Proteomic Analysis Methodology
The matrix that we used in the first part of our research, the cervicovaginal fluid, is a very complex sample to study. We have worked for several months to obtain a standardized, robust and reproducible technique, which today allows us to obtain very good results, around 6000 to 8000 proteins per sample.
Our methodology in the proteomics laboratory begins once the sample is taken from the volunteer, protease inhibitors are incorporated and it is frozen at -80ºC until processing. Once the sample is analyzed, it is thawed and the proteins are extracted using different chaotropic agents, in different buffers and pH, where finally a method based on Urea with Ammonium Carbonate is used. Subsequently, the proteins are subjected to quality evaluation by SDS-PAGE and subjected to Clean Up and digestion; The peptides are quantified and injected into the nanoHPLC (nanoELUTE, Bruker) for their chromatographic separation, this equipment is connected in line with the Bruker timsTOF Pro mass spectrometer, which collects the information based on the DDA (Data Dependent Acquisition) methodology.
One of the strategies that we have implemented in the laboratory is off-line fractionation, which allows us to perform Deep Proteomics, where we obtain a great depth of protein identification and a great coverage of peptides; For this, the peptides, before being analyzed in the mass spectrometer, are subjected to a chromatographic fractionation, using the Reverse pH strategy through the AKTA Avant 25â chromatograph from General Electric, where we collect a number of fractions depending on the experiment and the required depth, which can be from 8 to 24 fractions, each fraction is separated again in the nanoHPLC and the data is analyzed in the mass spectrometer.
The information measured by the timsTOF Pro is analyzed with software such as PEAKS Studio X, which translates the experimental information from the spectrometer to peptide and protein sequences with their respective relative intensity values, which are re-analyzed using applications based on the R language, using the bioconstructor package, where quantitative and statistical analyzes are carried out, generating graphs such as heatmap, volcano plots and lists of statistically significant differential expression proteins.
The analysis equipment
To perform deep proteomics, offline fractionation is performed on an AKTA AVANT chromatograph, which is an HPLC that allows peptides to be separated into simpler fractions. Each of these fractions are injected into the spectrometer. Additionally, we have the Direct Detect, which is an infrared spectrometer that allows us to determine the amount of peptides in the sample.
We also use a fluorescence equipment for protein quantification called Qubit, which allows us to know the concentration of proteins and, in turn, everything related to the quality of the proteins.
We have an electrophoresis system that consists of a chamber and a BIORAD brand power source, the SpeedVac that allows us to do the rotary concentration of the peptides and finally the Tims TOF Pro mass spectrometer, from Bruker, with its Nano HPLC ( nanoELUTE).
The spectrometer can work by itself, but it would not have the great performance that it has without the Nano HPLC, because of a very complex sample it could only resolve the most abundant; therefore what chromatography does is separate your sample and allow peptides to enter one by one or a group of peptides into the spectrometer. For example: if you take a complex sample that has thousands of peptides and inject it into the spectrometer alone, it will detect only the most abundant, which will be 100 or 200, on the other hand, with the Nano HPLC you can solve thousands.
After obtaining a result from the mass spectrometer, a search engine is used for analysis. In this case we have several algorithms; We mainly use PEAKS study X +, but we also use MaxQuant, MS Fragger for other experiments depending on the type of application.
The search engine returns the peptide / protein sequences as well as relative abundance values and spectral counts, and you take that data into statistical software. In this case, you work with R and with an application called Bio Conductor, where you can normalize the results based on the means. In order for the results to be comparable, we subsequently make the comparisons in times of change. What does this mean? We transform the data based on logarithm 2, versus a one-division control. With that we have quantitative data. And then we use pathway analysis software like IPA software.