Steps 1 – 6: Synthesis of peracetylated mannose

Timing: ~12 h

1|  Add D-mannose 1 (1.0 g, 5.5 mmol) in pyridine (3.5 ml, 44.9 mmol) and cool the mixture to 0 °C in an ice bath. Add acetic anhydride (3.9 ml, 44.1 mmol).

2 | Add acetic anhydride to the reaction mixture through the addition funnel (addition takes ~15 min).

3 | Stir the reaction mixture for 12 h (or overnight) under N2.

4 | Evaporate the solvent and dissolve the residue in 15 ml of dichloromethane (DCM).

5 | Wash the DCM layer with 10 ml dH2O 2–3 times and separate the layers.

6 | Dry the organic layer over Na2SO4 (2 g) and evaporate the solvent, filter through filter paper and evaporate the solvent using a rotary evaporator; a white solid (2) is obtained. Throughout this protocol, MgSO4 can be used instead of Na2SO4.

Pause point: The intermediate can be stored for several months in the refrigerator if necessary.

1 | Select the fluorescent protein fragments to be used. Several combinations of fluorescent protein fragments support bimolecular fluorescence complementation¹¹; those recommended for BiFC analysis are listed in Table 2. For most purposes, fragments of YFP truncated at residue 155 (designated YN155 and YC155) are recommended, because they exhibit a relatively high complementation efficiency when fused to many interaction partners, yet produce low fluorescence when fused to proteins that do not interact with each other². Fragments of YFP truncated at residue 173 (designated YN173 and YC173) can also be used¹¹, and may exhibit a different efficiency of complementation owing to differences in the steric constraints imposed by tethering of the fragments to the protein complex. Fragments of Venus (a mutated GFP with high fluorescence intensity)²⁰ truncated at either residue 155 or 173 (designated VN155 and VC155, or VN173 and VC173, respectively) produce a significantly brighter fluorescent signal when fused to specific interaction partners²¹. However, these fragments also produce a brighter signal when fused to proteins that do not selectively interact with each other²¹. These fragments have the great advantage that the bimolecular fluorescent complex is readily detectable at 37 °C, which avoids the incubation at 30 °C that is generally necessary to detect complementation using YFP fragments. Other combinations of fluorescent protein fragments can also be used, especially when using BiFC analysis for the visualization of multiple protein complexes in the same cell¹¹.

2 | Determine the sites where the fluorescent protein fragments can be fused to the putative interaction partners. Determine the positions of the fusions empirically to fulfill the three criteria described below.
First, ensure that the fusions allow the fragments of the fluorescent proteins to associate with each other if the putative partners interact. Information about the structure and location of the interaction interface may be useful to determine optimal positions for the fusions. However, this information is not essential because fusions that can be used for BiFC analysis can be identified by screening multiple combinations of fusion proteins for fluorescence complementation. One strategy for the identification of fusion proteins that allow bimolecular fluorescence complementation is to fuse each of the fluorescent protein fragments to the N- and C-terminal end of each interaction partner, and to test for complementation in all eight combinations that contain both fragments of the fluorescent protein (Fig. 2).

Second, confirm that fusions do not affect the localization or the stabilities of the proteins by comparing the localization and expression levels of the fusion proteins with those of wild-type proteins lacking the fusions; indirect immunofluorescence and immunoblot analyses can be used.

Third, test the fusion proteins for all known functions of the endogenous proteins to ensure that the fusions do not affect the functions of the proteins under investigation.

Troubleshooting

3 | Select linkers to connect the fragments to the proteins of interest. The linkers must provide flexibility for independent motion of the fluorescent protein fragments and the interaction partners, allowing the fragments to associate when the proteins interact. We have used the RSIAT and RPACKIPNDLKQKVMNH linker sequences in many fusion constructs used for BiFC analysis^{2, 11}. These linkers have been used for the visualization of interactions between many structurally unrelated proteins. The sequence AAANSSIDLISVPVDSR encoded by the multiple cloning sites of the pCMV-FLAG vector (Sigma) has also been successfully used as a linker in many BiFC experiments. A peptide sequence designed to be flexible, such as (GGGS)n, can also be used, although it can potentially affect the degradation of the fusion protein. Although these linker sequences have worked well for the proteins examined previously, it is possible that linkers of a different length or sequence are optimal for BiFC analysis of interactions between other proteins.

Troubleshooting

4 | Select a cell culture system. Choose a cell culture system that represents the biological context to be investigated, and allows efficient introduction of DNA into a large fraction of the cells. Cells that grow as an adherent monolayer are generally easier to image. The BiFC assay has been used for the analysis of protein interactions in many mammalian cell lines including COS-1, HEK293, HeLa, Hep3B, TN4, and NIH3T3 cells as well as in intact organisms^{2, 10, 12, 18, 19, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66.}

5 | Select a strategy for expression of the fusion proteins. Choose either transient expression (A) or stable expression (B) strategies, based on the purpose of the experiment.

Step 32: Data preprocessing

A GC-TOF-MS data analysis

Timing: 4–6 h for target list generation, 2–3 h for raw data processing of data acquired over 5 d

(i) Using LECO’s terminology, perform a ‘peak find’ data processing method with a single QC sample injected in the middle of the block experiment. The data processing method should have ‘Baseline’, ‘Peak Find’, ‘Calculate Area/Height’ and ‘Retention Index’ functions activated. Key parameters in this method are the baseline offset, data points to be averaged for smoothing, expected chromatographic peak width, maximum number of unknown peaks to find and the minimum signal-to-noise ratio for the (automatically selected) quantitation mass. All parameters are sensitive to the chromatographic performance obtained and must be selected to reflect this. From representative chromatograms acquired in the HUSERMET project, in which we analyzed thousands of human serum samples with GC-MS, baseline offset was set at 0.5, data points to be averaged for smoothing was set at automatic, peak width was set at 1.8 s and the maximum number of unknown peaks to find was set to 400. A signal/noise (S/N) threshold of 100:1 was used; this was an informed compromise between comprehensive reporting and the collation of spectra of sufficient quality to be reliably found subsequently. A retention index method is prepared in the software by compiling a method table containing the retention indices (1,000, 1,200, 1,500, 1,900 and 2,200), the observed retention time and the quantitation ions used to confirm the detection of each retention index compound.

(ii) Step 32A(i) produces a table of potential candidates for inclusion in a reference table and annotated with a retention index, mass spectrum and single quantitation ion. From this table, delete candidates whose mass spectrum does not contain fragment ions expected for TMS derivatives at m/z 73 and 147, and whose quantitation ion chromatogram indicates that a single mass spectral feature has been reported as multiple features (‘peak splitting’). In these cases, delete the features with lowest S/N while retaining the feature with the highest S/N. Manually edit the mass spectrum for the isotopically labeled internal standards to remove ions present in the unlabeled endogeneous metabolite. Assess the automatically chosen quantitation masses for accuracy, a high S/N ratio and no interference to peak shape from co-eluting derivatized metabolite peaks. Amend the quantitation mass if necessary. The metabolite peaks are then exported to a reference file created before Step 32A(i). Parameters in the reference table are set at 100,000,000 for tolerance (to ensure all peaks are matched and reported independent of peak area), 20 for RI deviation, 700 for match threshold, 2,500 for minimum area and 5.0 for S/N threshold.

(iii) A separate study sample can then be processed through the deconvolution software, as described in 32A(i), with the ‘Compare’ function also enabled. To do this, set the mass threshold setting at 50. Derivatized metabolic features uniquely detected in this sample are marked, the mass spectrum and quantitation masses are assessed as described above in Step 32A(ii) and then exported to the reference file. This process is performed for a range of samples from the study.

Critical step: In large-scale studies, we recommend performing Step 32A(iii) on samples from different experimental blocks to ensure that all derivatized metabolite peaks are present in the reference file.

(iv) Each peak in the reference file is named with a unique label (e.g., internal standard succinic d4 acid, sample peak X). At this stage, definitive identification of each peak can be performed. To do this, compare the retention index and mass spectrum of each metabolite with those recorded for authentic chemical standards and present in in-house libraries (e.g., Golm metabolome database or MMD in-house library) or in commercially available mass spectral libraries (e.g., NIST, EPA or NIST05 libraries) (see Experimental design). If a match to a retention time/index (± 10) and mass spectrum (match >70%) is observed, the identification can be described as definitive and the peak can be labeled metabolite name_definitive. If a match to only a mass spectrum is observed, the identification can be described as putative and the peak can be labeled ‘metabolite name_putative’.

(v) The final stage is used to define the most appropriate internal standard for each peak. This can be performed by analyzing 60 QC injections in a single block. Calculate the peak area ratio (peak area metabolite/peak area internal standard) for each metabolite peak associated with each internal standard and calculate the relative standard deviation (RSD) for each of these peaks for injections 6–60. The internal standard providing the lowest RSD is chosen as the internal standard for that metabolite.

(vi) Perform raw data processing using the reference table described above (Step 32A(i–v)) for all samples to reliably find and report the selected metabolic features in all samples. Process all the blocks using the appropriate set of parameters and internal standard selections. As noted, automatic feature detection and measurement achieves a high success rate (estimated to be in excess of 98%), which was further improved by manually inspecting the peak area measurements for each internal standard in each sample, and manually correcting where required. Further outlier rejection tests can be performed on a block basis before accepting data. This has led to the rejection of <1% of the injections performed.

Pause point: Archive processed data for future use.