Serum antibodies are an important class of biomarkers. In clinical medicine, specific antibodies are commonly monitored for the diagnosis of diseases such as HIV, malaria, and autoimmune diseases. Antibodies are also an important element of an immune response and are routinely used to evaluate responses to vaccines, clinical procedures, and other treatments. In basic and preclinical research, serum antibody profiling can facilitate the discovery of new antigens and guide vaccine design. A variety of high-throughput approaches, including SEREX,1
phage display,2, 3
and peptide/protein arrays,4, 5
have been developed to identify disease specific antibodies to protein and peptide antigens from the repertoire of antibodies present in serum. Carbohydrates are an equally important, though largely underappreciated, class of antigens. Antibodies to carbohydrates play a direct role in many clinical areas including blood transfusions, organ transplants, and responses to vaccines. Antibodies to carbohydrates also have tremendous potential as diagnostic and prognostic markers (for some recent examples see 6-9
). Traditional methods used to study carbohydrate-protein interactions, however, can be slow and/or require large amounts of material. For this reason, most studies have been restricted to a small number of carbohydrate antigens that are readily available in large quantities.
Recently, carbohydrate arrays (also referred to as glycan arrays) have emerged as powerful tools for evaluating binding of lectins, monoclonal antibodies, whole cells, viruses, and serum antibodies to carbohydrates.10-13
Carbohydrate arrays are analogous to protein and DNA arrays but contain many different glycans immobilized on a solid support in a spatially defined arrangement. The microarray format permits analysis of many potential binding interactions while using only minimal amounts of material. In recent years, carbohydrate arrays have seen applications in comparative human serum profiling.14-20
These studies use carbohydrate arrays to screen for differences in the serum antibody profiles between groups of individuals or within individuals who have undergone some treatment or exposure.
Three key issues for the successful application of carbohydrate microarray technology to serum profiling can be identified. First, it is essential to determine the technical/experimental variability of the assay. Without this information, one cannot determine whether observed changes or differences are real or artifactual. Microarray profiling is highly susceptible to experimental variability, and this issue is especially important for carbohydrate microarrays since they are a newer technology with limited validation. Most of the reports on glycan array reproducibility have focused on purified lectins or monoclonal antibodies and their binding to a small subset of array components. Human serum, however, contains a complex mixture of proteins, small molecules, and non-carbohydrate antibodies, which all contribute to variability and noise. For this reason, it is critical to assess variability with the target sample, human serum. To date, only one report regarding reproducibility with serum has been published for a carbohydrate array.21
In the study, the authors reported variability of signals for one component, Rha-α, using a single pooled serum sample of 10 individuals. Coefficient of variation (CV) values of 7-8% and 21-22% were reported for spot-to-spot and inter-slide measurements, respectively. It is not clear that reproducibility measurements for one component are applicable to other array components. Moreover, variability is typically highest between batches of array slides. No information was reported regarding batch-to-batch variability. Since variability may also depend on the serum sample, ideally one should test multiple serum samples and examine variability for as many array components as possible. Therefore, more comprehensive and systematic studies on bias, reproducibility, and the relationship between variability and carbohydrate structure for assays with serum are needed.
The second key issue is understanding biological variability. Information regarding the natural range and distribution of antibodies within a group of subjects is crucial for identifying disease related antibodies/profiles as biomarkers and for selecting appropriate control groups. Assessment is usually carried out by conducting pilot studies on sample groups, often comprised of healthy donors. For human serum profiling, many factors such as differences in lifestyle, exposure to pathogens, genetics, health, race, sex, age, and blood type could contribute to biological variability. Several research groups have conducted pilot studies involving evaluating human serum from healthy individuals on a carbohydrate array.15, 16, 21-24
However, the published studies have involved a limited number of serum samples, a limited number of carbohydrate antigens, or both. Furthermore, little or no information was provided regarding the donors. For example, 10 human serum samples were evaluated on a carbohydrate array of about 200 carbohydrates.22
The authors found antibody levels were “remarkably consistent among samples from 10 individuals”; however, it was not clear if a larger, more diverse set of serum samples would show similar consistency. Other studies have made use of pooled samples in evaluating anti-carbohydrate antibody levels in human serum.23, 25
Although convenient, pooling can obscure actual biological variance within the group. For example, individuals typically have high or low levels of antibodies to blood group A or B oligosaccharides depending on their blood type. A pooled sample, however, has an intermediate level of antibodies for both blood group antigens.
Finally, it is important to measure variations in the antibody levels in a healthy individual that occur over time and determine whether any differences observed are carbohydrate dependent. This is especially relevant when studying changes in an individual resulting from exposure to a pathogen, development of a disease, or response to treatment or immunization. One study aimed at this objective has been published. Dotan and coworkers23
report a longitudinal study showing anti-carbohydrate antibody profiles for 3 carbohydrate antigens in 7 individuals. With this small set of antigens, it is difficult to gain a broad view of temporal fluctuations. Furthermore, little or no information was reported regarding reproducibility, bias, and normalization in the context of assaying human serum.
We have previously described the development of a carbohydrate microarray in our laboratory.26-32
The array used for this study contained 128 different components including 98 structurally defined synthetic carbohydrates [as bovine serum albumin (BSA)/human serum albumin (HSA) conjugates], 24 natural glycoproteins, and 6 controls (see Table S1 in Supporting information
for a detailed list). Herein, we describe a systematic evaluation of experimental variability for our carbohydrate antigen microarray and design and implement an optimized assay, normalization method, and data processing protocol for obtaining reproducible measurements when conducting human serum profiling. We evaluate serum samples from a set of 48 healthy donors against all 122 antigens on the carbohydrate array. Associations between serum antibody levels and serum donor characteristics such as race, sex, blood type, age, and geographical location are evaluated. We also evaluate intra-individual variability of anti-carbohydrate antibody levels for 122 carbohydrate antigens in 7 healthy adults over periods ranging from 3 to 13 weeks, which allowed us to ascertain whether antibody levels are subject to temporal variations within this time frame.