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This site presents the code and results of the analyses described in the manuscript: “Single-cell profiling of BAL in preschool cystic fibrosis reveals macrophage dysregulation and ivacaftor-modified inflammatory programs in the early life lung”.

All the code and results of this analysis are available from GitHub at https://github.com/Oshlack/paediatric-cf-inflammation-citeseq. To reproduce the complete analysis follow the instructions on the getting started page.

Abstract

Aberrant inflammation and structural lung damage occurs early in life for people with cystic fibrosis (CF). Even in the era of CFTR modulators, anti-inflammatory therapy may still be needed to prevent establishment and lifelong consequences of bronchiectasis. In this study, we integrated transcriptome-wide single-cell RNA sequencing data and highly multiplexed surface protein expression to create the largest comprehensive paediatric lower airway atlas of >190,000 cells from 45 bronchoalveolar lavage (BAL) samples resulting in 43 immune and epithelial cell populations, all available for exploration on CELLxGENE. We then investigated inflammatory cell responses in children with CF to show widespread gene expression dysregulation of macrophage populations in the preschool CF lung. This included alterations in pathways associated with TNF and IFN signalling, cholesterol homeostasis, as well as pulmonary fibrosis, that were further altered by the early development of bronchiectasis. We showed that the CFTR modulator ivacaftor restores some of these macrophage-related functional deficits and reduces expression of pathways associated with neutrophil infiltration, however the modulator lumacaftor/ivacaftor did not result in any detectable changes in transcriptional response. This work represents a comprehensive, multi-omic single-cell analysis of BAL from preschool children and the results may inform the future development of anti-inflammatory therapy for children with CF.

Authors

Jovana Maksimovic1,2,9*, Shivanthan Shanthikumar 2,3,4*, George Howitt1,9, Gunjan Dixit1,9, Peter F Hickey5,6, William Ho5, Casey J.A. Anttila5, Daniel V. Brown5, Anne Senabouth7,8, Dominik Kaczorowski7,8, Daniela Amann-Zalcenstein5,6, Joseph E. Powell7,8, Sarath C. Ranganathan2,3,4, Alicia Oshlack1,9, Melanie R. Neeland2,3#.

1 Computational Biology Program, Peter MacCallum Cancer Centre, Parkville, VIC, Australia

2 Respiratory Diseases, Murdoch Children’s Research Institute, Parkville, VIC, Australia

3 Department of Paediatrics, University of Melbourne, Parkville, VIC, Australia

4 Respiratory and Sleep Medicine, Royal Children’s Hospital, Parkville, VIC, Australia

5 Advanced Genomics Facility, Advanced Technology and Biology Division, WEHI, Parkville, VIC, Australia

6 Department of Medical Biology, University of Melbourne, Parkville, VIC, Australia

7 Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, Australia

8 UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, NSW, Australia

9 Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, VIC, Australia

*Contributed equally

Analysis Overview

Follow the links below to view the different parts of the analysis.

Differential Expression Analysis between Conditions

  1. Differential expression analysis of macrophages
    1. Macrophages
    2. Alveolar macrophages
    3. APOC2+ macrophages
    4. CCL macrophages
    5. IFI21 macrophages
    6. Lipid macrophages
    7. Monocyte-derived macrophages
    8. Proliferating macrophages
  1. Differential expression analysis of T/NK and rare cells
    1. CD4 T-cells
    2. CD8 T-cells
    3. DC cells
    4. Epithelial cells
  1. Analyse cell types proportions
    1. Annotation level 1
    2. Annotation level 3, non macro. cells
    3. Annotation level 3, macro. cells

Licenses

The code in this analysis is covered by the MIT license and the written content on this website is covered by a Creative Commons CC-BY license.

Version Information

R version: R version 4.3.3 (2024-02-29)

Bioconductor version: 3.18


sessionInfo()
R version 4.3.3 (2024-02-29)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS 15.5

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: Australia/Melbourne
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
[1] workflowr_1.7.1

loaded via a namespace (and not attached):
 [1] jsonlite_1.8.8      compiler_4.3.3      BiocManager_1.30.22
 [4] renv_1.1.4          promises_1.2.1      Rcpp_1.0.12        
 [7] stringr_1.5.1       git2r_0.33.0        callr_3.7.3        
[10] later_1.3.2         jquerylib_0.1.4     yaml_2.3.8         
[13] fastmap_1.1.1       R6_2.5.1            knitr_1.50         
[16] tibble_3.2.1        rprojroot_2.0.4     bslib_0.6.1        
[19] pillar_1.9.0        rlang_1.1.6         utf8_1.2.4         
[22] cachem_1.0.8        stringi_1.8.3       httpuv_1.6.14      
[25] xfun_0.52           getPass_0.2-4       fs_1.6.6           
[28] sass_0.4.10         cli_3.6.5           magrittr_2.0.3     
[31] ps_1.7.6            digest_0.6.34       processx_3.8.3     
[34] rstudioapi_0.15.0   lifecycle_1.0.4     vctrs_0.6.5        
[37] evaluate_0.23       glue_1.8.0          whisker_0.4.1      
[40] fansi_1.0.6         rmarkdown_2.29      httr_1.4.7         
[43] tools_4.3.3         pkgconfig_2.0.3     htmltools_0.5.8.1