A bacterial stimulation assay for bronchoalveolar lavage immune cells from young children with cystic fibrosis.

Cystic Fibrosis (CF) is primarily a progressive lung disease, characterized by chronic pulmonary infections with opportunistic pathogens. Such infections typically commence early in life, producing an inflammatory response marked by IL-8 chemokine production and neutrophilic infiltration, major contributory factors in CF progression. Studying this inflammation, especially early in life, is critical for developing new strategies for preventing or slowing disruption to the structural integrity of the CF airways. However, evaluating the immune responses of bronchoalveolar lavage (BAL) cells from children with CF faces technical challenges, including contamination carried from the lung due to pre-existing infections and low cell number availability. Here, we describe a technique for preparing BAL cells from young children with CF and using those cells in a bacterial stimulation assay. Initial antibiotic treatment proved essential for preventing resident bacteria from overgrowing BAL cell cultures, or non-specifically activating the cells. ACTB, identified as an optimal reference gene, was validated for accurate analysis of gene expression in these cells. Pseudomonas aeruginosa and Staphylococcus aureus were used as bacterial stimulants to evaluate the immune response of BAL cells from young children with CF. Addition of gentamicin prevented bacterial overgrowth, although if added after 3 hours of culture an extremely variable response resulted, with the bacteria causing a suppressive effect in some cultures. Addition of gentamicin after 1 hour of culture completely prevented this suppressive effect. This technique was then able to reproducibly measure the IL-8 response to stimulation with S. aureus and P. aeruginosa, including co-stimulation with both bacteria.

Estrogen-induced immune changes within the normal mammary gland.

Breast cancer (BCa) incidence increases following aberrant hormone exposure, which has been linked to direct effects on estrogen receptor (ER)+ mammary epithelium. While estrogen exposure during mammary involution has been shown to drive tumour growth via neutrophils, the potential for the ER + immune microenvironment to mediate part (in addition to mammary epithelial cells) of hormonally controlled BCa risk during normal development has not been assessed. We collected mammary tissue, lymph nodes and blood from tumour naïve mice treated with, oophorectomy, estrogen (17ß estradiol) or Fulvestrant. Flow cytometry was used to examine the impact on the frequency of innate and adaptive immune cells. Oophorectomy and fulvestrant decreased the proportion of macrophages, particularly pro-tumour polarized M2 macrophages and neutrophils. Conversely, dendritic cells were increased by these therapies, as were eosinophils. Estrogen increased the proportion of M2 macrophages and to a lesser extent CD4-CD8- double negative and FoxP3+ regulatory T cells but decreased CD8 + T cells and B cells. Excluding eosinophils, these changes were restricted to the mammary tissue. This suggests that inhibiting estrogen action lowers the immune suppressive myeloid cells, increases in antigen presentation and eosinophil-mediated direct or indirect cytotoxic effects. In contrast, estrogen exposure, which drives BCa risk, increases the suppressive myeloid cells and reduces anti-tumour cytotoxic T cells. The impact of hormonal exposure on BCa risk, may in part be linked to its immune modulatory activity.

Artificial intelligence defines protein-based classification of thyroid nodules.

Determination of malignancy in thyroid nodules remains a major diagnostic challenge. Here we report the feasibility and clinical utility of developing an AI-defined protein-based biomarker panel for diagnostic classification of thyroid nodules: based initially on formalin-fixed paraffin-embedded (FFPE), and further refined for fine-needle aspiration (FNA) tissue specimens of minute amounts which pose technical challenges for other methods. We first developed a neural network model of 19 protein biomarkers based on the proteomes of 1724 FFPE thyroid tissue samples from a retrospective cohort. This classifier achieved over 91% accuracy in the discovery set for classifying malignant thyroid nodules. The classifier was externally validated by blinded analyses in a retrospective cohort of 288 nodules (89% accuracy; FFPE) and a prospective cohort of 294 FNA biopsies (85% accuracy) from twelve independent clinical centers. This study shows that integrating high-throughput proteomics and AI technology in multi-center retrospective and prospective clinical cohorts facilitates precise disease diagnosis which is otherwise difficult to achieve by other methods.

DPHL: A DIA Pan-human Protein Mass Spectrometry Library for Robust Biomarker Discovery.

To address the increasing need for detecting and validating protein biomarkers in clinical specimens, mass spectrometry (MS)-based targeted proteomic techniques, including the selected reaction monitoring (SRM), parallel reaction monitoring (PRM), and massively parallel data-independent acquisition (DIA), have been developed. For optimal performance, they require the fragment ion spectra of targeted peptides as prior knowledge. In this report, we describe a MS pipeline and spectral resource to support targeted proteomics studies for human tissue samples. To build the spectral resource, we integrated common open-source MS computational tools to assemble a freely accessible computational workflow based on Docker. We then applied the workflow to generate DPHL, a comprehensive DIA pan-human library, from 1096 data-dependent acquisition (DDA) MS raw files for 16 types of cancer samples. This extensive spectral resource was then applied to a proteomic study of 17 prostate cancer (PCa) patients. Thereafter, PRM validation was applied to a larger study of 57 PCa patients and the differential expression of three proteins in prostate tumor was validated. As a second application, the DPHL spectral resource was applied to a study consisting of plasma samples from 19 diffuse large B cell lymphoma (DLBCL) patients and 18 healthy control subjects. Differentially expressed proteins between DLBCL patients and healthy control subjects were detected by DIA-MS and confirmed by PRM. These data demonstrate that the DPHL supports DIA and PRM MS pipelines for robust protein biomarker discovery. DPHL is freely accessible at https://www.iprox.org/page/project.html?id=IPX0001400000.