Rat Kidney snRNA-seq Reference built from HSRA Rat

Milner AR, Johnson AC, Attipoe EM, Wu W, Challagundla L, Garrett MR. Methylseq, single-nuclei RNAseq, and discovery proteomics identify pathways associated with nephron-deficit CKD in the HSRA rat model. Am J Physiol Renal Physiol. 2025 Apr 1;328(4):F470-F488. doi: 10.1152/ajprenal.00258.2024. Epub 2025 Feb 21. PMID: 39982494; PMCID: PMC12224070.

This repository provides a reusable single-nucleus RNA-seq reference of the 4-week rat kidney from the HSRA rat model which was derived from the NIH Heterogeneous Stock (HS).

The resource is designed to support cell type annotation of rat kidney single-cell and single-nucleus datasets using SingleR (Bioconductor), Seurat label transfer, or Azimuth (local reference mapping). It includes annotated reference objects and example workflows.

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Overview

Single-cell and single-nucleus RNA-seq studies of the kidney require reliable reference datasets for accurate cell type annotation. While several resources exist for human and mouse, fewer are available for rat.

This dataset provides a curated reference of the juvenile rat kidney with annotated cell types and supporting marker genes. It is intended for use in annotation, cross-dataset comparison, and validation of kidney cell type markers. Currently it is compatibile with SingleR, Seurat label transfer, or Azimuth. Although generated from 4-week tissue, this reference may be applicable to both juvenile and adult rat kidney datasets.

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Data

Reference objects needed for annotation are available on Zenodo:

https://doi.org/10.5281/zenodo.19196715

Files:

• hsra_kidney_reference_dietseurat.rds
• hsra_kidney_reference_sce_aggr.rds
• azimuth_referenceidx.annoy
• azimuth_referenceref.Rds

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Cell Type Annotation

Annotations were assigned based on clustering, differential expression, and known kidney marker genes.

There are 14 celltypes included in the reference:

Podocytes, GC - Glomerular endothelial cells, PT-C - Proximal tubule-convoluted, PT-S - Proximal tubule-straight, DTL-LOH - Descending thin limb, ATL-LOH - Ascending thin limb, TAL-LOH - Thick ascending limb, DT-C - Distal tubule-convoluted, CT - Connecting tubule, CD-PC - Collecting duct-Principal Cell, CD-ICA - Collecting duct-Intercalated type A, CD-ICB - Collecting duct-Intercalated type B, MC - Mesenchymal-derived (Mesangial, Fibroblasts, etc), Proliferating

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Quick Start

SingleR annotation

library(SingleR)

#Load your query dataset (must be SingleCellExperiment)
#query_sce <- readRDS("your_query_sce.rds")

#Download reference from Zenodo 
ref <- readRDS("path/to/hsra_kidney_reference_sce_agg.rds")

pred <- SingleR(
  test = query_sce,
  ref = ref,
  labels = ref$celltype
)

table(pred$labels)

Or

Seurat label transfer

library(Seurat)
#Load your query dataset (Seurat object)
#query_obj <- readRDS("your_query_seurat.rds")

#Download reference from Zenodo
ref <- readRDS("path/to/hsra_kidney_reference_dietseurat.rds")

anchors <- FindTransferAnchors(
  reference = ref,
  query = query_obj,
  dims = 1:30
)

predictions <- TransferData(
  anchorset = anchors,
  refdata = ref$celltype,
  dims = 1:30
)

query_obj <- AddMetaData(query_obj, metadata = predictions)

Or

Azimuth (local mapping)

library(Seurat)
library(Azimuth)

#Download reference from Zenodo (idx.annoy and ref.Rds)

# query_obj <- readRDS("your_query_seurat.rds")

mapped <- RunAzimuth(
  query = query_obj,
  reference = "path/to/azimuth_reference_files"
)

head(mapped$predicted.celltype)

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Methods Summary

Single-nucleus RNA-seq data were processed using Seurat. Cells were filtered using standard quality control metrics, normalized, and clustered. Cell type annotations were assigned based on differential gene expression and established kidney marker genes.

Reference objects were generated for use with SingleR, Seurat label transfer workflows, and Azimuth local mapping. This reference was validated across independent rat kidney datasets.

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Repository Structure

hsra-rat-kidney-reference/
├── scripts/
│   ├── run_azimuth_example.R
│   ├── run_seurat_label_transfer.R
│   └── run_singler_example.R
└── README.md

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Associated Publication

https://doi.org/10.1152/ajprenal.00258.2024

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Citation

If you use this reference in your work, please cite:

Milner AR, Johnson AC, Attipoe EA, Wu W, Challagundla L, Garrett MR. Methylseq, single-nuclei RNAseq, and discovery proteomics identify pathways associated with nephron-deficit CKD in the HSRA rat model. American Journal of Physiology-Renal Physiology 2025 328:4, F470-F488

BibTeX

@article{doi:10.1152/ajprenal.00258.2024,
author = {Milner, Andrew R. and Johnson, Ashley C. and Attipoe, Esinam M. and Wu, Wenjie and Challagundla, Lavanya and Garrett, Michael R.},
title = {Methylseq, single-nuclei RNAseq, and discovery proteomics identify pathways associated with nephron-deficit CKD in the HSRA rat model},
journal = {American Journal of Physiology-Renal Physiology},
volume = {328},
number = {4},
pages = {F470-F488},
year = {2025},
doi = {10.1152/ajprenal.00258.2024},
    note ={PMID: 39982494}

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Limitations

This reference is derived from 4-week rat kidney tissue in the HSRA rat model. While it captures major kidney cell types, users should consider differences related to developmental stage, disease context, and species when applying this reference.