scholarly journals An Automated Microwell Platform for Large-Scale Single Cell RNA-Seq

2016 ◽  
Author(s):  
Jinzhou Yuan ◽  
Peter A. Sims
Keyword(s):  
Single Cell ◽  
Expression Profiling ◽  
Large Scale ◽  
Single Cell Analysis ◽  
Cell Analysis ◽  
Cell Capture ◽  
Rna Seq ◽  
Fluorescent Cell ◽  
Recent Developments ◽  
Improved Performance

Recent developments have enabled rapid, inexpensive RNA sequencing of thousands of individual cells from a single specimen, raising the possibility of unbiased and comprehensive expression profiling from complex tissues. Microwell arrays are a particularly attractive microfluidic platform for single cell analysis due to their scalability, cell capture efficiency, and compatibility with imaging. We report an automated microwell array platform for single cell RNA-Seq with significantly improved performance over previous implementations. We demonstrate cell capture efficiencies of >50%, compatibility with commercially available barcoded mRNA capture beads, and parallel expression profiling from thousands of individual cells. We evaluate the level of cross-contamination in our platform by both tracking fluorescent cell lysate in sealed microwells and with a human-mouse mixed species RNA-Seq experiment. Finally, we apply our system to comprehensively assess heterogeneity in gene expression of patient-derived glioma neurospheres and uncover subpopulations similar to those observed in human glioma tissue.

scholarly journals SIMLR: a tool for large-scale single-cell analysis by multi-kernel learning

2017 ◽  
Author(s):  
Bo Wang ◽  
Daniele Ramazzotti ◽  
Luca De Sano ◽  
Junjie Zhu ◽  
Emma Pierson ◽  
...  
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Single Cell Analysis ◽  
R Package ◽  
Supplementary Information ◽  
Cell Analysis ◽  
Rna Seq ◽  
A Cell ◽  
Supplementary Material ◽  
Public Datasets

AbstractMotivationWe here present SIMLR (Single-cell Interpretation via Multi-kernel LeaRning), an open-source tool that implements a novel framework to learn a cell-to-cell similarity measure from single-cell RNA-seq data. SIMLR can be effectively used to perform tasks such as dimension reduction, clustering, and visualization of heterogeneous populations of cells. SIMLR was benchmarked against state-of-the-art methods for these three tasks on several public datasets, showing it to be scalable and capable of greatly improving clustering performance, as well as providing valuable insights by making the data more interpretable via better a visualization.Availability and ImplementationSIMLR is available on GitHub in both R and MATLAB implementations. Furthermore, it is also available as an R package on [email protected] or [email protected] InformationSupplementary data are available at Bioinformatics online.

scholarly journals Advances in Single-Cell Printing

Micromachines ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 80
Author(s):  
Xiaohu Zhou ◽  
Han Wu ◽  
Haotian Wen ◽  
Bo Zheng
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Three Dimensional ◽  
Cell Isolation ◽  
Special Focus ◽  
Cell Analysis ◽  
Cell Array ◽  
Cell Printing ◽  
Recent Developments ◽  
Single Cell Isolation

Single-cell analysis is becoming an indispensable tool in modern biological and medical research. Single-cell isolation is the key step for single-cell analysis. Single-cell printing shows several distinct advantages among the single-cell isolation techniques, such as precise deposition, high encapsulation efficiency, and easy recovery. Therefore, recent developments in single-cell printing have attracted extensive attention. We review herein the recently developed bioprinting strategies with single-cell resolution, with a special focus on inkjet-like single-cell printing. First, we discuss the common cell printing strategies and introduce several typical and advanced printing strategies. Then, we introduce several typical applications based on single-cell printing, from single-cell array screening and mass spectrometry-based single-cell analysis to three-dimensional tissue formation. In the last part, we discuss the pros and cons of the single-cell strategies and provide a brief outlook for single-cell printing.

scholarly journals ASAP 2020 update: an open, scalable and interactive web-based portal for (single-cell) omics analyses

2020 ◽  
Vol 48 (W1) ◽  
pp. W403-W414
Author(s):  
Fabrice P A David ◽  
Maria Litovchenko ◽  
Bart Deplancke ◽  
Vincent Gardeux
Keyword(s):  
Big Data ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Omics Data ◽  
Cell Analysis ◽  
Rna Seq ◽  
Analysis Pipeline ◽  
Web Based ◽  
Data Analyses ◽  
The Web

Abstract Single-cell omics enables researchers to dissect biological systems at a resolution that was unthinkable just 10 years ago. However, this analytical revolution also triggered new demands in ‘big data’ management, forcing researchers to stay up to speed with increasingly complex analytical processes and rapidly evolving methods. To render these processes and approaches more accessible, we developed the web-based, collaborative portal ASAP (Automated Single-cell Analysis Portal). Our primary goal is thereby to democratize single-cell omics data analyses (scRNA-seq and more recently scATAC-seq). By taking advantage of a Docker system to enhance reproducibility, and novel bioinformatics approaches that were recently developed for improving scalability, ASAP meets challenging requirements set by recent cell atlasing efforts such as the Human (HCA) and Fly (FCA) Cell Atlas Projects. Specifically, ASAP can now handle datasets containing millions of cells, integrating intuitive tools that allow researchers to collaborate on the same project synchronously. ASAP tools are versioned, and researchers can create unique access IDs for storing complete analyses that can be reproduced or completed by others. Finally, ASAP does not require any installation and provides a full and modular single-cell RNA-seq analysis pipeline. ASAP is freely available at https://asap.epfl.ch.

scholarly journals Design of a large-scale femtoliter droplet array for single-cell analysis of drug-tolerant and drug-resistant bacteria

2013 ◽  
Vol 4 ◽  
Author(s):  
Ryota Iino ◽  
Yoshimi Matsumoto ◽  
Kunihiko Nishino ◽  
Akihito Yamaguchi ◽  
Hiroyuki Noji
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Single Cell Analysis ◽  
Resistant Bacteria ◽  
Drug Resistant ◽  
Cell Analysis ◽  
Drug Resistant Bacteria ◽  
Droplet Array

Recent developments in single-cell analysis

2004 ◽  
Vol 510 (2) ◽  
pp. 127-138 ◽  
Author(s):  
Xin Lu ◽  
Wei-Hua Huang ◽  
Zong-Li Wang ◽  
Jie-Ke Cheng
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Cell Analysis ◽  
Recent Developments

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 Single-cell analysis identifies a key role for Hhip in murine coronal suture development

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Greg Holmes ◽  
Ana S. Gonzalez-Reiche ◽  
Madrikha Saturne ◽  
Susan M. Motch Perrine ◽  
Xianxiao Zhou ◽  
...  
Keyword(s):  
Single Cell ◽  
Hedgehog Signaling ◽  
Bone Growth ◽  
Single Cell Analysis ◽  
Coronal Suture ◽  
Cell Analysis ◽  
Rna Seq ◽  
Wild Type ◽  
Calvarial Bone ◽  
Osteogenic Cells

AbstractCraniofacial development depends on formation and maintenance of sutures between bones of the skull. In sutures, growth occurs at osteogenic fronts along the edge of each bone, and suture mesenchyme separates adjacent bones. Here, we perform single-cell RNA-seq analysis of the embryonic, wild type murine coronal suture to define its population structure. Seven populations at E16.5 and nine at E18.5 comprise the suture mesenchyme, osteogenic cells, and associated populations. Expression of Hhip, an inhibitor of hedgehog signaling, marks a mesenchymal population distinct from those of other neurocranial sutures. Tracing of the neonatal Hhip-expressing population shows that descendant cells persist in the coronal suture and contribute to calvarial bone growth. In Hhip−/− coronal sutures at E18.5, the osteogenic fronts are closely apposed and the suture mesenchyme is depleted with increased hedgehog signaling compared to those of the wild type. Collectively, these data demonstrate that Hhip is required for normal coronal suture development.

scholarly journals Identification of KLF6/PSGs and NPY-Related USF2/CEACAM Transcriptional Regulatory Networks via Spinal Cord Bulk and Single-Cell RNA-Seq Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Xinbing Liu ◽  
Wei Gao ◽  
Wei Liu
Keyword(s):  
Spinal Cord ◽  
Single Cell ◽  
Regulatory Networks ◽  
Single Cell Analysis ◽  
Transcriptional Regulatory Network ◽  
Transcriptional Regulator ◽  
Cell Analysis ◽  
Rna Seq ◽  
Transcriptional Regulatory Networks ◽  
Transcriptional Regulatory

Background. To further understand the development of the spinal cord, an exploration of the patterns and transcriptional features of spinal cord development in newborn mice at the cellular transcriptome level was carried out. Methods. The mouse single-cell sequencing (scRNA-seq) dataset was downloaded from the GSE108788 dataset. Single-cell RNA-Seq (scRNA-Seq) was conducted on cervical and lumbar spinal V2a interneurons from 2 P0 neonates. Single-cell analysis using the Seurat package was completed, and marker mRNAs were identified for each cluster. Then, pseudotemporal analysis was used to analyze the transcription changes of marker mRNAs in different clusters over time. Finally, the functions of these marker mRNAs were assessed by enrichment analysis and protein-protein interaction (PPI) networks. A transcriptional regulatory network was then constructed using the TRRUST dataset. Results. A total of 949 cells were screened. Single-cell analysis was conducted based on marker mRNAs of each cluster, which revealed the heterogeneity of neonatal mouse spinal cord neuronal cells. Functional analysis of pseudotemporal trajectory-related marker mRNAs suggested that pregnancy-specific glycoproteins (PSGs) and carcinoembryonic antigen cell adhesion molecules (CEACAMs) were the core mRNAs in cluster 3. GSVA analysis then demonstrated that the different clusters had differences in pathway activity. By constructing a transcriptional regulatory network, USF2 was identified to be a transcriptional regulator of CEACAM1 and CEACAM5, while KLF6 was identified to be a transcriptional regulator of PSG3 and PSG5. This conclusion was then validated using the Genotype-Tissue Expression (GTEx) spinal cord transcriptome dataset. Conclusions. This study completed an integrated analysis of a single-cell dataset with the utilization of marker mRNAs. USF2/CEACAM1&5 and KLF6/PSG3&5 transcriptional regulatory networks were identified by spinal cord single-cell analysis.

scholarly journals Preparation of Tissues and Heterogeneous Cellular Samples for Single-Cell Analysis

2021 ◽  
Author(s):  
E. Celeste Welch ◽  
Anubhav Tripathi
Keyword(s):  
Sample Preparation ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Cell Analysis ◽  
Processing Times ◽  
Tissue Dissociation ◽  
Emerging Trends ◽  
Recent Developments ◽  
Preparation Techniques

While sample preparation techniques for the chemical and biochemical analysis of tissues are fairly well advanced, the preparation of complex, heterogenous samples for single-cell analysis can be difficult and challenging. Nevertheless, there is growing interest in preparing complex cellular samples, particularly tissues, for analysis via single-cell resolution techniques such as single-cell sequencing or flow cytometry. Recent microfluidic tissue dissociation approaches have helped to expedite the preparation of single cells from tissues through the use of optimized, controlled mechanical forces. Cell sorting and selective cellular recovery from heterogenous samples have also gained traction in biosensors, microfluidic systems, and other diagnostic devices. Together, these recent developments in tissue disaggregation and targeted cellular retrieval have contributed to the development of increasingly streamlined sample preparation workflows for single-cell analysis technologies, which minimize equipment requirements, enable lower processing times and costs, and pave the way for high-throughput, automated technologies. In this chapter, we survey recent developments and emerging trends in this field.

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