pubmed.ncbi.nlm.nih.gov

Defining cell-type specificity at the transcriptional level in human disease - PubMed

. 2013 Nov;23(11):1862-73.

doi: 10.1101/gr.155697.113. Epub 2013 Aug 15.

Affiliations

Defining cell-type specificity at the transcriptional level in human disease

Wenjun Ju et al. Genome Res. 2013 Nov.

Abstract

Cell-lineage-specific transcripts are essential for differentiated tissue function, implicated in hereditary organ failure, and mediate acquired chronic diseases. However, experimental identification of cell-lineage-specific genes in a genome-scale manner is infeasible for most solid human tissues. We developed the first genome-scale method to identify genes with cell-lineage-specific expression, even in lineages not separable by experimental microdissection. Our machine-learning-based approach leverages high-throughput data from tissue homogenates in a novel iterative statistical framework. We applied this method to chronic kidney disease and identified transcripts specific to podocytes, key cells in the glomerular filter responsible for hereditary and most acquired glomerular kidney disease. In a systematic evaluation of our predictions by immunohistochemistry, our in silico approach was significantly more accurate (65% accuracy in human) than predictions based on direct measurement of in vivo fluorescence-tagged murine podocytes (23%). Our method identified genes implicated as causal in hereditary glomerular disease and involved in molecular pathways of acquired and chronic renal diseases. Furthermore, based on expression analysis of human kidney disease biopsies, we demonstrated that expression of the podocyte genes identified by our approach is significantly related to the degree of renal impairment in patients. Our approach is broadly applicable to define lineage specificity in both cell physiology and human disease contexts. We provide a user-friendly website that enables researchers to apply this method to any cell-lineage or tissue of interest. Identified cell-lineage-specific transcripts are expected to play essential tissue-specific roles in organogenesis and disease and can provide starting points for the development of organ-specific diagnostics and therapies.

PubMed Disclaimer

Figures

Figure 1.
Figure 1.

Schematic overview of the in silico nanodissection workflow, an iterative approach for cell-lineage–specific gene prediction, validation, and functional analysis. Expert-curated literature annotations are iteratively combined with gene-expression data to predict genes specific to a cell lineage. These predictions are assessed, and the standards are refined. Validation of podocyte specificity of our predictions used publicly available resources followed by evaluation of intrarenal mRNA and protein expression analysis in correlation with clinical phenotypes to define regulation of predicted gene sets in human disease.

Figure 2.
Figure 2.

In silico nanodissection. Distribution of cell-type–specific prediction by percentile, estimated using a Gaussian kernel. Genes are ordered on the x-axis from worst (zero percentile) to best (100th percentile). The dotted line shows in silico nanodissection cutoff for the top 136 genes. Nanodissection successfully separates (area under the curve [AUC] 0.83) podocyte-specific genes (green) from genes specific to other renal cell lineages (glomerular endothelial in dark blue, glomerular mesangial in light blue, and tubular in red).

Figure 3.
Figure 3.

Evaluation of podocyte-specific genes based on qualified Human Protein Atlas (HPA) staining images. (A) HPA images demonstrate podocyte-specific pattern of positive standard markers and predicted genes. Staining pattern of positive standard markers: podocyte-specific in kidney (I): nephrin (NPHS1) and podocyte-specific in glomerulus: (II) SYNPO and (III) CD2AP. Exemplary staining patterns for de novo nanodissection predicted proteins (IV–IX): (IV) FGF1; (V) ARHGAP28; (VI) PRKAR2B; (VII) PCOLCE2; (VIII) GJA1; and (IX) ZDHHC6. (B) HPA-based distribution intrarenal protein staining pattern in random gene set, nanodissection-identified gene set, and the murine experimental approach-derived gene set: The in silico nanodissection approach (65%) significantly outperforms a random set of genes (12%) and the ex vivo murine experimental approach (23%) for identifying podocyte-specific genes. Gray bars show the proportion of genes with exclusively podoctye-specific staining within the kidney, and black bars show the proportion of genes with exclusively podocyte-specific staining within the glomerulus.

Figure 4.
Figure 4.

Regulation of predicted podocyte-specific gene set in human disease. (A) Box-and-whisker plot of glomerular mRNA expression of PCOLCE2 in biopsies from living donor controls (LD, n = 35), minimal change disease (MCD) patients (n = 12), and focal segmental glomerulosclerosis (FSGS) patients (n = 19). Asterisk denotes a significant differential expression (p < 0.05). (B) IHC staining of PCOLCE2 on kidney biopsies from controls (I) and FSGS patients (II). In comparison with control kidneys, PCOLCE2 signal disappears in FSGS patients. Images shown are the representative images in the glomerulus of controls (n = 5) and FSGS patients (n = 8). (C) Density plot of the association (Pearson correlation, x-axis) of the 136 predicted podocyte-specific genes (red) with renal function as quantified by GFR value, compared with density plot of repeatedly (100 times) randomized gene expression–GFR associations (black). The randomized set shows a distribution centered on zero (meaning no correlation with GFR), whereas the podocyte-specific genes show a skewed distribution toward positive correlation, indicating reduced gene expression is associated with impaired renal function. Correlation with GFR of the 136 transcripts across all renal diseases analyzed was significantly enriched compared with the permuted sample (p < 0.01). Black line indicates the correlation of PCOLCE2 mRNA level with GFR.

Comment in

  • Human disease: Cell types in profile.

    Flintoft L. Flintoft L. Nat Rev Genet. 2013 Oct;14(10):678. doi: 10.1038/nrg3580. Epub 2013 Sep 11. Nat Rev Genet. 2013. PMID: 24022698 No abstract available.

Similar articles

Cited by

References

    1. Beck LH, Bonegio RGB, Lambeau G, Beck DM, Powell DW, Cummins TD, Klein JB, Salant DJ 2009. M-type phospholipase A2 receptor as target antigen in idiopathic membranous nephropathy. N Engl J Med 361: 11–21 - PMC - PubMed
    1. Bennett MR, Czech KA, Arend LJ, Witte DP, Devarajan P, Potter SS 2007. Laser capture microdissection-microarray analysis of focal segmental glomerulosclerosis glomeruli. Nephron, Exp Nephrol 107: e30–e40 - PubMed
    1. Berthier CC, Bethunaickan R, Gonzalez-Rivera T, Nair V, Ramanujam M, Zhang W, Bottinger EP, Segerer S, Lindenmeyer M, Cohen CD, et al. 2012. Cross-species transcriptional network analysis defines shared inflammatory responses in murine and human lupus nephritis. J Immunol 189: 988–1001 - PMC - PubMed
    1. Bowman AW, Azzalini A 2010. R package sm: nonparametric smoothing methods (version 2.2-4)
    1. Brunskill EW, Georgas K, Rumballe B, Little MH, Potter SS 2011. Defining the molecular character of the developing and adult kidney podocyte. PLoS ONE 6: e24640. - PMC - PubMed

Publication types

MeSH terms