Nanocellulose aerogel inserts for quantitative lateral flow immunoassays

Biosens Bioelectron. 2021 Nov 15:192:113491. doi: 10.1016/j.bios.2021.113491. Epub 2021 Jul 9.

Abstract

The Lateral Flow Immuno Assay (LFIA) is a well-established technique that provides immediate results without high-cost laboratory equipment and technical skills from the users. However, conventional colorimetric LFIA strips suffer from high limits of detection, mainly due to the analysis of a limited sample volume, short reaction time between the target analyte and the conjugation molecules, and a weak optical signal. Thus, LFIAs are mainly employed as a medical diagnostic tool for qualitative and semi/quantitative detection, respectively. We applied a novel cellulose nanofiber (CNF) aerogel material incorporated into LFIA strips to increase the sample flow time, which in turn extends the binding interactions between the analyte of interest and the detection antibody, thus improving the limit of detection (LOD). Compared to a conventional LFIA strip, the longer sample flow time in the aerogel modified LFIA strips improved the LOD for the detection of mouse IgG in a buffer solution by a 1000-fold. The accomplished LOD (0.01 ng/mL) even outperformed specifications of a commercial ELISA kit by a factor of 10, and the CNF aerogel assisted LFIA was successfully applied to detect IgG in human serum with a LOD of 0.72 ng/mL. Next to the improved LOD, the aerogel assisted LFIA could quantify IgG samples in buffer and human serum in the concentration ranges of 0.17 ng/mL - 100 ng/mL (in buffer) and 4.6 ng/mL - 100 ng/mL (in human serum). The presented solution thus poses a unique potential to transform lateral flow assays into highly sensitive, fully quantitative point-of-care diagnostics.

Keywords: Extended sample flow time; Lateral flow immunoassay; Nanocellulose aerogel; Quantitative point-of-care diagnostics.

MeSH terms

  • Animals
  • Biosensing Techniques*
  • Colorimetry
  • Enzyme-Linked Immunosorbent Assay
  • Immunoassay
  • Limit of Detection
  • Mice