This repository contains the files and scripts used for the pre-processing and downstream analysis (main figures 4, 5a-i and supplementary figures 5-7) of the single cell macrophage dataset.

Leukaemia Exposure Alters the Transcriptional Profile and Function of BCR-ABL Negative Macrophages in the Bone Marrow Niche

Amy Dawson¹, Joana Bittencourt-Silvestre², Ya-Ching Hsieh³, Isabel van Loon¹, Giovanny Rodriguez Blanco³, Angela Ianniciello¹, Puneet Agarwal⁴, Hassan Almasoudi¹, Laura McCluskey², Lisa E. M. Hopcroft², Mary T. Scott¹, Pablo Baquero¹, Karen Dunn², David Vetrie¹, Seth B. Coffelt^1,3, Ravi Bhatia⁴, Helen Wheadon², Sara Zanivan^1,3, *Kristina Kirschner^1,3, *G. Vignir Helgason¹

1. Wolfson Wohl Cancer Research Centre, Institute of Cancer Sciences, University of Glasgow, Garscube Estate, Switchback Road, Bearsden, G61 1QH.
2. Paul O’Gorman Leukaemia Research Centre, Institute of Cancer Sciences, University of Glasgow, Gartnavel General Hospital, 21 Shelley Rd, Glasgow G12 0ZD.
3. Cancer Research UK Beatson Institute, Garscube Estate, Switchback Road, Bearsden, Glasgow, G61 1BD.
4. Division of Hematology and Oncology, Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA.

*Corresponding authors: G. Vignir Helgason; Vignir.Helgason@Glasgow.ac.uk and Kristina Kirschner; kristina.kirschner@glasgow.ac.uk

Macrophages are fundamental cells of the innate immune system that support normal haematopoiesis, fight infection and play roles in both anti-cancer immunity and tumour progression. However, the function of macrophages in myeloid leukaemias remains largely unknown due to difficulties in isolating non-transformed cells from those derived from the malignant clone. Here we use a state-of-the-art chimeric mouse model of chronic myeloid leukaemia (CML) to study the impact of the dysregulated bone marrow (BM) microenvironment on bystander macrophages. Utilising both single cell RNA sequencing (scRNA-seq) of Philadelphia chromosome (Ph) negative macrophages and secretory proteomics of murine c-Kit+ stem/progenitor cells we have uncovered that macrophages exposed to a CML environment are altered transcriptionally and have reduced phagocytic function. Comparison of CML exposed macrophages to control counterparts by scRNA-seq has demonstrated significant heterogeneity in bone marrow (BM) macrophages, with the CML niche driving two unique subpopulations of immature and anti-inflammatory macrophages. Furthermore, we have identified that CML exposed macrophages can be separated from their normal counterparts via differential expression of surface markers CD36 and TGFBI, thereby providing us with a novel strategy to isolate Ph- macrophages in a CML BM niche. Finally, we have demonstrated that the dysregulated CML protein secretome is partially responsible for the in vivo alterations of macrophages, uncovering aberrant production of the immune modulatory protein lactotransferrin (LTF), and showing exposure to CML secreted factors suppresses phagocytosis, mitochondrial respiration, and inflammatory gene expression in BM macrophages.

Pre-print now online at:

GEO link: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE204978

Access token: kruzmyowvduvvuj

Repository overview

This repository contains the Bash and R scripts pertaining to the single-cell RNA-seq analysis of the macrophage dataset. Additionally it contains DESeq2 outputs for the comparison of each cluster to the mean expression of all other clusters, and the .rnk files used for GSEA.

1. The Bash scripts folder contains the Bash scripts used for preprocessing, utilising the following command line tools:

   • FastQC v0.11.8
   • Trim_galore v0.5.0
   • Hisat2 v2.1.0
   • FeatureCounts v1.6.2

2. The R scripts folder contains the code relating to further filtering steps, normalisation and visulisation.

   • Clustering was performed using nearest neighbour from the Scran package followed by cluster walktrap from the igraph package.
   • T-SNE plots were produced using Scater. Markers for clusters were obtained using the findMarkers function in Scran and heatmaps were plotted using Scater. Violin plots were generated using ggplot2.
   • Differential expression was calculated using DESeq2 and Zinbwave, with each cluster being compared to the average of all the other clusters together.
   • GSEA44,45 was performed on a ranked list formed of the -log of the p-value of each gene, multiplied by -1 in case the log2 fold-change was negative, or 1, in case the log2 fold-change was positive.
   • Pathway analysis using Kegg, Reactome and Wikipathways was performed using http://www.webgestalt.org

3. The GSEA .rnk files file contains .rnk files for each cluster.

4. The DESeq2 outputs file contains DESeq2/Zinbwave differential gene expression tables.

5. The Figures and Figure legends and Supplementary Figures files contain all figures from the main text and the supplementary text respectivley.