Project description: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.
Project description: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), and then processed by scRNA-Seq or Multiome.
Project description: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. We propose GALILEO, a framework providing lineage tracing, transcriptomic, and chromatin accessibility information simultaneously at single-cell resolution (Multiome ATAC + gene expression on barcoded cells). The combination of single-cell lineage tracing with phenotypic assays allows to link a cell state with its fate.
Project description:We present Barcoded Oligonucleotides Ligated On RNA Amplified for Multiplexed and parallel In Situ analyses (BOLORAMIS), a reverse transcription-free method for spatially-resolved, targeted, in situ RNA identification of single or multiple targets. BOLORAMIS was demonstrated on a range of cell types and human cerebral organoids. Singleplex experiments to detect coding and non-coding RNAs in human iPSCs showed a stem-cell signature pattern. Specificity of BOLORAMIS was found to be 92% as illustrated by a clear distinction between human and mouse housekeeping genes in a co-culture system, as well as by recapitulation of subcellular localization of lncRNA MALAT1. Sensitivity of BOLORAMIS was quantified by comparing with single molecule FISH experiments and found to be 11%, 12% and 35% for GAPDH, TFRC and POL2RA respectively. To demonstrate BOLORAMIS for multiplexed gene analysis, we targeted 96 mRNAs within a co-culture of iNGN neurons and HMC3 human microglia cells. We used fluorescence in situ sequencing to detect error-robust 8-base barcodes associated with each of these genes. We then used this data to uncover the spatial relationship among cells and transcripts by performing single-cell clustering and gene-gene proximity analyses. We anticipate the BOLORAMIS technology for in situ RNA detection to find applications in basic and translational research.
Project description:Next generation sequencing (NGS) allows for sensitive quantification of DNA and RNA. It would be highly desirable to have a systematic equivalent for assaying cellular protein levels on living cells. We present a highly multiplexed, quantitative, and inexpensive sequencing-based proteomic method using genetically barcoded antibodies called Phage-antibody Next Generation Sequencing (PhaNGS). We demonstrate the utility of PhaNGS by showing how a set of 144 targeted Fab-phage can reliably detect changes in 44 targeted cell surface proteins in drug sensitive and resistant B-cells, or upon induction of the Myc oncogene.
Project description:SUM149PT, SUM185PE, SUM229PE, SUM159PT and SUM1315MO2 are triple negative breast cancer cell lines. They were sequenced to test the contrastive learning-based algorithm we developed to predict Afatinib sensitivity in TNBC.