Project description:In this study we describe a new method for GTO (Genomics Transcriptomics One-Tube) that allows for simeltaneous sequencing of RNA and DNA from a single cell in the same tube. This method relies on dual amplification. The first amplification is done immediately after the mRNA is transcribed to cDNA using SMART-Seq2 protocol (Picelli et al., 2014) to increase the level of cDNA to that comparable to gDNA. The second amplification with universal primers from SeqXE kit for whole genome amplification (from Sigma Aldrich) amplifies the cDNA and gDNA fragments to amounts required for library preparation. The reads generated from this mixed library are separated bioinformatically to exonic and nonexonic reads depending on where they align in the reference genome to generate the RNAseq and DNAseq data respectively. We present data generated from single in-vitro cells of multiple cell lines (A375, BT549, SKBR3, 315A) and also from single in-vivo cells isolated from tumors of an orthotopic tranplantation mouse model of pancreatic cancer (using KPC1199-EGFP cell line).
Project description:Single cell genome, DNA methylome, and transcriptome sequencing has been achieved separately. However, to analyze the regulation of RNA expression by genetic and epigenetic factors within an individual cell, it is necessary to analyze these omics simultaneously from the same single cell. Here we developed a single cell triple omics sequencing technique- scTrio-seq, to analyze the genome, DNA methylome, and transcriptome concurrently of a mammalian cell. 6 single human HepG2 cell line cells were sequenced using the newly developed scTrio-seq, other 2 HepG2 cells were sequenced using scRNA-seq and other 2 HepG2 cells were sequenced using scRRBS as technique control. 6 single mouse embryonic stem cells (mESCs) were sequenced using the newly developted scTrio-seq. Meanwhile, two scRNA-seq and two scRRBS were also completed using two mESCs separately. 26 single cells from hepatocellular carcinoma were sequenced using scTrio-seq to analyze the regulation relations between three omics of cancer cells.
Project description:An indepth analysis of Paneth cell transcriptome at single cell level has not been available. Existing intestinal epithelial cell scRNA dataset contain many cell types, where Paneth cells represent a small portion. We used a flow cytometry based approach to enrich and isolate relatively pure Panethc cells from a newly developed Paneth cell reporter mouse line (Lyz1-3'UTR-IRES-CreER; Rosa26R-tdTomato). Single cell RNA sequencing was performed on purified duodenal and ileal Paneth cells of mice housed under specific pathogen free condition.
Project description:Hass2017-PanRTK model for single cell
line
The model structure comprises
heterodimerization and receptor trafficking as described in detail
in the article below. For ligand input, set a respective
event. The illustrated event sets the EGF concentration to 2.5 nMol
in the model file.
This model is described in the article:
Predicting ligand-dependent
tumors from multi-dimensional signaling features.
Hass H, Masson K, Wohlgemuth S,
Paragas V, Allen JE, Sevecka M, Pace E, Timmer J, Stelling J,
MacBeath G, Schoeberl B, Raue A.
NPJ Syst Biol Appl 2017; 3: 27
Abstract:
Targeted therapies have shown significant patient benefit in
about 5-10% of solid tumors that are addicted to a single
oncogene. Here, we explore the idea of ligand addiction as a
driver of tumor growth. High ligand levels in tumors have been
shown to be associated with impaired patient survival, but
targeted therapies have not yet shown great benefit in
unselected patient populations. Using an approach of applying
Bagged Decision Trees (BDT) to high-dimensional signaling
features derived from a computational model, we can predict
ligand dependent proliferation across a set of 58 cell lines.
This mechanistic, multi-pathway model that features receptor
heterodimerization, was trained on seven cancer cell lines and
can predict signaling across two independent cell lines by
adjusting only the receptor expression levels for each cell
line. Interestingly, for patient samples the predicted tumor
growth response correlates with high growth factor expression
in the tumor microenvironment, which argues for a co-evolution
of both factors in vivo.
This model is hosted on
BioModels Database
and identified by:
MODEL1708210000.
To cite BioModels Database, please use:
Chelliah V et al. BioModels: ten-year
anniversary. Nucl. Acids Res. 2015, 43(Database
issue):D542-8.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to
the public domain worldwide. Please refer to
CC0
Public Domain Dedication for more information.