Biology subjects

Hueniken, K.

Publications and source records attributed to Hueniken, K..

2 recordsLinked to original sources

bioRxiv2024

Cell and Transcriptomic Diversity of Infrapatellar Fat Pad during Knee Osteoarthritis

ObjectivesIn this study, we employ a multi-omic approach to identify major cell types and subsets, and their transcriptomic profiles within the infrapatellar fat pad (IFP), and to determine differences in the IFP based on knee osteoarthritis (KOA), sex, and obesity status. MethodsSingle-nucleus RNA sequencing of 82,924 nuclei from 21 IFPs (n=6 healthy control and n=15 KOA donors), spatial transcriptomics and bioinformatic analysis were used to identify contributions of the IFP to KOA. We mapped cell subclusters from other white adipose tissues using publicly available literature. The diversity of fibroblasts within the IFP was investigated by bioinformatic analyses, comparing by KOA, sex, and obesity status. Metabolomics was used to further explore differences in fibroblasts by obesity status. ResultsWe identified multiple subclusters of fibroblasts, macrophages, adipocytes, and endothelial cells with unique transcriptomic profiles. Using spatial transcriptomics, we resolved distributions of cell types and their transcriptomic profiles, and computationally identified putative cell-cell communication networks. Furthermore, we identified transcriptomic differences in fibroblasts from KOA versus healthy control donor IFPs, female versus male KOA-IFPs, and obese versus normal body mass index (BMI) KOA-IFPs. Finally, using metabolomics, we defined differences in metabolite levels in supernatants of naive, profibrotic- and proinflammatory stimuli-treated fibroblasts from obese compared to normal BMI KOA-IFP. ConclusionsOverall, by employing a multi-omic approach, this study provides the first comprehensive map of cellular and transcriptomic diversity of human IFP and identifies IFP fibroblasts as a key cell type contributing to transcriptomic and metabolic differences related to KOA disease, sex, or obesity.

cell biology↗

bioRxiv2022

Cell-free Tumor Methylome Analysis of Small Cell Lung Cancer Patients Identifies Subgroups with Prognostic Associations

IntroductionSmall cell lung cancer (SCLC) is a highly aggressive type of cancer with a high risk of recurrence. The SCLC methylome may yield biologic insight but is understudied due to difficulty in acquiring primary patient tissue. Here, we comprehensively profile the SCLC methylome using cell-free methylated DNA immunoprecipitation sequencing (cfMeDIP-seq). MethodscfDNA was extracted from plasma samples collected from 74 SCLC patients prior to initiation of first-line treatment and from 20 non-cancer smoker participants. Genomic DNA (gDNA) was also extracted from paired peripheral blood leukocytes from the 74 SCLC patients and 7 accompanying circulating-tumour-cell patient-derived xenografts (CDX). cfDNA and gDNA were used as input for cfMeDIP-seq. We developed PeRIpheral blood leukocyte MEthylation (PRIME) subtraction as an algorithm to improve tumour specificity of cell-free methylome. ResultsSCLC total plasma cfDNA methylation profiles obtained using cfMeDIP-seq are representative of CDX tumour methylation. SCLC cfDNA methylation is distinct from non-cancer plasma. Using PRIME and k-means consensus clustering, we identified two SCLC methylome clusters with prognostic associations. These clusters had methylated biological pathways related to axon guidance, neuroactive ligand-receptor interaction, pluripotency of stem cells, and were differentially methylated at long noncoding RNA, LINEs, SINEs, retrotransposons, and other repeats features. ConclusionsWe have comprehensively profiled the SCLC methylome using cfMeDIP-seq in a large patient cohort and identified methylome clusters with prognostic associations. Our work demonstrates the potential of liquid biopsies in examining SCLC biology encoded in the methylome.

cancer biology↗