Pulmonary fibrosis (PF) is a common characteristic of heterogeneous and multifactorial lung disorders as they involve different genetic and environmental risk factors and many cell types and tissues. PF develops from a wide range of triggers through a dysregulated repair process that is perpetually "turned on”, resulting in an extensive impairment of organ function with high morbidity and mortality. Currently, with some exceptions, no drugs are approved and no treatment guidelines have been issued, however, none of the existing approaches blocks the fibrotic process nor determine its reversibility. Therefore, the validation and implementation of new technologies for biomarkers and drug discovery will be crucial to personalize patient care, prioritize lung transplants and stratify patients for drug studies, as well as, in the future, to predict response to therapies as they emerge. Multi-omic and spatial-omic technologies applied to the study of PF could be a means to approach these issues.
Numerous individual -omic studies on different lung disorders characterized by pulmonary fibrosis have been performed extrapolating a wide range of different molecules involved in distinct molecular pathways. Traditional transcriptomic and proteomic approaches have provided insight into disease progression by identifying discrete cellular subpopulations or microenvironmental signatures characteristic of normal or pathological tissues. These studies dramatically transformed biomedical research but there is a need for comprehensive and combined analyses to obtain a more complete scenario on PF and bioinformatics and system biology computational techniques which could be essential for data management, integration, classification, and prediction, to overcome these issues.
Moreover, traditional techniques do not examine how a given cellular state relates to its interactions with neighboring cells or its surrounding ECM with multiparametric characterization. Bulk-omic techniques mask tissue, cellular and sub-cellular heterogeneity by averaging gene expression or protein abundance across whole samples while RNA and protein subcellular localization is tightly controlled and intimately linked to the function. For this reason, it could be relevant to perform new studies of spatial-omics since the localizations of the molecules and their dynamics at the subcellular level is essential for a complete understanding of cell biology.
We invite investigators to contribute original research as well as review articles addressing recent advances on aspects regarding multi–omic and spatial-omic research on pulmonary fibrosis for a wider comprehension of this common process in different lung disorders. Research regarding the understanding of the molecular mechanisms at the basis of the diseases for personalized medicine and the discovery of new therapies.
Submissions may focus on, but are not limited to, the following:
· Analyses of the impact of multi-omics and spatial-omic approaches on pulmonary fibrosis diagnosis, prognosis, patient stratification, and response to treatments.
· Development and validation of biomarkers to predict diagnosis, prognosis and drug targets, and possible drug efficacy.
· Development and validation of multi-omics and spatial-omic molecular subtypes serving precision medicine
· Combined bioinformatics and artificial intelligence to mine new strategies and biomarkers for pulmonary fibrosis therapy
Keywords:
multiomics, spatialomics, PF, Pulmonary Fibrosis, diagnosis, drug targets, bioinformatics
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
Pulmonary fibrosis (PF) is a common characteristic of heterogeneous and multifactorial lung disorders as they involve different genetic and environmental risk factors and many cell types and tissues. PF develops from a wide range of triggers through a dysregulated repair process that is perpetually "turned on”, resulting in an extensive impairment of organ function with high morbidity and mortality. Currently, with some exceptions, no drugs are approved and no treatment guidelines have been issued, however, none of the existing approaches blocks the fibrotic process nor determine its reversibility. Therefore, the validation and implementation of new technologies for biomarkers and drug discovery will be crucial to personalize patient care, prioritize lung transplants and stratify patients for drug studies, as well as, in the future, to predict response to therapies as they emerge. Multi-omic and spatial-omic technologies applied to the study of PF could be a means to approach these issues.
Numerous individual -omic studies on different lung disorders characterized by pulmonary fibrosis have been performed extrapolating a wide range of different molecules involved in distinct molecular pathways. Traditional transcriptomic and proteomic approaches have provided insight into disease progression by identifying discrete cellular subpopulations or microenvironmental signatures characteristic of normal or pathological tissues. These studies dramatically transformed biomedical research but there is a need for comprehensive and combined analyses to obtain a more complete scenario on PF and bioinformatics and system biology computational techniques which could be essential for data management, integration, classification, and prediction, to overcome these issues.
Moreover, traditional techniques do not examine how a given cellular state relates to its interactions with neighboring cells or its surrounding ECM with multiparametric characterization. Bulk-omic techniques mask tissue, cellular and sub-cellular heterogeneity by averaging gene expression or protein abundance across whole samples while RNA and protein subcellular localization is tightly controlled and intimately linked to the function. For this reason, it could be relevant to perform new studies of spatial-omics since the localizations of the molecules and their dynamics at the subcellular level is essential for a complete understanding of cell biology.
We invite investigators to contribute original research as well as review articles addressing recent advances on aspects regarding multi–omic and spatial-omic research on pulmonary fibrosis for a wider comprehension of this common process in different lung disorders. Research regarding the understanding of the molecular mechanisms at the basis of the diseases for personalized medicine and the discovery of new therapies.
Submissions may focus on, but are not limited to, the following:
· Analyses of the impact of multi-omics and spatial-omic approaches on pulmonary fibrosis diagnosis, prognosis, patient stratification, and response to treatments.
· Development and validation of biomarkers to predict diagnosis, prognosis and drug targets, and possible drug efficacy.
· Development and validation of multi-omics and spatial-omic molecular subtypes serving precision medicine
· Combined bioinformatics and artificial intelligence to mine new strategies and biomarkers for pulmonary fibrosis therapy
Keywords:
multiomics, spatialomics, PF, Pulmonary Fibrosis, diagnosis, drug targets, bioinformatics
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.