Abstract

While complex sample pooling strategies have been developed for large-scale experiments with robotic liquid handling, many medium-scale experiments like mycotoxin screening by Enzyme-Linked Immunosorbent Assay (ELISA) are still conducted manually in 48- and 96-well plates. At this scale, the opportunity to save on reagent costs is offset by the increased costs of labor, materials, and risk-of-error caused by increasingly complex pooling strategies. This paper compares one-dimensional (1D), two-dimensional (2D), and Shifted Transversal Design (STD) pooling to study whether pooling affects assay accuracy and experimental cost and to provide guidance for when a human experimentalist might benefit from pooling. We approximated mycotoxin contamination in single corn kernels by fitting statistical distributions to experimental data (432 kernels for aflatoxin and 528 kernels for fumonisin) and used experimentally-validated Monte-Carlo simulation (10,000 iterations) to evaluate assay sensitivity, specificity, reagent cost, and pipetting cost. Based on the validated simulation results, assay sensitivity remains 100% for all four pooling strategies while specificity decreases as prevalence level rises. Reagent cost could be reduced by 70% and 80% in 48- and 96-well plates, with 1D and STD pooling being most reagent-saving respectively. Such a reagent-saving effect is only valid when prevalence level is < 21% for 48-well plates and < 13%-21% for 96-well plates. Pipetting cost will rise by 1.3-3.3 fold for 48-well plates and 1.2-4.3 fold for 96-well plates, with 1D pooling by row requiring the least pipetting. Thus, it is advisable to employ pooling when the expected prevalence level is below 21% and when the likely savings of up to 80% on reagent cost outweighs the increased materials and labor costs of up to 4 fold increases in pipetting.

Highlights

  • Pooling is defined in this paper as the act of taking an aliquot of equal volume from multiple samples and mixing them

  • Once the fitted distributions were determined to represent the data, they were used to draw random numbers that represented mycotoxin levels. These simulated mycotoxin levels were arranged in various combinations to reflect different plate layouts (48- or 96-well) and prevalence levels (1–47 or 1–95 positive samples)

  • While pooling theories have been well established and validated in a large-scale experimental setting, there remains an opportunity for practical instruction of pooling in medium-scale experiments [8]

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Summary

Introduction

Pooling is defined in this paper as the act of taking an aliquot of equal volume from multiple samples and mixing them. It is intended to rule out a large number of negative samples and detect a few positive samples at a lower cost.

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