Transcriptomic, epigenomic and spatial metabolomic cell profiling redefines regional human kidney anatomy

This repository documents the scripts to generate data for our manuscript studying human kidney anatomical regions with SHARE-seq and imaging mass spectrometry (Cell Metabolism 2024).

For citation (DOI: https://doi.org/10.1016/j.cmet.2024.02.015) (PMID: 38513647):

Li, H., Li, D., Ledru, N., Xuanyuan, Q., Wu, H., Asthana, A., Byers, L.N., Tullius, S.G., Orlando, G., Waikar, S.S. and Humphreys, B.D., 2024. Transcriptomic, epigenomic, and spatial metabolomic cell profiling redefines regional human kidney anatomy. Cell metabolism, 36(5), pp.1105-1125.

More information about MALDIpy, a package for single-cell analysis of MALDI-MS imaging mass spectrometry data, can be found in another GitHub repository: https://github.com/TheHumphreysLab/MALDIpy.

Raw data (.fastq), processed data (count matrix .h5 files and fragment .bed files), sublibrary primer sequences and metadata of the SHARE-seq data have been deposited in NCBI’s Gene Expression Omnibus and are available through GEO Series accession number GSE234788.

A searchable database is available at our Kidney Interactive Transcriptomics (K.I.T.) website: http://humphreyslab.com/SingleCell/.

Pre-processing of raw fastq files was performed as previously described (Ma et al. 2020, Cell): https://github.com/masai1116/SHARE-seq-alignment and https://github.com/masai1116/SHARE-seq-alignmentV2/.

Descriptions

1. Scripts for Figure 1

Peusdobulk analysis for scRNA-seq
Peusdobulk analysis for scATAC-seq
Peusdobulk analysis for co-embedded scRNA-seq and scATAC-seq
Quality control and single-cell clustering (scRNA-seq)
.bed file preprocessing, peak calling and metadata inclusion (scATAC-seq)
Quality control and single-cell clustering (scATAC-seq)
Circular UMAP visualization with plot1cell

2. Scripts for Figure 2

Single-cell and gene expression visualizations and correlation analysis (scRNA-seq)
Single-cell and gene expression visualizations and correlation analysis (scATAC-seq)
Calculating cell type proportions across samples or groups
Calculating sample or group distributions for each cell type
Calculating the consistency of cell cluster annotation between scRNA-seq and scATAC-seq

3. Scripts for Figure 3

Single-cell analysis of spatially resolved metabolomics data
Single-cell metabolite feature visualization and tissue projection

4. Scripts for Figure 4

Quality control and single-cell clustering for tL
Quality control and single-cell clustering for TAL
Quality control and single-cell clustering for distal tubular cells
Gene expression visualization and calculating cell type proportions across samples or groups

5. Scripts for Figure 5

Processing a total of 2111 metabolism associated genes only
Quality control and single-cell clustering with metabolism genes
Gene module scoring for metabolic profiles
Data visualization

6. Scripts for Figure 6&7

Acylcarnitine feature analysis in the spatially resolved metabolomics data
Monocle3-based trajectory analysis for PT cells
Marker visualization across predicted pseudotime
Cinical data integration and regression analysis

7. Scripts for other multimodal analysis (multiome_code)

Weighted Nearest Neighbour (WNN)
chromVAR
Cicero
RENIN

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