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Heterogeneous expression of cell surface markers in human myeloma cell lines: implications for disease modelling and therapeutic targeting

ABSTRACT: Heterogeneous expression of cell surface markers in human myeloma cell lines: implications for disease modelling and therapeutic targeting

PROVIDER: PRJNA1052711 | ENA |

REPOSITORIES: ENA

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Heterogeneous expression of cell surface markers in human myeloma cell lines: implications for disease modelling and therapeutic targeting

Project description:This RNAseq analysis is performed as a part of the work "Heterogeneous expression of cell surface markers in human myeloma cell lines: implications for disease modelling and therapeutic targeting", where expression levels both at the cell surface level and transcriptomic level are analysed.

2024-10-03 | GSE250236 | GEO

Computational identification of surface markers for isolating distinct subpopulations from heterogeneous cancer cell populations [scRNA-seq]

Project description:Intratumoral heterogeneity reduces treatment efficacy and complicates our understanding of tumor progression. There is a pressing need to understand how heterogeneous tumor cell subpopulations coexist within a tumor, yet biological systems to study these processes in vitro are limited. With the advent of single-cell RNA sequencing (scRNA-seq), it has been found that some cancer cell lines contain distinct subpopulations. These heterogeneous cell lines provide a unique opportunity to study coexistence and evolution between genetically similar cancer cell subpopulations in controlled experimental settings. Here, we present scEMD, a computational package that returns candidate surface makers maximally unique to transcriptomic subpopulations in scRNA-seq which may be used for FACS isolation. scEMD was experimentally validated using the MDA-MB-231 and MDA-MB-436 cell lines. ESAM and BST2/Tetherin were experimentally confirmed to identify and separate scRNA-seq cluster-matched subpopulations of MDA-MB-231 and MDA-MB-436 cells, respectively. Identification and enrichment of distinct subpopulations within cell line models using this computationally efficient and experimentally validated workflow paves the way for studies on the generation and maintenance of cellular heterogeneity in low-complexity in vitro systems.

2024-06-26 | GSE268249 | GEO

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Using intracellular markers to identify a novel set of surface markers for live cell purification from a heterogeneous hIPSC culture

Project description:We differentiated human iPSC to day 14 using our dopaminergic neuron protocol. At day 14, the cells were fixed, stained and FACS sorted in to the FOXA2/LMX1 double positive (wanted neural progenitor population) and double negative pools. We compared the RNA of the pools via microarray to identify surface markers in the double positive pool to be able to live purify the neural progenitors from the rest of the cells. Human embryonic stem cells (ESCs) and induced pluripotent stem cells (iPSCs) can provide sources for midbrain dopaminergic (mDA) neural progenitors (NPCs) for cell therapy to treat Parkinson’s disease (PD) patients. However, the well-known line-to-cell line variability in the differentiation capacity of individual cell lines needs to be improved for the success of this therapy. To address this issue, we sought to identify mDA NPC specific cell surface markers for fluorescence activated cell sorting (FACS). Through RNA isolation after sorting for NPCs based on staining for cell-specific transcription factors followed by microarray, we identified two positive cell surface markers (CORIN and CD166) and one negative cell surface marker (CXCR4) for mDA NPC sorting. These three markers can enrich dopaminergic NPCs to 90% purity, and the sorted NPCs more efficiently differentiate to mature dopaminergic neurons compared to unsorted or CORIN+ alone mDA NPCs. This surface marker identification strategy can be used broadly to facilitate isolation of cell subtypes of interest from heterogeneous cultures. Complete processed data are not available

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Computational identification of surface markers for isolating distinct subpopulations from heterogeneous cancer cell populations [scRNA-seq]

Project description:Computational identification of surface markers for isolating distinct subpopulations from heterogeneous cancer cell populations [scRNA-seq]

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Using intracellular markers to identify a novel set of surface markers for live cell purification from a heterogeneous hIPSC culture.

Project description:Human embryonic stem cells (ESCs) and induced pluripotent stem cells (iPSCs) can provide sources for midbrain dopaminergic (mDA) neural progenitors (NPCs) for cell therapy to treat Parkinson's disease (PD) patients. However, the well-known line-to-cell line variability in the differentiation capacity of individual cell lines needs to be improved for the success of this therapy. To address this issue, we sought to identify mDA NPC specific cell surface markers for fluorescence activated cell sorting (FACS). Through RNA isolation after sorting for NPCs based on staining for cell-specific transcription factors followed by microarray, we identified two positive cell surface markers (CORIN and CD166) and one negative cell surface marker (CXCR4) for mDA NPC sorting. These three markers can enrich floor plate NPCs to 90% purity, and the sorted NPCs more efficiently differentiate to mature dopaminergic neurons compared to unsorted or CORIN+ alone mDA NPCs. This surface marker identification strategy can be used broadly to facilitate isolation of cell subtypes of interest from heterogeneous cultures.

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Genome Wide Transcriptional Modelling of a 24hour timecourse of T-helper cell differentiation

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Project description:The myeloma cell surface proteome (“surfaceome”) not only determines tumor interaction with the microenvironment but serves as an emerging arena for therapeutic development. Here, we use glycoprotein capture proteomics to first define surface markers most-enriched on myeloma when compared to B-cell malignancy models, revealing unexpected biological signatures unique to malignant plasma cells. We next integrate our proteomic dataset with existing transcriptome databases, nominating CCR10 and TXNDC11 as possible monotherapeutic targets and CD48 as a promising co-target for increasing avidity of BCMA-directed cellular therapies. We further identify potential biomarkers of resistance to proteasome inhibitors and lenalidomide including changes in CD53, EVI2B, CD10, and CD33. Comparison of short-term treatment with chronic resistance delineates large differences in surface proteome profile under each type of drug exposure. Finally, we develop a miniaturized version of the surface proteomics protocol and present the first surface proteomic profile of a primary MM patient plasma cell sample. Our dataset provides a unique resource to advance the biological, therapeutic, and diagnostic understanding of myeloma.

2022-05-27 | PXD022553 | Pride

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