Cellular quiescence is a reversible, non-proliferative cell state that preserves stem cell function, supports tissue homeostasis, and enables dynamic responses to injury, stress, and infection. Quiescence is essential for adult stem-cell maintenance, regeneration, immune function, aging, and cancer persistence, yet the molecular mechanisms governing entry into, maintenance of, and exit from quiescence remain poorly understood. A major barrier to progress is the lack of experimentally validated quiescent-cell training datasets and generalizable computational tools for identifying quiescent cells across diverse biological contexts.
Our preliminary studies identified a quiescent-like Neural G0 state in human neural stem cells and led to the development of an experimental framework for enriching and characterizing quiescent cells by transcriptomic profiling. Comparative analyses of quiescent stem-cell populations revealed a conserved set of seven quiescence-associated genes, supporting the existence of a shared quiescence program across distinct developmental lineages. We hypothesize that quiescence is underpinned by a conserved transcriptional regulatory program shared across stem, progenitor, and immune cell populations, and that systematic characterization of quiescent cells across diverse lineages will reveal the fundamental molecular mechanisms governing quiescence.
To test this hypothesis, Aim 1 will determine whether seven core quiescence genes are necessary and sufficient to regulate quiescence entry, maintenance, and exit through gain- and loss-of-function studies in human stem-cell models, coupled with transcriptomic characterization of downstream regulatory programs. Aim 2 will construct a comprehensive compendium of quiescent stem, progenitor, and immune cell populations spanning human in vitro and mouse in vivo systems across all three germ layers using bulk RNA-seq and single-cell RNA sequencing (scRNA-seq). These datasets will be used to further define the conserved core quiescence program. Aim 3 will develop scQuiescNet, a generalizable deep-learning framework that integrates single-cell foundation models with out-of-distribution learning strategies to identify quiescent cells across diverse tissues, cell types, species, and experimental platforms.
The proposed studies will establish fundamental mechanistic insight into the regulation of quiescence, generate a comprehensive resource of experimentally validated quiescent cellular states, and provide the first generalizable computational framework for identifying quiescent cells from single-cell transcriptomic data. Together, these advances will accelerate discovery of conserved quiescence mechanisms and facilitate future applications in regenerative medicine, aging, immune biology, and cancer.
","end":"2031-01-01T00:00:00Z","funding":[{"name":"National Institutes of Health (nih.gov)","funder_id":{"type":"ror","identifier":"https://ror.org/01cwqze88"},"funding_status":"planned","dmproadmap_funded_affiliations":[{"name":"Arizona State University (asu.edu)","affiliation_id":{"type":"ror","identifier":"https://ror.org/03efmqc40"}}]}],"title":"Learning the core quiescence program to enable robust identification of quiescent cells"}],"dmp_id":{"type":"doi","identifier":"https://doi.org/10.48321/D1AD212614"},"registered":"2026-06-04T20:59:54Z","ethical_issues_exist":"unknown","language":"eng","dmproadmap_links":{"get":"https://https/api/v2/plans/154878"},"dataset":[{"description":"No individual datasets have been defined for this DMP.","type":"dataset","title":"Generic dataset","keyword":["Medical engineering","2.6 - Medical engineering"]}],"dmproadmap_privacy":"private","dmphub_modifications":[],"dmproadmap_template":{"title":"NIH Data Management and Sharing Plan (2026 Pilot DMS Format)","id":"21609016"},"dmproadmap_featured":"0","description":"Cellular quiescence is a reversible, non-proliferative cell state that preserves stem cell function, supports tissue homeostasis, and enables dynamic responses to injury, stress, and infection. Quiescence is essential for adult stem-cell maintenance, regeneration, immune function, aging, and cancer persistence, yet the molecular mechanisms governing entry into, maintenance of, and exit from quiescence remain poorly understood. A major barrier to progress is the lack of experimentally validated quiescent-cell training datasets and generalizable computational tools for identifying quiescent cells across diverse biological contexts.
Our preliminary studies identified a quiescent-like Neural G0 state in human neural stem cells and led to the development of an experimental framework for enriching and characterizing quiescent cells by transcriptomic profiling. Comparative analyses of quiescent stem-cell populations revealed a conserved set of seven quiescence-associated genes, supporting the existence of a shared quiescence program across distinct developmental lineages. We hypothesize that quiescence is underpinned by a conserved transcriptional regulatory program shared across stem, progenitor, and immune cell populations, and that systematic characterization of quiescent cells across diverse lineages will reveal the fundamental molecular mechanisms governing quiescence.
To test this hypothesis, Aim 1 will determine whether seven core quiescence genes are necessary and sufficient to regulate quiescence entry, maintenance, and exit through gain- and loss-of-function studies in human stem-cell models, coupled with transcriptomic characterization of downstream regulatory programs. Aim 2 will construct a comprehensive compendium of quiescent stem, progenitor, and immune cell populations spanning human in vitro and mouse in vivo systems across all three germ layers using bulk RNA-seq and single-cell RNA sequencing (scRNA-seq). These datasets will be used to further define the conserved core quiescence program. Aim 3 will develop scQuiescNet, a generalizable deep-learning framework that integrates single-cell foundation models with out-of-distribution learning strategies to identify quiescent cells across diverse tissues, cell types, species, and experimental platforms.
The proposed studies will establish fundamental mechanistic insight into the regulation of quiescence, generate a comprehensive resource of experimentally validated quiescent cellular states, and provide the first generalizable computational framework for identifying quiescent cells from single-cell transcriptomic data. Together, these advances will accelerate discovery of conserved quiescence mechanisms and facilitate future applications in regenerative medicine, aging, immune biology, and cancer.
","modified":"2026-06-04T21:07:51Z","title":"Learning the core quiescence program to enable robust identification of quiescent cells","dmproadmap_related_identifiers":[],"dmproadmap_external_system_identifier":"https://doi.org/10.48321/D1AD212614"}}],"errors":[],"page":1,"per_page":25}