Single cell RNA sequencing of KPC1199 cell line [mouse scRNA-seq]
Ontology highlight
ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE128794 | GEO | 2020/03/30
REPOSITORIES: GEO
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Project description:We first validated the robustness and high throughput capability of the GTO protocol in multiple cell lines. The key step to success of GTO is determining the optimum number of cycles for first amplification of cDNA. This step needs to be result in a balance between cDNA and gDNA reads so that the gene expression profiles generated are good while not comprimising on the CNA profile quality. This validation was done in SKBR3, A375 and BT549 cell lines. They were then compared to 315A control diploid cell line also processed by the GTO method. The scRNAseq and scDNAseq data generated by GTO was alsocompared to bulk RNAseq and DNAseq data generated in triplicates for each cell line by traditional methods. After the method was tested in cell lines, we next applied the method to mouse models to understand tumor biology. The mouse model we used here is an orthotopic transplantation model of pancreatic cancer in syngenic mice. EGFP positive KPC 1199 tumor cells were injected into the pancreas of adult wild-type mice to generate primary tumors in pancreas and metastases in liver, spleen, peritoneum and kidneys. These primary tumors and mets were then extracted and dissociated into single cell suspensions. The single cell suspensions were analyzed by flow cytometry with antibodies against CD45, Epcam and for endogeneous GFP to isolate single stromal and tumor cells. These cells were then processed by GTP protocol to analyze gDNA by CNV profiles and RNA through read counts from the same cell. To validate our data and also have controls, we generated scRNAseq gene expression data and scDNAseq CNA profiles from the 2D cells at the same passage number at which they were injected into the mice. The scRNAseq data was generated using Fluidigm C1 machine on theie medium IFC for mRNA seq according to user manual instructions. The scDNAseq data for CNA profiles was generated by sorting single nuclei into PCR plates and then using SeqXE kit from Sigma Aldrich for Whole genome amplification using their instructions again. The data analysis was done similar to scRNAseq or scDNAseq data from GTO.
2020-03-30 | GSE128790 | GEO
Project description:We first validated the robustness and high throughput capability of the GTO protocol in multiple cell lines. The key step to success of GTO is determining the optimum number of cycles for first amplification of cDNA. This step needs to be result in a balance between cDNA and gDNA reads so that the gene expression profiles generated are good while not comprimising on the CNA profile quality. This validation was done in SKBR3, A375 and BT549 cell lines. They were then compared to 315A control diploid cell line also processed by the GTO method. The scRNAseq and scDNAseq data generated by GTO was alsocompared to bulk RNAseq and DNAseq data generated in triplicates for each cell line by traditional methods. After the method was tested in cell lines, we next applied the method to mouse models to understand tumor biology. The mouse model we used here is an orthotopic transplantation model of pancreatic cancer in syngenic mice. EGFP positive KPC 1199 tumor cells were injected into the pancreas of adult wild-type mice to generate primary tumors in pancreas and metastases in liver, spleen, peritoneum and kidneys. These primary tumors and mets were then extracted and dissociated into single cell suspensions. The single cell suspensions were analyzed by flow cytometry with antibodies against CD45, Epcam and for endogeneous GFP to isolate single stromal and tumor cells. These cells were then processed by GTP protocol to analyze gDNA by CNV profiles and RNA through read counts from the same cell. To validate our data and also have controls, we generated scRNAseq gene expression data and scDNAseq CNA profiles from the 2D cells at the same passage number at which they were injected into the mice. The scRNAseq data was generated using Fluidigm C1 machine on theie medium IFC for mRNA seq according to user manual instructions. The scDNAseq data for CNA profiles was generated by sorting single nuclei into PCR plates and then using SeqXE kit from Sigma Aldrich for Whole genome amplification using their instructions again. The data analysis was done similar to scRNAseq or scDNAseq data from GTO.
2020-03-30 | GSE128785 | GEO
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2020-03-30 | GSE128793 | GEO
Project description:We first validated the robustness and high throughput capability of the GTO protocol in multiple cell lines. The key step to success of GTO is determining the optimum number of cycles for first amplification of cDNA. This step needs to be result in a balance between cDNA and gDNA reads so that the gene expression profiles generated are good while not comprimising on the CNA profile quality. This validation was done in SKBR3, A375 and BT549 cell lines. They were then compared to 315A control diploid cell line also processed by the GTO method. The scRNAseq and scDNAseq data generated by GTO was alsocompared to bulk RNAseq and DNAseq data generated in triplicates for each cell line by traditional methods. After the method was tested in cell lines, we next applied the method to mouse models to understand tumor biology. The mouse model we used here is an orthotopic transplantation model of pancreatic cancer in syngenic mice. EGFP positive KPC 1199 tumor cells were injected into the pancreas of adult wild-type mice to generate primary tumors in pancreas and metastases in liver, spleen, peritoneum and kidneys. These primary tumors and mets were then extracted and dissociated into single cell suspensions. The single cell suspensions were analyzed by flow cytometry with antibodies against CD45, Epcam and for endogeneous GFP to isolate single stromal and tumor cells. These cells were then processed by GTP protocol to analyze gDNA by CNV profiles and RNA through read counts from the same cell. To validate our data and also have controls, we generated scRNAseq gene expression data and scDNAseq CNA profiles from the 2D cells at the same passage number at which they were injected into the mice. The scRNAseq data was generated using Fluidigm C1 machine on theie medium IFC for mRNA seq according to user manual instructions. The scDNAseq data for CNA profiles was generated by sorting single nuclei into PCR plates and then using SeqXE kit from Sigma Aldrich for Whole genome amplification using their instructions again. The data analysis was done similar to scRNAseq or scDNAseq data from GTO.
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Project description:We first validated the robustness and high throughput capability of the GTO protocol in multiple cell lines. The key step to success of GTO is determining the optimum number of cycles for first amplification of cDNA. This step needs to be result in a balance between cDNA and gDNA reads so that the gene expression profiles generated are good while not comprimising on the CNA profile quality. This validation was done in SKBR3, A375 and BT549 cell lines. They were then compared to 315A control diploid cell line also processed by the GTO method. The scRNAseq and scDNAseq data generated by GTO was alsocompared to bulk RNAseq and DNAseq data generated in triplicates for each cell line by traditional methods. After the method was tested in cell lines, we next applied the method to mouse models to understand tumor biology. The mouse model we used here is an orthotopic transplantation model of pancreatic cancer in syngenic mice. EGFP positive KPC 1199 tumor cells were injected into the pancreas of adult wild-type mice to generate primary tumors in pancreas and metastases in liver, spleen, peritoneum and kidneys. These primary tumors and mets were then extracted and dissociated into single cell suspensions. The single cell suspensions were analyzed by flow cytometry with antibodies against CD45, Epcam and for endogeneous GFP to isolate single stromal and tumor cells. These cells were then processed by GTP protocol to analyze gDNA by CNV profiles and RNA through read counts from the same cell. To validate our data and also have controls, we generated scRNAseq gene expression data and scDNAseq CNA profiles from the 2D cells at the same passage number at which they were injected into the mice. The scRNAseq data was generated using Fluidigm C1 machine on theie medium IFC for mRNA seq according to user manual instructions. The scDNAseq data for CNA profiles was generated by sorting single nuclei into PCR plates and then using SeqXE kit from Sigma Aldrich for Whole genome amplification using their instructions again. The data analysis was done similar to scRNAseq or scDNAseq data from GTO.
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