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Comparison of high-throughput single-cell RNA-seq methods for ex vivo drug screening
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Molecular Precision Medicine. Uppsala University, Science for Life Laboratory, SciLifeLab.ORCID iD: 0000-0002-6242-6344
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Molecular Precision Medicine. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Women's and Children's Health, Pediatric oncological and neurological research.ORCID iD: 0000-0001-6505-4198
Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Molecular Precision Medicine.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala Univ Hosp, Dept Clin Chem & Pharmacol, S-75185 Uppsala, Sweden..
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2024 (English)In: NAR Genomics and Bioinformatics, E-ISSN 2631-9268, Vol. 6, no 1, article id lqae001Article in journal (Refereed) Published
Abstract [en]

Functional precision medicine (FPM) aims to optimize patient-specific drug selection based on the unique characteristics of their cancer cells. Recent advancements in high throughput ex vivo drug profiling have accelerated interest in FPM. Here, we present a proof-of-concept study for an integrated experimental system that incorporates ex vivo treatment response with a single-cell gene expression output enabling barcoding of several drug conditions in one single-cell sequencing experiment. We demonstrate this through a proof-of-concept investigation focusing on the glucocorticoid-resistant acute lymphoblastic leukemia (ALL) E/R+ Reh cell line. Three different single-cell transcriptome sequencing (scRNA-seq) approaches were evaluated, each exhibiting high cell recovery and accurate tagging of distinct drug conditions. Notably, our comprehensive analysis revealed variations in library complexity, sensitivity (gene detection), and differential gene expression detection across the methods. Despite these differences, we identified a substantial transcriptional response to fludarabine, a highly relevant drug for treating high-risk ALL, which was consistently recapitulated by all three methods. These findings highlight the potential of our integrated approach for studying drug responses at the single-cell level and emphasize the importance of method selection in scRNA-seq studies. Finally, our data encompassing 27 327 cells are freely available to extend to future scRNA-seq methodological comparisons.

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Oxford University Press, 2024. Vol. 6, no 1, article id lqae001
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Cell and Molecular Biology
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URN: urn:nbn:se:uu:diva-522872DOI: 10.1093/nargab/lqae001ISI: 001151552700002PubMedID: 38288374OAI: oai:DiVA.org:uu-522872DiVA, id: diva2:1838259
Funder
Swedish Research Council, 2019-01976Swedish Childhood Cancer Foundation, PR2022-0082Swedish Childhood Cancer Foundation, TJ2020-0039Swedish Childhood Cancer Foundation, PR2022-0082Göran Gustafsson Foundation for promotion of scientific research at Uppala University and Royal Institute of TechnologyEU, Horizon 2020, 824110 EASI-GenomicsUppsala UniversityAvailable from: 2024-02-15 Created: 2024-02-15 Last updated: 2024-02-15Bibliographically approved

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Gezelius, HenrikEnblad, Anna PiaLundmark, AndersÅberg, MartinBlom, KristinRudfeldt, JakobRaine, AmandaHarila-Saari, Arja H.Rendo, VerónicaAndersson, ClaesNordlund, Jessica

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Gezelius, HenrikEnblad, Anna PiaLundmark, AndersÅberg, MartinBlom, KristinRudfeldt, JakobRaine, AmandaHarila-Saari, Arja H.Rendo, VerónicaAndersson, ClaesNordlund, Jessica
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Molecular Precision MedicineScience for Life Laboratory, SciLifeLabPediatric oncological and neurological researchCancer Pharmacology and Computational MedicineDepartment of Medical SciencesNeurooncology and neurodegeneration
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