An optimized protocol for isolation of hepatic leukocytes retrieved from murine and NASH liver biopsies.

Immune dysregulation and inflammation by hepatic-resident leukocytes is considered a key step in disease progression of non-alcoholic fatty liver disease and non-alcoholic steatohepatitis toward cirrhosis and hepatocellular carcinoma. Here, we provide a protocol for isolation and characterization of liver-resident immune cells from fine-needle biopsies obtained from a rodent model and humans. We describe steps for isolating leukocytes, cell sorting, and RNA extraction and sequencing. We then detail procedures for low-input mRNA sequencing analyses.

Automation enables high-throughput and reproducible single-cell transcriptomics library preparation.

Next-generation sequencing (NGS) has revolutionized genomics, decreasing sequencing costs and allowing researchers to draw correlations between diseases and DNA or RNA changes. Technical advances have enabled the analysis of RNA expression changes between single cells within a heterogeneous population, known as single-cell RNA-seq (scRNA-seq). Despite resolving transcriptomes of cellular subpopulations, scRNA-seq has not replaced RNA-seq, due to higher costs and longer hands-on time. Here, we developed an automated workflow to increase throughput (up to 48 reactions) and to reduce by 75% the hands-on time of scRNA-seq library preparation, using the 10X Genomics Single Cell 3' kit. After gel bead-in-emulsion (GEM) generation on the 10X Genomics Chromium Controller, cDNA amplification was performed, and the product was normalized and subjected to either the manual, standard library preparation method or a fully automated, walk-away method using a Biomek i7 Hybrid liquid handler. Control metrics showed that both quantity and quality of the single-cell gene expression libraries generated were equivalent in size and yield. Key scRNA-seq downstream quality metrics, such as unique molecular identifiers count, mitochondrial RNA content, and cell and gene counts, further showed high correlations between automated and manual workflows. Using the UMAP dimensionality reduction technique to visualize all cells, we were able to further correlate the results observed between the manual and automated methods (R=0.971). The method developed here allows for the fast, error-free, and reproducible multiplex generation of high-quality single-cell gene expression libraries.

Automation of high-throughput mRNA-seq library preparation: a robust, hands-free and time efficient methodology.

Over the last decade, whole transcriptome profiling, also known as RNA-sequencing (RNA-seq), has quickly gained traction as a reliable method for unbiased assessment of gene expression. Integration of RNA-seq expression data into other omics datasets (e.g., proteomics, metabolomics, or epigenetics) solidifies our understanding of cell-specific regulatory patterns, yielding pathways to investigate the key rules of gene regulation. A limitation to efficient, at-scale utilization of RNA-seq is the time-demanding library preparation workflows, which is a 2-day or longer endeavor per cohort/sample size. To tackle this bottleneck, we designed an automated workflow that increases throughput capacity, while minimizing human error to enhance reproducibility. To this end, we converted the manual protocol of the NEBNext Directional Ultra II RNA Library Prep Kit for Illumina on the Beckman Coulter liquid handler, Biomek i7 Hybrid workstation. A total of 84 RNA samples were isolated from two human cell lines and subjected to comparative manual and automated library preparation methods. Qualitative and quantitative results indicated a high degree of similarity between libraries generated manually or through automation. Yet, there was a significant reduction in both hands-on and assay time from a 2-day manual to a 9-hour automated workflow. Using linear regression analysis, we found the Pearson correlation coefficient between libraries generated manually or by automation to be almost identical to a sample being sequenced twice (R²= 0.985 vs 0.983). This demonstrates that high-throughput automated workflows can be of great benefit to genomic laboratories by enhancing efficiency of library preparation, reducing hands-on time and increasing throughput potential.

RELACS nuclei barcoding enables high-throughput ChIP-seq.

Chromatin immunoprecipitation followed by deep sequencing (ChIP-seq) is an invaluable tool for mapping chromatin-associated proteins. Current barcoding strategies aim to improve assay throughput and scalability but intense sample handling and lack of standardization over cell types, cell numbers and epitopes hinder wide-spread use in the field. Here, we present a barcoding method to enable high-throughput ChIP-seq using common molecular biology techniques. The method, called RELACS (restriction enzyme-based labeling of chromatin in situ) relies on standardized nuclei extraction from any source and employs chromatin cutting and barcoding within intact nuclei. Barcoded nuclei are pooled and processed within the same ChIP reaction, for maximal comparability and workload reduction. The innovative barcoding concept is particularly user-friendly and suitable for implementation to standardized large-scale clinical studies and scarce samples. Aiming to maximize universality and scalability, RELACS can generate ChIP-seq libraries for transcription factors and histone modifications from hundreds of samples within three days.

Comprehensive analysis of DNA-methylation in mammalian tissues using MeDIP-chip.

Genome-wide mapping of epigenetic changes is essential for understanding the mechanisms involved in gene regulation during cell differentiation and embryonic development. DNA-methylation is one of these key epigenetic marks that is directly linked to gene expression is. Methylated DNA immunoprecipitation (MeDIP) is a recently devised method used to determine the distribution of DNA-methylation within functional regions (e.g., promoters) or in the entire genome robustly and cost-efficiently. This approach is based on the enrichment of methylated DNA with an antibody that specifically binds to 5-methyl-cytosine and can be combined with PCR, microarrays or high-throughput sequencing. This article outlines the experimental procedure of MeDIP-chip and provides a comprehensive summary of quality control strategies and primary data analysis.

DNA methylation analysis of chromosome 21 gene promoters at single base pair and single allele resolution.

Differential DNA methylation is an essential epigenetic signal for gene regulation, development, and disease processes. We mapped DNA methylation patterns of 190 gene promoter regions on chromosome 21 using bisulfite conversion and subclone sequencing in five human cell types. A total of 28,626 subclones were sequenced at high accuracy using (long-read) Sanger sequencing resulting in the measurement of the DNA methylation state of 580427 CpG sites. Our results show that average DNA methylation levels are distributed bimodally with enrichment of highly methylated and unmethylated sequences, both for amplicons and individual subclones, which represent single alleles from individual cells. Within CpG-rich sequences, DNA methylation was found to be anti-correlated with CpG dinucleotide density and GC content, and methylated CpGs are more likely to be flanked by AT-rich sequences. We observed over-representation of CpG sites in distances of 9, 18, and 27 bps in highly methylated amplicons. However, DNA sequence alone is not sufficient to predict an amplicon's DNA methylation status, since 43% of all amplicons are differentially methylated between the cell types studied here. DNA methylation in promoter regions is strongly correlated with the absence of gene expression and low levels of activating epigenetic marks like H3K4 methylation and H3K9 and K14 acetylation. Utilizing the single base pair and single allele resolution of our data, we found that i) amplicons from different parts of a CpG island frequently differ in their DNA methylation level, ii) methylation levels of individual cells in one tissue are very similar, and iii) methylation patterns follow a relaxed site-specific distribution. Furthermore, iv) we identified three cases of allele-specific DNA methylation on chromosome 21. Our data shed new light on the nature of methylation patterns in human cells, the sequence dependence of DNA methylation, and its function as epigenetic signal in gene regulation. Further, we illustrate genotype-epigenotype interactions by showing novel examples of allele-specific methylation.