ELISA-R: an R-based method for robust ELISA data analysis.

Enzyme-linked immunosorbent assay (ELISA) is a technique to detect the presence of an antigen or antibody in a sample. ELISA is a simple and cost-effective method that has been used for evaluating vaccine efficacy by detecting the presence of antibodies against viral/bacterial antigens and diagnosis of disease stages. Traditional ELISA data analysis utilizes a standard curve of known analyte, and the concentration of the unknown sample is determined by comparing its observed optical density against the standard curve. However, in the case of vaccine research for complicated bacteria such as Mycobacterium tuberculosis (Mtb), there is no prior information regarding the antigen against which high-affinity antibodies are generated and therefore plotting a standard curve is not feasible. Consequently, the analysis of ELISA data in this instance is based on a comparison between vaccinated and unvaccinated groups. However, to the best of our knowledge, no robust data analysis method exists for "non-standard curve" ELISA. In this paper, we provide a straightforward R-based ELISA data analysis method with open access that incorporates end-point titer determination and curve-fitting models. Our modified method allows for direct measurement data input from the instrument, cleaning and arranging the dataset in the required format, and preparing the final report with calculations while leaving the raw data file unchanged. As an illustration of our method, we provide an example from our published data in which we successfully used our method to compare anti-Mtb antibodies in vaccinated vs non-vaccinated mice.

Mucosal exposure to non-tuberculous mycobacteria elicits B cell-mediated immunity against pulmonary tuberculosis.

Bacille Calmette-Guerin (BCG) is the only licensed vaccine against Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis (TB) disease. However, BCG has limited efficacy, necessitating the development of better vaccines. Non-tuberculous mycobacteria (NTMs) are opportunistic pathogens present ubiquitously in the environment. TB endemic countries experience higher exposure to NTMs, but previous studies have not elucidated the relationship between NTM exposure and BCG efficacy against TB. Therefore, we develop a mouse model (BCG + NTM) to simulate human BCG immunization regime and continuous NTM exposure. BCG + NTM mice exhibit superior and prolonged protection against pulmonary TB, with increased B cell influx and anti-Mtb antibodies in serum and airways, compared with BCG alone. Notably, spatial transcriptomics and immunohistochemistry reveal that BCG + NTM mice formed B cell aggregates with features of germinal center development, which correlate with reduced Mtb burden. Our studies suggest a direct relationship between NTM exposure and TB protection, with B cells playing a crucial role.

A Whole Virion Vaccine for COVID-19 Produced via a Novel Inactivation Method and Preliminary Demonstration of Efficacy in an Animal Challenge Model.

The COVID-19 pandemic has generated intense interest in the rapid development and evaluation of vaccine candidates for this disease and other emerging diseases. Several novel methods for preparing vaccine candidates are currently undergoing clinical evaluation in response to the urgent need to prevent the spread of COVID-19. In many cases, these methods rely on new approaches for vaccine production and immune stimulation. We report on the use of a novel method (SolaVAX) for production of an inactivated vaccine candidate and the testing of that candidate in a hamster animal model for its ability to prevent infection upon challenge with SARS-CoV-2 virus. The studies employed in this work included an evaluation of the levels of neutralizing antibody produced post-vaccination, levels of specific antibody sub-types to RBD and spike protein that were generated, evaluation of viral shedding post-challenge, flow cytometric and single cell sequencing data on cellular fractions and histopathological evaluation of tissues post-challenge. The results from this preliminary evaluation provide insight into the immunological responses occurring as a result of vaccination with the proposed vaccine candidate and the impact that adjuvant formulations, specifically developed to promote Th1 type immune responses, have on vaccine efficacy and protection against infection following challenge with live SARS-CoV-2. This data may have utility in the development of effective vaccine candidates broadly. Furthermore, the results of this preliminary evaluation suggest that preparation of a whole virion vaccine for COVID-19 using this specific photochemical method may have potential utility in the preparation of one such vaccine candidate.

BCG-Prime and boost with Esx-5 secretion system deletion mutant leads to better protection against clinical strains of Mycobacterium tuberculosis.

Although vaccination with BCG prevents disseminated forms of childhood tuberculosis (TB), it does not protect against pulmonary infection or Mycobacterium tuberculosis (Mtb) transmission. In this study, we generated a complete deletion mutant of the Mtb Esx-5 type VII secretion system (Mtb Δesx-5). Mtb Δesx-5 was highly attenuated and safe in immunocompromised mice. When tested as a vaccine candidate to boost BCG-primed immunity, Mtb Δesx-5 improved protection against highly virulent Mtb strains in the murine and guinea pig models of TB. Enhanced protection provided by heterologous BCG-prime plus Mtb Δesx-5 boost regimen was associated with increased pulmonary influx of central memory T cells (TCM), follicular helper T cells (TFH) and activated monocytes. Conversely, lower numbers of T cells expressing exhaustion markers were observed in vaccinated animals. Our results suggest that boosting BCG-primed immunity with Mtb Δesx-5 is a potential approach to improve protective immunity against Mtb. Further insight into the mechanism of action of this novel prime-boost approach is warranted.

Cyto-Feature Engineering: A Pipeline for Flow Cytometry Analysis to Uncover Immune Populations and Associations with Disease.

Flow cytometers can now analyze up to 50 parameters per cell and millions of cells per sample; however, conventional methods to analyze data are subjective and time-consuming. To address these issues, we have developed a novel flow cytometry analysis pipeline to identify a plethora of cell populations efficiently. Coupled with feature engineering and immunological context, researchers can immediately extrapolate novel discoveries through easy-to-understand plots. The R-based pipeline uses Fluorescence Minus One (FMO) controls or distinct population differences to develop thresholds for positive/negative marker expression. The continuous data is transformed into binary data, capturing a positive/negative biological dichotomy often of interest in characterizing cells. Next, a filtering step refines the data from all identified cell phenotypes to populations of interest. The data can be partitioned by immune lineages and statistically correlated to other experimental measurements. The pipeline's modularity allows customization of statistical testing, adoption of alternative initial gating steps, and incorporation of other datasets. Validation of this pipeline through manual gating of two datasets (murine splenocytes and human whole blood) confirmed its accuracy in identifying even rare subsets. Lastly, this pipeline can be applied in all disciplines utilizing flow cytometry regardless of cytometer or panel design. The code is available at https://github.com/aef1004/cyto-feature_engineering.

Acquisition of High-Quality Spectral Flow Cytometry Data.

Flow cytometry allows the visualization of physical, functional, and/or biological properties of cells including antigens, cytokines, size, and complexity. With increasingly large flow cytometry panels able to analyze up to 50 parameters, there is a need to standardize flow cytometry protocols to achieve high-quality data that can be input into analysis algorithms. Without this clean data, algorithms may incorrectly categorize the cell populations present in the samples. In this protocol, we outline a comprehensive methodology to prepare samples for polychromatic flow cytometry. The use of multiple washing steps and rigorous controls creates high-quality data with good separation between cell populations. Experimental data acquired using this protocol can be analyzed via computational algorithms that perform end-to-end analysis. © 2020 by John Wiley & Sons, Inc. Basic Protocol 1: Preparation of single-cell suspension for flow cytometry Support Protocol 1: Lung preparation Support Protocol 2: Counting cells on a flow cytometer Basic Protocol 2: Surface and intracellular flow cytometry staining Support Protocol 3: Single-color bead controls.