scholarly journals Single-cell RNA-seq analysis of mouse preimplantation embryos by third-generation sequencing

PLoS Biology ◽  
2020 ◽  
Vol 18 (12) ◽  
pp. e3001017
Author(s):  
Xiaoying Fan ◽  
Dong Tang ◽  
Yuhan Liao ◽  
Pidong Li ◽  
Yu Zhang ◽  
...  
Keyword(s):  
Single Cell ◽  
Developmental Stages ◽  
Expression Patterns ◽  
Embryonic Stem ◽  
Preimplantation Embryos ◽  
Specific Gene ◽  
Third Generation ◽  
Specific Expression ◽  
Third Generation Sequencing ◽  
Generation Sequencing

The development of next generation sequencing (NGS) platform-based single-cell RNA sequencing (scRNA-seq) techniques has tremendously changed biological researches, while there are still many questions that cannot be addressed by them due to their short read lengths. We developed a novel scRNA-seq technology based on third-generation sequencing (TGS) platform (single-cell amplification and sequencing of full-length RNAs by Nanopore platform, SCAN-seq). SCAN-seq exhibited high sensitivity and accuracy comparable to NGS platform-based scRNA-seq methods. Moreover, we captured thousands of unannotated transcripts of diverse types, with high verification rate by reverse transcription PCR (RT-PCR)–coupled Sanger sequencing in mouse embryonic stem cells (mESCs). Then, we used SCAN-seq to analyze the mouse preimplantation embryos. We could clearly distinguish cells at different developmental stages, and a total of 27,250 unannotated transcripts from 9,338 genes were identified, with many of which showed developmental stage-specific expression patterns. Finally, we showed that SCAN-seq exhibited high accuracy on determining allele-specific gene expression patterns within an individual cell. SCAN-seq makes a major breakthrough for single-cell transcriptome analysis field.

scholarly journals SMOOTH-seq: single-cell genome sequencing of human cells on a third-generation sequencing platform

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Xiaoying Fan ◽  
Cheng Yang ◽  
Wen Li ◽  
Xiuzhen Bai ◽  
Xin Zhou ◽  
...  
Keyword(s):  
Single Cell ◽  
Genome Sequencing ◽  
Single Molecule ◽  
Human Cancer ◽  
Whole Genome ◽  
Third Generation ◽  
Sequencing Platform ◽  
Human Cancer Cell Lines ◽  
Third Generation Sequencing ◽  
Generation Sequencing

AbstractThere is no effective way to detect structure variations (SVs) and extra-chromosomal circular DNAs (ecDNAs) at single-cell whole-genome level. Here, we develop a novel third-generation sequencing platform-based single-cell whole-genome sequencing (scWGS) method named SMOOTH-seq (single-molecule real-time sequencing of long fragments amplified through transposon insertion). We evaluate the method for detecting CNVs, SVs, and SNVs in human cancer cell lines and a colorectal cancer sample and show that SMOOTH-seq reliably and effectively detects SVs and ecDNAs in individual cells, but shows relatively limited accuracy in detection of CNVs and SNVs. SMOOTH-seq opens a new chapter in scWGS as it generates high fidelity reads of kilobases long.

scholarly journals A human iPSC-astroglia neurodevelopmental model reveals divergent transcriptomic patterns in schizophrenia

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Attila Szabo ◽  
Ibrahim A. Akkouh ◽  
Matthieu Vandenberghe ◽  
Jordi Requena Osete ◽  
Timothy Hughes ◽  
...  
Keyword(s):  
Developmental Stages ◽  
Glutamate Uptake ◽  
Expression Patterns ◽  
Induced Pluripotent Stem Cell ◽  
Specific Gene ◽  
Specific Expression ◽  
Functional Changes ◽  
Neurodevelopmental Abnormalities ◽  
Induced Pluripotent

AbstractWhile neurodevelopmental abnormalities have been associated with schizophrenia (SCZ), the role of astroglia in disease pathophysiology remains poorly understood. In the present study, we used a human induced pluripotent stem cell (iPSC)-derived astrocyte model to investigate the temporal patterns of astroglia differentiation during developmental stages critical for SCZ using RNA sequencing. The model generated astrocyte-specific gene expression patterns during differentiation that corresponded well to astroglia-specific expression signatures of in vivo cortical fetal development. Using this model we identified SCZ-specific expression dynamics, and found that SCZ-associated differentially expressed genes were significantly enriched in the medial prefrontal cortex, striatum, and temporal lobe, targeting VWA5A and ADAMTS19. In addition, SCZ astrocytes displayed alterations in calcium signaling, and significantly decreased glutamate uptake and metalloproteinase activity relative to controls. These results implicate novel transcriptional dynamics in astrocyte differentiation in SCZ together with functional changes that are potentially important biological components of SCZ pathology.

scholarly journals Finding cell-specific expression patterns in the early Ciona embryo with single-cell RNA-seq

2017 ◽  
Author(s):  
Garth R. Ilsley ◽  
Ritsuko Suyama ◽  
Takeshi Noda ◽  
Nori Satoh ◽  
Nicholas M. Luscombe
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Patterns ◽  
Cell Types ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Stage ◽  
Specific Expression ◽  
Temporal Gene Expression

AbstractSingle-cell RNA-seq has been established as a reliable and accessible technique enabling new types of analyses, such as identifying cell types and studying spatial and temporal gene expression variation and change at single-cell resolution. Recently, single-cell RNA-seq has been applied to developing embryos, which offers great potential for finding and characterising genes controlling the course of development along with their expression patterns. In this study, we applied single-cell RNA-seq to the 16-cell stage of the Ciona embryo, a marine chordate and performed a computational search for cell-specific gene expression patterns. We recovered many known expression patterns from our single-cell RNA-seq data and despite extensive previous screens, we succeeded in finding new cell-specific patterns, which we validated by in situ and single-cell qPCR.

scholarly journals SOX2 Is Regulated Differently from NANOG and OCT4 in Human Embryonic Stem Cells during Early Differentiation Initiated with Sodium Butyrate

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Ade Kallas ◽  
Martin Pook ◽  
Annika Trei ◽  
Toivo Maimets
Keyword(s):  
Transcription Factors ◽  
Single Cell ◽  
Sodium Butyrate ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Embryonic Stem ◽  
Specific Expression ◽  
Self Renewal ◽  
Early Differentiation ◽  
Hes Cells

Transcription factors NANOG, OCT4, and SOX2 regulate self-renewal and pluripotency in human embryonic stem (hES) cells; however, their expression profiles during early differentiation of hES cells are unclear. In this study, we used multiparameter flow cytometric assay to detect all three transcription factors (NANOG, OCT4, and SOX2) simultaneously at single cell level and monitored the changes in their expression during early differentiation towards endodermal lineage (induced by sodium butyrate). We observed at least four distinct populations of hES cells, characterized by specific expression patterns of NANOG, OCT4, and SOX2 and differentiation markers. Our results show that a single cell can express both differentiation and pluripotency markers at the same time, indicating a gradual mode of developmental transition in these cells. Notably, distinct regulation of SOX2 during early differentiation events was detected, highlighting the potential importance of this transcription factor for self-renewal of hES cells during differentiation.

BUILDING CATALOGUE OF LIFE: ULTRAHIGH THROUGHPUT DNA BARCODING USING THIRD GENERATION SEQUENCING

2018 ◽  
Author(s):  
P.D.N. HEBERT ◽  
◽  
T.W.A. BRAUKMANN ◽  
S.W.J. PROSSER ◽  
S. RATNASINGHAM ◽  
...  
Keyword(s):  
Dna Barcoding ◽  
Third Generation ◽  
Third Generation Sequencing ◽  
Generation Sequencing

IsoDetect: Detection of splice isoforms from third generation long reads based on short feature sequences

2020 ◽  
Vol 15 ◽  
Author(s):  
Hongdong Li ◽  
Wenjing Zhang ◽  
Yuwen Luo ◽  
Jianxin Wang
Keyword(s):  
Sequence Similarity ◽  
Detection Methods ◽  
Sequence Information ◽  
Third Generation ◽  
Sequencing Data ◽  
Splice Isoforms ◽  
Third Generation Sequencing ◽  
Long Reads ◽  
Feature Sequence ◽  
Generation Sequencing

Aims: Accurately detect isoforms from third generation sequencing data. Background: Transcriptome annotation is the basis for the analysis of gene expression and regulation. The transcriptome annotation of many organisms such as humans is far from incomplete, due partly to the challenge in the identification of isoforms that are produced from the same gene through alternative splicing. Third generation sequencing (TGS) reads provide unprecedented opportunity for detecting isoforms due to their long length that exceeds the length of most isoforms. One limitation of current TGS reads-based isoform detection methods is that they are exclusively based on sequence reads, without incorporating the sequence information of known isoforms. Objective: Develop an efficient method for isoform detection. Method: Based on annotated isoforms, we propose a splice isoform detection method called IsoDetect. First, the sequence at exon-exon junction is extracted from annotated isoforms as the “short feature sequence”, which is used to distinguish different splice isoforms. Second, we aligned these feature sequences to long reads and divided long reads into groups that contain the same set of feature sequences, thereby avoiding the pair-wise comparison among the large number of long reads. Third, clustering and consensus generation are carried out based on sequence similarity. For the long reads that do not contain any short feature sequence, clustering analysis based on sequence similarity is performed to identify isoforms. Result: Tested on two datasets from Calypte Anna and Zebra Finch, IsoDetect showed higher speed and compelling accuracy compared with four existing methods. Conclusion: IsoDetect is a promising method for isoform detection. Other: This paper was accepted by the CBC2019 conference.

scholarly journals Comprehensive analysis of single cell ATAC-seq data with SnapATAC

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Rongxin Fang ◽  
Sebastian Preissl ◽  
Yang Li ◽  
Xiaomeng Hou ◽  
Jacinta Lucero ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Expression Patterns ◽  
Regulatory Elements ◽  
Cellular Heterogeneity ◽  
Specific Gene ◽  
Open Chromatin ◽  
Cell Type ◽  
Process Data ◽  
Cell Type Specific

AbstractIdentification of the cis-regulatory elements controlling cell-type specific gene expression patterns is essential for understanding the origin of cellular diversity. Conventional assays to map regulatory elements via open chromatin analysis of primary tissues is hindered by sample heterogeneity. Single cell analysis of accessible chromatin (scATAC-seq) can overcome this limitation. However, the high-level noise of each single cell profile and the large volume of data pose unique computational challenges. Here, we introduce SnapATAC, a software package for analyzing scATAC-seq datasets. SnapATAC dissects cellular heterogeneity in an unbiased manner and map the trajectories of cellular states. Using the Nyström method, SnapATAC can process data from up to a million cells. Furthermore, SnapATAC incorporates existing tools into a comprehensive package for analyzing single cell ATAC-seq dataset. As demonstration of its utility, SnapATAC is applied to 55,592 single-nucleus ATAC-seq profiles from the mouse secondary motor cortex. The analysis reveals ~370,000 candidate regulatory elements in 31 distinct cell populations in this brain region and inferred candidate cell-type specific transcriptional regulators.

scholarly journals Apollo: a sequencing-technology-independent, scalable and accurate assembly polishing algorithm

2020 ◽  
Vol 36 (12) ◽  
pp. 3669-3679 ◽  
Author(s):  
Can Firtina ◽  
Jeremie S Kim ◽  
Mohammed Alser ◽  
Damla Senol Cali ◽  
A Ercument Cicek ◽  
...  
Keyword(s):  
Genome Analysis ◽  
Supplementary Information ◽  
Third Generation ◽  
Sequencing Technology ◽  
Base Pairs ◽  
Sequencing Technologies ◽  
Third Generation Sequencing ◽  
Long Reads ◽  
Generation Sequencing ◽  
Large Genomes

Abstract Motivation Third-generation sequencing technologies can sequence long reads that contain as many as 2 million base pairs. These long reads are used to construct an assembly (i.e. the subject’s genome), which is further used in downstream genome analysis. Unfortunately, third-generation sequencing technologies have high sequencing error rates and a large proportion of base pairs in these long reads is incorrectly identified. These errors propagate to the assembly and affect the accuracy of genome analysis. Assembly polishing algorithms minimize such error propagation by polishing or fixing errors in the assembly by using information from alignments between reads and the assembly (i.e. read-to-assembly alignment information). However, current assembly polishing algorithms can only polish an assembly using reads from either a certain sequencing technology or a small assembly. Such technology-dependency and assembly-size dependency require researchers to (i) run multiple polishing algorithms and (ii) use small chunks of a large genome to use all available readsets and polish large genomes, respectively. Results We introduce Apollo, a universal assembly polishing algorithm that scales well to polish an assembly of any size (i.e. both large and small genomes) using reads from all sequencing technologies (i.e. second- and third-generation). Our goal is to provide a single algorithm that uses read sets from all available sequencing technologies to improve the accuracy of assembly polishing and that can polish large genomes. Apollo (i) models an assembly as a profile hidden Markov model (pHMM), (ii) uses read-to-assembly alignment to train the pHMM with the Forward–Backward algorithm and (iii) decodes the trained model with the Viterbi algorithm to produce a polished assembly. Our experiments with real readsets demonstrate that Apollo is the only algorithm that (i) uses reads from any sequencing technology within a single run and (ii) scales well to polish large assemblies without splitting the assembly into multiple parts. Availability and implementation Source code is available at https://github.com/CMU-SAFARI/Apollo. Supplementary information Supplementary data are available at Bioinformatics online.

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