scholarly journals Simultaneous multiplexed amplicon sequencing and transcriptome profiling in single cells

2018 ◽  
Author(s):  
Mridusmita Saikia ◽  
Philip Burnham ◽  
Sara H. Keshavjee ◽  
Michael F. Z. Wang ◽  
Michael Heyang ◽  
...  
Keyword(s):  
Infected Cell ◽  
Single Cell ◽  
B Lymphocytes ◽  
Light Chain ◽  
Single Cells ◽  
Variable Region ◽  
Transcriptome Profiling ◽  
Amplicon Sequencing ◽  
Single Cell Sequencing ◽  
Rna Targeting

AbstractWe describe Droplet Assisted RNA Targeting by single cell sequencing (DART-seq), a versatile technology that enables multiplexed amplicon sequencing and transcriptome profiling in single cells. We applied DART-seq to simultaneously characterize the non-A-tailed transcripts of a segmented dsRNA virus and the transcriptome of the infected cell. In addition, we used DART-seq to simultaneously determine the natively paired, variable region heavy and light chain amplicons and the transcriptome of B lymphocytes.

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.

scholarly journals The Development of an Effective Bacterial Single-Cell Lysis Method Suitable for Whole Genome Amplification in Microfluidic Platforms

Micromachines ◽  
2018 ◽  
Vol 9 (8) ◽  
pp. 367 ◽  
Author(s):  
Yuguang Liu ◽  
Dirk Schulze-Makuch ◽  
Jean-Pierre de Vera ◽  
Charles Cockell ◽  
Thomas Leya ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Bacterial Species ◽  
Cell Manipulation ◽  
Microfluidic Platforms ◽  
Gram Positive ◽  
Gram Negative ◽  
Genome Amplification ◽  
Single Cell Sequencing ◽  
Wide Range

Single-cell sequencing is a powerful technology that provides the capability of analyzing a single cell within a population. This technology is mostly coupled with microfluidic systems for controlled cell manipulation and precise fluid handling to shed light on the genomes of a wide range of cells. So far, single-cell sequencing has been focused mostly on human cells due to the ease of lysing the cells for genome amplification. The major challenges that bacterial species pose to genome amplification from single cells include the rigid bacterial cell walls and the need for an effective lysis protocol compatible with microfluidic platforms. In this work, we present a lysis protocol that can be used to extract genomic DNA from both gram-positive and gram-negative species without interfering with the amplification chemistry. Corynebacterium glutamicum was chosen as a typical gram-positive model and Nostoc sp. as a gram-negative model due to major challenges reported in previous studies. Our protocol is based on thermal and chemical lysis. We consider 80% of single-cell replicates that lead to >5 ng DNA after amplification as successful attempts. The protocol was directly applied to Gloeocapsa sp. and the single cells of the eukaryotic Sphaerocystis sp. and achieved a 100% success rate.

scholarly journals P02.10 FocuSCOPE: a single cell, multi-omics solution to simultaneously analyze tumor variants and microenvironment

2021 ◽  
Vol 9 (Suppl 1) ◽  
pp. A12.1-A12
Author(s):  
Y Arjmand Abbassi ◽  
N Fang ◽  
W Zhu ◽  
Y Zhou ◽  
Y Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Tumor Microenvironment ◽  
Single Cell ◽  
High Throughput ◽  
Immune Cells ◽  
Genetic Variants ◽  
Expression Profiles ◽  
Single Cells ◽  
Gene Expression Profiles ◽  
Single Cell Sequencing

Recent advances of high-throughput single cell sequencing technologies have greatly improved our understanding of the complex biological systems. Heterogeneous samples such as tumor tissues commonly harbor cancer cell-specific genetic variants and gene expression profiles, both of which have been shown to be related to the mechanisms of disease development, progression, and responses to treatment. Furthermore, stromal and immune cells within tumor microenvironment interact with cancer cells to play important roles in tumor responses to systematic therapy such as immunotherapy or cell therapy. However, most current high-throughput single cell sequencing methods detect only gene expression levels or epigenetics events such as chromatin conformation. The information on important genetic variants including mutation or fusion is not captured. To better understand the mechanisms of tumor responses to systematic therapy, it is essential to decipher the connection between genotype and gene expression patterns of both tumor cells and cells in the tumor microenvironment. We developed FocuSCOPE, a high-throughput multi-omics sequencing solution that can detect both genetic variants and transcriptome from same single cells. FocuSCOPE has been used to successfully perform single cell analysis of both gene expression profiles and point mutations, fusion genes, or intracellular viral sequences from thousands of cells simultaneously, delivering comprehensive insights of tumor and immune cells in tumor microenvironment at single cell resolution.Disclosure InformationY. Arjmand Abbassi: None. N. Fang: None. W. Zhu: None. Y. Zhou: None. Y. Chen: None. U. Deutsch: None.

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 MQuad enables clonal substructure discovery using single cell mitochondrial variants

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.

scholarly journals Longitudinal cancer evolution from single cells

2020 ◽  
Author(s):  
Daniele Ramazzotti ◽  
Fabrizio Angaroni ◽  
Davide Maspero ◽  
Gianluca Ascolani ◽  
Isabella Castiglioni ◽  
...  
Keyword(s):  
Single Cell ◽  
Tumor Heterogeneity ◽  
Single Cells ◽  
Clonal Evolution ◽  
Multiple Time ◽  
Cancer Evolution ◽  
Weighted Likelihood ◽  
Time Points ◽  
Single Cell Sequencing ◽  
The Impact

ABSTRACTThe rise of longitudinal single-cell sequencing experiments on patient-derived cell cultures, xenografts and organoids is opening new opportunities to track cancer evolution in single tumors and to investigate intra-tumor heterogeneity. This is particularly relevant when assessing the efficacy of therapies over time on the clonal composition of a tumor and in the identification of resistant subclones.We here introduce LACE (Longitudinal Analysis of Cancer Evolution), the first algorithmic framework that processes single-cell somatic mutation profiles from cancer samples collected at different time points and in distinct experimental settings, to produce longitudinal models of cancer evolution. Our approach solves a Boolean matrix factorization problem with phylogenetic constraints, by maximizing a weighted likelihood function computed on multiple time points, and we show with simulations that it outperforms state-of-the-art methods for both bulk and single-cell sequencing data.Remarkably, as the results are robust with respect to high levels of data-specific errors, LACE can be employed to process single-cell mutational profiles as generated by calling variants from the increasingly available scRNA-seq data, thus obviating the need of relying on rarer and more expensive genome sequencing experiments. This also allows to investigate the relation between genomic clonal evolution and phenotype at the single-cell level.To illustrate the capabilities of LACE, we show its application to a longitudinal scRNA-seq dataset of patient-derived xenografts of BRAFV600E/K mutant melanomas, in which we characterize the impact of concurrent BRAF/MEK-inhibition on clonal evolution, also by showing that distinct genetic clones reveal different sensitivity to the therapy. Furthermore, the analysis of a longitudinal dataset of breast cancer PDXs from targeted scDNA-sequencing experiments delivers a high-resolution characterization of intra-tumor heterogeneity, also allowing the detection of a late de novo subclone.

scholarly journals DART-seq

2019 ◽  
Author(s):  
Iwijn De Vlaminck ◽  
Mridusmita Saikia ◽  
Philip Burnham ◽  
Sara H. Keshavjee ◽  
Michael F. Z. Wang ◽  
...  
Keyword(s):  
Single Cell ◽  
Human Peripheral Blood ◽  
Single Cells ◽  
Amplicon Sequencing ◽  
Droplet Microfluidics ◽  
Cell Repertoire ◽  
Rna Sequences ◽  
Peripheral Blood Mononuclear ◽  
T4 Dna Ligase ◽  
B Cell Repertoire

Abstract Here we describe the step-by-step protocol of Droplet Assisted RNA Targeting by single cell sequencing \(DART-seq). DART-seq allows simultaneous multiplexed amplicon sequencing and transcriptome analysis in single cells. Using a simple and quick modification of commercially available barcoded beads we have expanded the application of droplet microfluidics based high throughput single cell RNA sequencing technology to include non-polyadenylated RNA transcripts. Specific probes targeting RNA of interest were attached to the beads using a T4 DNA Ligase based enzymatic reaction.The modified DART-seq beads were then used in standard Drop-seq platform to generate single cell libraries for sequencing. We have applied DART-seq to analyze reovirus RNA sequences in infected murine L cells. As a second application we used DART-seq to investigate the B cell repertoire in human peripheral blood mononuclear cell \(PBMC) samples.

scholarly journals Cellsnp-lite: an efficient tool for genotyping single cells

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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