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Review
. 2020 Jun;18(3):254-261.
doi: 10.1007/s11914-020-00583-6.

Transcriptomics: a Solution for Renal Osteodystrophy?

Aline Martin  1 , Valentin David  2
Affiliations
Review

Transcriptomics: a Solution for Renal Osteodystrophy?

Aline Martin et al. Curr Osteoporos Rep. 2020 Jun.

Abstract

Purpose of review: The molecular mechanisms of the bone disease associated with chronic kidney disease (CKD), called renal osteodystrophy (ROD), are poorly understood. New transcriptomics technologies may provide clinically relevant insights into the pathogenesis of ROD. This review summarizes current progress and limitations in the study and treatment of ROD, and in transcriptomics analyses of skeletal tissues.

Recent findings: ROD is characterized by poor bone quality and strength leading to increased risk of fracture. Recent studies indicate permanent alterations in bone cell populations during ROD. Single-cell transcriptomics analyses, successful at identifying specialized cell subpopulations in bone, have not yet been performed in ROD. ROD is a widespread poorly understood bone disease with limited treatment options. Transcriptomics analyses of bone are needed to identify the bone cell subtypes and their role in the pathogenesis of ROD, and to develop adequate diagnosis and treatment strategies.

Keywords: Bone and mineral metabolism; Bulk RNA sequencing; Chronic kidney disease; Renal osteodystrophy; Single-cell RNA sequencing; Transcriptomics.

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Figures

Figure 1:
Figure 1:. Purpose of transcriptomics analyses in renal osteodystrophy.
(A) 3D microtomography scans of femur diaphysis from healthy mice and mice with advanced CKD show increased cortical bone porosity in CKD. Workflow comparison between (B) bulk RNA sequencing (RNAseq) and (C) single cell RNAseq (scRNAseq) analyses illustrates high sensitivity detection of bulk gene expression at the tissue level (B) vs. cell subset identification and subset-specific detection of gene expression (C). Heatmaps represent decreased (blue) to increased (red) gene expression. Colored coded clusters in scRNAseq plots represent cell subsets identified and plotted according to distinct molecular signatures. (D) The combined benefits of each transcriptomics approach can be used to significantly narrow the window for diagnostic and treatment of ROD from disease- to mechanism-specific strategies.
See this image and copyright information in PMC

References

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