This code is associated with the paper from Russell et al., "Extreme heterogeneity of influenza virus infection in single cells". eLife, 2018. http://dx.doi.org/10.7554/eLife.32303

Single-cell sequencing of influenza-infected cells

This repository is an analysis of the transcriptional dynamics of influenza virus infection at the level of single cells.

Briefly, A549 cells were infected with A/WSN/1933 influenza virus at a low MOI. The transcriptomes of the cells were then sequenced on the Chromium 10X platform. The virus used was a mix of wildtype and virus with synonymous "barcodes" near the 3' end to help enable identification of co-infection. The virus populations were also "pure" in the sense that they were passaged at low MOI to avoid accumulation of defective particles. Multiple timepoints were collected and analyze.

Publication and data

The paper describing this work will be published in eLife with DOI 10.7554/eLife.32303. A pre-print of the initial version is on bioRxiv at DOI 10.1101/193995 (note that this pre-print version is changed in some modest ways in the final eLife publication).

The cell-gene matrix will be available on DataDryad with 10.5061/dryad.qp0t3. The deep-sequencing data are on GEO under accession GSE108041.

Authors

Alistair Russell, Cole Trapnell, Jesse Bloom.

Organization of this repository

The analysis is performed by a set of Jupyter notebooks.

1. The Python Jupyter notebook align_and_annotate.ipynb demultiplexes and aligns the reads, annotates the flu synonymous barcodes, and generates the cell-gene matrix. It requires installation of cellranger, which performs the demultiplexing and alignment. It also uses custom Python and bash scripts found in the ./scripts/ subdirectory, and requires installation of a few common Python modules. The notebook describes the software versions used.

2. The R Jupyter notebook monocle_analysis.ipynb analyzes the cell-gene matrix, making use of Monocle. It generates most of the figures as well and places them in ./paper/figures. The versions of R and associated packages are described in the notebook. If you just want to run this part of the analysis, download the cell-gene matrix from DataDryad into ./results/cellgenecounts/ and skip running the first notebook that creates this matrix.

3. The paper (in LaTex) is in the ./paper/ subdirectory. When compiled, the PDF is at ./paper/paper.pdf.

The Jupyter notebooks can be run via the bash script run_analysis.bash.

Input data

In addition to the notebooks / scripts themselves, the following input data is used:

1. The BCL files that contain the deep sequencing data are on the Bloom lab ngs directory, and are linked to directly in align_and_annotate.ipynb.

2. ./data/flu_sequences/ contains the influenza genomes for both the wildtype A/WSN/1933 virus and the variants with synonymous mutations barcoding the 3' end of the mRNA, as taken from the Bloom lab reverse-genetics plasmids used to grow these viruses.

3. ./data/h.all.v6.0.symbols.gmt contains gene sets for enrichment analysis as downloaded from GSEA.

Results and Conclusions

The final paper is in the ./paper/ subdirectory, and when the LaTex is compiled this is in the pdf ./paper/paper.pdf.

The Jupyter notebooks align_and_annotate.ipynb and monocle_analysis.ipynb contain detailed descriptions of the results.

All of the output from the analyses are written to the ./results/ subdirectory.