Biology subjects

Reichenberger, E. R.

Publications and source records attributed to Reichenberger, E. R..

3 recordsLinked to original sources

bioRxiv2025

Single-cell analysis reveals the cellular and transcriptional diversity of thyrocytes in the normal pediatric thyroid

To enhance the understanding of cellular heterogeneity within the pediatric thyroid, single-nuclei RNA sequencing was used to recover 38,069 non-pathogenic cells from thyroid tissue of three pediatric patients. The recovered cells were analyzed using the SWANS (Single Entity Workflow ANalysiS) pipeline (version 1.0). Analysis revealed seven major cell types: thyrocytes, endothelial cells, fibroblasts, C cells, T cells, B cells, and myeloid cells. Thyrocytes were the most prominent and heterogeneous cell type. Initially, two dominant thyrocyte subsets were identified based on transcriptional activity, which were subsequently subdivided into seven subclusters. Differentially expressed genes within each cluster support distinct cellular functions, including a metabolically active subset which may be involved in hormone synthesis and a subset involved in the transport of thyroid hormone into circulation. We identified an immune subpopulation originating predominantly from a single sample that was histologically and morphologically similar to the other two samples. This supports that transcriptional changes can be detected and used to identify populations of cells, even in the absence of histologically observable changes. This characterization represents the first comprehensive portraiture of pediatric thyroid gland cells and the first description of normal patient thyrocyte and stromal cell heterogeneity in the absence of adjacent malignancy.

cell biology↗

bioRxiv2025

KEAP1 mutations activate the NRF2 pathway to drive cell growth and migration, and attenuate drug response in thyroid cancer

The KEAP1/NRF2 pathway, a major regulator of the cellular oxidative stress response, is frequently activated in human cancers. Often mediated by loss-of-function mutations in KEAP1, this activation causes increased NRF2 transcriptional activity and constitutive activation of the antioxidant response. While KEAP1 mutations have been well documented in various cancers, their presence and role in thyroid carcinoma have remained largely unexplored. In this study, we sequenced pediatric thyroid tumors and analyzed publicly available datasets, identifying 81 KEAP1 mutations in tumors across a range of histologies. In these tumors, we further identified frequent biallelic loss of KEAP1 via 19p13.2 loss of heterozygosity (LOH). MAPK-activating alterations were found in a subset of KEAP1-mutant cases, but they were mutually exclusive with 19p13.2 LOH. Transcriptome analysis also revealed significant activation of the NRF2 pathway in KEAP1-mutant tumors. Four additional cases with similar transcriptional profiles but lacking mutational data were identified, likely representing putative KEAP1 mutants. Using in vitro cell line models, we then profiled the functional consequences of KEAP1 knockout in cells with and without known driver alterations. In these models, we show that KEAP1 loss leads to an NRF2-dependent upregulation of AKR1C3, GCLC, NQO1, along with increased proliferation and migration, irrespective of MAPK mutational status. We also demonstrate that loss of KEAP1 reduced sensitivity of RET fusion-positive cells to selpercatinib, consistent with previous reports that these alterations promote drug resistance in other malignancies. In this report, we comprehensively profile KEAP1 mutations in thyroid tumors, showing they are more prevalent and functionally significant than previously recognized. These findings position KEAP1 mutations as potential novel oncogenic drivers in thyroid cancer and support the integration of KEAP1/NRF2 pathway profiling into future studies and clinical frameworks.

cancer biology↗

bioRxiv2025

SWANS: A highly configurable analysis pipeline for single-cell and single-nuclei RNA-sequencing data

BackgroundSingle-cell RNA sequencing (scRNA-seq) is a powerful technique that enables the analysis of gene expression at the individual cell level. Bioinformatic tools for scRNA-seq data analysis have many different options throughout the typical scRNA-seq workflow (normalization, integration, annotation, clustering, and visualization), and the choice of method(s) and parameter(s) at each stage can impact results. ResultsHere, we introduce SWANS (v2.0), a configurable analysis pipeline that, in a single run, can employ multiple analysis methods, resolutions, and modifiable parameters. The resulting clustering arrangements, differential gene expression results, and other quantitative measurements can be dynamically visualized and compared in a Shiny interactive report to assist in choosing a single analysis schema for annotation and downstream analysis. Once a final approach is chosen, SWANS will perform differential gene expression (DGE) analysis based on experimental conditions and gene set enrichment analysis (GSEA) in addition to creating reports that display figures and interactive tables, quality control metrics, and benchmarking information. SWANS uses Snakemake as a workflow manager, Cell Ranger for alignment and gene expression quantification, Seurat for single cell data analysis, and additional single cell R packages for quality control and downstream single cell analysis. ConclusionSWANS is a tailorable pipeline that provides options for quality control, dimensionality reduction, clustering, differential gene expression analysis, gene set enrichment analysis, and trajectory analysis. Additionally, SWANS generates a series of reports that facilitate sharing large volumes of complex data in a clear and concise manner with other investigators.

bioinformatics↗