scholarly journals Dean flow assisted single cell and bead encapsulation for high performance single cell expression profiling

2019 ◽  
Author(s):  
Luoquan Li ◽  
Ping Wu ◽  
Zhaofeng Luo ◽  
Lei Wang ◽  
Weiping Ding ◽  
...  
Keyword(s):  
Single Cell ◽  
Expression Profiling ◽  
High Efficiency ◽  
Cell Concentration ◽  
Critical Role ◽  
Droplet Microfluidics ◽  
Sequencing Data ◽  
High Cell Concentration ◽  
Dean Flow ◽  
Cell Expression

AbstractSingle-cell RNA sequencing examines the transcriptome of individual cells and reveals the inter-cell transcription heterogeneity, playing a critical role in both scientific research and clinical applications. Recently, droplet microfluidics-based platform for expression profiling has been shown as a powerful tool to capture of the transcriptional information on single cell level. Despite the breakthrough this platform brought about, it required the simultaneous encapsulation of single cell and single barcoded bead, the incidence of which was very low. Suboptimal capturing efficiency limited the throughput of the Drop-seq platform. In this work, we leveraged the advance in inertial microfluidics-based cell sorting and designed a microfluidic chip for high efficiency cell-bead co-encapsulation, increasing the capturing rate by more than four folds. Specifically, we adopted spiral and serpentine channels and ordered cells/beads before the encapsulation region. We characterized the effect of cell concentration on the capturing rate and achieved a cell-bead co-capturing rate up to 3%. We tested this platform by co-encapsulating barcoded beads and human-mouse cell mixtures. The sequencing data distinguished the majority of human and mice expressions, with the doublet rate being as low as 5.8%, indicating that the simultaneous capturing of two or more cells in one droplet was minimal even when using high cell concentration. This chip design showed great potential in improving the efficiency for future single cell expression profiling.

scholarly journals Dean Flow Assisted Single Cell and Bead Encapsulation for High Performance Single Cell Expression Profiling

ACS Sensors ◽  
2019 ◽  
Vol 4 (5) ◽  
pp. 1299-1305 ◽  
Author(s):  
Luoquan Li ◽  
Ping Wu ◽  
Zhaofeng Luo ◽  
Lei Wang ◽  
Weiping Ding ◽  
...  
Keyword(s):  
Single Cell ◽  
Expression Profiling ◽  
High Performance ◽  
Dean Flow ◽  
Cell Expression

scholarly journals Resolving early mesoderm diversification through single-cell expression profiling

Nature ◽  
2016 ◽  
Vol 535 (7611) ◽  
pp. 289-293 ◽  
Author(s):  
Antonio Scialdone ◽  
Yosuke Tanaka ◽  
Wajid Jawaid ◽  
Victoria Moignard ◽  
Nicola K. Wilson ◽  
...  
Keyword(s):  
Single Cell ◽  
Expression Profiling ◽  
Cell Expression

scholarly journals Single cell analysis of lincRNA expression during human blastocyst differentiation identifies TERT(+) multi-lineage precursor cells

2016 ◽  
Author(s):  
Jens Durruthy-Durruthy ◽  
Mark Wossidlo ◽  
Vittorio Sebastiano ◽  
Gennadi Glinsky
Keyword(s):  
Single Cell ◽  
Expression Profiling ◽  
Single Cell Analysis ◽  
Precursor Cells ◽  
Human Embryos ◽  
Cell Analysis ◽  
Embryonic Cells ◽  
Human Blastocyst ◽  
Extinction Events ◽  
Cell Expression

SummaryChromosome instability and aneuploidies occur very frequently in human embryos, impairing proper embryogenesis and leading to cell cycle arrest, loss of cell viability, and developmental failures in 50-80% of cleavage-stage embryos. This high frequency of cellular extinction events represents a significant experimental obstacle challenging analyses of individual cells isolated from human preimplantation embryos. Here, we carried out single cell expression profiling analyses of 241 individual cells recovered from 32 human embryos during the early and late stages of viable human blastocyst differentiation. Classification of embryonic cells was performed solely based on expression patterns of human pluripotency-associated transcripts (HPAT), which represent a family of transposable element-derived lincRNAs highly expressed in human embryonic stem cells (hESCs) and regulating nuclear reprogramming and pluripotency induction. We then validated our findings by analyzing 1,708 individual embryonic cells recovered from more than 100 human embryos and 259 mouse embryonic cells at different stages of preimplantation embryogenesis. Our experiments demonstrate that segregation of human blastocyst cells into distinct sub-populations based on single-cell expression profiling of just three HPATs (HPAT-21; -2; and -15) appears to inform key molecular and cellular events of naïve pluripotency induction and accurately captures a full spectrum of cellular diversity during human blastocyst differentiation. HPAT’s expression-guided spatiotemporal reconstruction of human embryonic development inferred from single-cell expression analysis of viable blastocyst differentiation enabled identification of TERT(+) sub-populations, which are significantly enriched for cells expressing key naïve pluripotency regulatory genes and genetic markers of all three major lineages created during human blastocyst differentiation. Results of our analyses suggest that during early stages of preimplantation embryogenesis putative immortal multi-lineage precursor cells (iMPCs) are created, which then differentiate into trophectoderm, primitive endoderm and pluripotent epiblast lineages. We propose that cellular extinction events in cleavage-stage embryos are triggered by premature activation of HPAT lincRNAs reflecting failed iMPC’s creation attempts.HighlightsSingle cell analysis of 1,949 human & 259 mouse embryonic cellsIdentification of 5 most abundant HPAT lincRNAs in viable human blastocystsExpression profiling of just 3 lincRNAs captures cellular diversity of human blastocystsIdentification & characterization of TERT(+) multi-lineage precursor cellsMTTH/HPAT lincRNAs regulatory axis of naïve pluripotency induction in vivo

scholarly journals High-throughput single-cell DNA sequencing of AML tumors with droplet microfluidics

2017 ◽  
Author(s):  
Maurizio Pellegrino ◽  
Adam Sciambi ◽  
Sebastian Treusch ◽  
Robert Durruthy-Durruthy ◽  
Kaustubh Gokhale ◽  
...  
Keyword(s):  
Single Cell ◽  
Clonal Evolution ◽  
Droplet Microfluidics ◽  
Next Generation Sequencing Data ◽  
Sequencing Data ◽  
Single Cell Sequencing ◽  
Selection For ◽  
Genetic Profiles ◽  
Generation Sequencing

ABSTRACTTo enable the characterization of genetic heterogeneity in tumor cell populations, we developed a novel microfluidic approach that barcodes amplified genomic DNA from thousands of individual cancer cells confined to droplets. The barcodes are then used to reassemble the genetic profiles of cells from next generation sequencing data. Using this approach, we sequenced longitudinally collected AML tumor populations from two patients and genotyped up to 62 disease relevant loci across more than 16,000 individual cells. Targeted single-cell sequencing was able to sensitively identify tumor cells during complete remission and uncovered complex clonal evolution within AML tumors that was not observable with bulk sequencing. We anticipate that this approach will make feasible the routine analysis of heterogeneity in AML leading to improved stratification and therapy selection for the disease.

scholarly journals DoubletFinder: Doublet detection in single-cell RNA sequencing data using artificial nearest neighbors

2018 ◽  
Author(s):  
Christopher S. McGinnis ◽  
Lyndsay M. Murrow ◽  
Zev J. Gartner
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
False Negative ◽  
Droplet Microfluidics ◽  
Cell Capture ◽  
Sequencing Data ◽  
Putative Gene ◽  
Detection Tool ◽  
Single Cell Rna Sequencing

SUMMARYSingle-cell RNA sequencing (scRNA-seq) using droplet microfluidics occasionally produces transcriptome data representing more than one cell. These technical artifacts are caused by cell doublets formed during cell capture and occur at a frequency proportional to the total number of sequenced cells. The presence of doublets can lead to spurious biological conclusions, which justifies the practice of sequencing fewer cells to limit doublet formation rates. Here, we present a computational doublet detection tool – DoubletFinder – that identifies doublets based solely on gene expression features. DoubletFinder infers the putative gene expression profile of real doublets by generating artificial doublets from existing scRNA-seq data. Neighborhood detection in gene expression space then identifies sequenced cells with increased probability of being doublets based on their proximity to artificial doublets. DoubletFinder robustly identifies doublets across scRNA-seq datasets with variable numbers of cells and sequencing depth, and predicts false-negative and false-positive doublets defined using conventional barcoding approaches. We anticipate that DoubletFinder will aid in scRNA-seq data analysis and will increase the throughput and accuracy of scRNA-seq experiments.

scholarly journals SCSsim: an integrated tool for simulating single-cell genome sequencing data

2019 ◽  
Author(s):  
Zhenhua Yu ◽  
Fang Du ◽  
Xuehong Sun ◽  
Ao Li
Keyword(s):  
Single Cell ◽  
High Efficiency ◽  
Detection Efficiency ◽  
Comprehensive Evaluation ◽  
Supplementary Information ◽  
Sequencing Data ◽  
Single Cell Sequencing ◽  
Technical Issues ◽  
Cell Genome ◽  
User Intervention

Abstract Motivation Allele dropout (ADO) and unbalanced amplification of alleles are main technical issues of single-cell sequencing (SCS), and effectively emulating these issues is necessary for reliably benchmarking SCS-based bioinformatics tools. Unfortunately, currently available sequencing simulators are free of whole-genome amplification involved in SCS technique and therefore not suited for generating SCS datasets. We develop a new software package (SCSsim) that can efficiently simulate SCS datasets in a parallel fashion with minimal user intervention. SCSsim first constructs the genome sequence of single cell by mimicking a complement of genomic variations under user-controlled manner, and then amplifies the genome according to MALBAC technique and finally yields sequencing reads from the amplified products based on inferred sequencing profiles. Comprehensive evaluation in simulating different ADO rates, variation detection efficiency and genome coverage demonstrates that SCSsim is a very useful tool in mimicking single-cell sequencing data with high efficiency. Availability and implementation SCSsim is freely available at https://github.com/qasimyu/scssim. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data

GigaScience ◽  
2020 ◽  
Vol 9 (12) ◽  
Author(s):  
Matthew D Young ◽  
Sam Behjati
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Sequencing Data ◽  
Sequencing Technologies ◽  
Single Cell Rna Sequencing ◽  
Biological Interpretation ◽  
Downstream Analysis ◽  
Cell Expression ◽  
Rna Contamination

Abstract Background Droplet-based single-cell RNA sequence analyses assume that all acquired RNAs are endogenous to cells. However, any cell-free RNAs contained within the input solution are also captured by these assays. This sequencing of cell-free RNA constitutes a background contamination that confounds the biological interpretation of single-cell transcriptomic data. Results We demonstrate that contamination from this "soup" of cell-free RNAs is ubiquitous, with experiment-specific variations in composition and magnitude. We present a method, SoupX, for quantifying the extent of the contamination and estimating "background-corrected" cell expression profiles that seamlessly integrate with existing downstream analysis tools. Applying this method to several datasets using multiple droplet sequencing technologies, we demonstrate that its application improves biological interpretation of otherwise misleading data, as well as improving quality control metrics. Conclusions We present SoupX, a tool for removing ambient RNA contamination from droplet-based single-cell RNA sequencing experiments. This tool has broad applicability, and its application can improve the biological utility of existing and future datasets.

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