heterogeneity in cell populations. With their remarkably rapid pace, the single-cell RNA sequencing methodologies will potentially result in breakthroughs in clinical transplantation in the coming years.

Approaches to probe and perturb long noncoding RNA functions in diseases

Long noncoding RNAs (lncRNAs) are a class of RNA molecules exceeding 200 nucleotides in length that lack long open-reading frames. Transcribed predominantly by RNA polymerase II (>500nt), lncRNAs can undergo splicing and are produced from various regions of the genome, including intergenic regions, introns, and in antisense orientation to protein-coding genes. Aberrations in lncRNA expression or function have been associated with a wide variety of diseases, including cancer, cardiovascular diseases, diabetes, and neurodegeneration. Despite the growing recognition of select lncRNAs as key players in cellular processes and diseases, several challenges obscure a comprehensive understanding of their functional landscape. Recent technological innovations, such as in sequencing, affinity-based techniques, imaging, and RNA perturbation, have advanced functional characterization and mechanistic understanding of disease-associated lncRNAs.

Identification of Interpretable Clusters and Associated Signatures in Breast Cancer Single-Cell Data: A Topic Modeling Approach

Topic modeling is a popular technique in machine learning and natural language processing, where a corpus of text documents is classified into themes or topics using word frequency analysis. This approach has proven successful in various biological data analysis applications, such as predicting cancer subtypes with high accuracy and identifying genes, enhancers, and stable cell types simultaneously from sparse single-cell epigenomics data. The advantage of using a topic model is that it not only serves as a clustering algorithm, but it can also explain clustering results by providing word probability distributions over topics. Our study proposes a novel topic modeling approach for clustering single cells and detecting topics (gene signatures) in single-cell datasets that measure multiple omics simultaneously. We applied this approach to examine the transcriptional heterogeneity of luminal and triple-negative breast cancer cells using patient-derived xenograft models with acquired resistance to chemotherapy and targeted therapy. Through this approach, we identified protein-coding genes and long non-coding RNAs (lncRNAs) that group thousands of cells into biologically similar clusters, accurately distinguishing drug-sensitive and -resistant breast cancer types. In comparison to standard state-of-the-art clustering analyses, our approach offers an optimal partitioning of genes into topics and cells into clusters simultaneously, producing easily interpretable clustering outcomes. Additionally, we demonstrate that an integrative clustering approach, which combines the information from mRNAs and lncRNAs treated as disjoint omics layers, enhances the accuracy of cell classification.

Fast and flexible profiling of chromatin accessibility and total RNA expression in single nuclei using Microwell-seq3

Single cell chromatin accessibility profiling and transcriptome sequencing are the most widely used technologies for single-cell genomics. Here, we present Microwell-seq3, a high-throughput and facile platform for high-sensitivity single-nucleus chromatin accessibility or full-length transcriptome profiling. The method combines a preindexing strategy and a penetrable chip-in-a-tube for single nucleus loading and DNA amplification and therefore does not require specialized equipment. We used Microwell-seq3 to profile chromatin accessibility in more than 200,000 single nuclei and the full-length transcriptome in ~50,000 nuclei from multiple adult mouse tissues. Compared with the existing polyadenylated transcript capture methods, integrative analysis of cell type-specific regulatory elements and total RNA expression uncovered comprehensive cell type heterogeneity in the brain. Gene regulatory networks based on chromatin accessibility profiling provided an improved cell type communication model. Finally, we demonstrated that Microwell-seq3 can identify malignant cells and their specific regulons in spontaneous lung tumors of aged mice. We envision a broad application of Microwell-seq3 in many areas of research.

Emerging tools for uncovering genetic and transcriptomic heterogeneities in bacteria

Bacterial communities display an astonishing degree of heterogeneities among their constituent cells across both the genomic and transcriptomic levels, giving rise to diverse social interactions and stress-adaptation strategies indispensable for proliferating in the natural environment (Ackermann in Nat Rev Microbiol 13:497-508, 2015). Our knowledge about bacterial heterogeneities and their physiological ramifications critically depends on our ability to unambiguously resolve the genetic and phenotypic states of the individual cells that make up the population. In this short review, I highlight several recently developed methods for studying bacterial heterogeneities, primarily focusing on single-cell techniques based on advanced sequencing and microscopy technologies. I will discuss the working principle of each technique as well as the types of problems each technique is best positioned to address. With significant improvements in resolution and throughput, these emerging tools together offer unprecedented and complementary views of various types of heterogeneities found within bacterial populations, paving the way for mechanistic dissections and systematic interventions in laboratory and clinical settings.

Spatial multi-omics: novel tools to study the complexity of cardiovascular diseases

Spatial multi-omic studies have emerged as a promising approach to comprehensively analyze cells in tissues, enabling the joint analysis of multiple data modalities like transcriptome, epigenome, proteome, and metabolome in parallel or even the same tissue section. This review focuses on the recent advancements in spatial multi-omics technologies, including novel data modalities and computational approaches. We discuss the advancements in low-resolution and high-resolution spatial multi-omics methods which can resolve up to 10,000 of individual molecules at subcellular level. By applying and integrating these techniques, researchers have recently gained valuable insights into the molecular circuits and mechanisms which govern cell biology along the cardiovascular disease spectrum. We provide an overview of current data analysis approaches, with a focus on data integration of multi-omic datasets, highlighting strengths and weaknesses of various computational pipelines. These tools play a crucial role in analyzing and interpreting spatial multi-omics datasets, facilitating the discovery of new findings, and enhancing translational cardiovascular research. Despite nontrivial challenges, such as the need for standardization of experimental setups, data analysis, and improved computational tools, the application of spatial multi-omics holds tremendous potential in revolutionizing our understanding of human disease processes and the identification of novel biomarkers and therapeutic targets. Exciting opportunities lie ahead for the spatial multi-omics field and will likely contribute to the advancement of personalized medicine for cardiovascular diseases.

Single Cell Micro RNA Sequencing Library Preparation

Micro RNAs represent important post-transcriptional regulators in health and are involved in the onset of many diseases. Therefore, the further characterization of physiological miRNA functions is an important basic research question, and miRNAs even have high potential as biomarkers both for prognosis and diagnosis. In order to exploit this potential, it is mandatory to accurately quantify the miRNA expression not only in bulk but also on the single-cell level. Here, we describe a protocol, which facilitates miRNA sequencing library preparation of very low input samples, single cells, and even clinical samples such as circulating tumor cells. The protocol can be combined with different single-cell isolation methods (e.g., micromanipulation and FACS sorting). After cell lysis, sequencing adapters are ligated to the miRNAs, other ncRNA species, and adapter dimers are reduced by exonuclease digest, the miRNA library is reverse transcribed, amplified, and purified. Furthermore, quality controls are described to select only high-quality samples for sequencing.

CRISPR/Cas9-based depletion of 16S ribosomal RNA improves library complexity of single-cell RNA-sequencing in planarians

BACKGROUND

Single-cell RNA-sequencing (scRNA-seq) relies on PCR amplification to retrieve information from vanishingly small amounts of starting material. To selectively enrich mRNA from abundant non-polyadenylated transcripts, poly(A) selection is a key step during library preparation. However, some transcripts, such as mitochondrial genes, can escape this elimination and overwhelm libraries. Often, these transcripts are removed in silico, but whether physical depletion improves detection of rare transcripts in single cells is unclear.

RESULTS

We find that a single 16S ribosomal RNA is widely enriched in planarian scRNA-seq datasets, independent of the library preparation method. To deplete this transcript from scRNA-seq libraries, we design 30 single-guide RNAs spanning its length. To evaluate the effects of depletion, we perform a side-by-side comparison of the effects of eliminating the 16S transcript and find a substantial increase in the number of genes detected per cell, coupled with virtually complete loss of the 16S RNA. Moreover, we systematically determine that library complexity increases with a limited number of PCR cycles following CRISPR treatment. When compared to in silico depletion of 16S, physically removing it reduces dropout rates, retrieves more clusters, and reveals more differentially expressed genes.

CONCLUSIONS

Our results show that abundant transcripts reduce the retrieval of informative transcripts in scRNA-seq and distort the analysis. Physical removal of these contaminants enables the detection of rare transcripts at lower sequencing depth, and also outperforms in silico depletion. Importantly, this method can be easily customized to deplete any abundant transcript from scRNA-seq libraries.

Ushering in the era of tRNA medicines

Long viewed as an intermediary in protein translation, there is a growing awareness that tRNAs are capable of myriad other biological functions linked to human health and disease. These emerging roles could be tapped to leverage tRNAs as diagnostic biomarkers, therapeutic targets, or even as novel medicines. Furthermore, the growing array of tRNA-derived fragments, which modulate an increasingly broad spectrum of cellular pathways, is expanding this opportunity. Together, these molecules offer drug developers the chance to modulate the impact of mutations and to alter cell homeostasis. Moreover, because a single therapeutic tRNA can facilitate readthrough of a genetic mutation shared across multiple genes, such medicines afford the opportunity to define patient populations not based on their clinical presentation or mutated gene but rather on the mutation itself. This approach could potentially transform the treatment of patients with rare and ultrarare diseases. In this review, we explore the diverse biology of tRNA and its fragments, examining the past and present challenges to provide a comprehensive understanding of the molecules and their therapeutic potential.

Single-Nucleus RNA-Seq: Open the Era of Great Navigation for FFPE Tissue

Single-cell sequencing (scRNA-seq) has revolutionized our ability to explore heterogeneity and genetic variations at the single-cell level, opening up new avenues for understanding disease mechanisms and cell-cell interactions. Single-nucleus RNA-sequencing (snRNA-seq) is emerging as a promising solution to scRNA-seq due to its reduced ionized transcription bias and compatibility with richer samples. This approach will provide an exciting opportunity for in-depth exploration of billions of formalin-fixed paraffin-embedded (FFPE) tissues. Recent advancements in single-cell/nucleus gene expression workflows tailored for FFPE tissues have demonstrated their feasibility and provided crucial guidance for future studies utilizing FFPE specimens. In this review, we provide a broad overview of the nuclear preparation strategies, the latest technologies of snRNA-seq applicable to FFPE samples. Finally, the limitations and potential technical developments of snRNA-seq in FFPE samples are summarized. The development of snRNA-seq technologies for FFPE samples will lay a foundation for transcriptomic studies of valuable samples in clinical medicine and human sample banks.

Cell-free RNA for the liquid biopsy of gastrointestinal cancer

Gastrointestinal (GI) cancer includes many cancer types, such as esophageal, liver, gastric, pancreatic, and colorectal cancer. As the cornerstone of personalized medicine for GI cancer, liquid biopsy based on noninvasive biomarkers provides promising opportunities for early diagnosis and dynamic treatment management. Recently, a growing number of studies have demonstrated the potential of cell-free RNA (cfRNA) as a new type of noninvasive biomarker in body fluids, such as blood, saliva, and urine. Meanwhile, transcriptomes based on high-throughput RNA detection technologies keep discovering new cfRNA biomarkers. In this review, we introduce the origins and applications of cfRNA, describe its detection and qualification methods in liquid biopsy, and summarize a comprehensive list of cfRNA biomarkers in different GI cancer types. Moreover, we also discuss perspective studies of cfRNA to overcome its current limitations in clinical applications. This article is categorized under: RNA in Disease and Development > RNA in Disease.

CRISPR/Cas9-based depletion of 16S ribosomal RNA improves library complexity of single-cell RNA-sequencing

Background

Single-cell RNA-sequencing (scRNA-seq) relies on PCR amplification to retrieve information from vanishingly small amounts of starting material. To selectively enrich mRNA from abundant non-polyadenylated transcripts, poly(A) selection is a key step during library preparation. However, some transcripts, such as mitochondrial genes, can escape this elimination and overwhelm libraries. Often, these transcripts are removed in silico, but whether physical depletion improves detection of rare transcripts in single cells is unclear.

Results

We find that a single 16S ribosomal RNA is widely enriched in planarian scRNA-seq datasets, independent of the library preparation method. To deplete this transcript from scRNA-seq libraries, we design 30 single-guide RNAs spanning its length. To evaluate the effects of depletion, we perform a side-by-side comparison of the effects of eliminating the 16S transcript and find a substantial increase in the number of genes detected per cell, coupled with virtually complete loss of the 16S RNA. Moreover, we systematically determine that library complexity increases with a limited number of PCR cycles following CRISPR treatment. When compared to in silico depletion of 16S, physically removing it reduces dropout rates, retrieves more clusters, and reveals more differentially-expressed genes.

Conclusions

Our results show that abundant transcripts reduce the retrieval of informative transcripts in scRNA-seq and distort the analysis. Physical removal of these contaminants enables the detection of rare transcripts at lower sequencing depth, and also outperforms in silico depletion. Importantly, this method can be easily customized to deplete any abundant transcript from scRNA-seq libraries.

A ligation-independent sequencing method reveals tRNA-derived RNAs with blocked 3' termini

Despite the numerous sequencing methods available, the vast diversity in size and chemical modifications of RNA molecules makes the capture of the full spectrum of cellular RNAs a difficult task. By combining quasi-random hexamer priming with a custom template switching strategy, we developed a method to construct sequencing libraries from RNA molecules of any length and with any type of 3' terminal modification, allowing the sequencing and analysis of virtually all RNA species. Ligation-independent detection of all types of RNA (LIDAR) is a simple, effective tool to comprehensively characterize changes in small non-coding RNAs and mRNAs simultaneously, with performance comparable to separate dedicated methods. With LIDAR, we comprehensively characterized the coding and non-coding transcriptome of mouse embryonic stem cells, neural progenitor cells, and sperm. LIDAR detected a much larger variety of tRNA-derived RNAs (tDRs) compared to traditional ligation-dependent sequencing methods, and uncovered the presence of tDRs with blocked 3' ends that had previously escaped detection. Our findings highlight the potential of LIDAR to systematically detect all RNAs in a sample and uncover new RNA species with potential regulatory functions.

From words to complete phrases: insight into single-cell isoforms using short and long reads

The profiling of gene expression patterns to glean biological insights from single cells has become commonplace over the last few years. However, this approach overlooks the transcript contents that can differ between individual cells and cell populations. In this review, we describe early work in the field of single-cell short-read sequencing as well as full-length isoforms from single cells. We then describe recent work in single-cell long-read sequencing wherein some transcript elements have been observed to work in tandem. Based on earlier work in bulk tissue, we motivate the study of combination patterns of other RNA variables. Given that we are still blind to some aspects of isoform biology, we suggest possible future avenues such as CRISPR screens which can further illuminate the function of RNA variables in distinct cell populations.

Approaches for single-cell RNA sequencing across tissues and cell types

Single-cell sequencing of RNA (scRNA-seq) has advanced our understanding of cellular heterogeneity and signaling in developmental biology and disease. A large number of complementary assays have been developed to profile transcriptomes of individual cells, also in combination with other readouts, such as chromatin accessibility or antibody-based analysis of protein surface markers. As scRNA-seq technologies are advancing fast, it is challenging to establish robust workflows and up-to-date protocols that are best suited to address the large range of research questions. Here, we review scRNA-seq techniques from mRNA end-counting to total RNA in relation to their specific features and outline the necessary sample preparation steps and quality control measures. Based on our experience in dealing with the continuously growing portfolio from the perspective of a central single-cell facility, we aim to provide guidance on how workflows can be best automatized and share our experience in coping with the continuous expansion of scRNA-seq techniques.

High-throughput single nucleus total RNA sequencing of formalin-fixed paraffin-embedded tissues by snRandom-seq

Formalin-fixed paraffin-embedded (FFPE) tissues constitute a vast and valuable patient material bank for clinical history and follow-up data. It is still challenging to achieve single cell/nucleus RNA (sc/snRNA) profile in FFPE tissues. Here, we develop a droplet-based snRNA sequencing technology (snRandom-seq) for FFPE tissues by capturing full-length total RNAs with random primers. snRandom-seq shows a minor doublet rate (0.3%), a much higher RNA coverage, and detects more non-coding RNAs and nascent RNAs, compared with state-of-art high-throughput scRNA-seq technologies. snRandom-seq detects a median of >3000 genes per nucleus and identifies 25 typical cell types. Moreover, we apply snRandom-seq on a clinical FFPE human liver cancer specimen and reveal an interesting subpopulation of nuclei with high proliferative activity. Our method provides a powerful snRNA-seq platform for clinical FFPE specimens and promises enormous applications in biomedical research.

Small RNA transcriptome analysis using parallel single-cell small RNA sequencing

miRNA and other forms of small RNAs are known to regulate many biological processes. Single-cell small RNA sequencing can be used to profile small RNAs of individual cells; however, limitations of efficiency and scale prevent its widespread application. Here, we developed parallel single-cell small RNA sequencing (PSCSR-seq), which can overcome the limitations of existing methods and enable high-throughput small RNA expression profiling of individual cells. Analysis of PSCSR-seq data indicated that diverse cell types could be identified based on patterns of miRNA expression, and showed that miRNA content in nuclei is informative (for example, cell type marker miRNAs can be detected in isolated nuclei). PSCSR-seq is very sensitive: analysis of only 732 peripheral blood mononuclear cells (PBMCs) detected 774 miRNAs, whereas bulk small RNA analysis would require input RNA from approximately 10⁶ cells to detect as many miRNAs. We identified 42 miRNAs as markers for PBMC subpopulations. Moreover, we analyzed the miRNA profiles of 9,533 cells from lung cancer biopsies, and by dissecting cell subpopulations, we identified potentially diagnostic and therapeutic miRNAs for lung cancers. Our study demonstrates that PSCSR-seq is highly sensitive and reproducible, thus making it an advanced tool for miRNA analysis in cancer and life science research.

T-RHEX-RNAseq - a tagmentation-based, rRNA blocked, random hexamer primed RNAseq method for generating stranded RNAseq libraries directly from very low numbers of lysed cells

BACKGROUND

RNA sequencing has become the mainstay for studies of gene expression. Still, analysis of rare cells with random hexamer priming - to allow analysis of a broader range of transcripts - remains challenging.

RESULTS

We here describe a tagmentation-based, rRNA blocked, random hexamer primed RNAseq approach (T-RHEX-RNAseq) for generating stranded RNAseq libraries from very low numbers of FACS sorted cells without RNA purification steps.

CONCLUSION

T-RHEX-RNAseq provides an easy-to-use, time efficient and automation compatible method for generating stranded RNAseq libraries from rare cells.

Spatial mapping of the total transcriptome by in situ polyadenylation

Spatial transcriptomics reveals the spatial context of gene expression, but current methods are limited to assaying polyadenylated (A-tailed) RNA transcripts. Here we demonstrate that enzymatic in situ polyadenylation of RNA enables detection of the full spectrum of RNAs, expanding the scope of sequencing-based spatial transcriptomics to the total transcriptome. We demonstrate that our spatial total RNA-sequencing (STRS) approach captures coding RNAs, noncoding RNAs and viral RNAs. We apply STRS to study skeletal muscle regeneration and viral-induced myocarditis. Our analyses reveal the spatial patterns of noncoding RNA expression with near-cellular resolution, identify spatially defined expression of noncoding transcripts in skeletal muscle regeneration and highlight host transcriptional responses associated with local viral RNA abundance. STRS requires adding only one step to the widely used Visium spatial total RNA-sequencing protocol from 10x Genomics, and thus could be easily adopted to enable new insights into spatial gene regulation and biology.

Evidence for Existence of Multiple Functional Human Small RNAs Derived from Transcripts of Protein-Coding Genes

The human genome encodes a multitude of different noncoding transcripts that have been traditionally separated on the basis of their lengths into long (>200 nt) or small (<200 nt) noncoding RNAs. The functions, mechanisms of action, and biological relevance of the vast majority of both long and short noncoding transcripts remain unknown. However, according to the functional understanding of the known classes of long and small noncoding RNAs (sncRNAs) that have been shown to play crucial roles in multiple biological processes, it is generally assumed that many unannotated long and small transcripts participate in important cellular functions as well. Nevertheless, direct evidence of functionality is lacking for most noncoding transcripts, especially for sncRNAs that are often dismissed as stable degradation products of longer RNAs. Here, we developed a high-throughput assay to test the functionality of sncRNAs by overexpressing them in human cells. Surprisingly, we found that a significant fraction (>40%) of unannotated sncRNAs appear to have biological relevance. Furthermore, contrary to the expectation, the potentially functional transcripts are not highly abundant and can be derived from protein-coding mRNAs. These results strongly suggest that the small noncoding transcriptome can harbor multiple functional transcripts that warrant future studies.

High-throughput total RNA sequencing in single cells using VASA-seq

Most methods for single-cell transcriptome sequencing amplify the termini of polyadenylated transcripts, capturing only a small fraction of the total cellular transcriptome. This precludes the detection of many long non-coding, short non-coding and non-polyadenylated protein-coding transcripts and hinders alternative splicing analysis. We, therefore, developed VASA-seq to detect the total transcriptome in single cells, which is enabled by fragmenting and tailing all RNA molecules subsequent to cell lysis. The method is compatible with both plate-based formats and droplet microfluidics. We applied VASA-seq to more than 30,000 single cells in the developing mouse embryo during gastrulation and early organogenesis. Analyzing the dynamics of the total single-cell transcriptome, we discovered cell type markers, many based on non-coding RNA, and performed in vivo cell cycle analysis via detection of non-polyadenylated histone genes. RNA velocity characterization was improved, accurately retracing blood maturation trajectories. Moreover, our VASA-seq data provide a comprehensive analysis of alternative splicing during mammalian development, which highlighted substantial rearrangements during blood development and heart morphogenesis.

Applications of single-cell RNA sequencing in atopic dermatitis and psoriasis

Single-cell RNA sequencing (scRNA-seq) is a novel technology that characterizes molecular heterogeneity at the single-cell level. With the development of more automated, sensitive, and cost-effective single-cell isolation methods, the sensitivity and efficiency of scRNA-seq have improved. Technological advances in single-cell analysis provide a deeper understanding of the biological diversity of cells present in tissues, including inflamed skin. New subsets of cells have been discovered among common inflammatory skin diseases, such as atopic dermatitis (AD) and psoriasis. ScRNA-seq technology has also been used to analyze immune cell distribution and cell-cell communication, shedding new light on the complex interplay of components involved in disease responses. Moreover, scRNA-seq may be a promising tool in precision medicine because of its ability to define cell subsets with potential treatment targets and to characterize cell-specific responses to drugs or other stimuli. In this review, we briefly summarize the progress in the development of scRNA-seq technologies and discuss the latest scRNA-seq-related findings and future trends in AD and psoriasis. We also discuss the limitations and technical problems associated with current scRNA-seq technology.

Computational solutions for spatial transcriptomics

Transcriptome level expression data connected to the spatial organization of the cells and molecules would allow a comprehensive understanding of how gene expression is connected to the structure and function in the biological systems. The spatial transcriptomics platforms may soon provide such information. However, the current platforms still lack spatial resolution, capture only a fraction of the transcriptome heterogeneity, or lack the throughput for large scale studies. The strengths and weaknesses in current ST platforms and computational solutions need to be taken into account when planning spatial transcriptomics studies. The basis of the computational ST analysis is the solutions developed for single-cell RNA-sequencing data, with advancements taking into account the spatial connectedness of the transcriptomes. The scRNA-seq tools are modified for spatial transcriptomics or new solutions like deep learning-based joint analysis of expression, spatial, and image data are developed to extract biological information in the spatially resolved transcriptomes. The computational ST analysis can reveal remarkable biological insights into spatial patterns of gene expression, cell signaling, and cell type variations in connection with cell type-specific signaling and organization in complex tissues. This review covers the topics that help choosing the platform and computational solutions for spatial transcriptomics research. We focus on the currently available ST methods and platforms and their strengths and limitations. Of the computational solutions, we provide an overview of the analysis steps and tools used in the ST data analysis. The compatibility with the data types and the tools provided by the current ST analysis frameworks are summarized.

A curated human cellular microRNAome based on 196 primary cell types

BACKGROUND

An incomplete picture of the expression distribution of microRNAs (miRNAs) across human cell types has long hindered our understanding of this important regulatory class of RNA. With the continued increase in available public small RNA sequencing datasets, there is an opportunity to more fully understand the general distribution of miRNAs at the cell level.

RESULTS

From the NCBI Sequence Read Archive, we obtained 6,054 human primary cell datasets and processed 4,184 of them through the miRge3.0 small RNA sequencing alignment software. This dataset was curated down, through shared miRNA expression patterns, to 2,077 samples from 196 unique cell types derived from 175 separate studies. Of 2,731 putative miRNAs listed in miRBase (v22.1), 2,452 (89.8%) were detected. Among reasonably expressed miRNAs, 108 were designated as cell specific/near specific, 59 as infrequent, 52 as frequent, 54 as near ubiquitous, and 50 as ubiquitous. The complexity of cellular microRNA expression estimates recapitulates tissue expression patterns and informs on the miRNA composition of plasma.

CONCLUSIONS

This study represents the most complete reference, to date, of miRNA expression patterns by primary cell type. The data are available through the human cellular microRNAome track at the UCSC Genome Browser (https://genome.ucsc.edu/cgi-bin/hgHubConnect) and an R/Bioconductor package (https://bioconductor.org/packages/microRNAome/).

Single cell- and spatial 'Omics revolutionize physiology

Single cell multi- 'Omics and Spatial Transcriptomics are prominent technological highlights of recent years, and both fields still witness a ceaseless firework of novel approaches for high resolution profiling of additional omics layers. As all life processes in organs and organisms are based on the functions of their fundamental building blocks, the individual cells and their interactions, these methods are of utmost worth for the study of physiology in health and disease. Recent discoveries on embryonic development, tumor immunology, the detailed cellular composition and function of complex tissues like for example the kidney or the brain, different roles of the same cell type in different organs, the oncogenic program of individual tumor entities, or the architecture of immunopathology in infected tissue are based on single cell and spatial transcriptomics experiments. In this review, we will give a broad overview of technological concepts for single cell and spatial analysis, showing both advantages and limitations, and illustrate their impact with some particularly impressive case studies.

Transfer RNA-Derived Small RNAs: Novel Regulators and Biomarkers of Cancers

Transfer RNA-derived small RNAs (tsRNAs) are conventional non-coding RNAs (ncRNAs) with a length between18 and 40 nucleotides (nt) playing a crucial role in treating various human diseases including tumours. Nowadays, with the use of high-throughput sequencing technologies, it has been proven that certain tsRNAs are dysregulated in multiple tumour tissues as well as in the blood serum of cancer patients. Meanwhile, data retrieved from the literature show that tsRNAs are correlated with the regulation of the hallmarks of cancer, modification of tumour microenvironment, and modulation of drug resistance. On the other side, the emerging role of tsRNAs as biomarkers for cancer diagnosis and prognosis is promising. In this review, we focus on the specific characteristics and biological functions of tsRNAs with a focus on their impact on various tumours and discuss the possibility of tsRNAs as novel potential biomarkers for cancer diagnosis and prognosis.