The zebrafish kideny is more complex than ever anticipated!

Written By: Porter Alston | poalston@davidson.edu

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This web page was produced as an assignment for an undergraduate course at Davidson College. Featured Image from Britannica

Single-cell RNA sequencing on white blood cells of infected zebrafish kidneys by Hu et al. reveals the presence of multiple immune cell lineages, as well as conserved differential expression across multiple genes markers related to immune response and viral infection. The zebrafish kidney is now characterized as a secondary lymphoid organ crucial to immune defense, waste excretion, osmoregulation, as well as understanding immune function across diverse vertebrate species.

Single-cell RNA sequencing generates huge amounts of information about the transcriptome of the organism being studied. Using RNA sequencing, gene expression can be observed over time with thousands of genes measured simultaneously, and only a small number of samples are required. RNA sequencing data is similar to genomic data, however the coverage is proportional to the expression of a gene.¹ This typically requires an existing reference genome to map RNA sequence reads for identification of gene locations.³ In single-cell RNA sequencing, this information is then used to identify the cell-type based on known markers from the initial sample. Single-cell RNA sequencing allows researchers to map and compare the expression patterns of different cell types under variable conditions.^{1, 3} Single-cell RNA sequencing is proving its utility as it is increasingly implemented in developmental biology and immunology studies. 

Teleost, or ray-finned, fish account for the majority of currently existing fish populations. The immune systems of teleost fish are popularly studied due to their genetic relationships and similarities to vertebrate immune systems.² Importantly, teleost fish immunology has been crucial to understanding development of immune cells from signaling pathways as well as identifying genes which function in immune response. In teleost fish, the kidney functions as an ortholog of mammalian bone marrow, generating variable hematopoietic/ blood cell lineages and immunological responses to infection. However, little is known about the properties of kidney hematopoietic cells or the functionality of the kidney in the fish immune system. In order to further elucidate these interests, researchers conducted single-cell RNA sequencing on kidney tissues in zebrafish at variable levels of viral infection. Through a better understanding of immune development and function in the zebrafish kidney, researchers hope to learn more about viral immunology in humans.

Adult zebrafish were randomly divided into three treatment groups for comparison of gene expression in response to infection; the normal group (control), infected group, and vaccinated+infected group. Subsequently, kidney samples were collected at 7 days from the control and infected group and 21 days for the vaccinated+infected group. White blood cells were then isolated from the kidney samples and prepared for single-cell RNA sequencing. The RNA sequencing reads were mapped to genes of the zebrafish genome using a reference genome and individual cells were identified. After filtering the cell count to minimize overlap, the cell counts were 9,004 in the control group, 9,890 in the infected group, and 8,487 in the vaccinated+infected group.

Having conducted RNA sequencing of the white blood cells of the kidney, researchers then went about computational analysis and comparison of the different cells and their expression patterns. Cells were clustered based on the presence of cataloged gene markers and expression patterns. Using a principal component analysis, researchers identified 13 distinct cell clusters. Specific genes were pinpointed within each cluster and clear genetic boundaries were evident for each cluster based on heatmapping. A heatmap shows the expression pattern within a certain cell type of interest, and orders each cells’ pattern next to cells with the most similar patterns. In the end, the heatmap provides a stratified visualization of the change in expression patterns across the 13 clusters.

Having identified specific lineages of immune cells within the zebrafish kidney, researchers focused on the genes showing a significant difference in expression across cell types and the treatment groups. This is done through a computational algorithm which compares the presence of differentially expressed genes in one cell-type with the function of known genes from other similar species or cell types. This process is sometimes referred to as gene ontology enrichment analysis.  

The analysis of differential expression revealed significant changes in the abundance of diverse immune cells between the control and infected groups, with both upregulated and downregulated subsets of cells being found specific to the infected group. The expression patterns of cell types between treatment groups also showed significant conservation, supporting pathway specific development of immune responses in vertebrates. While more research needs to be done, it is clear that the kidney of zebrafish provides an adaptive response to viral infection as well as an active immunity. Infected group cells demonstrated a shift in expression patterns upon initial infection while both the infected group and viral+infected group displayed a permanent upregulation in certain immune cell subsets distinct from those seen in the control. 

A crucial backbone to RNA sequencing analysis and the research conducted in this paper is cell type analysis using principal component analysis plots or t-distributed stochastic neighbor embedding plots.³  The researchers use these programs to determine clustering of different cell types as well as further subset differences within types of cells. The researchers briefly state that these were the most optimal methods for separating cell types and subsets compared to other unsupervised cluster detection algorithms. While researchers use both principal components analysis and neighboring algorithms to cluster, both of these programs are designed to amplify differences in order to better cluster data.³ This amplification of differences can skew the data or separate like cells based on minor differences. In effect, it is very important how researchers display this information and communicate to the audience how differences were determined.

Ultimately, while the researchers found new subsets of immune cell types, and further characterized the genetic landscape of the kidney, more research must be done to further confirm these delineations. One possible remedy being proposed is the follow up analysis of single-cell RNA sequencing on the zebrafish spleen, an organ with similar lymphatic function to the kidney.² Researchers appear interested in conducting cluster analysis on immune cells of the spleen, hoping to find similar effects and pathways reflected in the immune response of the kidney, representing a system compatible and supporting a shared immune system response to inflection. The results of this study could help further validate the outcomes and use of cell clustering algorithms under certain conditions. Regardless, analysis of the zebrafish kidney by single-cell RNA sequencing has been crucial to identifying immune response pathways and genes critical to viral infections across multiple vertebrates, including humans.²

References:

(1)

Haque, A.; Engel, J.; Teichmann, S. A.; Lönnberg, T. A Practical Guide to Single-Cell RNA-Sequencing for Biomedical Research and Clinical Applications. Genome Medicine 2017, 9 (1), 75. https://doi.org/10.1186/s13073-017-0467-4.

(2)

Hu, C.; Zhang, N.; Hong, Y.; Tie, R.; Fan, D.; Lin, A.; Chen, Y.; Xiang, L.; Shao, J. Single-Cell RNA Sequencing Unveils the Hidden Powers of Zebrafish Kidney for Generating Both Hematopoiesis and Adaptive Antiviral Immunity. eLife 2024, 13. https://doi.org/10.7554/eLife.92424.2.

(3)

Scharl, T.; Grün, B. A Clustering Procedure for Three-Way RNA Sequencing Data Using Data Transformations and Matrix-Variate Gaussian Mixture Models: BMC Bioinformatics. BMC Bioinformatics 2024, 25 (1), 1–21. https://doi.org/10.1186/s12859-024-05717-6.

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