Summary: During host-pathogen encounters, the complex interactions between different immune cell-types can determine the outcome of infection. Advances in single cell RNA-seq (scRNA-seq) allow to probe this complexity of immunity, and afforded the basis for deconvolution algorithms that infer cell-type compositions from bulk RNA-seq measurements. However, immune activation, an important aspect of immune surveillance, is not represented in current algorithms. Here, using scRNA-seq of human peripheral blood cells infected with Salmonella, we developed a novel deconvolution algorithm to infer dynamic immune states from bulk measurements. We applied our dynamic deconvolution algorithm both to cohorts of healthy individuals challenged ex vivo with Salmonella and to cohorts of tuberculosis patients during different stages of disease. We revealed cell-type specific immune responses associated not only with ex vivo infection phenotype but also with clinical disease stage. We propose that our approach provides a predictive power to identify risk for disease, and can be applied to comprehensively study human infection outcome.
Overall Design: Frozen PBMCs from healthy individual were defrosted and infectd ex vivo with Salmonella enterica serovar Typhimurium.
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Growth Protocol: | Venous blood was drawn from the cubital vein of volunteers and PBMCS were isolated as described in Li et al. Cell 2016. The cells were counted and frozen until used. A day before experiment, the cells where defrosted, suspended in medium (RPMI 1640 with L- Glutamine supplemented with 10% heat inactivated fetal bovine serum and 1mM sodium pyruvate) and plated on untreated plates. A day after, the cells were collected from the dish. To avoid cell lost, the dish was washed with medium and the remaining cells were added to the collected cells. The cells were then manually counted with trypan blue. |
Treatment Protocol: | Salmonella strain used in this study was derived from the wild-type strain SL1344 containing GFP (pFPV25.1; Addgene). Cultures of Salmonella were grown in Luria-Bertani (LB) medium at 37℃ for 16 hours and used for PBMCs infection at MOI 5 for the exposed cells. After 30 min of internalization, the cells were washed and suspended with media containing 50 ug/ml gentamicin to eliminate Salmonella that were not internalized. The cells were incubated for 4 hours at 37℃ in 5% CO2 in non-treated cell culture plates. |
Extract Protocol: | 4 hours after infection, the cells were washed with PBS, resuspended in 120ul of 5mM EDTA and incubated 5min in RT to detach cells that might have attached the dish. The EDTA was then washed and the cells were counted with trypan blue, suspended with 0.04% BSA in PBS and directly used for single-cell sequencing by the Chromium Single Cell 3'Reagent version 2 kit and Chromium Controller (10X Genomics, CA, USA) as previously described at Zheng, G. X. Y. et al Nature Communications 2017. |
Library Construction Protocol: | Libraries were prepared by the Chromium Single Cell 3' Reagent version 2 kit and Chromium Controller (10X Genomics, CA, USA) as previously described at Zheng, G. X. Y. et al Nature communications 2017. |
Molecule Type: | poly(A)+ RNA |
Library Source: | |
Library Layout: | PAIRED |
Library Strand: | Forward |
Platform: | ILLUMINA |
Instrument Model: | Illumina NextSeq 500 |
Strand-Specific: | Specific |
Data Resource | GEN Sample ID | GEN Dataset ID | Project ID | BioProject ID | Sample ID | Sample Name | BioSample ID | Sample Accession | Experiment Accession | Release Date | Submission Date | Update Date | Species | Race | Ethnicity | Age | Age Unit | Gender | Source Name | Tissue | Cell Type | Cell Subtype | Cell Line | Disease | Disease State | Development Stage | Mutation | Phenotype | Case Detail | Control Detail | Growth Protocol | Treatment Protocol | Extract Protocol | Library Construction Protocol | Molecule Type | Library Layout | Strand-Specific | Library Strand | Spike-In | Strategy | Platform | Instrument Model | Cell Number | Reads Number | Gbases | AvgSpotLen1 | AvgSpotLen2 | Uniq Mapping Rate | Multiple Mapping Rate | Coverage Rate |
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