The Bolouri lab is interested in developing and applying computational systems immunology methods to better understand immune dysregulation in autoimmunity, cancer, and infection (most recently COVID-19). Our approach is inherently multi- and cross-disciplinary and is predicated on:
- Tight integration of biological and immunological insights
- Use of high-throughput technologies
- Methods derived from computer science, engineering, mathematics, statistics, and physics
We typically integrate data from diverse assays (e.g. single cell RNAseq, flow/mass cytometry, proteomics and epigenomics) with patient clinical data as well as curated data from databases/publications.
Multi-scale regulation of immune homeostasis and steady-state maintenance
The mammalian immune system is constantly undergoing self-renewal, while also interacting with environmental cues, self-antigens, the central nervous system and commensal microbiota. How does the healthy immune system maintain a balanced steady state in the midst of all these changes? And how are the regulatory mechanisms that maintain a healthy immune balance impacted in disease? Homeostasis and steady-state are well known to be regulated through combinations of self-reinforcing and stabilizing feedback loops. In the immune system, these feedbacks occur at multiple scales, within individual cells, through cell-cell interactions, and via organ and organism level regulatory signals.
In collaboration with the BRI Sound Life Project, we are leveraging recent developments in single cell omics and high-throughput cytometry to characterize the intra- and inter-cellular immune regulatory interactions in healthy humans.This collaborative project has two components:
- Using integrative bioinformatics tools to reverse-engineer regulatory interactions from single cell and bulk blood measurements from multiple individuals at multiple time points.
- Development of novel multi-scale and multi-modal data integration methods that optimally exploit existing knowledge from the literature, collaborating domain experts and curated databases.
Unbiased semi-supervised cell population labeling
Currently, automated data processing pipelines for high-throughput flow/mass cytometry and single-cell omics (e.g. scRNAseq, scATACseq) typically require ad-hoc, manual labeling of clusters of cells into known cell types. Automated approaches are attractive because they can enable unbiased/uniform processing of large numbers of samples while addressing batch to batch variations and other potential confounding factors. Existing approaches to automated cell labeling require either cell-type-specific markers or sets of differentially up/down regulated genes per cell type. However, in diseases involving immune dysregulation, changes in the transcriptional states of immune cells can affect signature and marker gene expression, confounding cellular identity and state. We are exploring semi-supervised and hybrid machine-learning methods that leverage domain expert knowledge and expert guided training to provide reliable cell clustering and labeling in specific immunological settings.
This collaborative project currently has three components:
- Clustering and labeling of single-cell omics data in disease settings where disease-related transcriptional changes such as activation and exhaustion complicate cell type clustering/labeling
- Automated population gating and removal of unwanted variation in mass cytometry (CyTOF) data
- Identification of novel cell populations/states in specific diseases using automated methods
Multi-omics disease trajectory stratification in COVID-19
The immune profile of COVID-19 patients changes dynamically over time and in response to drug treatments and other clinical interventions. The immune response is also impacted by sex, race/ethnicity, age and pre-existing comorbidities. The extent to which COVID-19 treatment should be tailored to specific patient characteristics remains unclear. As part of a BRI-wide longitudinal study of COVID-19 patients, we are interested in establishing clinically reliable patient stratification methods based on mass cytometry, clinical test results, demographic and other readily-available patient data. Analysis of long-term longitudinal data from the same patients is enabling identification of shared immune trajectories within patient groups. By mapping the molecular and cellular changes in trajectory clusters onto known regulatory immune interactions (see example figure below), we hope to establish causal and mechanistic models of distinct immune responses to SARS-CoV-2 infection. This is a rapidly evolving area of research. For current details please contact Dr. Bolouri.
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