scholarly journals Single-cell sequencing techniques from individual to multiomics analyses

2020 ◽  
Vol 52 (9) ◽  
pp. 1419-1427
Author(s):  
Yukie Kashima ◽  
Yoshitaka Sakamoto ◽  
Keiya Kaneko ◽  
Masahide Seki ◽  
Yutaka Suzuki ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Experimental Methods ◽  
Chromatin Accessibility ◽  
Data Sets ◽  
Transcriptome Data ◽  
Detailed Understanding ◽  
Single Cell Sequencing ◽  
Single Cell Rna Sequencing ◽  
Molecular Profiles

Abstract Here, we review single-cell sequencing techniques for individual and multiomics profiling in single cells. We mainly describe single-cell genomic, epigenomic, and transcriptomic methods, and examples of their applications. For the integration of multilayered data sets, such as the transcriptome data derived from single-cell RNA sequencing and chromatin accessibility data derived from single-cell ATAC-seq, there are several computational integration methods. We also describe single-cell experimental methods for the simultaneous measurement of two or more omics layers. We can achieve a detailed understanding of the basic molecular profiles and those associated with disease in each cell by utilizing a large number of single-cell sequencing techniques and the accumulated data sets.

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]

Endothelial reprogramming by disturbed flow revealed by single-cell RNA and chromatin accessibility study

2020 ◽  
Author(s):  
Aitor Andueza ◽  
Sandeep Kumar ◽  
Juyoung Kim ◽  
Dong-Won Kang ◽  
Hope L Mumme ◽  
...  
Keyword(s):  
Single Cell ◽  
Immune Cell ◽  
Single Cells ◽  
Chromatin Accessibility ◽  
Marker Genes ◽  
Hematopoietic Stem ◽  
Disturbed Flow ◽  
Single Cell Rna Sequencing ◽  
Left And Right ◽  
Stable Flow

SUMMARYDisturbed flow (d-flow) induces atherosclerosis by regulating gene expression in endothelial cells (ECs). For further mechanistic understanding, we carried out a single-cell RNA sequencing (scRNAseq) and scATACseq study using endothelial-enriched single-cells from the left- and right carotid artery exposed to d-flow (LCA) and stable-flow (s-flow in RCA) using the mouse partial carotid ligation (PCL) model. We found 8 EC clusters along with immune cells, fibroblasts, and smooth muscle cells. Analyses of marker genes, pathways, and pseudo-time revealed that ECs are highly heterogeneous and plastic. D-flow induced a dramatic transition of ECs from atheroprotective phenotypes to pro-inflammatory, mesenchymal (EndMT), hematopoietic stem cells, endothelial stem/progenitor cells, and an unexpected immune cell-like (EndICLT) phenotypes. While confirming KLF4/KLF2 as s-flow-sensitive transcription factor binding site, we also found those sensitive to d-flow (RELA, AP1, STAT1, and TEAD1). D-flow reprograms ECs from atheroprotective to pro-atherogenic phenotypes including EndMT and potentially EndICLT.

scholarly journals Simultaneous dimensionality reduction and integration for single-cell ATAC-seq data using deep learning

2021 ◽  
Author(s):  
Wolfgang Kopp ◽  
Altuna Akalin ◽  
Uwe Ohler
Keyword(s):  
Dimensionality Reduction ◽  
Single Cell ◽  
Learning Strategy ◽  
Single Cells ◽  
Simulated Data ◽  
Chromatin Accessibility ◽  
Noise Model ◽  
Data Sets ◽  
Batch Correction ◽  
Regulatory Processes

Advances in single-cell technologies enable the routine interrogation of chromatin accessibility for tens of thousands of single cells, shedding light on gene regulatory processes at an unprecedented resolution. Meanwhile, size, sparsity and high dimensionality of the resulting data continue to pose challenges for its computational analysis, and specifically the integration of data from different sources. We have developed a dedicated computational approach, a variational auto-encoder using a noise model specifically designed for single-cell ATAC-seq data, which facilitates simultaneous dimensionality reduction and batch correction via an adversarial learning strategy. We showcase both its individual advantages on carefully chosen real and simulated data sets, as well as the benefits for detailed cell type characterization via integrating multiple complex datasets.

scholarly journals Linking transcriptome and chromatin accessibility in nanoliter droplets for single-cell sequencing

2019 ◽  
Author(s):  
Song Chen ◽  
Blue B Lake ◽  
Kun Zhang
Keyword(s):  
Mrna Expression ◽  
Single Cell ◽  
Adult Mouse ◽  
Single Cells ◽  
Cell Types ◽  
Chromatin Accessibility ◽  
Single Cell Sequencing ◽  
Single Nucleus

Linked profiling of transcriptome and chromatin accessibility from single cells can provide unprecedented insights into cellular status. Here we developed a droplet-based Single-Nucleus chromatin Accessibility and mRNA Expression sequencing (SNARE-seq) assay, that we used to profile neonatal and adult mouse cerebral cortices. To demonstrate the strength of single-cell dual-omics profiling, we reconstructed transcriptome and epigenetic landscapes of cell types, uncovered lineage-specific accessible sites, and connected dynamics of promoter accessibility with transcription during neurogenesis.

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 Adapted single-cell consensus clustering (adaSC3)

2020 ◽  
Author(s):  
Cornelia Fuetterer ◽  
Thomas Augustin ◽  
Christiane Fuchs
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Single Cells ◽  
Linear Method ◽  
Principal Component ◽  
Data Sets ◽  
Consensus Clustering ◽  
Sequencing Data ◽  
Reduction Techniques ◽  
Single Cell Rna Sequencing

AbstractThe analysis of single-cell RNA sequencing data is of great importance in health research. It challenges data scientists, but has enormous potential in the context of personalized medicine. The clustering of single cells aims to detect different subgroups of cell populations within a patient in a data-driven manner. Some comparison studies denote single-cell consensus clustering (SC3), proposed by Kiselev et al. (Nat Methods 14(5):483–486, 2017), as the best method for classifying single-cell RNA sequencing data. SC3 includes Laplacian eigenmaps and a principal component analysis (PCA). Our proposal of unsupervised adapted single-cell consensus clustering (adaSC3) suggests to replace the linear PCA by diffusion maps, a non-linear method that takes the transition of single cells into account. We investigate the performance of adaSC3 in terms of accuracy on the data sets of the original source of SC3 as well as in a simulation study. A comparison of adaSC3 with SC3 as well as with related algorithms based on further alternative dimension reduction techniques shows a quite convincing behavior of adaSC3.

scholarly journals Molecular spikes: a gold standard for single-cell RNA counting

2021 ◽  
Author(s):  
Christoph Ziegenhain ◽  
Gert-Jan Hendriks ◽  
Michael Hagemann-Jensen ◽  
Rickard Sandberg
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Gold Standard ◽  
Single Cells ◽  
Single Cell Sequencing ◽  
Experimental Strategy ◽  
Single Cell Rna Sequencing

Molecule counting is central to single-cell sequencing, yet no experimental strategy to evaluate counting performance exists. Here, we introduce molecular spikes, novel RNA spike-ins containing inbuilt unique molecular identifiers that we use to identify critical experimental and computational conditions for accurate RNA counting across single-cell RNA-sequencing methods. The molecular spikes are a new gold standard that can be widely used to validate RNA counting in single cells.

scholarly journals Manifold alignment reveals correspondence between single cell transcriptome and epigenome dynamics

2017 ◽  
Author(s):  
Joshua D. Welch ◽  
Alexander J. Hartemink ◽  
Jan F. Prins
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Epigenetic Modification ◽  
Single Cells ◽  
Embryonic Stem ◽  
Genomic Data ◽  
Chromatin Accessibility ◽  
Data Sets ◽  
Cell Transcriptome ◽  
Dynamic Interplay

AbstractSingle cell genomic techniques promise to yield key insights into the dynamic interplay between gene expression and epigenetic modification. However, the experimental difficulty of performing multiple measurements on the same cell currently limits efforts to combine multiple genomic data sets into a united picture of single cell variation. We show that it is possible to construct cell trajectories, reflecting the changes that occur in a sequential biological process, from single cell ATAC-seq, bisulfite sequencing, and ChIP-seq data. In addition, we present an approach called MATCHER that computationally circumvents the experimental difficulties inherent in performing multiple genomic measurements on a single cell by inferring correspondence between single cell transcriptomic and epigenetic measurements performed on different cells of the same type. MATCHER works by first learning a separate manifold for the trajectory of each kind of genomic data, then aligning the manifolds to infer a shared trajectory in which cells measured using different techniques are directly comparable. Using scM&T-seq data, we confirm that MATCHER accurately predicts true single cell correlations between DNA methylation and gene expression without using known cell correspondence information. We also used MATCHER to infer correlations among gene expression, chromatin accessibility, and histone modifications in single mouse embryonic stem cells. These results reveal the dynamic interplay between epigenetic changes and gene expression underlying the transition from pluripotency to differentiation priming. Our work is a first step toward a united picture of heterogeneous transcriptomic and epigenetic states in single cells.

scholarly journals Efficient synergistic single-cell genome assembly

2014 ◽  
Author(s):  
Narjes S. Movahedi ◽  
Zeinab Taghavi ◽  
Mallory Embree ◽  
Harish Nagarajan ◽  
Karsten Zengler ◽  
...  
Keyword(s):  
Single Cell ◽  
De Novo ◽  
Single Cells ◽  
Data Sets ◽  
Protein Coding ◽  
Single Cell Sequencing ◽  
Assembly Method ◽  
Sequencing Experiment ◽  
Cell Genome ◽  
Lower Depth

As the vast majority of all microbes are unculturable, single-cell sequencing has become a significant method to gain insight into microbial physiology. Single-cell sequencing methods, currently powered by multiple displacement genome amplification (MDA), have passed important milestones such as finishing and closing the genome of a prokaryote. However, the quality and reliability of genome assemblies from single cells are still unsatisfactory due to uneven coverage depth and the absence of scattered chunks of the genome in the final collection of reads caused by MDA bias. In this work, our new algorithm Hybrid De novo Assembler (HyDA) demonstrates the power of co-assembly of multiple single-cell genomic data sets through significant improvement of the assembly quality in terms of predicted functional elements and length statistics. Co-assemblies contain significantly more base pairs and protein coding genes, cover more subsystems, and consist of longer contigs compared to individual assemblies by the same algorithm as well as state-of-the-art single-cell assemblers SPAdes and IDBA-UD. Hybrid De novo Assembler (HyDA) is also able to avoid chimeric assemblies by detecting and separating shared and exclusive pieces of sequence for input data sets. By replacing one deep single-cell sequencing experiment with a few single-cell sequencing experiments of lower depth, the co-assembly method can hedge against the risk of failure and loss of the sample, without significantly increasing sequencing cost. Application of the single-cell co-assembler HyDA to the study of three uncultured members of an alkane-degrading methanogenic community validated the usefulness of the co-assembly concept.

scholarly journals ChromVAR: Inferring transcription factor variation from single-cell epigenomic data

2017 ◽  
Author(s):  
Alicia N. Schep ◽  
Beijing Wu ◽  
Jason D. Buenrostro ◽  
William J. Greenleaf
Keyword(s):  
Transcription Factor ◽  
Single Cell ◽  
De Novo ◽  
Single Cells ◽  
R Package ◽  
Chromatin Accessibility ◽  
Data Sets ◽  
Sequence Motifs ◽  
Computational Approaches

AbstractSingle cell ATAC-seq (scATAC) yields sparse data that makes application of conventional computational approaches for data analysis challenging or impossible. We developed chromVAR, an R package for analyzing sparse chromatin accessibility data by estimating the gain or loss of accessibility within sets of peaks sharing the same motif or annotation while controlling for known technical biases. chromVAR enables accurate clustering of scATAC-seq profiles and enables characterization of known, or the de novo identification of novel, sequence motifs associated with variation in chromatin accessibility across single cells or other sparse epigenomic data sets.

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