High-Resolution Profiling of Human Vocal Fold Cellular Landscapes With Single-Nuclei RNA Sequencing.

INTRODUCTION: The function of the vocal folds (VFs) is determined by the phenotype, abundance, and distribution of differentiated cells within specific microenvironments. Identifying this histologic framework is crucial in understanding laryngeal disease. A paucity of studies investigating VF cellular heterogeneity has been undertaken. Here, we examined the cellular landscape of human VFs by utilizing single-nuclei RNA-sequencing. METHODS: Normal true VF tissue was excised from five patients undergoing pitch elevation surgery. Tissue was snap frozen in liquid nitrogen and subjected to cellular digestion and nuclear extraction. Nuclei were processed for single-nucleus sequencing using the 10X Genomics Chromium platform. Sequencing reads were assembled using cellranger and analyzed with the scanpy package in python. RESULTS: RNA sequencing revealed 18 global cell clusters. While many were of epithelial origin, expected cell types, such as fibroblasts, immune cells, muscle cells, and endothelial cells were present. Subcluster analysis defined unique epithelial, immune, and fibroblast subpopulations. CONCLUSION: This study evaluated the cellular heterogeneity of normal human VFs by utilizing single-nuclei RNA-sequencing. With further confirmation through additional spatial sequencing and microscopic imaging, a novel cellular map of the VFs may provide insight into new cellular targets for VF disease. LEVEL OF EVIDENCE: NA Laryngoscope, 134:3193-3200, 2024.

genomeSidekick: A user-friendly epigenomics data analysis tool.

Recent advances in epigenomics measurements have resulted in a preponderance of genomic sequencing datasets that require focused analyses to discover mechanisms governing biological processes. In addition, multiple epigenomics experiments are typically performed within the same study, thereby increasing the complexity and difficulty of making meaningful inferences from large datasets. One gap in the sequencing data analysis pipeline is the availability of tools to efficiently browse genomic data for scientists that do not have bioinformatics training. To bridge this gap, we developed genomeSidekick, a graphical user interface written in R that allows researchers to perform bespoke analyses on their transcriptomic and chromatin accessibility or chromatin immunoprecipitation data without the need for command line tools. Importantly, genomeSidekick outputs lists of up- and downregulated genes or chromatin features with differential accessibility or occupancy; visualizes omics data using interactive volcano plots; performs Gene Ontology analyses locally; and queries PubMed for selected gene candidates for further evaluation. Outputs can be saved using the user interface and the code underlying genomeSidekick can be edited for custom analyses. In summary, genomeSidekick brings wet lab scientists and bioinformaticians into a shared fluency with the end goal of driving mechanistic discovery.

Predicting Therapeutic Antibody Delivery into Human Head and Neck Cancers.

PURPOSE: The efficacy of antibody-based therapeutics depends on successful drug delivery into solid tumors; therefore, there is a clinical need to measure intratumoral antibody distribution. This study aims to develop and validate an imaging and computation platform to directly quantify and predict antibody delivery into human head and neck cancers in a clinical study. EXPERIMENTAL DESIGN: Twenty-four patients received systemic infusion of a near-infrared fluorescence-labeled therapeutic antibody followed by surgical tumor resection. A computational platform was developed to quantify the extent of heterogeneity of intratumoral antibody distribution. Both univariate and multivariate regression analyses were used to select the most predictive tumor biological factors for antibody delivery. Quantitative image features from the pretreatment MRI were extracted and correlated with fluorescence imaging of antibody delivery. RESULTS: This study not only confirmed heterogeneous intratumoral antibody distribution in-line with many preclinical reports, but also quantified the extent of interpatient, intertumor, and intratumor heterogeneity of antibody delivery. This study demonstrated the strong predictive value of tumor size for intratumoral antibody accumulation and its significant impact on antibody distribution in both primary tumor and lymph node metastasis. Furthermore, this study established the feasibility of using contrast-enhanced MRI to predict antibody delivery. CONCLUSIONS: This study provides a clinically translatable platform to measure antibody delivery into solid tumors and yields valuable insight into clinically relevant antibody tumor penetration, with implications in the selection of patients amenable to antibody therapy and the design of more effective dosing strategies.