Complex interactions between different host immune cell types can determine the outcome of pathogen infections. Advances in single cell RNA-sequencing (scRNA-seq) allow probing of these immune interactions, such as cell-type compositions, which are then interpreted by deconvolution algorithms using bulk RNA-seq measurements. However, not all aspects of immune surveillance are represented by current algorithms.
Using scRNA-seq of human peripheral blood cells infected with Salmonella, Weizmann Institute of Science researchers have developed a deconvolution algorithm for inferring cell-type specific infection responses from bulk measurements. They apply their dynamic deconvolution algorithm to a cohort of healthy individuals challenged ex vivo with Salmonella, and to three cohorts of tuberculosis patients during different stages of disease. The scientists reveal cell-type specific immune responses associated not only with ex vivo infection phenotype but also with clinical disease stage. They propose that our approach provides a predictive power to identify risk for disease, and human infection outcomes.
scRNA-seq based dynamic deconvolution to infer cell-type
composition and infection-induced states
a Illustration of the dynamic deconvolution approach: transformation of the scRNA-seq data into two properties that can be inferred from bulk measurements – immune cell-type composition and infection-induced cell state. Cell-type composition is represented as a one-dimensional vector, where kj is the number of cells from a specific cell type j. The infection-induced cell state (Sj) is represented as the induction of cell-type specific genes following infection. Using our deconvolution algorithm (equations at the bottom, see methods) we infer robust estimators for the relative abundance (Kj) and infection-induced state (Sj) of each cell type across individuals from bulk RNA-seq measurements, as illustrated on the right. b and c Reduction of the scRNA-seq data into two sets of genes which represent intrinsic cell-type properties (b) and cell-type specific infection-induced states (c). Cells are ordered by their cell type (color-coded at the bottom) and cell origin (white for naïve and black for exposed cells); see colorbar for expression levels. d Validation of our deconvolution algorithm using FACS experiment. Comparison between the percentages of each cell type as measured by FACS (x-axis) to the relative abundance by our deconvolution (y-axis). There is a high concordance between the deconvolution prediction and the cellular composition as determined by FACS. Each dot is the mean of 3–4 replicates for the FACS and bulk RNA-seq. Presented also are the standard error (SEM) for the replicates. e Validation of the infection-induced signatures in sorted populations. Presented are the expression levels of the intrinsic cell types (from b) and infection-induced marker genes (from c) in bulk measurements of sorted naïve and exposed NKT cells and monocytes. The NKT infection-induced state is upregulated following infection solely in the exposed NKT cells (left). Similarly, the monocytes cell-type signature is expressed exclusively in naïve and exposed monocytes, and the monocytes infection-induced signature is upregulated following infection exclusively in the exposed monocytes (right). Each sample is the mean of 2–4 technical replicates; cell-type signatures are color-coded (ndenotes the number of genes in each signature)
Availability – The code for the deconvolution algorithm is available at: https://github.com/noabossel/Dynamic-deconvolution-algorithm.