Small RNA-Sequencing for Analysis of Circulating miRNAs: Benchmark Study

Crustacean aquaculture is expected to be a major source of fishery commodities in the near future. Hemocytes are key players of the immune system in shrimps; however, their classification, maturation, and differentiation are still under debate. To date, only discrete and inconsistent information on the classification of shrimp hemocytes has been reported, showing that the morphological characteristics are not sufficient to resolve their actual roles. Our present study using single-cell RNA sequencing revealed six types of hemocytes of Marsupenaeus japonicus based on their transcriptional profiles. We identified markers of each subpopulation and predicted the differentiation pathways involved in their maturation. We also predicted cell growth factors that might play crucial roles in hemocyte differentiation. Different immune roles among these subpopulations were suggested from the analysis of differentially expressed immune-related genes. These results provide a unified classification of shrimp hemocytes, which improves the understanding of its immune system.

Mycobacterium tuberculosis (Mtb) remains a global human health threat and a significant cause of human morbidity and mortality. We document here the capture of Mtb transcripts in libraries designed to amplify eukaryotic mRNA. These reads are often considered spurious or nuisance and are rarely investigated. Because of early literature suggesting the possible presence of polyadenylated transcripts in Mtb RNA, we included the H37Rv Mtb reference genome when assembling scRNA seq libraries from fine needle aspirate samples from patients presenting at the TB clinic, Port Moresby General Hospital, Papua New Guinea. We used 10X Genomics single-cell RNA sequencing transcriptomics pipeline, which initiates mRNA amplification with poly-T primers on ~30-micron beads designed to capture, in this case, human mRNA associated with individual cells in the clinical samples. Utilizing the 10X Genomics Cell Ranger tool to align sequencing reads, we consistently detected bacterial small and large ribosomal subunit RNA sequences (rrs and rrl, respectively) and other bacterial gene transcripts in the cell culture and patient samples. We interpret Mtb reads associated with the host cell’s unique molecular identifier (UMI) and transcriptome to indicate infection of that individual host cell. The Mtb transcripts detected showed frequent sequence variation from the reference genome, with greater than 90% of the rrs or rrl reads from many clinical samples having at least 1 sequence difference compared to the H37Rv reference genome. The data presented includes only bacterial sequences from patients with TB infections that were confirmed by the hospital pathology lab using acid-fast microscopy and/or GeneXpert analysis. The repeated, non-random nature of the sequence variations detected in Mtb rrs and rrl transcripts from multiple patients, suggests that, even though this appears to be a stochastic process, there is possibly some selective pressure that limits the types and locations of sequence variation allowed. The variation does not appear to be entirely artefactual, and it is hypothesized that it could represent an additional mechanism of adaptation to enhance bacterial fitness against host defenses or chemotherapy.

Objective: Early prediction of patient response to chemotherapy can facilitate opportunities to avoid inefficient treatment and related toxicity. Cancer cells secrete a large amount of exosomes that contain various molecular constituents reflecting the originating tumor. This study aimed to examine whether circulating exosomal microRNA profile can predict the pathological complete response (pCR) to neoadjuvant chemotherapy in breast cancer patients. Method: Plasma exosomal microRNAs of 20 breast cancer patients treated with adriamycin/cyclophosphamide (60/600 mg/m2 of body surface area) neoadjuvant chemotherapy were extracted prior to and after the first chemotherapy cycle. The pathological response was classified into pCR and non-pCR by histopathological analysis after breast surgery. Exosomal miRNAs were extracted from plasma and analyzed by next generation sequencing (NGS) using Illumina high-throughput RNA sequencing technology. Sequence data were converted to FASTQ files and analyzed using CLC Genomics Workbench version 12.02 (Qiagen). Unique molecular identifiers (UMIs) were extracted and grouped into unique search sequences according to exact sequence of the small RNA sequence and the UMI. Reads were then mapped to MiRNA database miRbase v22 and quantified using the QIAseq miRNA Quantification workflow, which utilizes the RNA-seq Analysis tools in CLC Genomics Workbench (version 12.0.2). The unmapped reads from the quantifying step were then extracted and mapped to human genome GRCh38 using annotation hg38. Samples were analyzed for differential expression using the exact test in the CLC Genomics Workbench suite of Transcriptomics tools. Analysis included Principal Component Analysis, Unsupervised Clustering and Heat Maps, Empirical analysis of gene expression, and volcano plots of the expression difference between groups. miRNAs with post-treatment versus pre-treatment fold change (FC) > 1.5 and false discovery rate (FDR) adjusted p-value <0.05 were considered significant. Results: The histopathological assessment after the surgery identified pCR in 6 patients and non-pCR in 14 patients. The results from the miRNA-Seq analysis showed has-miR-141-3p was significantly down-regulated (FC>2, FDR=0.0003) in the group of patients with pCR, while 3 miRNAs (hsa-miR-34a-5p, hsa-miR-183-5p, and hsa-miR-182-5p) were significantly upregulated in the group of patients with non-pCR (FC>1.5, FDR<0.05). Conclusions: These new findings suggest the potential role of exosomal miR-141-3p, hsa-miR-34a-5p, hsa-miR-183-5p, and hsa-miR-182-5p as biomarkers for early prediction of pCR to neoadjuvant chemotherapy of breast cancer. The results from this study built a foundation for future research on the role of miRNAs in the response to neoadjuvant chemotherapy. Citation Format: Valentina K. Todorova, Stephanie Byrum, Allen Gies, Issam Makhoul. Circulating exosomal microRNAs as promising early biomarkers of neoadjuvant chemotherapy response in breast cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr LB-341.

Highly abundant microRNAs (miRNAs) in small RNA sequencing libraries make it difficult to obtain efficient measurements of more lowly expressed species. We present a new method that allows for the selective blocking of specific, abundant miRNAs during preparation of sequencing libraries. This technique is specific with little off-target effects and has no impact on the reproducibility of the measurement of non-targeted species. In human plasma samples, we demonstrate that blocking of highly abundant hsa-miR-16–5p leads to improved detection of lowly expressed miRNAs and more precise measurement of differential expression overall. Furthermore, we establish the ability to target a second abundant miRNA and to multiplex the blocking of two miRNAs simultaneously. For small RNA sequencing, this technique could fill a similar role as do ribosomal or globin removal technologies in messenger RNA sequencing.

During early embryonic stages, gene products generated by the embryo acquire control over embryonic development. At the 8- to 16-cell stage, major embryonic genome activation (EGA) occurs in bovine embryos. Morphological observations, such as size of blastomeres and distribution of microvilli, suggest heterogeneity of individual cells already at this developmental stage. To study this heterogeneity on the transcriptome level, we performed single-cell RNA sequencing (scRNA-seq) of 161 blastomeres from 14 in vitro-produced bovine embryos at Day 2 and Day 3 post-fertilization. After removing the zona pellucida, blastomeres were mechanically separated in Ca2+- and Mg2+-free PBS, individually collected, and lysed. Complementary DNA libraries were prepared by the single cell RNA-barcoding and sequencing (SCRB-Seq) protocol. Exogenous RNA was added for quality control and cell specific barcodes and unique molecular identifiers (UMI) were used to enable pooling of libraries and to exclude PCR duplicates, respectively. After sequencing (Illumina HiSEqn 1500; 50 nt reads; Illumina Inc., San Diego, CA, USA), UMI were counted with the published Drop-seq pipeline (45,000 UMI on average per library) and cells with UMI count <2.000 were removed. Data were normalized based on UMI and non-supervised clustering analyses of single-cell data were performed (SC3 and M3Drop R packages). The transcriptome profiles of all individual cells were assigned to 6 clusters with specific sets of genes. Sorting cells according to their transcriptome profiles by the CellTree R package (Bioconductor; https://bioconductor.org/packages/release/bioc/html/cellTree.html) resulted in a linear pseudo-timeline. Furthermore, this tool identified 6 groups of genes (topics). Each of them showed an over-representation of distinct Gene Ontology (GO) terms; topic 1, “translation” and “cell division”; topic 2, GO terms involved in translation, RNA splicing and cell division; topic 3, “translation”; topic 4, “ATP synthesis coupled proton transport”; topic 5, “mitochondrial translational elongation”; topic 6, “organic hydroxyl compound transport”. Moreover, increased expression of PCDH10 (protocadherin 10) was observed in the biologically pseudo-ordered more advanced blastomeres. This gene is known to be predominantly expressed in the inner cell mass (ICM) at the blastocyst stage, suggesting that these cells might become ICM. In summary, our study reveals developmental heterogeneity and hints to early lineage specification events in bovine embryos at the time of major EGA.

BackgroundSingle-cell RNA sequencing (scRNA-seq) enables the high-throughput quantification of transcriptional profiles in single cells. In contrast to bulk RNA-seq, additional preprocessing steps such as cell barcode identification or unique molecular identifier (UMI) deconvolution are necessary for preprocessing of data from single cell protocols. R packages that can easily preprocess data and rapidly visualize quality metrics and read alignments for individual cells across multiple samples or runs are still lacking.ResultsHere we present scruff, an R/Bioconductor package that preprocesses data generated from the CEL-Seq or CEL-Seq2 protocols and reports comprehensive data quality metrics and visualizations. scruff rapidly demultiplexes, aligns, and counts the reads mapped to genome features with deduplication of unique molecular identifier (UMI) tags. scruff also provides novel and extensive functions to visualize both pre- and post-alignment data quality metrics for cells from multiple experiments. Detailed read alignments with corresponding UMI information can be visualized at specific genome coordinates to display differences in isoform usage. The package also supports the visualization of quality metrics for sequence alignment files for multiple experiments generated by Cell Ranger from 10X Genomics. scruff is available as a free and open-source R/Bioconductor package.Conclusionsscruff streamlines the preprocessing of scRNA-seq data in a few simple R commands. It performs data demultiplexing, alignment, counting, quality report and visualization systematically and comprehensively, ensuring reproducible and reliable analysis of scRNA-seq data.

Emerging high-throughput sequencing methods for the analyses of complex structure of TCR and BCR repertoires give a powerful impulse to adaptive immunity studies. However, there are still essential technical obstacles for performing a truly quantitative analysis. Specifically, it remains challenging to obtain comprehensive information on the clonal composition of small lymphocyte populations, such as Ag-specific, functional, or tissue-resident cell subsets isolated by sorting, microdissection, or fine needle aspirates. In this study, we report a robust approach based on unique molecular identifiers that allows profiling Ag receptors for several hundred to thousand lymphocytes while preserving qualitative and quantitative information on clonal composition of the sample. We also describe several general features regarding the data analysis with unique molecular identifiers that are critical for accurate counting of starting molecules in high-throughput sequencing applications.

RNA-seq is a powerful tool to detect tissue-specific gene expression, splicing, and genetic variations associated with disease states. However, current RNA-seq approaches have limitations due to poor signal from low-abundant transcripts. Furthermore, tissue-derived RNA samples are often highly degraded, thereby limiting gene detection and suffering from potential sequencing artifacts. Here we show that target capture of sequencing libraries tagged with unique molecular identifiers (UMIs) using the xGen™ Prism DNA library prep kit, optimized for low-input and degraded samples, can overcome these obstacles. Directional and UMI-tagged RNA-seq libraries were constructed with RNA extracted from a FFPE RNA Fusion Reference Standard and captured with different designs of the xGen Pan-Cancer Panel spiked with probes for fusion genes. The first design strategy used IDT's stocked Pan Cancer V1.5 panel targeted to gDNA coordinates. The second design involved extracting all associated RefSeq NM transcripts associated with the gene list. Probes were designed to each transcript and duplicate probes were removed based on exact sequence match. The third strategy leveraged a multi-strain design, which created probes from fasta inputs, but removed probes with 90% or greater homology. Normalized expression was highly correlated (> 85%) between captured and uncaptured samples regardless of rRNA depletion prior to library prep. Captured samples had a greater depth of coverage with over 90% on target bases. In addition, our panel design strategies identified low frequency fusions with deep sequencing regardless of rRNA depletion prior to library prep. The multi-strain design was more effective in reducing redundant capture probes compared the other design strategies. Enhanced coverage and PCR de-duplication with UMIs allowed us to reproducibly measure expression over a wide range of RNA inputs (5-500 ng). We show that target capture of RNA-seq libraries reliably maintains expression information present in uncaptured libraries while increasing coverage for poorly expressed genes and low frequency fusions. In addition, the target captured libraries without rRNA depletion prior to library prep have comparable on-target rate and target coverage with rRNA-depleted, target captured libraries. The addition of UMIs to differentiate between PCR duplicates and unique starting molecules also makes it possible to reliably analyze even highly amplified libraries. (For research use only). Citation Format: Tzu-Chun Chen, Katelyn Larkin, Shale Dames, Hsiao-Yun Huang, Kevin Lai, Jessica Sheu, Timothy Barnes, Katia Star, Manqing Hong, Bosun Min, Ryan Demeter, Ashley Dvorak, Ushati Das Chakravarty, Patrick Lau, Steven Henck. High conversion library preparation with optimal hybridization capture panel design strategy in RNA-seq [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 327.

The emergence of single-cell RNA-seq (scRNA-seq) technology has made it possible to measure gene expression variations at cellular level. This breakthrough enables the investigation of a wider range of problems including analysis of splicing heterogeneity among individual cells. However, compared to bulk RNA-seq, scRNA-seq data are much noisier due to high technical variability and low sequencing depth. Here we propose SCATS (Single-Cell Analysis of Transcript Splicing) for differential splicing analysis in scRNA-seq, which achieves high sensitivity at low coverage by accounting for technical noise. SCATS models scRNA-seq data either with or without Unique Molecular Identifiers (UMIs). For non-UMI data, SCATS explicitly models technical noise by accounting for capture efficiency and amplification bias through the use of external spike-ins; for UMI data, SCATS models capture efficiency and further accounts for transcriptional burstiness. A key aspect of SCATS lies in its ability to group “exons” that originate from the same isoform(s). Grouping exons is essential in splicing analysis of scRNA-seq data as it naturally aggregates spliced reads across different exons, making it possible to detect splicing events even when sequencing depth is low. To evaluate the performance of SCATS, we analyzed both simulated and real scRNA-seq datasets and compared with existing methods including Census and DEXSeq. We show that SCATS has well controlled type I error rate, and is more powerful than existing methods, especially when splicing difference is small. In contrast, Census suffers from severe type I error inflation, whereas DEXSeq is more conservative. When applied to mouse brain scRNA-seq datasets, SCATS identified more differential splicing events with subtle difference across cell types compared to Census and DEXSeq. With the increasing adoption of scRNA-seq, we believe SCATS will be well-suited for various splicing studies. The implementation of SCATS can be downloaded from https://github.com/huyustats/SCATS.

Detection of extremely rare variant alleles, such as tumor DNA, within a complex mixture of DNA molecules is experimentally challenging due to sequencing errors. Barcoding of target DNA molecules in library construction for next-generation sequencing provides a way to identify and bioinformatically remove polymerase induced errors. During the barcoding procedure involving t consecutive PCR cycles, the DNA molecules become barcoded by Unique Molecular Identifiers (UMIs). Different library construction protocols utilize different values of t. The effect of a larger t and imperfect PCR amplifications in relation to UMI cluster sizes is poorly described.This paper proposes a branching process with growing immigration as a model describing the random outcome of t cycles of PCR barcoding. Our model discriminates between five different amplification rates r1, r2, r3, r4, r for different types of molecules associated with the PCR barcoding procedure. We study this model by focussing on Ct, the number of clusters of molecules sharing the same UMI, as well as Ct(m), the number of UMI clusters of size m. Our main finding is a remarkable asymptotic pattern valid for moderately large t. It turns out that E(Ct(m))/E(Ct)≈2−m for m=1,2,…, regardless of the underlying parameters (r1,r2,r3,r4,r). The knowledge of the quantities Ct and Ct(m) as functions of the experimental parameters t and (r1,r2,r3,r4,r) will help the users to draw more adequate conclusions from the outcomes of different sequencing protocols.

BackgroundCurrent quantification methods for mass spectrometry (MS)-based proteomics either do not provide sufficient control of variability or are difficult to implement for routine clinical testing.ResultsWe present here an integrated quantification (InteQuan) method that better controls pre-analytical and analytical variability than the popular quantification method using stable isotope-labeled standard peptides (SISQuan). We quantified 16 lung cancer biomarker candidates in human plasma samples in three assessment studies, using immunoaffinity depletion coupled with multiple reaction monitoring (MRM) MS. InteQuan outperformed SISQuan in precision in all three studies and tolerated a two-fold difference in sample loading. The three studies lasted over six months and encountered major changes in experimental settings. Nevertheless, plasma proteins in low ng/ml to low μg/ml concentrations were measured with a median technical coefficient of variation (CV) of 11.9% using InteQuan. The corresponding median CV using SISQuan was 15.3% after linear fitting. Furthermore, InteQuan surpassed SISQuan in measuring biological difference among clinical samples and in distinguishing benign versus cancer plasma samples.ConclusionsWe demonstrated that InteQuan is a simple yet robust quantification method for MS-based quantitative proteomics, especially for applications in biomarker research and in routine clinical testing.Electronic supplementary materialThe online version of this article (doi:10.1186/1559-0275-12-3) contains supplementary material, which is available to authorized users.

Robust Consensus Molecular Subtyping of Muscle-Invasive Bladder Cancer Via 3' RNA Sequencing of Formalin-Fixed Paraffin-Embedded Tissues: Potential Impact for Clinical and Trial Settings.

Gamma-delta T cells are a small fraction of T lymphocytes with only 1-5% of the overall T cell population, but they are an important subset that have unique contributions to both innate and adaptive immunity. Gamma-delta T cells can recognize a broad range of antigens to provide rapid responses to pathogens, and can also interact with both immune cells and non-immune tissue cells triggering regulatory and cytotoxic responses. These unique features make them ideal candidates that could be targeted to induce durable immunity in the context of different pathologies. There has been growing interest in understanding the contributions of gamma-delta T cells to immunology and developing efficient gamma-delta T-cell-based therapies for cancer, infectious disease, and autoimmune disease. In this study, we have implemented T cell repertoire sequencing for high throughput characterization. Gamma-delta T cells were enriched from tissues and peripheral blood mononuclear cells (PBMCs). RNA was extracted and used to generate full length TCR libraries. Unique molecular identifiers (UMIs) were incorporated to discretely barcode each mRNA molecule, enabling absolute quantitative ranking of TCR clone abundance. Full length immune repertoire sequencing facilitates the detection of distinct and shared clones in tissue and blood samples, enabling the identification of disease specific clones to evaluate immunotherapy effects. In addition, RNA sequencing was performed for gene expression analysis of gamma-delta T cells to identify cell phenotypes. Citation Format: Chen Song, Ariel Erijman, Bradley W. Langhorst, Pingfang Liu, Eileen T. Dimalanta, Theodore B. Davis. Immune repertoire sequencing facilitates gamma delta T cell clonal determination [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5628.

DNA methylation patterns are epigenetic modifications with direct implications in gene expression and chromatin structure regulation. Altered DNA methylation has been reported in various human cancers. The methylation profile of cell-free DNA (cfDNA) from plasma can be exploited to detect and diagnose tissue pathologies and is therefore of great diagnostic interest. Due to limitations of existing NGS library preparations, we present a new workflow, that integrates Unique Molecular Identifier (UMI) based error correction and optimizes library preparation and target enrichment. This workflow incorporates an engineered mutant ligase and proprietary methylated adapters that together prevent chimeras, suppress dimer-formation, and maximize conversion. Our library preparation provides >80% unique mapping efficiency and high conversion efficiency which creates superior sensitivity and specificity for detecting epigenetic signatures from a wide range of inputs (1-250 ng) and samples, including genomic DNA, FFPE DNA and cfDNA. This workflow is suitable for both genome-wide and targeted methylation sequencing. We implemented the target methyl-seq workflow to distinguish between fully methylation and hemimethylation on only one strand of the DNA by ligating in-line UMIs prior to target enrichment, followed by bisulfite conversion and PCR. Using a custom panel for targeted methylation sequencing, we detected >60% of CpG methylation in normal human samples and >95% of CpG methylation in in vitro methylated HCT116 samples. Employing ultra-deep sequencing to an average target depth of 10,000X followed by double-stranded consensus building analysis enables accurate low frequency methylation and hemimethylation detection. The studies presented here offer tools to advance epigenetics research. Citation Format: Hsiao-Yun Huang, Ushati Das Chakravarty, Kevin Lai, Timothy Barnes, Tzu-Chun Chen, Jessica Sheu, Ramses Lopez, Karissa Scott, Lynette Lewis, Anastasia Potts. Identification of symmetric methylation and hemimethylation patterns with optimal panel design, library preparation, and error correction [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 142.

Spatial transcriptomics is considered as an important part of spatiotemporal molecular images to bridge molecular information with clinical images. Of those potentials and opportunities, the excellent quality of human sample preparation and handling will ensure the precise and reliable information generated from clinical spatial transcriptome. The present study aims at defining potential factors that might influence the quality of spatial transcriptomics in lung cancer, para‐cancer, or normal tissues, pathological images of sections and the RNA integrity before spatial transcriptome sequencing. We categorised potential influencing factors from clinical aspects, including patient selection, pathological definition, surgical types, sample harvest, temporary preservation conditions and solutions, frozen approaches, transport and storage conditions and duration. We emphasis on the relationship between the combination of histological scores with RNA integrity number (RIN) and the unique molecular identifier (UMI), which is determines the quality of of spatial transcriptomics; however, we did not find significantly relevance between them. Our results showed that isolated times and dry conditions of sample are critical for the UMI and the quality of spatial transcriptomic samples. Thus, clinical procedures of sample preparation should be furthermore optimised and standardised as new standards of operation performance for clinical spatial transcriptome. Our data suggested that the temporary preservation time and condition of samples at operation room should be within 30 min and in ‘dry’ status. The direct cryo‐preservation within OCT media for human lung sample is recommended. Thus, we believe that clinical spatial transcriptome will be a decisive approach and bridge in the development of spatiotemporal molecular images and provide new insights for understanding molecular mechanisms of diseases at multi‐orientations.