Volume 15 Supplement 9
Pathway analysis and transcriptomics improve protein identification by shotgun proteomics from samples comprising small number of cells - a benchmarking study
© Sun et al.; licensee BioMed Central Ltd. 2014
Published: 8 December 2014
Proteomics research is enabled with the high-throughput technologies, but our ability to identify expressed proteome is limited in small samples. The coverage and consistency of proteome expression are critical problems in proteomics. Here, we propose pathway analysis and combination of microproteomics and transcriptomics analyses to improve mass-spectrometry protein identification from small size samples.
Multiple proteomics runs using MCF-7 cell line detected 4,957 expressed proteins. About 80% of expressed proteins were present in MCF-7 transcripts data; highly expressed transcripts are more likely to have expressed proteins. Approximately 1,000 proteins were detected in each run of the small sample proteomics. These proteins were mapped to gene symbols and compared with gene sets representing canonical pathways, more than 4,000 genes were extracted from the enriched gene sets. The identified canonical pathways were largely overlapping between individual runs. Of identified pathways 182 were shared between three individual small sample runs.
Current technologies enable us to directly detect 10% of expressed proteomes from small sample comprising as few as 50 cells. We used knowledge-based approaches to elucidate the missing proteome that can be verified by targeted proteomics. This knowledge-based approach includes pathway analysis and combination of gene expression and protein expression data for target prioritization. Genes present in both the enriched gene sets (canonical pathways collection) and in small sample proteomics data correspond to approximately 50% of expressed proteomes in larger sample proteomics data. In addition, 90% of targets from canonical pathways were estimated to be expressed. The comparison of proteomics and transcriptomics data, suggests that highly expressed transcripts have high probability of protein expression. However, approximately 10% of expressed proteins could not be matched with the expressed transcripts.
Proteomics is the large-scale study of proteins expressed in an organism, tissues, or cells.
Protein expression changes over time in response to various stimuli or to change of conditions. Cellular proteome is a set of proteins expressed in a specific cell type, across various subcellular locations, whereas human proteome is a set of proteins encoded by some 25,000 protein-coding genes of human genome . High-throughput sequencing has shown that more than 95% of human protein coding genes produce splice variant transcripts . More than 260,000 protein variants resulting from alternative splicing have been annotated to date . A wide variety of post-translational modifications (PTM) occur in proteins, often changing their structure and function. PTMs include phosphorylation, ubiquitination, glycosylation, methylation, acetylation, sulfation, oxidation, and nitrosylation, among many others . A widely accepted estimate is that more than 2 million protein variants make the posttranslated human proteome in any human individual . This estimate excludes natural recombinant proteins such as T cell receptors and antibodies and the majority of PTMs.
Molecular profiling of samples representing healthy or diseased states, pre- and postintervention, or time series during disease progression is important research topic in biomedicine. Such profiling supports the discovery and evaluation of cellular-level pathways of disease progression, characterization of biomarkers, identification of therapeutic targets, and their applications for improved diagnosis, prognosis, monitoring, and selection of therapies. This quest is supported by the emerging technologies of genomics, transcriptomics, and proteomics. Genomics technologies help identify variation of human genome and its role in disease associations, but they do not provide information about availability of transcripts (RNA expression). Transcriptomics studies the RNA transcribed from a particular genome under various conditions. Transcriptomic studies link the analysis of genome and proteome because they provide critical information about gene regulation and also about availability of mRNA for protein translation. Principal strengths of genomics and transcriptomics lay in the ability to amplify genetic material for extraction of genetic and transcript profiles even from a single cell . On the contrary, proteomics profiling requires utilization of larger numbers of cells. Identification of proteome from a single cell is currently available for the analysis of cell lines  as it allows for a very limited proteome analysis . Currently a single cell proteome analysis is not a viable option for clinical applications. Larger samples yield better proteome coverage, while smaller samples yield progressively smaller coverage of the expressed proteome. On the other hand, transcriptome analysis provides limited information: patterns of changes in gene expression do not necessarily correlate well with patterns of changes in protein expression . The limited correlation of transcript and protein expression is particularly notable in the study of human clinical samples. The reasons for such discrepancies include errors in measurement, noise in regulation of gene expression, presence of posttranslational modifications, variation in gene-specific regulation of translation, and varying dynamics of protein degradation under different conditions . Furthermore, genomic and transcriptomic studies cannot provide information on PTMs, and quantity of proteins. About a third of transcripts, although expressed, do not get translated into proteins  and the lifetime of proteins can differ by orders of magnitude even for proteins that have similar translation rates . Because of their limitations, genomics, transcriptomics, and proteomics complement each other - each of them provides a valuable but incomplete insight into molecular profiles that characterize healthy and diseased states represented by the studied sample.
To achieve clinical goals of proteomics, we need to target samples that often comprise extremely low numbers of cells. These include, among others, circulating tumor cells [12, 13], circulating endothelial cells [14–16], samples collected using fine needle aspirates , and samples collected by laser capture microdissection . Limited numbers of cells are available from samples that contain mixed normal and transformed (tumor) cells, which is a particular problem in tissue samples with early stage cancer . The latest microproteomics methods enable proteome profiling from tiny samples (less than 1,000 cells) and are thus suitable for moving the frontier of clinical applications. Such proteome studies can yield a few thousand proteins [20, 21], which was confirmed in this report. Deep proteomics can yield more than 10,000 proteins from cell line samples , but the number of proteins identifiable from clinical samples is usually much smaller.
Significant improvements in instrumentation (sensitivity, throughput, resolution of separation) , sample processing , and bioinformatics [22, 23] help comprehensive proteome profiling. Nevertheless, proteome profiling suffers from problems of incomplete data coverage and inconsistencies between individual runs . While this problem is less pronounced in protein identification using large samples derived from cell lines where vast majority of proteins are expressed ubiquitously [11, 25] this problem is pronounced when small samples are used. Goh et al.  have argued that biological networks analysis provides robust models and interpretations that increase coverage while the analysis of protein interaction groups and biological pathways help improve coverage of proteins and complement quantitative proteomics data. They defined biological networks as groups of genes or proteins that are linked through a shared set of functional relationships and pathways as well-described biological networks involved in metabolic and regulatory processes. Several methods for utilization of biological networks for improvement of identification (coverage) have been described, including analysis of overlaps, clique enrichment analysis, proteomics expansion pipeline, Maxlink, and shortest-path methods (reviewed in ). The improvement of inconsistencies between different runs can be pursued using overlap analysis, direct group analysis, and network-based analysis (reviewed in ). Gene expression profiles and pathway analysis can be used to define candidates for targeted proteomics  discovery and improve identification sensitivity of expressed proteins. Targeted proteomics is more sensitive than unbiased screening - the sensitivity of specific protein identification using targeted proteomics can be an order of magnitude larger than sensitivity of unbiased screening .
Here, we compared newly measured MCF-7 proteomics data from several small-size samples. The detected proteins were mapped to standardized gene names. The target proteins were predicted to be present in samples using enriched gene sets among canonical pathways collections in MSigDB . In addition, the expanded gene sets were compared to the transcriptomics data extracted from the literature. We benchmarked the consistency and coverage of proteomes identified in different small-sample runs and defined a strategy for proteome profiling and quantitation using the analysis of expressed canonical pathways.
Data set acquisition
107 of MCF-7 human breast adenocarcinoma cells (ATCC, Manassas, VA) were rinsed twice with 1 mL volumes of DPBS and lysed with sonication on ice in 8 M urea, 2 M thiourea, 5 mM TCEP in 25 mM ammonium bicarbonate (ABC), pH 8.4 for 15 min. Extracted proteins were processed using a previously described protocol. In brief, the extract was reduced in 5 mM TCEP for 30 min at room temperature and alkylated in 20 mM iodoacetamide (IAA) for 30 min in the darkness. Prior to digestion, the resulting lysate was diluted 10-fold with 25 mM ABC, pH 8.4 to bring the concentrations of urea and thiourea to 0.8 M and 0.2 M, respectively. Protein digestion was performed with endoproteinase Lys-C (sequencing grade, Promega, Madison, WI) for 4h at an enzyme/substrate (E:S) of approximately 1:50 and followed by an addition of sequencing grade trypsin (Promega) at an E:S ratio of approximately 1:50 and overnight digestion in a shaker at 37 °C. The total volume of the resulting digest was 1000 µL.
The monolithic microSPE and analytical porous layer open tubular (PLOT) columns were polymerized as described in . For LC separation, 5 cm of 50 μm i.d. PS-DVB monolithic SPE precolumn was connected with a 4.2 m PLOT using a PicoClear Tee (NewObjective, Woburn MA). Digested lysates were first loaded on the monolithic SPE precolumn at a flow rate of 200 nL/min using a NCS 3500 RS pump (Dionex, Sunnyvale, CA). Then, entrapped and desalted digests were eluted off the precolumn and separated on the PLOT column using a linear solvent gradient at a 20 nL/min flow rate. The separation was performed using a 4-hour gradient of 0%-27% mobile phase B (mobile phase A: 0.1% FA in water; mobile phase B, 0.1% FA in ACN). Nano ESI spray was enabled using an electrospray voltage of 1.1 kV and a distal coated tip (NewObjective) butt-to-butt connected with an outlet of the PLOT column via a zero dead volume PicoClear union (New Objective). Ion transfer tube temperature was set for 275 0C.
MS detection was performed using a top 12 MS/MS data-dependent scans on the Q Exactive (Thermo Fisher Scientific) mass spectrometer. Full MS scans were acquired over the range of m/z 380-1600 Th with resolution set to 70,000 and an automatic gain control (AGC) target set to 3x106. The 12 most intense parent ions excluding singly charged ions and ions with unassigned charges were selected for higer-energy collisional dissociation (HCD) fragmentation with a normalized collision energy (NCE) set to 28%. The MS/MS spectra were analyzed in the Orbitrap mass analyzer using resolution set to 17,500 and AGC set to 1x105. The isolation window was set to 2 m/z and dynamic exclusion was set to 60 s. The maximum ion injection time was set to 20 ms for full MS scans and 120 ms for MS/MS.
LC-MS/MS raw data files were analyzed using Proteome Discoverer 1.4 (Thermo Fisher Scientific) by two search engines Sequest HT (Thermo) and Mascot (Matrix Science) against the UniProt human database (2013 Jan version, containing 139905 sequences).
Carbamidomethylation (57.021 Da was set as a fixed modification and N-terminal acetylation, methionine oxidation and deamidation (NQ) were set as variable modifications. The precursor peptide mass tolerance was set to 10 ppm and fragment tolerance to 0.05 Da. The results of the searches were combined and validated using the Percolator module with filters set to high peptide identification confidence to achieve a false discovery rate (FDR) ≤1% for SEQUEST and ≤0.5% for Mascot. The proteomics data sets used in this study include proteome profiles from MCF-7 cell line. Proteins were identified from three 240 min gradient runs comprising 50 cells each. These results were compared to data sets comprising triplicate runs with samples comprising 100, 500, 1000, and 5000 MCF-7 cells.
Gene expression data of MCF-7 cells (GEO accession: GSE21946) studied in Patacsil et al.'s work  were downloaded from Gene Expression Omnibus (GEO) . The platform used in this study was Affymetrix Human Genome U133A 2.0 Array. Gene symbols were extracted from the platform data. The data from the Patacsil study was selected because it has both treated and control samples that show good reproducibility of results.
There were 8 samples in the array data and the expression levels of these 8 samples were averaged for each probe. For the genes with multiple probes, the highest average measurement was kept. The variation of gene expression between samples was minimal and we consider these data highly reproducible. For example, 97.7% of all transcripts showed the ratio of signals across 8 samples (max-min)/average at 0.2 (±10%) and 99.6% at 0.3 (±15%). 3) Gene sets/pathways data
Canonical pathways used in this study were downloaded from MSigDB 3.1 . There are 1452 gene sets included in this canonical pathways collection (CP collection). These gene sets represent well-described biological processes compiled by domain experts, which include gene sets derived mainly from BioCarta pathway database , KEGG pathway database , and Reactome pathway database . These canonical pathways mainly include metabolic and signalling pathways that are shared by all cell types.
The Common Repository of Adventitious Proteins  include proteins used in proteomics experiments, contaminants, or proteins used as quantitation standards. Proteins that were removed from proteomics data were: ALBU_BOVIN, ALDOA_RABIT, CAH2_BOVIN, CAS1_BOVIN, CAS2_BOVIN, DHE3_BOVIN, LYSC_LYSEN, TRY1_BOVIN and TRYP_PIG
Proteins without gene annotation
HGNC Gene Nomenclature
The gene names/symbols in proteomics data, transcriptomics data and gene sets in MSigDB are inconsistent in many cases. To solve this problem, all the approved gene symbols and their previous gene symbols/synonyms were downloaded from the HUGO Gene Nomenclature Committee - HGNC  on 8th April, 2013. We screened all genes in above datasets and all gene names/symbols were mapped to the names approved by HUGO.
Overlapping genes between proteomics and transcriptomics data
The genes names in the transcriptomics data were sorted in descending order of their RNA expression levels. The genes that were also detected in proteomics data were marked as 1, while the others were marked as 0. Sliding window of size 50 was applied here: the scores were added up for every consecutive 50 genes on the list. This method enables inspection of the overlaps between proteomics data and the transcriptomics data and find out protein content distribution relative to the transcript expression level.
Gene set enrichment analysis
where N is the number of genes in gene sets collections, K is the number of genes in gene set A, n is the number of proteins in PS, k is the number of proteins in PS that overlap with gene set A. The p-value of "if PS is enriched in A" will be calculated by summing up the probability from P(X=k) to P(X=n).
Gene set enrichment analysis was done for each run of proteomics data from 50-cell samples and the gene set used was CP collections. Gene sets with p-value below 0.01 were kept for further analysis.
Verification of predicted genes
Genes in mapped gene set were compared to the detected targets in each run and all detected targets in proteomics data as well.
Proteins/genes detected in proteomics data
Mass spectrometry proteomics analysis was performed in experiments that used samples of 50-, 100-, 500-, 1500-, and 5000-cell sample sizes. Each sample was run in triplicate. The Common Repository of Adventitious Proteins (cRAP) collects proteins that are commonly found in proteomics experiments due to accidents or contamination of protein samples and was used to eliminate these proteins. After removing cRAP and duplicate proteins from the lists, a total of 5,032 proteins were detected from the proteomics data.
Enrichment of proteomics data in expression level-grouped transcriptomics data.
Number of proteins transcripts
Number of mapped
% of proteins mapped to transcript data
% of all protein detected (within 4,957)
19.52% transcript set
Genes and their expression level in transcriptomics data
Mapping of proteomics data onto transcriptomics data
The analysis of the overlap between proteomics and transcriptomics data resulted in 3,989 proteins, representing 80.47% of the identified proteome. The remaining 19.53% of identified proteome was not available for transcript analysis (Table 1).
Sliding window analysis
To assess the relationship of transcript expression and presence of expressed proteins we analysed the presence of expressed proteins in the sliding window of 50 transcripts sorted from the highest to the lowest expression level. Transcripts that had high expression level were more likely to have their corresponding protein expressed ranging from 98-100% (transcript expression level >14) to approximately 10% (transcript expression level <6) (Figure 4). The protein presence numbers within the 50-member transcript sliding window drops linearly from the highest expression transcripts to become stable after around ~7,500 transcripts in the sorted list. This corresponds well with the selected threshold for the positives of transcript expression (≥6 units with 7,757 individual transcripts were deemed positive).
Enrichment of proteomics data in expression-level-grouped transcriptomics data
Given the results of concordance of protein and transcript expression shown in Figure 3, RNA expression level 6.0 was chosen as a threshold to cluster genes in transcriptomics data: higher expression group (genes with highest RNA expression level above or equal to 6.0) and lower expression group (genes with highest RNA expression level below 6.0).
The gene presence of proteomics data were further analysed in these two expression groups (Table 1). Proteomics data in genes with higher RNA expression level (greater than or equal to 6.0) is obviously more enriched than those in genes with lower RNA expression level.
Mapping of proteins from small samples to canonical pathways (CP collections)
Gene sets mapping from CP collections.
# proteins/genes detected
exist in CP1
# mapped gene sets
# genes in mapped gene sets
for 3 runs
The number of proteins in the smallest set, comprising proteins that were detected in each of the three runs, was 682 constituting 58.5%, 70.2% and 75.0% of proteins detected in the Runs 1, 2, and 3. The number of gene sets identified from each small sample proteomic run is very similar, 221-227, and they are similar to the "twice" group. Intersection yields the smallest number of mapped pathways, 190, while "union" group yielded 244 gene sets. These sets are largely overlapping indicating the subsets of the same proteomes have been captured in each run.
Verification of mapped genes derived from small sample
From mapped genes to proteomics data
From mapped genes to transcriptomics data
The overlapping numbers of mapped genes from 50-cell sample, detected proteins in all proteomic runs to transcriptomics data.
# mapped genes
Presence in transcriptomics data
# detected proteins
All proteomic runs
Alternatively, these differences may be attributed to differences in samples used in transcriptomic and proteomic experiments, but may also represent the products that are part of canonical pathways that do not have protein expression in the studied samples.
Discussion and conclusions
Small sample analysis of expressed proteome is critical for many clinical samples since they represent points in time for disease progression in individual patients. We used the MCF-7 breast cancer cell line to benchmark the number of proteins that can be detected by using microscale proteomics and have developed strategies to increase the coverage of protein detection.
Proteomics data suffer from problems of coverage and consistency. The problem becomes worse as the sample size diminishes. In this study, pathway analysis has been confirmed as a useful method for improving protein identification in proteomics data. Approximately 1,000 proteins were detected in each small sample run followed by the identification of approximately 4,000 possible expressed protein targets. The proteomics data from larger samples experimentally validated approximately half of these probable targets in this study. Comparing these 4,000 possible targets to transcriptomics data, more than 80% of targets are highly likely to be present, especially enriched in the group of higher RNA expression. Our estimate is that only 10% of predicted proteome by canonical pathways may represent false positives. In addition, it appears that the predicted proteomes based on each individual run, intersection of proteins from three runs, or union of proteins from all three runs will produce very similar predicted proteomes. This indicates a remarkable robustness of the method reported in this study.
Naming conventions and nomenclature raise a problem when processed data are derived from multiple sources. It is also a problem when data are derived from a single source at different time points because of the changes and updates of gene and protein names. We have used the standardized symbols and have mapped proteomics, transcriptomics, and gene set (pathway) data to the common list of HUGO gene symbols. Approximately 20% of detected proteome could not be mapped to the HUGO gene symbols because these proteins either did not have corresponding gene symbols, the names were ambiguous and could not be resolved, or the products have been removed from the recent database update as obsolete or redundant.
Proteomics technology has improved and we can detect a significant proportion of the expressed proteome from small samples, such as 50 cells samples. However this detection initially amounts to only 10% of the estimated total expressed proteome. Knowledge-based approaches are needed to elucidate the likely presence of proteins that can be subsequently detected by targeted proteomics. These KB-approaches include analysis of pathways and combination of gene expression and protein expression data. Using meta-analysis, we have shown that most of the proteins, perhaps 90%, identified as members of canonical pathways - pathways common for all cell types - are likely to be expressed as proteins. These proteins represent approximately 50% of the expressed proteome. The remaining proteins can be elucidated by the analysis of tissue-, organ-, process-, or disease-specific pathways. Furthermore, targets that are represented by highly expressed transcripts are more likely to be expressed as proteins (98-100% for transcripts that show highest expression levels). Approximately 10% of transcripts that show low or no expression have their proteins expressed, as detected in the proteomics runs. This may include a number of false positives due to different sources used in this study for the analysis of transcriptome and proteome, but it is highly likely that the majority of expressed proteins are real.
In summary, proteomics detection of protein expression from small samples can be enriched by pathway analysis followed by targeted proteomics. Furthermore, gene expression data can be used for prioritization of potential targets for deep proteomics screening. This study has provided benchmark results that will facilitate proteogenomics studies for detecting expressed proteomes from samples comprising small numbers of cells.
Contribution Number 1044 from the Barnett Institute.
The cost of this publication was funded by Vladimir Brusic.
This article has been published as part of BMC Genomics Volume 15 Supplement 9, 2014: Thirteenth International Conference on Bioinformatics (InCoB2014): Computational Biology. The full contents of the supplement are available online at http://www.biomedcentral.com/bmcgenomics/supplements/15/S9.
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