VIB-IRC-Molecular Cell Biology / Bioinformatics Core Facility
Stageonderwerp 2009-2010 en voorstel stage-onderwerp (2010-2011) 5: MirTra: een gebruiksvriendelijke web-tool voor de simultane visualisatie van de transcriptionele en post-transcriptionele regulatie mogelijkheden van eukaryote genen (Dr. Pieter De Bleser)
Bulk RNA-seq was the preferred method for transcriptome analysis for a long time, but the last few years a competitor has shown up. Single cell RNA-seq (scRNA-seq) uses the same principle as bulk RNA-seq methods, but can do transcriptome analysis on a cellular level. This can be useful for the identification of cell types, specific cell response to disease and many more applications. To date scRNA-seq is especially used in human and mouse research, but recently Arabidopsis thaliana researchers started to get interested in the technique.
To see if the technique can be used on plant cells, a small experiment was set up to analyse the difference between the root of wild type Arabidopsis and TMO5 induced Arabidopsis. The overexpression of TMO5 promotes lateral growth of the root, resulting in a shorter but thicker root. The data was analysed using the same pipeline as human or mouse samples.The quality of the sample is as good or even better than most of the analysed human and mouse samples. Different cell types can be identified and it is also possible to use trajectory inference methods to identify a developmental path. Based on these results, we can conclude that scRNA-seq can be used to get insight in Arabidopsis on a cellular level.
Biologists have a lot of knowledge on a specific subject, but mostly lack the ability to perform bioinformatic analysis. This can be a problem for scRNA-seq data, since a bioinformatician mostly lacks the biological knowledge to interpret the results himself. To address this problem, a Shiny web application named “tSNEtool” was developed. This application uses the results of a scRNA-seq experiment and brings them to the biologist in an interactive way. By using a dimensionality reduction method, t-distributed stochastic neighbour embedding (tSNE) in this case, the high dimensional results can be visualised in a two-dimensional plot. This visualisation method makes it possible to identify different subpopulations among all cells. The expression of a certain gene can also be projected on this plot. Besides the visualisation, the application can also be used to determine differentially expressed genes among different subpopulations. This helps in the identification of the different subpopulations. To make this identification easier, an annotation method is implemented that uses marker genes of a specific cell type to annotate the different subpopulations. tSNEtool can also be used to perform a gene ontology analysis on a specific subpopulation to get a better understanding of the biological function of these cells.
Samenvatting eindwerk 1 2012-2013:
Tags: bioinformatics |
Address
Technologiepark 927
9052 Zwijnaarde
Belgium |
Contacts
Traineeship supervisor
Pieter De Bleser
09/3313693 pieterdb@dmbr.vib-ugent.be |
Traineeship supervisor
Dr. Frans van Roy
|
Traineeship supervisor
Claude Libert
|
Traineeship supervisor
Liesbet Martens
bioit@irc.vib-ugent.be |