scholarly journals Single organoid RNA-sequencing reveals high organoid-to-organoid variability

2021 ◽  
Author(s):  
Kristin Gehling ◽  
Swati Parekh ◽  
Farina Schneider ◽  
Marcel Kirchner ◽  
Vangelis Kondylis ◽  
...  
Keyword(s):  
Gene Expression ◽  
Experimental Design ◽  
Personalized Medicine ◽  
Culture Condition ◽  
Ips Cells ◽  
The Other ◽  
Great Promise ◽  
Rna Seq ◽  
Systematic Analysis ◽  
Differential Gene

Over the last decades, organoids have been established from the majority of tissue resident stem and iPS cells. They hold great promise for our understanding of mammalian organ development, but also for the study of disease or even personalized medicine. In recent years, several reports hinted at intraculture organoid variability, but a systematic analysis of such a heterogeneity has not been performed before. Here, we used RNA-seq of individual organoids to address this question. Importantly, we find that batch-to-batch variation is very low, even when prepared by different researchers. On the other hand, there is organoid-to-organoid variability within a culture. Using differential gene expression, we did not identify specific pathways that drive this variability, pointing towards possible effects of the microenvironment within the culture condition. Taken together, our study provides a framework for organoid researchers to properly consider experimental design.

scholarly journals Design and power analysis for multi-sample single cell genomics experiments

2020 ◽  
Author(s):  
Katharina T. Schmid ◽  
Cristiana Cruceanu ◽  
Anika Böttcher ◽  
Heiko Lickert ◽  
Elisabeth B. Binder ◽  
...  
Keyword(s):  
Gene Expression ◽  
Experimental Design ◽  
Single Cell ◽  
Expression Analysis ◽  
Quantitative Trait ◽  
Power Analysis ◽  
Rna Seq ◽  
Wide Range ◽  
Differential Gene ◽  
Number Of Cells

AbstractBackgroundThe identification of genes associated with specific experimental conditions, genotypes or phenotypes through differential expression analysis has long been the cornerstone of transcriptomic analysis. Single cell RNA-seq is revolutionizing transcriptomics and is enabling interindividual differential gene expression analysis and identification of genetic variants associated with gene expression, so called expression quantitative trait loci at cell-type resolution. Current methods for power analysis and guidance of experimental design either do not account for the specific characteristics of single cell data or are not suitable to model interindividual comparisons.ResultsHere we present a statistical framework for experimental design and power analysis of single cell differential gene expression between groups of individuals and expression quantitative trait locus analysis. The model relates sample size, number of cells per individual and sequencing depth to the power of detecting differentially expressed genes within individual cell types. Power analysis is based on data driven priors from literature or pilot experiments across a wide range of application scenarios and single cell RNA-seq platforms. Using these priors we show that, for a fixed budget, the number of cells per individual is the major determinant of power.ConclusionOur model is general and allows for systematic comparison of alternative experimental designs and can thus be used to guide experimental design to optimize power. For a wide range of applications, shallow sequencing of high numbers of cells per individual leads to higher overall power than deep sequencing of fewer cells. The model is implemented as an R package scPower.

scholarly journals Differential Gene Expression by RNA-Seq Analysis of the Primo Vessel in the Rabbit Lymph

2019 ◽  
Vol 12 (1) ◽  
pp. 11-19 ◽  
Author(s):  
Jun-Young Shin ◽  
Sang-Heon Choi ◽  
Da-Woon Choi ◽  
Ye-Jin An ◽  
Jae-Hyuk Seo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Rna Seq ◽  
Differential Gene

scholarly journals RNAflow: An Effective and Simple RNA-Seq Differential Gene Expression Pipeline Using Nextflow

Genes ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1487
Author(s):  
Marie Lataretu ◽  
Martin Hölzer
Keyword(s):  
Gene Expression ◽  
Homo Sapiens ◽  
Standard Technique ◽  
Common Species ◽  
Rna Seq ◽  
Rna Molecules ◽  
Gene Filtering ◽  
Differential Gene ◽  
High Level ◽  
Very High

RNA-Seq enables the identification and quantification of RNA molecules, often with the aim of detecting differentially expressed genes (DEGs). Although RNA-Seq evolved into a standard technique, there is no universal gold standard for these data’s computational analysis. On top of that, previous studies proved the irreproducibility of RNA-Seq studies. Here, we present a portable, scalable, and parallelizable Nextflow RNA-Seq pipeline to detect DEGs, which assures a high level of reproducibility. The pipeline automatically takes care of common pitfalls, such as ribosomal RNA removal and low abundance gene filtering. Apart from various visualizations for the DEG results, we incorporated downstream pathway analysis for common species as Homo sapiens and Mus musculus. We evaluated the DEG detection functionality while using qRT-PCR data serving as a reference and observed a very high correlation of the logarithmized gene expression fold changes.

Molecular cloning of stage specific mRNAs from amoebae and Plasmodia of Physarum polycephalum

1986 ◽  
Vol 64 (12) ◽  
pp. 1294-1302 ◽  
Author(s):  
Dominick Pallotta ◽  
André Laroche ◽  
Anne Tessier ◽  
Thomas Shinnick ◽  
Gérald Lemieux
Keyword(s):  
Gene Expression ◽  
Molecular Cloning ◽  
Physarum Polycephalum ◽  
Developmental Stages ◽  
The Other ◽  
Cdna Libraries ◽  
Northern Hybridization ◽  
Cdna Sequences ◽  
Specific Mrnas ◽  
Differential Gene

We constructed cDNA libraries from plasmodia and amoebal poly(A)+ RNA of Physarum polycephalum. The libraries were screened by differential hybridization with labeled poly(A)+ RNA of amoebae and plasmodia. The 136 plasmodial specific clones that gave the strongest hybridization signals were analysed in detail. From this group six different cDNA sequences were found. Four of the cDNAs each accounted for between 1 and 4.8% of all the clones in the library and represented abundant mRNAs. Two other clones constituted 0.2 and 0.4% of the total library. Seventeen clones in the amoebal library were amoebal specific. From these clones, seven different sequences were found. One of the sequences was present in nine clones (1.2%) of the library and considered abundant. The other six sequences were each found in only one or two clones. The specificity of these amoebal and plasmodial mRNAs was confirmed by Northern hybridization. Our results show that amoebae and plasmodia have different mRNA populations, which are most likely the result of differential gene expression in these two developmental stages.

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