Het gebruik van MALDI-TOF-massaspectrometrie en een aangepaste database te Bacteriën inheemse karakteriseren naar een unieke Cave Milieu (Kartchner Caverns, AZ, USA)

The overall goal of this procedure is to develop, apply, and demonstrate a detailed procedure for library based bacterial identification using MALDI to mass spectrometry. This is accomplished by first acquiring mass spectra of bacteria using a maloff mass spectrometer. In this study, mass spectra of 15 bacteria isolated from a caustic environment are collected the resulting peaks form, characteristic patterns or fingerprints of the bacteria analyzed.

These mass spectra are then imported into software and a model database is constructed. Next, the reference mass spectra in the model database are processed and analyzed using cluster analysis, peak matching, and statistical analysis. Finally, mass spectra of blind coded bacteria, which are randomly selected from these 15 bacteria are collected again and compared to the reference spectra in the model database for identification results show that bacteria can be correctly identified either based on similarity coefficients or potential biomarkers and peak classes.

Begin this procedure with deposition of one microliter of protein extract containing no viable cells onto a stainless steel maldi target plate and allow it to dry. Then overlay the dried protein extract with one microliter of matrix solution and allow it to dry. Then deposit one microliter of CBR standard onto the target plate after it dries, overlay the CBR with one microliter of matrix solution and allow it to dry.

Finally, deposit two microliters of matrix solution onto the target plate as a negative control. Use the multi to mass spectrometer equipped with the nitrogen laser and operated using Bruker flex control software. Collect each mass spectrum in positive linear mode by accumulation of 500 laser shots in 100 shot increments.

Set the ion source one voltage to 20, kilovolts the ion source, two voltage to 18.15 kilovolts and the lens voltage to 9.05 kilovolts. These parameters are instrument specific and may require optimization. Set the mass to charge range for automated spectrum evaluation from two to 20 kilodaltons per charge.

Then set the minimum resolution threshold at 100. Select the OID peak detection algorithm. Set the signal to noise ratio threshold at two, and the minimum intensity threshold at 100.

Finally, set peak accumulation to 500 for database design. Create a new database in bio numerics 7.1 using the new database wizard. Then create a spectrum experiment type such as maldi, using commands in the experiment types panel.

Next, create the levels using the database design panel, navigate to the database menu and add new levels using the level add new level command here. Create species level, biological replicate level, and technical level. To import the raw mass spectra.

First, export the raw mass spectra as text files using flex analysis by navigating the file menu and clicking the export mass spectrum command. Then import the raw mass spectra into the database in the level of the technical replicates. Pre-process the raw mass spectra by importing and resampling using a quadratic fitting algorithm.

Then perform a baseline subtraction using a rolling disc with a size of 50 points compute noise using the continuous wavelet transformation or CWT. Smooth the spectra using a Kaiser window with a window size of 20 points and a beta of 10 points. Then perform a second baseline subtraction using a rolling disc with a size of 200 points.

Finally, detect the peaks using the CWT with a minimum signal to noise ratio of 10 after pre-processing safe characteristic patterns of each mass spectrum, such as peak lists, containing peak sizes, peak intensities, and signal to noise ratio. In the next, create composite mass spectra from pre-processed spectra using the summarized command in the analysis menu. Choose biological replicate as the target level.Here.

Combine mass spectra of 10 technical replicates of the same colony to yield a composite mass spectrum for that colony, resulting in three composite mass spectra for that isolate at the biological replicate level. Finally, summarize the three composite spectrum to create one composite spectrum for that isolate at the species level. To perform similarity based cluster analysis and multidimensional scaling, create groups with colors.

First, select the three biological composite mass spectra and click the create new group from selection command in the group's menu to create a group for the corresponding isolate. Then design a color automatically used for these three mass spectra. Perform cluster analysis by navigating the clustering menu and clicking the calculate cluster analysis command.

On the comparison settings, page one, select the Pearson correlation and leave other parameters as default. On page two, select unweighted pair group method with arithmetic mean. Then click finish.

Obtain a multidimensional scaling plot using the multidimensional scaling command in the statistics menu to perform peak matching. Click on the spectrum type maldi in the experiments panel. Then select layout.

Show image, individual peaks of spectra are shown. Then perform peak matching using the do peak matching command in the spectra menu to identify peak classes perform two-way clustering by clicking statistics matrix.Mining. The intensity of the peaks matched to the peak classes is represented as a heat map to identify bacteria using the similarity coefficient based method, create a comparison and generate a rogram based on mass spectra at the technical replicate level as performed for cluster analysis, save the rogram for comparison of similarity.

Select an unknown mass spectrum and click the analysis. Identify selected entries. The identification dialogue box appears.

Select the comparison based classifier type, and click next. On the next page, choose the saved gram as a reference comparison, and then click Next. Choose the basic similarity as an identification method and again, click next, choose the maximum similarity as the scoring method.

Type in the appropriate threshold values and minimum difference values for each parameter, and then click next. Once the calculations are completed, the identification window appears in the results panel. The members of the database that best match the unknown are listed.

A rogram based on clustering analysis shows a similarity between each isolate in the database. The dataset contains spectra of 15 different species with three composite spectra for each species. Two-way cluster analysis can help researchers identify potential biomarkers for a species or strains within a species.

Here, peak intensity is represented by colors, green meaning low intensity, and red meaning high intensity. Finally, based on these analysis, the mass spectrum of an unknown can be compared to the gram and a similarity coefficient based identification is achieved.