Low-input RNA Research Articles

T cell receptor repertoire detection from dry blood spots

Abstract Immune repertoire amplification of T cell receptors (TCRs) coupled with high throughput sequencing provides detailed insight into the immune system. A common starting sample type is total RNA extracted from whole blood by venipuncture. However, the costs, participant burden, regulatory constraints, and logistics associated with venipuncture and RNA handling are major barriers to clinical application or community-based research on various diseases. This study aims to identify TCR repertoires from dry blood spots (DBS), a method that could help collect real-world data for biomarker applications. Finger-prick blood was collected onto a Whatman filter card, and RNA was extracted from DBS. Fully automated multiplex PCR was performed to generate a TCRβ chain library for next generation sequencing (NGS) analysis of unique CDR3s (uCDR3). According to statistical analysis and laboratory confirmation, forty 2-mm punch disks from the filter cards were enough to detect the shared top clones and have a strong correlation in uCDR3 discovery with whole blood. uCDR3 discovery was not affected by storage temperatures (room temperature or −20°C) or storage durations (1, 14, and 28 days) when compared to whole blood. About 74 – 90% of top 50 uCDR3 clones of whole blood could also be detected from DBS. The DBS-based TCR repertoire profiling method is minimally invasive, provides convenient sampling, and incorporates fully automated library preparation. The system is sensitive to low RNA input, and the results are highly correlated with whole blood uCDR3 discovery allowing study scale-up to better understand the relationship and mutual influences between the immune repertoire and various disease states.

Isolating, Sequencing and Analyzing Extracellular MicroRNAs from Human Mesenchymal Stem Cells.

Extracellular and circulating RNAs (exRNA) are produced by many cell types of the body and exist in numerous bodily fluids such as saliva, plasma, serum, milk and urine. One subset of these RNAs are the posttranscriptional regulators - microRNAs (miRNAs). To delineate the miRNAs produced by specific cell types, in vitro culture systems can be used to harvest and profile exRNAs derived from one subset of cells. The secreted factors of mesenchymal stem cells are implicated in alleviating numerous diseases and is used as the in vitro model system here. This paper describes the process of collection, purification of small RNA and library generation to sequence extracellular miRNAs. ExRNAs from culture media differ from cellular RNA by being low RNA input samples, which calls for optimized procedures. This protocol provides a comprehensive guide to small exRNA sequencing from culture media, showing quality control checkpoints at each step during exRNA purification and sequencing.

High-throughput, Efficient, and Unbiased Capture of Small RNAs from Low-input Samples for Sequencing

MicroRNAs hold great promise as biomarkers of disease. However, there are few efficient and robust methods for measuring microRNAs from low input samples. Here, we develop a high-throughput sequencing protocol that efficiently captures small RNAs while minimizing inherent biases associated with library production. The protocol is based on early barcoding such that all downstream manipulations can be performed on a pool of many samples thereby reducing reagent usage and workload. We show that the optimization of adapter concentrations along with the addition of nucleotide modifications and random nucleotides increases the efficiency of small RNA capture. We further show, using unique molecular identifiers, that stochastic capture of low input RNA rather than PCR amplification influences the biased quantitation of intermediately and lowly expressed microRNAs. Our improved method allows the processing of tens to hundreds of samples simultaneously while retaining high efficiency quantitation of microRNAs in low input samples from tissues or bodily fluids.

High throughput sequencing of T-cell receptor repertoire using dry blood spots

BackgroundImmunology research, particularly next generation sequencing (NGS) of the immune T-cell receptor β (TCRβ) repertoire, has advanced progression in several fields, including treatment of various cancers and autoimmune diseases. This study aimed to identify the TCR repertoires from dry blood spots (DBS), a method that will help collecting real-world data for biomarker applications.MethodsFinger-prick blood was collected onto a Whatman filter card. RNA was extracted from DBS of the filter card, and fully automated multiplex PCR was performed to generate a TCRβ chain library for next generation sequencing (NGS) analysis of unique CDR3s (uCDR3).ResultsWe demonstrated that the dominant clonotypes from the DBS results recapitulated those found in whole blood. According to the statistical analysis and laboratory confirmation, 40 of 2-mm punch disks from the filter cards were enough to detect the shared top clones and have strong correlation in the uCDR3 discovery with whole blood. uCDR3 discovery was neither affected by storage temperatures (room temperature versus − 20 °C) nor storage durations (1, 14, and 28 days) when compared to whole blood. About 74–90% of top 50 uCDR3 clones of whole blood could also be detected from DBS. A low rate of clonotype sharing, 0.03–1.5%, was found among different individuals.ConclusionsThe DBS-based TCR repertoire profiling method is minimally invasive, provides convenient sampling, and incorporates fully automated library preparation. The system is sensitive to low RNA input, and the results are highly correlated with whole blood uCDR3 discovery allowing study scale-up to better understand the relationship and mutual influences between the immune and diseases.

NanoPARE: parallel analysis of RNA 5' ends from low-input RNA.

Diverse RNA 5′ ends are generated through both transcriptional and post-transcriptional processes. These important modes of gene regulation often vary across cell types and can contribute to the diversification of transcriptomes and thus cellular differentiation. Therefore, the identification of primary and processed 5′ ends of RNAs is important for their functional characterization. Methods have been developed to profile either RNA 5′ ends from primary transcripts or the products of RNA degradation genome-wide. However, these approaches either require high amounts of starting RNA or are performed in the absence of paired gene-body mRNA-seq data. This limits current efforts in RNA 5′ end annotation to whole tissues and can prevent accurate RNA 5′ end classification due to biases in the data sets. To enable the accurate identification and precise classification of RNA 5′ ends from standard and low-input RNA, we developed a next-generation sequencing-based method called nanoPARE and associated software. By integrating RNA 5′ end information from nanoPARE with gene-body mRNA-seq data from the same RNA sample, our method enables the identification of transcription start sites at single-nucleotide resolution from single-cell levels of total RNA, as well as small RNA-mediated cleavage events from at least 10,000-fold less total RNA compared to conventional approaches. NanoPARE can therefore be used to accurately profile transcription start sites, noncapped RNA 5′ ends, and small RNA targeting events from individual tissue types. As a proof-of-principle, we utilized nanoPARE to improve Arabidopsis thaliana RNA 5′ end annotations and quantify microRNA-mediated cleavage events across five different flower tissues.

A comparative analysis of library prep approaches for sequencing low input translatome samples

BackgroundCell type-specific ribosome-pulldown has become an increasingly popular method for analysis of gene expression. It allows for expression analysis from intact tissues and monitoring of protein synthesis in vivo. However, while its utility has been assessed, technical aspects related to sequencing of these samples, often starting with a smaller amount of RNA, have not been reported. In this study, we evaluated the performance of five library prep protocols for ribosome-associated mRNAs when only 250 pg-4 ng of total RNA are used.ResultsWe obtained total and RiboTag-IP RNA, in three biological replicates. We compared 5 methods of library preparation for Illumina Next Generation sequencing: NuGEN Ovation RNA-Seq system V2 Kit, TaKaRa SMARTer Stranded Total RNA-Seq Kit, TaKaRa SMART-Seq v4 Ultra Low Input RNA Kit, Illumina TruSeq RNA Library Prep Kit v2 and NEBNext® Ultra™ Directional RNA Library Prep Kit using slightly modified protocols each with 4 ng of total RNA. An additional set of samples was processed using the TruSeq kit with 70 ng, as a ‘gold standard’ control and the SMART-Seq v4 with 250 pg of total RNA. TruSeq-processed samples had the best metrics overall, with similar results for the 4 ng and 70 ng samples. The results of the SMART-Seq v4 processed samples were similar to TruSeq (Spearman correlation > 0.8) despite using lower amount of input RNA. All RiboTag-IP samples had an increase in the intronic reads compared with the corresponding whole tissue, suggesting that the IP captures some immature mRNAs. The SMARTer-processed samples had a higher representation of ribosomal and non-coding RNAs leading to lower representation of protein coding mRNA. The enrichment or depletion of IP samples compared to corresponding input RNA was similar across all kits except for SMARTer kit.ConclusionRiboTag-seq can be performed successfully with as little as 250 pg of total RNA when using the SMART-Seq v4 kit and 4 ng when using the modified protocols of other library preparation kits. The SMART-Seq v4 and TruSeq kits resulted in the highest quality libraries. RiboTag IP RNA contains some immature transcripts.

Refined RIP-seq protocol for epitranscriptome analysis with low input materials.

N6-Methyladenosine (m6A) accounts for approximately 0.2% to 0.6% of all adenosine in mammalian mRNA, representing the most abundant internal mRNA modifications. m6A RNA immunoprecipitation followed by high-throughput sequencing (MeRIP-seq) is a powerful technique to map the m6A location transcriptome-wide. However, this method typically requires 300 μg of total RNA, which limits its application to patient tumors. In this study, we present a refined m6A MeRIP-seq protocol and analysis pipeline that can be applied to profile low-input RNA samples from patient tumors. We optimized the key parameters of m6A MeRIP-seq, including the starting amount of RNA, RNA fragmentation, antibody selection, MeRIP washing/elution conditions, methods for RNA library construction, and the bioinformatics analysis pipeline. With the optimized immunoprecipitation (IP) conditions and a postamplification rRNA depletion strategy, we were able to profile the m6A epitranscriptome using 500 ng of total RNA. We identified approximately 12,000 m6A peaks with a high signal-to-noise (S/N) ratio from 2 lung adenocarcinoma (ADC) patient tumors. Through integrative analysis of the transcriptome, m6A epitranscriptome, and proteome data in the same patient tumors, we identified dynamics at the m6A level that account for the discordance between mRNA and protein levels in these tumors. The refined m6A MeRIP-seq method is suitable for m6A epitranscriptome profiling in a limited amount of patient tumors, setting the ground for unraveling the dynamics of the m6A epitranscriptome and the underlying mechanisms in clinical settings.

Next Generation Sequencing Analysis of Total Small Noncoding RNAs from Low Input RNA from Dried Blood Sampling.

Circulating miRNAs are favored for biomarker candidates as they can reflect tissue specific miRNA dysregulation in disease contexts. Moreover, they have the additional advantage that they can be monitored in a minimally invasive manner. Blood-borne miRNAs are therefore currently characterized to identify, describe, and validate their potential suitability as biomarkers; however, sampling and as well miRNA detection methods limit these studies in terms of sensitivity but also practicability in clinical, at-home, or low-resource sampling of high-quality circulating RNA samples. We describe here a novel and innovative method of circulating RNA microsampling from minimal volume dried blood samples with direct enrichment for small RNA fractions in combination with ligation free library preparation. We evaluated crucial parameters for efficient library preparation from low RNA inputs of 50 pg for efficient dissection not only of miRNAs but also isomiRs, piRNAs, and lincRNAs. We compared these data to classical microarrays and characterize the technical reproducibility and its sensitivity. We demonstrate and evaluate a method for easy low resource sampling and NGS analysis of circulating RNAs providing a powerful tool for massive cohort and remote patient monitoring.

Abstract 1492: High-efficiency, strand-specific, low-bias library preparation method for transcriptome profiling of low-input RNA

Abstract RNA Sequencing (RNA-seq) is a powerful technique for transcriptome analysis. It is widely used for gene expression analysis, detection of mutations, fusion transcripts, alternative splicing, and the study of post transcriptional modifications. RNA-seq is emerging as a powerful tool for molecular diagnostics as well, providing insights into the biological pathways and molecular mechanisms associated with disease progression including cancer. Recent improvements in next generation sequencing technologies (NGS) and sample barcoding strategies have resulted in significant cost reduction. As RNA-seq is increasingly adopted for molecular diagnostics, the quality and reproducibility of library preparation methods become more important. In addition, demand for library preparation methods that produce successful NGS libraries from very low input RNA or precious clinical samples is increasing. To achieve these goals, we have developed an RNA-seq library preparation method which can be used across a wide range of input RNA (5-1000 ng total RNA). GC content analysis, gene body coverage and gene expression correlation show that these important parameters remain consistent across varying inputs, even though input amounts vary by two orders of magnitude. As a result, our method has increased sensitivity and specificity for low-abundance transcripts, and reduced PCR duplicates and sequence bias, delivering high quality strand-specific data. Our method is compatible with poly A-tail enriched and ribosomal RNA depleted samples as well as RNA extracted from FFPE (formaldehyde fixed paraffin embedded) tissue samples. Citation Format: Anetta Nowosielska-Vecchietti. High-efficiency, strand-specific, low-bias library preparation method for transcriptome profiling of low-input RNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1492.

Abstract 429: Optimization of an RNA sequencing method for low-quantity degraded samples

Abstract RNA-sequencing (RNA-seq) is an effective tool for gene expression analysis, promising to be a diagnostic tool for patient stratification and individualized therapy. However, it has been challenging to apply RNA-seq to low-quantity and degraded RNA derived from formalin fixed paraffin embedded clinical samples. The Illumina TruSeq® RNA Access approach, utilizing capture probes targeting known exons to enrich for coding RNAs, has shown high performance to profile poor quality RNA samples at input amounts at or above 20 ng. Here, we have further optimized the Illumina TruSeq® RNA Access method for samples with low quality and low quantity, and have compared results to those obtained with libraries prepared with SMARTer® Stranded Total RNA-Seq- Pico Input Kit. Libraries were prepared using 1 ng and 10 ng of degraded RNA from Banked FFPE liver specimens, and were compared to libraries prepared using the standard RNA Access method with an input of 100 ng. With the optimized low-input RNA Access method, the alignment rate was consistently high (95-97%) across both input amounts, with 82-84% of aligned reads mapped to the transcriptome. This is comparable to results obtained with the standard 100 ng input. Gene detection however, changed as a function of input, with ~ 15,900 genes detected at an input of 10 ng, and ~ 13,500 detected at 1 ng of input. In contrast, the SMARTer method detected a relatively constant number of genes (~ 14,500 genes) at both input amounts, albeit with a lower % aligned reads to transcriptome (46-47%), and an increased % reads aligned to Ribosomal RNA when compared to RNA Access. Thus, while both methods are capable of comparable levels of performance, the RNA Access method is more cost-effective from a sequencing standpoint. These characteristics were maintained across a group of additional FFPE-derived samples tested by both methods. Taken together, these results demonstrate that an optimized RNA Access method has consistent mapping performance and a high gene detection rate, and is thus suitable for the analysis of low-quantity degraded RNA. Citation Format: Dan Su, Jason G. Powers, Thomas A. Halsey, Patrick Hurban. Optimization of an RNA sequencing method for low-quantity degraded samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 429.

Evaluation of commercially available small RNASeq library preparation kits using low input RNA

BackgroundEvolving interest in comprehensively profiling the full range of small RNAs present in small tissue biopsies and in circulating biofluids, and how the profile differs with disease, has launched small RNA sequencing (RNASeq) into more frequent use. However, known biases associated with small RNASeq, compounded by low RNA inputs, have been both a significant concern and a hurdle to widespread adoption. As RNASeq is becoming a viable choice for the discovery of small RNAs in low input samples and more labs are employing it, there should be benchmark datasets to test and evaluate the performance of new sequencing protocols and operators. In a recent publication from the National Institute of Standards and Technology, Pine et al., 2018, the investigators used a commercially available set of three tissues and tested performance across labs and platforms.ResultsIn this paper, we further tested the performance of low RNA input in three commonly used and commercially available RNASeq library preparation kits; NEB Next, NEXTFlex, and TruSeq small RNA library preparation. We evaluated the performance of the kits at two different sites, using three different tissues (brain, liver, and placenta) with high (1 μg) and low RNA (10 ng) input from tissue samples, or 5.0, 3.0, 2.0, 1.0, 0.5, and 0.2 ml starting volumes of plasma. As there has been a lack of robust validation platforms for differentially expressed miRNAs, we also compared low input RNASeq data with their expression profiles on three different platforms (Abcam Fireplex, HTG EdgeSeq, and Qiagen miRNome).ConclusionsThe concordance of RNASeq results on these three platforms was dependent on the RNA expression level; the higher the expression, the better the reproducibility. The results provide an extensive analysis of small RNASeq kit performance using low RNA input, and replication of these data on three downstream technologies.

Robust Sub-nanomolar Library Preparation for High Throughput Next Generation Sequencing

BackgroundCurrent library preparation protocols for Illumina HiSeq and MiSeq DNA sequencers require ≥2 nM initial library for subsequent loading of denatured cDNA onto flow cells. Such amounts are not always attainable from samples having a relatively low DNA or RNA input; or those for which a limited number of PCR amplification cycles is preferred (less PCR bias and/or more even coverage). A well-tested sub-nanomolar library preparation protocol for Illumina sequencers has however not been reported. The aim of this study is to provide a much needed working protocol for sub-nanomolar libraries to achieve outcomes as informative as those obtained with the higher library input (≥ 2 nM) recommended by Illumina’s protocols.ResultsExtensive studies were conducted to validate a robust sub-nanomolar (initial library of 100 pM) protocol using PhiX DNA (as a control), genomic DNA (Bordetella bronchiseptica and microbial mock community B for 16S rRNA gene sequencing), messenger RNA, microRNA, and other small noncoding RNA samples. The utility of our protocol was further explored for PhiX library concentrations as low as 25 pM, which generated only slightly fewer than 50% of the reads achieved under the standard Illumina protocol starting with > 2 nM.ConclusionsA sub-nanomolar library preparation protocol (100 pM) could generate next generation sequencing (NGS) results as robust as the standard Illumina protocol. Following the sub-nanomolar protocol, libraries with initial concentrations as low as 25 pM could also be sequenced to yield satisfactory and reproducible sequencing results.

A technical assessment of the porcine ejaculated spermatozoa for a sperm-specific RNA-seq analysis

ABSTRACTThe study of the boar sperm transcriptome by RNA-seq can provide relevant information on sperm quality and fertility and might contribute to animal breeding strategies. However, the analysis of the spermatozoa RNA is challenging as these cells harbor very low amounts of highly fragmented RNA, and the ejaculates also contain other cell types with larger amounts of non-fragmented RNA. Here, we describe a strategy for a successful boar sperm purification, RNA extraction and RNA-seq library preparation. Using these approaches our objectives were: (i) to evaluate the sperm recovery rate (SRR) after boar spermatozoa purification by density centrifugation using the non-porcine-specific commercial reagent BoviPureTM; (ii) to assess the correlation between SRR and sperm quality characteristics; (iii) to evaluate the relationship between sperm cell RNA load and sperm quality traits and (iv) to compare different library preparation kits for both total RNA-seq (SMARTer Universal Low Input RNA and TruSeq RNA Library Prep kit) and small RNA-seq (NEBNext Small RNA and TailorMix miRNA Sample Prep v2) for high-throughput sequencing. Our results show that pig SRR (~22%) is lower than in other mammalian species and that it is not significantly dependent of the sperm quality parameters analyzed in our study. Moreover, no relationship between the RNA yield per sperm cell and sperm phenotypes was found. We compared a RNA-seq library preparation kit optimized for low amounts of fragmented RNA with a standard kit designed for high amount and quality of input RNA and found that for sperm, a protocol designed to work on low-quality RNA is essential. We also compared two small RNA-seq kits and did not find substantial differences in their performance. We propose the methodological workflow described for the RNA-seq screening of the boar spermatozoa transcriptome.Abbreviations: FPKM: fragments per kilobase of transcript per million mapped reads; KRT1: keratin 1; miRNA: micro-RNA; miscRNA: miscellaneous RNA; Mt rRNA: mitochondrial ribosomal RNA; Mt tRNA: mitochondrial transference RNA; OAZ3: ornithine decarboxylase antizyme 3; ORT: osmotic resistance test; piRNA: Piwi-interacting RNA; PRM1: protamine 1; PTPRC: protein tyrosine phosphatase receptor type C; rRNA: ribosomal RNA; snoRNA: small nucleolar RNA; snRNA: small nuclear RNA; SRR: sperm recovery rate; tRNA: transfer RNA

Evaluation of biological and technical variations in low-input RNA-Seq and single-cell RNA-Seq

Copyright © 2018 Inderscience Enterprises Ltd. Background: Low-input or single-cell RNA-Seq are widely used today, but two technical questions remain: 1) in technical replicates, what proportion of noises comes from input RNA quantity rather than variation of bioinformatics tools?; 2) In single neurons, whether variation in gene expression is attributable to biological heterogeneity or just random noise? To examine the sources of variability, we have generated RNA-Seq data from low-input (10/100/1000pg) reference RNA samples and 38 single neurons from human brains. Results: For technical replicates, the quantity of input RNA is negatively correlated with expression variation. For genes in the medium- and high-expression groups, input RNA amount explains most of the variation, whereas bioinformatic pipelines explain some variation for the low-expression group. The t-distributed stochastic neighbour embedding (t-SNE) method reveals data-inherent aggregation of low-input replicate data, and suggests heterogeneity of single pyramidal neuron transcriptome. Interestingly, expression variation in single neurons is biologically relevant. Conclusions: We found that differences in bioinformatics pipelines do not present a major source of variation.

Evaluation of biological and technical variations in low-input RNA-Seq and single-cell RNA-Seq

Background: Low-input or single-cell RNA-Seq are widely used today, but two technical questions remain: 1) in technical replicates, what proportion of noises comes from input RNA quantity rather than variation of bioinformatics tools?; 2) In single neurons, whether variation in gene expression is attributable to biological heterogeneity or just random noise? To examine the sources of variability, we have generated RNA-Seq data from low-input (10/100/1000pg) reference RNA samples and 38 single neurons from human brains. Results: For technical replicates, the quantity of input RNA is negatively correlated with expression variation. For genes in the medium- and high-expression groups, input RNA amount explains most of the variation, whereas bioinformatic pipelines explain some variation for the low-expression group. The t-distributed stochastic neighbour embedding (t-SNE) method reveals data-inherent aggregation of low-input replicate data, and suggests heterogeneity of single pyramidal neuron transcriptome. Interestingly, expression variation in single neurons is biologically relevant. Conclusions: We found that differences in bioinformatics pipelines do not present a major source of variation.

Laser Capture Microdissection-Based RNA-Seq of Barley Grain Tissues.

Spatiotemporal patterning throughout the plant body depends to a large degree on cell- and tissue-specific expression of genes. Subsequently, for a better understanding of cell and tissue differentiation processes during plant development it is important to conduct transcript analyses in individual cells or tissue types rather than in bulk tissues. Laser capture microdissection (LCM) provides a useful method for isolating specific cell types from complex tissue structures for downstream applications. Contrasting to mammalian cells, the texture of plant cells is more critical due to hard, cellulose-rich cell walls, large vacuoles, and air spaces which complicates tissue preparation and extraction of macromolecules, like DNA and RNA. In particular, developing barley seeds (i.e. grains) depict cell types with differences in osmomolarity (meristematic, differentiating and degenerating tissues) and contain high amounts of the main storage product starch. In this study, we report about methods allowing tissue-specific transcriptome profiling by RNA-seq of developing barley grain tissues from low-input RNA amounts. Details on tissue preparation, laser capture microdissection, RNA isolation, and linear mRNA amplification to produce high-quality samples for Illumina sequencing are provided. Particular emphasis was placed on the influence of the mRNA amplification step on the transcriptome data and the fidelity of deduced expression levels obtained by the developed methods. Analysis of RNA-seq data confirmed sample processing as a highly reliable and reproducible procedure that was also used for transcriptome analyses of different tissue types from barley plants.

Robust transcriptional signatures for low-input RNA samples based on relative expression orderings

BackgroundIt is often difficult to obtain sufficient quantity of RNA molecules for gene expression profiling under many practical situations. Amplification from low-input samples may induce artificial signals.ResultsWe compared the expression measurements of low-input mRNA samples, from 25 pg to 1000 pg mRNA, which were amplified and profiled by Smart-seq, DP-seq and CEL-seq techniques using the Illumina HiSeq 2000 platform, with those of the paired high-input (50 ng) mRNA samples. Even with 1000 pg mRNA input, we found that thousands of genes had at least 2 folds-change of expression levels in the low-input samples compared with the corresponding paired high-input samples. Consequently, a transcriptional signature based on quantitative expression values and determined from high-input RNA samples cannot be applied to low-input samples, and vice versa. In contrast, the within-sample relative expression orderings (REOs) of approximately 90% of all the gene pairs in the high-input samples were maintained in the paired low-input samples with 1000 pg input mRNA molecules. Similar results were observed in the low-input total RNA samples amplified and profiled by the Whole-Genome DASL technique using the Illumina HumanRef-8 v3.0 platform. As a proof of principle, we developed REOs-based signatures from high-input RNA samples for discriminating cancer tissues and showed that they can be robustly applied to low-input RNA samples.ConclusionsREOs-based signatures determined from the high-input RNA samples can be robustly applied to samples profiled with the low-input RNA samples, as low as the 1000 pg and 250 pg input samples but no longer stable in samples with less than 250 pg RNA input to a certain degree.

Distinct and common expression of receptors for inflammatory mediators in vagal nodose versus jugular capsaicin-sensitive/TRPV1-positive neurons detected by low input RNA sequencing.

Capsaicin-sensitive sensory C-fibers derived from vagal ganglia innervate the visceral organs, and respond to inflammatory mediators and noxious stimuli. These neurons play an important role in maintenance of visceral homeostasis, and contribute to the symptoms of visceral inflammatory diseases. Vagal sensory neurons are located in two ganglia, the jugular ganglia (derived from the neural crest), and the nodose ganglia (from the epibranchial placodes). The functional difference, especially in response to immune mediators, between jugular and nodose neurons is not fully understood. In this study, we microscopically isolated murine nodose and jugular capsaicin-sensitive / Trpv1-expressing C-fiber neurons and performed transcriptome profiling using ultra-low input RNA sequencing. RNAseq detected genes with significantly differential expression in jugular and nodose neurons, which were mostly involved in neural functions. Transcriptional regulators, including Cited1, Hoxb5 and Prdm12 showed distinct expression patterns in the two C-fiber neuronal populations. Common and specific expression of immune receptor proteins was characterized in each neuronal type. The expression of immune receptors that have received little or no attention from vagal sensory biologists is highlighted including receptors for certain chemokines (CXCLs), interleukins (IL-4) and interferons (IFNα, IFNγ). Stimulation of immune receptors with their cognate ligands led to activation of the C-fibers in isolated functional assays.

Single molecule counting and assessment of random molecular tagging errors with transposable giga-scale error-correcting barcodes

BackgroundRNA-Seq measures gene expression by counting sequence reads belonging to unique cDNA fragments. Molecular barcodes commonly in the form of random nucleotides were recently introduced to improve gene expression measures by detecting amplification duplicates, but are susceptible to errors generated during PCR and sequencing. This results in false positive counts, leading to inaccurate transcriptome quantification especially at low input and single-cell RNA amounts where the total number of molecules present is minuscule.To address this issue, we demonstrated the systematic identification of molecular species using transposable error-correcting barcodes that are exponentially expanded to tens of billions of unique labels.ResultsWe experimentally showed random-mer molecular barcodes suffer from substantial and persistent errors that are difficult to resolve. To assess our method’s performance, we applied it to the analysis of known reference RNA standards. By including an inline random-mer molecular barcode, we systematically characterized the presence of sequence errors in random-mer molecular barcodes. We observed that such errors are extensive and become more dominant at low input amounts.ConclusionsWe described the first study to use transposable molecular barcodes and its use for studying random-mer molecular barcode errors. Extensive errors found in random-mer molecular barcodes may warrant the use of error correcting barcodes for transcriptome analysis as input amounts decrease.

Differing molecular response of young and advanced maternal age human oocytes to IVM.

What effect does maternal age have on the human oocyte's molecular response to in vitro oocyte maturation? Although polyadenylated transcript abundance is similar between young and advanced maternal age (AMA) germinal vesicle (GV) oocytes, metaphase II (MII) oocytes exhibit a divergent transcriptome resulting from a differential response to in vitro oocyte maturation. Microarray studies considering maternal age or maturation stage have shown that either of these factors will affect oocyte polyadenylated transcript abundance in human oocytes. However, studies considering both human oocyte age and multiple stages simultaneously are limited to a single study that examined transcript levels for two genes by qPCR. Thus, polyadenylated RNA sequencing (RNA-Seq) could provide novel insight into age-associated aberrations in gene expression in GV and MII oocytes. The effect of maternal age (longitudinal analysis) on polyadenylated transcript abundance at different stages was analyzed by examining single GV and single in vitro matured MII oocytes derived from five young (YNG; < 30 years; average age 26.8; range 20-29) and five advanced maternal age (AMA; ≥40 years; average age 41.6 years; range 40-43 years) patients. Thus, a total of 10 YNG (5 GV and 5 MII) and 10 AMA (5 GV and 5 MII) oocytes were individually processed for RNA-Seq analysis. Patients undergoing infertility treatment at the Colorado Center for Reproductive Medicine (Lone Tree, CO, USA) underwent ovarian stimulation with FSH and received hCG for final follicular maturation prior to ultrasound guided oocyte retrieval. Unused GV oocytes obtained at retrieval were donated for transcriptome analysis. Single oocytes were stored (at -80°C in PicoPure RNA Extraction Buffer; Thermo Fisher Scientific, USA) immediately upon verification of immaturity or after undergoing in vitro oocyte maturation (24 h incubation), representing GV and MII samples, respectively. After isolating RNA and generating single oocyte RNA-Seq libraries (SMARTer Ultra Low Input RNA HV kit; Clontech, USA), Illumina sequencing (100 bp paired-end reads on HiSeq 2500) and bioinformatics analysis (CLC Genomics Workbench, DESeq2, weighted gene correlation network analysis (WGCNA), Ingenuity Pathway Analysis) were performed. A total of 12 770 genes were determined to be expressed in human oocytes (reads per kilobase per million mapped reads (RPKM) > 0.4 in at least three of five replicates for a minimum of one sample type). Differential gene expression analysis between YNG and AMA oocytes (within stage) identified 1 and 255 genes that significantly differed (adjusted P < 0.1 and log2 fold change >1) in polyadenylated transcript abundance for GV and MII oocytes, respectively. These genes included CDK1, NLRP5 and PRDX1, which have been reported to affect oocyte developmental potential. Despite the similarity in transcript abundance between GV oocytes irrespective of age, divergent expression patterns emerged during oocyte maturation. These age-specific differentially expressed genes were enriched (FDR < 0.05) for functions and pathways associated with mitochondria, cell cycle and cytoskeleton. Gene modules generated by WGCNA (based on gene expression) and patient traits related to oocyte quality (e.g. age and blastocyst development) were correlated (P < 0.05) and enriched (FDR < 0.05) for functions and pathways associated with oocyte maturation. Raw data from this study can be accessed through GSE95477. The human oocytes used in the current study were obtained from patients with varying causes of infertility (e.g. decreased oocyte quality and oocyte quality-independent factors), possibly affecting oocyte gene expression. Oocytes in this study were retrieved at the GV stage following hCG administration and the MII oocytes were derived by IVM of patient oocytes. Although the approach has the benefit of identifying intrinsic differences between samples, it may not be completely representative of in vivo matured oocytes. Transcriptome profiles of YNG and AMA oocytes, particularly at the MII stage, suggest that aberrant transcript abundance may contribute to the age-associated decline in fertility. J.M.R. was supported by an Austin Eugene Lyons Fellowship awarded by the University of California, Davis. The Eunice Kennedy Shriver National Institute of Child Health and Human Development of the National Institutes of Health (awarded to P.J.R.; R01HD070044) and the Fertility Laboratories of Colorado partly supported the research presented in this manuscript.