The Statistical Value of Raw Fluorescence Signal in Luminex xMAP Based Multiplex Immunoassays

Abstract

Tissue samples (plasma, saliva, serum or urine) from 169 patients classified as either normal or having one of seven possible diseases are analysed across three 96-well plates for the presences of 37 analytes using cytokine inflammation multiplexed immunoassay panels. Censoring for concentration data caused problems for analysis of the low abundant analytes. Using fluorescence analysis over concentration based analysis allowed analysis of these low abundant analytes. Mixed-effects analysis on the resulting fluorescence and concentration responses reveals a combination of censoring and mapping the fluorescence responses to concentration values, through a 5PL curve, changed observed analyte concentrations. Simulation verifies this, by showing a dependence on the mean florescence response and its distribution on the observed analyte concentration levels. Differences from normality, in the fluorescence responses, can lead to differences in concentration estimates and unreliable probabilities for treatment effects. It is seen that when fluorescence responses are normally distributed, probabilities of treatment effects for fluorescence based t-tests has greater statistical power than the same probabilities from concentration based t-tests. We add evidence that the fluorescence response, unlike concentration values, doesn’t require censoring and we show with respect to differential analysis on the fluorescence responses that background correction is not required.

Rescue of rhesus macaques from the lethality of aerosolized ricin toxin

“…Resulting P values were corrected with the Benjamini, Krieger, and Yekutieli method to control FDR. Due to the low abundance of many of the inflammatory markers in serum and BALF examined by Luminex, all analyses were performed on log 2 -transformed raw fluorescent intensity values to avoid the need to censor values (48). A P value less than 0.05 was considered significant.…”

Discovery of Distinct Immune Phenotypes Using Machine Learning in Pulmonary Arterial Hypertension

“…We did not derive absolute protein concentration from this signal, as MFI does not require detection limit censoring, better quantifies analytes with low abundance, and has greater statistical power in downstream analysis. 19 Data pre-processing involved background fluorescence subtraction and robust quantile normalization. We adjusted for plate and batch effects by empirical Bayes methodology (online supplement p.4), 20 and visualized adjustments with principal component analysis (Online Figure I).…”

Magnetic Particles: Their Applications from Sample Preparations to Biosensing Platforms

“…The use of magnetic particles enables the high washing efficiency of the target separation step to rapidly remove the unwanted components, resulting in the enhancement of the limit of detection. Based on those advantages, widely used fluorescence-based technologies for multiple/highly sensitive detections using the magnetic beads, such as Luminex xMAP [58][59][60][61], magnetic modulation biosensing (MMB) [62], Quanterix Simoa [63,64] and magnetically-modulated optical nanoprobes (MagMOONs) [65,66], have emerged. A recent study by Wang et al pointed out a microfluidic biosensing device modification platform for Salmonella typhimurium measurement using fluorescent labeling and video processing on smartphones.…”