Importance of specimen pretreatment for the low-level detection of mycobacterial lipoarabinomannan in human serum†
Patient care and prevention of disease outbreaks rely heavily on the performance of diagnostic tests. These tests are typically carried out in serum, urine, and other complex sample matrices, but are often plagued by a number of matrix effects such as nonspecific adsorption and complexation with circulating proteins. This paper demonstrates the importance of sample pretreatment to overcome matrix effects, enabling the low-level detection of a disease marker for tuberculosis (TB). The impact of pretreatment is illustrated by detecting a cell wall component unique to mycobacteria, lipoarabinomannan (LAM). LAM is a major virulence factor in the infectious pathology of Mycobacterium tuberculosis (Mtb) and has been successfully detected in the body fluids of TB-infected individuals; however, its clinical sensitivity – identifying patients with active infection – remains problematic. This and the companion paper show that the detection of LAM in an immunoassay is plagued by its complexation with proteins and other components in serum. Herein, we present the procedures and results from an investigation of several different pretreatment schemes designed to disrupt complexation and thereby improve detection. These sample pretreatment studies, aimed at determining the optimal conditions for complex disruption, were carried out by using a LAM simulant derived from the nonpathogenic M. smegmatis, a mycobacterium often used as a model for Mtb. We have found that a perchloric acid-based pretreatment step improves the ability to detect this simulant by ∼1500× with respect to that in untreated serum. This paper describes the approach to pretreatment, how pretreatment improves the detection of the LAM simulant in human serum, and the results from a preliminary investigation to identify possible contributors to complexation by fractionating serum according to molecular weight. The companion paper applies this pretreatment approach to assays of TB patient samples.