Summary. The regulation of alveolar regeneration of injured lungs by exosomal signals in acute respiratory distress syndrome (ARDS) are incompletely studied. We have identified exosomal proteins in bronchoalveolar lavage fluid (BALF) of ARDS patients that are closely association with the severity of lung injury. In ARDS, clinical severity is graded by the ratio of arterial oxygen tension (PaO2 in mmHg) to the fraction of inspired oxygen (FiO2). We have identified stem cell-related niche proteins that are present in mild (200£PaO2/FiO2<300 mmHg) to moderate ARDS (100£PaO2/FiO2<200 mmHg) but reduced in severe ARDS (PaO2/FiO2<100 mmHg). A key pathological process in ARDS is damage to the alveolar epithelium, which has been demonstrated in numerous preclinical and clinical studies to be associated with ARDS severity. Therefore, we hypothesize that exosomal signals for lung stem cell-mediated re-alveolarization are determinants of ARDS outcomes. Several findings support this hypothesis. 1) Exosomes are effective autocrine/paracrine pathways for stem cell lineage in injured organs. 2) Exosomal proteins are protected from catalysis by enzymes in inflamed tissues. 3) Unbiased high throughput proteomic analysis and advanced bioinformatic platforms have successfully identified novel biomarkers for other diseases. 4) Although the etiologies of ARDS are diverse, the repair processes mediated by epithelial stem/progenitor cells regulated by niches seem to be similar based on preclinical studies. Our objective is to test this hypothesis with BALF samples and patients' clinical data from NIH/NHLBI-supported clinical trials, prioritize niche molecules, and validate the results in genetically bioengineered mice and organoids of alveolar type 2 (AT2) epithelial cells. We will apply novel “exosomics” approaches, cutting-edge bioinformatics, three-dimensional culture models, robust cell origin tracking, and supervised machine learning algorithms. There are three specific aims: Aim 1 is designed to identify exosomal signaling pathways and networks in lavage that regulate the lineage of lung stem cells for re-alveolarization. We hypothesize that stem cell-mediated re- alveolarization has been suppressed in severe ARDS patients due to the disruption of key exosomal niches. We will prioritize differentially expressed exosomal proteins and related signaling pathways and networks using R packages. Aim 2 is designed to detect and optimize regenerative predictors for re-alveolarization of injured lungs. We hypothesize that the significantly differential exosomal molecules in lavage will be associated with clinical data in ARDS patients. We will perform unbiased clustering and supervised machine learning algorithms to develop models for discovering critical signals for lung regeneration. The prioritized exosomal predictors will be compared with traditional whole-protein markers for accuracy and applicability. Aim 3 will validate selected exosomal signals for AT2-mediated re-...