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Project leish-ME: IDO1/PD-L1 in infected microenvironments

IL-32 producing CD8+ memory T cells and Tregs define the IDO1/PD-L1 niche in human cutaneous leishmaniasis skin lesions.

Nidhi S. Dey1, Shoumit Dey1, Naj Brown1, Sujai Senarathne2, Luiza Campos Reis3,¥, Ritika Sengupta4, Jose Angelo L. Lindoso5,6, Sally James5, Lesley Gilbert5, Mitali Chatterjee4, Hiro Goto3, Shalindra Ranasinghe2 and Paul M. Kaye1*

1York Biomedical Research Institute and Skin Research Centre, Hull York Medical School, University of York, UK. 2Department of Parasitology, Faculty of Medical Sciences, University of Sri Jayewardenepura, Sri Lanka. 3Department of Preventive Medicine, Instituto de Medicina Tropical de São Paulo, Faculdade de Medicina, Universidade de São Paulo, Brazil. 4Department of Pharmacology, Institute of Postgraduate Medical Education and Research, Kolkata, India. 5Secretaria de Saúde do Estado de São Paulo, Instituto de Infectologia Emílio Ribas, São Paulo, SP, Brasil 6University of São Paulo, Faculty of Medicine, Department of Infectious and Parasitic Diseases, São Paulo, SP, Brazil. 7Technology Facility, Department of Biology, University of York, York, YO10 5DD

*Correspondence: Paul M. Kaye, paul.kaye@york.ac.uk ¥ Current address: Escuela Profesional de Medicina Humana, Facultad de Medicina, Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas, Chachapoyas, Peru.

Key words: Human cutaneous leishmaniasis, Host directed therapy, skin, IL-32, CD8 memory T cells, Regulatory T cells, Prognosis, Spatial Transcriptomics, IDO1, PD-L1, myeloid cells.

Abstract

Human cutaneous leishmaniasis (CL) is characterised by chronic skin pathology. Experimental and clinical data suggest that immune checkpoints (ICs) play a crucial role in disease outcome but the cellular and molecular niches that facilitate IC expression during leishmaniasis are ill-defined. We previously showed that in Sri Lankan patients with CL two ICs, indoleamine 2,3-dioxygenase 1 (IDO1) and programmed death-ligand 1 (PD-L1) are enriched in lesional skin and that reduced PD-L1 expression early after treatment onset predicts cure rate following antimonial therapy. Here, we use spatial cell interaction mapping to identify IL-32-expressing CD8+ memory cells and regulatory T cells as key components of the IDO1 / PD-L1 niche in a cohort of Sri Lankan CL patients. This finding was confirmed in patients with distinct forms of dermal leishmaniasis in Brazil and India. Furthermore, in our Sri Lankan cohort the abundance of IL-32+ cells and IL-32+CD8+ T cells at treatment onset was prognostic for rate of cure. This study provides a unique spatial perspective on the expression of key ICs in these important skin diseases and a novel route to identify biomarkers of treatment response.

Software requirements:

OS: Tested on

  1. Windows 11
  2. Ubuntu 22.04.03 LTS

Suggested computing requirements:

  1. Processor 3 GHz and above
  2. RAM above 32GB

Software

  1. R version 4.1.2 (2021-11-01) and RStudio 2023.09.1
  2. Packages and versions needed for all Visium based analysis

sessionInfo() R version 4.1.2 (2021-11-01) Platform: x86_64-pc-linux-gnu (64-bit) Running under: Ubuntu 22.04.3 LTS

Matrix products: default BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0 LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0

locale: [1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C LC_TIME=en_GB.UTF-8 LC_COLLATE=en_GB.UTF-8 LC_MONETARY=en_GB.UTF-8
[6] LC_MESSAGES=en_GB.UTF-8 LC_PAPER=en_GB.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C

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

other attached packages: [1] reshape2_1.4.4 VennDiagram_1.7.3 futile.logger_1.4.3 stringr_1.5.0 corrplot_0.92 gplots_3.1.3
[7] spatstat_3.0-7 spatstat.linnet_3.1-3 spatstat.model_3.2-8 rpart_4.1.16 spatstat.explore_3.2-5 nlme_3.1-155
[13] spatstat.random_3.2-1 spatstat.geom_3.2-7 spatstat.data_3.0-3 ggpubr_0.6.0 RColorBrewer_1.1-3 patchwork_1.1.3
[19] ggplot2_3.4.4 sqldf_0.4-11 RSQLite_2.3.2 gsubfn_0.7 proto_1.0.0 dplyr_1.1.3
[25] SeuratObject_5.0.0 Seurat_4.4.0

loaded via a namespace (and not attached): [1] backports_1.4.1 spam_2.10-0 plyr_1.8.9 igraph_1.5.1 lazyeval_0.2.2 sp_2.1-1
[7] splines_4.1.2 listenv_0.9.0 scattermore_1.2 digest_0.6.33 htmltools_0.5.6.1 fansi_1.0.5
[13] magrittr_2.0.3 memoise_2.0.1 tensor_1.5 cluster_2.1.2 ROCR_1.0-11 limma_3.50.3
[19] globals_0.16.2 matrixStats_1.0.0 spatstat.sparse_3.0-3 colorspace_2.1-0 blob_1.2.4 ggrepel_0.9.4
[25] xfun_0.41 crayon_1.5.2 tcltk_4.1.2 jsonlite_1.8.7 progressr_0.14.0 survival_3.2-13
[31] zoo_1.8-12 glue_1.6.2 polyclip_1.10-6 gtable_0.3.4 leiden_0.4.3 car_3.1-2
[37] future.apply_1.11.0 abind_1.4-5 scales_1.2.1 futile.options_1.0.1 DBI_1.1.3 rstatix_0.7.2
[43] miniUI_0.1.1.1 Rcpp_1.0.11 viridisLite_0.4.2 xtable_1.8-4 reticulate_1.34.0 bit_4.0.5
[49] dotCall64_1.1-0 htmlwidgets_1.6.2 httr_1.4.7 ellipsis_0.3.2 ica_1.0-3 farver_2.1.1
[55] pkgconfig_2.0.3 uwot_0.1.16 deldir_1.0-9 utf8_1.2.4 labeling_0.4.3 tidyselect_1.2.0
[61] rlang_1.1.1 later_1.3.1 munsell_0.5.0 tools_4.1.2 cachem_1.0.8 cli_3.6.1
[67] generics_0.1.3 broom_1.0.5 ggridges_0.5.4 evaluate_0.23 fastmap_1.1.1 yaml_2.3.7
[73] goftest_1.2-3 knitr_1.45 bit64_4.0.5 fitdistrplus_1.1-11 caTools_1.18.2 purrr_1.0.2
[79] RANN_2.6.1 pbapply_1.7-2 future_1.33.0 mime_0.12 formatR_1.14 compiler_4.1.2
[85] rstudioapi_0.15.0 plotly_4.10.3 png_0.1-8 ggsignif_0.6.4 spatstat.utils_3.0-4 tibble_3.2.1
[91] stringi_1.7.12 lattice_0.20-45 Matrix_1.6-1.1 vctrs_0.6.4 pillar_1.9.0 lifecycle_1.0.3
[97] lmtest_0.9-40 RcppAnnoy_0.0.21 data.table_1.14.8 cowplot_1.1.1 bitops_1.0-7 irlba_2.3.5.1
[103] httpuv_1.6.12 R6_2.5.1 promises_1.2.1 KernSmooth_2.23-20 gridExtra_2.3 parallelly_1.36.0
[109] codetools_0.2-18 lambda.r_1.2.4 MASS_7.3-55 gtools_3.9.4 chron_2.3-61 withr_2.5.2
[115] sctransform_0.4.1 mgcv_1.8-39 parallel_4.1.2 tidyr_1.3.0 rmarkdown_2.25 carData_3.0-5
[121] Rtsne_0.16 shiny_1.7.5.1

  1. Giotto - Requires specific commit installation: Please use the following link to run directly on R; depends on the remotes library: remotes::install_github("RubD/Giotto@0c8c2866e881b1c6b35ddc97c24dcb58b555c375")
  2. Packages and versions required for CoxMx analysis

sessionInfo() R version 4.1.2 (2021-11-01) Platform: x86_64-pc-linux-gnu (64-bit) Running under: Ubuntu 22.04.3 LTS

Matrix products: default BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0 LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0

locale: [1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C LC_TIME=en_GB.UTF-8 LC_COLLATE=en_GB.UTF-8 LC_MONETARY=en_GB.UTF-8
[6] LC_MESSAGES=en_GB.UTF-8 LC_PAPER=en_GB.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C

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

other attached packages: [1] Giotto_2.0.0.9021 ggplot2_3.4.4 reticulate_1.34.0 rcartocolor_2.1.1 dplyr_1.1.3 remotes_2.4.2.1

loaded via a namespace (and not attached): [1] Rcpp_1.0.11 rstudioapi_0.15.0 knitr_1.45 magrittr_2.0.3 rappdirs_0.3.3 tidyselect_1.2.0 munsell_0.5.0 lattice_0.20-45
[9] colorspace_2.1-0 R6_2.5.1 rlang_1.1.1 fansi_1.0.5 tools_4.1.2 grid_4.1.2 data.table_1.14.8 gtable_0.3.4
[17] xfun_0.41 png_0.1-8 utf8_1.2.4 cli_3.6.1 withr_2.5.2 tibble_3.2.1 lifecycle_1.0.3 Matrix_1.6-1.1
[25] vctrs_0.6.4 glue_1.6.2 compiler_4.1.2 pillar_1.9.0 generics_0.1.3 scales_1.2.1 jsonlite_1.8.7 pkgconfig_2.0.3

  1. For Seurat based sub-clustering of CosMx data; the following packages are used:

sessionInfo() R version 4.1.2 (2021-11-01) Platform: x86_64-pc-linux-gnu (64-bit) Running under: Ubuntu 22.04.3 LTS

Matrix products: default BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0 LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0

locale: [1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C LC_TIME=en_GB.UTF-8 LC_COLLATE=en_GB.UTF-8 LC_MONETARY=en_GB.UTF-8
[6] LC_MESSAGES=en_GB.UTF-8 LC_PAPER=en_GB.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C

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

other attached packages: [1] reshape2_1.4.4 VennDiagram_1.7.3 futile.logger_1.4.3 stringr_1.5.0 corrplot_0.92 gplots_3.1.3
[7] spatstat_3.0-7 spatstat.linnet_3.1-3 spatstat.model_3.2-8 rpart_4.1.16 spatstat.explore_3.2-5 nlme_3.1-155
[13] spatstat.random_3.2-1 spatstat.geom_3.2-7 spatstat.data_3.0-3 ggpubr_0.6.0 RColorBrewer_1.1-3 patchwork_1.1.3
[19] ggplot2_3.4.4 sqldf_0.4-11 RSQLite_2.3.2 gsubfn_0.7 proto_1.0.0 dplyr_1.1.3
[25] SeuratObject_5.0.0 Seurat_4.4.0

loaded via a namespace (and not attached): [1] backports_1.4.1 spam_2.10-0 plyr_1.8.9 igraph_1.5.1 lazyeval_0.2.2 sp_2.1-1
[7] splines_4.1.2 listenv_0.9.0 scattermore_1.2 digest_0.6.33 htmltools_0.5.6.1 fansi_1.0.5
[13] magrittr_2.0.3 memoise_2.0.1 tensor_1.5 cluster_2.1.2 ROCR_1.0-11 globals_0.16.2
[19] matrixStats_1.0.0 spatstat.sparse_3.0-3 colorspace_2.1-0 blob_1.2.4 ggrepel_0.9.4 xfun_0.41
[25] tcltk_4.1.2 jsonlite_1.8.7 progressr_0.14.0 survival_3.2-13 zoo_1.8-12 glue_1.6.2
[31] polyclip_1.10-6 gtable_0.3.4 leiden_0.4.3 car_3.1-2 future.apply_1.11.0 abind_1.4-5
[37] scales_1.2.1 futile.options_1.0.1 DBI_1.1.3 rstatix_0.7.2 miniUI_0.1.1.1 Rcpp_1.0.11
[43] viridisLite_0.4.2 xtable_1.8-4 reticulate_1.34.0 bit_4.0.5 dotCall64_1.1-0 htmlwidgets_1.6.2
[49] httr_1.4.7 ellipsis_0.3.2 ica_1.0-3 pkgconfig_2.0.3 uwot_0.1.16 deldir_1.0-9
[55] utf8_1.2.4 tidyselect_1.2.0 rlang_1.1.1 later_1.3.1 munsell_0.5.0 tools_4.1.2
[61] cachem_1.0.8 cli_3.6.1 generics_0.1.3 broom_1.0.5 ggridges_0.5.4 fastmap_1.1.1
[67] goftest_1.2-3 knitr_1.45 bit64_4.0.5 fitdistrplus_1.1-11 caTools_1.18.2 purrr_1.0.2
[73] RANN_2.6.1 pbapply_1.7-2 future_1.33.0 mime_0.12 formatR_1.14 compiler_4.1.2
[79] rstudioapi_0.15.0 plotly_4.10.3 png_0.1-8 ggsignif_0.6.4 spatstat.utils_3.0-4 tibble_3.2.1
[85] stringi_1.7.12 lattice_0.20-45 Matrix_1.6-1.1 vctrs_0.6.4 pillar_1.9.0 lifecycle_1.0.3
[91] lmtest_0.9-40 RcppAnnoy_0.0.21 data.table_1.14.8 cowplot_1.1.1 bitops_1.0-7 irlba_2.3.5.1
[97] httpuv_1.6.12 R6_2.5.1 promises_1.2.1 KernSmooth_2.23-20 gridExtra_2.3 parallelly_1.36.0
[103] codetools_0.2-18 lambda.r_1.2.4 MASS_7.3-55 gtools_3.9.4 chron_2.3-61 withr_2.5.2
[109] sctransform_0.4.1 mgcv_1.8-39 parallel_4.1.2 tidyr_1.3.0 carData_3.0-5 Rtsne_0.16
[115] shiny_1.7.5.1

Running time: All R files run sequentially will take approximately 2 hours to run on a windows computer with 64GB RAM on a 4 core 3.4GHz machine

Data files to run the analysis are available on Zenodo - 10.5281/zenodo.10402126

Instructions for generating figures and analysis for leish-ME

Description of technology used, associated code file and object it is dependent on

Sri Lankan (SL) Cutaneous Leishmaniasis (CL) - Visium

1. sl2_cl_integration.Rmd 
2. sl2_cl_downstream.Rmd
     * Uses: with47_48_integrated_nd2117slcl_dims15_res0.9_prepsct.rds
3. sl2_cl_cell2location.Rmd
     * Uses: with47_48_integrated_nd2117slcl_dims15_res0.9_prepsct.rds

Sri Lankan (SL) Cutaneous Leishmaniasis (CL) - CosMx

4. giotto_sl2_config.Rmd 
     * Uses: complete_giotto_object_PostAnalysis.RData
5. giotto_sl2_downstream.Rmd
     * Dependent on giotto_sl2_config.Rmd
6. cosmx_mac_sub_clustering.Rmd
     * Uses: SL2_seurat_object.Rds
7. cosmx_mac_sub_clustered_downstream.Rmd
     * Uses: cosmx_myeloid_clusterf.Rds
8. cosmx_spatial_plots.Rmd 
     * cosmx_clusterf.Rds

Indian (IN) Post Kala Azar Dermal Leishmaniasis (PKDL) - Visium

9. in_pkdl_integration.Rmd 
     * Uses .h5 files to generate lesion_PKDL_merged.Rds
10. in_pkdl_downstream.Rmd
     * Uses lesion_PKDL_merged.Rds

Brazilian (BZ) Cutaneous Leishmaniasis (CL) - Visium

11. br_cl_integration.Rmd 
     * Uses .h5 files to generate lesion_BRCL_skin_merged.Rds
12. br_cl_downstream.Rmd
     * Uses lesion_BRCL_skin_merged.Rds
Figure number File name
1A Images
1B Images
1C sl2_cl_downstream.Rmd
1D sl2_cl_downstream.Rmd
1E sl2_cl_downstream.Rmd
1F sl2_cl_downstream.Rmd
1G sl2_cl_downstream.Rmd
1H sl2_cl_downstream.Rmd
1I sl2_cl_downstream.Rmd
1J sl2_cl_downstream.Rmd; plotted in GraphPad
1K sl2_cl_cell2location.Rmd
1L sl2_cl_cell2location.Rmd; plotted in GraphPad
Supp. 1A Images
Supp. 1B Images
Supp. 1C sl2_cl_downstream.Rmd
Supp. 1D sl2_cl_downstream.Rmd
Supp. 1E-K sl2_cl_downstream.Rmd
Supp. 2A sl2_cl_downstream.Rmd
Supp. 2B sl2_cl_downstream.Rmd
Supp. 2C-L sl2_cl_cell2location.Rmd
2A Image
2A giotto_sl2_config.Rmd
2B giotto_sl2_config.Rmd
2C giotto_sl2_config.Rmd
2D giotto_sl2_configcosmx_mac_sub_clustered_downstream.Rmd
2E cosmx_spatial_plots.Rmd
2F cosmx_mac_sub_clustered_downstream.Rmd
2G cosmx_mac_sub_clustered_downstream.Rmd
2H cosmx_mac_sub_clustered_downstream.Rmd
2I cosmx_mac_sub_clustered_downstream.Rmd
2J cosmx_mac_sub_clustered_downstream.Rmd
Supp 3A Images
Supp. 3B giotto_sl2_config.Rmd. Plotted in GraphPad
Supp. 3C-H giotto_sl2_config.Rmd
3A sl2_cl_downstream.Rmd
3B sl2_cl_downstream.Rmd
3C sl2_cl_downstream.Rmd. Plotted in GraphPad
3D-K sl2_cl_downstream.Rmd
3L sl2_cl_cell2location.Rmd
3M-O sl2_cl_downstream.Rmd
Supp. 4A sl2_cl_downstream.Rmd
4A giotto_sl2_downstream.Rmd
4B giotto_sl2_downstream.Rmd
4C giotto_sl2_downstream.Rmd. Analysed in Excel, plotted in GraphPad
4D giotto_sl2_downstream.Rmd
4E giotto_sl2_downstream.Rmd
4F Image
4G-I giotto_sl2_downstream.Rmd Analysed in Excel, plotted in GraphPad and UpsetR shiny app
5 Images, image analysis done in Strataquest and graphed in GraphPad
6A-F giotto_sl2_downstream.Rmd; Graphed in GraphPad
6G-I giotto_sl2_downstream.Rmd
6J Analysed in excel and graphed in GraphPad
7A-D images
7E br_cl_downstream.Rmd
7F in_pkdl_downstream.Rmd
7G br_cl_downstream.Rmd
7H in_pkdl_downstream.Rmd
7I br_cl_downstream.Rmd
7J in_pkdl_downstream.Rmd
7K in_pkdl_downstream.Rmd
7L br_cl_downstream.Rmd
7M Data from br_cl_downstream.Rmd,in_pkdl_downstream.Rmd and sl2_cl_downstream.Rmd. Plotted in Venny.
Supp. 5A-D Images
Supp. 5E in_pkdl_downstream.Rmd
Supp. 5F in_pkdl_downstream.Rmd
Ext. 8h in_pkdl_downstream.Rmd
8A-C Flow data analysed in FlowJo and graphed in FlowJo and GraphPad
7D-O Images; using data from source files plotted in R

###Figure reproduction - Please use option 2 until publication Option1 : Start from scratch using raw 10x files (available post publication)

Download all samples/10x files from GSMXXXX into source/'Visium_slide_name'/
Start with sl2_cl_integration.Rmd, in_pkdl_integration.Rmd  or br_cl_integration.Rmd

Option2 : Start from prepared Rds containing primary clustering analysis - 10.5281/zenodo.10402126

1. Download all Rds files from the Zenodo repository - 10.5281/zenodo.10402126 and keep them in your working folder.
2. Open Rmd file as per the figure needed from table above on RStudio. 
3. Define working folder in the path (Find instructions in the Rmd file). 
4. Create a sub-folder in your wokring path 'R'. All outputs will be saved here
5. Run sequentially, chunk by chunk after all requisite packages are installed

Cell2location: Deconvoluting spatial spots

Cell2location v0.1 (https://cell2location.readthedocs.io/en/latest/) was run by following the instructions as per the tool's tutorial for mapping lymph nodes. The code was run on University of York's HPC, namely, Viking using GPU node with 1 GPU, 1 node utilising 40GB RAM in approximately 2.5 hours. 1 hour for reference modelling and 1.5 hours for modelling the spatial data to calculate cell abundances in Visium spots

Cell2location was installed in its own environment as per the instructions
RAW single cell data (10.1126/science.aba6500) and RAW spatial data (this study) was used as input to cell2location
Output was stored as a model.pt file and anndata file containing q05 abundances as metadata
Python scripts were submitted to a job manager (slurm) on the HPC as a shell script with the following batch parameters and script

#!/bin/bash #SBATCH --job-name=XXXX #SBATCH --mail-type=END #SBATCH --mail-user=nidhi.dey@york.ac.uk #SBATCH --ntasks=1 #SBATCH --cpus-per-task=1 #SBATCH --mem=40gb #SBATCH --time=04:00:00 #SBATCH --output=cell2loc.log #SBATCH --account=XXXX #SBATCH --partition=gpu #SBATCH --gres=gpu:1

module load system/CUDA/10.0.130 
module load lang/Miniconda3/4.9.2

source activate cell2loc_env2

command -v python

python config.py

source deactivate

Abundances can be accessed from the final python object as metadata

adata_vis.obs[adata_vis.uns['mod']['factor_names']] = adata_vis.obsm['q05_cell_abundance_w_sf']

You can write this as a CSV file to explore abundances further as in sl2_cl_cell2location.Rmd

License

This project is covered under the MIT License.

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