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Biology subjects

McIlwain, D. R.

Publications and source records attributed to McIlwain, D. R..

3 recordsLinked to original sources

Single-cell profiling of Ebola virus infection in vivo reveals viral and host transcriptional dynamics

Ebola virus (EBOV) causes epidemics with high case fatality rates, yet remains understudied due to the challenge of experimentation in high-containment and outbreak settings. To better understand EBOV infection in vivo, we used single-cell transcriptomics and CyTOF-based single-cell protein quantification to characterize peripheral immune cell activity during EBOV infection in rhesus monkeys. We obtained 100,000 transcriptomes and 15,000,000 protein profiles, providing insight into pathogenesis. We find that immature, proliferative monocyte-lineage cells with reduced antigen presentation capacity replace conventional circulating monocyte subsets within days of infection, while lymphocytes upregulate apoptosis genes and decline in abundance. By quantifying viral RNA abundance in individual cells, we identify molecular determinants of tropism and examine temporal dynamics in viral and host gene expression. Within infected cells, we observe that EBOV down-regulates STAT1 mRNA and interferon signaling, and up-regulates putative pro-viral genes (e.g., DYNLL1 and HSPA5), nominating cellular pathways the virus manipulates for its replication. Overall, this study sheds light on EBOV tropism, replication dynamics, and elicited immune response, and provides a framework for characterizing interactions between hosts and emerging viruses in a maximum containment setting.

microbiology

Integration of Mechanistic Immunological Knowledge into a Machine Learning Pipeline Increases Predictive Power

The dense network of interconnected cellular signaling responses quantifiable in peripheral immune cells provide a wealth of actionable immunological insights. While high-throughput single-cell profiling techniques, including polychromatic flow and mass cytometry, have matured to a point that enables detailed immune profiling of patients in numerous clinical settings, limited cohort size together with the high dimensionality of data increases the possibility of false positive discoveries and model overfitting. We introduce a machine learning platform, the immunological Elastic-Net (iEN), which incorporates immunological knowledge directly into the predictive models. Importantly, the algorithm maintains the exploratory nature of the high-dimensional dataset, allowing for the inclusion of immune features with strong predictive power even if not consistent with prior knowledge. In three independent studies our method demonstrates improved predictive power for clinically-relevant outcomes from mass cytometry data generated from whole blood, as well as a large simulated dataset.

bioinformatics

Coordinated cellular neighborhoods orchestrate antitumoral immunity at the colorectal cancer invasive front

Antitumoral immunity requires organized, spatially nuanced interactions between components of the immune tumor microenvironment (iTME). Understanding this coordinated behavior in effective versus ineffective tumor control will advance immunotherapies. We optimized CO-Detection by indEXing (CODEX) for para ffin-em bedded tissue microarrays, enabling profiling of 140 tissue regions from 35 advanced-stage colorectal cancer (CRC) patients with 56 protein markers simultaneously. We identified nine conserved, distinct cellular neighborhoods (CNs)-a collection of components characteristic of the CRC iTME. Enrichment of PD-1+CD4+ T cells only within a granulocyte CN positively correlated with survival in a high-risk patient subset. Coupling of tumor and immune CNs, fragmentation of T cell and macrophage CNs, and disruption of inter-CN communication was associated with inferior outcomes. This study provides a framework for interrogating complex biological processes, such as antitumoral immunity, demonstrating an example of how tumors can disrupt imm une functionality through interference in the concerted action of cells and spatial domains.

immunology