scholarly journals An eQTL landscape of kidney tissue in human nephrotic syndrome

2018 ◽  
Author(s):  
Christopher E. Gillies ◽  
Rosemary Putler ◽  
Rajasree Menon ◽  
Edgar Otto ◽  
Kalyn Yasutake ◽  
...  
Keyword(s):  
Nephrotic Syndrome ◽  
Kidney Tissue ◽  
Human Kidney ◽  
Eqtl Analysis ◽  
Rna Seq ◽  
Cell Type ◽  
Cell Type Specificity ◽  
Disease Research ◽  
Genetics Of Gene Expression ◽  
Eqtl Data

AbstractExpression 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 eQTLs studies of human kidney. Here, we used whole genome sequencing (WGS) and microdissected glomerular (GLOM) & tubulointerstitial (TI) transcriptomes from 187 patients 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 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 1767 TI eQTLs at FDR <0.05. 14% and 19% of GLOM & 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 & TI genes, primarily at the HLA region.In this study of NS patients, we discovered GLOM & 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 publicly available for browsing and download at http://nephqtl.org.

scholarly journals Single cell RNA‐seq in the sea urchin embryo show marked cell‐type specificity in the Delta/Notch pathway

2019 ◽  
Vol 86 (8) ◽  
pp. 931-934 ◽  
Author(s):  
Stephany Foster ◽  
Yee Voan Teo ◽  
Nicola Neretti ◽  
Nathalie Oulhen ◽  
Gary M. Wessel
Keyword(s):  
Single Cell ◽  
Sea Urchin ◽  
Notch Pathway ◽  
Rna Seq ◽  
Cell Type ◽  
Cell Type Specificity ◽  
Sea Urchin Embryo ◽  
Marked Cell

scholarly journals High throughput single cell RNA-seq of developing mouse kidney and human kidney organoids reveals a roadmap for recreating the kidney

2017 ◽  
Author(s):  
Alexander N. Combes ◽  
Belinda Phipson ◽  
Luke Zappia ◽  
Kynan T. Lawlor ◽  
Pei Xuan Er ◽  
...  
Keyword(s):  
Single Cell ◽  
Kidney Tissue ◽  
Human Kidney ◽  
Population Based ◽  
Mouse Kidney ◽  
Rna Seq ◽  
Kidney Morphogenesis ◽  
Human Ipsc ◽  
Kidney Organoids

AbstractRecent advances in our capacity to differentiate human pluripotent stem cells to human kidney tissue are moving the field closer to novel approaches for renal replacement. Such protocols have relied upon our current understanding of the molecular basis of mammalian kidney morphogenesis. To date this has depended upon population based-profiling of non-homogenous cellular compartments. In order to improve our resolution of individual cell transcriptional profiles during kidney morphogenesis, we have performed 10x Chromium single cell RNA-seq on over 6000 cells from the E18.5 developing mouse kidney, as well as more than 7000 cells from human iPSC-derived kidney organoids. We identified 16 clusters of cells representing all major cell lineages in the E18.5 mouse kidney. The differentially expressed genes from individual murine clusters were then used to guide the classification of 16 cell clusters within human kidney organoids, revealing the presence of distinguishable stromal, endothelial, nephron, podocyte and nephron progenitor populations. Despite the congruence between developing mouse and human organoid, our analysis suggested limited nephron maturation and the presence of ‘off target’ populations in human kidney organoids, including unidentified stromal populations and evidence of neural clusters. This may reflect unique human kidney populations, mixed cultures or aberrant differentiation in vitro. Analysis of clusters within the mouse data revealed novel insights into progenitor maintenance and cellular maturation in the major renal lineages and will serve as a roadmap to refine directed differentiation approaches in human iPSC-derived kidney organoids.

scholarly journals Single-cell mapper (scMappR): using scRNA-seq to infer the cell-type specificities of differentially expressed genes

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Dustin J Sokolowski ◽  
Mariela Faykoo-Martinez ◽  
Lauren Erdman ◽  
Huayun Hou ◽  
Cadia Chan ◽  
...  
Keyword(s):  
Single Cell ◽  
Differentially Expressed Genes ◽  
Cell Types ◽  
Differentially Expressed ◽  
Rna Seq ◽  
Kidney Regeneration ◽  
Cell Type ◽  
Cell Type Specificity ◽  
Cost Constraints ◽  
Mouse Tissues

Abstract RNA sequencing (RNA-seq) is widely used to identify differentially expressed genes (DEGs) and reveal biological mechanisms underlying complex biological processes. RNA-seq is often performed on heterogeneous samples and the resulting DEGs do not necessarily indicate the cell-types where the differential expression occurred. While single-cell RNA-seq (scRNA-seq) methods solve this problem, technical and cost constraints currently limit its widespread use. Here we present single cell Mapper (scMappR), a method that assigns cell-type specificity scores to DEGs obtained from bulk RNA-seq by leveraging cell-type expression data generated by scRNA-seq and existing deconvolution methods. After evaluating scMappR with simulated RNA-seq data and benchmarking scMappR using RNA-seq data obtained from sorted blood cells, we asked if scMappR could reveal known cell-type specific changes that occur during kidney regeneration. scMappR appropriately assigned DEGs to cell-types involved in kidney regeneration, including a relatively small population of immune cells. While scMappR can work with user-supplied scRNA-seq data, we curated scRNA-seq expression matrices for ∼100 human and mouse tissues to facilitate its stand-alone use with bulk RNA-seq data from these species. Overall, scMappR is a user-friendly R package that complements traditional differential gene expression analysis of bulk RNA-seq data.

scholarly journals A novel method to identify cell-type specific regulatory variants and their role in cancer risk

2021 ◽  
Author(s):  
Cynthia A Kalita ◽  
Alexander Gusev
Keyword(s):  
Cancer Risk ◽  
Cell Types ◽  
New Method ◽  
Rna Seq ◽  
Cell Type ◽  
Regulatory Variants ◽  
Specific Effects ◽  
Allele Specific ◽  
Cell Type Specific ◽  
Eqtl Data

Background: Expression quantitative trait loci (eQTLs) have been crucial in providing an understanding of how genetic variants influence gene expression. However, eQTLs are known to exert cell type specific effects, and existing methods to identify cell type specific QTLs in bulk data require large sample sizes. Results: Here, we propose DeCAF (DEconvoluted cell type Allele specific Function), a new method to identify cell-fraction (cf) QTLs in tumors by leveraging both allelic and total expression information. Applying DeCAF to RNA-seq data from TCGA, we identified 3,664 genes with cfQTLs (at 10% FDR) in 14 cell types, a 5.63x increase in discovery over conventional interaction-eQTL mapping. cfQTLs replicated in external cell type specific eQTL data and were more enriched for cancer risk than conventional eQTLs. The intersection of tumor-specific QTL effects (tsQTLs) with GWAS loci identified rs4765621 and SCARB1, which has been previously linked to renal cell carcinoma (RCC) progression and experimentally validated in tumors. Conclusions: Our new method, DeCAF, empowers the discovery of biologically meaningful cfQTLs from bulk RNA-seq data in moderately sized studies. Our study contributes to a better understanding of germline mechanisms underlying the anticancer immune response as well as cfQTLs contributing to cancer risk.

scholarly journals Optimized design of single-cell RNA sequencing experiments for cell-type-specific eQTL analysis

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Igor Mandric ◽  
Tommer Schwarz ◽  
Arunabha Majumdar ◽  
Kangcheng Hou ◽  
Leah Briscoe ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Statistical Power ◽  
Eqtl Analysis ◽  
Optimized Design ◽  
Rna Seq ◽  
Cell Type ◽  
Specific Expression ◽  
Single Cell Rna Sequencing ◽  
Cell Type Specific

Abstract Single-cell RNA-sequencing (scRNA-Seq) is a compelling approach to directly and simultaneously measure cellular composition and state, which can otherwise only be estimated by applying deconvolution methods to bulk RNA-Seq estimates. However, it has not yet become a widely used tool in population-scale analyses, due to its prohibitively high cost. Here we show that given the same budget, the statistical power of cell-type-specific expression quantitative trait loci (eQTL) mapping can be increased through low-coverage per-cell sequencing of more samples rather than high-coverage sequencing of fewer samples. We use simulations starting from one of the largest available real single-cell RNA-Seq data from 120 individuals to also show that multiple experimental designs with different numbers of samples, cells per sample and reads per cell could have similar statistical power, and choosing an appropriate design can yield large cost savings especially when multiplexed workflows are considered. Finally, we provide a practical approach on selecting cost-effective designs for maximizing cell-type-specific eQTL power which is available in the form of a web tool.

scholarly journals Brain expression quantitative trait locus and network analysis reveals downstream effects and putative drivers for brain-related diseases

2021 ◽  
Author(s):  
Niek de Klein ◽  
Ellen A. Tsai ◽  
Martijn Vochteloo ◽  
Denis Baird ◽  
Yunfeng Huang ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Network Analysis ◽  
Genome Wide Association Study ◽  
Brain Regions ◽  
Specific Gene ◽  
Eqtl Analysis ◽  
Rna Seq ◽  
Cell Type ◽  
The Brain ◽  
Unique Genes

Gaining insight into the downstream consequences of non-coding variants is an essential step towards the identification of therapeutic targets from genome-wide association study (GWAS) findings. Here we have harmonized and integrated 8,727 RNA-seq samples with accompanying genotype data from multiple brain-regions from 14 datasets. This sample size enabled us to perform both cis- and trans-expression quantitative locus (eQTL) mapping. Upon comparing the brain cortex cis-eQTLs (for 12,307 unique genes at FDR<0.05) with a large blood cis-eQTL analysis (n=31,684 samples), we observed that brain eQTLs are more tissue specific than previously assumed. We inferred the brain cell type for 1,515 cis-eQTLs by using cell type proportion information. We conducted Mendelian Randomization on 31 brain-related traits using cis-eQTLs as instruments and found 159 significant findings that also passed colocalization. Furthermore, two multiple sclerosis (MS) findings had cell type specific signals, a neuron-specific cis-eQTL for CYP24A1 and a macrophage specific cis-eQTL for CLECL1. To further interpret GWAS hits, we performed trans-eQTL analysis. We identified 2,589 trans-eQTLs (at FDR<0.05) for 373 unique SNPs, affecting 1,263 unique genes, and 21 replicated significantly using single-nucleus RNA-seq data from excitatory neurons. We also generated a brain-specific gene-coregulation network that we used to predict which genes have brain-specific functions, and to perform a novel network analysis of Alzheimer's disease (AD), amyotrophic lateral sclerosis (ALS), multiple sclerosis (MS) and Parkinson's disease (PD) GWAS data. This resulted in the identification of distinct sets of genes that show significantly enriched co-regulation with genes inside the associated GWAS loci, and which might reflect drivers of these diseases.

scholarly journals Precision Sub-phenotyping of Histologically Stable Kidney Transplants by a Composite Molecular and Cellular Instability Score

2019 ◽  
Author(s):  
D. Rychkov ◽  
S. Sur ◽  
M. Sirota ◽  
M. M. Sarwal
Keyword(s):  
Graft Loss ◽  
Graft Function ◽  
Kidney Tissue ◽  
Human Kidney ◽  
Cell Types ◽  
Specific Cell ◽  
Cell Type ◽  
Tissue Samples ◽  
Post Transplant ◽  
Definition Of

AbstractAcute Rejection (AR) is the main cause of the graft dysfunction and premature graft loss, and diagnosis of rejection before advanced histological injury is crucial to salvage graft function. However, recent molecular studies have highlighted the unrecognized burden of sub-clinical graft rejection when graft function is preserved, and a dichotomy exists, of a histologically normal biopsy with molecular signatures of AR. Conversely, significant variation also exists in the definition of a stable allograft, defined as a transplant with absence of clinical AR, with absence of histological inflammation, though published studies have highlighted that some of these stable samples will not have stable immune quiescence, as they may be molecularly similar to AR. Thus, refining the definition of a stable allograft as one that is clinically, histologically and molecularly quiescent is critical, as the inclusion of stable allografts in mechanistic and clinical studies are vital to provide a normal, non-injured comparative group for all interrogative studies on understanding allograft injury.With this goal in mind, we analyzed publicly available transcriptional data across 4,845 human kidney tissue samples from 38 Gene Expression Omnibus (GEO) datasets, inclusive of 510 allograft biopsy samples with AR, 1,154 renal allograft biopsies classified in each dataset as histological stable (hSTA), and 609 normal kidney (donor) samples. By applying a machine learning model, a substantive number of hSTA samples were found to be molecularly similar to AR (mAR); these have been reclassified in this study as clinical and histological stable samples with transcriptional signatures overlapping with AR (hSTA/mAR), with the predominant expression of a subset of 6 genes (KLF4, CENPJ, KLF2, PPP1R15A, FOSB, TNFAIP3). To understand the cellular sources of these molecular signals, we utilized xCell, a cell type enrichment tool and interrogated 64 specific cell types to identify 5 (CD4+ Tcm, CD4+ Tem, CD8+ Tem, NK cells, and Th1 cells) that were also highly predictive for classification of the AR phenotype in these studies. A combined gene and cell-type specific InstaScore (AUC 0.99) was developed using gene and cell subtype data to re-phenotype all hSTA allografts. This clearly defined two disparate hSTA biopsies: those that are both histologically and molecularly quiescent (hSTA/mSTA) or those that are histologically quiescent but molecularly similar to AR (hSTA/mAR). The clinical utility of the Instability Score was subsequently assessed by independent validation on a serial set of post-transplant hSTA biopsies, where strong significant correlation was observed between the score on 6 month post-transplant hSTA graft biopsies, where hSTA/mAR samples had a significant change in graft function and graft loss at 5 year follow-up.In conclusion, our computational approach of precision sub-phenotyping of hSTA allografts by the InstaScore identifies discrepancies in the current recognition of a stable allograft by histology alone. Precision molecular sub-typing of the hSTA allograft into the hSTA/mSTA group is an important deliverable for selection of “true” STA samples for mechanistic studies, and into the hSTA/mAR group, for accurate prediction of subsequent patient clinical outcomes, and real time treatment stratification for hSTA/mAR allografts to positively impact long-term graft survival.

scholarly journals In situ classification of cell types in human kidney tissue using 3D nuclear staining

2020 ◽  
Author(s):  
Andre Woloshuk ◽  
Suraj Khochare ◽  
Aljohara Fahad Almulhim ◽  
Andrew McNutt ◽  
Dawson Dean ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Kidney Tissue ◽  
Human Kidney ◽  
Cell Types ◽  
Learning Approach ◽  
Cell Type ◽  
Cell Classification

AbstractTo understand the physiology and pathology of disease, capturing the heterogeneity of cell types within their tissue environment is fundamental. In such an endeavor, the human kidney presents a formidable challenge because its complex organizational structure is tightly linked to key physiological functions. Advances in imaging-based cell classification may be limited by the need to incorporate specific markers that can link classification to function. Multiplex imaging can mitigate these limitations, but requires cumulative incorporation of markers, which may lead to tissue exhaustion. Furthermore, the application of such strategies in large scale 3-dimensional (3D) imaging is challenging. Here, we propose that 3D nuclear signatures from a DNA stain, DAPI, which could be incorporated in most experimental imaging, can be used for classifying cells in intact human kidney tissue. We developed an unsupervised approach that uses 3D tissue cytometry to generate a large training dataset of nuclei images (NephNuc), where each nucleus is associated with a cell type label. We then devised various supervised machine learning approaches for kidney cell classification and demonstrated that a deep learning approach outperforms classical machine learning or shape-based classifiers. Specifically, a custom 3D convolutional neural network (NephNet3D) trained on nuclei image volumes achieved a balanced accuracy of 80.26%. Importantly, integrating NephNet3D classification with tissue cytometry allowed in situ visualization of cell type classifications in kidney tissue. In conclusion, we present a tissue cytometry and deep learning approach for in situ classification of cell types in human kidney tissue using only a DNA stain. This methodology is generalizable to other tissues and has potential advantages on tissue economy and non-exhaustive classification of different cell types.

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