Enhancing single-cell transcriptomics using interposed anchor oligonucleotide sequences

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.

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.

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.

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.

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.

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.

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.

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.

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.

Antibody repertoire sequencing (Rep-seq) has been widely used to reveal repertoire dynamics and to interrogate antibodies of interest at single nucleotide-level resolution. However, polymerase chain reaction (PCR) amplification introduces extensive artifacts including chimeras and nucleotide errors, leading to false discovery of antibodies and incorrect assessment of somatic hypermutations (SHMs) which subsequently mislead downstream investigations. Here, a novel approach named DUMPArts, which improves the accuracy of antibody repertoires by labeling each sample with dual barcodes and each molecule with dual unique molecular identifiers (UMIs) via minimal PCR amplification to remove artifacts, is developed. Tested by ultra-deep Rep-seq data, DUMPArts removed inter-sample chimeras, which cause artifactual shared clones and constitute approximately 15% of reads in the library, as well as intra-sample chimeras with erroneous SHMs and constituting approximately 20% of the reads, and corrected base errors and amplification biases by consensus building. The removal of these artifacts will provide an accurate assessment of antibody repertoires and benefit related studies, especially mAb discovery and antibody-guided vaccine design.

Background: Circulating tumor DNA (ctDNA) analysis holds potential for minimal residual disease (MRD) detection in early stage breast cancer. However, sensitivity for MRD is limited due to low ctDNA levels in early stage patients and limited blood volumes. Loss of input DNA during library preparation, limited multiplexing or low sensitivity of current molecular methods further limit accuracy. To address this gap, we have developed TARgeted DIgital Sequencing (TARDIS), a novel method for simultaneous analysis of multiple patient-specific mutations in plasma DNA. Methods: Using tumor exome sequencing, we identify and prioritize somatic founder mutations, design nested primers and evaluate them for multiplex performance. Using 5-10 ng input plasma DNA, we perform 1) targeted linear pre-amplification to improve downstream molecular conversion, 2) single-stranded adapter ligation to incorporate unique molecular identifiers (UMIs) and 3) targeted PCR to prepare sequencing-ready libraries. The resulting sequencing reads have fixed target-specific ends and variable ligation ends. We utilize fragment size and UMIs to group sequencing reads into read families. To ensure specificity, we require targeted mutations are supported by 2 or more read families. Results: To assess analytical performance, we targeted 8 mutations in cell-free DNA reference samples with 0.25%-2% mutation allele fractions (AFs). Precision across 7-16 replicates at each AF level agreed with expectations of Poisson distribution, demonstrating effective analysis of ˜70% of input DNA. At 2%, 1%, 0.5% and 0.25% AFs, variant-level sensitivity was 96.4%, 96.4%, 91.1% and 65.8%, approaching the theoretical limit given input DNA. At 0.25% AF, 3-7 mutations were detected per sample, achieving 100% sample-level sensitivity. In 16 wild-type replicates, no targeted mutations were called (100% specificity). Averaging multiple mutations improved precision in sample-level AF estimates. Mean AFs from 8 mutations for the 2% sample were 2.34%-2.80% (5.8% CV). In 6 patients with breast cancer treated with neoadjuvant therapy (NAT), we analyzed 8-18 patient-specific mutations (mean 11.8). Before treatment, ctDNA was detected in 5/6 patients at mean AFs of 0.02%-1.19% (mean 0.40%), supported by 2-10 mutations (mean 5.6). Of these 5 patients, 4 had residual disease after NAT and ctDNA was detected pre-operatively or during NAT in 3/4 patients. 1 patient achieved pathological Complete Response and ctDNA was undetectable after NAT. Conclusions: Preliminary results suggest TARDIS enables accurate MRD detection after neoadjuvant therapy in patients with early stage breast cancer. On-going work is expanding this analysis to include additional patients and investigate the clinical validity of peri-operative ctDNA monitoring. Summary of clinical resultsPatientPre-NAT Stage (TNM)SubtypeNo. of Mutations TargetedBaseline ctDNA (AF%, No. of Mutations)ctDNA after or during NAT (AF%, No. of Mutations)Residual Tumor (TNM)1T3 N1ER+ PR+ HER2-8+ (0.02%, 2)-T2 N12T3 N0TNBC12+ (0.29%, 6)+ (0.01%, 1)T1a N03T2 N1TNBC18+ (1.19%, 10)+ (0.01%, 1)T1mi N04T3 N1TNBC10+ (0.02%, 3)+ (0.05%, 3)T3 N15T2 N0TNBC9+ (0.46%, 7)-pathCR6T1c N1TNBC14--pathCR Citation Format: McDonald BR, Contente-Cuomo T, Sammut S-J, Ernst B, Odenheimer-Bergman A, Perdigones N, Chin S-F, Farooq M, Cronin PA, Anderson KS, Kosiorek H, Northfelt D, McCullough A, Patel B, Caldas C, Pockaj B, Murtaza M. Multiplexed targeted digital sequencing of circulating tumor DNA to detect minimal residual disease in early and locally advanced breast cancer [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P4-01-21.

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.

Adaptive immune receptor (AIR) repertoire diversity assays are susceptible to biases arising from variations in conditions in the RT-PCR and next-generation sequencing (NGS) steps. We designed synthetic TCR and BCR spike-in controls to mitigate these biases and to serve as universal standards for any PCR-based immune receptor profiling assay. We synthesized 48 BCR constructs representing different IGH, IGK, and IGL genes and 39 TCR constructs for TRB, TRA, TRG, and TRD genes. The spike-in controls were tested as (16 × 3) BCR constructs and (13 × 3) TCR Triplex isoform pools added to multiple samples in the same batch to detect cross-contamination across samples. We successfully discriminated between controls and background sequences by combining a unique molecular identifier (UMI)-based correction strategy with spike-in controls at the data analysis step. We also tested 48 BCR and 39 TCR Premixed Controls by spiking into peripheral blood mononuclear cells (PBMC) RNA samples before reverse transcription using Cellecta’s DriverMap™ Adaptive Immune Receptor Profiling Assay that uses a multiplex RT-PCR approach with gene-specific primers and UMIs. We successfully used it to evaluate assay performance by adding premixed controls at different concentrations. Results showed a linear trend as the number of spike-in molecules increased. Our analysis revealed an average sequencing error rate of 0.4%-0.8% per base, aligning with the reported error rate range of Illumina sequencing. This suggests the reliability of our spike-in controls, which can be used to rectify biases in the AIR protocol and accurately estimate error and mutation rates for the DriverMap AIR assay or any other sequencing-based immune receptor profiling assay. This innovative approach enhances the robustness of immune receptor profiling technology, facilitating more accurate assessments of repertoire diversity. Citation Format: Alex Chenchik, Tianbing Liu, Mikhail Makhanov, Dongfang Hu, Khadija Ghias, Paul Diehl. Universal synthetic TCR/BCR spike-in controls to evaluate immune receptor profiling assay and next-generation sequencing performance [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 339.

ATAC-seq (Assay for Transposase-Accessible Chromatin with high-throughput sequencing) provides an efficient way to analyze nucleosome-free regions and has been applied widely to identify transcription factor footprints. Both applications rely on the accurate quantification of insertion events of the hyperactive transposase Tn5. However, due to the presence of the PCR amplification, it is impossible to accurately distinguish independently generated identical Tn5 insertion events from PCR duplicates using the standard ATAC-seq technique. Removing PCR duplicates based on mapping coordinates introduces increasing bias towards highly accessible chromatin regions. To overcome this limitation, we establish a UMI-ATAC-seq technique by incorporating unique molecular identifiers (UMIs) into standard ATAC-seq procedures. UMI-ATAC-seq can rescue about 20% of reads that are mistaken as PCR duplicates in standard ATAC-seq in our study. We demonstrate that UMI-ATAC-seq could more accurately quantify chromatin accessibility and significantly improve the sensitivity of identifying transcription factor footprints. An analytic pipeline is developed to facilitate the application of UMI-ATAC-seq, and it is available at https://github.com/tzhu-bio/UMI-ATAC-seq.

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