| People | Locations | Statistics |
|---|---|---|
| Naji, M. |
| |
| Motta, Antonella |
| |
| Aletan, Dirar |
| |
| Mohamed, Tarek |
| |
| Ertürk, Emre |
| |
| Taccardi, Nicola |
| |
| Kononenko, Denys |
| |
| Petrov, R. H. | Madrid |
|
| Alshaaer, Mazen | Brussels |
|
| Bih, L. |
| |
| Casati, R. |
| |
| Muller, Hermance |
| |
| Kočí, Jan | Prague |
|
| Šuljagić, Marija |
| |
| Kalteremidou, Kalliopi-Artemi | Brussels |
|
| Azam, Siraj |
| |
| Ospanova, Alyiya |
| |
| Blanpain, Bart |
| |
| Ali, M. A. |
| |
| Popa, V. |
| |
| Rančić, M. |
| |
| Ollier, Nadège |
| |
| Azevedo, Nuno Monteiro |
| |
| Landes, Michael |
| |
| Rignanese, Gian-Marco |
|
Pigram, Paul
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (10/10 displayed)
- 2023New insight into degradation mechanisms of conductive and thermally resistant polyaniline filmscitations
- 2023Comparison of Tiling Artifact Removal Methods in Secondary Ion Mass Spectrometry Imagescitations
- 2023Two-Dimensional and Three-Dimensional Time-of-Flight Secondary Ion Mass Spectrometry Image Feature Extraction Using a Spatially Aware Convolutional Autoencodercitations
- 2023Exploring the Relationship between Polymer Surface Chemistry and Bacterial Attachment Using ToF‐SIMS and Self‐Organizing mapscitations
- 2022Applications of multivariate analysis and unsupervised machine learning to ToF-SIMS images of organic, bioorganic, and biological systems
- 2020ToF-SIMS and machine learning for single-pixel molecular discrimination of an acrylate polymer microarray
- 2020Analyzing 3D Hyperspectral ToF-SIMS Depth Profile Data Using Self-Organizing Map-Relational Perspective Mappingcitations
- 2018Distinguishing chemically similar polyamide materials with ToF-SIMS using self-organizing maps and a universal data matrixcitations
- 2017Determining the limit of detection of surface bound antibodycitations
- 2016Chromium functionalized diglyme plasma polymer coating enhances enzyme-linked immunosorbent assay performancecitations
Places of action
| Organizations | Location | People |
|---|
article
Determining the limit of detection of surface bound antibody
Abstract
Determination of a limit of detection (LoD) for surface bound antibodies is crucial for the development and deployment of sensitive bioassays. The measurement of very low concentrations of surface bound antibodies is also important in the manufacturing of pharmaceutical products such as antibody-conjugated pharmaceuticals. Low concentrations are required to avoid an immune response from the target host. Enzyme-linked immunosorbent assay (ELISA), x-ray photoelectron spectroscopy (XPS), and time-of-flight secondary ion mass spectrometry (ToF-SIMS) were used to determine the LoD for the surface bound antibody (antiepidermal growth factor receptor antibody) on silicon substrates. Antibody solution concentrations between 10 μg/ml and 1 ng/ml and a control (antibody-free buffer solution) were employed, and the detection performance of each technique was compared. For this system, the ELISA LoD was 100 ng/ml and the XPS LoD was 1 μg/ml, corresponding to an estimated surface concentration of 49 ± 7 ng/cm2 using a 1 μg/ml solution. Due to the multivariate complexity of ToF-SIMS data, analysis was carried out using three different methods, peak ratio calculations, principal component analysis, and artificial neural network analysis. The use of multivariate analysis with this dataset offers an unbiased analytical approach based on the peaks selected from ToF-SIMS data. The results estimate a ToF-SIMS LoD between applied antibody concentrations of 10 and 100 ng/mL. For surface bound antibodies on a silicon substrate, the LoD is below an estimated surface concentration of 49 ng/cm2. The authors have determined the LoD for this system using ELISA, XPS, and ToF-SIMS with multivariate analyses, with ToF-SIMS offering an order of magnitude better detection over ELISA and 2 orders of magnitude better detection over XPS