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Biology subjects

Skene, P.

Publications and source records attributed to Skene, P..

3 recordsLinked to original sources

Multi-lineage transcriptional and cell communication signatures define pathways in individuals at-risk for developing rheumatoid arthritis that initiate and perpetuate disease

Rheumatoid arthritis (RA) is a systemic autoimmune disease arising from loss of tolerance and autoantibody development in at-risk individuals. Targeted therapies yield variable responses and current preventive strategies delay but do not stop disease onset. We propose that a set of transcription factors (TFs) and their downstream pathways regulate inflammatory cell communication networks in at-risk populations and RA. These networks enable multiple pathogenic cell types and mediators and could account for variable responses to targeted agents. To test this hypothesis, we identified anti-citrullinated protein antibody (ACPA)-positive at-risk individuals, patients with early and established RA and healthy controls. Single cell chromatin accessibility and transcriptomic profiles from blood mononuclear r cells were integrated and identified share pathogenic mechanisms, especially SUMOylation, RUNX2, YAP1, NOTCH3, and {beta}-Catenin Pathways. Surprisingly, this signature was found in multiple cell types. Individualized gene expression patterns were then confirmed in RA synovium. Cell communication analysis revealed that multiple lineages can deliver a core set of pro-inflammatory mediators to receiver cells. Longitudinal analysis showed that the signature cell types evolve in individual at-risk participants. Cell-type-specific signature pathways could contribute to the differing clinical responses to targeted therapies. This study describes how a common clinical phenotype could arise from multiple pathogenic mechanisms.

systems biology↗

SPARROW reveals cell states and functions influenced by microenvironment zones in complex tissues.

Spatially resolved transcriptomics technologies have significantly enhanced our ability to understand cellular characteristics within tissue contexts. However, they present a trade-off between spatial resolution and transcriptome coverage. This limitation, compounded with analytical tools treating cell type inference and cellular neighbourhood identification as separate processes, hinders a unified understanding of tissue features across scales. Our computational framework, SPARROW, infers cell types and delineates cellular organization patterns as microenvironment zones using an interconnected architecture. SPARROW algorithmically achieves single cell spatial resolution and whole transcriptome coverage by integrating spatially resolved transcriptomics and scRNA-seq data. Using SPARROW, we identified established and novel microenvironment zone-specific ligand-receptor mediated interactions in human tonsils, discoveries that would not be possible using either modality alone. Moreover, SPARROW uncovered novel cell states in the mouse hypothalamus, underscoring the influence of microenvironment zones on cell identities. Lastly, through its common latent spaces that facilitate cross-tissue comparisons, SPARROW revealed distinct inflammation states between different lymph node tissues. Overall, SPARROW integrates cellular gene expression with spatial organization, providing a comprehensive characterization of tissue features across scales and samples.

bioinformatics↗

PALMO: a comprehensive platform for analyzing longitudinal multi-omics data

Longitudinal bulk and single-cell omics data is increasingly generated for biological and clinical research but is challenging to analyze due to its many intrinsic types of variations. We present PALMO (https://github.com/aifimmunology/PALMO), a platform that contains five analytical modules to examine longitudinal bulk and single-cell multi-omics data from multiple perspectives, including decomposition of sources of variations within the data, collection of stable or variable features across timepoints and participants, identification of up- or down-regulated markers across timepoints of individual participants, and investigation on samples of same participants for possible outlier events. We tested PALMO performance on a complex longitudinal multi-omics dataset of five data modalities on the same samples and six external datasets of diverse background. Both PALMO and our longitudinal multi-omics dataset can be valuable resources to the scientific community.

immunology↗