Targeted absolute transcript quantification in single cells after whole transcriptome amplification v1 (protocols.io.389grz6)

protocols.io ◽  
2019 ◽  
Author(s):  
Franziska Durst ◽  
Ana Grujovic ◽  
Iris Ganser ◽  
Martin Hoffmann ◽  
Peter Ugocsai ◽  
...  
PLoS ONE ◽  
2019 ◽  
Vol 14 (8) ◽  
pp. e0216442 ◽  
Author(s):  
Franziska C. Durst ◽  
Ana Grujovic ◽  
Iris Ganser ◽  
Martin Hoffmann ◽  
Peter Ugocsai ◽  
...  

2019 ◽  
Author(s):  
Franziska C. Durst ◽  
Ana Grujovic ◽  
Iris Ganser ◽  
Martin Hoffmann ◽  
Peter Ugocsai ◽  
...  

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.


2017 ◽  
Vol 4 (S) ◽  
pp. 102
Author(s):  
Xiaoyang (Alice) Wang ◽  
Chip Lomas ◽  
Craig Betts ◽  
Aaron Walker ◽  
Christina Fan ◽  
...  

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.


2020 ◽  
Vol 48 (18) ◽  
pp. e107-e107 ◽  
Author(s):  
Tamim Abdelaal ◽  
Soufiane Mourragui ◽  
Ahmed Mahfouz ◽  
Marcel J T Reinders

Abstract Single-cell technologies are emerging fast due to their ability to unravel the heterogeneity of biological systems. While scRNA-seq is a powerful tool that measures whole-transcriptome expression of single cells, it lacks their spatial localization. Novel spatial transcriptomics methods do retain cells spatial information but some methods can only measure tens to hundreds of transcripts. To resolve this discrepancy, we developed SpaGE, a method that integrates spatial and scRNA-seq datasets to predict whole-transcriptome expressions in their spatial configuration. Using five dataset-pairs, SpaGE outperformed previously published methods and showed scalability to large datasets. Moreover, SpaGE predicted new spatial gene patterns that are confirmed independently using in situ hybridization data from the Allen Mouse Brain Atlas.


2020 ◽  
Author(s):  
Li Lin ◽  
Minfang Song ◽  
Yong Jiang ◽  
Xiaojing Zhao ◽  
Haopeng Wang ◽  
...  

ABSTRACTNormalization with respect to sequencing depth is a crucial step in single-cell RNA sequencing preprocessing. Most methods normalize data using the whole transcriptome based on the assumption that the majority of transcriptome remains constant and are unable to detect drastic changes of the transcriptome. Here, we develop an algorithm based on a small fraction of constantly expressed genes as internal spike-ins to normalize single cell RNA sequencing data. We demonstrate that the transcriptome of single cells may undergo drastic changes in several case study datasets and accounting for such heterogeneity by ISnorm improves the performance of downstream analyzes.


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