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Singh A, Xue A, Tai J, Mbadugha F, Obi P, Mascarenhas R, Tyagi A, Siena A, Chen YG. A scalable and cost-efficient rRNA depletion approach to enrich RNAs for molecular biology investigations. RNA (NEW YORK, N.Y.) 2024; 30:728-738. [PMID: 38485192 PMCID: PMC11098455 DOI: 10.1261/rna.079761.123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 02/16/2024] [Indexed: 05/18/2024]
Abstract
Transcriptomics analyses play pivotal roles in understanding the complex regulatory networks that govern cellular processes. The abundance of rRNAs, which account for 80%-90% of total RNA in eukaryotes, limits the detection and investigation of other transcripts. While mRNAs and long noncoding RNAs have poly(A) tails that are often used for positive selection, investigations of poly(A)- RNAs, such as circular RNAs, histone mRNAs, and small RNAs, typically require the removal of the abundant rRNAs for enrichment. Current approaches to deplete rRNAs for downstream molecular biology investigations are hampered by restrictive RNA input masses and high costs. To address these challenges, we developed rRNA Removal by RNaseH (rRRR), a method to efficiently deplete rRNAs from a wide range of human, mouse, and rat RNA inputs and of varying qualities at a cost 10- to 20-fold cheaper than other approaches. We used probe-based hybridization and enzymatic digestion to selectively target and remove rRNA molecules while preserving the integrity of non-rRNA transcripts. Comparison of rRRR to two commercially available approaches showed similar rRNA depletion efficiencies and comparable off-target effects. Our developed method provides researchers with a valuable tool for investigating gene expression and regulatory mechanisms across a wide range of biological systems at an affordable price that increases the accessibility for researchers to enter the field, ultimately advancing our understanding of cellular processes.
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Affiliation(s)
- Amrita Singh
- Department of Immunobiology, Yale University School of Medicine, New Haven, Connecticut 06519, USA
| | - Amy Xue
- Department of Immunobiology, Yale University School of Medicine, New Haven, Connecticut 06519, USA
| | - Justin Tai
- Department of Immunobiology, Yale University School of Medicine, New Haven, Connecticut 06519, USA
| | - Faith Mbadugha
- Department of Immunobiology, Yale University School of Medicine, New Haven, Connecticut 06519, USA
| | - Prisca Obi
- Department of Immunobiology, Yale University School of Medicine, New Haven, Connecticut 06519, USA
| | - Romario Mascarenhas
- Department of Immunobiology, Yale University School of Medicine, New Haven, Connecticut 06519, USA
| | - Antariksh Tyagi
- Yale Center for Genome Analysis, Yale University School of Medicine, New Haven, Connecticut 06519, USA
| | - Adamo Siena
- Department of Immunobiology, Yale University School of Medicine, New Haven, Connecticut 06519, USA
| | - Y Grace Chen
- Department of Immunobiology, Yale University School of Medicine, New Haven, Connecticut 06519, USA
- Department of Genetics, Yale University School of Medicine, New Haven, Connecticut 06519, USA
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2
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Maden SK, Kwon SH, Huuki-Myers LA, Collado-Torres L, Hicks SC, Maynard KR. Challenges and opportunities to computationally deconvolve heterogeneous tissue with varying cell sizes using single-cell RNA-sequencing datasets. Genome Biol 2023; 24:288. [PMID: 38098055 PMCID: PMC10722720 DOI: 10.1186/s13059-023-03123-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 11/24/2023] [Indexed: 12/17/2023] Open
Abstract
Deconvolution of cell mixtures in "bulk" transcriptomic samples from homogenate human tissue is important for understanding disease pathologies. However, several experimental and computational challenges impede transcriptomics-based deconvolution approaches using single-cell/nucleus RNA-seq reference atlases. Cells from the brain and blood have substantially different sizes, total mRNA, and transcriptional activities, and existing approaches may quantify total mRNA instead of cell type proportions. Further, standards are lacking for the use of cell reference atlases and integrative analyses of single-cell and spatial transcriptomics data. We discuss how to approach these key challenges with orthogonal "gold standard" datasets for evaluating deconvolution methods.
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Affiliation(s)
- Sean K Maden
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Sang Ho Kwon
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Louise A Huuki-Myers
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Leonardo Collado-Torres
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Stephanie C Hicks
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA.
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, USA.
| | - Kristen R Maynard
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA.
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA.
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA.
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3
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Song K, Elboudwarej E, Zhao X, Zhuo L, Pan D, Liu J, Brachmann C, Patterson SD, Yoon OK, Zavodovskaya M. RNA-seq RNAaccess identified as the preferred method for gene expression analysis of low quality FFPE samples. PLoS One 2023; 18:e0293400. [PMID: 37883360 PMCID: PMC10602291 DOI: 10.1371/journal.pone.0293400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 10/11/2023] [Indexed: 10/28/2023] Open
Abstract
Clinical tumor tissues that are preserved as formalin-fixed paraffin-embedded (FFPE) samples result in extensive cross-linking, fragmentation, and chemical modification of RNA, posing significant challenges for RNA-seq-based gene expression profiling. This study sought to define an optimal RNA-seq protocol for FFPE samples. We employed a common RNA extraction method and then compared RNA-seq library preparation protocols including RNAaccess, RiboZero and PolyA in terms of sequencing quality and concordance of gene expression using FFPE and case-matched fresh-frozen (FF) triple-negative breast cancer (TNBC) tissues. We found that RNAaccess, a method based on exome capture, produced the most concordant results. Applying RNAaccess to FFPE gastric cancer tissues, we established a minimum RNA DV200 requirement of 10% and a RNA input amount of 10ng that generated highly reproducible gene expression data. Lastly, we demonstrated that RNAaccess and NanoString platforms produced highly concordant expression profiles from FFPE samples for shared genes; however, RNA-seq may be preferred for clinical biomarker discovery work because of the broader coverage of the transcriptome. Taken together, these results support the selection of RNA-seq RNAaccess method for gene expression profiling of FFPE samples. The minimum requirements for RNA quality and input established here may allow for inclusion of clinical FFPE samples of sub-optimal quality in gene expression analyses and ultimately increasing the statistical power of such analyses.
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Affiliation(s)
- Kai Song
- Gilead Sciences, Inc., Foster City, California, United States of America
| | - Emon Elboudwarej
- Gilead Sciences, Inc., Foster City, California, United States of America
| | - Xi Zhao
- Gilead Sciences, Inc., Foster City, California, United States of America
| | - Luting Zhuo
- Gilead Sciences, Inc., Foster City, California, United States of America
| | - David Pan
- Gilead Sciences, Inc., Foster City, California, United States of America
| | - Jinfeng Liu
- Gilead Sciences, Inc., Foster City, California, United States of America
| | - Carrie Brachmann
- Gilead Sciences, Inc., Foster City, California, United States of America
| | - Scott D. Patterson
- Gilead Sciences, Inc., Foster City, California, United States of America
| | - Oh Kyu Yoon
- Gilead Sciences, Inc., Foster City, California, United States of America
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4
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Hippen AA, Omran DK, Weber LM, Jung E, Drapkin R, Doherty JA, Hicks SC, Greene CS. Performance of computational algorithms to deconvolve heterogeneous bulk ovarian tumor tissue depends on experimental factors. Genome Biol 2023; 24:239. [PMID: 37864274 PMCID: PMC10588129 DOI: 10.1186/s13059-023-03077-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 09/29/2023] [Indexed: 10/22/2023] Open
Abstract
BACKGROUND Single-cell gene expression profiling provides unique opportunities to understand tumor heterogeneity and the tumor microenvironment. Because of cost and feasibility, profiling bulk tumors remains the primary population-scale analytical strategy. Many algorithms can deconvolve these tumors using single-cell profiles to infer their composition. While experimental choices do not change the true underlying composition of the tumor, they can affect the measurements produced by the assay. RESULTS We generated a dataset of high-grade serous ovarian tumors with paired expression profiles from using multiple strategies to examine the extent to which experimental factors impact the results of downstream tumor deconvolution methods. We find that pooling samples for single-cell sequencing and subsequent demultiplexing has a minimal effect. We identify dissociation-induced differences that affect cell composition, leading to changes that may compromise the assumptions underlying some deconvolution algorithms. We also observe differences across mRNA enrichment methods that introduce additional discrepancies between the two data types. We also find that experimental factors change cell composition estimates and that the impact differs by method. CONCLUSIONS Previous benchmarks of deconvolution methods have largely ignored experimental factors. We find that methods vary in their robustness to experimental factors. We provide recommendations for methods developers seeking to produce the next generation of deconvolution approaches and for scientists designing experiments using deconvolution to study tumor heterogeneity.
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Affiliation(s)
- Ariel A Hippen
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
| | - Dalia K Omran
- Penn Ovarian Cancer Research Center, Department of Obstetrics and Gynecology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Lukas M Weber
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Euihye Jung
- Penn Ovarian Cancer Research Center, Department of Obstetrics and Gynecology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ronny Drapkin
- Penn Ovarian Cancer Research Center, Department of Obstetrics and Gynecology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Stephanie C Hicks
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Casey S Greene
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
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5
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Butto T, Mungikar K, Baumann P, Winter J, Lutz B, Gerber S. Nuclei on the Rise: When Nuclei-Based Methods Meet Next-Generation Sequencing. Cells 2023; 12:cells12071051. [PMID: 37048124 PMCID: PMC10093037 DOI: 10.3390/cells12071051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 03/22/2023] [Accepted: 03/27/2023] [Indexed: 04/03/2023] Open
Abstract
In the last decade, we have witnessed an upsurge in nuclei-based studies, particularly coupled with next-generation sequencing. Such studies aim at understanding the molecular states that exist in heterogeneous cell populations by applying increasingly more affordable sequencing approaches, in addition to optimized methodologies developed to isolate and select nuclei. Although these powerful new methods promise unprecedented insights, it is important to understand and critically consider the associated challenges. Here, we provide a comprehensive overview of the rise of nuclei-based studies and elaborate on their advantages and disadvantages, with a specific focus on their utility for transcriptomic sequencing analyses. Improved designs and appropriate use of the various experimental strategies will result in acquiring biologically accurate and meaningful information.
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Affiliation(s)
- Tamer Butto
- Institute for Pharmaceutical and Biomedical Sciences, Johannes Gutenberg-University, 55128 Mainz, Germany
- Correspondence: (T.B.); (S.G.); Tel.: +49-(0)6131-39-27331 (S.G.)
| | - Kanak Mungikar
- Institute of Human Genetics, University Medical Center Mainz, 55131 Mainz, Germany
| | - Peter Baumann
- Faculty of Biology, Johannes Gutenberg-University, 55128 Mainz, Germany
- Institute of Molecular Biology (IMB), 55128 Mainz, Germany
| | - Jennifer Winter
- Institute of Human Genetics, University Medical Center Mainz, 55131 Mainz, Germany
- Leibniz Institute for Resilience Research (LIR), 55122 Mainz, Germany
| | - Beat Lutz
- Leibniz Institute for Resilience Research (LIR), 55122 Mainz, Germany
- Institute of Physiological Chemistry, University Medical Center Mainz, 55128 Mainz, Germany
| | - Susanne Gerber
- Institute of Human Genetics, University Medical Center Mainz, 55131 Mainz, Germany
- Correspondence: (T.B.); (S.G.); Tel.: +49-(0)6131-39-27331 (S.G.)
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6
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Library adaptors with integrated reference controls improve the accuracy and reliability of nanopore sequencing. Nat Commun 2022; 13:6437. [PMID: 36307482 PMCID: PMC9616880 DOI: 10.1038/s41467-022-34028-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 10/11/2022] [Indexed: 12/25/2022] Open
Abstract
Library adaptors are short oligonucleotides that are attached to RNA and DNA samples in preparation for next-generation sequencing (NGS). Adaptors can also include additional functional elements, such as sample indexes and unique molecular identifiers, to improve library analysis. Here, we describe Control Library Adaptors, termed CAPTORs, that measure the accuracy and reliability of NGS. CAPTORs can be integrated within the library preparation of RNA and DNA samples, and their encoded information is retrieved during sequencing. We show how CAPTORs can measure the accuracy of nanopore sequencing, evaluate the quantitative performance of metagenomic and RNA sequencing, and improve normalisation between samples. CAPTORs can also be customised for clinical diagnoses, correcting systematic sequencing errors and improving the diagnosis of pathogenic BRCA1/2 variants in breast cancer. CAPTORs are a simple and effective method to increase the accuracy and reliability of NGS, enabling comparisons between samples, reagents and laboratories, and supporting the use of nanopore sequencing for clinical diagnosis.
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7
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Potemkin N, Cawood SMF, Treece J, Guévremont D, Rand CJ, McLean C, Stanton JAL, Williams JM. A method for simultaneous detection of small and long RNA biotypes by ribodepleted RNA-Seq. Sci Rep 2022; 12:621. [PMID: 35022475 PMCID: PMC8755727 DOI: 10.1038/s41598-021-04209-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 11/24/2021] [Indexed: 11/09/2022] Open
Abstract
RNA sequencing offers unprecedented access to the transcriptome. Key to this is the identification and quantification of many different species of RNA from the same sample at the same time. In this study we describe a novel protocol for simultaneous detection of coding and non-coding transcripts using modifications to the Ion Total RNA-Seq kit v2 protocol, with integration of QIASeq FastSelect rRNA removal kit. We report highly consistent sequencing libraries can be produced from both frozen high integrity mouse hippocampal tissue and the more challenging post-mortem human tissue. Removal of rRNA using FastSelect was extremely efficient, resulting in less than 1.5% rRNA content in the final library. We identified > 30,000 unique transcripts from all samples, including protein-coding genes and many species of non-coding RNA, in biologically-relevant proportions. Furthermore, the normalized sequencing read count for select genes significantly negatively correlated with Ct values from qRT-PCR analysis from the same samples. These results indicate that this protocol accurately and consistently identifies and quantifies a wide variety of transcripts simultaneously. The highly efficient rRNA depletion, coupled with minimized sample handling and without complicated and high-loss size selection protocols, makes this protocol useful to researchers wishing to investigate whole transcriptomes.
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Affiliation(s)
- Nikita Potemkin
- Department of Anatomy, School of Biomedical Sciences, University of Otago, P.O. Box 56, Dunedin, New Zealand
- Brain Health Research Centre, Brain Research New Zealand-Rangahau Roro Aotearoa, University of Otago, Dunedin, New Zealand
| | - Sophie M F Cawood
- Department of Anatomy, School of Biomedical Sciences, University of Otago, P.O. Box 56, Dunedin, New Zealand
- Brain Health Research Centre, Brain Research New Zealand-Rangahau Roro Aotearoa, University of Otago, Dunedin, New Zealand
| | - Jackson Treece
- Department of Anatomy, School of Biomedical Sciences, University of Otago, P.O. Box 56, Dunedin, New Zealand
| | - Diane Guévremont
- Department of Anatomy, School of Biomedical Sciences, University of Otago, P.O. Box 56, Dunedin, New Zealand
- Brain Health Research Centre, Brain Research New Zealand-Rangahau Roro Aotearoa, University of Otago, Dunedin, New Zealand
| | - Christy J Rand
- Department of Anatomy, School of Biomedical Sciences, University of Otago, P.O. Box 56, Dunedin, New Zealand
| | - Catriona McLean
- Victorian Brain Bank, The Florey Institute of Neuroscience and Mental Health, Melbourne, VIC, Australia
- Anatomical Pathology, The Alfred Hospital, Melbourne, VIC, Australia
| | - Jo-Ann L Stanton
- Department of Anatomy, School of Biomedical Sciences, University of Otago, P.O. Box 56, Dunedin, New Zealand
| | - Joanna M Williams
- Department of Anatomy, School of Biomedical Sciences, University of Otago, P.O. Box 56, Dunedin, New Zealand.
- Brain Health Research Centre, Brain Research New Zealand-Rangahau Roro Aotearoa, University of Otago, Dunedin, New Zealand.
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8
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Baldwin A, Morris AR, Mukherjee N. An Easy, Cost-Effective, and Scalable Method to Deplete Human Ribosomal RNA for RNA-seq. Curr Protoc 2021; 1:e176. [PMID: 34165268 DOI: 10.1002/cpz1.176] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
RNA sequencing (RNA-seq) is a powerful and increasingly prevalent method to characterize and quantify the transcriptome. Ribosomes are extremely abundant, however, and approximately 80% of total RNA is ribosomal RNA (rRNA). Therefore, to detect and quantify less abundant yet biologically important transcripts such as messenger RNA (mRNA) and long noncoding RNAs (lncRNA), it is essential to minimize the rRNA being sequenced. Although commercial methods exist to deplete rRNA from total RNA samples before sequencing, they are expensive and require specific amounts of input RNA, and the most commonly used kit is no longer available as a stand-alone product. Here, we present an optimized rRNA depletion protocol using RNase H and DNA oligonucleotides complementary to human rRNA transcripts. This protocol includes guidelines for DNA oligo preparation, RNA:DNA hybridization, RNase H cleavage and RNA cleanup, and benchmarking of rRNA depletion. The method is flexible because the user can include additional complementary DNA oligos directed against any abundant transcript in their particular system. Furthermore, the performance of this rRNA depletion approach is comparable to or better than that of commercial kits, at a fraction of the cost and across a wide range of input RNA amounts. © 2021 Wiley Periodicals LLC. Basic Protocol: Specific depletion of rRNA transcripts from human total RNA Support Protocol: Preparation of the rRNA depletion DNA oligonucleotide pool.
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Affiliation(s)
- Amber Baldwin
- University of Colorado Anschutz School of Medicine, Aurora, Colorado
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9
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Haile S, Corbett RD, LeBlanc VG, Wei L, Pleasance S, Bilobram S, Nip KM, Brown K, Trinh E, Smith J, Trinh DL, Bala M, Chuah E, Coope RJN, Moore RA, Mungall AJ, Mungall KL, Zhao Y, Hirst M, Aparicio S, Birol I, Jones SJM, Marra MA. A Scalable Strand-Specific Protocol Enabling Full-Length Total RNA Sequencing From Single Cells. Front Genet 2021; 12:665888. [PMID: 34149808 PMCID: PMC8209500 DOI: 10.3389/fgene.2021.665888] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 04/21/2021] [Indexed: 12/14/2022] Open
Abstract
RNA sequencing (RNAseq) has been widely used to generate bulk gene expression measurements collected from pools of cells. Only relatively recently have single-cell RNAseq (scRNAseq) methods provided opportunities for gene expression analyses at the single-cell level, allowing researchers to study heterogeneous mixtures of cells at unprecedented resolution. Tumors tend to be composed of heterogeneous cellular mixtures and are frequently the subjects of such analyses. Extensive method developments have led to several protocols for scRNAseq but, owing to the small amounts of RNA in single cells, technical constraints have required compromises. For example, the majority of scRNAseq methods are limited to sequencing only the 3' or 5' termini of transcripts. Other protocols that facilitate full-length transcript profiling tend to capture only polyadenylated mRNAs and are generally limited to processing only 96 cells at a time. Here, we address these limitations and present a novel protocol that allows for the high-throughput sequencing of full-length, total RNA at single-cell resolution. We demonstrate that our method produced strand-specific sequencing data for both polyadenylated and non-polyadenylated transcripts, enabled the profiling of transcript regions beyond only transcript termini, and yielded data rich enough to allow identification of cell types from heterogeneous biological samples.
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Affiliation(s)
- Simon Haile
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada
| | - Richard D Corbett
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada
| | - Veronique G LeBlanc
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada
| | - Lisa Wei
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada
| | - Stephen Pleasance
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada
| | - Steve Bilobram
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada
| | - Ka Ming Nip
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada
| | - Kirstin Brown
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada
| | - Eva Trinh
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada
| | - Jillian Smith
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada
| | - Diane L Trinh
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada
| | - Miruna Bala
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada
| | - Eric Chuah
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada
| | - Robin J N Coope
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada
| | - Richard A Moore
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada
| | - Andrew J Mungall
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada
| | - Karen L Mungall
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada
| | - Yongjun Zhao
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada
| | - Martin Hirst
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada
| | - Samuel Aparicio
- Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada
| | - Inanc Birol
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada.,Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
| | - Steven J M Jones
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada.,Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
| | - Marco A Marra
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada.,Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
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10
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Krieg E, Gupta K, Dahl A, Lesche M, Boye S, Lederer A, Shih WM. A smart polymer for sequence-selective binding, pulldown, and release of DNA targets. Commun Biol 2020; 3:369. [PMID: 32651444 PMCID: PMC7351716 DOI: 10.1038/s42003-020-1082-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Accepted: 06/17/2020] [Indexed: 11/26/2022] Open
Abstract
Selective isolation of DNA is crucial for applications in biology, bionanotechnology, clinical diagnostics and forensics. We herein report a smart methanol-responsive polymer (MeRPy) that can be programmed to bind and separate single- as well as double-stranded DNA targets. Captured targets are quickly isolated and released back into solution by denaturation (sequence-agnostic) or toehold-mediated strand displacement (sequence-selective). The latter mode allows 99.8% efficient removal of unwanted sequences and 79% recovery of highly pure target sequences. We applied MeRPy for the depletion of insulin, glucagon, and transthyretin cDNA from clinical next-generation sequencing (NGS) libraries. This step improved the data quality for low-abundance transcripts in expression profiles of pancreatic tissues. Its low cost, scalability, high stability and ease of use make MeRPy suitable for diverse applications in research and clinical laboratories, including enhancement of NGS libraries, extraction of DNA from biological samples, preparative-scale DNA isolations, and sorting of DNA-labeled non-nucleic acid targets.
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Affiliation(s)
- Elisha Krieg
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA.
- Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, USA.
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA.
- Leibniz-Institut für Polymerforschung Dresden e.V., Dresden, Germany.
- School of Science, Technische Universität Dresden, Dresden, Germany.
| | - Krishna Gupta
- Leibniz-Institut für Polymerforschung Dresden e.V., Dresden, Germany
- Biotechnology Center (BIOTEC), Technische Universität Dresden, Dresden, Germany
| | - Andreas Dahl
- DRESDEN-concept Genome Center, Center for Molecular and Cellular Bioengineering, Technische Universität Dresden, Dresden, Germany
| | - Mathias Lesche
- DRESDEN-concept Genome Center, Center for Molecular and Cellular Bioengineering, Technische Universität Dresden, Dresden, Germany
| | - Susanne Boye
- Leibniz-Institut für Polymerforschung Dresden e.V., Dresden, Germany
| | - Albena Lederer
- Leibniz-Institut für Polymerforschung Dresden e.V., Dresden, Germany
- School of Science, Technische Universität Dresden, Dresden, Germany
| | - William M Shih
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA.
- Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, USA.
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA.
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