Biology subjects

Cappuccio, A.

Publications and source records attributed to Cappuccio, A..

4 recordsLinked to original sources

bioRxiv2024

Innate immune epigenomic landscape following controlled human influenza virus infection

Viral infections can induce changes in innate immunity that persist after virus clearance. Here, we used blood samples from a human influenza H3N2 challenge study to perform comprehensive multi-omic analyses. We detected remodeling of immune programs in innate immune cells after resolution of the infection that was proportional in magnitude to the level of prior viral load. We found changes associated with suppressed inflammation including decreased cytokine and AP-1 gene expression as well as decreased accessibility at AP-1 targets and interleukin-related gene promoter regions. We also found decreased histone deacetylase gene expression, increased MAP kinase gene expression, and increased accessibility at interferon-related gene promoter regions. Genes involved in inflammation and epigenetic-remodeling showed modulation of gene-chromatin site regulatory circuit activity. These results reveal a coordinated rewiring of the epigenetic landscape in innate immune cells induced by mild influenza virus infection.

immunology↗

bioRxiv2023

Automated single-cell omics end-to-end framework with data-driven batch inference

To facilitate single-cell multi-omics analysis and improve reproducibility, we present SPEEDI (Single-cell Pipeline for End to End Data Integration), a fully automated end-to-end framework for batch inference, data integration, and cell type labeling. SPEEDI introduces data-driven batch inference and transforms the often heterogeneous data matrices obtained from different samples into a uniformly annotated and integrated dataset. Without requiring user input, it automatically selects parameters and executes pre-processing, sample integration, and cell type mapping. It can also perform downstream analyses of differential signals between treatment conditions and gene functional modules. SPEEDIs data-driven batch inference method works with widely used integration and cell-typing tools. By developing data-driven batch inference, providing full end-to-end automation, and eliminating parameter selection, SPEEDI improves reproducibility and lowers the barrier to obtaining biological insight from these valuable single-cell datasets. The SPEEDI interactive web application can be accessed at https://speedi.princeton.edu/.

bioinformatics↗

bioRxiv2020

ICELLNET: a transcriptome-based framework to dissect intercellular communication

Cell-to-cell communication can be inferred from ligand-receptor expression in cell transcriptomic datasets. However, important challenges remain: 1) global integration of cell-to-cell communication, 2) biological interpretation, and 3) application to individual cell population transcriptomic profiles. We developed ICELLNET, a transcriptomic-based framework integrating: 1) an original expert-curated database of ligand-receptor interactions accounting for multiple subunits expression, 2) quantification of communication scores, 3) the possibility to connect a cell population of interest with 31 reference human cell types (BioGPS), and 4) three visualization modes to facilitate biological interpretation. We applied ICELLNET to uncover different communication in breast cancer associated fibroblast (CAF) subsets. ICELLNET also revealed autocrine IL-10 as a switch to control human dendritic cell communication with up to 12 other cell types, four of which were experimentally validated. In summary, ICELLNET is a global, versatile, biologically validated, and easy-to-use framework to dissect cell communication from single or multiple cell-based transcriptomic profile(s).

systems biology↗

bioRxiv2019

Deciphering the combinatorial interaction landscape

From cellular activation to drug combinations, the control of biological systems involves multiple stimuli that can elicit complex nonlinear interactions. To elucidate the functions and logic of stimulus interactions, we developed SAIL (Synergistic/Antagonistic Interaction Learner). SAIL uses a machine learning classifier trained to categorize interactions across a complete taxonomy of possible combinatorial effects. The strategy resolves the most informative interactions, and helps infer their functions and regulatory mechanisms. SAIL-predicted interaction mechanisms controlling key immune functions were experimentally validated. SAIL can integrate results from multiple datasets to derive general properties of how cells respond to multiple stimuli. Using public immunological datasets, we assembled a fine-grained landscape of [~]30000 interactions. Analysis of the landscape shows the context-dependent functions of individual modulators, and reveals a probabilistic algebra that links the separate and combined stimulus effects. SAIL is available through a user friendly interface to resolve the effect of stimulus and drug combinations.

bioinformatics↗