Comparison of Normal and Metaplastic Epithelium in Patients with Stable versus Persistently Symptomatic Severe Asthma Using Laser-Capture Microdissection and Data-Independent Acquisition-Mass Spectrometry

Laser capture microdissection as a method for investigating the human hair follicle microbiome reveals region-specific differences in the bacteriome profile

OBJECTIVE

Human hair follicles (HFs) are populated by a rich and diverse microbiome, traditionally evaluated by methods that inadvertently sample the skin microbiome and/or miss microbiota located in deeper HF regions. Thereby, these methods capture the human HF microbiome in a skewed and incomplete manner. This pilot study aimed to use laser-capture microdissection of human scalp HFs, coupled with 16S rRNA gene sequencing to sample the HF microbiome and overcome these methodological limitations.

RESULTS

HFs were laser-capture microdissected (LCM) into three anatomically distinct regions. All main known core HF bacterial colonisers, including Cutibacterium, Corynebacterium and Staphylococcus, were identified, in all three HF regions. Interestingly, region-specific variations in α-diversity and microbial abundance of the core microbiome genera and Reyranella were identified, suggestive of variations in microbiologically relevant microenvironment characteristics. This pilot study therefore shows that LCM-coupled with metagenomics is a powerful tool for analysing the microbiome of defined biological niches. Refining and complementing this method with broader metagenomic techniques will facilitate the mapping of dysbiotic events associated with HF diseases and targeted therapeutic interventions.

Cell-intrinsic differences between human airway epithelial cells from children and adults

The airway epithelium is a protective barrier that is maintained by the self-renewal and differentiation of basal stem cells. Increasing age is a principle risk factor for chronic lung diseases, but few studies have explored age-related molecular or functional changes in the airway epithelium. We retrieved epithelial biopsies from histologically normal tracheobronchial sites from pediatric and adult donors and compared their cellular composition and gene expression profile (in laser capture-microdissected whole epithelium, fluorescence-activated cell-sorted basal cells, and basal cells in cell culture). Histologically, pediatric and adult tracheobronchial epithelium was similar in composition. We observed age-associated changes in RNA sequencing studies, including higher interferon-associated gene expression in pediatric epithelium. In cell culture, pediatric cells had higher colony formation ability, sustained in vitro growth, and outcompeted adult cells in a direct competitive proliferation assay. Our results demonstrate cell-intrinsic differences between airway epithelial cells from children and adults in both homeostatic and proliferative states.

Resolving Clinical Phenotypes into Endotypes in Allergy: Molecular and Omics Approaches

Allergic diseases are highly complex with respect to pathogenesis, inflammation, and response to treatment. Current efforts for allergic disease diagnosis have focused on clinical evidence as a binary outcome. Although outcome status based on clinical phenotypes (observable characteristics) is convenient and inexpensive to measure in large studies, it does not adequately provide insight into the complex molecular determinants of allergic disease. Individuals with similar clinical diagnoses do not necessarily have similar disease etiologies, natural histories, or responses to treatment. This heterogeneity contributes to the ineffective response to treatment leading to an annual estimated cost of $350 billion in the USA alone. There has been a recent focus to deconvolute the clinical heterogeneity of allergic diseases into specific endotypes using molecular and omics approaches. Endotypes are a means to classify patients based on the underlying pathophysiological mechanisms involving distinct functions or treatment response. The advent of high-throughput molecular omics, immunophenotyping, and bioinformatics methods including machine learning algorithms is facilitating the development of endotype-based diagnosis. As we move to the next decade, we should truly start treating clinical endotypes not clinical phenotype. This review highlights current efforts taking place to improve allergic disease endotyping via molecular omics profiling, immunophenotyping, and machine learning approaches in the context of precision diagnostics in allergic diseases. Graphical Abstract.