Linked-read analysis identifies mutations in single cell DNA sequencing data

AbstractWhole-genome sequencing of DNA from single cells has the potential to reshape our understanding of the mutational heterogeneity in normal and disease tissues. A major difficulty, however, is distinguishing artifactual mutations that arise from DNA isolation and amplification from true mutations. Here, we describe linked-read analysis (LiRA), a method that utilizes phasing of somatic single nucleotide variants with nearby germline variants to identify true mutations, thereby allowing accurate estimation of somatic mutation rates at the single cell level.

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Accurate and scalable variant calling from single cell DNA sequencing data with ProSolo

Nature Communications ◽

10.1038/s41467-021-26938-w ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

David Lähnemann ◽

Johannes Köster ◽

Ute Fischer ◽

Arndt Borkhardt ◽

Alice C. McHardy ◽

...

Keyword(s):

Dna Sequencing ◽

Single Cell ◽

Single Cells ◽

Variant Calling ◽

Sequencing Data ◽

Computationally Efficient ◽

Single Nucleotide Variants ◽

Efficient Manner ◽

Single Nucleotide ◽

Amplification Bias

AbstractAccurate single cell mutational profiles can reveal genomic cell-to-cell heterogeneity. However, sequencing libraries suitable for genotyping require whole genome amplification, which introduces allelic bias and copy errors. The resulting data violates assumptions of variant callers developed for bulk sequencing. Thus, only dedicated models accounting for amplification bias and errors can provide accurate calls. We present ProSolo for calling single nucleotide variants from multiple displacement amplified (MDA) single cell DNA sequencing data. ProSolo probabilistically models a single cell jointly with a bulk sequencing sample and integrates all relevant MDA biases in a site-specific and scalable—because computationally efficient—manner. This achieves a higher accuracy in calling and genotyping single nucleotide variants in single cells in comparison to state-of-the-art tools and supports imputation of insufficiently covered genotypes, when downstream tools cannot handle missing data. Moreover, ProSolo implements the first approach to control the false discovery rate reliably and flexibly. ProSolo is implemented in an extendable framework, with code and usage at: https://github.com/prosolo/prosolo

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ProSolo: Accurate Variant Calling from Single Cell DNA Sequencing Data

10.1101/2020.04.27.064071 ◽

2020 ◽

Author(s):

David Lähnemann ◽

Johannes Köster ◽

Ute Fischer ◽

Arndt Borkhardt ◽

Alice C. McHardy ◽

...

Keyword(s):

Dna Sequencing ◽

Single Cell ◽

Single Cells ◽

Variant Calling ◽

Sequencing Data ◽

Single Nucleotide Variants ◽

Single Nucleotide ◽

Biologically Relevant ◽

Amplification Bias ◽

Missing Genotypes

ABSTRACTObtaining accurate mutational profiles from single cell DNA is essential for the analysis of genomic cell-to-cell heterogeneity at the finest level of resolution. However, sequencing libraries suitable for genotyping require whole genome amplification, which introduces allelic bias and copy errors. As a result, single cell DNA sequencing data violates the assumptions of variant callers developed for bulk sequencing, which when applied to single cells generate significant numbers of false positives and false negatives. Only dedicated models accounting for amplification bias and errors will be able to provide more accurate calls.We present ProSolo, a probabilistic model for calling single nucleotide variants from multiple displacement amplified single cell DNA sequencing data. It introduces a mechanistically motivated empirical model of amplification bias that improves the quantification of genotyping uncertainty. To account for amplification errors, it jointly models the single cell sample with a bulk sequencing sample from the same cell population—also enabling a biologically relevant imputation of missing genotypes for the single cell. Through these innovations, ProSolo achieves substantially higher performance in calling and genotyping single nucleotide variants in single cells in comparison to all state-of-the-art tools. Moreover, ProSolo implements the first approach to control the false discovery rate reliably and flexibly; not only for single nucleotide variant calls, but also for artefacts of single cell methodology that one may wish to identify, such as allele dropout.ProSolo’s model is implemented into a flexible framework, encouraging extensions. The source code and usage instructions are available at: https://github.com/prosolo/prosolo

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Critical downstream analysis steps for single-cell RNA sequencing data

Briefings in Bioinformatics ◽

10.1093/bib/bbab105 ◽

2021 ◽

Author(s):

Zilong Zhang ◽

Feifei Cui ◽

Chen Lin ◽

Lingling Zhao ◽

Chunyu Wang ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Noisy Data ◽

Single Cell Level ◽

Cell Type ◽

Sequencing Data ◽

Cell Level ◽

Bioinformatics Tool ◽

Single Cell Rna Sequencing ◽

Downstream Analysis

Abstract Single-cell RNA sequencing (scRNA-seq) has enabled us to study biological questions at the single-cell level. Currently, many analysis tools are available to better utilize these relatively noisy data. In this review, we summarize the most widely used methods for critical downstream analysis steps (i.e. clustering, trajectory inference, cell-type annotation and integrating datasets). The advantages and limitations are comprehensively discussed, and we provide suggestions for choosing proper methods in different situations. We hope this paper will be useful for scRNA-seq data analysts and bioinformatics tool developers.

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Microfluidic Platforms Designed for Morphological and Photosynthetic Investigations of Chlamydomonas reinhardtii on a Single-Cell Level

Cells ◽

10.3390/cells11020285 ◽

2022 ◽

Vol 11 (2) ◽

pp. 285

Author(s):

Eszter Széles ◽

Krisztina Nagy ◽

Ágnes Ábrahám ◽

Sándor Kovács ◽

Anna Podmaniczki ◽

...

Keyword(s):

Chlamydomonas Reinhardtii ◽

Single Cell ◽

Single Cells ◽

Fluorescence Induction ◽

Photochemical Quenching ◽

Support Surface ◽

Single Cell Level ◽

Microfluidic Platforms ◽

Cell Level ◽

Non Photochemical Quenching

Chlamydomonas reinhardtii is a model organism of increasing biotechnological importance, yet, the evaluation of its life cycle processes and photosynthesis on a single-cell level is largely unresolved. To facilitate the study of the relationship between morphology and photochemistry, we established microfluidics in combination with chlorophyll a fluorescence induction measurements. We developed two types of microfluidic platforms for single-cell investigations: (i) The traps of the “Tulip” device are suitable for capturing and immobilizing single cells, enabling the assessment of their photosynthesis for several hours without binding to a solid support surface. Using this “Tulip” platform, we performed high-quality non-photochemical quenching measurements and confirmed our earlier results on bulk cultures that non-photochemical quenching is higher in ascorbate-deficient mutants (Crvtc2-1) than in the wild-type. (ii) The traps of the “Pot” device were designed for capturing single cells and allowing the growth of the daughter cells within the traps. Using our most performant “Pot” device, we could demonstrate that the FV/FM parameter, an indicator of photosynthetic efficiency, varies considerably during the cell cycle. Our microfluidic devices, therefore, represent versatile platforms for the simultaneous morphological and photosynthetic investigations of C. reinhardtii on a single-cell level.

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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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Response of Listeria monocytogenes to Disinfection Stress at the Single-Cell and Population Levels as Monitored by Intracellular pH Measurements and Viable-Cell Counts

Applied and Environmental Microbiology ◽

10.1128/aem.02625-08 ◽

2009 ◽

Vol 75 (13) ◽

pp. 4550-4556 ◽

Cited By ~ 27

Author(s):

Vicky G. Kastbjerg ◽

Dennis S. Nielsen ◽

Nils Arneborg ◽

Lone Gram

Keyword(s):

Listeria Monocytogenes ◽

Single Cell ◽

Intracellular Ph ◽

Bacterial Population ◽

Single Cells ◽

Viable Cell ◽

Population Subdivision ◽

Single Cell Level ◽

Cell Level ◽

Cell Counts

ABSTRACT Listeria monocytogenes has a remarkable ability to survive and persist in food production environments. The purpose of the present study was to determine if cells in a population of L. monocytogenes differ in sensitivity to disinfection agents as this could be a factor explaining persistence of the bacterium. In situ analyses of Listeria monocytogenes single cells were performed during exposure to different concentrations of the disinfectant Incimaxx DES to study a possible population subdivision. Bacterial survival was quantified with plate counting and disinfection stress at the single-cell level by measuring intracellular pH (pHi) over time by fluorescence ratio imaging microscopy. pHi values were initially 7 to 7.5 and decreased in both attached and planktonic L. monocytogenes cells during exposure to sublethal and lethal concentrations of Incimaxx DES. The response of the bacterial population was homogenous; hence, subpopulations were not detected. However, pregrowth with NaCl protected the planktonic bacterial cells during disinfection with Incimaxx (0.0015%) since pHi was higher (6 to 6.5) for the bacterial population pregrown with NaCl than for cells grown without NaCl (pHi 5 to 5.5) (P < 0.05). The protective effect of NaCl was reflected by viable-cell counts at a higher concentration of Incimaxx (0.0031%), where the salt-grown population survived better than the population grown without NaCl (P < 0.05). NaCl protected attached cells through drying but not during disinfection. This study indicates that a population of L. monocytogenes cells, whether planktonic or attached, is homogenous with respect to sensitivity to an acidic disinfectant studied on the single-cell level. Hence a major subpopulation more tolerant to disinfectants, and hence more persistent, does not appear to be present.

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Combined Molecular Genetic and Cytogenetic Analysis from Single Cells after Isothermal Whole-Genome Amplification

Clinical Chemistry ◽

10.1373/clinchem.2011.162131 ◽

2011 ◽

Vol 57 (7) ◽

pp. 1032-1041 ◽

Cited By ~ 28

Author(s):

Thomas Kroneis ◽

Jochen B Geigl ◽

Amin El-Heliebi ◽

Martina Auer ◽

Peter Ulz ◽

...

Keyword(s):

Single Cell ◽

Tumor Cells ◽

Whole Genome Amplification ◽

Single Cells ◽

Molecular Genetic ◽

Target Cells ◽

Whole Genome ◽

Single Cell Level ◽

Cell Level ◽

Genome Amplification

BACKGROUND Analysis of chromosomal aberrations or single-gene disorders from rare fetal cells circulating in the blood of pregnant women requires verification of the cells' genomic identity. We have developed a method enabling multiple analyses at the single-cell level that combines verification of the genomic identity of microchimeric cells with molecular genetic and cytogenetic diagnosis. METHODS We used a model system of peripheral blood mononuclear cells spiked with a colon adenocarcinoma cell line and immunofluorescence staining for cytokeratin in combination with DNA staining with the nuclear dye TO-PRO-3 in a preliminary study to define candidate microchimeric (tumor) cells in Cytospin preparations. After laser microdissection, we performed low-volume on-chip isothermal whole-genome amplification (iWGA) of single and pooled cells. RESULTS DNA fingerprint analysis of iWGA aliquots permitted successful identification of all analyzed candidate microchimeric cell preparations (6 samples of pooled cells, 7 samples of single cells). Sequencing of 3 single-nucleotide polymorphisms was successful at the single-cell level for 20 of 32 allelic loci. Metaphase comparative genomic hybridization (mCGH) with iWGA products of single cells showed the gains and losses known to be present in the genomic DNA of the target cells. CONCLUSIONS This method may be instrumental in cell-based noninvasive prenatal diagnosis. Furthermore, the possibility to perform mCGH with amplified DNA from single cells offers a perspective for the analysis of nonmicrochimeric rare cells exhibiting genomic alterations, such as circulating tumor cells.

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VASC: dimension reduction and visualization of single cell RNA sequencing data by deep variational autoencoder

10.1101/199315 ◽

2017 ◽

Cited By ~ 6

Author(s):

Dongfang Wang ◽

Jin Gu

Keyword(s):

Dimension Reduction ◽

Single Cell ◽

Rna Sequencing ◽

Original Data ◽

Marker Genes ◽

Single Cell Level ◽

Sequencing Data ◽

Cell Level ◽

Variational Autoencoder ◽

Single Cell Rna Sequencing

AbstractSingle cell RNA sequencing (scRNA-seq) is a powerful technique to analyze the transcriptomic heterogeneities in single cell level. It is an important step for studying cell sub-populations and lineages based on scRNA-seq data by finding an effective low-dimensional representation and visualization of the original data. The scRNA-seq data are much noiser than traditional bulk RNA-Seq: in the single cell level, the transcriptional fluctuations are much larger than the average of a cell population and the low amount of RNA transcripts will increase the rate of technical dropout events. In this study, we proposed VASC (deep Variational Autoencoder for scRNA-seq data), a deep multi-layer generative model, for the unsupervised dimension reduction and visualization of scRNA-seq data. It can explicitly model the dropout events and find the nonlinear hierarchical feature representations of the original data. Tested on twenty datasets, VASC shows superior performances in most cases and broader dataset compatibility compared with four state-of-the-art dimension reduction methods. Then, for a case study of pre-implantation embryos, VASC successfully re-establishes the cell dynamics and identifies several candidate marker genes associated with the early embryo development.

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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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Generation of genetic tools for gauging multiple gene expression at single cell level

Applied and Environmental Microbiology ◽

10.1128/aem.02956-20 ◽

2021 ◽

Author(s):

Marta Mellini ◽

Massimiliano Lucidi ◽

Francesco Imperi ◽

Paolo Visca ◽

Livia Leoni ◽

...

Keyword(s):

Gene Expression ◽

Image Processing ◽

Single Cell ◽

Fluorescent Protein ◽

Single Cells ◽

Phenotypic Heterogeneity ◽

Single Cell Level ◽

Cell Level ◽

Multiple Gene ◽

Genetic Tools

Key microbial processes in many bacterial species are heterogeneously expressed in single cells of bacterial populations. However, the paucity of adequate molecular tools for live, real-time monitoring of multiple gene expression at the single cell level has limited the understanding of phenotypic heterogeneity. In order to investigate phenotypic heterogeneity in the ubiquitous opportunistic pathogen Pseudomonas aeruginosa, a genetic tool that allows gauging multiple gene expression at the single cell level has been generated. This tool, named pRGC, consists in a promoter-probe vector for transcriptional fusions that carries three reporter genes coding for the fluorescent proteins mCherry, green fluorescent protein (GFP) and cyan fluorescent protein (CFP). The pRGC vector has been characterized and validated via single cell gene expression analysis of both constitutive and iron-regulated promoters, showing clear discrimination of the three fluorescence signals in single cells of a P. aeruginosa population, without the need of image-processing for spectral crosstalk correction. In addition, two pRGC variants have been generated for either i) integration of the reporter gene cassette into a single neutral site of P. aeruginosa chromosome, that is suitable for long-term experiments in the absence of antibiotic selection, or ii) replication in bacterial genera other than Pseudomonas. The easy-to-use genetic tools generated in this study will allow rapid and cost-effective investigation of multiple gene expression in populations of environmental and pathogenic bacteria, hopefully advancing the understanding of microbial phenotypic heterogeneity. IMPORTANCE Within a bacterial population single cells can differently express some genes, even though they are genetically identical and experience the same chemical and physical stimuli. This phenomenon, known as phenotypic heterogeneity, is mainly driven by gene expression noise and results in the emergence of bacterial sub-populations with distinct phenotypes. The analysis of gene expression at the single cell level has shown that phenotypic heterogeneity is associated with key bacterial processes, including competence, sporulation and persistence. In this study, new genetic tools have been generated that allow easy cloning of up to three promoters upstream of distinct fluorescent genes, making it possible to gauge multiple gene expression at the single cell level by fluorescent microscopy, without the need of advanced image-processing procedures. A proof of concept has been provided by investigating iron-uptake and iron-storage gene expression in response to iron availability in P. aeruginosa.

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