μDamID: a microfluidic approach for imaging and sequencing protein-DNA interactions in single cells

Mapping Intimacies ◽

10.1101/706903 ◽

2019 ◽

Author(s):

Nicolas Altemose ◽

Annie Maslan ◽

Andre Lai ◽

Jonathan A. White ◽

Aaron M. Streets

Keyword(s):

Single Cell ◽

Fluorescent Proteins ◽

Single Cells ◽

Chromatin Organization ◽

Joint Analysis ◽

Nuclear Localization Sequence ◽

Sequencing Data ◽

Dna Interactions ◽

Interaction Sites ◽

Protein Dna Interactions

AbstractGenome regulation depends on carefully programmed protein-DNA interactions that maintain or alter gene expression states, often by influencing chromatin organization. Most studies of these interactions to date have relied on bulk methods, which in many systems cannot capture the dynamic single-cell nature of these interactions as they modulate cell states. One method allowing for sensitive single-cell mapping of protein-DNA interactions is DNA adenine methyltransferase identification (DamID), which records a protein’s DNA-binding history by methylating adenine bases in its vicinity, then selectively amplifies and sequences these methylated regions. These interaction sites can also be visualized using fluorescent proteins that bind to methyladenines. Here we combine these imaging and sequencing technologies in an integrated microfluidic platform (μDamID) that enables single-cell isolation, imaging, and sorting, followed by DamID. We apply this system to generate paired single-cell imaging and sequencing data from a human cell line, in which we map and validate interactions between DNA and nuclear lamina proteins, providing a measure of 3D chromatin organization and broad gene regulation patterns. μDamID provides the unique ability to compare paired imaging and sequencing data for each cell and between cells, enabling the joint analysis of the nuclear localization, sequence identity, and variability of protein-DNA interactions.

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A tale of two measurements: protein-DNA interactions and gene expression in single cells.

10.33540/343 ◽

2020 ◽

Author(s):

◽

Corina Maria Markodimitraki

Keyword(s):

Gene Expression ◽

Single Cells ◽

Dna Interactions ◽

Protein Dna Interactions

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Quality assessment of single-cell RNA sequencing data by coverage skewness analysis

10.1101/2019.12.31.890269 ◽

2019 ◽

Author(s):

Imad Abugessaisa ◽

Shuhei Noguchi ◽

Melissa Cardon ◽

Akira Hasegawa ◽

Kazuhide Watanabe ◽

...

Keyword(s):

Quality Assessment ◽

Single Cell ◽

Rna Sequencing ◽

Single Cells ◽

Assessment Method ◽

Poor Quality ◽

Sequencing Data ◽

Single Cell Rna Sequencing ◽

Gene Coverage ◽

The Impact

AbstractAnalysis and interpretation of single-cell RNA-sequencing (scRNA-seq) experiments are compromised by the presence of poor quality cells. For meaningful analyses, such poor quality cells should be excluded to avoid biases and large variation. However, no clear guidelines exist. We introduce SkewC, a novel quality-assessment method to identify poor quality single-cells in scRNA-seq experiments. The method is based on the assessment of gene coverage for each single cell and its skewness as a quality measure. To validate the method, we investigated the impact of poor quality cells on downstream analyses and compared biological differences between typical and poor quality cells. Moreover, we measured the ratio of intergenic expression, suggesting genomic contamination, and foreign organism contamination of single-cell samples. SkewC is tested in 37,993 single-cells generated by 15 scRNA-seq protocols. We envision SkewC as an indispensable QC method to be incorporated into scRNA-seq experiment to preclude the possibility of scRNA-seq data misinterpretation.

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SCELLECTOR: ranking amplification bias in single cells using shallow sequencing

BMC Bioinformatics ◽

10.1186/s12859-020-03858-y ◽

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.

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Estimating the Allele-Specific Expression of SNVs From 10× Genomics Single-Cell RNA-Sequencing Data

Genes ◽

10.3390/genes11030240 ◽

2020 ◽

Vol 11 (3) ◽

pp. 240 ◽

Cited By ~ 2

Author(s):

Prashant N. M. ◽

Hongyu Liu ◽

Pavlos Bousounis ◽

Liam Spurr ◽

Nawaf Alomran ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Single Cells ◽

Sequencing Data ◽

Specific Expression ◽

Single Nucleotide ◽

Healthy Donors ◽

Allele Expression ◽

Single Cell Rna Sequencing ◽

Allele Specific

With the recent advances in single-cell RNA-sequencing (scRNA-seq) technologies, the estimation of allele expression from single cells is becoming increasingly reliable. Allele expression is both quantitative and dynamic and is an essential component of the genomic interactome. Here, we systematically estimate the allele expression from heterozygous single nucleotide variant (SNV) loci using scRNA-seq data generated on the 10×Genomics Chromium platform. We analyzed 26,640 human adipose-derived mesenchymal stem cells (from three healthy donors), sequenced to an average of 150K sequencing reads per cell (more than 4 billion scRNA-seq reads in total). High-quality SNV calls assessed in our study contained approximately 15% exonic and >50% intronic loci. To analyze the allele expression, we estimated the expressed variant allele fraction (VAFRNA) from SNV-aware alignments and analyzed its variance and distribution (mono- and bi-allelic) at different minimum sequencing read thresholds. Our analysis shows that when assessing positions covered by a minimum of three unique sequencing reads, over 50% of the heterozygous SNVs show bi-allelic expression, while at a threshold of 10 reads, nearly 90% of the SNVs are bi-allelic. In addition, our analysis demonstrates the feasibility of scVAFRNA estimation from current scRNA-seq datasets and shows that the 3′-based library generation protocol of 10×Genomics scRNA-seq data can be informative in SNV-based studies, including analyses of transcriptional kinetics.

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Scirpy: A Scanpy extension for analyzing single-cell T-cell receptor sequencing data

10.1101/2020.04.10.035865 ◽

2020 ◽

Author(s):

Gregor Sturm ◽

Tamas Szabo ◽

Georgios Fotakis ◽

Marlene Haider ◽

Dietmar Rieder ◽

...

Keyword(s):

T Cell ◽

Single Cell ◽

Large Scale ◽

Single Cells ◽

Cell Receptor ◽

Sequencing Data ◽

Seamless Integration ◽

T Cell Phenotypes ◽

Cell Phenotypes

AbstractSummaryAdvances in single-cell technologies have enabled the investigation of T cell phenotypes and repertoires at unprecedented resolution and scale. Bioinformatic methods for the efficient analysis of these large-scale datasets are instrumental for advancing our understanding of adaptive immune responses in cancer, but also in infectious diseases like COVID-19. However, while well-established solutions are accessible for the processing of single-cell transcriptomes, no streamlined pipelines are available for the comprehensive characterization of T cell receptors. Here we propose Scirpy, a scalable Python toolkit that provides simplified access to the analysis and visualization of immune repertoires from single cells and seamless integration with transcriptomic data.Availability and implementationScirpy source code and documentation are available at https://github.com/icbi-lab/scirpy.

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Gene set inference from single-cell sequencing data using a hybrid of matrix factorization and variational autoencoders

10.1101/740415 ◽

2019 ◽

Author(s):

Soeren Lukassen ◽

Foo Wei Ten ◽

Roland Eils ◽

Christian Conrad

Keyword(s):

Neural Network ◽

Single Cell ◽

Network Model ◽

Neural Network Model ◽

Matrix Factorization ◽

Latent Variable ◽

Single Cells ◽

Sequencing Data ◽

Gene Set ◽

Gene Sets

AbstractRecent advances in single-cell RNA sequencing (scRNA-Seq) have driven the simultaneous measurement of the expression of 1,000s of genes in 1,000s of single cells. These growing data sets allow us to model gene sets in biological networks at an unprecedented level of detail, in spite of heterogenous cell populations. Here, we propose an unsupervised deep neural network model that is a hybrid of matrix factorization and conditional variational autoencoders (CVA), which utilizes weights as matrix factorizations to obtain gene sets, while class-specific inputs to the latent variable space facilitate a plausible identification of cell types. This artificial neural network model seamlessly integrates functional gene set inference, experimental batch effect correction, and static gene identification, which we conceptually prove here for three single-cell RNA-Seq datasets and suggest for future single-cell-gene analytics.

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Exploring cell-specific miRNA regulation with single-cell miRNA-mRNA co-sequencing data

10.1101/2020.10.14.340299 ◽

2020 ◽

Author(s):

Junpeng Zhang ◽

Lin Liu ◽

Taosheng Xu ◽

Wu Zhang ◽

Chunwen Zhao ◽

...

Keyword(s):

Single Cell ◽

Regulatory Networks ◽

Single Cells ◽

Small Scale ◽

Mirna Regulation ◽

Sequencing Data ◽

Resolution Level ◽

Novel Strategy ◽

Cell Cell

AbstractBackgroundExisting computational methods for studying miRNA regulation are mostly based on bulk miRNA and mRNA expression data. However, bulk data only allows the analysis of miRNA regulation regarding a group of cells, rather than the miRNA regulation unique to individual cells. Recent advance in single-cell miRNA-mRNA co-sequencing technology has opened a way for investigating miRNA regulation at single-cell level. However, as currently single-cell miRNA-mRNA co-sequencing data is just emerging and only available at small-scale, there is a strong need of novel methods to exploit existing single-cell data for the study of cell-specific miRNA regulation.ResultsIn this work, we propose a new method, CSmiR (Cell-Specific miRNA regulation) to use single-cell miRNA-mRNA co-sequencing data to identify miRNA regulatory networks at the resolution of individual cells. We apply CSmiR to the miRNA-mRNA co-sequencing data in 19 K562 single-cells to identify cell-specific miRNA-mRNA regulatory networks to understand miRNA regulation in each K562 single-cell. By analyzing the obtained cell-specific miRNA-mRNA regulatory networks, we observe that the miRNA regulation in each K562 single-cell is unique. Moreover, we conduct detailed analysis on the cell-specific miRNA regulation associated with the miR-17/92 family as a case study. Finally, through exploring cell-cell similarity matrix characterized by cell-specific miRNA regulation, CSmiR provides a novel strategy for clustering single-cells to help understand cell-cell crosstalk.ConclusionsTo the best of our knowledge, CSmiR is the first method to explore miRNA regulation at a single-cell resolution level, and we believe that it can be a useful method to enhance the understanding of cell-specific miRNA regulation.

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MQuad enables clonal substructure discovery using single cell mitochondrial variants

10.1101/2021.03.27.437331 ◽

2021 ◽

Author(s):

Aaron Wing Cheung Kwok ◽

Chen Qiao ◽

Rongting Huang ◽

Mai-Har Sham ◽

Joshua W. K. Ho ◽

...

Keyword(s):

Dna Sequencing ◽

Single Cell ◽

Single Cells ◽

High Sensitivity ◽

Copy Number Variations ◽

Sequencing Data ◽

Single Nucleotide ◽

Single Cell Sequencing ◽

Mtdna Variants ◽

Python Package

AbstractMitochondrial mutations are increasingly recognised as informative endogenous genetic markers that can be used to reconstruct cellular clonal structure using single-cell RNA or DNA sequencing data. However, there is a lack of effective computational methods to identify informative mtDNA variants in noisy and sparse single-cell sequencing data. Here we present an open source computational tool MQuad that accurately calls clonally informative mtDNA variants in a population of single cells, and an analysis suite for complete clonality inference, based on single cell RNA or DNA sequencing data. Through a variety of simulated and experimental single cell sequencing data, we showed that MQuad can identify mitochondrial variants with both high sensitivity and specificity, outperforming existing methods by a large extent. Furthermore, we demonstrated its wide applicability in different single cell sequencing protocols, particularly in complementing single-nucleotide and copy-number variations to extract finer clonal resolution. MQuad is a Python package available via https://github.com/single-cell-genetics/MQuad.

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Cellsnp-lite: an efficient tool for genotyping single cells

10.1101/2020.12.31.424913 ◽

2021 ◽

Author(s):

Xianjie Huang ◽

Yuanhua Huang

Keyword(s):

Single Cell ◽

Single Cells ◽

Basic Research ◽

Substantial Improvement ◽

Data Sets ◽

Sequencing Data ◽

Single Cell Sequencing ◽

Memory Efficiency ◽

Computational Speed ◽

Cell Data

AbstractSummarySingle-cell sequencing is an increasingly used technology and has promising applications in basic research and clinical translations. However, genotyping methods developed for bulk sequencing data have not been well adapted for single-cell data, in terms of both computational parallelization and simplified user interface. Here we introduce a software, cellsnp-lite, implemented in C/C++ and based on well supported package htslib, for genotyping in single-cell sequencing data for both droplet and well based platforms. On various experimental data sets, it shows substantial improvement in computational speed and memory efficiency with retaining highly concordant results compared to existing methods. Cellsnp-lite therefore lightens the genetic analysis for increasingly large single-cell data.AvailabilityThe source code is freely available at https://github.com/single-cell-genetics/[email protected]

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