Immunogold labeling

Machine learning has been utilized in bio-imaging in recent years, however as it is relatively new and evolving, some researchers who wish to utilize machine learning tools have limited access because of a lack of programming knowledge. In electron microscopy (EM), immunogold labeling is commonly used to identify the target proteins, however the manual annotation of the gold particles in the images is a time-consuming and laborious process. Conventional image processing tools could provide semi-automated annotation, but those require that users make manual adjustments for every step of the analysis. To create a new high-throughput image analysis tool for immuno-EM, I developed a deep learning pipeline that was designed to deliver a completely automated annotation of immunogold particles in EM images. The program was made accessible for users without prior programming experience and was also expanded to be used on different types of immuno-EM images.

This study established a lateral flow competitive immunoassay with a colloidal gold-monoclonal antibody probe for the qualitative detection of sailfish serum albumin. The test involves mixing a tissue homogenate with a colloidal gold-monoclonal antibody suspension and applying the mixture to a strip of plastic-backed nitrocellulose membrane. The presence of albumin in a target sample competed with adsorbed antigen and prevented the appearance of a pink color on the nitrocellulose membrane. A non-target sample yielded a pink color when gold-labeled monoclonal antibodies bound to sailfish albumin previously absorbed to the nitrocellulose. Three gold particle sizes for antibody conjugation were evaluated and of these, 41 nm was optimal. The optimal pH for conjugation of anti-sailfish antibody to colloidal gold was 7.0. The assay requires only five minutes to perform and utilizes two solutions.