• 1.

    Eng C-HL, et al. Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH+. Nature 2019; 568:235239. doi: 10.1038/s41586-019-1049-y

  • 2.

    Rudman-Melnick V, et al. Single-cell profiling of AKI in a murine model reveals novel transcriptional signatures, profibrotic phenotype, and epithelial-to-stromal crosstalk. J Am Soc Nephrol 2020; 31:27932814. doi: 10.1681/ASN.2020010052

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 3.

    Ståhl PL, et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 2016; 353:7882. doi: 10.1126/science.aaf2403

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 4.

    10X Genomics. Visium Spatial Gene Expression. Map the whole transcriptome within the tissue context. 2022. https://www.10xgenomics.com/products/spatial-gene-expression

    • Search Google Scholar
    • Export Citation
  • 5.

    Dixon EE, et al. Spatially resolved transcriptomic analysis of acute kidney injury in a female murine model. J Am Soc Nephrol 2022; 33:279289. doi: 10.1681/ASN.2021081150

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 6.

    Kirita Y, et al. Cell profiling of mouse acute kidney injury reveals conserved cellular responses to injury. Proc Natl Acad Sci USA 2020; 117:1587415883. doi: 10.1073/pnas.2005477117

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 7.

    Park J, et al. Single cell-transcriptomics of the mouse kidney reveals potential cellular targets of kidney disease. Science 2018; 360:758763. doi: 10.1126/science.aar2131

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 8.

    Melo Ferreira R, et al. Integration of spatial and single-cell transcriptomics localizes epithelial cell-immune cross-talk in kidney injury. JCI Insight 2021; 6:e147703. doi: 10.1172/jci.insight.147703

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 9.

    Janosevic D, et al. The orchestrated cellular and molecular responses of the kidney to endotoxin define a precise sepsis timeline. Elife 2021; 10:e62270. doi: 10.7554/eLife.62270

    • Crossref
    • Search Google Scholar
    • Export Citation

Visualizing the Kidney Transcriptome: Spatial Transcriptomics Take Center Stage

  • 1 Eryn E. Dixon, PhD, is a Postdoctoral Research Scholar with the Washington University School of Medicine in St. Louis, MO.
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The advancements of single-cell and nucleus RNA sequencing (sc/snRNAseq) have shifted our approach to defining cell types and states relevant to human health. These technologies have provided detailed insight into the transcriptome (all of the expressed messenger RNA [mRNA] of a single cell, tissue, or sample) of a single cell. However, this process, requiring dissociation of tissue to the level of the single cell or nucleus, obscures the structural context of each cell within the tissue. Therefore, sc/snRNAseq studies have been limited by their inability to capture data essential to understanding these cellular microenvironments (surrounding cells, extracellular matrix, and signaling

The advancements of single-cell and nucleus RNA sequencing (sc/snRNAseq) have shifted our approach to defining cell types and states relevant to human health. These technologies have provided detailed insight into the transcriptome (all of the expressed messenger RNA [mRNA] of a single cell, tissue, or sample) of a single cell. However, this process, requiring dissociation of tissue to the level of the single cell or nucleus, obscures the structural context of each cell within the tissue. Therefore, sc/snRNAseq studies have been limited by their inability to capture data essential to understanding these cellular microenvironments (surrounding cells, extracellular matrix, and signaling molecules that affect the response of a cell of interest) and the overall transcriptional landscape of the kidney. In response, researchers have been optimizing and implementing new strategies that account for both the two- and three-dimensional architecture of tissues as a whole. These efforts are known as spatially resolved transcriptomics.

The idea of visualizing transcript localization is not necessarily new. For years, researchers have used techniques, such as in situ hybridization (ISH; a technique for the visualization of a specific segment of a target nucleic acid [DNA or RNA] using a single-stranded DNA or RNA probe that can be detected using radioactivity, enzymes, or immunofluorescence), to localize one gene transcript at a time. But, in recent years, these gold-standard molecular biology techniques have evolved from visualization of single gene targets to robust and technically challenging sequencing methods that support hundreds to thousands of different gene transcripts. Regardless of the method, the driving motivation of this methodology is to cultivate an efficient platform for the visualization of gene expression and localization within intact tissue across the transcriptome.

There are two main categories of spatially resolved transcriptomics, including ISH-based and next-generation sequencing (NGS)-based (also known as barcoding-based spatially resolved transcriptomics). Both categories are gaining momentum with the advent of specialized computational analysis pipelines and streamlined processes for sequencing library generation. However, implementation of a specific modality of spatially resolved transcriptomics depends on the research question. For cellular or subcellular localization of a pre-specified profile of target genes, ISH-based spatially resolved transcriptomics will be the best option. To capture transcriptome-wide (unbiased) expression and localization changes in the kidney, NGS-based spatially resolved transcriptomics—a large-scale and high-throughput technology that rapidly determines the whole genome or transcriptome by reading millions of DNA or RNA sequences in parallel instead of previous methods that processed one at a time—would be needed.

ISH-based spatially resolved transcriptomics examples: sequential fluorescence ISH (seqFISH) and MERFISH

ISH-based spatially resolved transcriptomics were built on the foundation of original, gold-standard ISH protocols. Therefore, to scale up the basic ISH protocol, ISH-based spatially resolved transcriptomics use repeated binding, known as serial hybridization, of RNA target-specific primer probes to transcripts of interest, which are imaged to capture the binding of each probe (1). This serial hybridization creates unique molecular identifiers, corresponding to each target, and allows for the identification of hundreds of genes. Following the image capture of these probes, advanced computational analysis integrated with immunofluorescent images of markers that delineate tubule borders, or membrane markers for cell borders, can be used to segment tissue structures down to the cell or even subcellular level, resulting in highly resolved localization of gene targets. These ISH-based spatially resolved transcriptomic approaches have not yet been directly applied to kidney research. However, RNAscope, a similar but lower-throughput technology, has been used to exhibit the localization of multiple target genes (Figure 1). In the future, kidney researchers may be able to leverage the higher sensitivity and throughput of seqFISH, MERFISH, and other related ISH-based spatial transcriptomics to visualize genes of interest with unknown expression patterns by simultaneously looking at the expression and localization of gene panels that characterize potential neighboring cell types.

Figure 1
Figure 1

ISH-based visualization techniques

Citation: Kidney News 14, 4

NGS-based spatially resolved transcriptomics example: Visium

Barcoding-based spatially resolved transcriptomics have rapidly evolved from the seminal work of Ståhl, Salmén, and co-workers (3), where tissue was mounted on a specialized slide, covered with an array of oligonucleotides and positional barcodes. From this tissue slice, cellular mRNA could be captured by the probes underneath it on the slide and synthesized into cDNA (3). Enzymes could then be used to release this cDNA for generation of sequencing libraries, now with a special spatial barcode that can be used to map expression back to a specific area of the tissue. Curated gene targets are not necessary for this approach, and the whole transcriptome can be sequenced, providing an unbiased method for discovery of gene expression and localization changes. This technology, commercially available as the 10X Genomics Visium platform (4), can thus generate transcriptome-wide spatial atlases overlaid on hematoxylin and eosin or immunofluorescent staining (Figure 2). Although the barriers to barcoding-based spatially resolved transcriptomics or Visium technologies have decreased because of open-source computational toolboxes, a substantial remaining disadvantage is the resolution. Arrayed, probe-embedded spots are too large and too distant to resolve single cells, but cellular deconvolution pipelines have helped determine the contributions of multiple cell types.

Figure 2
Figure 2

Spatially resolved transcriptomics of kidney tissue

Citation: Kidney News 14, 4

How can spatially resolved transcriptomics be applied to kidney study in health and disease?

sc/snRNAseq has provided great insight into the cellular composition of the kidney at many different stages in both animal and human models (6, 7). With the identification of new cell types and states, it is critical to investigate how these cells communicate and the physiological relevance of these local signaling microenvironments. For example, how do we better understand the interactions of injured kidney cells with other epithelial cells and the interstitium? In the past year, multiple groups (5, 8, 9) have focused on spatially resolved transcriptomics for various models of acute kidney injury and chronic kidney disease in both mouse and human kidneys. Although these projects differ in model systems, the efforts share similar goals, including the deconvolution of spatially resolved transcriptomics data to the level of the cell and assessment of cell-to-cell interactions. Ultimately, these spatial atlases of sepsis and ischemia reperfusion injury models demonstrate changes in immune and epithelial cell interactions that are consistent with the disease states they represent (5, 8, 9). Beyond determining localization and expression changes of tubule segment-specific markers and other genes of interest, spatially resolved transcriptomics can reveal how communication within tubule microenvironments responds to genetic variation and disease. The pairing of the spatially resolved transcriptomics pipeline with sc/snRNAseq and other sequencing modalities, such as assay for transposase-accessible chromatin with sequencing (ATACseq), will build a multi-dimensional picture of how the kidney responds to health and disease states, leading us closer to defining mechanisms of kidney physiology.

References

  • 1.

    Eng C-HL, et al. Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH+. Nature 2019; 568:235239. doi: 10.1038/s41586-019-1049-y

  • 2.

    Rudman-Melnick V, et al. Single-cell profiling of AKI in a murine model reveals novel transcriptional signatures, profibrotic phenotype, and epithelial-to-stromal crosstalk. J Am Soc Nephrol 2020; 31:27932814. doi: 10.1681/ASN.2020010052

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 3.

    Ståhl PL, et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 2016; 353:7882. doi: 10.1126/science.aaf2403

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 4.

    10X Genomics. Visium Spatial Gene Expression. Map the whole transcriptome within the tissue context. 2022. https://www.10xgenomics.com/products/spatial-gene-expression

    • Search Google Scholar
    • Export Citation
  • 5.

    Dixon EE, et al. Spatially resolved transcriptomic analysis of acute kidney injury in a female murine model. J Am Soc Nephrol 2022; 33:279289. doi: 10.1681/ASN.2021081150

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 6.

    Kirita Y, et al. Cell profiling of mouse acute kidney injury reveals conserved cellular responses to injury. Proc Natl Acad Sci USA 2020; 117:1587415883. doi: 10.1073/pnas.2005477117

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 7.

    Park J, et al. Single cell-transcriptomics of the mouse kidney reveals potential cellular targets of kidney disease. Science 2018; 360:758763. doi: 10.1126/science.aar2131

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 8.

    Melo Ferreira R, et al. Integration of spatial and single-cell transcriptomics localizes epithelial cell-immune cross-talk in kidney injury. JCI Insight 2021; 6:e147703. doi: 10.1172/jci.insight.147703

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 9.

    Janosevic D, et al. The orchestrated cellular and molecular responses of the kidney to endotoxin define a precise sepsis timeline. Elife 2021; 10:e62270. doi: 10.7554/eLife.62270

    • Crossref
    • Search Google Scholar
    • Export Citation
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