The described protocol provides an optimized quantitative proteomics analysis of tissue samples using two approaches: label-based and label free quantitation. Label-based approaches have the advantage of more accurate quantitation of proteins, while a label-free approach is more cost-effective and used to analyze hundreds of samples of a cohort.
Recent advances in mass spectrometry have resulted in deep proteomic analysis along with the generation of robust and reproducible datasets. However, despite the considerable technical advancements, sample preparation from biospecimens such as patient blood, CSF, and tissue still poses considerable challenges. For identifying biomarkers, tissue proteomics often provides an attractive sample source to translate the research findings from the bench to the clinic. It can reveal potential candidate biomarkers for early diagnosis of cancer and neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease, etc. Tissue proteomics also yields a wealth of systemic information based on the abundance of proteins and helps to address interesting biological questions.
Quantitative proteomics analysis can be grouped into two broad categories: a label-based and a label-free approach. In the label-based approach, proteins or peptides are labeled using stable isotopes such as SILAC (stable isotope labeling with amino acids in cell culture) or by chemical tags such as ICAT (isotope-coded affinity tags), TMT (tandem mass tag) or iTRAQ (isobaric tag for relative and absolute quantitation). Label-based approaches have the advantage of more accurate quantitation of proteins and using isobaric labels, multiple samples can be analyzed in a single experiment. The label-free approach provides a cost-effective alternative to label-based approaches. Hundreds of patient samples belonging to a particular cohort can be analyzed and compared with other cohorts based on clinical features. Here, we have described an optimized quantitative proteomics workflow for tissue samples using label-free and label-based proteome profiling methods, which is crucial for applications in life sciences, especially biomarker discovery-based projects.
Proteomics technologies have the potential to enable the identification and quantification of potential candidate markers that can aid in the detection and prognostication of the disease1. Recent advancements in the field of mass spectrometry have accelerated clinical research at the protein level. Researchers are trying to address the challenge of complicated pathobiology of several diseases using mass spectrometry-based proteomics, which now offers increased sensitivity for protein identification and quantification2. Accurate quantitative measurement of proteins is crucial to comprehend the dynamic and spatial cooperation among proteins in healthy and diseased individuals3; however, such analysis on a proteome-wide scale is not easy.
One major limitation of proteomic profiling of clinical specimens is the complexity of biological samples. Many different types of samples have been investigated to study the disease proteome, such as cell lines, plasma, and tissues4,5. Cell lines are widely used as models in in vitro experiments to mimic different stages of disease progression. However, one major limitation with cell lines is that they easily acquire genotypic and phenotypic changes during the process of cell culture6. Body fluids such as plasma could be an attractive source for biomarker discovery; however, due to the highly abundant proteins and dynamic range of protein concentration, plasma proteomics is a bit more challenging7. Here, peptides originated from the most abundant proteins can suppress those derived from the low abundant proteins even if the mass/charge ratio is the same6. Although there have been advancements in the depletion and fractionation technologies in the last few years, getting good coverage still remains a major limitation of plasma proteomics8,9. The use of tissues for proteomic investigation of disease biology is preferred as tissue samples are most proximal to the disease sites and offer high physiological and pathological information to provide better insights into the disease biology10,11.
In this manuscript, we have provided a simplified protocol for the quantitative proteomics of tissue samples. We have used a buffer containing 8 M urea for the tissue lysate preparation as this buffer is compatible with mass spectrometry-based investigations. However, it is mandatory to clean the peptides to remove salts before injecting them into the mass spectrometer. One important point to remember is to reduce the urea concentration to less than 1 M before adding trypsin for protein digestion as trypsin exhibits low activity at 8 M urea concentration. We have explained two approaches of quantitative global proteomics: label-based quantification using iTRAQ (isobaric tags for relative and absolute quantification) and label-free quantification (LFQ). The iTRAQ-based quantitative proteomics is mainly used for comparing multiple samples varying in their biological condition (e.g., normal versus disease or treated samples). The approach utilizes isobaric reagents to label the N-terminal primary amines of peptides12. The iTRAQ reagents contain one N-methyl piperazine reporter group, a balancer group, and one N-hydroxy succinimide ester group that reacts with N-terminal primary amines of peptides13. Digested peptides from each condition are labeled with a particular iTRAQ reagent. Following the labeling, the reaction is stopped and labeled peptides from different conditions are pooled into a single tube. This combined sample mixture is analyzed by mass spectrometer for identification and quantification. After the MS/MS analysis, reporter ion fragments with low molecular masses are generated and the ion intensities of these reporter ions are used for the quantification of the proteins.
Another approach, label-free quantification is used to determine the relative number of proteins in complex samples without labeling peptides with stable isotopes.
This study was reviewed and approved by institutional review boards and the ethics committee of the Indian Institute of Technology Bombay (IITB-IEC/2016/026). The patients/participants provided their written consent to participate in this study.
1. Tissue lysate preparation
NOTE: Perform all the following steps on the ice to keep the proteases inactive. Make sure the scalpels and any tubes used are sterile to avoid any cross-contamination.
2. Protein quantification and quality check of tissue lysates
3. Enzymatic digestion of proteins
NOTE: The steps for enzymatic digestion are shown in Figure 1a.
4. Desalting of digested peptides
NOTE: To perform the desalting of peptides, use C18 stage tips.
5. Quantification of desalted peptides
6. Label-free quantitation (LFQ) of the digested peptides
NOTE: For label-free quantitation, use the LC and MS parameters mentioned in the Supplementary File 2. A high coverage data was obtained when three biological replicates of the same type of the sample were run in the mass spectrometer.
7. Label-based quantitation (iTRAQ) of digested peptides
NOTE: Label-based quantification can be performed using different isobaric labels such as iTRAQ or TMT reagents, etc. Here, iTRAQ 4-plex was used for the labeling of digested peptides from three tissue samples. The procedure of iTRAQ 4-plex labeling is mentioned below.
8. Data analysis
We have used two different approaches for discovery proteomics: label-free and label-based proteomics approaches. The protein profile of tissue samples on SDS-PAGE showed the intact proteins and could be considered for proteomic analysis (Figure 2A). The quality control check of the instrument was monitored via system suitability software and it showed the day-wise variation in the instrument performance (Figure 2B). We observed 91% sequence coverage of the BSA sample in 30 min of LC gradient (Figure 2C). The LC gradient was optimized using 500 ng of commercial HeLa cell digest and we observed 2425 proteins in a 2 h gradient as compared to 1488 proteins in a 1 h gradient (Figure 2D). We were able to identify, on an average, 2428 proteins across all three technical replicates of a pool tissue sample (Figure 2E).
The optimized LC and MS parameters were applied to three different biological tissue samples (Figure 3 and Supplementary File 2). The chromatogram showed good reproducibility between three different biological tissue samples. We identified 2725, 2748, and 2718 quantifiable proteins from tissue samples 1, 2, and 3, respectively, using a label-free based approach. We observed that 151 proteins were common in the first and second LFQ experiments, 163 proteins were shared between the second and third LFQ experiments, and 187 proteins were shared between the first and third experiments, while 2190 proteins were common in all three tissue samples (Figure 4A).
We inspected the chromatogram and checked the iTRAQ labels and found it to be present in almost all MS/MS spectrums. The three sets have been run for the iTRAQ experiment. Proteins number obtained from each set were 2455, 2285, and 2307, respectively. 287 proteins were found to be common in sample 1 and sample 2, 183 proteins were common in sample 2 and sample 3, and 195 proteins were common in sample 1 and sample 3. The total number of proteins common in all three samples was 1557 (Figure 4B).
We compared total peptide spectral matches (PSMs), peptide groups, total proteins, protein groups, and the number of proteins obtained after 1% FDR from the LFQ and iTRAQ experiment (Figure 4C).
Figure 1: Workflow for tissue proteomics. (A) The sample processing steps to prepare samples from tissue lysate for the MS analysis. (B) Steps for label-free quantitation. (C) Steps for label-based quantitation. (D) Steps for data analysis using a proteome discoverer. Please click here to view a larger version of this figure.
Figure 2: Quality check control of tissue samples and reproducibility of the instrument. (A) Quality check of tissue lysates on 12% SDS-PAGE (B) Monitoring of some peptides of BSA using Panorama to check the instrument variability across the different days. (C) The sequence coverage of BSA in three technical replicates. (D) Optimization of LC parameters for tissue samples. (E) The number of peptide spectral matches, peptides, and proteins in three different biological samples. Please click here to view a larger version of this figure.
Figure 3: LC and MS parameters for proteomics analysis of tissue sample. (A,B) The liquid chromatography gradient used to separate the peptides for label-free quantitation (A) and label-based quantitation (B) of the tissue sample. (C) The MS parameters for label-free quantitation and label-based quantitation. Please click here to view a larger version of this figure.
Figure 4: Label-free and label-based quantitation of tissue sample. (A) Venn diagram represents the common and exclusive proteins in tissue samples 1, 2, and 3 of the label-free experiment. (B) Venn diagram represents the common and exclusive proteins in tissue samples 1, 2, and 3 of the label-based experiment. (C) The comparative analysis of the number of peptide spectral matches (PSMs), peptide groups, total proteins, protein groups, and protein number after 1% FDR in label-free quantitation (LFQ) and label-based experiment (iTRAQ). Please click here to view a larger version of this figure.
Supplementary Files. Please click here to download this File.
Tissue proteomics of biological samples enables us to explore new potential biomarkers associated with different stages of disease progression. It also explains the mechanism of signaling and pathways associated with disease progression. The described protocol for tissue quantitative proteomics analysis provides reproducible good coverage data. Most of the steps have been adapted from the manufacturer's instructions. In order to obtain high-quality data, the following steps are most crucial. Hence, extra care should be given while performing these steps.
The incomplete digestion of proteins and contamination of keratin may provide less coverage of proteins (n < 1000), thereby affecting the overall experiment. The pH of samples (pH 8) and concentration of urea in the samples (less than 1 M) will ensure the efficient digestion of proteins. The use of fresh buffer and handling of samples with care will reduce the chances of keratin contamination. iTRAQ reagents are extremely costly and it requires a sophisticated MS platform to perform the MS/MS and software to analyze the data. The proteomics experiments are sensitive to contamination from salts, peptide quantification, and labeling efficiency of iTRAQ/TMT reagents. Before the MS/MS analysis, ensure digested peptides are properly desalted to reduce the background noise in the data. In the case of the iTRAQ technique, fragmentation of the attached tag generates a low molecular mass reporter ion that can be used to relatively quantify the peptides and the proteins from which they have originated, whereas for label-free approach, the area under the curve is considered for quantitation. To increase the confidence in the quantitation of proteins, independent validation experiments especially, MRM/PRM should be performed.
The analysis of tissue samples using two quantitation methods (label-free and label-based proteomics) has been described to obtain a good coverage of proteins. The label-free quantitative proteomics approach offers several advantages for its use in clinical studies. The samples are run independently, and this is particularly important for studies that are undertaken for a patient cohort as there are a large number of samples to be analyzed via mass spectrometer. Using a technical replicate such as a pool of optimized peptides, one can ensure good reproducibility even if the samples are run at different time points. This approach has been used in large cohort studies such as the CPTAC, which is an effort of many international communities14.
The potential targets emerging from the study could be considered for validation using targeted proteomics approaches. We conclude that the projects based on tissue samples analysis could be heavily benefitted from the detailed workflows of quantitative proteomics provided in this study. The mentioned steps will help to optimize the method and map the proteome of tissue samples. The selection of quantitative proteomic techniques may depend upon the number of samples, availability of MS platforms, and the biological question to be addressed.
The authors have nothing to disclose.
We acknowledge MHRD-UAY Project (UCHHATAR AVISHKAR YOJANA), project #34_IITB to SS and MASSFIITB Facility at IIT Bombay supported by the Department of Biotechnology (BT/PR13114/INF/22/206/2015) to carry out all MS-related experiments.
Reagents | |||
Acetonitrile (MS grade) | Fisher Scientific | A/0620/21 | |
Bovine Serum Albumin | HiMedia | TC194-25G | |
Calcium chloride | Fischer Scienific | BP510-500 | |
Formic acid (MS grade) | Fisher Scientific | 147930250 | |
Iodoacetamide | Sigma | 1149-25G | |
Isopropanol (MS grade) | Fisher Scientific | Q13827 | |
Magnesium Chloride | Fischer Scienific | BP214-500 | |
Methanol (MS grade) | Fisher Scientific | A456-4 | |
MS grade water | Pierce | 51140 | |
Phosphate Buffer Saline | HiMedia | TL1006-500ML | |
Protease inhibitor cocktail | Roche Diagnostics | 11873580001 | |
Sodium Chloride | Merck | DF6D661300 | |
TCEP | Sigma | 646547 | |
Tris Base | Merck | 648310 | |
Trypsin (MS grade) | Pierce | 90058 | |
Bradford Reagent | Bio-Rad | 5000205 | |
Urea | Merck | MB1D691237 | |
Supplies | |||
Hypersil Gold C18 column | Thermo | 25002-102130 | |
Micropipettes | Gilson | F167380 | |
Stage tips | MilliPore | ZTC18M008 | |
Zirconia/Silica beads | BioSpec products | 11079110z | |
Equipment | |||
Bead beater (Homogeniser) | Bertin Minilys | P000673-MLYS0-A | |
Microplate reader (spectrophotometer) | Thermo | MultiSkan Go | |
pH meter | Eutech | CyberScan pH 510 | |
Probe Sonicator | Sonics Materials, Inc | VCX 130 | |
Shaking Drybath | Thermo | 88880028 | |
Orbitrap Fusion mass spectrometer | Thermo | FSN 10452 | |
Nano LC | Thermo | EASY-nLC1200 | |
Vacuum concentrator | Thermo | Savant ISS 110 | |
Software | |||
Proteome Discoverer | Thrermo | Proteome Discoverer 2.2.0.388 |