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Heritable states influence stem cell fates upon acquisition of cancer mutations

ABSTRACT: Heritable states influence stem cell fates upon acquisition of cancer mutations

PROVIDER: PRJNA1106425 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR28845400_1.fastq.gz	Fastqsanger.gz
	SRR28845400_2.fastq.gz	Fastqsanger.gz
	SRR28845401_1.fastq.gz	Fastqsanger.gz
	SRR28845401_2.fastq.gz	Fastqsanger.gz
	SRR28845402_1.fastq.gz	Fastqsanger.gz

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Heritable states influence stem cell fates upon acquisition of cancer mutations

Project description:Tumors with the same driver mutations can display a striking variation in their progression and treatment response, but the origins of this variation are still unclear. In this study, we use state-fate analysis to unveil that heritable stem cell states can influence how individual cells respond to the acquisition of the same cancer mutation. We develop a new methodological pipeline, single-cell Tracking of Recombinase Activation And Clonal Kinetics, and apply it to hematopoietic stem cells carrying Cre/Flp-conditional leukemia alleles. Tracking the gene expression changes and expansion kinetics of a common set of stem cell clones, with and without the same myeloid leukemia mutations, we unveil a striking heterogeneity in the malignant fates of diverse stem cells. First, we define that heritable clonal states persist in expansion cultures and cause the selection of a small group of clones with a specific fitness signature. Then, using mouse models of the most frequent initiating mutations, we define that these pre-existent stem cell states influence the mutation-induced changes in expansion, fate, and malignant gene expression programs. Initiating driver mutations increase the survival probability of clones with low fitness through enhancing their stemness programs. Surprisingly, the fate of high-fitness stem-cell clones is sometimes reversed, producing more mature leukemias, yet still carrying markers of their cell of origin. We further validate these HSC-of-origin signatures in bulk and single-cell RNAseq datasets from cancer patients. Our findings suggest that aggressive premalignant clonal expansions arise from low-fitness stem cells more frequently than previously expected.

2024-09-17 | GSE266232 | GEO

Tissue-specific non-heritable influence drive uterine immune system variation

Project description:Tissue-specific non-heritable influence drive uterine immune system variation

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Tissue-specific non-heritable influence drive uterine immune system variation (ATAC-seq)

Project description:Tissue-specific non-heritable influence drive uterine immune system variation (ATAC-seq)

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Tissue-specific non-heritable influence drive uterine immune system variation (RNA-seq)

Project description:Tissue-specific non-heritable influence drive uterine immune system variation (RNA-seq)

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Tissue-specific non-heritable influence drive uterine immune system variation (RNA-seq)

Project description:Human twin studies have revealed the combined contribution of heritable and non-heritable (environmental) factors in shaping peripheral blood immune system variability. However, the heterogeneity within tissue immune compartments and the contribution of tissue-specific external factors remain unexplored. The human uterus is a tissue under constant regeneration exposed to distinct environmental factors. To assess uterine immune system variation, we have performed a transcriptomic and epigenetic analysis of uterine and peripheral blood immune cells in monozygotic twins and longitudinal samples in relation to heritable versus non-heritable factors.

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Tissue-specific non-heritable influence drive uterine immune system variation (ATAC-seq)

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Influence of

Project description:Influence of

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Evaluating the influence of MS-acquisition parameters on DDA label-free proteomics analyses

Project description:Label-free proteomics enables the unbiased quantification of thousands of proteins across large sample cohorts. Commonly used mass spectrometry-based proteomic workflows rely on data dependent acquisition (DDA). However, its stochastic selection of peptide features for fragmentation-based identification inevitably results in high rates of missing values, which prohibits the integration of larger cohorts as the number of recurrently detected peptides is a limiting factor. Peptide identity propagation (PIP) can mitigate this challenge, allowing to transfer sequencing information between samples. However, despite the promise of these approaches, current methods remain limited either in sensitivity or reliability and there is a lack of robust and widely applicable software. Here we prepared a tool spike-in data set which can be used to evaluate the influence of changing Top-N, gradient length and sample injection amounts on DDA label-free proteomics results. It also includes analysis by data-independent acquisition (DIA) which allows direct comparison of DDA and DIA for label-free proteomics analyses.

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