Mosaic loss of Y chromosome is associated with aging and epithelial injury in chronic kidney disease

Parker C. Wilson¹, Amit Verma¹, Yasuhiro Yoshimura², Yoshiharu Muto², Haikuo Li², Nicole P. Malvin², Eryn E. Dixon², Benjamin D. Humphreys^2,3

¹ Division of Diagnostic Innovation, Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
² Division of Nephrology, Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
³ Department of Developmental Biology, Washington University in St. Louis, St. Louis, MO, USA

Welcome to our GitHub repository!
Here you will find analysis scripts for our manuscript where we use single cell sequencing to detect loss of Y chromosome (LOY) and other mosaic chromosomal alterations (mCA) in chronic kidney disease. Please contact the corresponding author, Dr. Parker Wilson, with questions or comments. If you use any of the code or workflows in this repository please cite our manuscript in Genome Biology link

Wilson, P.C., Verma, A., Yoshimura, Y. et al.
Mosaic loss of Y chromosome is associated with aging and epithelial injury in chronic kidney disease.
Genome Biol 25, 36 (2024). https://doi.org/10.1186/s13059-024-03173-2

The code associated with this publication has been deposited in Zenodo
It has been released under an open source Apache License version 2.0

Visit the Wilson lab website:
www.parkerwilsonlab.com

Visit the Humphreys lab website:
www.humphreyslab.com

Check out our interactive datasets with Kidney Interactive mulTiomics (KIT):
http://humphreyslab.com/SingleCell/

Find us on Twitter:
@parkercwilson @HumphreysLab

Find us on Docker Hub:
p4rkerw@dockerhub

Contents

• Data Availability
• Single cell multiome preprocessing and analysis workflow
• snATAC kidney preprocessing and analysis workflow
• KPMP scRNA-seq preprocessing and analysis workflow
• Visium spatial preprocessing and analysis workflow
• snATAC leukocyte preprocessing and analysis workflow
• Figures

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Data availability

Single cell multiome and snATAC-seq data generated for this manuscript (multiomes: 6, snATAC-seq: 5) and cellranger-arc v2.0 / cellranger-atac v2.1 gene and peak count matrices can be found here:
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE232222

Visium spatial sequencing data generated for this manuscript can be found here:
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE232431

Sequencing data generated for previously-published kidney single cell multiomes (n=3) can be found here: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE220289

Sequencing data generated for previously-published kidney snATAC-seq (n=17) can be found here: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE151302
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE195460
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE172008
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE200547

Sequencing data for previously-published leukocyte snATAC-seq (n=20) can be found here:
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE181064

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Single cell multiome preprocessing and analysis workflow

1. Align and count each multiome library (multi_aggr_prep_kidney/cellranger/cellranger_arc_count.sh) Libraries were generated from a nuclear dissociation and aligned to refdata-cellranger-arc-GRCh38-2020-A-2.0.0, which can be downloaded from the 10X genomics website: https://support.10xgenomics.com/.

2. Aggregate the libraries using the multi_aggr_prep_kidney/cellranger/multi_aggr.csv file (multi_aggr_prep_kidney/cellranger/cellranger_arc_aggr.sh)

3. Identify doublet barcodes with AMULET (multi_aggr_prep_kidney/step0_amulet.sh)

4. Bin multiome ATAC fragments into 1Mb bins with epiAneufinder. Exclude barcodes with < 10,000 fragments. Generate cytoband_counts10k.rds file for each library. (multi_aggr_prep_kidney/step1_multi_bin_fragments.R)

5. Run QC and preprocessing routing with Seurat and annotate barcodes (multi_aggr_prep_kidney/step2_multi_prep.R).
   single cell multiome barcodes can be found in this repository.

6. Count and normalize chrY RNA transcripts. Count and normalize ATAC fragments for all chromosomes. Classify LOY using a gaussian finite mixture model. (multi_aggr_prep_kidney/step3_multi_loy.R)

7. Find differentially expressed genes for LOY vs XY cells for all cell types with and without age adjustment (multi_aggr_prep_kidney/analysis/find_deg.R)

8. Find differentially accessible chromatin regions for LOY vs XY cells for all cell types with and without age adjustment (multi_aggr_prep_kidney/analysis/find_dar.R)

9. Run epiAneufinder using 1Mb bins with default settings on autosomal chromosomes (multi_aggr_prep_kidney/step4_multi_epianeufinder.R)

10. Estimate autosomal CNV burden using epiAneufinder output (multi_aggr_prep_kidney/step5_cnv_burden.R)

11. Run gene set enrichment analysis on differentially expressed genes that differentiate LOY vs XY cells in the proximal convoluted tubule and other cell types. (multi_aggr_prep_kidney/step6_gsea.R)

12. Run chromVAR to estimate TF motif activities (multi_aggr_prep_kidney/step7_chromvar.R)

13. Find differentially activity of TF motifs with chromVAR for LOY vs XY cells for all cell types (multi_aggr_prep_kidney/analysis/find_chromvar.R)

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snATAC kidney preprocessing and analysis workflow

1. Align and count each ATAC library (atac_aggr_prep_kidney/cellranger/cellranger_atac_count.sh)
   Libraries were generated from a nuclear dissociation and aligned to refdata-cellranger-arc-A-2.0.0 which can be downloaded from the 10X genomics website: https://support.10xgenomics.com/.

2. Aggregate the snATAC libraries using the atac_aggr_prep_kidney/cellranger/atac_aggr_22.csv file (atac_aggr_prep_kidney/cellranger/cellranger_atac_aggr.sh)

3. Identify doublet barcodes with AMULET (atac_aggr_prep_kidney/step0_amulet.sh)

4. Run standard Signac QC on the aggregated snATAC data, perform batch effect correction with Harmony, transfer cell type labels from a previously-published snRNA-seq atlas and visualize cell-specific markers. (atac_aggr_prep_kidney/step1_prep.R)

5. Annotate barcodes (atac_aggr_prep_kidney/step2_anno.R)
   snATAC barcodes can be found in this repository.

6. Run chromVAR to estimate TF motif activities (atac_aggr_prep_kidney/step3_chromvar.R)

7. Bin ATAC fragments into 1Mb bins with epiAneufinder. Exclude barcodes with < 10,000 fragments. Generate cytoband_counts10k.rds file for each library. (atac_aggr_prep_kidney/step4_atac_bin_fragments.R)

8. Run epiAneufinder using 1Mb bins with default settings on autosomal chromosomes (atac_aggr_prep_kidney/step5_multi_epianeufinder.R)

9. Count and normalize ATAC fragments for all chromosomes. Classify LOY using a density threshold model. (multi_aggr_prep_kidney/step6_atac_loy.R)

10. Estimate autosomal CNV burden using epiAneufinder output (atac_aggr_prep_kidney/step7_cnv_burden.R)

11. Find differentially accessible regions for LOY vs XY cells for all cell types with and without age adjustment (atac_aggr_prep_kidney/analysis/find_dar.R)

12. Find differentially activity of TF motifs with chromVAR for LOY vs XY cells for all cell types (atac_aggr_prep_kidney/analysis/find_chromvar.R)

13. Run gene set enrichment analysis on differentially accessible regions that differentiate LOY vs XY cells in the proximal convoluted tubule and other cell types. (atac_aggr_prep_kidney/step8_gsea.R)

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KPMP scRNA-seq preprocessing and analysis workflow

1. Download the KPMP dataset in h5seurat format from the KPMP website "c798e11b-bbde-45dd-bd91-487f27c93f8f_WashU-UCSD_HuBMAP_KPMP-Biopsy_10X-R_12032021.h5Seurat"

2. Run QC and preprocessing routing with Seurat, predict cell types using label transfer from a previously-published snRNA-seq atlas, perform batch effect correction with Harmony and annotate barcodes. Count and normalize chrY transcripts and assign LOY using density threshold model (rna_aggr_prep_kidney/step1_kpmp.R)
   snRNA barcodes used for the analysis can be found in this github repository

3. Find differentially expressed genes that differentiate LOY vs XY cells (rna_aggr_prep_kidney/analysis/find_deg.R)

4. Gene set enrichment analysis for LOY vs XY differentially expressed genes (and other comparisons) (rna_aggr_prep_kidney/step2_gsea.R)

5. Project the single cell multiome atlas onto the KPMP atlas to harmonize cell type annotations (rna_aggr_prep_kidney/step3_harmonize.R)

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Visium spatial preprocessing and analysis workflow

0. Align Visium datasets to refdata-gex-GRCh38-2020-A with spaceranger v2.0.0

1. Integrate and annotate Visium datasets (spatial_kidney/step1_spatial.R)
   Visium barcodes used for the analysis can be found in this github repository

2. Find neighborhood-specific differentially expressed genes (spatial_kidney/step2_deg.R)

3. Perform gene set enrichment analysis of differentially expressed genes for injured proximal tubule neighborhood (spatial_kidney/step3_gsea.R)

4. Analyze secreted intercellular ligand-receptor interactions with CellChat (spatial_kidney/step4_cellchat.R)

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snATAC leukocyte preprocessing and analysis workflow

1. Align and count each ATAC library. Libraries were aligned to refdata-cellranger-arc-A-2.0.0 which can be downloaded from the 10X genomics website: https://support.10xgenomics.com/.

2. Identify doublet barcodes with AMULET.

3. Aggregate the snATAC libraries using the atac_aggr_prep_leuk/cellranger/atac_aggr_rccleuk.csv file (atac_aggr_prep_leuk/cellranger/cellranger_atac_aggr.sh)

4. Run standard Signac QC on the aggregated snATAC data, perform batch effect correction with Harmony and remove doublet barcodes (atac_aggr_prep_kidney/step1_prep.R)

5. Bin ATAC fragments into 1Mb bins with epiAneufinder. Exclude barcodes with < 10,000 fragments. Generate cytoband_counts10k.rds file for each library. (atac_aggr_prep_leuk/step2_atac_bin_fragments.R)

6. Annotate barcodes using bridge transfer from a publicly-available leukocyte atlas (atac_aggr_prep_leuk/step3_atac_anno.R). https://www.10xgenomics.com/resources/datasets/pbmc-from-a-healthy-donor-granulocytes-removed-through-cell-sorting-10-k-1-standard-2-0-0
   snATAC barcodes used for the analysis can be found in this github repository

7. Count and normalize ATAC fragments for all chromosomes. Classify LOY using a density threshold model. (atac_aggr_prep_leuk/step4_atac_loy.R)

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Figures

Scripts for generating figures in the manuscript.