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Pan Y, Cao W, Mu Y, Zhu Q. Microfluidics Facilitates the Development of Single-Cell RNA Sequencing. BIOSENSORS 2022; 12:bios12070450. [PMID: 35884253 PMCID: PMC9312765 DOI: 10.3390/bios12070450] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Revised: 06/20/2022] [Accepted: 06/21/2022] [Indexed: 12/12/2022]
Abstract
Single-cell RNA sequencing (scRNA-seq) technology provides a powerful tool for understanding complex biosystems at the single-cell and single-molecule level. The past decade has been a golden period for the development of single-cell sequencing, with scRNA-seq undergoing a tremendous leap in sensitivity and throughput. The application of droplet- and microwell-based microfluidics in scRNA-seq has contributed greatly to improving sequencing throughput. This review introduces the history of development and important technical factors of scRNA-seq. We mainly focus on the role of microfluidics in facilitating the development of scRNA-seq technology. To end, we discuss the future directions for scRNA-seq.
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Affiliation(s)
- Yating Pan
- Research Center for Analytical Instrumentation, State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China; (Y.P.); (W.C.)
- College of Life Sciences, Zhejiang University, Hangzhou 310058, China
| | - Wenjian Cao
- Research Center for Analytical Instrumentation, State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China; (Y.P.); (W.C.)
| | - Ying Mu
- Research Center for Analytical Instrumentation, State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China; (Y.P.); (W.C.)
- Correspondence: (Y.M.); (Q.Z.); Tel.: +86-88208383 (Y.M.); +86-88208383 (Q.Z.)
| | - Qiangyuan Zhu
- Research Center for Analytical Instrumentation, State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China; (Y.P.); (W.C.)
- Huzhou Institute of Zhejiang University, Huzhou 313002, China
- Correspondence: (Y.M.); (Q.Z.); Tel.: +86-88208383 (Y.M.); +86-88208383 (Q.Z.)
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Ochoa E, Lee S, Lan-Leung B, Dias RP, Ong KK, Radley JA, Pérez de Nanclares G, Martinez R, Clark G, Martin E, Castaño L, Bottolo L, Maher ER. ImprintSeq, a novel tool to interrogate DNA methylation at human imprinted regions and diagnose multilocus imprinting disturbance. Genet Med 2022; 24:463-474. [PMID: 34906518 DOI: 10.1016/j.gim.2021.10.011] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 10/19/2021] [Indexed: 01/14/2023] Open
Abstract
PURPOSE Disruptions of genomic imprinting are associated with congenital imprinting disorders (CIDs) and other disease states, including cancer. CIDs are most often associated with altered methylation at imprinted differentially methylated regions (iDMRs). In some cases, multiple iDMRs are affected causing multilocus imprinting disturbances (MLIDs). The availability of accurate, quantitative, and scalable high-throughput methods to interrogate multiple iDMRs simultaneously would enhance clinical diagnostics and research. METHODS We report the development of a custom targeted methylation sequencing panel that covered most relevant 63 iDMRs for CIDs and the detection of MLIDs. We tested it in 70 healthy controls and 147 individuals with CIDs. We distinguished loss and gain of methylation per differentially methylated region and classified high and moderate methylation alterations. RESULTS Across a range of CIDs with a variety of molecular mechanisms, ImprintSeq performed at 98.4% sensitivity, 99.9% specificity, and 99.9% accuracy (when compared with previous diagnostic testing). ImprintSeq was highly sensitive for detecting MLIDs and enabled diagnostic criteria for MLID to be proposed. In a child with extreme MLID profile a probable genetic cause was identified. CONCLUSION ImprintSeq provides a novel assay for clinical diagnostic and research studies of CIDs, MLIDs, and the role of disordered imprinting in human disease states.
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Affiliation(s)
- Eguzkine Ochoa
- Department of Medical Genetics, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Sunwoo Lee
- Department of Medical Genetics, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Benoit Lan-Leung
- Department of Medical Genetics, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Renuka P Dias
- Department of Endocrinology and Diabetes, Birmingham Children's Hospital, Birmingham Women's and Children's NHS Foundation Trust, Birmingham, United Kingdom; Institute of Metabolism and Systems Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Ken K Ong
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom; Department of Paediatrics, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Jessica A Radley
- West Midlands Regional Clinical Genetics Service and Birmingham Health Partners, Birmingham Women's and Children's NHS Foundation Trust, Birmingham, United Kingdom; London North West Regional Genetics Service, St. Mark's and Northwick Park hospitals, Harrow, Middlesex, United Kingdom
| | - Gustavo Pérez de Nanclares
- Biocruces Bizkaia Health Research Institute, Hospital Universitario Cruces, CIBERDEM, CIBERER, Endo-ERN, University of the Basque Country (UPV-EHU), Bizkaia, Spain
| | - Rosa Martinez
- Biocruces Bizkaia Health Research Institute, Hospital Universitario Cruces, CIBERDEM, CIBERER, Endo-ERN, University of the Basque Country (UPV-EHU), Bizkaia, Spain
| | - Graeme Clark
- Department of Medical Genetics, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom; Stratified Medicine Core Laboratory NGS Hub, Department of Medical Genetics, University of Cambridge, Cambridge, United Kingdom
| | - Ezequiel Martin
- Stratified Medicine Core Laboratory NGS Hub, Department of Medical Genetics, University of Cambridge, Cambridge, United Kingdom
| | - Luis Castaño
- Biocruces Bizkaia Health Research Institute, Hospital Universitario Cruces, CIBERDEM, CIBERER, Endo-ERN, University of the Basque Country (UPV-EHU), Bizkaia, Spain
| | - Leonardo Bottolo
- Department of Medical Genetics, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom; The Alan Turing Institute, London, United Kingdom; MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Eamonn R Maher
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom.
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Gnodi E, Meneveri R, Barisani D. Celiac disease: From genetics to epigenetics. World J Gastroenterol 2022; 28:449-463. [PMID: 35125829 PMCID: PMC8790554 DOI: 10.3748/wjg.v28.i4.449] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Revised: 06/16/2021] [Accepted: 01/11/2022] [Indexed: 02/06/2023] Open
Abstract
Celiac disease (CeD) is a multifactorial autoimmune disorder spread worldwide. The exposure to gluten, a protein found in cereals like wheat, barley and rye, is the main environmental factor involved in its pathogenesis. Even if the genetic predisposition represented by HLA-DQ2 or HLA-DQ8 haplotypes is widely recognised as mandatory for CeD development, it is not enough to explain the total predisposition for the disease. Furthermore, the onset of CeD comprehend a wide spectrum of symptoms, that often leads to a delay in CeD diagnosis. To overcome this deficiency and help detecting people with increased risk for CeD, also clarifying CeD traits linked to disease familiarity, different studies have tried to make light on other predisposing elements. These were in many cases genetic variants shared with other autoimmune diseases. Since inherited traits can be regulated by epigenetic modifications, also induced by environmental factors, the most recent studies focused on the potential involvement of epigenetics in CeD. Epigenetic factors can in fact modulate gene expression with many mechanisms, generating more or less stable changes in gene expression without affecting the DNA sequence. Here we analyze the different epigenetic modifications in CeD, in particular DNA methylation, histone modifications, non-coding RNAs and RNA methylation. Special attention is dedicated to the additional predispositions to CeD, the involvement of epigenetics in developing CeD complications, the pathogenic pathways modulated by epigenetic factors such as microRNAs and the potential use of epigenetic profiling as biomarker to discriminate different classes of patients.
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Affiliation(s)
- Elisa Gnodi
- School of Medicine and Surgery, University of Milano-Bicocca, Monza 20900, Italy
| | - Raffaella Meneveri
- School of Medicine and Surgery, University of Milano-Bicocca, Monza 20900, Italy
| | - Donatella Barisani
- School of Medicine and Surgery, University of Milano-Bicocca, Monza 20900, Italy
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Ochoa E, Zuber V, Bottolo L. Accurate Measurement of DNA Methylation: Challenges and Bias Correction. Methods Mol Biol 2022; 2432:25-47. [PMID: 35505205 DOI: 10.1007/978-1-0716-1994-0_3] [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] [Indexed: 06/14/2023]
Abstract
DNA methylation is a key epigenetic modification involved in gene regulation whose contribution to disease susceptibility is still not fully understood. As the cost of genome sequencing technologies continues to drop, it will soon become commonplace to perform genome-wide quantification of DNA methylation at a single base-pair resolution. However, the demand for its accurate quantification might vary across studies. When the scope of the analysis is to detect regions of the genome with different methylation patterns between two or more conditions, e.g., case vs control; treatments vs placebo, accuracy is not crucial. This is the case in epigenome-wide association studies used as genome-wide screening of methylation changes to detect new candidate genes and regions associated with a specific disease or condition. If the aim of the analysis is to use DNA methylation measurements as a biomarker for diseases diagnosis and treatment (Laird, Nat Rev Cancer 3:253-266, 2003; Bock, Epigenomics 1:99-110, 2009), it is instead recommended to produce accurate methylation measurements. Furthermore, if the objective is the detection of DNA methylation in subclonal tumor cell populations or in circulating tumor DNA or in any case of mosaicism, the importance of accuracy becomes critical. The aim of this chapter is to describe the factors that could affect the precise measurement of methylation levels and a recent Bayesian statistical method called MethylCal and its extension that have been proposed to minimize this problem.
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Affiliation(s)
- Eguzkine Ochoa
- Department of Medical Genetics, University of Cambridge, Cambridge, UK
- Cambridge NIHR Biomedical Research Centre, Cambridge, UK
| | - Verena Zuber
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Leonardo Bottolo
- Department of Medical Genetics, University of Cambridge, Cambridge, UK.
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
- The Alan Turing Institute, London, UK.
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Kapsner LA, Zavgorodnij MG, Majorova SP, Hotz-Wagenblatt A, Kolychev OV, Lebedev IN, Hoheisel JD, Hartmann A, Bauer A, Mate S, Prokosch HU, Haller F, Moskalev EA. BiasCorrector: Fast and accurate correction of all types of experimental biases in quantitative DNA methylation data derived by different technologies. Int J Cancer 2021; 149:1150-1165. [PMID: 33997972 DOI: 10.1002/ijc.33681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Revised: 04/25/2021] [Accepted: 04/29/2021] [Indexed: 11/06/2022]
Abstract
Quantification of DNA methylation in neoplastic cells is crucial both from mechanistic and diagnostic perspectives. However, such measurements are prone to different experimental biases. Polymerase chain reaction (PCR) bias results in an unequal recovery of methylated and unmethylated alleles at the sample preparation step. Post-PCR biases get introduced additionally by the readout processes. Correcting the biases is more practicable than optimising experimental conditions, as demonstrated previously. However, utilisation of our earlier developed algorithm strongly necessitates automation. Here, we present two R packages: rBiasCorrection, the core algorithms to correct biases; and BiasCorrector, its web-based graphical user interface frontend. The software detects and analyses experimental biases in calibration DNA samples at a single base resolution by using cubic polynomial and hyperbolic regression. The correction coefficients from the best regression type are employed to compensate for the bias. Three common technologies-bisulphite pyrosequencing, next-generation sequencing and oligonucleotide microarrays-were used to comprehensively test BiasCorrector. We demonstrate the accuracy of BiasCorrector's performance and reveal technology-specific PCR- and post-PCR biases. BiasCorrector effectively eliminates biases regardless of their nature, locus, the number of interrogated methylation sites and the detection method, thus representing a user-friendly tool for producing accurate epigenetic results.
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Affiliation(s)
- Lorenz A Kapsner
- Medical Center for Information and Communication Technology, Universitätsklinikum Erlangen, Erlangen, Germany
| | - Mikhail G Zavgorodnij
- Functional Analysis and Operational Equations, Voronezh State University, Voronezh, Russia
| | - Svetlana P Majorova
- Higher Mathematics and Physical Mathematical Modelling, Voronezh State Technical University, Voronezh, Russia
| | - Agnes Hotz-Wagenblatt
- Omics IT and Data Management Core Facility, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Germany
| | - Oleg V Kolychev
- Research Center, Zhukovsky-Gagarin Academy, Voronezh, Russia
| | - Igor N Lebedev
- Laboratory of Cytogenetics, Research Institute of Medical Genetics, Tomsk National Research Medical Center, Tomsk, Russia
| | - Jörg D Hoheisel
- Functional Genome Analysis, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Germany
| | - Arndt Hartmann
- Institute of Pathology, Universitätsklinikum Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Andrea Bauer
- Functional Genome Analysis, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Germany
| | - Sebastian Mate
- Medical Center for Information and Communication Technology, Universitätsklinikum Erlangen, Erlangen, Germany
| | - Hans-Ulrich Prokosch
- Medical Center for Information and Communication Technology, Universitätsklinikum Erlangen, Erlangen, Germany.,Chair of Medical Informatics, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Florian Haller
- Institute of Pathology, Universitätsklinikum Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Evgeny A Moskalev
- Institute of Pathology, Universitätsklinikum Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
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