A protocol for single-cell transcriptomics from cryopreserved renal tissue and urine for the Accelerating Medicine Partnership (AMP) RA/SLE network

Rao, Deepak A.; Berthier, Céline C.; Arazi, Arnon; Davidson, Anne; Liu, Yanyan; Browne, Edward P.; Eisenhaure, Thomas; Chicoine, Adam; Lieb, David; Smilek, Dawn E.; Tosta, Patti; Lederer, James A.; Brenner, Michael B.; Hildeman, David; Woodle, E. Steve; Wofsy, David; Anolik, Jennifer H.; Kretzler, Matthias; Hacohen, Nir; Diamond, Betty

doi:10.1101/275859

Cited by 12 publications

(14 citation statements)

References 23 publications

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“…Single-cell dissociations were done as reported. 37 Individual cells were labeled with barcodes, and transcripts within each cell were tagged with distinct UMIs (unique molecular identifiers) in order to determine absolute transcript abundance. The library quality was assessed with ''high sensitivity cDNA arrays'' on an Agilent Bioanalyzer (Thermo Fischer) platform.…”

Section: Generation Of Single-cell Rna-seq Datamentioning

confidence: 99%

An eQTL Landscape of Kidney Tissue in Human Nephrotic Syndrome

Gillies¹,

Putler²,

Menon³

et al. 2018

The American Journal of Human Genetics

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Expression quantitative trait loci (eQTL) studies illuminate the genetics of gene expression and, in disease research, can be particularly illuminating when using the tissues directly impacted by the condition. In nephrology, there is a paucity of eQTL studies of human kidney. Here, we used whole-genome sequencing (WGS) and microdissected glomerular (GLOM) and tubulointerstitial (TI) transcriptomes from 187 individuals with nephrotic syndrome (NS) to describe the eQTL landscape in these functionally distinct kidney structures. Using MatrixEQTL, we performed cis-eQTL analysis on GLOM (n = 136) and TI (n = 166). We used the Bayesian "Deterministic Approximation of Posteriors" (DAP) to fine-map these signals, eQTLBMA to discover GLOM- or TI-specific eQTLs, and single-cell RNA-seq data of control kidney tissue to identify the cell type specificity of significant eQTLs. We integrated eQTL data with an IgA Nephropathy (IgAN) GWAS to perform a transcriptome-wide association study (TWAS). We discovered 894 GLOM eQTLs and 1,767 TI eQTLs at FDR < 0.05. 14% and 19% of GLOM and TI eQTLs, respectively, had >1 independent signal associated with its expression. 12% and 26% of eQTLs were GLOM specific and TI specific, respectively. GLOM eQTLs were most significantly enriched in podocyte transcripts and TI eQTLs in proximal tubules. The IgAN TWAS identified significant GLOM and TI genes, primarily at the HLA region. In this study, we discovered GLOM and TI eQTLs, identified those that were tissue specific, deconvoluted them into cell-specific signals, and used them to characterize known GWAS alleles. These data are available for browsing and download via our eQTL browser, "nephQTL."

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Section: Generation Of Single-cell Rna-seq Datamentioning

confidence: 99%

An eQTL Landscape of Kidney Tissue in Human Nephrotic Syndrome

Gillies¹,

Putler²,

Menon³

et al. 2018

The American Journal of Human Genetics

Self Cite

160

145

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“…Biopsy tissue can also be frozen as intact tissue at the collection site using methods similar to those used to cryopreserve viable cells 51,52 . Cryopreserved tissue can then be thawed, dissociated and analyzed at a later time.…”

Section: Kidney Tissue Collection and Processingmentioning

confidence: 99%

“…In the AMP RA/SLE Network experience, cryopreservation of intact kidney tissue provided increased total cell yields compared with cryopreservation of dissociated kidney cells 52 . Whether tissue cryopreservation and subsequent dissociation is better than tissue dissociation followed by cryopreservation might vary depending on the tissue type although, in general, maintaining cells in their microenvironment is likely to induce less stress than removing cells from their native cell-cell contacts.…”

Section: Kidney Tissue Collection and Processingmentioning

confidence: 99%

“…The 10X Genomics platform, for example, uses a droplet-based scRNA-seq method, which typically detects the expression of a smaller number of genes and therefore a lower threshold for this parameter is often applied 58 . In addition, RNA yields and quality may vary with cell type -leukocytes, for example, often demonstrate good viability whereas healthy parenchymal cells are more challenging to isolate and study 51,52 . It is possible that this challenge is exacerbated in diseased kidneys, as they likely harbor stressed cells that might be especially vulnerable to the manipulations needed to preserve and dissociate tissue.…”

Section: Quality Control Metricsmentioning

confidence: 99%

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Design and application of single-cell RNA sequencing to study kidney immune cells in lupus nephritis

et al. 2019

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The immune mechanisms that cause tissue injury in lupus nephritis have been challenging to define. The advent of high-dimensional cellular analyses, such as single-cell RNA sequencing, has enabled detailed characterization of the cell populations present in small biopsy samples of affected kidney tissue. In parallel, the development of methods that cryopreserve kidney biopsy specimens in a manner that preserves intact, viable cells, has enabled the uniform analysis of tissue samples collected at multiple sites and across many geographic areas and demographic cohorts by high-dimensional platforms. The application of these methods to kidney biopsy samples from patients with lupus nephritis has begun to define the phenotypes of both infiltrating and resident immune cells, as well as parenchymal cells, present in nephritic kidneys. The detection of similar immune cell populations in urine suggests that it might be possible to noninvasively monitor immune activation in kidneys. Once applied to large patient cohorts, these high-dimensional studies might enable patient stratification according to patterns of immune cell activation in the kidney or identify disease features that can be used as surrogate measures of efficacy in clinical trials. Applied broadly across multiple inflammatory kidney diseases, these studies promise to enormously expand our understanding of renal inflammation in the next decade.

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“…We combined and analyzed three scRNA-Seq datasets generated from healthy portions of tumor-nephrectomy samples specifically harvested for single cell analysis using 10X Genomics methodology. Single cell dissociations were done as reported 39 . Individual cells were labeled with barcodes, and transcripts within each cell were tagged with distinct UMIs (Unique Molecular Identifiers) in order to determine absolute transcript abundance.…”

Section: Generation Of Single Cell Rna-seq Datamentioning

confidence: 99%