An in situ sequencing approach maps PLASTOCHRON1 at the boundary between indeterminate and determinate cells.

The plant shoot apex houses the shoot apical meristem, a highly organized and active stem-cell tissue where molecular signaling in discrete cells determines when and where leaves are initiated. We optimized a spatial transcriptomics approach, in situ sequencing (ISS), to colocalize the transcripts of 90 genes simultaneously on the same section of tissue from the maize (Zea mays) shoot apex. The RNA ISS technology reported expression profiles that were highly comparable with those obtained by in situ hybridizations (ISHs) and allowed the discrimination between tissue domains. Furthermore, the application of spatial transcriptomics to the shoot apex, which inherently comprised phytomers that are in gradual developmental stages, provided a spatiotemporal sequence of transcriptional events. We illustrate the power of the technology through PLASTOCHRON1 (PLA1), which was specifically expressed at the boundary between indeterminate and determinate cells and partially overlapped with ROUGH SHEATH1 and OUTER CELL LAYER4 transcripts. Also, in the inflorescence, PLA1 transcripts localized in cells subtending the lateral primordia or bordering the newly established meristematic region, suggesting a more general role of PLA1 in signaling between indeterminate and determinate cells during the formation of lateral organs. Spatial transcriptomics builds on RNA ISH, which assays relatively few transcripts at a time and provides a powerful complement to single-cell transcriptomics that inherently removes cells from their native spatial context. Further improvements in resolution and sensitivity will greatly advance research in plant developmental biology.

Single-nucleus RNA sequencing of plant tissues using a nanowell-based system.

Single-cell genomics provides unprecedented potential for research on plant development and environmental responses. Here, we introduce a generic procedure for plant nucleus isolation combined with nanowell-based library preparation. Our method enables the transcriptome analysis of thousands of individual plant nuclei. It serves as an alternative to the use of protoplast isolation, which is currently a standard methodology for plant single-cell genomics, although it can be challenging for some plant tissues. We show the applicability of our nucleus isolation method by using different plant materials from different species. The potential of our single-nucleus RNA sequencing method is shown through the characterization of transcriptomes of seedlings and developing flowers from Arabidopsis thaliana. We evaluated the transcriptome dynamics during the early stages of anther development, identified stage-specific activities of transcription factors regulating this process, and predicted potential target genes of these transcription factors. Our nucleus isolation procedure can be applied in different plant species and tissues, thus expanding the toolkit for plant single-cell genomics experiments.

Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput.

Single-cell RNA-seq can precisely resolve cellular states, but applying this method to low-input samples is challenging. Here, we present Seq-Well, a portable, low-cost platform for massively parallel single-cell RNA-seq. Barcoded mRNA capture beads and single cells are sealed in an array of subnanoliter wells using a semipermeable membrane, enabling efficient cell lysis and transcript capture. We use Seq-Well to profile thousands of primary human macrophages exposed to Mycobacterium tuberculosis.

Going the Distance: Optimizing RNA-Seq Strategies for Transcriptomic Analysis of Complex Viral Genomes.

Transcriptome profiling has become routine in studies of many biological processes. However, the favored approaches such as short-read Illumina RNA sequencing are giving way to long-read sequencing platforms better suited to interrogating the complex transcriptomes typical of many RNA and DNA viruses. Here, we provide a guide-tailored to molecular virologists-to the ins and outs of viral transcriptome sequencing and discuss the strengths and weaknesses of the major RNA sequencing technologies as tools to analyze the abundance and diversity of the viral transcripts made during infection.

Portable sequencer in the fight against infectious disease.

Infectious disease is still a major threat in the world today. Five decades ago, it was considered soon to be eradicated, but the adaptation of pathogens to environmental pressure, such as antimicrobials, encouraged the emergence and reemergence of infectious disease. The fight with infectious disease starts with prevention, diagnosis, and treatment. Diagnosis can be upheld by observing the cause of disease under the microscope or detecting the presence of nucleic acid and proteins of the pathogens. The molecular techniques span from classical polymerase chain reaction (PCR) to sequencing the nucleic acid composition. Here, we are reviewing the works have been undertaken to utilize a portable sequencer, MinION, in various aspects of infectious disease management.

Freiburg RNA tools: a central online resource for RNA-focused research and teaching.

The Freiburg RNA tools webserver is a well established online resource for RNA-focused research. It provides a unified user interface and comprehensive result visualization for efficient command line tools. The webserver includes RNA-RNA interaction prediction (IntaRNA, CopraRNA, metaMIR), sRNA homology search (GLASSgo), sequence-structure alignments (LocARNA, MARNA, CARNA, ExpaRNA), CRISPR repeat classification (CRISPRmap), sequence design (antaRNA, INFO-RNA, SECISDesign), structure aberration evaluation of point mutations (RaSE), and RNA/protein-family models visualization (CMV), and other methods. Open education resources offer interactive visualizations of RNA structure and RNA-RNA interaction prediction as well as basic and advanced sequence alignment algorithms. The services are freely available at http://rna.informatik.uni-freiburg.de.

SPAR: small RNA-seq portal for analysis of sequencing experiments.

The introduction of new high-throughput small RNA sequencing protocols that generate large-scale genomics datasets along with increasing evidence of the significant regulatory roles of small non-coding RNAs (sncRNAs) have highlighted the urgent need for tools to analyze and interpret large amounts of small RNA sequencing data. However, it remains challenging to systematically and comprehensively discover and characterize sncRNA genes and specifically-processed sncRNA products from these datasets. To fill this gap, we present Small RNA-seq Portal for Analysis of sequencing expeRiments (SPAR), a user-friendly web server for interactive processing, analysis, annotation and visualization of small RNA sequencing data. SPAR supports sequencing data generated from various experimental protocols, including smRNA-seq, short total RNA sequencing, microRNA-seq, and single-cell small RNA-seq. Additionally, SPAR includes publicly available reference sncRNA datasets from our DASHR database and from ENCODE across 185 human tissues and cell types to produce highly informative small RNA annotations across all major small RNA types and other features such as co-localization with various genomic features, precursor transcript cleavage patterns, and conservation. SPAR allows the user to compare the input experiment against reference ENCODE/DASHR datasets. SPAR currently supports analyses of human (hg19, hg38) and mouse (mm10) sequencing data. SPAR is freely available at https://www.lisanwanglab.org/SPAR.

An RNA-based signature enables high specificity detection of circulating tumor cells in hepatocellular carcinoma.

Circulating tumor cells (CTCs) are shed into the bloodstream by invasive cancers, but the difficulty inherent in identifying these rare cells by microscopy has precluded their routine use in monitoring or screening for cancer. We recently described a high-throughput microfluidic CTC-iChip, which efficiently depletes hematopoietic cells from blood specimens and enriches for CTCs with well-preserved RNA. Application of RNA-based digital PCR to detect CTC-derived signatures may thus enable highly accurate tissue lineage-based cancer detection in blood specimens. As proof of principle, we examined hepatocellular carcinoma (HCC), a cancer that is derived from liver cells bearing a unique gene expression profile. After identifying a digital signature of 10 liver-specific transcripts, we used a cross-validated logistic regression model to identify the presence of HCC-derived CTCs in nine of 16 (56%) untreated patients with HCC versus one of 31 (3%) patients with nonmalignant liver disease at risk for developing HCC (P < 0.0001). Positive CTC scores declined in treated patients: Nine of 32 (28%) patients receiving therapy and only one of 15 (7%) patients who had undergone curative-intent ablation, surgery, or liver transplantation were positive. RNA-based digital CTC scoring was not correlated with the standard HCC serum protein marker alpha fetoprotein (P = 0.57). Modeling the sequential use of these two orthogonal markers for liver cancer screening in patients with high-risk cirrhosis generates positive and negative predictive values of 80% and 86%, respectively. Thus, digital RNA quantitation constitutes a sensitive and specific CTC readout, enabling high-throughput clinical applications, such as noninvasive screening for HCC in populations where viral hepatitis and cirrhosis are prevalent.

StructureFold2: Bringing chemical probing data into the computational fold of RNA structural analysis.

The secondary structure of an RNA is often implicit to its function. Recently, various high-throughput RNA structure probing techniques have been developed to elucidate important RNA structure-function relationships genome-wide. These techniques produce unwieldy experimental data sets that require evaluation with unique computational pipelines. Herein, we present StructureFold2, a user-friendly set of analysis tools that makes precise data processing and detailed downstream analyses of such data sets both available and practical. StructureFold2 processes high-throughput reads sequenced from libraries prepared after experimental probing for reverse transcription (RT) stops generated by chemical modification of RNA at solvent accessible residues. This pipeline is able to analyze reads generated from a variety of structure-probing chemicals (e.g. DMS, glyoxal, SHAPE). Notably, StructureFold2 offers a new fully featured suite of utilities and tools to guide a user through multiple types of analyses. A particular emphasis is placed on analyzing the reactivity patterns of transcripts, complementing their use as folding restraints for predicting RNA secondary structure. StructureFold2 is hosted as a Github repository and is available at (https://github.com/StructureFold2/StructureFold2).

Deep sequencing approaches for the analysis of prokaryotic transcriptional boundaries and dynamics.

The identification of the protein-coding regions of a genome is straightforward due to the universality of start and stop codons. However, the boundaries of the transcribed regions, conditional operon structures, non-coding RNAs and the dynamics of transcription, such as pausing of elongation, are non-trivial to identify, even in the comparatively simple genomes of prokaryotes. Traditional methods for the study of these areas, such as tiling arrays, are noisy, labour-intensive and lack the resolution required for densely-packed bacterial genomes. Recently, deep sequencing has become increasingly popular for the study of the transcriptome due to its lower costs, higher accuracy and single nucleotide resolution. These methods have revolutionised our understanding of prokaryotic transcriptional dynamics. Here, we review the deep sequencing and data analysis techniques that are available for the study of transcription in prokaryotes, and discuss the bioinformatic considerations of these analyses.

Fine needle aspirates of kidneys: a promising tool for RNA sequencing in native and transplanted kidneys.

BACKGROUND: Transcriptome analysis is emerging as emerging as a promising tool to enhance precision of diagnosis and monitoring in solid organ transplantation. Clinical progress has however been hampered by the current reliance on samples from core needle biopsies. This proof-of-principle study examined whether fine needle aspirates, being less invasive, permit the ascertainment of the identical molecular information as core biopsies. METHODS: We collected fine needles aspirates from various needle sizes (G19, 21, 23, 25) and the corresponding core biopsies (G16 needle) of non-tumor tissue of full nephrectomy specimens from patients suffering from clear cell renal cell carcinoma (n = 11). RNA expression patterns of two gene sets (156 genes) were executed using targeted RNA sequencing in samples from fine needle vs. core needle samples. A subgroup of kidneys (n = 6) also underwent whole transcriptome RNA sequencing from core biopsies of tumor and peri-tumoral normal tissue (Tru Seq RNA Access, Illumina). RESULTS: Samples from all needle sizes except two G25 aspirates yielded RNA potentially suitable for sequencing of both gene sets. The mRNA expression patterns of the two gene sets were highly correlated between fine needle aspirates (G23) and corresponding (G16) core biopsies (r = 0.985 and 0.982, respectively). This close correlation was further documented by heat map, Principal Component Analyses (PCA) and whole transcription RNA sequencing. The similarity between fine neddle aspirates and core needle biopsies was additionally confirmed in the subgroup with complete RNA sequencing. CONCLUSIONS: Fine needle biopsies yield similar genomic information to core needle biopsies. The less invasive nature of fine needle biopsies may therefore permit more frequent molecular monitoring and a more targeted use of core needle biopsies in native and especially in transplanted kidneys.

Accurate identification of A-to-I RNA editing in human by transcriptome sequencing.

RNA editing enhances the diversity of gene products at the post-transcriptional level. Approaches for genome-wide identification of RNA editing face two main challenges: separating true editing sites from false discoveries and accurate estimation of editing levels. We developed an approach to analyze transcriptome sequencing data (RNA-seq) for global identification of RNA editing in cells for which whole-genome sequencing data are available. We applied the method to analyze RNA-seq data of a human glioblastoma cell line, U87MG. Around 10,000 DNA-RNA differences were identified, the majority being putative A-to-I editing sites. These predicted A-to-I events were associated with a low false-discovery rate (∼5%). Moreover, the estimated editing levels from RNA-seq correlated well with those based on traditional clonal sequencing. Our results further facilitated unbiased characterization of the sequence and evolutionary features flanking predicted A-to-I editing sites and discovery of a conserved RNA structural motif that may be functionally relevant to editing. Genes with predicted A-to-I editing were significantly enriched with those known to be involved in cancer, supporting the potential importance of cancer-specific RNA editing. A similar profile of DNA-RNA differences as in U87MG was predicted for another RNA-seq data set obtained from primary breast cancer samples. Remarkably, significant overlap exists between the putative editing sites of the two transcriptomes despite their difference in cell type, cancer type, and genomic backgrounds. Our approach enabled de novo identification of the RNA editome, which sets the stage for further mechanistic studies of this important step of post-transcriptional regulation.

Next-generation sequencing identifies the natural killer cell microRNA transcriptome.

Natural killer (NK) cells are innate lymphocytes important for early host defense against infectious pathogens and surveillance against malignant transformation. Resting murine NK cells regulate the translation of effector molecule mRNAs (e.g., granzyme B, GzmB) through unclear molecular mechanisms. MicroRNAs (miRNAs) are small noncoding RNAs that post-transcriptionally regulate the translation of their mRNA targets, and are therefore candidates for mediating this control process. While the expression and importance of miRNAs in T and B lymphocytes have been established, little is known about miRNAs in NK cells. Here, we used two next-generation sequencing (NGS) platforms to define the miRNA transcriptomes of resting and cytokine-activated primary murine NK cells, with confirmation by quantitative real-time PCR (qRT-PCR) and microarrays. We delineate a bioinformatics analysis pipeline that identified 302 known and 21 novel mature miRNAs from sequences obtained from NK cell small RNA libraries. These miRNAs are expressed over a broad range and exhibit isomiR complexity, and a subset is differentially expressed following cytokine activation. Using these miRNA NGS data, miR-223 was identified as a mature miRNA present in resting NK cells with decreased expression following cytokine activation. Furthermore, we demonstrate that miR-223 specifically targets the 3' untranslated region of murine GzmB in vitro, indicating that this miRNA may contribute to control of GzmB translation in resting NK cells. Thus, the sequenced NK cell miRNA transcriptome provides a valuable framework for further elucidation of miRNA expression and function in NK cell biology.

Digital transcriptome profiling from attomole-level RNA samples.

Accurate profiling of minute quantities of RNA in a global manner can enable key advances in many scientific and clinical disciplines. Here, we present low-quantity RNA sequencing (LQ-RNAseq), a high-throughput sequencing-based technique allowing whole transcriptome surveys from subnanogram RNA quantities in an amplification/ligation-free manner. LQ-RNAseq involves first-strand cDNA synthesis from RNA templates, followed by 3' polyA tailing of the single-stranded cDNA products and direct single molecule sequencing. We applied LQ-RNAseq to profile S. cerevisiae polyA+ transcripts, demonstrate the reproducibility of the approach across different sample preparations and independent instrument runs, and establish the absolute quantitative power of this method through comparisons with other reported transcript profiling techniques and through utilization of RNA spike-in experiments. We demonstrate the practical application of this approach to define the transcriptional landscape of mouse embryonic and induced pluripotent stem cells, observing transcriptional differences, including over 100 genes exhibiting differential expression between these otherwise very similar stem cell populations. This amplification-independent technology, which utilizes small quantities of nucleic acid and provides quantitative measurements of cellular transcripts, enables global gene expression measurements from minute amounts of materials and offers broad utility in both basic research and translational biology for characterization of rare cells.

Fast accessibility-based prediction of RNA-RNA interactions.

MOTIVATION: Currently, the best RNA-RNA interaction prediction tools are based on approaches that consider both the inter- and intramolecular interactions of hybridizing RNAs. While accurate, these methods are too slow and memory-hungry to be employed in genome-wide RNA target scans. Alternative methods neglecting intramolecular structures are fast enough for genome-wide applications, but are too inaccurate to be of much practical use. RESULTS: A new approach for RNA-RNA interaction was developed, with a prediction accuracy that is similar to that of algorithms that explicitly consider intramolecular structures, but running at least three orders of magnitude faster than RNAup. This is achieved by using a combination of precomputed accessibility profiles with an approximate energy model. This approach is implemented in the new version of RNAplex. The software also provides a variant using multiple sequences alignments as input, resulting in a further increase in specificity. AVAILABILITY: RNAplex is available at www.bioinf.uni-leipzig.de/Software/RNAplex.

[RNA-Seq and its applications: a new technology for transcriptomics].

The transcriptome is the complete set of transcripts for certain type of cells or tissues in a specific developmental stage or physiological condition. Transcriptome analysis can provide a comprehensive understanding of molecular mechanisms involved in specific biological processes and diseases from the information on gene structure and function. Transcriptome has been challenging due to the efficient and fast procedures of RNA-seq. RNA-seq, refers to the use of high-throughput sequencing technologies to sequence cDNA library transcribed from all RNAs in tissues or cells, can be used to quantify, profile, and discover RNA transcripts by sequence reads. Thus, the transcripts can then be mapped on the reference genome to get comprehensive genetic information, such as transcription localization and alternative splicing status. RNA-Seq has been widely used in biological, medical, clinical and pharmaceutical research. The detailed principles, technical characteristics and applications of RNA-seq are reviewed here, and the challenges and application potentials of RNA-seq in the future are also discussed. This will present the useful information for other researchers.

Characterizing Genetic Parts and Devices Using RNA Sequencing.

Synthetic genetic circuits are composed of many parts that must interact and function together to produce a desired pattern of gene expression. A challenge when assembling circuits is that genetic parts often behave differently within a circuit, potentially impacting the desired functionality. Existing debugging methods based on fluorescent reporter proteins allow for only a few internal states to be monitored simultaneously, making diagnosis of the root cause impossible for large systems. Here, we present a tool called the Genetic Analyzer which uses RNA sequencing data to simultaneously characterize all transcriptional parts (e.g., promoters and terminators) and devices (e.g., sensors and logic gates) in complex genetic circuits. This provides a complete picture of the inner workings of a genetic circuit enabling faults to be easily identified and fixed. We construct a complete workflow to coordinate the execution of the various data processing and analysis steps and explain the options available when adapting these for the characterization of new systems.

Advances in single-cell sequencing: insights from organ transplantation.

Single-cell RNA sequencing (scRNA-seq) is a comprehensive technical tool to analyze intracellular and intercellular interaction data by whole transcriptional profile analysis. Here, we describe the application in biomedical research, focusing on the immune system during organ transplantation and rejection. Unlike conventional transcriptome analysis, this method provides a full map of multiple cell populations in one specific tissue and presents a dynamic and transient unbiased method to explore the progression of allograft dysfunction, starting from the stress response to final graft failure. This promising sequencing technology remarkably improves individualized organ rejection treatment by identifying decisive cellular subgroups and cell-specific interactions.

Low-input ATAC&mRNA-seq protocol for simultaneous profiling of chromatin accessibility and gene expression.

We present a simple, fast, and robust protocol (low-input ATAC&mRNA-seq) to simultaneously generate ATAC-seq and mRNA-seq libraries from the same cells in limited cell numbers by coupling a simplified ATAC procedure using whole cells with a novel mRNA-seq approach that features a seamless on-bead process including direct mRNA isolation from the cell lysate, solid-phase cDNA synthesis, and direct tagmentation of mRNA/cDNA hybrids for library preparation. It enables dual-omics profiling from limited material when joint epigenome and transcriptome analyses are needed. For complete details on the use and execution of this protocol, please refer to Li et al. (2021).