Lisa A. Maier

We compared traditional data-dependent acquisition mass spectrometry (DDA-MS) with the increasingly adopted data-independent acquisition (DIA-MS) to evaluate their relative utility for large-scale quantitative biofluid proteomics of lung compartments, specifically paired bronchoalveolar lavage (BAL) cells and bronchoalveolar lavage fluid (BALF). Using beryllium-related granulomatous lung disease as a focused model, we analyzed BALF and BAL cells from beryllium-sensitized (BeS) individuals using both acquisition strategies to assess proteome depth, quantitative completeness, and analytical robustness. In BAL cells, 5,640 proteins were identified by DDA-MS and 5,227 by DIA-MS; however, DIA-MS yielded markedly improved quantitative completeness, with 5,178 proteins (∼99%) quantified across all samples compared with 3,539 (∼63%) quantified by DDA-MS. While 3,397 proteins were quantified by both methods, DIA-MS uniquely quantified 1,781 lower-abundance proteins. Proteins identified by both DIA and DDA-MS approaches revealed pathways associated with granulomatous inflammation, including Toll-like receptor, clathrin-mediated endocytosis, sirtuin, and C-type lectin receptor signaling, whereas DIA-MS resolved additional pathways, such as the complement cascade, coagulation system, and JAK/IL–6–type cytokine signaling. In BALF, although more proteins were identified by DDA-MS than by DIA-MS (2,069 vs 1,742), DIA-MS achieved greater quantitative completeness, with 1,695 proteins quantified across all samples compared with 1,050 using DDA-MS, underscoring its suitability for biomarker-oriented analyses in lung fluid compartments. Together, these results support DIA-MS as a robust and sensitive platform for quantitative lung proteomics and discovery of disease-relevant protein signatures.

Introduction Sarcoidosis is a heterogeneous granulomatous disease with highly variable clinical trajectories, yet no validated biomarkers exist to distinguish progressive sarcoidosis (P-sarcoidosis) from non-progressive disease (NP-sarcoidosis). This lack of tractable biomarkers limits early risk stratification and impedes therapeutic decision-making. Preliminary data from our group suggest that P-sarcoidosis and NP-sarcoidosis may be differentiated by blood-derived and peripheral blood mononuclear cell (PBMC)-derived molecular signatures, as well as ex vivo granuloma biogenesis in response to putative disease-causing antigens. This protocol describes a multi-omic study aimed at identifying mechanistically grounded, clinically translatable biomarkers that distinguish P-sarcoidosis from NP-sarcoidosis. Methods and analysis We will perform an integrative proteomic and transcriptomic analysis across three biological compartments: ex vivo granuloma model, PBMCs and plasma. Participants with clinically adjudicated P-sarcoidosis or NP-sarcoidosis will provide blood samples for multi-omic profiling. P-sarcoidosis versus NP-sarcoidosis phenotype will be assessed based on changes in spirometry, diffusing capacity for carbon monoxide, chest radiography and need for treatment for pulmonary symptoms. Patient-reported outcomes will also be recorded. Data-driven computational approaches will be used to identify molecular pathways associated with granuloma formation and disease persistence and to develop a classifier that distinguishes P-sarcoidosis from NP-sarcoidosis. Rigorous internal validation, feature-selection procedures and statistical controls for high-dimensional data will be applied. Candidate biomarkers emerging from multi-compartment integration will be prioritised based on biological coherence, reproducibility and clinical feasibility. Ethics and dissemination The study protocol has been approved by the Biomedical Research Alliance of New York, serving as a single Institutional Review Board (IRB) for the project (IRB # 23–02-503), as well as at National Jewish Health (IRB# HS-4091), University of Minnesota (STUDY00020121/SITE00002051) and The Ohio State University (IRB# 2023X0140). All participants will provide informed consent prior to enrolment. Results will be disseminated through peer-reviewed publications, scientific conferences and presentations to patients and advocacy groups. De-identified datasets and analytic workflows will be shared in accordance with institutional policies and data-sharing agreements.

Rationale: Activated pulmonary CD4+ T lymphocytes are a critical part of sarcoidosis pathogenesis. Objectives: To identify molecular changes associated with sarcoidosis risk primarily and progression secondarily, we profiled and integrated DNA methylome and transcriptome data in lung CD4+ T cells. Methods: Bronchoalveolar lavage (BAL) CD4+ T cells were isolated from 29 sarcoidosis (16 progressive [SarcP] and 13 non-progressive [SarcNP]) cases and 19 healthy controls. mRNA was sequenced and DNA methylation profiled on Illumina HumanMethylationEPIC arrays. Linear models were fit to test for effect of diagnosis, adjusting for age and sex. Methylation models were adjusted for unknown batch effects, inflation and bias. We used Data Integration Analysis for Biomarker discovery using Latent cOmponents (DIABLO) to integrate methylome and transcriptome datasets and validated targets using single cell RNA-seq. Measurements and Main Results: Using transcriptome-wide significance, differentially expressed (DE) transcripts were identified in sarcoidosis (39), SarcNP (23), and SarcP (7) compared to controls. Using genome-wide significance, 37 CpGs and 47 regions were identified as differentially methylated (DM) in sarcoidosis compared to controls. Focusing on DE genes, we identified DM CpGs in 11 genes associated with sarcoidosis, 3 with SarcNP and 7 with SarcP. DIABLO first latent component distinguished cases and controls based on transcripts and CpGs associated with T cell signaling, T-cell differentiation, apoptosis, and epigenetic regulation. Among DE/DM transcripts identified by integrative omic analysis and validated by single cell RNA-seq is IL18 receptor accessory protein ( IL18RAP ). Conclusions: Integrative methylome–transcriptome profiling of lung CD4⁺ T cells identified coordinated immune and epigenetic alterations, including IL18RAP , as candidate biomarkers.

Although health effects of ambient air pollution are well established in the general population, the impact of exposure in working populations remains poorly understood. Outdoor workers are disproportionately exposed to ambient air pollution, particularly with increasing wildfire smoke events and global climate change. An international interdisciplinary group of experts including worker representation assembled to review the current state of knowledge regarding the impact of occupational air pollution exposure on worker health and develop recommendations for research and actions to evaluate, mitigate, and regulate occupational air pollution exposure. The group identified health risks likely resulting from air pollution based on studies of the general population, noting that additional risks may be encountered from coexposures, as well as exertion increasing the work of breathing. High-risk groups were identified, including agricultural workers, construction workers, and wildland firefighters; others working in warehouses and indoor spaces are likely at risk via ambient air pollutant infiltration. It was estimated that at least 20 million outdoor U.S. workers are exposed to air pollution at work, which limits productivity and increases absenteeism. Participants recommended using air quality to guide work modifications and adoption of the hierarchy of exposure controls as a model to reduce exposures, as used by some states and proposed by the National Institute for Occupational Safety and Health for agricultural and other outdoor workers. Existing research supports the urgent need for policies to protect workers from exposure. Research gaps remain, including medical surveillance strategies, improved technology to protect workers, and studies specifically evaluating the impacts of occupational air pollution exposure.

Objective. Sarcoidosis is a granulomatous disease affecting the lungs in over 90% of patients. Qualitative assessment of chest CT by radiologists is standard clinical practice and reliable quantification of disease from CT would support ongoing efforts to identify sarcoidosis phenotypes. Standard imaging feature engineering techniques such as radiomics suffer from extreme sensitivity to image acquisition and processing, potentially impeding generalizability of research to clinical populations. In this work, we instead investigate approaches to engineering variogram-based features with the intent to identify a robust, generalizable pipeline for image quantification in the study of sarcoidosis. Approach. For a cohort of more than 300 individuals with sarcoidosis, we investigated 24 feature engineering pipelines differing by decisions for image registration to a template lung, empirical and model variogram estimation methods, and feature harmonization for CT scanner model, and subsequently 48 sets of phenotypes produced through unsupervised clustering. We then assessed sensitivity of engineered features, phenotypes produced through unsupervised clustering, and sarcoidosis disease signal strength to pipeline. Main results. We found that variogram features had low to mild association with scanner model and associations were reduced by image registration. For each feature type, features were also typically robust to all pipeline decisions except image registration. Strength of disease signal as measured by association with pulmonary function testing and some radiologist visual assessments was strong (optimistic AUC ≈ 0.9, p ≪ 0.0001 in models for architectural distortion, conglomerate mass, fibrotic abnormality, and traction bronchiectasis) and fairly consistent across engineering approaches regardless of registration and harmonization for CT scanner. Significance. Variogram-based features appear to be a suitable approach to image quantification in support of generalizable research in pulmonary sarcoidosis.

Background: Despite the utility of the beryllium lymphocyte proliferation test (BeLPT), distinguishing sarcoidosis, a disease of unknown etiology, from chronic beryllium disease (CBD), has long posed a diagnostic challenge. It is unclear if beryllium‐exposed sarcoidosis cases (Be‐exp‐Sarc) are clinically distinct from CBD, or are misdiagnosed cases of CBD.Methods: We performed a case‐case study of 40 beryllium‐exposed individuals diagnosed with Be‐exp‐Sarc compared to 40 frequency‐matched CBD cases. We compared demographics, exposure, clinical, physical, and radiographic characteristics and HLA DBPI E69 genotype.Results: Compared to CBD, Be‐exp‐Sarc cases were diagnosed at a younger age, had lower lung function, were less likely to have normal radiographic imaging, were more likely to have massive adenopathy and extra‐thoracic manifestations and were more likely to have been prescribed systemic immunosuppressive therapy. Be‐exp‐Sarc tended to have fewer years of beryllium exposure, but there were no significant differences in the amount of beryllium exposure. HLA DPBI E69 was present in 53% of Be‐exp‐Sarc cases, not different from the general population, versus 92% of CBD cases (p < 0.001).Conclusions: While a number of differences were observed, the only absolute distinguishing features were lack of confirmed beryllium sensitization in Be‐exp‐Sarc and lack of extra‐thoracic manifestations in CBD. These findings suggest that Be‐exp‐Sarc may be distinct from CBD, and beryllium or some other workplace exposure may possibly play an as yet to be defined etiologic role, although the possibility that these cases could be due to selection bias from heightened surveillance in beryllium workforces cannot be excluded.