scholarly journals Bacterial Single Cell Whole Transcriptome Amplification in Microfluidic Platform Shows Putative Gene Expression Heterogeneity

2019 ◽  
Vol 91 (13) ◽  
pp. 8036-8044 ◽  
Author(s):  
Yuguang Liu ◽  
Patricio Jeraldo ◽  
Jin Sung Jang ◽  
Bruce Eckloff ◽  
Jin Jen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Putative Gene ◽  
Microfluidic Platform ◽  
Gene Expression Heterogeneity ◽  
Whole Transcriptome Amplification ◽  
Whole Transcriptome

scholarly journals Targeted transcript quantification in single disseminated cancer cells after whole transcriptome amplification

2019 ◽  
Author(s):  
Franziska C. Durst ◽  
Ana Grujovic ◽  
Iris Ganser ◽  
Martin Hoffmann ◽  
Peter Ugocsai ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Single Cell ◽  
Cancer Cells ◽  
Cell Lines ◽  
Single Cells ◽  
Qpcr Assay ◽  
Transcript Expression ◽  
Whole Transcriptome Amplification ◽  
Whole Transcriptome

AbstractGene expression analysis of rare or heterogeneous cell populations such as disseminated cancer cells (DCCs) requires a sensitive method allowing reliable analysis of single cells. Therefore, we developed and explored the feasibility of a quantitative PCR (qPCR) assay to analyze single-cell cDNA pre-amplified using a previously established whole transcriptome amplification (WTA) protocol. We carefully selected and optimized multiple steps of the protocol, e.g. re-amplification of WTA products, quantification of amplified cDNA yields and final qPCR quantification, to identify the most reliable and accurate workflow for quantitation of gene expression of the ERBB2 gene in DCCs. We found that absolute quantification outperforms relative quantification. We then validated the performance of our method on single cells of established breast cancer cell lines displaying distinct levels of HER2 protein. The different protein levels were faithfully reflected by transcript expression across the tested cell lines thereby proving the accuracy of our approach. Finally, we applied our method on patient-derived breast cancer DCCs. Here, we were able to measure ERBB2 expression levels in all HER2-positive DCCs. In addition, we could detect ERBB2 transcript expression even in HER2-negative DCCs, suggesting post-transcriptional mechanisms of HER2 loss in anti-HER2-treated DCCs. In summary, we developed a reliable single-cell qPCR assay applicable to measure distinct levels of ERBB2 in DCCs.

scholarly journals ID: 1027 High-Throughput, Single Cell Whole Transcriptome Sequencing Analysis of Cancer Cells with the New BD FACSMelody™ Cell Sorter and BD™ Precise assay

2017 ◽  
Vol 4 (S) ◽  
pp. 102
Author(s):  
Xiaoyang (Alice) Wang ◽  
Chip Lomas ◽  
Craig Betts ◽  
Aaron Walker ◽  
Christina Fan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cancer Cells ◽  
Cell Line ◽  
Selection Criteria ◽  
Single Cells ◽  
Principal Component ◽  
Jurkat Cells ◽  
Sequencing Analysis ◽  
Whole Transcriptome

Gene expression studies performed on bulk samples might obscure the understanding of complex samples. Gene expression analyses performed on single cells, however, can offer a powerful method to resolve sample heterogeneity and reveal hidden biology. Optimal sample preparation is critical to obtain high quality gene expression data from single cells.Historically, single cells or small numbers of cells were isolated and prepared by limiting dilutions, laser capture microdissection, or microfluidics technologies, or fluorescence-activated cell sorting (FACS). FACS sorting enables highthroughput processing of a heterogeneous mixture of cells and ensures the delivery of single cells or a small number ofcells into a chosen receptacle to meet the selection criteria at a purity level that is unmatched by other approaches.Furthermore, by FACS, the single cell selection criteria can be based on surface marker expression, cell size, and granularity(represented by scatter). Sorted cells can be used for any downstream application including next generation sequencing(NGS).In this study, the new, easy-to-use BD FACSMelody™ sorter was applied to sort individual cancer cells. Jurkat cells (a Tleukemia cell line), and T47D cells (a breast cancer cell line) were mixed, stained, analyzed, and sorted on a BD FACSMelody system. The individual cell’s whole transcriptome was interrogated using BD™ Precise Single Cell WTA (whole transcriptome amplification) Assay. Principal component analysis was applied to cluster the sorted Jurkat and T47D-cell populations.

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 Single-cell nucleosome mapping reveals the molecular basis of gene expression heterogeneity

2014 ◽  
Vol 111 (24) ◽  
pp. E2462-E2471 ◽  
Author(s):  
E. C. Small ◽  
L. Xi ◽  
J.-P. Wang ◽  
J. Widom ◽  
J. D. Licht
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Molecular Basis ◽  
Gene Expression Heterogeneity ◽  
Nucleosome Mapping

scholarly journals Mapping single-cell transcriptomes to copy number evolutionary trees

2021 ◽  
Author(s):  
Pedro F Ferreira ◽  
Jack Kuipers ◽  
Niko Beerenwinkel
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Copy Number ◽  
Evolutionary Dynamics ◽  
Real Data ◽  
Selective Advantage ◽  
Tumor Evolution ◽  
Clustering Methods ◽  
Gene Expression Heterogeneity ◽  
Cell Gene Expression

Cancer arises and evolves by the accumulation of somatic mutations that provide a selective advantage. The interplay of mutations and their functional consequences shape the evolutionary dynamics of tumors and contribute to different clinical outcomes. In the absence of scalable methods to jointly assay genomic and transcriptomic profiles of the same individual cell, the two data modalities are usually measured separately and need to be integrated computationally. Here, we introduce SCATrEx, a statistical model to map single-cell gene expression data onto the evolutionary history of copy number alterations of the tumor. SCATrEx jointly assigns cancer cells assayed with scRNA-seq to copy number profiles arranged in a copy number aberration tree and augments the tree with clone-specific clusters. Our simulations show that SCATrEx improves over both state-of-the-art unsupervised clustering methods and cell-to-clone assignment methods. In an application to real data, we observe that SCATrEx finds inter-clone and intra-clone gene expression heterogeneity not detectable using other integration methods. SCATrEx will allow for a better understanding of tumor evolution by jointly analysing the genomic and transcriptomic changes that drive it.

scholarly journals Designing single-cell experiments to harvest fluctuation noise while rejecting measurement noise

2021 ◽  
Author(s):  
Huy D Vo ◽  
Brian E Munsky
Keyword(s):  
Gene Expression ◽  
Measurement Error ◽  
Single Cell ◽  
Information Matrix ◽  
Experiment Design ◽  
Error Modeling ◽  
Measurement Noise ◽  
Noise Characteristics ◽  
Design Variables ◽  
Gene Expression Heterogeneity

Measurement error is a complicating factor that could reduce or distort the information contained in an experiment. This problem becomes even more serious in the context of experiments to measure single-cell gene expression heterogeneity, in which important quantities such as RNA and protein copy numbers are themselves subjected to the inherent randomness of biochemical reactions. Yet, it is not clear how measurement noise should be managed, in addition to other experiment design variables such as sampling size and frequency, in order to ensure that the collected data provides useful insights on the gene expression mechanism of interest. To address these experiment design challenges, we propose a model-centric framework that makes explicit use of measurement error modeling and Fisher Information Matrix-based criteria to decide between experimental methods. This unified approach not only allows us to see how different noise characteristics affect uncertainty in parameter estimation, but also enables a systematic approach to designing hybrid experiments that combine different measurement methods.

scholarly journals Epidermal Wnt signalling regulates transcriptome heterogeneity and proliferative fate in neighbouring cells

2017 ◽  
Author(s):  
Arsham Ghahramani ◽  
Giacomo Donati ◽  
Nicholas M. Luscombe ◽  
Fiona M. Watt
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Fate ◽  
Single Cell Analysis ◽  
Transcriptional Response ◽  
Wnt Signalling ◽  
Beta Catenin ◽  
Dependent Manner ◽  
Self Renewal ◽  
Gene Expression Heterogeneity

AbstractCanonical Wnt/beta-catenin signalling regulates self-renewal and lineage selection within the mouse epidermis. Although the transcriptional response of keratinocytes that receive a Wnt signal is well characterised, little is known about the mechanism by which keratinocytes in proximity to the Wntreceiving cell are co-opted to undergo a change in cell fate. To address this, we performed single-cell mRNA-Seq on mouse keratinocytes co-cultured with and without the presence of beta-catenin activated neighbouring cells. We identified seven distinct cell states in cultures that had not been exposed to the beta-catenin stimulus and show that the stimulus redistributes wild type subpopulation proportions. Using temporal single-cell analysis we reconstruct the cell fate changes induced by neighbour Wnt activation. Gene expression heterogeneity was reduced in neighbouring cells and this effect was most dramatic for protein synthesis associated genes. The changes in gene expression were accompanied by a shift from a quiescent to a more proliferative stem cell state. By integrating imaging and reconstructed sequential gene expression changes during the state transition we identified transcription factors, including Smad4 and Bcl3, that were responsible for effecting the transition in a contact-dependent manner. Our data indicate that non cell-autonomous Wnt/beta-catenin signalling decreases transcriptional heterogeneity and further our understanding of how epidermal Wnt signalling orchestrates regeneration and self-renewal.

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