BackgroundRecent biological discoveries have shown that clustering large datasets is essential for better understanding biology in many areas. Spectral clustering in particular has proven to be a powerful tool amenable for many applications. However, it cannot be directly applied to large datasets due to time and memory limitations. To address this issue, we have modified spectral clustering by adding an information preserving sampling procedure and applying a post-processing stage. We call this entire algorithm SamSPECTRAL.ResultsWe tested our algorithm on flow cytometry data as an example of large, multidimensional data containing potentially hundreds of thousands of data points (i.e., "events" in flow cytometry, typically corresponding to cells). Compared to two state of the art model-based flow cytometry clustering methods, SamSPECTRAL demonstrates significant advantages in proper identification of populations with non-elliptical shapes, low density populations close to dense ones, minor subpopulations of a major population and rare populations.ConclusionsThis work is the first successful attempt to apply spectral methodology on flow cytometry data. An implementation of our algorithm as an R package is freely available through BioConductor.
The immune response in humans is usually assessed using immunogenicity assays to provide biomarkers as correlates of protection (CoP). Flow cytometry is the assay of choice to measure intracellular cytokine staining (ICS) of cell-mediated immune (CMI) biomarkers. For CMI analysis, the integrated mean fluorescence intensity (iMFI) was introduced as a metric to represent the total functional CMI response as a CoP. iMFI is computed by multiplying the relative frequency (percent positive) of cells expressing a particular cytokine with the MFI of that population, and correlates better with protection in challenge models than either the percentage or the MFI of the cytokine-positive population. While determination of the iMFI as a CoP can readily be accomplished in animal models that allow challenge/protection experiments, this is not feasible in humans for ethical reasons. As a first step toward extending the iMFI concept to humans, we investigated the correlation of the iMFI derived from a human innate immune response ICS assay with functional cytokine release into the culture supernatant, as innate cytokines need to be released to have a functional impact. Next, we developed a quantitatively more correlative mathematical approach for calculating the functional response of cytokine-producing cells by incorporating the assignment of different weights to the magnitude (frequency of cytokine-positive cells) and the quality (the MFI) of the observed innate immune response. We refer to this model as generalized iMFI. ' 2010 International Society for Advancement of Cytometry Key termsGiMFI; correlation analysis; functional response; culture supernatant; cytokine; flow cytometry; antigen-presenting cells; integrated mean fluorescent intensity WHILE direct measurement of protection from infection after a defined challenge provides the most meaningful information in vaccine trials, in human studies, intermediate biomarkers [e.g., antibody titers or various measurements of cell-mediated immunity (CMI)] are used as correlates or surrogates of protection (1). The CMI response is often determined by measuring cytokines within the cell or secreted in serum or in culture supernatant. Given that cytokines exert their function mostly after being secreted, both approaches potentially measure different aspects of CMI, yet are often used interchangeably. To our knowledge, a direct correlative comparison of these two approaches has not been conducted. While quantification of secreted cytokines can be conducted using enzyme-linked immunosorbent assay (ELISA) or multiplex bead arrays, these methods do not identify the specific cell source of these secreted cytokines. Alternatively, flow cytometric analysis of intracellular cytokine staining (ICS) is able to identify the specific cell/s producing a given cytokine but it does not allow their absolute quantification. ICS results are determined as either percent positive cells or as mean fluorescent intensity (MFI) of a population of cytokine-producing cells, with both measurements co...
Collectively, our findings indicate that MAIT cells serve important but diverse roles in human cancers. Our work provides useful models and resources that employ gene expression data platforms to enable future studies in the realm of MAIT cell biology.
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