Multiplexed cytokine profiling identifies diagnostic signatures for latent tuberculosis and reactivation risk stratification.

Active tuberculosis (TB) is caused by Mycobacterium tuberculosis (Mtb) bacteria and is characterized by multiple phases of infection, leading to difficulty in diagnosing and treating infected individuals. Patients with latent tuberculosis infection (LTBI) can reactivate to the active phase of infection following perturbation of the dynamic bacterial and immunological equilibrium, which can potentially lead to further Mtb transmission. However, current diagnostics often lack specificity for LTBI and do not inform on TB reactivation risk.

Risk assessment of latent tuberculosis infection through a multiplexed cytokine biosensor assay and machine learning feature selection.

Accurate detection and risk stratification of latent tuberculosis infection (LTBI) remains a major clinical and public health problem. We hypothesize that multiparameter strategies that probe immune responses to Mycobacterium tuberculosis can provide new diagnostic insights into not only the status of LTBI infection, but also the risk of reactivation. After the initial proof-of-concept study, we developed a 13-plex immunoassay panel to profile cytokine release from peripheral blood mononuclear cells stimulated separately with Mtb-relevant and non-specific antigens to identify putative biomarker signatures.

Identification of lipid biomarkers of metastatic potential and gene expression (HER2/p53) in human breast cancer cell cultures using ambient mass spectrometry.

In breast cancer, overexpression of human epidermal growth factor receptor 2 (HER2) correlates with overactivation of lipogenesis, mutation of tumor suppressor p53, and increased metastatic potential. The mechanisms through which lipids mediate p53, HER2, and metastatic potential are largely unknown. We have developed a desorption electrospray ionization mass spectrometry (DESI-MS) method to identify lipid biomarkers of HER2/p53 expression, metastatic potential, and disease state (viz.

Precision immunoprofiling to reveal diagnostic signatures for latent tuberculosis infection and reactivation risk stratification.

Latent tuberculosis infection (LTBI) is estimated in nearly one quarter of the world's population, and of those immunocompetent and infected ~10% will proceed to active tuberculosis (TB). Current diagnostics cannot definitively identify LTBI and provide no insight into reactivation risk, thereby defining an unmet diagnostic challenge of incredible global significance. We introduce a new machine-learning-driven approach to LTBI diagnostics that leverages a high throughput, multiplexed cytokine detection technology and powerful bioinformatics to reveal multi-marker signatures for LTBI diagnosis and risk stratification.

A Guide to Quantitative Biomarker Assay Development using Whispering Gallery Mode Biosensors.

Whispering gallery mode (WGM) sensors are a class of powerful analytical techniques defined by the measurement of changes in the local refractive index at or near the sensor surface. When functionalized with target-specific capture agents, analyte binding can be measured with very low limits of detection. There are many geometric manifestations of WGM sensors, with chip-integrated silicon photonic devices first commercialized because of the robust, wafer-scale device fabrication, facile optical interrogation, and amenability to the creation of multiplexed sensor arrays.