Gene Expression Profiles of AHNAK2, DCSTAMP, FN1, and TERT Correlate With Mutational Status and Recurrence in Papillary Thyroid Carcinoma.

Papillary thyroid carcinoma (PTC), the most common malignancy of follicular cell derivation, is generally associated with good prognosis. Nevertheless, it is important to identify patients with aggressive PTCs and unfavorable outcome. Molecular markers such as BRAFV600E mutation and TERT promoter mutations have been proposed for risk stratification. While TERT promoter mutations have been frequently associated with aggressive PTCs, the association of BRAFV600E mutation with increased recurrence and mortality is less clear and has been controversially discussed. The aim of the present study was to analyze whether differentially expressed genes can predict BRAFV600E mutations as well as TERT promoter mutations in PTCs. RNA sequencing identified a large number of differentially expressed genes between BRAFV600E and BRAFwildtype PTCs. Of those, AHNAK2, DCSTAMP, and FN1 could be confirmed in a larger cohort (n = 91) to be significantly upregulated in BRAFV600E mutant PTCs using quantitative RT-PCR. Moreover, individual PTC expression values of DCSTAMP and FN1 were able to predict the BRAFV600E mutation status with high sensitivity and specificity. The expression of TERT was detected in all PTCs harboring TERT promoter mutations and in 19% of PTCs without TERT promoter mutations. Tumors with both TERT expression and TERT promoter mutations were particularly associated with aggressive clinicopathological features and a shorter recurrence-free survival. Altogether, it will be interesting to explore the biological function of AHNAK2, DCSTAMP, and FN1 in PTC in more detail. The analysis of their expression patterns could allow the characterization of PTC subtypes and thus enabling a more individualized surgical and medical treatment.

Using single-cell chromatin accessibility sequencing to characterize CD4+ T cells from murine tissues.

The Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) is a cutting-edge technology that enables researchers to assess genome-wide chromatin accessibility and to characterize cell type specific gene-regulatory programs. Recent technological progress allows for using this technology also on the single-cell level. In this article, we describe the whole value chain from the isolation of T cells from murine tissues to a complete bioinformatic analysis workflow. We start with methods for isolating scATAC-seq-ready CD4+ T cells from murine tissues such as visceral adipose tissue, skin, colon, and secondary lymphoid tissues such as the spleen. We describe the preparation of nuclei and quality control parameters during library preparation. Based on publicly available sequencing data that was generated using these protocols, we describe a step-by-step bioinformatic analysis pipeline for data pre-processing and downstream analysis. Our analysis workflow will follow the R-based bioinformatics framework ArchR, which is currently well established for scATAC-seq datasets. All in all, this work serves as a one-stop shop for generating and analyzing chromatin accessibility landscapes in T cells.

Using combined single-cell gene expression, TCR sequencing and cell surface protein barcoding to characterize and track CD4+ T cell clones from murine tissues.

Single-cell gene expression analysis using sequencing (scRNA-seq) has gained increased attention in the past decades for studying cellular transcriptional programs and their heterogeneity in an unbiased manner, and novel protocols allow the simultaneous measurement of gene expression, T-cell receptor clonality and cell surface protein expression. In this article, we describe the methods to isolate scRNA/TCR-seq-compatible CD4+ T cells from murine tissues, such as skin, spleen, and lymph nodes. We describe the processing of cells and quality control parameters during library preparation, protocols for multiplexing of samples, and strategies for sequencing. Moreover, we describe a step-by-step bioinformatic analysis pipeline from sequencing data generated using these protocols. This includes quality control, preprocessing of sequencing data and demultiplexing of individual samples. We perform quantification of gene expression and extraction of T-cell receptor alpha and beta chain sequences, followed by quality control and doublet detection, and methods for harmonization and integration of datasets. Next, we describe the identification of highly variable genes and dimensionality reduction, clustering and pseudotemporal ordering of data, and we demonstrate how to visualize the results with interactive and reproducible dashboards. We will combine different analytic R-based frameworks such as Bioconductor and Seurat, illustrating how these can be interoperable to optimally analyze scRNA/TCR-seq data of CD4+ T cells from murine tissues.