Single-cell profiling of Ebola virus infectionin vivoreveals viral and host transcriptional dynamics

Abstract

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. over-activation of innate and adaptive immunity underlies much of EVD pathology, rather than solely virus-mediated cytotoxicity (Geisbert et al., 2003a(Geisbert et al., , 2003b .

Species-specific quantification of circulating ebolavirus burden using VP40-derived peptide variants

“…Plasma EBOV viral loads detected in these animals at day 4 post-infection ranged from 2.9 × 10 6 for NHP 7284 and 1.2 × 10 9 Geq /mL for NHP 436. At day 6 post-infection, EBOV viral titer increased to 5.5–7.3× 10 9 Geq /mL, consistent with the viral load detected in a previous NHP study[ 26 ].…”

“…MALDI-TOF MS analysis of EBOV and SUDV recombinant VP40 protein digests detected six and nine of the 17 tryptic peptides detected by LC-MS within a precursor mass error of 1 Da, and both the p8 and p12 biomarker Longitudinal plasma samples were collected from two non-human primates (NHPs) at days 4 to 7 after their infection with EBOV, and analyzed by RT-PCR and Western blot to determine the correlation between circulating viral load and VP40 concentration (S6 Fig) . Plasma EBOV viral loads detected in these animals at day 4 post-infection ranged from 2.9 × 10 6 for NHP 7284 and1.2 × 10 9 Geq /mL for NHP 436. At day 6 post-infection, EBOV viral titer increased to 5.5-7.3× 10 9 Geq /mL, consistent with the viral load detected in a previous NHP study [26].…”

Gene signatures and host-parasite interactions revealed by dual single-cell profiling of Plasmodium vivax liver infection

“…However, the mechanisms by which IFN suppression is achieved in infected cells or the impact of host genetics and host-dependent IFN responses, key determinants of clinical outcome in HCV infections (Sheahan et al, 2014), remain unknown. In contrast, we described upregulation of innate immune response pathways in uninfected bystander cells, similar to those observed in viral infections that help control spreading of the virus to neighboring cells (Kotliar et al, 2020; Sheahan et al, 2014). In the context of malaria, this phenomenon might prime or protect cells from a secondary infection, as suggested by the inhibition of malaria re-infection by IFN in a rodent model of Plasmodium (Liehl et al, 2013, 2015).…”

Plasma from patients with bacterial sepsis or severe COVID-19 induces suppressive myeloid cell production from hematopoietic progenitors in vitro

“…Gene expression signatures similar to that of MS1 have also been described recently in severe SARS-CoV-2 infection (28,36) but have not been systematically analyzed across multiple cohorts. To determine whether MS1 cells are similarly expanded in severe COVID-19, we analyzed four independent COVID-19 scRNA-seq datasets (7,28,29,31) independently and identified gene expression programs in CD14 + monocytes from each dataset using an unbiased cNMF method (Fig.…”