METHODS TO ASSESS BINDING AGENT SPECIFICITY

Abstract

The present invention relates to methods for assessing binding agent specificity, in particular antibody specificity. The present invention thus provides a method of analysing a mixture of polypeptides comprising the steps of: (i) separating the polypeptides in the mixture into a plurality of fractions; (ii) contacting a first aliquot of two or more of the fractions with a plurality of different binding agents attached to one or more solid supports and detecting the binding of the polypeptides to the binding agents in each fraction; (iii) assessing the amino acid composition of the polypeptides in a second aliquot of said fractions by mass spectrometry; and (iv) correlating the binding results detected in step (ii) and the mass spectrometry results from step (iii) to assess the specificity of the binding agents for a polypeptide of interest.

Claims

1. A method of analysing a mixture of polypeptides comprising the steps of: (i) separating the polypeptides in the mixture into a plurality of fractions; (ii) contacting a first aliquot of two or more of the fractions with a plurality of different binding agents attached to one or more solid supports and detecting the binding of the polypeptides to the binding agents in each fraction; (iii) assessing the amino acid composition of the polypeptides in a second aliquot of said fractions by mass spectrometry; and (iv) correlating the binding results detected in step (ii) and the mass spectrometry results from step (iii) to assess the specificity of the binding agents for a polypeptide of interest.

2. The method of claim 1 further comprising the steps of: (v) determining one or more fractions which are enriched for a particular polypeptide of interest; (vi) contacting the one or more fractions with a binding agent to said polypeptide of interest attached to one or more solid supports; (vii) disrupting the binding agents of step (vi) from the associated polypeptides; and (viii) contacting the released polypeptides with a plurality of binding agents attached to one or more solid supports and detecting the binding of the polypeptides to the binding agents.

3. The method of claim 1 further comprising the steps of: (v) determining one or more fractions which are enriched for a particular polypeptide of interest; (vi) contacting the one or more fractions with a binding agent to the polypeptide of interest attached to one or more solid supports; (vii) disrupting the binding agents of step (vi) from the associated polypeptides; (viii) contacting the released polypeptides with a soluble binding agent that binds specifically to a first epitope on the polypeptide of interest; and (ix) contacting the polypeptides bound to said soluble binding agent with a plurality of binding agents attached to one or more solid supports and detecting the binding of the binding agents attached to the one or more solid supports to the polypeptides of interest.

4. The method of claim 1 further comprising the steps of: (v) determining one or more fractions which are enriched for a particular polypeptide of interest; (vi) contacting the one or more fractions with a binding agent to the polypeptide of interest attached to one or more solid supports; (vii) disrupting the binding agents of step (vi) from the associated polypeptides; and (viii) assessing the amino acid composition of the released polypeptides by mass spectrometry (MS).

5. The method of claim 4, wherein the disruption step (vii) is carried out by treating the solid support with a proteolytic enzyme to generate peptides that can be analysed by MS.

6. The method of claim 4, wherein the MS analysis is multiplexed using addressable bar codes, preferably where the addressable bar code is a stable isotope or is a physical parameter specific for proteins in a certain fraction.

7. The method of claim 1, wherein the separation step (i) is comprised of the following steps: (i.a) separation of polypeptides in the mixture into a plurality of fractions; (i.b) contacting a first aliquot of two or more of the fractions with a plurality of different binding agents attached to one or more solid supports and detecting the binding of the polypeptides to the binding agents in each fraction; (i.c) determining one or more fractions which are enriched for a particular polypeptide of interest; (i.d) separating the enriched fractions into a plurality of fractions.

8. The method of claim 7 where the steps in claim 7 are repeated one or more times.

9. The method of claim 2 wherein the binding agents of step (vii) are disrupted from the associated polypeptides using successive solutions with increasing stringency.

10. The method of claim 2 wherein the disruption of step (vii) is carried out using a nonionic surfactant, preferably a polysorbate-type non-ionic surfactant, more preferably polysorbate 20.

11. The method of claim 10 wherein a further or second disruption is carried out at step (vii) using an anionic surfactant, preferably an organosulphate surfactant, more preferably sodium dodecyl sulphate.

12. The method of claim 1 further comprising carrying out steps (i) to (iv) in respect of one or more further mixtures of polypeptides, preferably one or more further cell types.

13. The method of claim 1 wherein step (i) comprises separating the polypeptides on the basis of one or more physical parameters and/or subcellular locations and/or mixtures of polypeptides.

14. The method of claim 13 wherein the one or more physical parameters are selected from the list consisting of differential mass, acidity, basicity, charge, hydrophobicity and binding to different affinity ligands.

15. The method of claim 1 wherein step (i) is carried out using one or more techniques selected from the list consisting of gel electrophoresis, size exclusion chromatography, liquid chromatography, dialysis, filtration, ion exchange separation and iso-electric focusing.

16. (canceled)

17. The method of claim 1 wherein the binding agent of step (ii) is selected from the list consisting of antibodies or antigen-binding fragments thereof, aptamers or other nucleic acid based binding agents, affibodies, polypeptides, peptides, oligonucleotides, T-cell receptors, MHC molecules and mixtures thereof.

18. The method of claim 2 wherein the binding agent of any one of steps (vi) or (viii) is selected from the list consisting of antibodies or antigen-binding fragments thereof, aptamers or other nucleic acid based binding agents, affibodies, polypeptides, peptides, oligonucleotides, T-cell receptors, MHC molecules and mixtures thereof.

19. The method of claim 1 wherein the step (i) comprises separating the polypeptides in the mixture into at least four fractions, preferably at least twelve fractions, more preferably at least twenty four fractions, more preferably at least forty eight fractions, more preferably at least ninety six fractions, more preferably at least 200 fractions.

20. The method of claim 1 wherein the binding agents attached to one or more solid supports are attached in an array on the surface of one or more planar substrates and/or a planar substrate comprising three-dimensional surface structures.

21. The method of claim 1 wherein the binding agents are attached to a plurality of particles, each particle having attached thereon multiple copies of the same binding agent.

22. The method of claim 21 wherein a first set of particles having attached thereon multiple copies of the same binding agent have a different detectable feature from a further set of particles having multiple copies of a binding agent that are different to those attached to the first set of particles.

23. The method of claim 22 wherein the detectable feature is based on fluorescence, isotopes, preferably radioactive isotopes or non-radioactive (stable) isotopes, luminescence, size or acoustic properties.

24. The method of claim 23 wherein the detectable feature is in the form of at least one type of dye molecule attached to the particle, preferably at least three types of dye molecules attached to the particle.

25-27. (canceled)

28. The method of claim 1 further comprising attaching at least one label to the mixture of polypeptides or the one or more further mixtures of polypeptides.

29. The method of claim 28 wherein the step of attaching the label or labels to the mixture of polypeptides or the one or more further mixtures of polypeptides is carried out prior to step (i) or after step (i).

30. The method of claim 28 wherein a different label is attached to the mixture of polypeptides or the one or more further mixtures of polypeptides of each fraction.

31. The method of claim 28 wherein the label is attached to the polypeptides via a peptide, a polypeptide, an oligonucleotide, or an enzyme substrate.

32. The method of claim 28 wherein the or each label is selected from the list consisting of a hapten, a fluorescent dye, a luminescent dye, a radioactive isotope, a non-radioactive isotope and a mixture thereof.

33. The method of claim 32 wherein the hapten is biotin or digoxigenin.

34. The method of claim 1 wherein step (iv) is carried out by determining the correlation between the binding results of step (ii) in a chosen set of fractions and the MS results of step (iii) in the same fractions; or wherein step (iv) is carried out by measuring the overlap between the binding results of step (ii) and the MS results of step (iii).

35. The method of claim 1, wherein the binding results of step (ii) in a chosen set of fractions and the MS results of step (iii) in the same fractions are in the form of sets of numerical data which are then correlated in step (iv).

36. The method of claim 1, wherein a correlation which is statistically significant with a probability of p<0.20, p<0.15, p<0.10 or p<0.05 is indicative of a binding agent that is specific for the polypeptide of interest.

37. The method of claim 1 wherein step (iv) comprises processing either the binding results of step (ii) and/or the MS results of step (iii) in order to make direct comparisons between the binding results and the MS results.

38. The method of claim 37 wherein the processing comprises (a) upscaling or downscaling the binding results of step (ii) so that they can be compared against the MS results of step (iii); (b) upscaling or downscaling the MS results of step (iii) so that they can be compared against the binding results of step (ii); or (c) upscaling or downscaling both the binding results of step (ii) and the MS results of step (iii) so that the results can be compared against one another.

39. The method of claim 38 wherein the upscaling and/or downscaling is carried out so that the maximum binding signal value with respect to either a series of fractions or all fractions analysed is the same as, or corresponds to, the maximum relative abundance with respect to either a series of fractions or all fractions analysed as determined by MS.

40. The method of claim 1, wherein step (iv) comprises the steps of: a) determining the relative abundance of the polypeptide of interest within each fraction from the mass spectrometry results from step (iii); b) plotting the binding signal intensity for a polypeptide binding to a specific binding agent detected in step (ii) against each fraction; c) overlaying the relative abundance data determined in step a) with the binding results of step b); and d) determining the level of overlap between the mass spectrometry results and the binding results; or wherein step (iv) comprises the steps of: a) determining the relative binding signal intensity for a polypeptide binding to a specific binding agent detected in step (ii) within each fraction; b) plotting the abundance of the polypeptide of interest within each fraction from the mass spectrometry results from step (iii) against each fraction; c) overlaying the relative binding signal intensity data determined in step a) with the abundance results of step b); and d) determining the level of overlap between the mass spectrometry results and the binding results.

41. The method of claim 34 wherein a correlation or level of overlap of more than 80%, preferably 85%, more preferably 90%, is indicative of a binding agent that is specific for the polypeptide of interest.

42. The method of claim 34 wherein step (i) forms one or more series of continuous fractions and wherein step (iv) further comprises calculating a wide index, wherein the wide index is calculated by a) determining the MS centre by determining the fraction with the highest signal intensity or abundance of the polypeptide of interest obtained from the MS data in relation to a series of fractions or in relation to all the fractions; b) calculating the sum of the binding signal intensity from the binding agent array analysis in step (ii) measured in the fraction corresponding to the MS centre and the two immediate neighbouring fractions on each side of the MS centre divided by the sum of the binding signal intensity measured in either a series of fractions or all fractions.

43. The method of claim 42, wherein a wide index of more than 0.70, preferably 0.80, more preferably 0.90 is indicative of a binding agent that is specific for the polypeptide of interest.

44. The method of claim 34 wherein step (i) forms one or more series of continuous fractions and wherein step (iv) further comprises calculating a core index, wherein the core index is calculated by: a) determining the MS centre by determining the fraction with the highest signal intensity or abundance of the polypeptide of interest obtained from the MS data in relation to a series of fractions or in relation to all the fractions; b) calculating the sum of the binding signal intensity from the binding agent array analysis in step (ii) measured in the fraction corresponding to the MS centre and the two immediate neighbouring fractions divided by the sum of the binding signal intensity measured in either a series of fractions or all fractions.

45. The method of claim 44, wherein a core index of more than 0.70, preferably 0.80, more preferably 0.90 is indicative of a binding agent that is specific for the polypeptide of interest.

46. The method of claim 34 wherein step (iv) further comprises calculating a signal index, wherein the signal index is calculated by dividing the maximal binding signal intensity from the binding agent array analysis in step (ii), taken from either a series of fraction or all analysed fractions, by the median binding signal intensity.

47. The method of claim 46 wherein a signal index of more than 3, preferably 4, more preferably 5, is indicative of a binding agent that has an adequate level of sensitivity.

48. The method of claim 34 wherein step (iv) further comprises determining the absolute signal intensity, wherein the absolute signal intensity is the maximal binding signal intensity from the binding agent array analysis measured in step (ii) for a particular binding agent.

49. The method of claim 48 wherein an absolute signal intensity of more than 1500, preferably 2500, more preferably 3500, is indicative of a binding agent that has an adequate level of sensitivity.

50. The method of claim 1 wherein the correlation is either carried out or determined using a computer algorithm.

51. The method of claim 1 wherein in step (iii) the amino acid sequences of the polypeptides is determined.

52. The method of claim 1 wherein the mass spectrometry carried out in step (iii) is liquid chromatography mass spectrometry.

53-56. (canceled)

57. The method of claim 1, further comprising the step of stable isotope metabolic labelling of cells prior to step (i).

58-59. (canceled)

Description

[0295] The invention will now be further described in the following Examples and with reference to the figures in which:

[0296] FIG. 1 A schematic of a preferred method of the present invention. Cells from eight different cell types (represented by the petri dishes A to H) are lysed, and soluble proteins in cell lysates are labelled with amine- or thiol-reactive derivatives of a hapten such as biotin. Unreacted biotin is removed through the use of centrifugation filter units. The proteins are then denatured and separated by gel electrophoresis. During a typical separation, twelve fractions from the eight different samples labelled A to H (in this case cell types) are harvested, and transferred to a 96 well microplate. A liquid handling robot is used for precise transfer of liquid fraction aliquots from the master plate to two replicate plates. One of these two is supplemented with bead-based antibody arrays (marked WMAP, which stands for Western Microsphere Affinity Proteomics, in the figure). The plate is kept at 4 to 8 C. at constant agitation overnight in order to allow binding of the antibodies to the proteins. The plate is next subjected to centrifugation to pellet the beads in order to remove unbound protein and resuspended in washing buffer. After two washes, fluorescent streptavidin is added so that the biotin label on the captured proteins can be detected and so that the beads with captured protein can be separated from beads without captured protein. Finally, the beads are analysed using a flow cytometer.

[0297] The second plate is processed for analysis of peptides by mass spectrometry (marked MS in the figure). The sample processing used here involves the addition of beads with immobilised streptavidin to all liquid fractions. Biotinylated proteins bind indiscriminately to the beads. The beads are washed in order to remove unbound proteins and treated with trypsin in order to obtain peptides useful for mass spectrometry and analysed by liquid chromatography mass spectrometry.

[0298] The approach described above yields two sets of numerical data. The MS data (dashed line) represent the reference for validation of antibody specificity with respect to one protein specifically. Multiple dashed lines may be formed with respect to the same protein in each different cell type (i.e. each mixture of polypeptides), see for example FIG. 2. The WMAP data is presented as a solid line. The graph presenting both the WMAP data and the MS data would be produced for each antibody in the antibody array (WMAP data line) and the corresponding protein of interest (MS data line for the target protein which should be bound by the antibody). The proportion of overlap (correlation) of the signal curves from the WMAP data with that of the MS data provides a measure of the specificity of the antibody for the protein of interest (the specificity index).

[0299] FIG. 2 Algorithm used to assess sensitivity and specificity of antibodies. Plots of binding signal (fluorescence) intensity (antibody array signal) derived from WMAP (solid lines) analysis of protein captured by an antibody to the protein Akt1, and of relative abundance of Akt1 derived from MS (dashed lines) (y axis) for each of twelve size fractions (x axis), obtained using the method as described in FIG. 1. The polypeptide of interest was Akt 1. Cell lysates obtained from three different cell types were analysed, RT4 cells (squares), U2OS cells (circles) and HeLa cells (triangles).

[0300] A computer algorithm was used to identify the fraction with the highest signal intensity measured by MS (in this case fraction 10, hereafter referred to as the MS centre). The algorithm next calculates several indexes based on the antibody signal. The core index is the sum of the binding signal intensity from the antibody array analysis (antibody or binding agent array signal) measured in the fraction corresponding to the MS centre and the two immediate neighbouring fractions, i.e. the fraction each side of the MS centre (in this case fractions 9 to 11) divided by the sum of signal measured in all twelve fractions (total signal). The wide index (width index) is the sum of the binding signal intensity (antibody array signal) measured in the two immediate neighbouring fractions on each side of the MS centre (in this case fractions 8 to 12) divided by the total signal. The fractions that form the core and wide areas are shown in FIG. 2A. The signal index is the maximal signal intensity (from antibody or binding array analysis) with respect to all fractions analysed (for this case for all cell-types analysed) divided by the median signal. Maximal and median signal intensities are shown in FIG. 2A. The absolute signal intensity is the value for the maximal signal intensity measured by binding agent (antibody) array analysis. Finally, the algorithm determines the overall correlation between the signal values obtained with antibody array analysis (antibody array signal) and MS (this overall correlation can be referred to as the specificity index).

[0301] The antibody analysed in FIG. 2A has a core index of 0.86, a width index of 0.89 and a specificity index (correlation) of 0.98. FIGS. 2B to 2D show results obtained with three different antibodies to Akt1 in the same antibody array. FIG. 2B shows an antibody with a strong signal (maximum or absolute median fluorescence intensity (MFI) of greater than 20,000), but low core and wide indexes (i.e. a broad peak). This indicates lower specificity (lower specificity index) than the antibody shown in FIG. 2A. FIGS. 2C and 2D show antibodies that have an absolute (or maximum) MFI of below 3000, and in addition FIG. 2C has an extra peak in fraction 6. The antibodies of FIGS. 2C and 2D therefore have a low core, wide and signal indexes.

[0302] FIG. 3 Examples of antibodies identified as specific or cross-reactive. Plots of binding signal intensity (antibody or binding agent array signal) derived from WMAP (solid lines) and of relative abundance derived from MS (dashed lines) (y axis) for each of twelve size fractions (x axis), obtained using the method as described in FIG. 1. The polypeptides of interest were RBL2 (FIGS. 3A and 3B, a 128 kDa polypeptide) and beta-actin (ACTB) (FIGS. 3C to 3E, a 41 kDa polypeptide). Each plot represents a different antibody to the appropriate polypeptide of interest. Cell lysates obtained from three different cell types were analysed, RT4 cells (squares), U2OS cells (circles) and HeLa cells (triangles). FIGS. 3A and 3C show cross-reactive antibodies, as little overlap is seen between the antibody reactivity profile (solid lines) and the MS profile (dashed lines). FIGS. 3B, 3D and 3E show specific antibodies with a high level of overlap in antibody reactivity profile (solid lines) and the MS profile (dashed lines), indicating antibodies that are specific for RBL2 or ACTB. However, the antibody of FIG. 3E has a low absolute signal intensity (MFI of less than 1500) and so it can be concluded that the antibody in FIG. 3D is more sensitive than the antibody shown in FIG. 3E.

[0303] FIG. 4 Massive parallel assessment of antibody performance. Heatmaps showing reactivity profiles of hundreds of antibodies across fractions obtained from primary T cells immediately after isolation from blood or after 24 or 48 hours of in vitro activation with the mitogen Concanavalin A. 272 antibodies were analysed in FIG. 4A and 93 antibodies were analysed in FIG. 4B. The proteins (y-axis) were sorted in ascending order (top-down) according to predicted mass. With this formatting the distribution pattern of the proteins in the map is predictable. Thus, the signal maximum (grey pixels) for the smallest proteins is expected to appear in the top left corner and the signal is expected to distribute along the diagonal to the bottom right with increasing protein mass. Corresponding heatmaps for results obtained for the antibody targets by MS are shown in the right half of each map. The map in FIG. 4A shows results obtained with strict criteria (threshold) for antibody validation, specifically a specificity index of greater than 80%, a signal index of greater than 4, an absolute signal intensity of greater than 5000 and a core index of greater than 0.7. The strict criteria in FIG. 4A is evident from the similarity between the antibody reactivity profiles and the target distribution profiles as measured by MS. The antibodies shown in FIG. 4B did not satisfy the criteria (threshold) used in A, but satisfied less strict criteria, specifically a specificity index of between 70 and 80%, a signal index of between 3 and 4, an absolute signal intensity of greater than 2000 and a core index of less than 0.7. The pattern of signal distribution is more complex than in FIG. 4A and less similar to the MS profiles.

[0304] FIG. 5 Targeted immunoprecipitation followed by antibody array analysis. Proteins from different subcellular compartments in CD4+ T cells were separated and analysed with antibody arrays and flow cytometry. The line charts (FIG. 5A) show signal from biotinylated protein captured by indicated specificity, i.e. anti-CD3e or anti-CD247 (y-axis, log scale) plotted against SEC fraction number (1 to 24). The sub-cellular locations analysed were (1) cytosol, (2) organelles, (3) nucleus and cytoskeleton and (4) membrane. Fractions containing high levels of membrane-associated targets for anti-CD3e and the associated protein CD247 (CD3zeta), were identified (longer arrows). Antibodies were then used to immunoprecipitate their respective targets from a separate aliquot of the fraction. After overnight incubation, the beads were first subjected to very mild elution conditions (1% Tween in phosphate buffered saline at 22 C., shaking for 30 minutes) and then to harsh elution (0.1% sodium dodcecyl sulfate solution at 95 C.). Eluted proteins were next analysed with bead-based antibody arrays (FIG. 5A, bottom right panel). The bar plots show signal intensity for the ten microsphere subsets with the highest signal with respect to CD247 capture (FIG. 5B) and CD3e capture (FIG. 5C) (log scale).

[0305] FIG. 6 Reactivity patterns of antibodies that passed or failed validation on basis of overlap in chromatograms. The heatmaps show binder chromatograms for antibodies (left half) alongside MS chromatograms for the intended antibody targets. Proteins from six cell lines (Jurkat, U2O5, HeLa, A431, RT4, MCF7) were labelled with biotin and separated by preparative gel electrophoresis (Gelfree-8100). Three gels with different separation ranges were used (5%, 8% or 10% acrylamide). The proteins were next analyzed as outlined in FIG. 1. The x- and y-axis in each map corresponds to Gelfree fraction number, and antibodies/proteins, respectively. The largest and smallest proteins appear at the top and bottom in each map, respectively. Since protein mass increases along the y-axis as well as with fraction number (x-axis), the expected pattern is a continuum of bands from the lower left to the upper right in each map. In FIG. 6A the map shows reactivity patterns of 1060 antibodies that passed criteria for signal to noise (signal index) and peak position (core index) set by a computer algorithm. The similarity between data obtained with antibodies and MS, respectively, can be noted. In contrast, FIG. 6B shows results obtained with antibodies that failed to meet the same criteria. The results obtained with these antibodies do not recapitulate the MS data.

[0306] FIG. 7 Reactivity patterns of antibodies that passed or failed validation on basis of overlap in chromatograms and correlation. The heatmaps show relative protein levels measured in six cell lines (same sequence as in FIG. 6) by antibody array analysis and MS, alongside transcriptomics data (mRNA) retrieved from two published datasets. The original data set contained 12 data points (from 12 fractions) per antibody (antibody data) and antibody target (MS data). Here, the sum of five data points centered around the maximum value were used to calculate a single value for protein abundance (a wide index). All antibodies shown in the figure passed criteria for signal to noise (signal index) ratio of 4 or more. The 302 antibodies in the top map also passed criteria for overlap with the MS chromatogram (4 median) as well as criteria for correlation between antibody and MS data (correlation of 0.7). The similarity in patterns observed for antibody array data (MAP) and MS data can be noted. It can also be noted that a similar pattern is observed for the mRNA data. The mRNA data represent an independent control since they were retrieved from an article published by a different laboratory. The lower heatmap was organized according to the relative abundance measured with antibodies in experiment 1. Part of the pattern was reproduced in experiment 2 (a replicate experiment). However, there is no corresponding pattern in the MS or mRNA data. The antibodies therefore failed validation.

[0307] FIG. 8. Correlation of results obtained with antibody array analysis and MS. The charts show signal intensity (y-axis, log scale) obtained with four different antibodies to CDKN1A (solid lines) plotted against fraction number (Gelfree preparative gel electrophoresis, 10% gel). The dashed lines show MS signal intensity for CDKN1A in the same fractions. Antibody 1 failed the criterion for sensitivity (signal index) since the strongest signal was less than four-fold higher than the median. Antibody 2 bound two targets, but passed the criterion for chromatogram overlap (peak position, core index) since the tallest antibody reactivity peak did not deviate by more than one fraction from the signal maximum of CDKN1A as determined by MS (dashed lines). However, antibody 2 failed to meet the specificity criteria since the correlation with MS data was lower than the threshold of 0.7 for the reactivity profile (all data points) and relative protein abundance (sum of datapoints in the wide index, corresponding to five datapoints centered around the maximum signal). Antibodies 3 and 4 passed all criteria. The correlation was higher than 0.9 which yields a statistical significance better than p=0.05 (see legend to FIG. 9).

[0308] FIG. 9 Assessment of significance of correlations. The heatmap in FIG. 9A shows 8901 MS chromatograms from two experiments. Six human cell lines were cultured in the presence of amino acids with stable isotopes. The cells were lysed, and proteins were labelled with biotin and separated by preparative gel electrophoresis. Labelled proteins in each lysate were separated using three gels with different separation range (5%, 8% or 10% acrylamide). The proteins were processed and analyzed by shotgun MS analysis as described in FIG. 1. The proteins in the dataset were sorted according to the type of gel used for separation and then in descending order according to predicted mass. To assess random correlation, the values in each row of data from each experiment were correlated to those in the row below. The line chart (FIG. 9B) shows frequency (y axis) of random correlations in datasets obtained by analyzing fractions obtained by gel electrophoresis by MS. Spreadsheet functions were used to determine the frequency of data series with indicated correlations in the datasets shown in FIG. 9A. The horizontal line indicates a significance of 0.05. Random correlations were determined by correlation of data in neighboring rows.

[0309] FIG. 10. Correlations for all data points measured across a series of fractions are more reproducible than correlations for relative protein abundance.

[0310] The dot plots show distribution of correlations between results obtained by antibody array analysis and MS in two experiments. Arrays with content of 2406 antibodies were used to analyze 12 fractions of cellular proteins obtained by gel electrophoresis. An aliquot of the same fractions were analyzed by shotgun MS. Two types of correlations were performed in each experiment: The MAP/MS profile correlation is the correlation of all signal values obtained with MAP and MS, respectively in fractions 1-12 (overall correlation). Relative protein abundance was measured as the sum of signal values in five fractions centered around the fraction with the maximal signal in fractions from each cell type (wide index). The R.sup.2 values represent squared Pearson correlations.

[0311] FIG. 11 Downstream analysis

[0312] The line plot shows signal intensity (y-axis, log scale) for beta-actin plotted against fraction number. Solid lines indicate streptavidin fluorescence intensity measured by antibody array analysis. Dashed lines show MS signal intensity measured for actin-beta in the same fractions. Jurkat cells were cultured in media containing isotope-labelled amino acids. The cells were lysed, and the proteins were labelled with biotin, denatured and separated according to size using a Gelfree 8100 instrument for preparative gel electrophoresis. Twelve fractions were incubated with a bead-based antibody array. The arrays were washed, labelled with fluorescent streptavidin and analyzed by flow cytometry. The plot shows signal intensity measured for a subset of beads coupled with anti-beta actin (ACTB). The strongest signal was observed in fraction 8 MS data confirmed that this was the fraction most highly enriched for beta-actin. Beads with anti-beta-actin were used to capture the antibody target from 0.1 ug of protein from fraction 8. The beads were subjected to on-bead trypsin digestion and the peptides were sequenced by MS. The bar graph in the lower left hand panel shows MS signal intensity for indicated proteins that contained isotope-labelled amino acids. The signal for beta-actin was almost hundred times higher than those measured for any other sample-derived protein. The bar graph in the lower right panel shows MS signal intensity for proteins that did not contain SILAC label. These proteins therefore represent contamination. Note that gamma actin (ACTG1) is on the list of contaminants. This protein is highly homologous to beta-actin, and if this protein was not identified as contamination, one would have falsely assumed that the anti-beta-actin antibody cross-reacted with gamma-actin.

[0313] FIG. 12 Downstream analysis

[0314] The line plot shows streptavidin fluorescence intensity (y-axis, log scale) plotted against fraction number. Jurkat cells were cultured in media containing isotope-labelled amino acids. The cells were lysed, and the proteins were labelled with biotin, denatured and separated according to size using a Gelfree 8100 instrument for preparative gel electrophoresis. Twelve fractions were incubated with a bead-based antibody array. The arrays were washed, labelled with fluorescent streptavidin and analyzed by flow cytometry. The plot shows signal intensity measured for a subset of beads coupled with anti-Rel A (RELA). The strongest signal was observed in fraction 8. Beads with anti-Rel A were used to capture the antibody target from 10 ug or 1 ug of protein from fraction 8. The beads were subjected to on-bead trypsin digestion, and the peptides were sequenced by MS. The bar graph in the lower left hand panel shows MS signal intensity for indicated proteins that contained isotope-labelled amino acids. When 1 ug of protein was used as source, RELA was the only protein detected. When 10 ug was used, there was also a signal from HSPA2, but the signal from RELA was more than 10 times stronger. The bar graph in the lower right hand panel shows MS signal for proteins without stable isotopes. These represent contamination. Many of these have far higher signal intensity than RelA, and several are proteins that are found in Jurkat cells. Without SILAC labeling it would therefore be difficult to exclude that they represent cross-reactivity of the RELA antibody.

EXAMPLES

General Materials and Methods

Covalent Coupling of Protein G and Fluorescent Dyes to Particles to Form Colour-Coded Particles

[0315] Polymer particles (6 or 8 m, PMMA, amine-functionalised, www.Bangslabs.com) were reacted with sulfo-SPDP (Sigma) (3 mg per gram of particles) at 10% solids in PBS 1 mM EDTA 1% Tween 20 (PBT) for 30 minutes at 22 C. under constant rotation. The particles were pelleted by centrifugation at 500 g for 5 minutes, washed once in PBT, and reduced with 5 mM TCEP (Sigma) for 20 minutes at 37 C. Particles were pelleted, washed once in 100 mM MES pH 5 (MES-5) and resuspended at 10% solids in MES-5. Protein G (Fitzgerald Industries) was dissolved at 5 mg/ml in PBS, reacted with 100 ug/ml Sulfo-SMCC (30 minutes, 22 C.) and transferred to MES-5 using G-50 spin columns. Two milligrams of protein G-SMCC was added per gram of particles under constant vortexing. After 30 minutes of rotation at 22 C., particles were resuspended in 100 mM MES pH 6 containing 1 mM EDTA 1% Tween 20 with 1 mM TCEP (MES-6-TCEP) and stored at 4 C. until labeling with fluorescent dyes. Particles were stable for several weeks in this buffer. Fluorescent labeling was performed by incubating equal aliquots of particles at 1% solids with a serially diluted fluorescent maleimide for 30 minutes at 22 C. Differently labeled aliquots were washed twice in MES-6-TCEP and split in new aliquots, each of which were reacted with different concentrations of the next dye. The sequence used here was Alexa 488, Alexa 647, Pacific blue (all in MES-6) and Pacific Orange (PBT). The starting concentrations were 50 ng/ml for Alexa 488 and Alexa 647, 25 ng/ml for Pacific Blue, and 500 ng/ml for Pacific Orange. The dilutions were between two and three-fold. This method enables populations of particles to be prepared, each with a different colour code that can be distinguished from each other for example by an appropriate flow cytometer.

Binding of Antibodies to Color-Coded Particles

[0316] Before coupling of antibodies, particles were suspended in PBS casein block buffer (www.piercenet.com) for 24 hours at 4 C. Polyclonal antibodies (2 g for 10 l of 10% bead suspension) were added to particles suspended in casein-PBS block buffer. The particles were rotated for 30 minutes at 22 C. Polyclonals from rabbit and goat can be coupled directly to particles with protein G. For binding of mouse monoclonal antibodies, particles were first reacted with subclass-specific goat-anti-mouse IgG Fc (Jackson Immunoresearch), then with the mAbs. After three washes in PBT, a small aliquot of all particles was added to a single vial and labeled with phycoerythrin (PE) conjugated anti-mouse, anti-rabbit and anti-goat IgG to assess antibody binding. The particles were resuspended in PBT with 50% trehalose and 40 g/ml non-immune gamma globulins from goat and mouse to prevent crossover of specific antibodies between particles. Particles with different antibodies were mixed and stored frozen in aliquots at 70 C. Control experiments showed that freezing did not affect performance of the arrays (not shown). Approximately 5% of the particle populations were coupled to polyclonal non-immune immunoglobulins mouse and goat IgG and used as reference for background.

Cells

[0317] Human leukocytes were obtained from buffy coats from healthy blood donors. CD4 T cells were isolated using a RosetteSep kit (STEMCELL technologies Inc.). The U2OS and RT4 cell lines were obtained from ATCC. The cell lines HeLa (ovarian carcinoma) U2OS and RT4 were cultured in RPMI with 20 mM HEPES and 5% fetal bovine serum.

Cell Lysis and Labeling of Proteins

[0318] For separation by gel electrophoresis, cells may be lysed in a solution containing 140 mM NaCl, 30 mM HEPES pH 7.4, 0.3% Sodium Dodecyl Sulphate (SDS) and 1 Mm TCEP. Lysed cells were immediately heated to 90 C. for 10 min. Total cell lysates prepared for separation of proteins under native conditions, are typically prepared by lysing of cells in a solution containing 140 mM NaCl, 30 Mm HEPES pH 7.4, 1% dodecyl maltoside, and commercially available cocktails of inhibitors for proteases and phosphatases. Subcellular fractions may be prepared using commercially available kits from e.g. Thermo Scientific. For covalent labeling of proteins, cell lysates are supplemented with amine-reactive biotin (e.g 500 g/ml biotin-PEO-4-NHS) or thiol-reactive biotin (e.g. biotin-PEG2, maleimide) and the samples are incubated for 20 minutes at 22 C. Free label was removed through the use of centrifugation filter units.

Gel Electrophoresis

[0319] Biotinylated cellular proteins were supplemented with Sodium Dodecyl Sulfate (SDS) and heated. The denatured proteins were next subjected to gel electrophoresis using a GELFREE 8100 instrument (Expedeon Ltd, UK) to separate the proteins into liquid fractions according to size using conditions recommended by the manufacturer. During a typical separation, twelve fractions from up to eight samples were harvested, and transferred to a 96 well microplate. A liquid handling robot (CyBio SELMA) was used for precise transfer of liquid fraction aliquots from the master plate to two replicate plates.

[0320] The difference between a Western Blot and carrying out electrophoresis using the commercially available instrument Gelfree 8100 is that this instrument yields liquid fractions with size separated proteins. The instrument is used with gel cassettes, and running buffers according to the manufacturer's instructions. Proteins are loaded into cassettes useful for parallel separation of proteins from up to eight samples. During electrophoretic separation, proteins migrate through a gel, and liquid fractions containing proteins with a narrow size range are collected at different time points in separate sample collection chambers. Small proteins migrate fast and are collected first. The manufacturer recommends the use of 10% Tris-Acetate gels for separation of proteins with a mass of 15-100 kDa, 8% gels for resolution between 35-150 kDa and 5% gels for resolution between 75-500 kDa.

Incubation of Labeled Proteins with Antibody Arrays

[0321] Mixtures of colour-coded particles with antibodies bound thereto were thawed, pelleted and resuspended in PBS casein block buffer (Pierce) with 40 g/ml of mouse and goat gammaglobulins. Ten microliters of the suspension was added to each well of one of the replicate plates (polypropylene 96 well PCR plates, from Axygen Inc). Biotinylated proteins (25 l) were added by a liquid handling robot as described above, the wells capped and plates constantly agitated overnight at between 4 and 8 C. Particles were then pelleted by centrifugation washed at least two times in PBT and labeled with 10 l streptavidin-phycoerythrin (PE) (2 g/ml in PBS with 2% fetal bovine serum, streptavidin-PE was obtained from Jackson Immunoresearch (www.JiREurope.com)). Labeled particles were washed twice in PBT, resuspended in 200 l PBT and analysed using a flow cytometer.

Flow Cytometry

[0322] An LSRII flow cytometer was used to collect data. The flow cytometer is used to read the microsphere fluorescent colour-codes and to measure fluorescence from the streptavidin reporter molecule. Pacific Blue and Pacific Orange were excited by a 405 laser using 450 and 530 band pass filters, respectively. Alexa 488 and Phycoerythrin (PE) were excited by a 488 nm laser and light collected through 530BP and 585BP filters, respectively. Alexa 647 was excited by a 633 nm laser and light collected through a 655BP filter.

Mass Spectrometry

[0323] Biotinylated proteins in the second replicate plate were captured onto agarose beads covalently coupled with streptavidin. Following repeated washing steps in salt- and detergent-free media, the particles were suspended in a solution containing the proteolytic trypsin to facilitate digestion of the captured proteins. Peptides were solubilized in 0.1% formic acid and loaded onto a nano-liquid chromatography column interfaced directly into a mass spectrometer (liquid chromatography mass spectrometry).

Data Analysis

[0324] Flow cytometry data were processed through R script analysis (Stuchly et al., 2012, Cytometry Part A 81 (2), 120-129). Raw mass spectrometry data files were processed with MaxQuant in order to identify proteins. These yield two sets of numerical data which can be correlated, where the MS data represents the reference for assessment of antibody specificity. An example of the type of data obtained is shown in FIG. 1, where the dotted lines represent the MS data and the solid lines represent the flow cytometry (antibody binding) data. The proportion of the signal that overlaps with that measured by MS is considered as the specificity index. Determination of specificity index, core index, wide index, signal index and absolute signal intensity was carried out as discussed in FIG. 2 using computer algorithms.

Stable Isotope Labeling with Amino Acids in Culture (SILAC)
Isotopically labelled amino acids were purchased from Cambridge Isotope Laboratories, Inc. (USA): L-Lysine (13C6, 15N2)cat. no. CNLM-291-H-PK; L-Lysine (1306)cat. no. CLM-2247-H-PK; L-Arginine (D7, 15N4)cat. no. DNLM-7543-PK; L-Arginine (1306)cat. no. CLM-2265-H-PK. Jurkat and A431 cells were labelled with heavy amino acids (Lysine 13C6, 15N2; Arg 15N4, D7). RT4 and HeLa cells were labelled with medium amino acids (Lysine 13C6; Arg 13C6). U2-OS and MCF7 were labelled with light amino acids. First, the cells were adapted to dialyzed FBS. All cell lines were grown in RPMI 1640 (without lysine, arginine and glycine) supplemented with 10% dialyzed FBS (Sigma, cat. no. F0392-100 ML), penicillin/streptomycin, 1.1494253 mM light L-arginine, 0.2739726 mM light L-Lysine hydrochloride and 2.0547945 mM light L-glutamine. The cells were passaged at least 5 times to assess the effect of dialyzed FBS on growth and morphology. During this stage the cells were maintained in standard T25 flasks. After adaptation, the cell lines were grown in RPMI 1640 medium (no lysine, arginine, glycine) supplemented with 10% dialyzed FBS, penicillin/streptomycin and either heavy, medium or light amino acids. The cells were grown for at least 5 population doublings to ensure maximal incorporation of the labels.

Example 1Antibody Specificity Analysis Using Parallel Mass Spectrometry and Antibody Array

Materials and Methods

[0325] Antibody specificity analysis was carried out in accordance with FIG. 1. The methods for carrying out the fractionation and Western Microsphere Affinity Proteomics (WMAP) analysis are discussed above and are detailed in International patent publication WO 2009/080370.

[0326] Cells from three different cell types (RT4 cells, U2OS cells and HeLa cells), or alternatively from primary CD4 T cells that are either unstimulated, stimulated with the mitogen concanavalin A for 24 hours or stimulated with concanavalin A for 48 hours, were lysed, and soluble proteins in cell lysates were denatured and were labelled with biotin as described above. The proteins were then further denatured and separated by gel electrophoresis using a GELFREE 8100 instrument as described above. A liquid handling robot was used for precise transfer of liquid fraction aliquots from the master plate to two replicate plates.

[0327] The wells of one of these two replicate plates was supplemented with bead-based antibody arrays as described above and analysed using flow cytometry.

The other plate was processed for analysis of peptides by mass spectrometry as described above.

[0328] The approach described above yields two sets of numerical data. Data was analysed as described above.

Results and Discussion

[0329] As shown in FIG. 2, the use of MS in parallel with WMAP is able to distinguish between antibodies that bind specifically (e.g. with high specificity) to Akt1 (FIG. 2A) from antibodies that do not (or bind with lower specificity) (FIG. 2B). The most specific antibodies show good overlap between the WMAP antibody array data (solid lines) and the MS data (dotted lines). This method is also able to provide information on antibody sensitivity, for example the antibodies shown in FIGS. 2C and 2D are less sensitive than the antibody of FIG. 2A, based on the maximum (or absolute) MFI.

[0330] The ability of the method to distinguish specific antibodies from non-specific antibodies is again shown with respect to anti-RBL2 antibodies (FIGS. 3A and 3B) and with respect to anti-beta actin antibodies (FIGS. 3C to 3E). Since the MS data represent the gold standard, one can safely conclude that the antibodies in charts B and D are specific (good overlap of WMAP and MS data) while those in A and C are not (little overlap of WMAP and MS data), i.e. are cross-reactive or non-specific antibodies. Since flow cytometry has a high dynamic range for fluorescence detection, one can also conclude that the antibody in D is more sensitive than the one in E, based on the maximum (or absolute) MFI.

[0331] Through the use of heat maps as shown in FIG. 4, the parallel MS and WMAP analysis can be carried out with respect to a large number of antibodies, and so antibody screening can straightforwardly be carried out. The level of precision seen in the heat maps is highly unexpected for a relatively crude fractionation of a total cell lysate.

Example 2Elution of Proteins from Anti-CD3E and Anti-CD247 Antibodies

Materials and Methods

[0332] CD4+ T cells were lysed and labelled as described above for native proteins. Separation was carried out with respect to four subcellular locations (i.e. subcellular fractionation), namely (1) cytosol, (2) organelles, (3) nucleus and cytoskeleton and (4) membrane locations, using established methods, and with respect to size using size exclusion chromatography. The fractions were then separated and analysed with antibody arrays and flow cytometry as described above.

[0333] The flow cytometry data was processed through R script analysis in order to determine the fraction with the highest levels of membrane-associated targets for anti-CD3e and anti-CD247 antibodies (shown by the longer arrows in FIG. 5A). An aliquot of that fraction was taken from the master plate and captured with an anti-CD3e antibody or with an anti-CD247 antibody attached to particles. After two washes in ice-cold PBT, the proteins bound to the antibodies were eluted with a 30 minute incubation in 1% Tween 20 in PBS at 22 C. under constant agitation. The eluent was transferred to further antibody array (where antibodies were attached to colour-coded particles) as described above and analysed using flow cytometry.

[0334] A further elution was carried out in order elute proteins still bound to the antibodies in a solution of 0.1 SDS at 95 C. The eluent was transferred to further antibody array as described above and analysed using flow cytometry.

Results and Discussion

[0335] The results are shown in FIG. 5. While the arrays contain 576 antibodies to a wide range of proteins, anti-CD3e and anti-CD247 antibodies pull down components of the T cell receptor complex (CD3e, CD247, Zap70, Trat1 and LCK). In both cases, native mild elution allows detection with multiple different antibodies to CD3e. Some of these antibodies do not detect protein after denatured elution (heat+SDS), and they are therefore likely to bind to conformation-dependent epitopes that are lost during denaturing conditions. The results show that two antibodies to different components of a complex pull down similar proteins. This allows direct assessment of the specificity of individual antibodies.

[0336] This example shows not only that surprisingly mild elution conditions can be used in combination with the WMAP analysis but also that such mild elution conditions advantageously allow for the analysis of conformation-dependent epitopes and the identification of antibodies that bind to such epitopes.

Example 3Array Based Antibody Validation

Materials and Methods

Cell Lines and Culture Conditions:

[0337] The human Urinary Bladder Papilloma cell line RT4 (cat. no. 300326) and the Human Osteosarcoma cell line U2-OS (cat. no. 300364) were purchased from CLS Cell Lines Service (Germany). The acute T-cell leukemia cell line Jurkat (clone E6-1, cat. no. ATCC TIB-152), the epidermoid carcinoma epithelial cell line A-431 (cat. no. ATCC CRL-1555), the mammary gland adenocarcinoma cell line MCF7 (cat. no. ATCC HTB-22) were purchased from ATCC. The cervical adenocarcinoma cell line HeLa was a kind gift from M.S. Rdland (Oslo University Hospital, Oslo, Norway). The cell lines used in the study were authenticated by STR analysis via an external service provider (Identicell, Aarhus, Denmark). HeLa, RT4, A431, U2-OS, MCF7 and Jurkat cells were grown in RPMI 1640 medium supplemented with 10% FBS and penicillin/streptomycin. The cells were cultivated in a humidified atmosphere with 5% CO2 at 37 C. The cells were maintained in standard T75 flasks and expanded in T175 flasks prior to harvest.

Stable Isotope Labeling with Amino Acids in Culture (SILAC):

[0338] Isotopically labelled amino acids were purchased from Cambridge Isotope Laboratories, Inc. (USA): L-Lysine (13C6, 15N2)cat. no. CNLM-291-H-PK; L-Lysine (13C6)cat. no. CLM-2247-H-PK; L-Arginine (D7, 15N4)cat. no. DNLM-7543-PK; L-Arginine (13C6)cat. no. CLM-2265-H-PK. Jurkat and A431 cells were labelled with heavy amino acids (Lysine 13C6, 15N2; Arg 15N4, D7). RT4 and HeLa cells were labelled with medium amino acids (Lysine 13C6; Arg 13C6). U2-OS and MCF7 were labelled with light amino acids. First, the cells were adapted to dialyzed FBS. All cell lines were grown in RPMI 1640 (without lysine, arginine and glycine) supplemented with 10% dialyzed FBS (Sigma, cat. no. F0392-100 ML), penicillin/streptomycin, 1.1494253 mM light L-arginine, 0.2739726 mM light L-Lysine hydrochloride and 2.0547945 mM light L-glutamine. The cells were passaged at least 5 times to assess the effect of dialyzed FBS on growth and morphology. During this stage the cells were maintained in standard T25 flasks. After adaptation, the cell lines were grown in RPMI 1640 medium (no lysine, arginine, glycine) supplemented with 10% dialyzed FBS, penicillin/streptomycin and either heavy, medium or light amino acids. The cells were grown for at least 5 population doublings to ensure maximal incorporation of the labels.

Cell Lysis:

[0339] Adherent cells (A431, HeLa, MCF7, U2-OS, RT4) were harvested by trypsinization, followed by two washes in PBS (Sigma, cat. no. D8537). Suspension cells (Jurkat) were washed twice in PBS before lysis. The pellets were then re-suspended in SDS lysis buffer (15 mM NaCl, 30 mM HEPES pH 7.4, 1 mM EDTA, 2 mM MgCl2, 0.3% SDS) supplemented with protease inhibitor cocktail (Sigma, cat. no. P8340-5 ML), 1 mM TCEP, 1 mM PMSF, 1 mM NaF, 1 mM Na3VO4 and incubated for 10 min at 95 C. Buffer volume used was equal to 15 cell pellet volumes. The lysates were cooled on ice to room temperature and 250 units of benzonase (Semba Biosciences, cat. no. R1006E) was added. The samples were incubated for 30 min at 37 C., centrifuged at 14000 g for 5 min, aliquoted and stored at 70 C. Protein concentration was measured using DirectDetect assay free cards using the Direct Detect instrument (MerckMillipore)

Biotinylation of Sample Proteins:

[0340] Protein (300 g) from each cell type was supplemented with sulfo-NHS-LC-Biotin and Biotin-PEG2-maleimide (both at 0.5 mg/ml, www.proteochem.com). The samples were incubated 30 min on ice. Free biotin and salts were removed by buffer exchange using 10 kDa Amicon filters (MerckMillipore, cat. no. UFC501096). The sample was added to the filter and centrifuged at 14000g for 10 min, and the flow through was discarded. Deionized water (450 l) was added on top of the filter and centrifugation was repeated. The procedure was repeated four times. After the last step, 50 l of water was added to the filter, which was then inverted and placed in a clean collection tube. The filters were centrifuged at 2000g for 2 min. Protein concentration was determined using the DirectDetect instrument (MerckMillipore).

Preparative Gel Electrophoresis by Gelfree 8100:

[0341] A Gelfree 8100 instrument (Expedeon, UK) was used to obtain liquid fractions with size-separated proteins using installed programs for gels with three different separation ranges: Tris-Acetate 5% (80-300 kDa), TA 8% (35-90 kDa), 10% (15-70 kDa). For each separation, a total of 150 g protein was supplemented with SDS-sample buffer for Gelfree separation (Expedeon UK). Fractions (150 l) were harvested at 12 time points as recommended by the manufacturer and transferred to a 96 well plate. The fractions were stored at 70 C. until use.

Solid-Phase-Aided Sample Preparation (Solid-PhASP) of Peptides for Mass Spectrometry (MS):

[0342] 50 l of each fraction from the Gelfree separation was transferred to a 96 well PCR plate pre-filled with 100 l PBS (Axygen cat no 732-0662). Five microliters of a 50% streptavidin sepharose slurry was added (http://www.gelifesciences.com/). Prior to use, the streptavidin beads were treated with the 50/ml of Bissulfosuccinimidyl suberate (BS3) for 15 min at 22 C. crosslink the streptavidin and thereby minimize release of streptavidin-derived peptides during on-bead trypsin digestion. Microwell plates with sample proteins and streptavidin beads were sealed with caps and rotated for 30 min at 22 C. to immobilize biotinylated proteins. The sepharose beads were next washed twice in PBS with 1% DDM to remove detergents, twice with deionized water and resuspended in 100 l ammonium carbonate buffer. At this point beads with separated proteins from three SILAC-labelled cell types were mixed to allow multiplexed MS. Trypsin (1 g) was added to each well, and the plate was incubated with constant shaking overnight at 37 C. The streptavidin beads were pelleted by centrifugation and the supernatant containing peptides was transferred to a Sep-Pak tC18 Elution filter plate (Waters, cat. no. 186002318). The resin was pre-activated using 100 l acetonitrile (Sigma), followed by equilibration with 200 l of 0.1% formic acid in water. Peptides were passed through the filter plate using a vacuum manifold. The resin was then washed twice with 200 l of 0.1% formic acid in water. The peptides were eluted in two subsequent rounds, each time using 80 l 80% acetonitrile with 0.1% formic acid in water. The samples were dried using a Concentrator Plus vacuum concentrator (Eppendorf) and the volume was adjusted to 12 l using 0.1% formic acid in water. The samples were stored at 20 C. until use.

Mass Spectrometry:

[0343] Peptides were analyzed on QExactive plus Orbitrap mass spectrometer coupled to Easy-nLC1000 liquid chromatographer (both ThermoFisher Scientific). LC was equipped with a 50 cm PepMap RSLCC18 column with a diameter of 75 m (ThermoFisher Scientific, cat. no. ES803). Water with 0.1% formic acid was used as solvent A and acetonitrile with 0.1% formic acid was used as solvent B. The gradient was as follows: 2% B to 7% B in 5 min; 7% B to 30% B in 55 min; 30% B to 90% B in 2 min; 90% B for 20 min. Solvent flow was set to 300 nl/min and column temperature was kept at 60 C. The mass spectrometer was operated in the data-dependent mode to automatically switch between MS and MS/MS acquisition. Survey full scan MS spectra (from m/z 400 to 1,200) were acquired in the Orbitrap with resolution R=70,000 at m/z 200 (after accumulation to a target of 3,000,000 ions in the quadruple). The method used allowed sequential isolation of the most intense multiply-charged ions, up to ten, depending on signal intensity, for fragmentation on the HCD cell using high-energy collision dissociation at a target value of 100,000 charges or maximum acquisition time of 100 ms. MS/MS scans were collected at 17,500 resolution at the Orbitrap cell. Target ions already selected for MS/MS were dynamically excluded for 30 seconds. General mass spectrometry conditions were: electrospray voltage 2.1 kV; no sheath and auxiliary gas flow, heated capillary temperature of 250 C., normalized HCD collision energy 25%. Ion selection threshold was set to 5e4 counts. Isolation width of 3.0 Da was used.

Analysis of MS Data:

[0344] MS raw files were submitted to MaxQuant software version 1.5.2.8 for protein identification. Parameters were set as follows: no fixed modification; protein N-acetylation and methionine oxidation as variable modifications. When applicable, the following SILAC labels were selected: Lys8; Arg11; Lys6; Arg6. First search error window of 20 ppm and mains search error of 6 ppm. Trypsin without proline restriction enzyme option was used, with two allowed miscleavages. Minimal unique peptides were set to 1, and FDR allowed was 0.01 (1%) for peptide and protein identification. The reviewed Uniprot human database was used (retrieved June 2015). Generation of reversed sequences was selected to assign FDR rates.

Microsphere-Based Antibody Arrays.

[0345] Microspheres with up to 500 fluorescent bar codes are commercially available from Luminex corporation. The procedure for production of the in-house arrays used here has been described in detail previously (Wu et al., Molecular and Cellular Proteomics: MCP 8: 245-257, 2009; Slaastad et al., Proteomics 11, 4578-4582, 2011). Briefly, amine functionalized polymethyl-metha-acrylate (PMMA) microspheres (Bangs Laboratories, IN, USA) first reacted with the hetero-bifunctional crosslinker succinimidyl 3-(2-pyridyldithio)propionate (SPDP, 50 g/ml, Sigma) and reduced with 5 mM TCEP (Sigma) to obtain thiol-functionalized beads. The thiol groups were first used as binding sites for maleimide-derivatized Protein G (ProSpec-Tany TechnoGene Ltd, IL). Remaining thiols were used to bind serially diluted solutions of malemide-derivatives of fluorescent dyes: Alexa-750 (three levels), Alexa-488 (six levels), Alexa-647 (six levels), Pacific Orange (four levels) and Pacific Blue (four levels). Antibodies from rabbit and goat were coupled directly to protein-G beads. For binding of mouse antibodies, the beads were first coupled with goat antibodies to mouse IgG subclasses (Jackson lmmunoresearch). Bar-coded microspheres were kept separate in 384 well plates until completion of the antibody coupling step. The beads were next mixed suspended in PBS Casein Block buffer (Thermo Fisher) and stored at 70 C. until use.

Antibody Array Analysis.

[0346] Aliquots (15 l) of the fractions obtained by GelFree separation (see above) were added to a microwell plate pre-filled with 150 l PBT. The samples were next supplemented with 10 l of a solution containing bead-based antibody arrays suspended in PBS casein block buffer supplemented with immunoglobulins (20 g/ml) from human, mouse and goat IgG. The plate was sealed with plastic film and rotated overnight at 4-8 C. The plate was next centrifuged at 1000g to pellet the beads. The supernatant containing unbound protein was harvested and stored frozen. The beads were next washed twice in PBT and labelled with R-Phycoerythrin-conjugated streptavidin (10 g/ml in PBS with 0.1% bovine serum albumin, Jackson Immunoresearch). Following two washes with PBT, the beads were resuspended in PBS with 0.1% bovine serum albumin and analyzed by flow cytometry.

Flow Cytometry.

[0347] Microsphere-based antibody arrays were analyzed using an Attune flow cytometer (Thermo) equipped with a 96 plate sample loader and four lasers: 405 nm (Pacific Blue, Pacific Orange), 488 nm (Alexa-488), 567 nm (R-Phycoerythrin) and 633 nm (Alexa-647, Cy7). The emission filters were standard for the instrument, except for the use of a 520 nm band bass filter for detection of Pacific Orange.

Analysis of Flow Cytometric Data.

[0348] Flow cytometry data were processed using a freely available R-application dedicated for analysis of MAP data (Stuchly et al., 2012, supra). The application identifies microsphere subsets on basis of their color codes and exports values for median R-Phycoerythrin fluorescence for each subset.

Statistics:

[0349] The MS and flow cytometry procedures described above yield two sets of numerical data which can be correlated. All correlations reported are Pearson correlations for linear data. To assess the frequency of random correlations in MAP-MS and transcriptomics datasets, the proteins/mRNA identifiers were first sorted according to predicted mass and then in alphabetical order. We next assessed correlations between data in neighboring rows. Correlations between series of six values corresponding to relative abundance of proteins or mRNA were assessed for MS and transcriptomics data. For MAP and MS data we also assessed the overall correlation between all data points in fractions 3-12 in all samples. The results in FIG. 9B show that the frequencies of random correlations of 0.9 are around 5%, which corresponds to statistical significance (p<0.05). The rationale for choosing a lower cut-off for validation is that the average correlation between results in the two MS datasets was 0.6, and fewer than 40% of the correlations were higher than 0.9 (data not shown). The same was true for correlations between the two transcriptomics datasets (data not shown). We re-analyzed data from biological replicates in the MaxQB database and obtained similar results (Geiger et al., Molecular and Cellular Proteomics: MCP 11, M111 014050, 2012). Thus, the precision that can be obtained with orthogonal data is limited by the reproducibility of the methods used to generate reference data. However, a significance of 0.05-0.15 for discrimination between proteins with the same mass is clearly better than the current industry standard, which is a band near or at a predicted position and no sample named as positive and negative control.

Results and Discussion

[0350] The method described in this Example is analogous to a multiplexed Western Blot (WB) with MS data as a direct reference to assess specificity (FIG. 1). The first steps are the same as for standard WB (materials and methods). Thus, proteins from six human cell lines were heated in the presence of sodium dodecyl sulphate (SDS) and separated by polyacrylamide gel electrophoresis (PAGE). However, to facilitate multiplexed analysis with antibody arrays, we labelled the sample proteins with biotin and used the Gelfree 8100 instrument (Expedeon, UK) for preparative PAGE. The instrument yielded 12 liquid fractions with size-separated, biotinylated proteins from each sample (FIG. 1). An aliquot of each fraction was analysed with microsphere-based antibody arrays and flow cytometry (microsphere affinity proteomics, MAP, Wu et al., 2009, supra). A second aliquot was processed with a new semi-automated method (Solid-PhASP) to obtain peptides for MS (FIG. 1 and FIG. 9A). Analysis by MAP resolved antibody targets as peaks of reactivity across the fractions, and PAGE-MS data for the intended targets served as reference to identify peaks that correspond to specific binding (FIG. 1, numerical data not shown). 2412 antibodies were used.

[0351] Text files with data from two PAGE-MAP/MS experiments (data not shown) were used as input in computerized antibody validation (CAVA, supplementary software, supplementary protocol). The algorithm focusses on fractions 3-12, which contain the best resolved proteins. The first steps in the validation process are assessment of signal to noise ratio (signal index) and peak position (or core index) (FIGS. 6A and 6B, FIG. 8). The threshold for signal to noise (signal index) was set to a four-fold difference between the strongest and the median MAP signal measured across all samples. CAVA next determines if the tallest MAP peak overlaps with the MS peak for the intended target in the same sample. A deviation of one fraction is accepted for this core index (or peak position) (FIG. 8).

[0352] The result of the first two steps was visualized as heatmaps formatted as digital WBs (FIGS. 6A and 6B). Thus, the largest protein appears on top, and the remainder are organized in descending order according to predicted mass to mimic their positions on a standard WB. Since protein mass increases along the y-axis as well as with fraction number (x-axis), the expected pattern is a continuum of bands from the lower left to the upper right in each map. The MS data in the digital WBs showed the expected pattern (FIGS. 6A and 6B). The same was true for targets of antibodies that passed thresholds for sensitivity and peak position (FIG. 6A). By contrast, the reactivity pattern of antibodies that failed to meet these criteria was dominated by background signal (FIG. 6B).

[0353] Thus, through the use of heatmaps as shown in FIGS. 6A and 6B, one can visualize the results of a computer algorithm used to process results from parallel analysis of fractionated proteins by MAP and MS to identify specific antibodies. The maps in FIG. 6A shows antibody reactivity patterns (left half) that closely resemble the MS data for the corresponding targets (right half). The antibodies were identified on the basis of computerized assessment of the signal index (SI) as having an SI of four or more. The algorithm also determined that the maximal antibody signal was measured in the same fraction as the maximal MS signal or in one of the immediate neighboring fractions. The antibodies in FIG. 6B failed to meet these criteria, and the heatmap shows a clear difference between their reactivity patterns and the MS data.

[0354] The heatmaps shown in FIG. 7 serve to further illustrate how a computer algorithm can be used to process data from parallel analysis of fractionated proteins with antibody arrays and MS. In these heatmaps, the 12 data points from analysis of fractionated samples are compressed to a single value corresponding to the wide index (sum of signal measured in five fractions centered around the maximum). The wide index serves as proxy for protein abundance. With this analysis, the signature of the protein is the relative abundance in different cell lines (J Jurkat, U U2OS, H HeLa, A A431, R RT4, M MCF7). The computer algorithm identified 302 antibodies with reactivity patterns that had correlations of 0.9 or better with MS data. This is observed as similarity between the heatmaps for MAP and MS data in the upper heatmap. The heatmaps to the right show that a similar pattern was observed for differential mRNA expression. The mRNA data were retrieved from two published datasets and therefore serve as an independent reference (Uhlen et al Science 2016, Klijn C. et al Nat Biotechnol 33, 306-312 (2015)). The lower heatmap shows results obtained with antibodies that failed to meet critera for correlation between MAP and MS data. This is observed as a difference between the heatmaps shown for antibody reactivity and MS and mRNA data.

[0355] A key feature of the present invention is that the analysis of relative protein abundance in a series of fractions yields a chromatogram that serves as a signature for the protein of interest. Antibody validation is based on correlation of chromatograms obtained when the fractions are analyzed with antibody arrays and MS, respectively. We provide an example to illustrate how one can use MS data to determine the level of correlation required to obtain statistical significance.

[0356] The heatmap in FIG. 9A and the line chart in FIG. 9B serve to illustrate how results from shotgun MS analysis can be used to assess the significance of correlations. The heatmap in FIG. 9 visualizes the entire MS dataset obtained by measuring fractions from six cell lines separated by three gels with different separation range (5%, 8% or 10% acrylamide). The proteins were processed and analyzed by shotgun MS analysis as described in FIG. 1. The proteins in the dataset were sorted according to the type of gel used for separation and then in descending order according to predicted mass. Since protein mass also increases with fraction number (x-axis), the expected pattern is a continuum of bands/pixels along the diagonal from bottom left to top right in each map. To assess random correlations, the data in each row were correlated to those in the row below. The line chart in FIG. 9B shows the frequency/significance (y-axis) of random correlations indicated on the x-axis, and the horizonal line indicates a frequency of 0.05, which is often used as a threshold for significance in statistics. The two lines correspond to results obtained in two separate experiments. Thus, one can readily observe that a correlation of 0.8-0.9 is statistically significant.

[0357] The dot plots in FIG. 10 serve to illustrate the added value of analyzing fractionated samples as compared to measuring protein abundance. The left dot plot show overall correlations in experiment 1 plotted against those in experiment 2 (i.e. correlation all datapoints obtained by paired analysis of 12 fractions by MAP and MS, respectively). The squared R value was 0.7 which indicates that highly similar correlations were observed in the two experiments. The dot plot to the right shows corresponding results for measurements of the wide index (i.e. sum of five fractions centered around the maximum as proxy for relative protein abundance) The squared R value was 0.25. The results show that correlations for dataseries consisting of all data points are more reproducible (i.e. higher correlation between the two experiments) than what is achieved by measuring protein abundance. This result is surprising and underscores the added value of analyzing fractionated samples.

Example 4Mass Spectrometry Analysis of Monomeric Proteins Captured from Enriched Page Fractions

Materials and Methods

[0358] Stable Isotope Labeling with Amino Acids in Culture (SILAC):

[0359] Human T cell acute leukemia cells (Jurkat) were adapted to culture in medium with dialyzed fetal bovine serum (FBS) by culture in RPMI 1640 (without lysine, arginine and glycine) supplemented with 10% dialyzed FBS (Sigma, cat. no. F0392-100 ML), penicillin/streptomycin, 1.1494253 mM light L-arginine, 0.2739726 mM light L-Lysine hydrochloride and 2.0547945 mM light L-glutamine. The cells were passaged at least 5 times to assess the effect of dialyzed FBS on growth and morphology. After adaptation, the cell lines were grown in RPMI 1640 medium (no lysine, arginine, glycine) supplemented with 10% dialyzed FBS, penicillin/streptomycin and heavy isotope acids (Lysine 13C6, 15N2; Arg 15N4, D7). The cells were grown for at least 5 population doublings to ensure maximal incorporation of the labels.

[0360] The methods for preparation of cell lysates, labeling of proteins with biotin, separation by Gelfree 8100 and analysis by MAP and MS are described above.

Immunoprecipitation and Mass Spectrometry:

[0361] Indicated amounts of biotinylated proteins from Gelfree 8100 fractions was diluted in 1 ml PBS with with 0.1% casein (Thermo Fisher, cat no. 37528). Polymer beads coupled covalently with Protein A/G (Prospec, IL) and then with indicated antibodies were added (1 ul 10% solids). The mixture was incubated overnight at 4-8 C. with constant shaking. The beads were pelleted by centrifugation and washed twice in PBS with 0.1% dodecyl maltoside. The beads were next resuspended in 100 l ammonium carbonate buffer, and 100 ng trypsin (Promega) was added. After 15 min incubation at 21 C., the beads were pelleted and the supernatant was harvested. Peptides were processed for mass spectrometry as described above.

Results and Discussion:

[0362] The line chart in FIG. 11 shows signal intensity (y-axis) for beta-actin measured by mass spectrometry (dashed line) and antibody array analysis (solid line, anti-beta-actin antibody GTX629630, GeneTex, USA), plotted against fraction number (Gelfree 8100 fractionation, 10% gel). The maximum signal was observed in fraction 8, and the trace for the target of the antibody closely resembles the MS signal for beta actin. The antibody is therefore readily identified as a good candidate for more expensive validation by immunoprecipitation and mass spectrometry (IP-MS).

[0363] One microliter of fraction (8) with an estimated content of as little as 100 ng protein was used as source for immunoprecipitation with anti-beta actin antibody. The immune-precipitate was processed for MS analysis as described above. The bar graph in the middle shows MS signal intensity for indicated proteins with SILAC labeling (log scale), while the graph to the right shows signal for proteins without SILAC label.

[0364] The results show that only five proteins in the immunoprecipitate contained the SILAC label, and more than 90% of the total MS signal for SILAC-labelled proteins corresponded to the antibody target (beta-actin, ACTB). A large number of additional proteins were observed (right bar chart). However, these did not contain the SILAC label and therefore represent sample contamination. The signals from contaminating proteins were up to ten-fold stronger than that observed with SILAC-labelled beta-actin. While some of the contaminating proteins represent keratins that are known to be common contaminants, many are broadly expressed cellular proteins, and the list also contains non-keratin proteins. Collectively, the results obtained by paired antibody array and MS analysis and the downstream analysis by IP-MS provide definitive evidence that the antibody to beta-actin is more than 90% specific for the intended target.

[0365] The solid line in the line chart in FIG. 12 shows signal intensity for anti-RELA (y-axis, log scale) plotted against Gelfree fraction number. The dashed line shows MS signal for RELA. The trace obtained with the antibody closely resembles the MS signal for the intended target. The antibody is therefore clearly a good candidate for definitive validation by IP-MS. The bar chart in the middle shows MS signal for SILAC-labelled proteins. Rel A was detected in immunoprecipitates from 1 ul and 10 ul Gelfree fraction, corresponding to an estimated 10 ug and 1 ug of protein, and the protein, and intended antibody target constituted more than 90% of the total MS signal for SILAC-labelled proteins. The bar chart to the right shows presence of a large number of proteins with higher MS signal intensity than that measured for RELA. However, these proteins did not contain the SILAC label and therefore represent contamination. Collectively, the results obtained by paired antibody array and MS analysis and the downstream analysis by IP-MS provide definitive evidence that the antibody to RELA is more than 90% specific for the intended target.

[0366] Established protocols for IP-MS describe the use of 0.5-5 mg of sample protein (Marcon, E. et al., Nat Methods, 12, 725-731 (2015); Malovannaya A. et al, Cell, 145, 787-799 (2011). Here, we used as little as 1 ug to detect RELA and 100 ng for detection of beta-actin. Thus, the sensitivity of method described in the present invention is three orders of magnitude higher. Moreover, immunoprecipitates obtained using established protocols contain an average of at least 200 proteins as compared to five proteins or less with the method described here (Marcon, E. et al., Nat Methods, 12, 725-731 (2015). The most comprehensive study to date concluded that the precision of specificity assessment in IP-MS is limited to showing that the intended target is among the top-three most abundant proteins in the immunoprecipitate (Marcon, E. et al., Nat Methods, 12, 725-731 (2015). A second large study concluded that our analysis provides indication, but NOT a conclusive proof for identities of secondary (cross-reacting) antigens. Malovannaya A. et al, Cell, 145, 787-799 (2011), supplementary Table 1). The results obtained with the method described in the present invention are therefore surprising and clearly more definitive.

[0367] We conclude that paired analysis of fractionated proteins with antibody arrays and MS is helpful to select antibodies that are likely to be specific and therefore worth the investment of more expensive and definitive downstream analysis by IP-MS. It is also clear that this method will be useful to identify the targets of antibodies that cross-react. In paired array and MS analysis of fractions, one would identify an antibody reactivity peak that does not overlap with the MS signal. The antibody can then be used to immunoprecipitate the target from the enriched fraction for identification by IP-MS. Finally, some antibodies may show a reactivity peak when shotgun MS does not show a signal for the intended target. A negative MS signal is not definitive evidence for lack of protein expression. IP-MS is more sensitive than shotgun MS. One can therefore identify targets of antibodies to low abundance proteins that are not detected by shotgun MS.