Optimization of a chromogenic Limulus amebocyte lysate (LAL) assay for automated endotoxin detection.
D M Bussey, K. Tsuji
- 发表年份
- 1985
- 引用次数
- 4
摘要
The chromogenic LAL assay was optimized using a LAL reagent (Lot 261, Associates of Cape Cod, Woods Hole, MA) designed for the gel-clot assay, and a chromogenic substrate, SPECTROZYME™ (American Diagnostica, Greenwich, CT). The chromogenic assay procedure involves the incubation of the LAL reagent and endotoxin standard or sample, followed by the subsequent incubation with the chromogenic substrate. Acetic acid is added to stop the enzymatic reaction and the yellow color formed is read spectrophotometrically at 405 nm. The assay was optimal when the 50-test vial of LAL reagent was reconstituted with 10 ml of 50 mM tris-buffer, pH 7.5 for the incubation of the LAL-endotoxin solution, and the 2 mM chromogenic substrate was prepared in 25 mM tris-buffer, pH 9.0 for the subsequent incubation to release p-nitroaniline(pNA) at pH 8.2. The optimum temperature for the entire reaction was 37 °C. The linear range of the standard curve for endotoxin detection and the sensitivity of the assay were dependent on the length of the LAL-endotoxin incubation and subsequent incubation periods. The linear region of the assay with incubation of the LAL and endotoxin for 15 min, followed by 3 min of subsequent incubation with the chromogenic substrate, was 0.1–0.6 endotoxin units (EU)/ml (r = 0.99). When the LAL-endotoxin incubation and the subsequent incubation times were increased to 30 and 5 min, respectively, the chromogenic LAL detected subpicogram quantities of endotoxin (0.005 EU/ml). The stability of the LAL reagent and the effect of other variables (e.g., ionic strength, buffer type and freezing) on LAL reactivity are discussed. The chromogenic LAL assay developed has been automated using a Zymate robotic system.
关键词
相关论文
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
Fractional Differential Equations
Igor Podlubný
2025
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991
Genetic Programming: On the Programming of Computers by Means of Natural Selection
John R. Koza
1992