Facilitating the Validation of Novel Protein Biomarkers for Dementia: An Optimal Workflow for the Development of Sandwich Immunoassays

Abstract

Different neurodegenerative disorders, such as Alzheimer's disease (AD) and frontotemporal dementia (FTD), lead to dementia syndromes. Dementia will pose a huge impact on society and thus it is essential to develop novel tools that are able to detect the earliest, most sensitive, discriminative, and dynamic biomarkers for each of the disorders. To date, the most common assays used in large-scale protein biomarker analysis are enzyme-linked immunosorbent assays (ELISA), such as the sandwich immunoassays, which are sensitive, practical, and easily implemented. However, due to the novelty of many candidate biomarkers identified during proteomics screening, such assays or the antibodies that specifically recognize the desired marker are often not available. The development and optimization of a new ELISA should be carried out with considerable caution since a poor planning can be costly, ineffective, time consuming, and it may lead to a misinterpretation of the findings. Previous guidelines described either the overall biomarker development in more general terms (i.e., the process from biomarker discovery to validation) or the specific steps of performing an ELISA procedure. However, a workflow describing and guiding the main issues in the development of a novel ELISA is missing. Here, we describe a specific and detailed workflow to develop and validate new ELISA for a successful and reliable validation of novel dementia biomarkers. The proposed workflow highlights the main issues in the development of an ELISA and covers several critical aspects, including production, screening, and selection of specific antibodies until optimal fine-tuning of the assay. Although these recommendations are designed to analyze novel biomarkers for dementia in cerebrospinal fluid, they are generally applicable for the development of immunoassays for biomarkers in other human body fluids or tissues. This workflow is designed to maximize the quality of the developed ELISA using a time- and cost-efficient strategy. This will facilitate the validation of the dementia biomarker candidates ultimately allowing accurate diagnostic conclusions.

Keywords: AD; CSF; ELISA; FTD; dementia; guidelines; novel biomarkers; workflow.

Figure 1

Pipeline reflecting the development on novel protein biomarker candidates. Biomarker development pipeline is divided into four main phases. Biomarker development starts with a low throughput screening of samples in the unbiased phase to a high throughput analysis in the latest clinical validation stage, where hundreds to thousands of samples are evaluated for the clinical assessment of the biomarker candidate. “Analytes” and “Samples” refer to the number of different protein targets or samples, respectively, that are evaluated in each phase. LC-MS/MS, liquid chromatography tandem mass spectrometry; MRM, multiple reaction monitoring; IHC, immunohistochemistry; WB, Western blotting. Figure adapted from Rifai et al. (11).

Figure 2

Recommended workflow for the development of a novel ELISA. Workflow to facilitate the development and analytical validation of assays for the validation of novel biomarker candidates. The process is divided into four different steps (orange rectangles). In each step, different analyses are performed (dark gray rectangles) and specific questions are addressed before moving into the next phase (light gray circles). When the different criteria in a specific phase cannot be reached, changes should be performed one phase back. If a specific ELISA is already available, it should undergo a validation process for the targeted matrix (step 3). JPND-BIOMARKAPD guidelines are published in this special issue by Andreasson and colleagues.

Figure 3

Antibody design and production. (A) Schematic representation of a protein amino acid sequence in denatured/reducing conditions (upper) or in its native form (bottom). Green amino acids represent the peptides recognized by unbiased proteomics in the discovery phase. Antibodies (Y symbol), glycol groups (black dots), and disulfide bonds (dotted line) are also represented. Specific epitopes detected in the unbiased proteomics approach might be available in denatured conditions but might be masked in native conditions. (B) Graphical representation of a transmembrane protein using Protter (20), in which all protein characteristics are included. (C) Time-line differences in the production of polyclonal and monoclonal antibodies.

Figure 4

Recommended experiments during antibody testing for an optimal selection of antibodies. (A) Example of a dot blot against CSF, human brain, or antigenic peptide using two antibodies recognizing two different epitopes of the same protein at different concentrations. (B) Two different samples analyzed by Western blot under reducing (+DTT) and non-reducing (−DTT) conditions and detected by two antibodies (Ab1 and Ab2) detecting two different epitopes of the same protein. Only Ab 2 can detect the protein of interest in CSF in native conditions. (C) Schematic representation exemplifying a checkerboard titration to test different antibody pairs for ELISA development. In this set-up, one antibody (Ab1) is used as capture antibody in three different concentrations (2 and 1 μg/mL and no capture antibody (−)). Four different antibodies are tested as detection antibodies (Ab 2–5) in four different dilutions (1:100, 1:1000, 1:5000, and no detection antibody (−)). A fixed concentration of the standard protein/peptide is used in every well (i.e., 0.5 μg/mL). The best combination will be the one giving the highest signal using the lowest amount of antibody (higher dilution) and with the lowest background (signal when no antibody is used).