scholarly journals Combined single-cell profiling of expression and DNA methylation reveals splicing regulation and heterogeneity

2018 ◽  
Author(s):  
Stephanie M. Linker ◽  
Lara Urban ◽  
Stephen Clark ◽  
Mariya Chhatriwala ◽  
Shradha Amatya ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Alternative Splicing ◽  
Single Cell ◽  
Single Cells ◽  
Exon Skipping ◽  
Sequence Composition ◽  
Local Sequence ◽  
Single Cell Profiling ◽  
Induced Pluripotent ◽  
Cell Variation

AbstractBackgroundAlternative splicing is a key regulatory mechanism in eukaryotic cells and increases the effective number of functionally distinct gene products. Using bulk RNA sequencing, splicing variation has been studied across human tissues and in genetically diverse populations. This has identified disease-relevant splicing events, as well as associations between splicing and genomic variations, including sequence composition and conservation. However, variability in splicing between single cells from the same tissue or cell type and its determinants remain poorly understood.ResultsWe applied parallel DNA methylation and transcriptome sequencing to differentiating human induced pluripotent stem cells to characterize splicing variation (exon skipping) and its determinants. Our results shows that variation in single-cell splicing can be accurately predicted based on local sequence composition and genomic features. We observe moderate but consistent contributions from local DNA methylation profiles to splicing variation across cells. A combined model that is built based on sequence as well as DNA methylation information accurately predicts different splicing modes of individual cassette exons (AUC=0.85). These categories include the conventional inclusion and exclusion patterns, but also more subtle modes of cell-to-cell variation in splicing. Finally, we identified and characterized associations between DNA methylation and splicing changes during cell differentiation.ConclusionsOur study yields new insights into alternative splicing at the single-cell level and reveals a previously underappreciated link between DNA methylation variation and splicing.

scholarly journals Accurate prediction of single-cell DNA methylation states using deep learning

2016 ◽  
Author(s):  
Christof Angermueller ◽  
Heather J. Lee ◽  
Wolf Reik ◽  
Oliver Stegle
Keyword(s):  
Dna Methylation ◽  
Single Cell ◽  
Deep Neural Networks ◽  
Single Cells ◽  
Cell Types ◽  
Computational Approach ◽  
Methylation Data ◽  
Sequence Composition ◽  
Technological Advances ◽  
Genome Wide

AbstractRecent technological advances have enabled assaying DNA methylation at single-cell resolution. Current protocols are limited by incomplete CpG coverage and hence methods to predict missing methylation states are critical to enable genome-wide analyses. Here, we report DeepCpG, a computational approach based on deep neural networks to predict DNA methylation states from DNA sequence and incomplete methylation profiles in single cells. We evaluated DeepCpG on single-cell methylation data from five cell types generated using alternative sequencing protocols, finding that DeepCpG yields substantially more accurate predictions than previous methods. Additionally, we show that the parameters of our model can be interpreted, thereby providing insights into the effect of sequence composition on methylation variability.

scholarly journals Normalizing and denoising protein expression data from droplet-based single cell profiling

2020 ◽  
Author(s):  
Matthew P. Mulè ◽  
Andrew J. Martins ◽  
John S. Tsang
Keyword(s):  
Protein Expression ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Single Cells ◽  
R Package ◽  
Expression Data ◽  
Cell Capture ◽  
Noise Component ◽  
Single Cell Profiling ◽  
Cell Variation

AbstractRecent methods enable simultaneous measurement of protein expression with the transcriptome in single cells by combining protein labeling with DNA barcoded antibodies followed by droplet based single cell capture and sequencing (e.g. CITE-seq). While data normalization and denoising have received considerable attention for single cell RNA-seq data, such methods for protein data have been less explored. Here we showed that a major source of noise in CITE-seq data originated from unbound antibody encapsulated in droplets. We also found that the counts of isotype controls and those of the “negative” population inferred from all protein counts of each cell are significantly correlated, suggesting that their covariation likely reflects cell-to-cell differences due to technical factors such as non-specific antibody binding and droplet-to-droplet differences in capture efficiency of the DNA tags. Motivated by these observations, we developed a normalization method for CITE-seq protein expression data called Denoised and Scaled by Background (DSB). DSB corrects for 1) protein-specific background noise as reflected by empty droplets, 2) the technical cell-to-cell variation as captured by the latent noise component described above. DSB normalization improves separation between positive and negative populations for each protein, centers the negative-staining population around zero, and can improve unbiased protein expression-based clustering. DSB is available through the open source R package “DSB” via a single function call and can be readily integrated with existing single cell analysis workflows, including those in Bioconductor and Seurat.

scholarly journals SCELLECTOR: ranking amplification bias in single cells using shallow sequencing

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Vivekananda Sarangi ◽  
Alexandre Jourdon ◽  
Taejeong Bae ◽  
Arijit Panda ◽  
Flora Vaccarino ◽  
...  
Keyword(s):  
Single Cell ◽  
Multiple Displacement Amplification ◽  
Single Cells ◽  
Sequencing Data ◽  
High Coverage ◽  
Amplification Bias ◽  
Single Cell Profiling ◽  
High Concordance ◽  
Human Neuronal Cells

Abstract Background The study of mosaic mutation is important since it has been linked to cancer and various disorders. Single cell sequencing has become a powerful tool to study the genome of individual cells for the detection of mosaic mutations. The amount of DNA in a single cell needs to be amplified before sequencing and multiple displacement amplification (MDA) is widely used owing to its low error rate and long fragment length of amplified DNA. However, the phi29 polymerase used in MDA is sensitive to template fragmentation and presence of sites with DNA damage that can lead to biases such as allelic imbalance, uneven coverage and over representation of C to T mutations. It is therefore important to select cells with uniform amplification to decrease false positives and increase sensitivity for mosaic mutation detection. Results We propose a method, Scellector (single cell selector), which uses haplotype information to detect amplification quality in shallow coverage sequencing data. We tested Scellector on single human neuronal cells, obtained in vitro and amplified by MDA. Qualities were estimated from shallow sequencing with coverage as low as 0.3× per cell and then confirmed using 30× deep coverage sequencing. The high concordance between shallow and high coverage data validated the method. Conclusion Scellector can potentially be used to rank amplifications obtained from single cell platforms relying on a MDA-like amplification step, such as Chromium Single Cell profiling solution.

scholarly journals scSPLAT, a scalable plate-based protocol for single cell WGBS library preparation

2021 ◽  
Author(s):  
Amanda Raine ◽  
Anders Lundmark ◽  
Alva Annett ◽  
Ann-Christin Wiman ◽  
Marco Cavalli ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Single Cell ◽  
Single Cells ◽  
K562 Cells ◽  
Epigenetic Mark ◽  
Library Preparation ◽  
New Development ◽  
Liver Nuclei ◽  
Pooling Strategy ◽  
Methyl Cytosine

DNA methylation is a central epigenetic mark that has diverse roles in gene regulation, development, and maintenance of genome integrity. 5 methyl cytosine (5mC) can be interrogated at base resolution in single cells by using bisulfite sequencing (scWGBS). Several different scWGBS strategies have been described in recent years to study DNA methylation in single cells. However, there remain limitations with respect to cost-efficiency and yield. Herein, we present a new development in the field of scWGBS library preparation; single cell Splinted Ligation Adapter Tagging (scSPLAT). scSPLAT employs a pooling strategy to facilitate sample preparation at a higher scale and throughput than previously possible. We demonstrate the accuracy and robustness of the method by generating data from 225 single K562 cells and from 309 single liver nuclei and compare scSPLAT against other scWGBS methods.

scholarly journals Chronic opioid treatment arrests neurodevelopment and alters synaptic activity in human midbrain organoids

2021 ◽  
Author(s):  
Hye Sung Kim ◽  
Yang Xiao ◽  
Xuejing Chen ◽  
Siyu He ◽  
Jongwon Im ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Cell Lineage ◽  
Induced Pluripotent Stem Cell ◽  
Synaptic Activity ◽  
Gene Expressions ◽  
Opioid Treatment ◽  
Cell Type Specific ◽  
Induced Pluripotent ◽  
The Impact

SummaryThe impact of long-term opioid exposure on the embryonic brain is crucial to healthcare due to the surging number of pregnant mothers with an opioid dependency. Current studies on the neuronal effects are limited due to human brain inaccessibility and cross-species differences among animal models. Here, we report a model to assess cell-type specific responses to acute and chronic fentanyl treatment, as well as fentanyl withdrawal, using human induced pluripotent stem cell (hiPSC)-derived midbrain organoids. Single cell mRNA sequencing (25,510 single cells in total) results suggest that chronic fentanyl treatment arrests neuronal subtype specification during early midbrain development and alters the pathways associated with synaptic activities and neuron projection. Acute fentanyl treatment, however, increases dopamine release but does not induce significant changes in gene expressions of cell lineage development. To date, our study is the first unbiased examination of midbrain transcriptomics with synthetic opioid treatment at the single cell level.

scholarly journals Droplet-based Single-cell Total RNA-seq Reveals Differential Non-Coding Expression and Splicing Patterns during Mouse Development

2021 ◽  
Author(s):  
Fredrik Salmen ◽  
Joachim De Jonghe ◽  
Tomasz S. Kaminski ◽  
Anna Alemany ◽  
Guillermo Parada ◽  
...  
Keyword(s):  
Alternative Splicing ◽  
Single Cell ◽  
Single Cells ◽  
Droplet Microfluidics ◽  
Mammalian Development ◽  
Mouse Development ◽  
Protein Coding ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

In recent years, single-cell transcriptome sequencing has revolutionized biology, allowing for the unbiased characterization of cellular subpopulations. However, most methods amplify the termini of polyadenylated transcripts capturing only a small fraction of the total cellular transcriptome. This precludes the detection of many long non-coding, short non-coding and non-polyadenylated protein-coding transcripts. Additionally, most workflows do not sequence the full transcript hindering the analysis of alternative splicing. We therefore developed VASA- seq to detect the total transcriptome in single cells. VASA-seq is compatible with both plate- based formats and droplet microfluidics. We applied VASA-seq to over 30,000 single cells in the developing mouse embryo during gastrulation and early organogenesis. The dynamics of the total single-cell transcriptome result in the discovery of novel cell type markers many based on non-coding RNA, an in vivo cell cycle analysis and an improved RNA velocity characterization. Moreover, it provides the first comprehensive analysis of alternative splicing during mammalian development.

scholarly journals Single-cell multi-omic profiling of chromatin conformation and DNA methylation

2019 ◽  
Author(s):  
Dong-Sung Lee ◽  
Chongyuan Luo ◽  
Jingtian Zhou ◽  
Sahaana Chandran ◽  
Angeline Rivkin ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Single Cell ◽  
Genome Organization ◽  
Single Cells ◽  
Cell Types ◽  
Cell Type ◽  
Cell Level ◽  
3D Genome ◽  
Heterogeneous Samples ◽  
Single Data

Abstract The ability to profile epigenomic features in single cells is facilitating the study of the variation in transcription regulation at the single cell level. Single cell methods have also facilitated the generation of cell-type resolved transcriptomic and epigenetic profiles of lineages derived from complex heterogeneous samples. However, integrating different epigenetic features remain challenging, as many current methods profile a single data type at at time. Furthermore, some epigenetic features, such as 3D genome organization, are intrinsically variable between single cells of the same lineage, so it remains unclear how well these methods may resolve cell-types from complex mixtures. Here we describe a method for profiling 3D genome organization and DNA methylation in single cells. This protocol accompanies Lee et al. (Nature Methods 2019) after peer review to aid potential users in applying the method to their own samples.

scholarly journals Single-cell phenotyping of human induced pluripotent stem cells by high-throughput imaging

2015 ◽  
Author(s):  
Hans Christian Volz ◽  
Florian Heigwer ◽  
Tatjana Wuest ◽  
Marta Galach ◽  
Jochen Utikal ◽  
...  
Keyword(s):  
Stem Cells ◽  
Induced Pluripotent Stem Cells ◽  
Single Cell ◽  
High Throughput ◽  
Pluripotent Stem Cells ◽  
Single Cells ◽  
Phenotypic Variability ◽  
Screening Experiments ◽  
Induced Pluripotent ◽  
High Throughput Imaging

Single-cell phenotyping promises to yield insights into biological responses in heterogeneous cell populations. We developed a method based on single-cell analysis to phenotype human induced pluripotent stem cells (hIPSC) by high-throughput imaging. Our method uses markers for morphology and pluripotency as well as social features to characterize perturbations using a meta-phenotype based on mapping single cells to distinct phenotypic classes. Analysis of perturbations on a single cell level enhances the applicability of human induced pluripotent stem cells (hIPSC) for screening experiments taking the inherently increased phenotypic variability of these cells into account. We adapted miniaturized culture conditions to allow for the utilization of hIPSC in RNA interference (RNAi) high-throughput screens and single cell phenotyping by image analysis. We identified key regulators of pluripotency in hIPSC masked in a population-averaged analysis and we confirmed several candidate genes (SMG1, TAF1) and assessed their effect on pluripotency.

scholarly journals BRIE2: computational identification of splicing phenotypes from single-cell transcriptomic experiments

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yuanhua Huang ◽  
Guido Sanguinetti
Keyword(s):  
Alternative Splicing ◽  
Single Cell ◽  
Rna Splicing ◽  
Single Cells ◽  
Computational Method ◽  
Cell Level ◽  
Transcriptomic Data ◽  
Alternative Transcripts ◽  
Computational Identification ◽  
Selection Of

AbstractRNA splicing is an important driver of heterogeneity in single cells through the expression of alternative transcripts and as a determinant of transcriptional kinetics. However, the intrinsic coverage limitations of scRNA-seq technologies make it challenging to associate specific splicing events to cell-level phenotypes. BRIE2 is a scalable computational method that resolves these issues by regressing single-cell transcriptomic data against cell-level features. We show that BRIE2 effectively identifies differential disease-associated alternative splicing events and allows a principled selection of genes that capture heterogeneity in transcriptional kinetics and improve RNA velocity analyses, enabling the identification of splicing phenotypes associated with biological changes.

scholarly journals scNMT-seq enables joint profiling of chromatin accessibility DNA methylation and transcription in single cells

2017 ◽  
Author(s):  
Stephen J. Clark ◽  
Ricard Argelaguet ◽  
Chantriolnt-Andreas Kapourani ◽  
Thomas M. Stubbs ◽  
Heather J. Lee ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Single Cell ◽  
Single Cells ◽  
Embryonic Stem ◽  
Transcriptome Profiling ◽  
Chromatin Accessibility ◽  
Open Chromatin ◽  
Dynamic Coupling ◽  
Single Cell Sequencing ◽  
Molecular Layers

AbstractParallel single-cell sequencing protocols represent powerful methods for investigating regulatory relationships, including epigenome-transcriptome interactions. Here, we report a novel single-cell method for parallel chromatin accessibility, DNA methylation and transcriptome profiling. scNMT-seq (single-cell nucleosome, methylation and transcription sequencing) uses a GpC methyltransferase to label open chromatin followed by bisulfite and RNA sequencing. We validate scNMT-seq by applying it to differentiating mouse embryonic stem cells, finding links between all three molecular layers and revealing dynamic coupling between epigenomic layers during differentiation.

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