The Clinical Utility of a Tiered Approach to Pediatric Glioma Molecular Characterization for Resource-Limited Settings.

PURPOSE: Molecular characterization is key to optimally diagnose and manage cancer. The complexity and cost of routine genomic analysis have unfortunately limited its use and denied many patients access to precision medicine. A possible solution is to rationalize use-creating a tiered approach to testing which uses inexpensive techniques for most patients and limits expensive testing to patients with the highest needs. Here, we tested the utility of this approach to molecularly characterize pediatric glioma in a cost- and time-sensitive manner. METHODS: We used a tiered testing pipeline of immunohistochemistry (IHC), customized fusion panels or fluorescence in situ hybridization (FISH), and targeted RNA sequencing in pediatric gliomas. Two distinct diagnostic algorithms were used for low- and high-grade gliomas (LGGs and HGGs). The percentage of driver alterations identified, associated testing costs, and turnaround time (TAT) are reported. RESULTS: The tiered approach successfully characterized 96% (95 of 99) of gliomas. For 82 LGGs, IHC, targeted fusion panel or FISH, and targeted RNA sequencing solved 35% (29 of 82), 29% (24 of 82), and 30% (25 of 82) of cases, respectively. A total of 64% (53 of 82) of samples were characterized without targeted RNA sequencing. Of 17 HGG samples, 13 were characterized by IHC and four were characterized by targeted RNA sequencing. The average cost per sample was more affordable when using the tiered approach as compared with up-front targeted RNA sequencing in LGG ($405 US dollars [USD] v $745 USD) and HGGs ($282 USD v $745 USD). The average TAT per sample was also shorter using the tiered approach (10 days for LGG, 5 days for HGG v 14 days for targeted RNA sequencing). CONCLUSION: Our tiered approach molecularly characterized 96% of samples in a cost- and time-sensitive manner. Such an approach may be feasible in neuro-oncology centers worldwide, particularly in resource-limited settings.

Massive RNA sequencing effort proposed.

U.S. plan would harness the "RNome" for medicine and more-but funding is uncertain.

Single-cell RNA cap and tail sequencing (scRCAT-seq) reveals subtype-specific isoforms differing in transcript demarcation.

The differences in transcription start sites (TSS) and transcription end sites (TES) among gene isoforms can affect the stability, localization, and translation efficiency of mRNA. Gene isoforms allow a single gene diverse functions across different cell types, and isoform dynamics allow different functions over time. However, methods to efficiently identify and quantify RNA isoforms genome-wide in single cells are still lacking. Here, we introduce single cell RNA Cap And Tail sequencing (scRCAT-seq), a method to demarcate the boundaries of isoforms based on short-read sequencing, with higher efficiency and lower cost than existing long-read sequencing methods. In conjunction with machine learning algorithms, scRCAT-seq demarcates RNA transcripts with unprecedented accuracy. We identified hundreds of previously uncharacterized transcripts and thousands of alternative transcripts for known genes, revealed cell-type specific isoforms for various cell types across different species, and generated a cell atlas of isoform dynamics during the development of retinal cones.

Programmable low-cost DNA-based platform for viral RNA detection.

Detection of viruses is critical for controlling disease spread. Recent emerging viral threats, including Zika virus, Ebola virus, and SARS-CoV-2 responsible for coronavirus disease 2019 (COVID-19) highlight the cost and difficulty in responding rapidly. To address these challenges, we develop a platform for low-cost and rapid detection of viral RNA with DNA nanoswitches that mechanically reconfigure in response to specific viruses. Using Zika virus as a model system, we show nonenzymatic detection of viral RNA with selective and multiplexed detection between related viruses and viral strains. For clinical-level sensitivity in biological fluids, we paired the assay with sample preparation using either RNA extraction or isothermal preamplification. Our assay requires minimal laboratory infrastructure and is adaptable to other viruses, as demonstrated by quickly developing DNA nanoswitches to detect SARS-CoV-2 RNA in saliva. Further development and field implementation will improve our ability to detect emergent viral threats and ultimately limit their impact.

Cumulus provides cloud-based data analysis for large-scale single-cell and single-nucleus RNA-seq.

Massively parallel single-cell and single-nucleus RNA sequencing has opened the way to systematic tissue atlases in health and disease, but as the scale of data generation is growing, so is the need for computational pipelines for scaled analysis. Here we developed Cumulus-a cloud-based framework for analyzing large-scale single-cell and single-nucleus RNA sequencing datasets. Cumulus combines the power of cloud computing with improvements in algorithm and implementation to achieve high scalability, low cost, user-friendliness and integrated support for a comprehensive set of features. We benchmark Cumulus on the Human Cell Atlas Census of Immune Cells dataset of bone marrow cells and show that it substantially improves efficiency over conventional frameworks, while maintaining or improving the quality of results, enabling large-scale studies.

A Simple, Cost-Effective, and Robust Method for rRNA Depletion in RNA-Sequencing Studies.

The profiling of gene expression by RNA sequencing (RNA-seq) has enabled powerful studies of global transcriptional patterns in all organisms, including bacteria. Because the vast majority of RNA in bacteria is rRNA, it is standard practice to deplete the rRNA from a total RNA sample such that the reads in an RNA-seq experiment derive predominantly from mRNA. One of the most commonly used commercial kits for rRNA depletion, the Ribo-Zero kit from Illumina, was recently discontinued abruptly and for an extended period of time. Here, we report the development of a simple, cost-effective, and robust method for depleting rRNA that can be easily implemented by any lab or facility. We first developed an algorithm for designing biotinylated oligonucleotides that will hybridize tightly and specifically to the 23S, 16S, and 5S rRNAs from any species of interest. Precipitation of these oligonucleotides bound to rRNA by magnetic streptavidin-coated beads then depletes rRNA from a complex, total RNA sample such that ∼75 to 80% of reads in a typical RNA-seq experiment derive from mRNA. Importantly, we demonstrate a high correlation of RNA abundance or fold change measurements in RNA-seq experiments between our method and the Ribo-Zero kit. Complete details on the methodology are provided, including open-source software for designing oligonucleotides optimized for any bacterial species or community of interest.IMPORTANCE The ability to examine global patterns of gene expression in microbes through RNA sequencing has fundamentally transformed microbiology. However, RNA-seq depends critically on the removal of rRNA from total RNA samples. Otherwise, rRNA would comprise upward of 90% of the reads in a typical RNA-seq experiment, limiting the reads coming from mRNA or requiring high total read depth. A commonly used kit for rRNA subtraction from Illumina was recently unavailable for an extended period of time, disrupting routine rRNA depletion. Here, we report the development of a "do-it-yourself" kit for rapid, cost-effective, and robust depletion of rRNA from total RNA. We present an algorithm for designing biotinylated oligonucleotides that will hybridize to the rRNAs from a target set of species. We then demonstrate that the designed oligonucleotides enable sufficient rRNA depletion to produce RNA-seq data with 75 to 80% of reads coming from mRNA. The methodology presented should enable RNA-seq studies on any species or metagenomic sample of interest.

Rapid and Cost-Efficient Enterovirus Genotyping from Clinical Samples Using Flongle Flow Cells.

Enteroviruses affect millions of people worldwide and are of significant clinical importance. The standard method for enterovirus identification and genotyping still relies on Sanger sequencing of short diagnostic amplicons. In this study, we assessed the feasibility of nanopore sequencing using the new flow cell "Flongle" for fast, cost-effective, and accurate genotyping of human enteroviruses from clinical samples. PCR amplification of partial VP1 gene was performed from multiple patient samples, which were multiplexed together after barcoding PCR and sequenced multiple times on Flongle flow cells. The nanopore consensus sequences obtained from mapping reads to a reference database were compared to their Sanger sequence counterparts. Using clinical specimens sampled over different years, we were able to correctly identify enterovirus species and genotypes for all tested samples, even when doubling the number of barcoded samples on one flow cell. Average sequence identity across sequencing runs was >99.7%. Phylogenetic analysis showed that the consensus sequences achieved with Flongle delivered accurate genotyping. We conclude that the new Flongle-based assay with its fast turnover time, low cost investment, and low cost per sample represents an accurate, reproducible, and cost-effective platform for enterovirus identification and genotyping.

Comparison of Sanger sequencing for hepatitis C virus genotyping with a commercial line probe assay in a tertiary hospital.

BACKGROUND: The technique most frequently used to genotype HCV is quantitative RT-PCR. This technique is unable to provide an accurate genotype/subtype for many samples; we decided to develop an in-house method with the goal of accurately identifying the genotype of all samples. As a Belgium National Centre of reference for hepatitis, we developed in-house sequencing not only for 5'UTR and core regions starting from VERSANT LiPA amplicons but also for NS5B regions. The sequencing of VERSANT LiPA amplicons might be useful for many laboratories worldwide using the VERSANT LiPA assay to overcome undetermined results. METHODS: 100 samples from Hepatitis C virus infected patients analysed by the VERSANT HCV Genotype 2.0 LiPA Assay covering frequent HCV types and subtypes were included in this study. NS5B, 5'UTR and Core home-made sequencing were then performed on these samples. The sequences obtained were compared with the HCV genomic BLAST bank. RESULTS: All the samples were characterised by the VERSANT LiPA assay (8 G1a, 17 G1b, 6 G2, 11 G3, 13 G4, and 10 G6). It was not possible to discriminate between G6 and G1 by the VERSANT LiPA assay for 8 samples and 27 had an undetermined genotype. Forty-one samples were sequenced for the three regions: NS5B, 5'UTR and Core. Twenty-three samples were sequenced for two regions: 5' UTR and Core and 36 samples were sequenced only for NS5B. Of the 100 samples included, 64 samples were analysed for 5'UTR and Core sequencing and 79 samples were analysed for NS5B sequencing. The global agreement between VERSANT LiPA assay and sequencing was greater than 95%. CONCLUSIONS: In this study, we describe a new, original method to confirm HCV genotypes of samples not discriminated by a commercial assay, using amplicons already obtained by the screening method, here the VERSANT LiPA assay. This method thus saves one step if a confirmation assay is needed and might be of usefulness for many laboratories worldwide performing VERSANT LiPA assay in particular.

BART-Seq: cost-effective massively parallelized targeted sequencing for genomics, transcriptomics, and single-cell analysis.

We describe a highly sensitive, quantitative, and inexpensive technique for targeted sequencing of transcript cohorts or genomic regions from thousands of bulk samples or single cells in parallel. Multiplexing is based on a simple method that produces extensive matrices of diverse DNA barcodes attached to invariant primer sets, which are all pre-selected and optimized in silico. By applying the matrices in a novel workflow named Barcode Assembly foR Targeted Sequencing (BART-Seq), we analyze developmental states of thousands of single human pluripotent stem cells, either in different maintenance media or upon Wnt/ß-catenin pathway activation, which identifies the mechanisms of differentiation induction. Moreover, we apply BART-Seq to the genetic screening of breast cancer patients and identify BRCA mutations with very high precision. The processing of thousands of samples and dynamic range measurements that outperform global transcriptomics techniques makes BART-Seq first targeted sequencing technique suitable for numerous research applications.

CEL-Seq2-Single-Cell RNA Sequencing by Multiplexed Linear Amplification.

Single-cell RNA sequencing has revolutionized the way we look at cell populations. Of the methods available, CEL-Seq was the first to use linear RNA amplification. With early barcoding and 3' sequencing, it is sensitive, cost-effective and easy to perform. Here we describe a protocol for performing CEL-Seq2 on sorted cells, which can be performed without any special equipment.

Seq-Well: A Sample-Efficient, Portable Picowell Platform for Massively Parallel Single-Cell RNA Sequencing.

Seq-Well is a low-cost picowell platform that can be used to simultaneously profile the transcriptomes of thousands of cells from diverse, low input clinical samples. In Seq-Well, uniquely barcoded mRNA capture beads and cells are co-confined in picowells that are sealed using a semipermeable membrane, enabling efficient cell lysis and mRNA capture. The beads are subsequently removed and processed in parallel for sequencing, with each transcript's cell of origin determined via the unique barcodes. Due to its simplicity and portability, Seq-Well can be performed almost anywhere.

Miniaturization and optimization of 384-well compatible RNA sequencing library preparation.

Preparation of high-quality sequencing libraries is a costly and time-consuming component of metagenomic next generation sequencing (mNGS). While the overall cost of sequencing has dropped significantly over recent years, the reagents needed to prepare sequencing samples are likely to become the dominant expense in the process. Furthermore, libraries prepared by hand are subject to human variability and needless waste due to limitations of manual pipetting volumes. Reduction of reaction volumes, combined with sub-microliter automated dispensing of reagents without consumable pipette tips, has the potential to provide significant advantages. Here, we describe the integration of several instruments, including the Labcyte Echo 525 acoustic liquid handler and the iSeq and NovaSeq Illumina sequencing platforms, to miniaturize and automate mNGS library preparation, significantly reducing the cost and the time required to prepare samples. Through the use of External RNA Controls Consortium (ERCC) spike-in RNAs, we demonstrated the fidelity of the miniaturized preparation to be equivalent to full volume reactions. Furthermore, detection of viral and microbial species from cell culture and patient samples was also maintained in the miniaturized libraries. For 384-well mNGS library preparations, we achieved cost savings of over 80% in materials and reagents alone, and reduced preparation time by 90% compared to manual approaches, without compromising quality or representation within the library.

Comparative Analysis of Droplet-Based Ultra-High-Throughput Single-Cell RNA-Seq Systems.

Since its establishment in 2009, single-cell RNA sequencing (RNA-seq) has been a major driver behind progress in biomedical research. In developmental biology and stem cell studies, the ability to profile single cells confers particular benefits. Although most studies still focus on individual tissues or organs, the recent development of ultra-high-throughput single-cell RNA-seq has demonstrated potential power in characterizing more complex systems or even the entire body. However, although multiple ultra-high-throughput single-cell RNA-seq systems have attracted attention, no systematic comparison of these systems has been performed. Here, with the same cell line and bioinformatics pipeline, we developed directly comparable datasets for each of three widely used droplet-based ultra-high-throughput single-cell RNA-seq systems, inDrop, Drop-seq, and 10X Genomics Chromium. Although each system is capable of profiling single-cell transcriptomes, their detailed comparison revealed the distinguishing features and suitable applications for each system.

Transcriptomic heterogeneity in multifocal prostate cancer.

BACKGROUND: Commercial gene expression assays are guiding clinical decision making in patients with prostate cancer, particularly when considering active surveillance. Given heterogeneity and multifocality of primary prostate cancer, such assays should ideally be robust to the coexistence of unsampled higher grade disease elsewhere in the prostate in order to have clinical utility. Herein, we comprehensively evaluated transcriptomic profiles of primary multifocal prostate cancer to assess robustness to clinically relevant multifocality. METHODS: We designed a comprehensive, multiplexed targeted RNA-sequencing assay capable of assessing multiple transcriptional classes and deriving commercially available prognostic signatures, including the Myriad Prolaris Cell Cycle Progression score, the Oncotype DX Genomic Prostate Score, and the GenomeDX Decipher Genomic Classifier. We applied this assay to a retrospective, multi-institutional cohort of 156 prostate cancer samples. Derived commercial biomarker scores for 120 informative primary prostate cancer samples from 44 cases were determined and compared. RESULTS: Derived expression scores were positively correlated with tumor grade (rS = 0.53-0.73; all P < 0.001), both within the same case and across the entire cohort. In cases of extreme grade-discordant multifocality (co-occurrence of grade group 1 [GG1] and ≥GG4 foci], gene expression scores were significantly lower in low- (GG1) versus high-grade (≥GG4) foci (all P < 0.001). No significant differences in expression scores, however, were observed between GG1 foci from prostates with and without coexisting higher grade cancer (all P > 0.05). CONCLUSIONS: Multifocal, low-grade and high-grade prostate cancer foci exhibit distinct prognostic expression signatures. These findings demonstrate that prognostic RNA expression assays performed on low-grade prostate cancer biopsy tissue may not provide meaningful information on the presence of coexisting unsampled aggressive disease. FUNDING: Prostate Cancer Foundation, National Institutes of Health (U01 CA214170, R01 CA183857, University of Michigan Prostate Specialized Program of Research Excellence [S.P.O.R.E.] P50 CA186786-05, Weill Cornell Medicine S.P.O.R.E. P50 CA211024-01A1), Men of Michigan Prostate Cancer Research Fund, University of Michigan Comprehensive Cancer Center core grant (2-P30-CA-046592-24), A. Alfred Taubman Biomedical Research Institute, and Department of Defense.

Emerging approaches for detection of methylation sites in RNA.

RNA methylations play a significant regulatory role in diverse biological processes. Although the transcriptome-wide discovery of unknown RNA methylation sites is essential to elucidate their function, the development of a bigger variety of detection approaches is desirable for multiple reasons. Many established detection methods for RNA modifications heavily rely on the specificity of the respective antibodies. Thus, the development of antibody-independent transcriptome-wide methods is beneficial. Even the antibody-independent high-throughput sequencing-based methods are liable to produce false-positive or false-negative results. The development of an independent method for each modification could help validate the detected modification sites. Apart from the transcriptome-wide methods for methylation detection de novo, methods for monitoring the presence of a single methylation at a determined site are also needed. In contrast to the transcriptome-wide detection methods, the techniques used for monitoring purposes need to be cheap, fast and easy to perform. This review considers modern approaches for site-specific detection of methylated nucleotides in RNA. We also discuss the potential of third-generation sequencing methods for direct detection of RNA methylations.

Mantis: A Fast, Small, and Exact Large-Scale Sequence-Search Index.

Sequence-level searches on large collections of RNA sequencing experiments, such as the NCBI Sequence Read Archive (SRA), would enable one to ask many questions about the expression or variation of a given transcript in a population. Existing approaches, such as the sequence Bloom tree, suffer from fundamental limitations of the Bloom filter, resulting in slow build and query times, less-than-optimal space usage, and potentially large numbers of false-positives. This paper introduces Mantis, a space-efficient system that uses new data structures to index thousands of raw-read experiments and facilitates large-scale sequence searches. In our evaluation, index construction with Mantis is 6× faster and yields a 20% smaller index than the state-of-the-art split sequence Bloom tree (SSBT). For queries, Mantis is 6-108× faster than SSBT and has no false-positives or -negatives. For example, Mantis was able to search for all 200,400 known human transcripts in an index of 2,652 RNA sequencing experiments in 82 min; SSBT took close to 4 days.

Mapping the Mouse Cell Atlas by Microwell-Seq.

Single-cell RNA sequencing (scRNA-seq) technologies are poised to reshape the current cell-type classification system. However, a transcriptome-based single-cell atlas has not been achieved for complex mammalian systems. Here, we developed Microwell-seq, a high-throughput and low-cost scRNA-seq platform using simple, inexpensive devices. Using Microwell-seq, we analyzed more than 400,000 single cells covering all of the major mouse organs and constructed a basic scheme for a mouse cell atlas (MCA). We reveal a single-cell hierarchy for many tissues that have not been well characterized previously. We built a web-based "single-cell MCA analysis" pipeline that accurately defines cell types based on single-cell digital expression. Our study demonstrates the wide applicability of the Microwell-seq technology and MCA resource.

A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles.

We previously piloted the concept of a Connectivity Map (CMap), whereby genes, drugs, and disease states are connected by virtue of common gene-expression signatures. Here, we report more than a 1,000-fold scale-up of the CMap as part of the NIH LINCS Consortium, made possible by a new, low-cost, high-throughput reduced representation expression profiling method that we term L1000. We show that L1000 is highly reproducible, comparable to RNA sequencing, and suitable for computational inference of the expression levels of 81% of non-measured transcripts. We further show that the expanded CMap can be used to discover mechanism of action of small molecules, functionally annotate genetic variants of disease genes, and inform clinical trials. The 1.3 million L1000 profiles described here, as well as tools for their analysis, are available at https://clue.io.