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Adapting the 10x Genomics Platform for single-cell RNA-seq in yeast reveals the importance of stochastic gene expression during the lag phase

ABSTRACT: Adapting the 10x Genomics Platform for single-cell RNA-seq in yeast reveals the importance of stochastic gene expression during the lag phase

PROVIDER: PRJNA605000 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR11029235.fastq.gz	Fastqsanger.gz
	SRR11029236.fastq.gz	Fastqsanger.gz
	SRR11029237.fastq.gz	Fastqsanger.gz
	SRR11029238.fastq.gz	Fastqsanger.gz
	SRR11029239.fastq.gz	Fastqsanger.gz

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Adapting the 10x Genomics Platform for single-cell RNA-seq in yeast reveals the importance of stochastic gene expression during the lag phase

Project description:Isogenic population of yeast cells was grown in a maltose-glucose-maltose media shift schema. Time point samples were taken throughout the experiment, and were analyzed for single-cell gene expression using 10x Genomics platform.

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Project description:We report the application of conventional ChIP-seq analysis for LAG-1 chromatin state in germline. We identified 137 genes putatively regulated by LAG-1 that's overlapped by three independent biological replicates. These genes included previously known targets lst-1, sygl-1, and mir-61/250.

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Next Generation Sequencing for Transcriptomes Analysis for LAG-1 time course by auxin treatment.

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Project description:We previously identified and validated a pre-treatment peripheral blood biomarker, characterized by a high frequency of LAG-3+ lymphocytes, that predicts resistance in patients receiving anti-PD-1 immune checkpoint blockade. To better understand the mechanism of aPD-1 resistance and select rational therapies to overcome resistance, we identified murine tumor models with a high LAG-3+ lymphocyte frequency (LAG-3hi), which were resistant to aPD-1 therapy, and LAG-3lo murine tumor models that were aPD-1 sensitive, recapitulating the predictive biomarker we previously described in patients. As we hypothesized, LAG-3hi tumor-bearing mice were sensitive to aPD-1+aLAG-3 therapy and the benefit of combination therapy was CD8+ T cell-dependent. Unexpectedly, the benefit of combination therapy was enhanced in LAG-3hi (but not LAG-3lo) mice with depletion of CD4+ T cells. Furthermore, responses to aPD-1+aLAG-3 significantly correlated with T regulatory cell (Treg) phenotypic plasticity in LAG-3hi mice, suggesting a specific role for Tregs in the mechanism of response to this combination. Using Treg fate tracking Foxp3GFP-Cre-ERT2 x ROSAYFP reporter mice we demonstrate that expanded populations of unstable Tregs correlate with response to combination therapy in LAG-3hi mice. Complementing this preclinical data, an increased proportion of unstable Tregs also significantly correlated with higher response rate and improved survival after aPD-1+aLAG-3 therapy in a cohort of patients with metastatic melanoma (n=117). These data indicate that Treg phenotypic plasticity is a key mechanism in aPD-1+aLAG-3 responsiveness, which may represent both a novel biomarker to aid patient selection and a rationale therapeutic target to expand benefit for a subset of PD-1 refractory patients.

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