scAmpi-A versatile pipeline for single-cell RNA-seq analysis from basics to clinics

Abstract

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful technique to decipher tissue composition at the single-cell level and to inform on disease mechanisms, tumor heterogeneity, and the state of the immune microenvironment. Although multiple methods for the computational analysis of scRNA-seq data exist, their application in a clinical setting demands standardized and reproducible workflows, targeted to extract, condense, and display the clinically relevant information. To this end, we designed scAmpi (Single Cell Analysis mRNA pipeline), a workflow that facilitates scRNA-seq analysis from raw read processing to informing on sample composition, clinically relevant gene and pathway alterations, and in silico identification of personalized candidate drug treatments. We demonstrate the value of this workflow for clinical decision making in a molecular tumor board as part of a clinical study.

Fig 1. Overview of the workflow implemented in scAmpi, showing a tumor sample analysis as an example.

Starting from droplet-based 10x Genomics raw data (A), genome-wide read counts for each cell are generated (B). This gene-by-cell count matrix is the basis for cell type prediction (C) and unsupervised clustering (D) to determine the cell type composition and tumor heterogeneity. Subsequent steps include gene expression (E) and gene set (F) analysis, and drug candidate identification (G).

Fig 2. Examples of scAmpi’s basic scRNA-seq quality control plots of a melanoma sample.

The scatter plot in (A) shows cells colored by their respective category of applied filters. The vertical and horizontal lines indicate the chosen thresholds applied for the minimum number of genes (x-axis) and maximum fraction of reads mapping to mitochondrial genes per cell (y-axis), respectively. In (B), the UMAP embedding (after normalization) of all cells is shown, with cells colored by estimated cell-cycle phase. In (C), the same UMAP is shown, this time with cells colored by the fraction of reads mapping to mitochondrial genes.

Fig 3. Sample composition and interpretation of a melanoma sample.

In (A) the UMAP embedding is colored by cell type label (left) and cluster (right), with major cell type populations highlighted in the figure. For a complete overview of cell types, see Fig B in S2 Text. In (B), the enrichment of the MAPK pathway is exemplified. In (C), UMAPs showing the gene expression of CCND1 and CDK4 are shown as selected examples of individual gene expression plots. The UMAP in (D) shows the drug candidate identification result for the drug palbociclib.