Transcriptomic responses of the human kidney to acute injury at single cell resolution

  1. Christian Hinze
  2. Christine Kocks
  3. Janna Leiz
  4. Nikos Karaiskos
  5. Anastasiya Boltengagen
  6. Christopher Mark Skopnik
  7. Jan Klocke
  8. Jan-Hendrik Hardenberg
  9. Helena Stockmann
  10. Inka Gotthardt
  11. Benedikt Obermayer
  12. Laleh Haghverdi
  13. Emanuel Wyler
  14. Markus Landthaler
  15. Sebastian Bachmann
  16. Andreas C. Hocke
  17. Victor Corman
  18. Jonas Busch
  19. Wolfgang Schneider
  20. Nina Himmerkus
  21. Markus Bleich
  22. Kai-Uwe Eckardt
  23. Philipp Enghard
  24. Nikolaus Rajewsky
  25. Kai M. Schmidt-Ott

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Abstract

Background

Acute kidney injury (AKI) occurs frequently in critically ill patients and is associated with adverse outcomes. Cellular mechanisms underlying AKI and kidney cell responses to injury remain incompletely understood.

Methods

We performed single-nuclei transcriptomics, bulk transcriptomics, molecular imaging studies, and conventional histology on kidney tissues from 8 individuals with severe AKI (stage 2 or 3 according to Kidney Disease: Improving Global Outcomes (KDIGO) criteria). Specimens were obtained within 1-2 hours after individuals had succumbed to critical illness associated with respiratory infections, with 4 of 8 individuals diagnosed with COVID-19. Control kidney tissues were obtained post-mortem or after nephrectomy from individuals without AKI.

Results

High-depth single cell-resolved gene expression data of human kidneys affected by AKI revealed enrichment of novel injury-associated cell states within the major cell types of the tubular epithelium, in particular in proximal tubules, thick ascending limbs and distal convoluted tubules. Four distinct, hierarchically interconnected injured cell states were distinguishable and characterized by transcriptome patterns associated with oxidative stress, hypoxia, interferon response, and epithelial-to-mesenchymal transition, respectively. Transcriptome differences between individuals with AKI were driven primarily by the cell type-specific abundance of these four injury subtypes rather than by private molecular responses. AKI-associated changes in gene expression between individuals with and without COVID-19 were similar.

Conclusion

The study provides an extensive resource of the cell type-specific transcriptomic responses associated with critical illness-associated AKI in humans, highlighting recurrent disease-associated signatures and inter-individual heterogeneity. Personalized molecular disease assessment in human AKI may foster the development of tailored therapies.

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  1. ScreenIT

    SciScore for 10.1101/2021.12.15.472619: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    RandomizationThe training set included randomly selected cells (2/3 of cells) from injured mouse PT subclusterings (Suppl. Fig. S6)
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Recombinant DNA
    SentencesResources
    ), Newark, CA) was used to perform chromogenic in situ hybridization on formalin-fixed paraffin embedded mouse kidney sections with probes directed against IFITM3 (#1062531-C1, ACD), IGFBP7 (#316681, ACD) and IL18 (#400301, ACD).
    #1062531-C1
    suggested: None
    Software and Algorithms
    SentencesResources
    Provided FASTQ files were aligned using STAR and the same genome as for the snRNA-seq data (GRCh38 3.0.0 with SARS-CoV1/2), reads were then counted using featureCounts57 with -p -t exon -O -g gene_id -s 0.
    STAR
    suggested: (STAR, RRID:SCR_004463)
    Differential gene expression analysis: Differential gene expression analysis was performed using the DESeq2 pacakge (version 1.28.1).
    DESeq2
    suggested: (DESeq, RRID:SCR_000154)
    DESeq dataset was generated by: dds <- DESeqDataSetFromMatrix(countData = my.count.data, colData = col.data, design = ∼ condition + perc.mt), followed by: dds <- estimateSizeFactors(dds, type=‘poscounts’) dds <- DESeq(dds) “condition” was either AKI or control, my.count.
    DESeq
    suggested: (DESeq, RRID:SCR_000154)
    Pathway enrichment analysis: Differentially expressed genes were analyzed, separately, for genes up- and downregulated in AKI versus controls using the MSigDB web interface: http://www.gsea-msigdb.org/gsea/msigdb/annotate.jsp with the hallmark gene sets and curated gene sets (C1) from Biocarta,
    Biocarta
    suggested: (BioCarta Pathways, RRID:SCR_006917)
    Kegg, Reactome and WikiPathways
    Reactome
    suggested: (Reactome, RRID:SCR_003485)
    WikiPathways
    suggested: (WikiPathways, RRID:SCR_002134)
    Multinomial classification was performed using the glmnet package version 2.0-16.
    glmnet
    suggested: (glmnet, RRID:SCR_015505)

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    These findings suggest that precision approaches like single cell transcriptomics maybe suitable tools to overcome the current limitations in diagnosing and treating subtypes of AKI.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

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  2. Version published to 10.1101/2021.12.15.472619v1 on bioRxiv