Identification of Genetic Predisposition in Noncirrhotic Portal Hypertension Patients With Multiple Renal Cysts by Integrated Analysis of Whole-Genome and Single-Cell RNA Sequencing

Background and Aims: The multiple renal cysts (MRC) occur in some patients with noncirrhotic portal hypertension (NCPH) could be a subset of ciliopathy. However, the potential genetic influencers and/or determinants in NCPH with MRC are largely unknown. The aim of this study was to explore the potential candidate variants/genes associated with those patients.Methods: 8,295 cirrhotic patients with portal hypertension were enrolled in cohort 1 and 267 patients affected with NCPH were included in cohort 2. MRC was defined as at least two cysts in both kidneys within a patient detected by ultrasonography or computed tomography. Whole-genome sequencing (WGS) was performed in nine patients (four from cohort 1 and five from cohort 2). Then we integrated WGS and publicly available single-cell RNA sequencing (scRNA-seq) to prioritize potential candidate genes. Genes co-expressed with known pathogenic genes within same cell types were likely associated NCPH with MRC.Results: The prevalence of MRC in NCPH patients (19.5%, 52/267) was significantly higher than cirrhotic patients (6.2%, 513/8,295). Further, the clinical characteristics of NCPH patients with MRC were distinguishable from cirrhotic patients, including late-onset, more prominent portal hypertension however having preserved liver functions. In the nine whole genome sequenced patients, we identified three patients with early onset harboring compound rare putative pathogenic variants in the known disease gene PKHD1. For the remaining patients, by assessing cilia genes profile in kidney and liver scRNA-seq data, we identified CRB3 was the most co-expressed gene with PKHD1 that highly expressed in ureteric bud cell, kidney stromal cell and hepatoblasts. Moreover, we found a homozygous variant, CRB3 p.P114L, that caused conformational changes in the evolutional conserved domain, which may associate with NCPH with MRC.Conclusion: ScRNA-seq enables unravelling cell heterogeneity with cell specific gene expression across multiple tissues. With the boosting public accessible scRNA-seq data, we believe our proposed analytical strategy would effectively help disease risk gene identification.

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Comparison of high-throughput single-cell RNA sequencing data processing pipelines

Briefings in Bioinformatics ◽

10.1093/bib/bbaa116 ◽

2020 ◽

Author(s):

Mingxuan Gao ◽

Mingyi Ling ◽

Xinwei Tang ◽

Shun Wang ◽

Xu Xiao ◽

...

Keyword(s):

Data Processing ◽

Single Cell ◽

Rna Sequencing ◽

High Throughput ◽

Large Scale ◽

Evaluation Framework ◽

Integrated Analysis ◽

Sequencing Data ◽

Single Experiment ◽

Single Cell Rna Sequencing

Abstract With the development of single-cell RNA sequencing (scRNA-seq) technology, it has become possible to perform large-scale transcript profiling for tens of thousands of cells in a single experiment. Many analysis pipelines have been developed for data generated from different high-throughput scRNA-seq platforms, bringing a new challenge to users to choose a proper workflow that is efficient, robust and reliable for a specific sequencing platform. Moreover, as the amount of public scRNA-seq data has increased rapidly, integrated analysis of scRNA-seq data from different sources has become increasingly popular. However, it remains unclear whether such integrated analysis would be biassed if the data were processed by different upstream pipelines. In this study, we encapsulated seven existing high-throughput scRNA-seq data processing pipelines with Nextflow, a general integrative workflow management framework, and evaluated their performance in terms of running time, computational resource consumption and data analysis consistency using eight public datasets generated from five different high-throughput scRNA-seq platforms. Our work provides a useful guideline for the selection of scRNA-seq data processing pipelines based on their performance on different real datasets. In addition, these guidelines can serve as a performance evaluation framework for future developments in high-throughput scRNA-seq data processing.

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Comparison of High-Throughput Single-Cell RNA Sequencing Data Processing Pipelines

10.1101/2020.02.09.940221 ◽

2020 ◽

Cited By ~ 2

Author(s):

Mingxuan Gao ◽

Mingyi Ling ◽

Xinwei Tang ◽

Shun Wang ◽

Xu Xiao ◽

...

Keyword(s):

Data Processing ◽

Single Cell ◽

Rna Sequencing ◽

High Throughput ◽

Large Scale ◽

Evaluation Framework ◽

Integrated Analysis ◽

Sequencing Data ◽

Single Experiment ◽

Single Cell Rna Sequencing

AbstractWith the development of single-cell RNA sequencing (scRNA-seq) technology, it has become possible to perform large-scale transcript profiling for tens of thousands of cells in a single experiment. Many analysis pipelines have been developed for data generated from different high-throughput scRNA-seq platforms, bringing a new challenge to users to choose a proper workflow that is efficient, robust and reliable for a specific sequencing platform. Moreover, as the amount of public scRNA-seq data has increased rapidly, integrated analysis of scRNA-seq data from different sources has become increasingly popular. How-ever, it remains unclear whether such integrated analysis would be biased if the data were processed by different upstream pipelines. In this study, we encapsulated seven existing high-throughput scRNA-seq data processing pipelines with Nextflow, a general integrative workflow management framework, and evaluated their performances in terms of running time, computational resource consumption, and data processing consistency using nine public datasets generated from five different high-throughput scRNA-seq platforms. Our work provides a useful guideline for the selection of scRNA-seq data processing pipelines based on their performances on different real datasets. In addition, these guidelines can serve as a performance evaluation framework for future developments in high-throughput scRNA-seq data processing.

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Overcoming Genetic Drop-outs in Variants-based Lineage Tracing from Single-cell RNA Sequencing Data

10.1101/2020.01.01.892562 ◽

2020 ◽

Author(s):

Tianshi Lu ◽

Seongoh Park ◽

James Zhu ◽

Xiaowei Zhan ◽

Xinlei Wang ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Developmental Process ◽

Variant Calling ◽

Lineage Tracing ◽

Whole Genome ◽

Sequencing Data ◽

Bayesian Hierarchical ◽

Single Cell Rna Sequencing ◽

Drop Outs

ABSTRACTLineage tracing provides key insights into the fates of individual cells in complex tissues. Recent works on lineage reconstruction based on the single-cell expression data are suitable for short time frames while tracing lineage based on more stable genetic markers is needed for studies that span time scales over months or years. However, variant calling from the single-cell RNA sequencing (scRNA-Seq) data suffers from “genetic drop-outs”, including low coverage and allelic bias, which presents significant obstacles for lineage reconstruction. Prior studies focused only on mitochondrial (chrM) variants and need to be expanded to the whole genome to capture more variants with clearer physiological meaning. However, non-chrM variants suffer even more severe drop-outs than chrM variants, although drop-outs affect all variants. We developed strategies to overcome genetic drop-outs in scRNA-Seq-derived whole genomic variants for accurate lineage tracing, and we developed SClineger, a Bayesian Hierarchical model, to implement our approach. Our validation analyses on a series of sequencing protocols demonstrated the necessity of correction for genetic drop-outs and consideration of variants in the whole genome, and also showed the improvement that our approach provided. We showed that genetic-based lineage tracing is applicable for single-cell studies of both tumors and non-tumor tissues using our approach, and can reveal novel biological insights not afforded by expressional analyses. Interestingly, we showed that cells of various lineages grew under the spatial constraints of their respective organs during the developmental process. Overall, our work provides a powerful tool that can be applied to the large amounts of already existing scRNA-Seq data to construct the lineage histories of cells and derive new knowledge.

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