scholarly journals Identifying transposable element expression dynamics and heterogeneity during development at the single-cell level with a processing pipeline scTE

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jiangping He ◽  
Isaac A. Babarinde ◽  
Li Sun ◽  
Shuyang Xu ◽  
Ruhai Chen ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Embryonic Stem ◽  
Cell Heterogeneity ◽  
Cell Type ◽  
Cell Level ◽  
Single Cell Sequencing ◽  
Processing Pipeline ◽  
Sequencing Technologies ◽  
Disease Specific

AbstractTransposable elements (TEs) make up a majority of a typical eukaryote’s genome, and contribute to cell heterogeneity in unclear ways. Single-cell sequencing technologies are powerful tools to explore cells, however analysis is typically gene-centric and TE expression has not been addressed. Here, we develop a single-cell TE processing pipeline, scTE, and report the expression of TEs in single cells in a range of biological contexts. Specific TE types are expressed in subpopulations of embryonic stem cells and are dynamically regulated during pluripotency reprogramming, differentiation, and embryogenesis. Unexpectedly, TEs are expressed in somatic cells, including human disease-specific TEs that are undetectable in bulk analyses. Finally, we apply scTE to single-cell ATAC-seq data, and demonstrate that scTE can discriminate cell type using chromatin accessibly of TEs alone. Overall, our results classify the dynamic patterns of TEs in single cells and their contributions to cell heterogeneity.

scholarly journals Unveiling transposable element expression heterogeneity in cell fate regulation at the single-cell level

2020 ◽  
Author(s):  
Jiangping He ◽  
Isaac A. Babarinde ◽  
Li Sun ◽  
Shuyang Xu ◽  
Ruhai Chen ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Single Cells ◽  
Embryonic Stem ◽  
Cell Type ◽  
Cell Level ◽  
Single Cell Sequencing ◽  
Processing Pipeline ◽  
Sequencing Technologies ◽  
Disease Specific

AbstractTransposable elements (TEs) make up a majority of a typical eukaryote’s genome, and contribute to cell heterogeneity and fate in unclear ways. Single cell-sequencing technologies are powerful tools to explore cells, however analysis is typically gene-centric and TE activity has not been addressed. Here, we developed a single-cell TE processing pipeline, scTE, and report the activity of TEs in single cells in a range of biological contexts. Specific TE types were expressed in subpopulations of embryonic stem cells and were dynamically regulated during pluripotency reprogramming, differentiation, and embryogenesis. Unexpectedly, TEs were expressed in somatic cells, including human disease-specific TEs that are undetectable in bulk analyses. Finally, we applied scTE to single cell ATAC-seq data, and demonstrate that scTE can discriminate cell type using chromatin accessibly of TEs alone. Overall, our results reveal the dynamic patterns of TEs in single cells and their contributions to cell fate and heterogeneity.

scholarly journals Application of single-cell sequencing technologies in pancreatic cancer

2021 ◽  
Author(s):  
Mastan Mannarapu ◽  
Begum Dariya ◽  
Obul Reddy Bandapalli
Keyword(s):  
Pancreatic Cancer ◽  
Single Cell ◽  
Tumor Cells ◽  
Circulating Tumor Cells ◽  
Single Cells ◽  
Evolutionary Relationship ◽  
Intratumor Heterogeneity ◽  
Sequencing Technology ◽  
Single Cell Sequencing ◽  
Sequencing Technologies

AbstractPancreatic cancer (PC) is the third lethal disease for cancer-related mortalities globally. This is mainly because of the aggressive nature and heterogeneity of the disease that is diagnosed only in their advanced stages. Thus, it is challenging for researchers and clinicians to study the molecular mechanism involved in the development of this aggressive disease. The single-cell sequencing technology enables researchers to study each and every individual cell in a single tumor. It can be used to detect genome, transcriptome, and multi-omics of single cells. The current single-cell sequencing technology is now becoming an important tool for the biological analysis of cells, to find evolutionary relationship between multiple cells and unmask the heterogeneity present in the tumor cells. Moreover, its sensitivity nature is found progressive enabling to detect rare cancer cells, circulating tumor cells, metastatic cells, and analyze the intratumor heterogeneity. Furthermore, these single-cell sequencing technologies also promoted personalized treatment strategies and next-generation sequencing to predict the disease. In this review, we have focused on the applications of single-cell sequencing technology in identifying cancer-associated cells like cancer-associated fibroblast via detecting circulating tumor cells. We also included advanced technologies involved in single-cell sequencing and their advantages. The future research indeed brings the single-cell sequencing into the clinical arena and thus could be beneficial for diagnosis and therapy of PC patients.

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 Profiling Epigenetic Age in Single Cells

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 677-677
Author(s):  
Alexandre Trapp ◽  
Csaba Kerepesi ◽  
Vadim Gladyshev
Keyword(s):  
Stem Cells ◽  
Single Cell ◽  
Single Cells ◽  
Probabilistic Approach ◽  
Embryonic Stem ◽  
Chronological Age ◽  
Cell Type ◽  
Cpg Dinucleotides ◽  
Epigenetic Age ◽  
Quantitative Changes

Abstract DNA methylation of a defined set of CpG dinucleotides emerged as a critical and precise biomarker of the aging process. Multi-variate machine learning models, known as epigenetic clocks, can exploit quantitative changes in the methylome to predict the age of bulk tissue with remarkable accuracy. However, intrinsic sparsity and digitized methylation in individual cells have so far precluded the assessment of aging in single cell data. We developed scAge, a probabilistic approach to determine the epigenetic age of single cells, and validated our results in mice. scAge tissue-specific and multi-cell type single cell clocks correctly recapitulated the chronological age of the original tissue, while uncovering the inherent heterogeneity that exists at the single-cell level. The data suggested that while cells in a tissue age in a coordinated fashion, some cells age more or less rapidly than others. We showed that individual embryonic stem cells exhibit an age close to zero, that certain stem cells in a tissue showed a reduced age compared to their chronological age, and that early embryogenesis is associated with the reduction of epigenetic age in individual cells, the latter supporting a natural rejuvenation event during gastrulation. scAge is both robust against the low coverage that is characteristic of single cell sequencing techniques and is flexible for studying any cell type and mammalian organism of interest. We demonstrate the potential for accurate epigenetic age profiling at single-cell resolution.

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 Profiling epigenetic age in single cells

2021 ◽  
Author(s):  
Alexandre Trapp ◽  
Csaba Kerepesi ◽  
Vadim N Gladyshev
Keyword(s):  
Stem Cells ◽  
Single Cell ◽  
Single Cells ◽  
Probabilistic Approach ◽  
Embryonic Stem ◽  
Chronological Age ◽  
Cell Type ◽  
Cpg Dinucleotides ◽  
Epigenetic Age ◽  
Quantitative Changes

DNA methylation of a defined set of CpG dinucleotides emerged as a critical and precise biomarker of the aging process. Multi-variate machine learning models, known as epigenetic clocks, can exploit quantitative changes in the methylome to predict the age of bulk tissue with remarkable accuracy. However, intrinsic sparsity and digitized methylation in individual cells have so far precluded the assessment of aging in single cell data. Here, we present scAge, a probabilistic approach to determine the epigenetic age of single cells, and validate our results in mice. scAge tissue-specific and multi-cell type single cell clocks correctly recapitulate chronological age of the original tissue, while uncovering the inherent heterogeneity that exists at the single-cell level. The data suggest that while tissues age in a coordinated fashion, some cells age more or less rapidly than others. We show that individual embryonic stem cells exhibit an age close to zero, that certain stem cells in a tissue show a reduced age compared to their chronological age, and that early embryogenesis is associated with the reduction of epigenetic age of individual cells, the latter supporting a natural rejuvenation event during gastrulation. scAge is both robust against the low coverage that is characteristic of single cell sequencing techniques and is flexible for studying any cell type and vertebrate organism of interest. This study demonstrates for the first time the potential for accurate epigenetic age profiling at single-cell resolution.

scholarly journals scHiCTools: A computational toolbox for analyzing single-cell Hi-C data

2021 ◽  
Vol 17 (5) ◽  
pp. e1008978
Author(s):  
Xinjun Li ◽  
Fan Feng ◽  
Hongxi Pu ◽  
Wai Yan Leung ◽  
Jie Liu
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Three Dimensional ◽  
Dimensional Euclidean Space ◽  
Cell Level ◽  
Computational Tools ◽  
Contact Maps ◽  
Sequencing Technologies ◽  
Different Dimensions ◽  
Cell Cycle Dynamics

Single-cell Hi-C (scHi-C) sequencing technologies allow us to investigate three-dimensional chromatin organization at the single-cell level. However, we still need computational tools to deal with the sparsity of the contact maps from single cells and embed single cells in a lower-dimensional Euclidean space. This embedding helps us understand relationships between the cells in different dimensions, such as cell-cycle dynamics and cell differentiation. We present an open-source computational toolbox, scHiCTools, for analyzing single-cell Hi-C data comprehensively and efficiently. The toolbox provides two methods for screening single cells, three common methods for smoothing scHi-C data, three efficient methods for calculating the pairwise similarity of cells, three methods for embedding single cells, three methods for clustering cells, and a build-in function to visualize the cells embedding in a two-dimensional or three-dimensional plot. scHiCTools, written in Python3, is compatible with different platforms, including Linux, macOS, and Windows.

scholarly journals Lineage recording of zebrafish embryogenesis reveals historical and ongoing lineage commitments

2020 ◽  
Author(s):  
Zhuoxin Chen ◽  
Chang Ye ◽  
Zhan Liu ◽  
Shanjun Deng ◽  
Xionglei He ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Cell Lineage ◽  
Cell Types ◽  
Lineage Tracing ◽  
Cell Type ◽  
Single Cell Sequencing ◽  
Sequencing Technologies ◽  
Development And Differentiation ◽  
Zebrafish Embryogenesis

AbstractIt has been challenging to characterize the lineage relationships among cells in vertebrates, which comprise a great number of cells. Fortunately, recent progress has been made by combining the CRISPR barcoding system with single-cell sequencing technologies to provide an unprecedented opportunity to track lineage at single-cell resolution. However, due to errors and/or dropouts introduced by amplification and sequencing, reconstruction of accurate lineage relationships in complex organisms remains a challenge. Thus, improvements in both experimental design and computational analysis are necessary for lineage inference. In this study, we employed single-cell Lineage tracing On Endogenous Scarring Sites (scLOESS), a lineage recording strategy based on the CRISPR-Cas9 system, to trace cell fate commitments for zebrafish larvae. With rigorous quality control, we demonstrated that lineage commitments of complex organisms could be inferred from a limited number of barcoding sites. Together with cell-type characterization, our method could homogenously recover lineage information. In combination with the cell-type and lineage information, we depicted the development histories for germ layers as well as cell types. Furthermore, when combined with trajectory analysis, our methods could capture and resolve the ongoing lineage commitment events to gain further biological insights into later development and differentiation in complex organisms.

scholarly journals Total mRNA Quantification in Single Cells: Sarcoma Cell Heterogeneity

Cells ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 759 ◽  
Author(s):  
Emma Jonasson ◽  
Lisa Andersson ◽  
Soheila Dolatabadi ◽  
Salim Ghannoum ◽  
Pierre Åman ◽  
...  
Keyword(s):  
Single Cell ◽  
Dynamic Range ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Human Fibroblasts ◽  
Mrna Levels ◽  
Cell Analysis ◽  
Cell Heterogeneity ◽  
Cell Type ◽  
Sarcoma Cell

Single-cell analysis enables detailed molecular characterization of cells in relation to cell type, genotype, cell state, temporal variations, and microenvironment. These studies often include the analysis of individual genes and networks of genes. The total amount of RNA also varies between cells due to important factors, such as cell type, cell size, and cell cycle state. However, there is a lack of simple and sensitive methods to quantify the total amount of RNA, especially mRNA. Here, we developed a method to quantify total mRNA levels in single cells based on global reverse transcription followed by quantitative PCR. Standard curve analyses of diluted RNA and sorted cells showed a wide dynamic range, high reproducibility, and excellent sensitivity. Single-cell analysis of three sarcoma cell lines and human fibroblasts revealed cell type variations, a lognormal distribution of total mRNA levels, and up to an eight-fold difference in total mRNA levels among the cells. The approach can easily be combined with targeted or global gene expression profiling, providing new means to study cell heterogeneity at an individual gene level and at a global level. This method can be used to investigate the biological importance of variations in the total amount of mRNA in healthy as well as pathological conditions.

scholarly journals Analysis of Phase-Specific Gene Expression at the Single-Cell Level in the White-Opaque Switching System ofCandida albicans

2001 ◽  
Vol 183 (12) ◽  
pp. 3761-3769 ◽  
Author(s):  
Anja Strauß ◽  
Sonja Michel ◽  
Joachim Morschhäuser
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Reporter Gene ◽  
Single Cells ◽  
Phenotypic Switching ◽  
Specific Gene ◽  
Single Cell Level ◽  
Switching System ◽  
Cell Type ◽  
Cell Level

ABSTRACT The opportunistic fungal pathogen Candida albicanscan switch spontaneously and reversibly between different cell forms, a capacity that may enhance adaptation to different host niches and evasion of host defense mechanisms. Phenotypic switching has been studied intensively for the white-opaque switching system of strain WO-1. To facilitate the molecular analysis of phenotypic switching, we have constructed homozygous ura3 mutants from strain WO-1 by targeted gene deletion. The two URA3 alleles were sequentially inactivated using theMPA R -flipping strategy, which is based on the selection of integrative transformants carrying a mycophenolic acid (MPA) resistance marker that is subsequently deleted again by site-specific, FLP-mediated recombination. To investigate a possible cell type-independent switching in the expression of individual phase-specific genes, two different reporter genes that allowed the analysis of gene expression at the single-cell level were integrated into the genome, using URA3 as a selection marker. Fluorescence microscopic analysis of cells in which aGFP reporter gene was placed under the control of phase-specific promoters demonstrated that the opaque-phase-specificSAP1 gene was detectably expressed only in opaque cells and that the white-phase-specific WH11 gene was detectably expressed only in white cells. WhenMPA R was used as a reporter gene, it conferred an MPA-resistant phenotype on opaque but not white cells in strains expressing it from the SAP1 promoter, which was monitored at the level of single cells by a significantly enlarged size of the corresponding colonies on MPA-containing indicator plates. Similarly, white but not opaque cells became MPA resistant whenMPA R was placed under the control of the WH11 promoter. The analysis of these reporter strains showed that cell type-independent phase variation in the expression of the SAP1 and WH11 genes did not occur at a detectable frequency. The expression of these phase-specific genes of C. albicans in vitro, therefore, is tightly linked to the cell type.

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