- Rheumatologie-App Injektionstechnik
- Neue Studien
- Kongress Highlights
- EULAR 2018
- EULAR 2017
- EULAR 2016
- EULAR 2015
- EULAR 2014
- ACR 2017
- ACR 2015
- ACR 2014
- Rheuma Top 2018
- Rheuma Top 2017
- Rheuma Top 2015
- Rheuma Top 2014
- SlideSet RA
ACR 2017 | Daily Highlights
Kidney and Skin Single-Cell RNA Sequencing in Lupus Nephritis Provides Mechanistic Insights and Novel Potential Biomarkers
Authors: Evan Der1, Hemant Suryawanshi2, Saritha Ranabothu3, Beatrice Goilav4, H. Michael Belmont5, Peter M. Izmirly6, Nicole Bornkamp5, Nicole Jordan7, Tao Wang1, Ming Wu6, Judith A. James8, Joel M. Guthridge9, Soumya Raychaudhuri10, Thomas Tuschl11, Jill P. Buyon12 and Chaim Putterman13, 1Albert Einstein College of Medicine, Bronx, NY, 2The Rockefeller University, New York, NY, 3Nephrology, Children's Hospital at Montefiore, Bronx, NY, 4Albert Einstein College of Medicine/Montefiore Medical Center, New York, NY, 5Medicine, New York University School of Medicine, New York, NY, 6New York University School of Medicine, New York, NY, 7Montefiore Medical Center, New York, NY, 8Arthritis & Clinical Immunology Program, Oklahoma Medical Research Foundation, Oklahoma City, OK, 9Arthritis and Clinical Immunology Program, Oklahoma Medical Research Foundation, Oklahoma City, OK, 10Divisions of Genetics and Rheumatology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 11Rockefeller University, New York, NY, 12Rheumatology, New York University School of Medicine, New York, NY, 13Division of Rheumatology, Albert Einstein College of Medicine, Bronx, NY, USA, Bronx, NY
Classification and treatment decisions in lupus nephritis (LN) are largely based on renal histology. Single-cell RNAseq (scRNAseq) analysis may accurately differentiate types of renal involvement at the transcriptomic level, and better inform treatment decisions and prognosis. Through our involvement with the accelerating medicines partnership (AMP) network our objectives were to use scRNAseq profiles of renal constituent cells to distinguish between proliferative and membranous LN subclasses, and explore the use of scRNAseq of skin cells to discover biomarkers in a readily obtainable tissue.
scRNAseq was performed on ~2 mg kidney tissue collected from clinically indicated renal biopsies, and skin biopsies obtained at the time of renal biopsies, in 20 SLE patients. scRNAseq was performed using Fluidigm C1 HT Integrated Fluidic Circuits and cDNA libraries were prepared using the Nextera XT DNA Library Prep Kit followed by NextSeq (Illumina) sequencing.
A total of 1616 renal cells and 2392 skin cells were sequenced from LN and healthy control skin and kidney biopsies. Cell-types were determined using principal component analysis and tSNE plotting, resulting in the definitive identification of keratinocytes (N = 2004 cells), tubular cells (N=936 cells), fibroblasts (N=422), endothelial cells (N=154), and leukocytes (N=129). Genes identified by differential expression analysis of tubular cells (Fig. 1) and keratinocytes originating from patients with proliferative (class III or IV) (N=7 patients) and membranous (class V) nephritis (N=6 patients) were subjected to gene ontology pathway analysis. Tubular cells in patients with proliferative nephritis demonstrated upregulated TNF signaling (p<.001), including the transcription factor FOS, as compared to patients with membranous nephropathy. Moreover, the VEGF signaling (p<.001) pathway was upregulated, as was chemokine activity (p<.001) including CCL2 and CXCL3. Interestingly, keratinocytes from non-lesional skin of patients with proliferative nephritis also demonstrated upregulated TNF signaling (p<.01) as compared to those with membranous nephritis.
scRNAseq from small amounts of renal biopsy tissue in SLE can differentiate between the different classes of LN, and provide important insights into potential pathogenic mechanisms. Further, due to the systemic nature of the disease, transcriptomic changes in the skin of LN patients can provide a useful source of biomarkers and may reflect important information concerning concurrent kidney pathological events.
E. Der, None; H. Suryawanshi, None; S. Ranabothu, None; B. Goilav, None; H. M. Belmont, None; P. M. Izmirly, None; N. Bornkamp, None; N. Jordan, None; T. Wang, None; M. Wu, None; J. A. James, None; J. M. Guthridge, None; S. Raychaudhuri, Pfizer Inc, 2,Roche Pharmaceuticals, 2; T. Tuschl, None; J. P. Buyon, None; C. Putterman, None.
Precision medicine is one of the hot topics in medicine nowadays. Our current approach us using clinical and routine laboratory markers to diagnose and classify patients and to select treatments. This approach leads to disease diagnoses with a wide heterogeneity in term of prognosis, disease severity and treatment responses. Instead, precision medicine is using combined clinical, biological, imaging and other biomarkers to allow further sub-classification of the diseases and to better predict treatment responses. The vision of this approach is that with this more precise information it is possible to homogenize patients with certain diagnoses in terms of specific pathogenesis, disease outcome, and treatment selection.
The current study is a prime example on the journey to precision medicine. The use of small kidney tissue samples obtained by routine kidney biopsies in patients with lupus nephritis proves feasibility of such an approach also in a clinical practice setting. The overlapping results in the analysis of skin and kidney biopsies suggest that future approaches might be even more feasible by taking skin rather than kidney biopsies. Certain markers like TNF signaling pathways were able to cluster patients into the different forms of lupus nephritis, supporting the practicability of sub-classification and identification of pathways for targeted therapies. Future studies now have to analyze in lupus as well as in all other rheumatic diseases that certain expression patterns from tissues, optimally combined with other imaging and clinical markers, indeed are able to better predict disease outcome and responses to therapy than the current approaches.
Prof. Dr. Oliver Distler