Profiling Genome-Wide in Single Cells

2020 ◽  
pp. 221-240
Author(s):  
António Galvão ◽  
Gavin Kelsey
Keyword(s):  
Single Cells ◽  
Genome Wide

scholarly journals DNA Analysis by Restriction Enzyme (DARE) enables concurrent genomic and epigenomic characterization of single cells

2019 ◽  
Vol 47 (19) ◽  
pp. e122-e122
Author(s):  
Ramya Viswanathan ◽  
Elsie Cheruba ◽  
Lih Feng Cheow
Keyword(s):  
Dna Methylation ◽  
Restriction Enzyme ◽  
Copy Number ◽  
Dna Analysis ◽  
Whole Genome Amplification ◽  
Single Cells ◽  
Whole Genome ◽  
Copy Number Alterations ◽  
Genome Wide

Abstract Genome-wide profiling of copy number alterations and DNA methylation in single cells could enable detailed investigation into the genomic and epigenomic heterogeneity of complex cell populations. However, current methods to do this require complex sample processing and cleanup steps, lack consistency, or are biased in their genomic representation. Here, we describe a novel single-tube enzymatic method, DNA Analysis by Restriction Enzyme (DARE), to perform deterministic whole genome amplification while preserving DNA methylation information. This method was evaluated on low amounts of DNA and single cells, and provides accurate copy number aberration calling and representative DNA methylation measurement across the whole genome. Single-cell DARE is an attractive and scalable approach for concurrent genomic and epigenomic characterization of cells in a heterogeneous population.

scholarly journals Genome-wide copy number profiling of single cells in S-phase reveals DNA-replication domains

2013 ◽  
Vol 41 (6) ◽  
pp. e66-e66 ◽  
Author(s):  
Niels Van der Aa ◽  
Jiqiu Cheng ◽  
Ligia Mateiu ◽  
Masoud Zamani Esteki ◽  
Parveen Kumar ◽  
...  
Keyword(s):  
Dna Replication ◽  
Copy Number ◽  
Single Cells ◽  
S Phase ◽  
Genome Wide

scholarly journals Random forest based similarity learning for single cell RNA sequencing data

2018 ◽  
Author(s):  
Maziyar Baran Pouyan ◽  
Dennis Kostka
Keyword(s):  
Data Analysis ◽  
Random Forest ◽  
Single Cells ◽  
R Package ◽  
Similarity Learning ◽  
Sequencing Data ◽  
Genome Wide ◽  
Step Procedure ◽  
Exploratory Data ◽  
Cell Cell

AbstractMotivationGenome-wide transcriptome sequencing applied to single cells (scRNA-seq) is rapidly becoming an assay of choice across many fields of biological and biomedical research. Scientific objectives often revolve around discovery or characterization of types or sub-types of cells, and therefore obtaining accurate cell–cell similarities from scRNA-seq data is critical step in many studies. While rapid advances are being made in the development of tools for scRNA-seq data analysis, few approaches exist that explicitly address this task. Furthermore, abundance and type of noise present in scRNA-seq datasets suggest that application of generic methods, or of methods developed for bulk RNA-seq data, is likely suboptimal.ResultsHere we present RAFSIL, a random forest based approach to learn cell–cell similarities from scRNA-seq data. RAFSIL implements a two-step procedure, where feature construction geared towards scRNA-seq data is followed by similarity learning. It is designed to be adaptable and expandable, and RAFSIL similarities can be used for typical exploratory data analysis tasks like dimension reduction, visualization, and clustering. We show that our approach compares favorably with current methods across a diverse collection of datasets, and that it can be used to detect and highlight unwanted technical variation in scRNA-seq datasets in situations where other methods fail. Overall, RAFSIL implements a flexible approach yielding a useful tool that improves the analysis of scRNA-seq data.Availability and ImplementationThe RAFSIL R package is available online at www.kostkalab.net/software.html

scholarly journals Tandem Directional CTCF Sites Balance Protocadherin Promoter Usage

2019 ◽  
Author(s):  
Qiang Wu ◽  
Ya Guo ◽  
Yujia Lu ◽  
Jingwei Li ◽  
Yonghu Wu ◽  
...  
Keyword(s):  
Single Cells ◽  
Computational Simulation ◽  
Ctcf Binding ◽  
Monoallelic Expression ◽  
3D Genome ◽  
Genome Wide ◽  
A Genome ◽  
Mammalian Genomes ◽  
Wide Scale ◽  
Promoter Usage

ABSTRACTCTCF is a key insulator-binding protein and mammalian genomes contain numerous CTCF-binding sites (CBSs), many of which are organized in tandem arrays. Here we provide direct evidence that CBSs, if located between enhancers and promoters in the Pcdhα and β-globin clusters, function as an enhancer-blocking insulator by forming distinct directional chromatin loops, regardless whether enhancers contain CBS or not. Moreover, computational simulation and experimental capture revealed balanced promoter usage in cell populations and stochastic monoallelic expression in single cells by large arrays of tandem variable CBSs. Finally, gene expression levels are negatively correlated with CBS insulators located between enhancers and promoters on a genome-wide scale. Thus, single CBS insulators ensure proper enhancer insulation and promoter activation while tandem-arrayed CBS insulators determine balanced promoter usage. This finding has interesting implications on the role of topological insulators in 3D genome folding and developmental gene regulation.

scholarly journals Single-cell ATAC-seq Signal Extraction and Enhancement with SCATE

2019 ◽  
Author(s):  
Zhicheng Ji ◽  
Weiqiang Zhou ◽  
Hongkai Ji
Keyword(s):  
Single Cell ◽  
State Of The Art ◽  
Single Cells ◽  
Regulatory Elements ◽  
Single Cell Sequencing ◽  
Statistical Framework ◽  
Genome Wide ◽  
Cell Subpopulations ◽  
Accessible Chromatin ◽  
Regulatory Landscape

AbstractSingle-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) is the state-of-the-art technology for analyzing genome-wide regulatory landscape in single cells. Single-cell ATAC-seq data are sparse and noisy. Analyzing such data is challenging. Existing computational methods cannot accurately reconstruct activities of individual cis-regulatory elements (CREs) in individual cells or rare cell subpopulations. We present a new statistical framework, SCATE, that adaptively integrates information from co-activated CREs, similar cells, and publicly available regulome data to substantially increase the accuracy for estimating activities of individual CREs. We show that using SCATE, one can better reconstruct the regulatory landscape of a heterogeneous sample.

scholarly journals Antibody-guided Chromatin Tagmentation (ACT-seq)

2019 ◽  
Author(s):  
Benjamin Carter ◽  
Keji Zhao ◽  
Wai Lim Ku ◽  
Jee Youn Kang ◽  
Qingsong Tang
Keyword(s):  
Single Cell ◽  
Specific Antibody ◽  
Binding Proteins ◽  
Single Cells ◽  
Protein A ◽  
Histone Variants ◽  
Chromatin Binding ◽  
Histone Tail ◽  
Genome Wide ◽  
Tn5 Transposase

Abstract ACT-seq is a streamlined method for mapping genome-wide distributions of histone tail modifications, histone variants, and chromatin-binding proteins in a small number of or single cells. ACT-seq utilizes a fusion of Tn5 transposase to Protein A that is targeted to chromatin by a specific antibody, allowing chromatin fragmentation and sequence tag insertion specifically at genomic sites presenting the relevant antigen. The Tn5 transposase enables the use of an index multiplexing strategy (iACT-seq), which enables construction of thousands of single-cell libraries in one day by a single researcher without the need for drop-based fluidics or visual sorting. The protocol described here is intended for use with bulk-cell samples. The single-cell iACT-seq protocol is separate.

scholarly journals Tracking the embryonic stem cell transition from ground state pluripotency

2016 ◽  
Author(s):  
Tüzer Kalkan ◽  
Nelly Olova ◽  
Mila Roode ◽  
Carla Mulas ◽  
Heather J. Lee ◽  
...  
Keyword(s):  
Ground State ◽  
Es Cells ◽  
Single Cells ◽  
Embryonic Stem ◽  
List Type ◽  
Es Cell ◽  
Genome Wide ◽  
Developmental Progression ◽  
A Genome ◽  
Lineage Priming

SummaryMouse embryonic stem (ES) cells are locked into self-renewal by shielding from inductive cues. Release from this ground state in minimal conditions offers a system for delineating developmental progression from naive pluripotency. Here we examined the initial transition of ES cells. The population behaves asynchronously. We therefore exploited a short-half-life Rex1::GFP reporter to isolate cells either side of exit from naive status. Extinction of ES cell identity in single cells is acute. It occurs only after near-complete elimination of naïve pluripotency factors, but precedes appearance of lineage specification markers. Cells newly departed from the ES cell state exhibit global transcriptome features consistent with features of early post-implantation epiblast and distinct from primed epiblast. They also exhibit a genome-wide increase in DNA methylation, intermediate between early and late epiblast. These findings are consistent with the proposition that naïve cells transition to a discrete formative phase of pluripotency preparatory to lineage priming.HighlightsThe Rex1 destabilized GFP reporter demarcates naive pluripotency.Exit from the naive state is asynchronous in the population.Transition is relatively acute in individual cells and precedes lineage priming.Transcriptome and DNA methylome reflect events in the pre-gastrulation embryo.

Multi-centre evaluation of a comprehensive preimplantation genetic test through haplotyping-by-sequencing

2019 ◽  
Vol 34 (8) ◽  
pp. 1608-1619 ◽  
Author(s):  
Heleen Masset ◽  
Masoud Zamani Esteki ◽  
Eftychia Dimitriadou ◽  
Jos Dreesen ◽  
Sophie Debrock ◽  
...  
Keyword(s):  
Genome Sequencing ◽  
Copy Number ◽  
Reference Data ◽  
Single Cells ◽  
Patent Application ◽  
Polymorphic Variant ◽  
Whole Genome ◽  
Reduced Representation ◽  
Allelic Frequencies ◽  
Genome Wide

Abstract STUDY QUESTION Can reduced representation genome sequencing offer an alternative to single nucleotide polymorphism (SNP) arrays as a generic and genome-wide approach for comprehensive preimplantation genetic testing for monogenic disorders (PGT-M), aneuploidy (PGT-A) and structural rearrangements (PGT-SR) in human embryo biopsy samples? SUMMARY ANSWER Reduced representation genome sequencing, with OnePGT, offers a generic, next-generation sequencing-based approach for automated haplotyping and copy-number assessment, both combined or independently, in human single blastomere and trophectoderm samples. WHAT IS KNOWN ALREADY Genome-wide haplotyping strategies, such as karyomapping and haplarithmisis, have paved the way for comprehensive PGT, i.e. leveraging PGT-M, PGT-A and PGT-SR in a single workflow. These methods are based upon SNP array technology. STUDY DESIGN, SIZE, DURATION This multi-centre verification study evaluated the concordance of PGT results for a total of 225 embryos, including 189 originally tested for a monogenic disorder and 36 tested for a translocation. Concordance for whole chromosome aneuploidies was also evaluated where whole genome copy-number reference data were available. Data analysts were kept blind to the results from the reference PGT method. PARTICIPANTS/MATERIALS, SETTING, METHODS Leftover blastomere/trophectoderm whole genome amplified (WGA) material was used, or secondary trophectoderm biopsies were WGA. A reduced representation library from WGA DNA together with bulk DNA from phasing references was processed across two study sites with the Agilent OnePGT solution. Libraries were sequenced on an Illumina NextSeq500 system, and data were analysed with Agilent Alissa OnePGT software. The embedded PGT-M pipeline utilises the principles of haplarithmisis to deduce haplotype inheritance whereas both the PGT-A and PGT-SR pipelines are based upon read-count analysis in order to evaluate embryonic ploidy. Concordance analysis was performed for both analysis strategies against the reference PGT method. MAIN RESULTS AND THE ROLE OF CHANCE PGT-M analysis was performed on 189 samples. For nine samples, the data quality was too poor to analyse further, and for 20 samples, no result could be obtained mainly due to biological limitations of the haplotyping approach, such as co-localisation of meiotic crossover events and nullisomy for the chromosome of interest. For the remaining 160 samples, 100% concordance was obtained between OnePGT and the reference PGT-M method. Equally for PGT-SR, 100% concordance for all 36 embryos tested was demonstrated. Moreover, with embryos originally analysed for PGT-M or PGT-SR for which genome-wide copy-number reference data were available, 100% concordance was shown for whole chromosome copy-number calls (PGT-A). LIMITATIONS, REASONS FOR CAUTION Inherent to haplotyping methodologies, processing of additional family members is still required. Biological limitations caused inconclusive results in 10% of cases. WIDER IMPLICATIONS OF THE FINDINGS Employment of OnePGT for PGT-M, PGT-SR, PGT-A or combined as comprehensive PGT offers a scalable platform, which is inherently generic and thereby, eliminates the need for family-specific design and optimisation. It can be considered as both an improvement and complement to the current methodologies for PGT. STUDY FUNDING/COMPETING INTEREST(S) Agilent Technologies, the KU Leuven (C1/018 to J.R.V. and T.V.) and the Horizon 2020 WIDENLIFE (692065 to J.R.V. and T.V). H.M. is supported by the Research Foundation Flanders (FWO, 11A7119N). M.Z.E, J.R.V. and T.V. are co-inventors on patent applications: ZL910050-PCT/EP2011/060211- WO/2011/157846 ‘Methods for haplotyping single cells’ and ZL913096-PCT/EP2014/068315 ‘Haplotyping and copy-number typing using polymorphic variant allelic frequencies’. T.V. and J.R.V. are co-inventors on patent application: ZL912076-PCT/EP2013/070858 ‘High-throughput genotyping by sequencing’. Haplarithmisis (‘Haplotyping and copy-number typing using polymorphic variant allelic frequencies’) has been licensed to Agilent Technologies. The following patents are pending for OnePGT: US2016275239, AU2014345516, CA2928013, CN105874081, EP3066213 and WO2015067796. OnePGT is a registered trademark. D.L., J.T. and R.L.R. report personal fees during the conduct of the study and outside the submitted work from Agilent Technologies. S.H. and K.O.F. report personal fees and other during the conduct of the study and outside the submitted work from Agilent Technologies. J.A. reports personal fees and other during the conduct of the study from Agilent Technologies and personal fees from Agilent Technologies and UZ Leuven outside the submitted work. B.D. reports grants from IWT/VLAIO, personal fees during the conduct of the study from Agilent Technologies and personal fees and other outside the submitted work from Agilent Technologies. In addition, B.D. has a patent 20160275239 - Genetic Analysis Method pending. The remaining authors have no conflicts of interest.

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