Journal of Proteome Research ARTICLE Figure 1. Biological variation of LC MS/MS proteomics. (A) Venn diagrams show protein expression overlap for shotgun proteomic inventories in SW480APC and SW480Null (top). Overlap in three biological replicates is shown for SW480APC (bottom left) and SW480Null (bottom right). Spearman ranked correlations for replicate to replicate comparisons shown in parentheses (B) Stacked plots show percentage of proteins identiﬁed in one, two or three biological replicates in SW80APC and SW480Null. transcript and protein turnover rates as well as post-transciptional regulation, transcriptomic proﬁling alone may not provide an accurate assessment of the eﬀects of a single gene change. Therefore, we examined the eﬀects of a single gene alteration on the proteome and transcriptome. We chose to study the eﬀects of restoration of APC in a colorectal cancer cell line because of the central impor- tance of this gene in colorectal carcinoma development. Shotgun proteomic analysis was performed on three biological replicates of the isogenic SW480Null (mutant APC) and SW- 480APC (APC restored) cell lines (15 IEF fractions per replicate, 2 repeat injections per fraction), as described in Experimental Methods. In three biological replicate analyses we observed aver- ages of 26 100 (9.5%CV) and 24 300 (12.6%CV) conﬁdently matched spectra in SW480APC and SW480Null, respectively. These values corresponded to 4855 (7.4%CV) and 4795 (8.0%CV) protein groups in SW480APC and SW480Null, respectively. Spectral counting was used to determine relative diﬀerences in protein expression between SW480APC and SW480Null. This approach to analysis of shotgun proteomic data provides an alternative quantitative approach to isotope labeling approaches, such as SILAC.12,13,31 Previous studies have shown that spectral counting can achieve reproducible characterization of deﬁned proteomic diﬀerences across multiple laboratories and the quasi- likelihood modeling approach to compare data sets can correct for instrument-to-instrument variations and interlaboratory variation.23,32,33 Moreover, spectral counting-based quantitative proteomic analysis can be universally applied to many types of biospecimens including cell lines, frozen tissues, formalin ﬁxed tissues and blood plasma.16,34 A single discovery platform pro- vides a straightforward way of comparing protein expression signatures across multiple sample types. Therefore, mutation speciﬁc proteomic proﬁles in cell culture models can easily be weighed against proteomic data sets in more complex clinical samples. Although conventional search criteria (2 peptides per pro- teins, 5% peptide-level FDR) were applied to the data set, protein FDR levels remain considerably high (25.7%) after initial data- base searching. False positive peptides tend to be distributed across a large number of proteins. However, in general only a few spectra map to these false positive proteins (average 1.6 spectral counts per protein per cell line). Therefore, by applying a minimum spectra-per-protein ﬁltering criterion, protein FDR improved sub- stantially. After removal of proteins with fewer than 6 spectral counts (minimum one spectrum per biological replicate), a total of 5002 proteins (3.8% protein FDR) remained. Of these 99.2% were detected in both SW480APC and SW480Null (Figure 1A). A majority of proteins was observed in all three biological replicates (SW480APC, 84% and SW480Null, 85%), while relatively few proteins were found in just a single biological replicate (SW480APC, 3% and SW480Null, 3.4%) (Figure 1B). There- fore, 97% of all proteins were observed in at least two of the three biological replicates, indicating the considerable reproducibility of the platform. Individual protein spectral counts in biological replicates (average of Rep1 vs Rep2, Rep 1 vs Rep3, Rep 2 vs Rep 3) show good overall correlation (SW480APC, r = 0.85 and SW480Null, r = 0.88, Spearman ranked correlation). Proteomic and Transcriptomic Comparison To compare proteomic and transcriptomic proﬁles in the SW480APC model, supervised clustering analysis of shotgun proteomic data and corresponding microarray data was carried out (Figure 2). The analysis was limited to 111 diﬀerentially ex- pressed proteins (<0.1 adjusted quasi p-value) that were also detectable by microarray analysis. Clustering analysis revealed that a plurality of proteins and transcripts (76%, 85/111 proteins) show the same directionality of expression change between SW480APC and SW480Null, that is, most proteins down- regulated in SW480APC are also down-regulated at the tran- script level. One distinct cluster of 14 proteins was up-regulated in SW480APC in shotgun proteomics but down-regulated in microarray (Figure 2, red box). This set included cell adhesion proteins CTNNA1 and ACTN1 and phosphatases PPP4R1 and PTPRF. Further analysis of SW480APC/SW80Null log2 ratios for the set of 111 proteins/genes between microarray and proteo- mics revealed only modest correlation (r = 0.503, Spearman ranked correlation). Transcripts found to be signiﬁcantly diﬀerentially expressed by microarray (top 3% ranked by adjusted p-value, 122 genes/proteins) showed similar correlation with corresponding proteomic log2 ratios (r = 0.63, Spearman ranked correlation). It is likely that diﬀerences in turnover rates for proteins and transcripts as well post-transcriptional regulation events 1187 dx.doi.org/10.1021/pr2009109 |J. Proteome Res. 2012, 11, 1184–1195