Isolation of high yield and quality RNA from human precision-cut lung slices for RNA-sequencing and computational integration with larger patient cohorts

Precision-cut lung slices (PCLS) have gained increasing interest as a model to study lung biology/disease and screening novel therapeutics. In particular, PCLS derived from human tissue can better recapitulate some aspects of lung biology/disease as compared to animal models. Several experimental readouts have been established for use with PCLS, but obtaining high yield and quality RNA for downstream analysis has remained challenging. This is particularly problematic for utilizing the power of next-generation sequencing techniques, such as RNA-sequencing (RNA-seq), for non-biased and high through-put analysis of PCLS human cohorts. In the current study, we present a novel approach for isolating high quality RNA from a small amount of tissue, including diseased human tissue, such as idiopathic pulmonary fibrosis (IPF). We show that the RNA isolated using this method has sufficient quality for RT-qPCR and RNA-seq analysis. Furthermore, the RNA-seq data from human PCLS could be used in several established computational pipelines, including deconvolution of bulk RNA-seq data using publicly available single-cell RNA-seq data. Deconvolution using Bisque revealed a diversity of cell populations in human PCLS, including several immune cell populations, which correlated with cell populations known to be present and aberrant in human disease.

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Isolation of high yield and quality RNA from human precision-cut lung slices for RNA-sequencing and computational integration with larger patient cohorts

10.1101/2020.08.21.254516 ◽

2020 ◽

Author(s):

John Stegmayr ◽

Hani N. Alsafadi ◽

Wojciech Langwiński ◽

Anna Niroomand ◽

Sandra Lindstedt ◽

...

Keyword(s):

Rna Sequencing ◽

Human Tissue ◽

Immune Cell ◽

High Yield ◽

Cell Populations ◽

Data Sets ◽

Rna Seq ◽

Yield And Quality ◽

Novel Approach ◽

Precision Cut Lung Slices

AbstractPrecision-cut lung slices (PCLS) have gained increasing interest as a model to study lung biology and disease, as well as for screening novel therapeutics. In particular, PCLS derived from human tissue can better recapitulate some aspects of lung biology and disease as compared to PCLS derived from animals (e.g. clinical heterogeneity), but access to human tissue is limited. A number of different experimental readouts have been established for use with PCLS, but obtaining high yield and quality RNA for downstream gene expression analysis has remained challenging. This is particularly problematic for utilizing the power of next-generation sequencing techniques, such as RNA-sequencing (RNA-seq), for non-biased and high through-put analysis of PCLS human cohorts. In the current study, we present a novel approach for isolating high quality RNA from a small amount of tissue, including diseased human tissue, such as idiopathic pulmonary fibrosis (IPF). We show that the RNA isolated using this method is of sufficient quality for both RT-qPCR and RNA-seq analysis. Furthermore, the RNA-seq data from human PCLS was comparable to data generated from native tissue and could be used in several established computational pipelines, including deconvolution of bulk RNA-seq data using publicly available single-cell RNA-seq data sets. Deconvolution using Bisque revealed a diversity of cell populations in human PCLS derived from distal lung tissue, including several immune cell populations, which correlated with cell populations known to be present and aberrant in human disease, such as IPF.

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Enhancing droplet-based single-nucleus RNA-seq resolution using the semi-supervised machine learning classifier DIEM

10.1101/786285 ◽

2019 ◽

Cited By ~ 4

Author(s):

Marcus Alvarez ◽

Elior Rahmani ◽

Brandon Jew ◽

Kristina M. Garske ◽

Zong Miao ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Cell Types ◽

Supervised Machine Learning ◽

Data Sets ◽

Rna Seq ◽

Novel Approach ◽

Single Nucleus ◽

Downstream Analysis

AbstractSingle-nucleus RNA sequencing (snRNA-seq) measures gene expression in individual nuclei instead of cells, allowing for unbiased cell type characterization in solid tissues. Contrary to single-cell RNA seq (scRNA-seq), we observe that snRNA-seq is commonly subject to contamination by high amounts of extranuclear background RNA, which can lead to identification of spurious cell types in downstream clustering analyses if overlooked. We present a novel approach to remove debris-contaminated droplets in snRNA-seq experiments, called Debris Identification using Expectation Maximization (DIEM). Our likelihood-based approach models the gene expression distribution of debris and cell types, which are estimated using EM. We evaluated DIEM using three snRNA-seq data sets: 1) human differentiating preadipocytes in vitro, 2) fresh mouse brain tissue, and 3) human frozen adipose tissue (AT) from six individuals. All three data sets showed various degrees of extranuclear RNA contamination. We observed that existing methods fail to account for contaminated droplets and led to spurious cell types. When compared to filtering using these state of the art methods, DIEM better removed droplets containing high levels of extranuclear RNA and led to higher quality clusters. Although DIEM was designed for snRNA-seq data, we also successfully applied DIEM to single-cell data. To conclude, our novel method DIEM removes debris-contaminated droplets from single-cell-based data fast and effectively, leading to cleaner downstream analysis. Our code is freely available for use at https://github.com/marcalva/diem.

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Single Cell RNA Sequencing of Rare Immune Cell Populations

Frontiers in Immunology ◽

10.3389/fimmu.2018.01553 ◽

2018 ◽

Vol 9 ◽

Cited By ~ 39

Author(s):

Akira Nguyen ◽

Weng Hua Khoo ◽

Imogen Moran ◽

Peter I. Croucher ◽

Tri Giang Phan

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Immune Cell ◽

Cell Populations ◽

Single Cell Rna Sequencing

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Generation of High Yielding and Fragrant Rice (Oryza sativa L.) Lines by CRISPR/Cas9 Targeted Mutagenesis of Three Homoeologs of Cytochrome P450 Gene Family and OsBADH2 and Transcriptome and Proteome Profiling of Revealed Changes Triggered by Mutations

Plants ◽

10.3390/plants9060788 ◽

2020 ◽

Vol 9 (6) ◽

pp. 788 ◽

Cited By ~ 5

Author(s):

Babar Usman ◽

Gul Nawaz ◽

Neng Zhao ◽

Yaoguang Liu ◽

Rongbai Li

Keyword(s):

Grain Size ◽

Cytochrome P450 ◽

Grain Yield ◽

Rna Sequencing ◽

Oryza Sativa L ◽

Targeted Mutagenesis ◽

High Yield ◽

Yield And Quality ◽

Target Sites ◽

Cytochrome P450 Family

The significant increase in grain yield and quality are often antagonistic but a constant demand for breeders and consumers. Some genes related to cytochrome P450 family are known for rice organ growth but their role in controlling grain yield is still unknown. Here, we generated new rice mutants with high yield and improved aroma by simultaneously editing three cytochrome P450 homoeologs (Os03g0603100, Os03g0568400, and GL3.2) and OsBADH2 with the CRISPR/Cas9 system, and RNA-sequencing and proteomic analysis were performed to unveil the subsequent changes. High mutation efficiency was achieved in both target sites of each gene and the mutations were predominantly only deletions, while insertions were rare, and no mutations were detected in the five most likely off-target sites against each sgRNA. Mutants exhibited increased grain size, 2-acetyl-1-pyrroline (2AP) content, and grain cell numbers while there was no change in other agronomic traits. Transgene-DNA-free mutant lines appeared with a frequency of 44.44% and homozygous mutations were stably transmitted, and bi-allelic and heterozygous mutations followed Mendelian inheritance, while the inheritance of chimeric mutations was unpredictable. Deep RNA sequencing and proteomic results revealed the regulation of genes and proteins related to cytochrome P450 family, grain size and development, and cell cycle. The KEGG and hub-gene and protein network analysis showed that the gene and proteins related to ribosomal and photosynthesis pathways were mainly enriched, respectively. Our findings provide a broad and detailed basis to understand the role of CRISPR/Cas9 in rice yield and quality improvement.

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Full-Length Transcriptome: A Reliable Alternative for Single-Cell RNA-Seq Analysis in the Spleen of Teleost Without Reference Genome

Frontiers in Immunology ◽

10.3389/fimmu.2021.737332 ◽

2021 ◽

Vol 12 ◽

Author(s):

Lixing Huang ◽

Ying Qiao ◽

Wei Xu ◽

Linfeng Gong ◽

Rongchao He ◽

...

Keyword(s):

Data Analysis ◽

B Cells ◽

Single Cell ◽

Reference Genome ◽

Immune Cell ◽

Cell Types ◽

Full Length ◽

Cell Populations ◽

Epinephelus Coioides ◽

Rna Seq

Fish is considered as a supreme model for clarifying the evolution and regulatory mechanism of vertebrate immunity. However, the knowledge of distinct immune cell populations in fish is still limited, and further development of techniques advancing the identification of fish immune cell populations and their functions are required. Single cell RNA-seq (scRNA-seq) has provided a new approach for effective in-depth identification and characterization of cell subpopulations. Current approaches for scRNA-seq data analysis usually rely on comparison with a reference genome and hence are not suited for samples without any reference genome, which is currently very common in fish research. Here, we present an alternative, i.e. scRNA-seq data analysis with a full-length transcriptome as a reference, and evaluate this approach on samples from Epinephelus coioides-a teleost without any published genome. We show that it reconstructs well most of the present transcripts in the scRNA-seq data achieving a sensitivity equivalent to approaches relying on genome alignments of related species. Based on cell heterogeneity and known markers, we characterized four cell types: T cells, B cells, monocytes/macrophages (Mo/MΦ) and NCC (non-specific cytotoxic cells). Further analysis indicated the presence of two subsets of Mo/MΦ including M1 and M2 type, as well as four subsets in B cells, i.e. mature B cells, immature B cells, pre B cells and early-pre B cells. Our research will provide new clues for understanding biological characteristics, development and function of immune cell populations of teleost. Furthermore, our approach provides a reliable alternative for scRNA-seq data analysis in teleost for which no reference genome is currently available.

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MAJIQ-SPEL: Web-Tool to interrogate classical and complex splicing variations from RNA-Seq data

10.1101/136077 ◽

2017 ◽

Author(s):

Christopher J. Green ◽

Matthew R. Gazzara ◽

Yoseph Barash

Keyword(s):

Alternative Splicing ◽

Rna Sequencing ◽

Experimental Validation ◽

Ucsc Genome Browser ◽

Rna Seq ◽

Web Tool ◽

Rt Pcr ◽

Design Algorithm ◽

Gene Isoforms ◽

Downstream Analysis

AbstractAnalysis of RNA sequencing (RNA-Seq) data have highlighted the fact that most genes undergo alternative splicing (AS) and that these patterns are tightly regulated. Many of these events are complex, resulting in numerous possible isoforms that quickly become difficult to visualize, interpret, and experimentally validate. To address these challenges, We developed MAJIQ-SPEL, a web-tool that takes as input local splicing variations (LSVs) quantified from RNA-Seq data and provides users with visualization and quantification of gene isoforms associated with those. Importantly, MAJIQ-SPEL is able to handle both classical (binary) and complex (non-binary) splicing variations. Using a matching primer design algorithm it also suggests users possible primers for experimental validation by RT-PCR and displays those, along with the matching protein domains affected by the LSV, on UCSC Genome Browser for further downstream analysis.Availability: Program and code will be available at http://majiq.biociphers.org/majiq-spel

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HGG-26. SINGLE-CELL RNA-SEQ OF PEDIATRIC HIGH-GRADE GLIOMAS IDENTIFIES COMMON ONCOGENIC PROCESSES AMONG DISTINCT TUMOR HISTOLOGIES

Neuro-Oncology ◽

10.1093/neuonc/noab090.090 ◽

2021 ◽

Vol 23 (Supplement_1) ◽

pp. i22-i22

Author(s):

John DeSisto ◽

Andrew Donson ◽

Rui Fu ◽

Bridget Sanford ◽

Kent Riemondy ◽

...

Keyword(s):

Dna Methylation ◽

Single Cell ◽

Immune Cells ◽

Immune Cell ◽

Treatment Modalities ◽

Patient Specific ◽

Cell Populations ◽

High Grade ◽

Methylation Data ◽

Rna Seq

Abstract Background Pediatric high-grade glioma (PHGG) is a deadly childhood brain tumor that responds poorly to treatment. PHGG comprises two major subtypes: cortical tumors with wild-type H3K27 and diffuse midline gliomas (DMG) that occur in the midline and have characteristic H3K27M mutations. Cortical PHGG is heterogeneous with multiple molecular subtypes. In order to identify underlying commonalities in cortical PHGG that might lead to better treatment modalities, we performed molecular profiling, including single-cell RNA-Seq (scRNA-Seq), on PHGG samples from Children’s Hospital Colorado. Methods Nineteen cortical PHGG tumor samples, one DMG and one normal margin sample obtained at biopsy were disaggregated to isolate viable cells. Fifteen were glioblastomas (GBM), including five with epithelioid and/or giant cell features and five radiation-induced glioblastomas (RIG). There were also four non-GBM PHGG. We performed scRNA-Seq using 10X Genomics v.3 library preparation to enable capture of infiltrating immune cells. We also performed bulk RNA-Seq and DNA methylation profiling. Results After eliminating patient-specific and cell-cycle effects, RIG, epithelioid GBM, and other GBM each formed identifiable subgroups in bulk RNA-Seq and scRNA-Seq datasets. In the scRNA-Seq data, clusters with cells from multiple tumor samples included a PDGFRA-positive population expressing oligodendrocyte progenitor markers, astrocytic, mesenchymal and stemlike populations, macrophage/monocyte immune cells, and a smaller T-cell population. Analyses of DNA methylation data showed PDGFRA and CDK4 amplification and CDKN2A deletion are common alterations among PHGG. Inferred copy number variation analysis of the single-cell data confirmed that individual tumors include populations that both include and lack the molecular alterations identified in the methylation data. RNA velocity studies to define tumor cells of origin and further analyses of the immune cell populations are underway. Conclusions Single-cell analysis of PHGG confirms a large degree of tumor heterogeneity but also shows that PHGG have stemlike, mesenchymal and immune cell populations with common characteristics.

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Transcriptome Analysis of Ovarian and Uterine Clear Cell Malignancies

Frontiers in Oncology ◽

10.3389/fonc.2020.598579 ◽

2020 ◽

Vol 10 ◽

Author(s):

Jill Alldredge ◽

Leslie Randall ◽

Gabriela De Robles ◽

Anshu Agrawal ◽

Dan Mercola ◽

...

Keyword(s):

Cellular Proliferation ◽

Immune Cell ◽

Clear Cell ◽

Early Stage ◽

Expression Profiles ◽

Uterine Cancer ◽

Cell Populations ◽

Rna Seq ◽

Sequencing Data ◽

Ovarian Cancers

PurposeOvarian and uterine clear cell carcinomas (CCCs) are rare but associated with poor prognosis. This study explored RNA transcription patterns characteristic of these tumors.Experimental DesignRNA sequencing (RNA-seq) of 11 ovarian CCCs and five uterine CCCs was performed and compared to publicly available data from high grade serous ovarian cancers (HGSOCs). Ingenuity Pathway Analyses were performed. CIBERSORT analyses estimated relative fractions of 22 immune cell types in each RNA-seq sample. Sequencing data was correlated with PD-L1 immunohistochemical expression.ResultsRNA-seq revealed 1,613 downregulated and 1,212 upregulated genes (corrected p < 0.05, |FC |≥10) in ovarian CCC versus HGSOC. Two subgroups were identified in the ovarian CCC, characterized by ethnicity and expression differences in ARID1A. There were 3,252 differentially expressed genes between PD-L1+/− ovarian CCCs, revealing immune response, cell death, and DNA repair networks, negatively correlated with PD-L1 expression, whereas cellular proliferation networks positively correlated with expression. In clear cell ovarian versus clear cell uterine cancer, 1,607 genes were significantly upregulated, and 109 genes were significantly downregulated (corrected p < 0.05, |FC|≥10). Comparative pathway analysis of late and early stage ovarian CCCs revealed unique metabolic and PTEN pathways, whereas uterine CCCs had unique Wnt/Ca+, estrogen receptor, and CCR5 signaling. CIBERSORT analysis revealed that activated mast cells and regulatory T cell populations were relatively enriched in uterine CCCs. The PD-L1+ ovarian CCCs had enriched resting NK cells and memory B cell populations, while PD-L1− had enriched CD8 T-cells, monocytes, eosinophils, and activated dendritic cells.ConclusionsUnique transcriptional expression profiles distinguish clear cell uterine and ovarian cancers from each other and from other more common histologic subtypes. These insights may aid in devising novel therapeutics.

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The Role of RNA-Sequencing as a New Genetic Diagnosis Tool

Current Genetic Medicine Reports ◽

10.1007/s40142-021-00199-x ◽

2021 ◽

Vol 9 (2) ◽

pp. 13-21

Author(s):

Philippa D. K. Curry ◽

Krystyna L. Broda ◽

Christopher J. Carroll

Keyword(s):

Rna Sequencing ◽

Rare Diseases ◽

Diagnostic Yield ◽

Neuromuscular Disorders ◽

Genetic Diagnosis ◽

Rna Seq ◽

Novel Approach ◽

Whole Exome ◽

Diagnosis Tool

Abstract Purpose of Review Whole exome sequencing (WES) and whole-genome sequencing (WGS) are frontline approaches for the genetic diagnosis of rare diseases. However, WES/WGS fails in up to 75% of cases. Transcriptomics via RNA-sequencing (RNA-Seq) is a novel approach that aims to increase the diagnostic yield in rare diseases. Recent Findings Recent publications focus on the success of RNA-Seq for increasing diagnosis rates in WES/WGS-negative patients in up to 36% of cases, across a range of different diseases, sample sizes, and tissue types. Summary RNA-Seq is beneficial for aiding prioritisation of causative variants currently not detected or often overlooked by WES/WGS alone. An improvement in diagnostic yields has been demonstrated using multiple source tissues, with muscle and fibroblasts being the most representative, but the more accessible blood still demonstrating diagnostic success, particularly in neuromuscular disorders. The introduction of RNA-Seq to the genetic diagnosis toolbox promises to be a useful complementary tool to WES/WGS for improving genetic diagnosis in patients with rare disease.

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The R package Rsubread is easier, faster, cheaper and better for alignment and quantification of RNA sequencing reads

10.1101/377762 ◽

2018 ◽

Cited By ~ 7

Author(s):

Yang Liao ◽

Gordon K. Smyth ◽

Wei Shi

Keyword(s):

Rna Sequencing ◽

High Performance ◽

R Package ◽

Ease Of Use ◽

Rna Seq ◽

Counting Functions ◽

Unix Command ◽

R Programming ◽

Downstream Analysis ◽

True Values

AbstractThe first steps in the analysis of RNA sequencing (RNA-seq) data are usually to map the reads to a reference genome and then to count reads by gene, by exon or by exon-exon junction. These two steps are at once the most common and also typically the most expensive computational steps in an RNA-seq analysis. These steps are typically undertaken using Unix command-line or Python software tools, even when downstream analysis is to be undertaken using R.We present Rsubread, a Bioconductor software package that provides high-performance alignment and counting functions for RNA-seq reads. Rsubread provides the ease-of-use of the R programming environment, creating a matrix of read counts directly as an R object ready for downstream analysis. It has no software dependencies other than R itself. Using SEQC data and simulations, we compare Rsubread to the popular non-R tools TopHat2, STAR and HTSeq. We also compare to counting functions provided in the Bioconductor infrastructure packages. We show that Rsubread is faster, uses less memory and produces read count summaries that more accurately correlate with true values. The results show that users can adopt the R environment for alignment and quantification without suffering any loss of performance.

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