An algorithm-assisted automated identification and enumeration system for sensitive hydrogen sulfide sensing under dark field microscopy

Abstract

Hydrogen sulfide (H₂S) is an active physiological molecule, and its intracellular level has great significance to life functions. In this study, an effective and sensitive method was developed for H₂S sensing with dark field microscopy (DFM). The proposed method employed AuNPs as the signal source, DFM as the readout system, and an intelligence algorithm as the image processing and output systems, respectively. The AuNP surface was modified with azido and alkynyl in advance, and then added into a tube cap. As the H₂S evaporated from the solution and selectively reduced azido to amino, the click chemistry reaction was inhibited, which resulted in the AuNPs being well dispersed in the solution; otherwise, AuNP aggregation occurred. The scattering colour of single AuNPs could be easily distinguished from that of AuNP aggregations with DFM, and the number or ratio of single AuNPs could also be easily obtained by the custom algorithm. The results showed that the H₂S content could be linearly analyzed in a range from 2–80 μM. Furthermore, the proposed sensing strategy has been applied for H₂S detection in cell lysate. Compared with the traditional colorimetric method, the results showed no significant difference, indicating the good prospects of the algorithm and proposed H₂S sensing method.