Ichthyosis molecular fingerprinting shows profound Th17-skewing and a unique barrier genomic signature
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA427924 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:The purpose of this study was to analyze the genomic signatures and profiles of skin from ichthyosis (various subtypes) vs. healthy patients. The analysis strategy was to study differentially expressed genes common to the ichthyosis shared phenotype, as well as individual ichthyosis subtypes, and compare and contrast to the genomic profiles of atopic dermatitis and psoriasis.
2018-10-10 | GSE108640 | GEO
Project description: Not available
| S-EPMC7195861 | biostudies-literature
Project description:In this study we performed the first global RNA-seq analysis in 54 ichthyosis patients (7 Netherton syndrome/NS, 13 epidermolytic ichthyosis/EI, 16 lamellar ichthyosis/LI, 18 congenital ichthyosiform erythroderma/CIE) and 40 healthy controls. Differentially expressed genes (DEGs) were defined based on fold changes/FCH>2 and false discovery rate/FDR<0.05 criteria. We found robust and significant Th22/Th17 skewing in all subtypes (e.g. IL-17A/C/F, S100A7/8/9/12; p<0.001) with modest changes in Th2 pathway, primarily in NS, and Th1 skewing in CIE.
2022-03-06 | GSE192832 | GEO
Project description:Gene expression fingerprinting of ameloblasts shows changes in the endocytosis/cytoskeleton interface across stages.
| PRJNA551132 | ENA
Project description:Skin microbiome ichthyosis
| PRJEB60267 | ENA
Project description:Background: Skewing root patterns provide key insights into root growth strategies and mechanism that produce root architectures Roots exhibit skewing and waving when grown on a tilted, impenetrable surface, and while the genetics guiding these morphologies have been examined, the underlying molecular mechanisms of skewing and waving remain unclear. In this study, transcriptome data were derived from two Arabidopsis ecotypes, WS and Col-0, under three tilted growth conditions in order to identify candidate genes involved in skewing. WS is a skewing ecotype. Col-0 is a non-skewing ecotype. Results: This work identifies a number of genes that are likely involved in skewing, using growth conditions that differentially affect skewing and waving. Comparing the gene expression profiles of WS and Col-0 in different tilted growth conditions identified 11 candidate genes as potentially involved in the control of skewing. These 11 genes are involved in several different cellular processes, including sugar transport, salt signaling, cell wall organization, and hormone signaling. Conclusions: Many of the 11 identified genes are involved in signaling and perception, rather than the physical restructuring of roots, leading to the conclusion that root skewing is enabled through diverse environmental signaling pathways. These findings revealed further insights into the molecular mechanisms behind root skewing. This work investigated the transcriptional differences between skewing and non-skewing roots. Comparisons within WS revealed gees that that responded to the angle of growth (Agp) during the process of skewing. These genes were cross referenced with transcripts differing between the WS and Col-0 genotypes to refine the list of genes that are most probably be involved in root skewing. More of the highly probable skew gene candidates (HPSGC) are associated with environmental sensing (e.g. salt, sugar, hormones, darkness) than with physical growth differences (e.g. cell wall remodeling, cell division, cell elongation). Thus, the root behavior of skewing appears to be primarily driven by pathways that respond to disparate signals from the root local environment. Future studies could investigate the HPSGC to find the specific pathways and molecular mechanisms contributing to root skewing.
2016-08-31 | GSE83242 | GEO
Project description:Entomological DNA fingerprinting in honey
| PRJEB102161 | ENA
Project description:Single-cell perturbation dictionaries systematically measure how cells respond to genetic and chemical perturbations, creating the opportunity to assign causal interpretations to observational data. We introduce RNA fingerprinting, a statistical framework that maps transcriptional responses from new experiments onto reference perturbation dictionaries. RNA fingerprinting learns representations of perturbations, or ``fingerprints," from single-cell data, then probabilistically assigns query cells to one or more candidate perturbations. We benchmark our method across ground-truth datasets, demonstrating accurate assignments at single-cell resolution, scalability to genome-wide screens, and the ability to resolve combinatorial perturbations. We demonstrate its broad utility across diverse biological settings: identifying context-specific regulators of p53 under ribosomal stress, characterizing drug mechanisms of action and dose-dependent off-target effects, and uncovering cytokine-driven B cell heterogeneity during secondary influenza infection in vivo. Together, these results establish RNA fingerprinting as a versatile framework for interpreting single-cell datasets by linking cellular states to the underlying perturbations which generated them.
2026-07-27 | GSE339463 | GEO
Project description:Single-cell perturbation dictionaries systematically measure how cells respond to genetic and chemical perturbations, creating the opportunity to assign causal interpretations to observational data. We introduce RNA fingerprinting, a statistical framework that maps transcriptional responses from new experiments onto reference perturbation dictionaries. RNA fingerprinting learns representations of perturbations, or ``fingerprints," from single-cell data, then probabilistically assigns query cells to one or more candidate perturbations. We benchmark our method across ground-truth datasets, demonstrating accurate assignments at single-cell resolution, scalability to genome-wide screens, and the ability to resolve combinatorial perturbations. We demonstrate its broad utility across diverse biological settings: identifying context-specific regulators of p53 under ribosomal stress, characterizing drug mechanisms of action and dose-dependent off-target effects, and uncovering cytokine-driven B cell heterogeneity during secondary influenza infection in vivo. Together, these results establish RNA fingerprinting as a versatile framework for interpreting single-cell datasets by linking cellular states to the underlying perturbations which generated them.
2026-07-27 | GSE339461 | GEO
Project description:Single-cell perturbation dictionaries systematically measure how cells respond to genetic and chemical perturbations, creating the opportunity to assign causal interpretations to observational data. We introduce RNA fingerprinting, a statistical framework that maps transcriptional responses from new experiments onto reference perturbation dictionaries. RNA fingerprinting learns representations of perturbations, or ``fingerprints," from single-cell data, then probabilistically assigns query cells to one or more candidate perturbations. We benchmark our method across ground-truth datasets, demonstrating accurate assignments at single-cell resolution, scalability to genome-wide screens, and the ability to resolve combinatorial perturbations. We demonstrate its broad utility across diverse biological settings: identifying context-specific regulators of p53 under ribosomal stress, characterizing drug mechanisms of action and dose-dependent off-target effects, and uncovering cytokine-driven B cell heterogeneity during secondary influenza infection in vivo. Together, these results establish RNA fingerprinting as a versatile framework for interpreting single-cell datasets by linking cellular states to the underlying perturbations which generated them.
2026-07-27 | GSE339457 | GEO