Mass spectrometry based qualification of antibodies for plasma proteomics

There is a strong need for procedures that enable context and application dependent validation of antibodies. Here we describe a high-throughput approach for the detailed assessment of the selectivity of antibodies in plasma by PLasma Immunocapture Mass Spectrometry (PLIMS). The utility of PLIMS is demonstrated by determining the enrichment profiles of 157 antibodies targeting 120 proteins in EDTA plasma. Applying four classification categories (ON-target, CO-target, OFF-target and NO-target), it was found that 60% (44/60) of antibodies directed against denoted plasma proteins qualified for plasma assays. Among these, 85% (60/71) co-enriched another protein besides their respective target. As shown for several antibodies against IGFBP2, PLIMS was furthermore capable to describe known and explore novel protein complexes in plasma. In summary, PLIMS provides detailed insights into antibody selectivity in the context of plasma, thus will contribute as a valuable procedure towards to the generation of more reliable affinity-based plasma proteomics data.

MS, the rate increased to 59% ( Table 1). It is noteworthy that failed qualification could be due to limited affinity of the antibody, limited assay sensitivity or absence of the target in the used pools of plasma derived from healthy donors.
Current options for assessing antibodies for plasma assays can include protein arrays (12) or Western blot (WB) (13). For both assays, a surplus of antibodies is diluted in a solution and applied onto supports that present the antigens. Hence, there is no competition for binding sites between potential on-and off-targets. The composition of plasma (90% of protein content is assigned to 20 proteins) further poses a challenge for WB in terms of resolution in terms of separation efficiency. Nevertheless, we compared the classifications obtained with PLIMS and WB and found that the assessment of 13 out of 104 antibodies (12%) provided supportive evidence by both methods (Fig. 1 d-e, Table 2). For antibodies raised against plasma proteins, the success rates for PLIMS (53%) was though higher than for WB (32%) ( Table 2). When considering cellular proteins, the success rates were more similar: 35% for PLIMS and 31% for WB. For WB however, uncertainty does remain. Even bands detected at the predicted molecular weight could still represent the recognition of off-targets. Consequently, PLIMS provides an unequivocal identification of the target and could elucidate ambiguous WB results (see C1orf64, CEP162, E2F7, Supplementary Excel Table). Moreover, we found application dependent recognition for five antibodies generated for IL6R. All five were classified as target-specific using protein arrays, however only three detected IL6R in plasma (Supplementary Excel Table).
Acknowledging the requirements of identifying high-responding peptides for the MS analysis, the demand on instrumentation infrastructure and data analysis, as well as increased consumption of antibody (1 µg) and sample (100 µl) in per PLIMS assay, our data shows that PLIMS does provide more detailed and reliable information than WB.
Applying immuno-capture before MS analysis has been reported to improve the sensitivity of protein quantification (14)(15)(16). Hence, PLIMS may also be used to qualify antibodies for lower abundant proteins, including those that presently remain undetectable for other MS protocols. With PLIMS, 9 extracellular proteins (e.g. CXCL8, TGFA, BDNF) and 18 cellular proteins (e.g. S100PBP, CASP2, STIM1) were detected that were not found in the PaxDB plasma integrated dataset (Fig. 1e, Table). Peptides from such findings can now be applied to develop targeted MS assays (SRM or PRM). For almost 50 % of the polyclonal antibodies validated in our studies, the identified peptides aligned with the sequence of the protein fragments used to generate the antibodies ( Supplementary Fig. 2). This will allow to use isotope labelled versions of these fragments as standards for quantification (17). Such targeted MS assays can indeed serve as cross-platform validation method of antibody based discoveries.

Supplementary Excel
Besides evaluating antibodies in terms of on-target selectivity, we observed the possibility to use PLIMS to study co-enrichments. Antibodies for CCL16 (HPA042909) and SERPINA4 (HPA002869) also enriched CCL18 and SERPINA6 besides intended targets, respectively. Both co-targets belong to the same family as the expected target and share a high sequence homology (Supplementary note 2).
For IGFBP2, three antibodies were tested (HPA077723, HPA045140, HPA004754) of which the latter two were raised against the same antigen. As shown in Fig. 2b and PLIMS now provides supportive evidence in plasma (Supplementary Fig. 6). For the third binder (HPA004754), IFGBP2 was only enriched upon prior heat treatment plasma (Fig. 2b). The differential performance of polyclonal antibodies raised against the same antigen (HPA045140, HPA004754) confirms the necessity to investigate each of the different batches and lots of antibodies. For CDL5, evidence for known (IgM) and new (C1orf64 with Calm1) components of protein complexes (Supplementary Table 2), with PLIMS indicating an interaction between CD5L and IgM's J-chain (20).
In summary, PIMS enabled the systematic assessment of 157 antibodies, of which 74 antibodies were validated for plasma assays. We included 127 antibodies targeting proteins with a previously described disease association, and using PLIMS, we detected analytes circulating at low abundance as well as those found in complexes. We purpose to use PLIMS and the classification scheme as a standard approach for the assessment and selection of antibodies for proteomics assays in plasma.
Appropriate validation schemes need to apply experimental condition resembling those of the intended application. With a context and assay dependent selectivity of antibodies, PLIMS provides the required detailed insights when choosing antibodies for development the development of solid phase immunoassay as well as when assessing affinity reagents emerging from highly multiplexed screening approaches. The large number of proposed candidate biomarkers that, however, did not reach a clinical highlights again the need to devote more attention to validation(21). Lack in robustness of the analytical method is one the major pitfalls that makes it difficult to proceed from discovery to targeted validation. PLIMS can serve as an important tool to qualify affinity reagents for their use in plasma proteomics and empowers the development and application of specific, robust and reliable immunoassays(1).  Antibodies were covalently coupled to magnetic beads and processed with plasma from healthy donors using an automated bead handler. The captured proteins were digested and analyzed using a Thermo Q-Exactive HF mass spectrometer. Raw data files were searched and normalized using MaxLFQ. A zscore was generated for every protein and antibody to rank proteins specifically enriched by their respective antibody. The classification of the antibodies was based on their enrichment profiles using the z-score > 3 as cut-off.as: (1)

Figure 2. Enrichment profiles.
(a) The plots depict the z-score (x-axis) and LFQ-intensity (y-axis) to illustrate the four enrichment categories specificity, selectivity and protein complexes. ON-target, CO-target, OFF-target, and NOtarget. Each plot states the intended target protein, antibody ID and the number of replicates of PLIMS assays that were performed. The dots in each plot represent those identifications that were present in all the replicated assays for a respective antibody. Green dots refer to those proteins that were enriched with z-score >5 and peptides detected with LFQ intensity levels > 1e+07. Orange dots refer to proteins enriched with a z-score >3 and LFQ intensity > 1e+07. Red dots refer to those proteins with z-score >3 but a lower LFQ intensity < 1e+07. Text labels denote the expected target and additionally identified targets. A complete list of proteins identified and relative z-scores are available in Supplementary Excel Table. (b) Three antibodies raised against IGFBP2 (HPA077723, HPA045140, HPA004754) were investigated for their enrichment profile in plasma. Using PLIMS, these binders reveal co-target enrichments of IGF1 and IGF2. HPA004754 did reveal a concordant co-target enrichment when using heat treated plasma (56°C for 30 min), and IGF2 was identified in two isoforms: P01344-02 with a zscore = 11.0, and P01344 (indicated as *IGF2) with a z-score = 8.4. HPA077723 and HPA004754 also shared BCHE as co-target. Green circles underline those proteins that were co-enriched alongside IGFBP2. NH refers to no heat treatment, H refers to heat treatment.

Sample collection
Human EDTA plasma from healthy individuals (50% females) was obtained from Seralab (Sera Laboratories International Ltd). Aliquots of plasma (0.5 mL) were stored in cryogenic vials at -80 ºC and thawed at 4 ºC before use.

Target genes selection
Information about target proteins their functions and involvement in diseases were collected through literature  Atlas. In addition 3 normal IgG pools from rabbit (Bethyl Laboratories),mouse and rat (both Santa Cruz Biotechnology) were included as negative controls.

Antibody coupling to magnetic beads
Covalent coupling of antibody to magnetic beads (MagPlex, Luminex Corp.) was performed as previously described (22). Briefly antibodies are cross-linked to carboxylated beads through a sulfo-NHS (sulfo-NHS (n-hydroxysulfosuccinimide, Thermo) plus EDC (carbodiimide) reaction (EDC 10 mg, Thermo). After beads activation, antibodies diluted in MES buffer are incubated two hours at room temperature then washed and stored in blocking buffer at 4 ºC.

Immunocapture-mass spectrometry
Aliquots of EDTA plasma were diluted in assay buffer: 0.5% w/v PVA (

LC-MS/MS
MS analysis was performed using a Q-Exactive HF (Thermo) operated in a data dependent mode, equipped with an Ultimate 3000 RSLC nanosystem (Dionex). Samples were injected into a C18 guard desalting column and then into a 50 cm x 75μm ID Easy spray analytical column packed with 2μm C18 (Thermo) for RPLC. Elution was performed in a linear gradient of Buffer B (90% ACN, 5% DMSO, 0.1% FA) from 3 % to 43% in 50 min at 250 nL/min. Buffer B was step increased to 45% in 5 min and to 99% in 2 minutes and then hold for 10 minutes. Buffer A for the chromatography was added of DMSO (90% water, 5% ACN, 5% DMSO, 0.1% FA). Full MS scan (300-1600 m/z) proceeded at resolution of 60,000. Precursors were isolated with a width of 2 m/z and put on the exclusion list for 60 s. The top five most abundant ions were selected for higher energy collision dissociation (HCD). Single and unassigned charge states were rejected from precursor selection. In MS/MS, a max ion injection time of 250 ms and AGC target of 1E5 were applied.

Data analysis
Shotgun MS data search was performed on MaxQuant(23) using the integrated algorithm MaxLFQ. Data analysis and representation was performed on the environment for statistical computing and graphics R(24) using "ggplot2", "matrixStats","pheatmap". Alignments between protein and prEST sequences was performed using the Clustal Omega program available at EMBL-EBI 4.

Funding from the Swedish Foundation for Strategic Research, Swedish Cancer Society and Swedish
Research Council is gratefully acknowledged.

COMPETING FINANCIAL INTERESTS
The authors have on conflicts of interest.

Selection of antibodies
MaxQuant LFQ and z-score ranking