Unknown,Transcriptomics,Genomics,Proteomics

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Multiplexed Single Cell gene expression profile of barcoded SUM159PT cells

ABSTRACT: Cancer is a heterogeneous disease, where multiple, phenotypically distinct subpopulations co-exist. Tumour evolution is a result of a complex interplay of genetic and epigenetic factors. To predict the molecular drivers of distinct cancer responses, we apply single-cell lineage tracing (scRNA-Seq of barcoded cells) on a triple-negative breast cancer model. SUM159PT cells infected with a lentiviral barcode library (Perturb-seq Library) were sorted according to the presence of BFP signal, treated or not with paclitaxel (PTX), multiplexed with MULTI-Seq protocol, and then processed by scRNA-Seq.

INSTRUMENT(S): Illumina NovaSeq 6000

ORGANISM(S): Homo sapiens

SUBMITTER: Francesca Nadalin

PROVIDER: E-MTAB-13066 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:Pancreatic cancer is a complex disease with a desmoplastic stroma, extreme hypoxia, and inherent resistance to therapy. Understanding the signaling and adaptive response of such an aggressive cancer is key to making advances in therapeutic efficacy and understanding disease progression. Redox factor-1 (Ref-1), a redox signaling protein, regulates the DNA binding activity of several transcription factors, including HIF-1. The conversion of HIF-1 from an oxidized to reduced state leads to enhancement of its DNA binding. In our previously published work, knockdown of Ref-1 under normoxia resulted in altered gene expression patterns on pathways including EIF2, protein kinase A, and mTOR. In this study, single cell RNA sequencing (scRNA-seq) and proteomics were used to explore the effects of Ref-1 on metabolic pathways under hypoxia.Results: We also integrated the scRNA data analysis with the proteomic analysis and found that the differentially expressed genes and pathways identified from the scRNA-seq data are highly consistent to the significant proteins observed in the proteomics data, especially for the upregulated cell cycle and transcription pathways and downregulated metabolic, apoptosis and signaling pathways under hypoxia. Conclusion: The scRNA-seq and proteomics data consistently demonstrated down-regulated central metabolism pathways in APE1/Ref-1 knockdown vs scrambled control under both normoxia and hypoxia conditions. Experimental Methods: scRNA-seq comparing pancreatic cancer cells expressing less than 20% of the Ref-1 protein was analyzed using left truncated mixture Gaussian model. Matched samples were also collected for bulk proteomic analysis of the four conditions. scRNA-seq data was validated using proteomics and qRT-PCR. Ref-1’s role in mitochondrial function was confirmed using mitochondrial function assays and qRT-PCR. Results: We also integrated the scRNA data analysis with the proteomic analysis and found that the differentially expressed genes and pathways identified from the scRNA-seq data are highly consistent to the significant proteins observed in the proteomics data, especially for the upregulated cell cycle and transcription pathways and downregulated metabolic, apoptosis and signaling pathways under hypoxia. Conclusion: The scRNA-seq and proteomics data consistently demonstrated down-regulated central metabolism pathways in APE1/Ref-1 knockdown vs scrambled control under both normoxia and hypoxia conditions.

Project description:"The main goal of the project is to develop a new generation of bioinformatics resources for the integrative analysis of multiple types of omics data. These resources include both novel statistical methodologies as well as user-friendly software implementations. STATegra methods address many aspects of the omics data integration problem such as the design of multiomics experiments, integrative transcriptional and regulatory networks, integrative variable selection, data fusion, integration of public domain data, and integrative pathway analysis. To support method development STATegra uses a model biological system, namely the differentiation process of mouse pre-B-cells. The STATegra consortium generated data focused on a critical step in the differentiation of B lymphocytes, which are key components of the adaptive immune system. Transcription factors of the Ikaros family are central to the normal differentiation of B cell progenitors and their expression increases in response to developmental stage-specific signals to terminate the proliferation of B cell progenitors and to initiate their differentiation. In particular, a novel biological system that models the transition from the pre-BI stage to the pre-BII subsequent stage, where B cell progenitors undergo growth arrest and differentiation, was used. The approach involves a pre-B cell line, B3 , and an inducible version of the Ikaros transcription factor, Ikaros-ERt2. Ikaros factors act to down-regulate genes that drive proliferation and to simultaneously up-regulate the expression of genes that promote the differentiation of B cell progenitors. Hence, in the B3 system, before induction of Ikaros, cells proliferate and their gene expression pattern is similar to proliferating B cell progenitors in vivo. Following Ikaros induction, B3 cells undergo gene expression changes that resemble those that occur in vivo during the transition from cycling to resting pre-B cells, followed by a marked reduction in cellular proliferation and by G1 arrest. On this system the consortium has created a high-quality data collection consisting of a replicated time course using seven different omics platforms: RNA-seq, miRNA-seq, ChIP-seq, DNase-seq, Methyl-seq, proteomics and metabolomics, which is used to assess and to validate STATegra methods."

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Multiplexed Single Cell gene expression profile of barcoded SUM159PT cells

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