Proteomic Measurements Research Articles

The utility of protein and mRNA correlation

Transcriptomic, proteomic, and metabolomic measurements are revolutionizing the way we model and predict cellular behavior, and multi-omic comparisons are being published with increased regularity. Some have expected a trivial and predictable correlation between mRNA and protein; however, the manifest complexity of biological regulation suggests a more nuanced relationship. Indeed, observing this lack of strict correlation provides clues for new research topics, and has the potential for transformative biological insight.

Development of an enhanced metaproteomic approach for deepening the microbiome characterization of the human infant gut.

The establishment of early life microbiota in the human infant gut is highly variable and plays a crucial role in host nutrient availability/uptake and maturation of immunity. Although high-performance mass spectrometry (MS)-based metaproteomics is a powerful method for the functional characterization of complex microbial communities, the acquisition of comprehensive metaproteomic information in human fecal samples is inhibited by the presence of abundant human proteins. To alleviate this restriction, we have designed a novel metaproteomic strategy based on double filtering (DF) the raw samples, a method that fractionates microbial from human cells to enhance microbial protein identification and characterization in complex fecal samples from healthy premature infants. This method dramatically improved the overall depth of infant gut proteome measurement, with an increase in the number of identified low-abundance proteins and a greater than 2-fold improvement in microbial protein identification and quantification. This enhancement of proteome measurement depth enabled a more extensive microbiome comparison between infants by not only increasing the confidence of identified microbial functional categories but also revealing previously undetected categories.

Abstract 2483: An antibody-independent, complementary SRM strategy for ultrasensitive and multiplexed quantification of cancer biomarker candidates

Abstract Stable-isotope dilution coupled to targeted mass spectrometry (MS), applying selected reaction monitoring (SRM), provides good selectivity, reproducibility, sensitivity, and multiplexing capability rendering it highly suitable for robust, high-throughput and cost-effective verification of cancer biomarker candidates. The ability of SRM-MS to detect and quantify low abundance proteins present in blood plasma/serum at low ng/mL or even lower levels, however, relies on the front-end enrichment through specific affinity reagents (e.g., antibodies). To address this limitation, we have recently developed two antibody-independent, targeted quantification capabilities that enable protein quantification at low or sub ng/mL levels in plasma/serum. The first new capability, PRISM (high-pressure high-resolution separations with intelligent selection and multiplexing)-SRM, uses high resolution basic pH reversed-phase capillary liquid chromatographic (LC) separation with on-line SRM monitoring for the enrichment of target peptides and intelligent selection of target fractions for subsequent LC-SRM analysis that uses very short LC gradient to achieve better sensitivity and improved sample throughput. Application of PRISM-SRM enables highly sensitivity detection of several prostate cancer relevant proteins, e.g., prostate-specific antigen (PSA) and anterior gradient 2 (AGR2) proteins at ∼100 pg/mL level in serum, and distinct TMPRSS2-ERG fusion protein isoforms in cell line and tumor tissue samples. The front-end sample prefractionation often requires a relatively large amount of starting material (e.g., 25 ug in PRISM-SRM analysis) and increased instrument analysis time, and undesired sample losses are often encountered during multistep sample preparation. These can be effectively overcome by the second new capability, long-gradient separations coupled with SRM (LG-SRM). Our results show that LG-SRM enables reliable quantification of plasma proteins at low ng/mL levels in nondepleted human plasma/serum. Comparing to fractionation-based SRM methods, LG-SRM offers better sample throughput, requires minute amounts of starting materials (e.g., ∼4 μg), and provides at least 3-fold greater multiplexing capacity than conventional LC-SRM due to ∼3-fold increase in average peak widths. The complementary SRM strategy encompasses both PRISM-SRM and LG-SRM and is therefore highly attractive for the sensitive and multiplexed quantification of cancer biomarker candidates - PRISM-SRM offers ∼10X higher sensitivity, but LG-SRM requires much less sample and can be readily implemented. It is anticipated that these affinity reagent-free, highly sensitive proteomics measurements made possible by this complementary SRM strategy will bring new biological insights and perspectives to prostate cancer research as well as other cancers. Citation Format: Tujin Shi, Jintang He, Chaochao Wu, Thomas L. Fillmore, Athena A. Schepmoes, Mark Rubin, Javed Siddiqui, John Wei, Arul Chinnaiyan, Alvin Liu, Richard D. Smith, Jacob Kagan, Sudhir Srivastava, Karin D. Rodland, Wei-Jun Qian, Tao Liu, David G. Camp. An antibody-independent, complementary SRM strategy for ultrasensitive and multiplexed quantification of cancer biomarker candidates. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 2483. doi:10.1158/1538-7445.AM2014-2483

Abstract 2499: Application of a multiplexed fluorescence microscopy method (MultiOmyxTM) to dissect proteomic biomarkers of (18)F-fluorodeoxy-glucose ((18)FDG) uptake in breast cancer

Abstract Background: While routinely collected human tumor specimen can now readily be examined for genetic alterations on a genome wide scale, multi-parameter measurements of protein expression remain challenging. We developed a multiplexed fluorescence microscopy method (MultiOmyxTM) for the quantitative characterization of multiple analytes in formalin-fixed paraffin-embedded tissue (PMID23818604). We now applied this platform to human breast cancer samples to determine the expression of 25 proteins at a single-cell level and examine their relationship to tumor uptake of the Positron Emission Tomography (PET) radiotracer (18)F-fluorodeoxy-glucose ((18)FDG). Methodology: We stained a single 5 µm section of breast carcinoma from each of 18 patients with antibodies against members of growth factor signaling pathways (HER2, IGF1R, PTEN, p-EGFR, p-PDK1, p-ERK1/2, p-S6 Ribosomal Protein, p-4EBP1, p-eIF4E), the glycolysis pathway (Glut-1, HK2, LDH-A), hormone receptors (ER, PR, AR), tumor cell proliferation markers (KI-67 and phospho-histone H3), and markers of hypoxia (HIF-1α, CA IX), and angiogenesis (CD31). 28-30 representative fields of view (FOVs) were randomly selected in each tumor, each comprising 300-500 cells. Images were automatically separated into subcellular and histopathological compartments based on the staining of tumor cells with a panel of segmentation markers (NaK-ATPase, pan-Cadherin, pan-cytokeratin, S6, and DAPI). A breast pathologist assessed and annotated the histologic composition of each FOV. Further analysis focused on FOVs (n=390) containing only invasive ductal carcinoma without admixed DCIS or normal breast tissue. Multivariate analysis between marker expression and FDG uptake was performed using logistic regression and Cox proportional hazard models. All patients had (18)-FDG PET within four weeks prior to collection of the tumor specimen. Results: Staining results with our MultiOmyxTM platform correlated closely with the results from CLIA-certified single marker biomarker assays (e.g., ER IHC and HER2 FISH assay) performed independently on the same set of samples (PMID: 21646475). Nuclear ER staining was associated with low FDG uptake (p=0.02). KI-67 was higher in tumors with high FDG uptake (p=0.04). K-median clustering identified molecular breast cancer subtypes. Conclusions: Our study illustrates the feasibility of quantitative proteomic measurements using a new in-situ multiplexed fluorescence microscopy platform on only a single routinely collected FFPE tissue section. Quantitative staining results from many thousand cells per tumor support the previously reported relationship between hormone receptor status, tumor cell proliferation, and FDG-uptake in breast cancer. Multiple protein markers showed a high degree of intra- and inter-tumoral heterogeneity. Citation Format: Anup Sood, Alexandra M. Miller, Fiona Ginty, Elizabeth McDonough, Yunxia Sui, Alexander Bordwell, Qing Li, Sireesha Kaanumalle, Zhengyu Pang, Franklin Torres, Edi Brogi, Steven Larson, Ingo Mellinghoff. Application of a multiplexed fluorescence microscopy method (MultiOmyxTM) to dissect proteomic biomarkers of (18)F-fluorodeoxy-glucose ((18)FDG) uptake in breast cancer. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 2499. doi:10.1158/1538-7445.AM2014-2499

Ovarian endometriosis signatures established through discovery and directed mass spectrometry analysis.

New molecular information on potential therapeutic targets or tools for noninvasive diagnosis for endometriosis are important for patient care and treatment. However, surprisingly few efforts have described endometriosis at the protein level. In this work we enumerate the proteins in patient endometrium and ovarian endometrioma by extensive and comprehensive analysis of minute amounts of cryosectioned tissues in a three-tiered mass spectrometric approach. Quantitative comparison of the tissues revealed 214 differentially expressed proteins in ovarian endometrioma and endometrium. These proteins are reported here as a resource of SRM (selected reaction monitoring) assays that are unique, standardized, and openly available. Pathway analysis of the proteome measurements revealed a potential role for Transforming growth factor β-1 in ovarian endometriosis development. Subsequent mRNA microarray analysis further revealed clear ovarian endometrioma specificity for a subset of these proteins, which was also supported by further in silico studies. In this process two important proteins emerged, Calponin-1 and EMILIN-1, that were additionally confirmed in ovarian endometrioma tissues by immunohistochemistry and Western blotting. This study provides the most comprehensive molecular description of ovarian endometriosis to date and researchers with new molecular methods and tools for high throughput patient screening using the SRM assays.

Bayesian Proteoform Modeling Improves Protein Quantification of Global Proteomic Measurements.

As the capability of mass spectrometry-based proteomics has matured, tens of thousands of peptides can be measured simultaneously, which has the benefit of offering a systems view of protein expression. However, a major challenge is that with an increase in throughput, protein quantification estimation from the native measured peptides has become a computational task. A limitation to existing computationally-driven protein quantification methods is that most ignore protein variation, such as alternate splicing of the RNA transcript and post-translational modifications or other possible proteoforms, which will affect a significant fraction of the proteome. The consequence of this assumption is that statistical inference at the protein level, and consequently downstream analyses, such as network and pathway modeling, have only limited power for biomarker discovery. Here, we describe a Bayesian model (BP-Quant) that uses statistically derived peptides signatures to identify peptides that are outside the dominant pattern, or the existence of multiple over-expressed patterns to improve relative protein abundance estimates. It is a research-driven approach that utilizes the objectives of the experiment, defined in the context of a standard statistical hypothesis, to identify a set of peptides exhibiting similar statistical behavior relating to a protein. This approach infers that changes in relative protein abundance can be used as a surrogate for changes in function, without necessarily taking into account the effect of differential post-translational modifications, processing, or splicing in altering protein function. We verify the approach using a dilution study from mouse plasma samples and demonstrate that BP-Quant achieves similar accuracy as the current state-of-the-art methods at proteoform identification with significantly better specificity. BP-Quant is available as a MatLab ® and R packages at https://github.com/PNNL-Comp-Mass-Spec/BP-Quant.

Development of a new ion mobility time-of-flight mass spectrometer

A new ion mobility spectrometer (IMS) platform was developed to improve upon the sensitivity and reproducibility of our previous platforms, and further enhance IMS–MS utility for broad ‘pan-omics’ measurements. The new platform incorporated an improved electrospray ionization source and interface for enhanced sensitivity, and providing the basis for further benefits based upon implementation of multiplexed IMS. The ion optics included electrodynamic ion funnels at both the entrance and exit of the IMS, an ion funnel trap for ion injection, and a design in which nearly all ion optics (e.g., drift rings, ion funnels) were fabricated using printed circuit board technology. The IMS resolving power achieved was ∼73 for singly-charged ions, very close to the predicted diffusion-limited resolving power (∼75). The platform’s performance evaluation (e.g., for proteomics measurements) include LC–IMS–TOF MS datasets for 30 technical replicates for a trypsin digested human serum, and included platform performance in each dimension (LC, IMS and MS) separately.

Translational Research: From Biological Discovery to Public Benefit (or Not)

Advances in biology are occurring at a breathtaking pace today, from genetic insights facilitated by the Human Genome Project and next generation DNA sequencing technologies, to global nucleic acid and proteomic expression measurement using new high-throughput methods. Less publicized in recent years, yet still the central driver of progress, are the steadily proceeding biological insights gained through tried and true hypothesis-driven investigation into the complex worlds of metabolism, growth, development, and regulation. Certainly, the basic science ecosystem is productive and this portends well for the myriad new applications that will benefit mankind; drugs, vaccines, devices, and related economic growth—or perhaps not—in stark contrast to the generation of fundamental biological knowledge are inefficiencies in applying this information to real-world problems, especially those of the clinic. While investigation hums along at light speed, translation often does not. The good news is that obstacles to progress are tractable. The bad news, however, is that these problems are difficult. The present paper examines translational research from multiple perspectives, beginning with a historical account and proceeding to the current state of the art. Included are descriptions of successes and challenges, along with conjecture on how the field may need to evolve in the future.

Elucidation of Zymomonas mobilis physiology and stress responses by quantitative proteomics and transcriptomics.

Zymomonas mobilis is an excellent ethanologenic bacterium. Biomass pretreatment and saccharification provides access to simple sugars, but also produces inhibitors such as acetate and furfural. Our previous work has identified and confirmed the genetic change of a 1.5-kb deletion in the sodium acetate tolerant Z. mobilis mutant (AcR) leading to constitutively elevated expression of a sodium proton antiporter encoding gene nhaA, which contributes to the sodium acetate tolerance of AcR mutant. In this study, we further investigated the responses of AcR and wild-type ZM4 to sodium acetate stress in minimum media using both transcriptomics and a metabolic labeling approach for quantitative proteomics the first time. Proteomic measurements at two time points identified about eight hundreds proteins, or about half of the predicted proteome. Extracellular metabolite analysis indicated AcR overcame the acetate stress quicker than ZM4 with a concomitant earlier ethanol production in AcR mutant, although the final ethanol yields and cell densities were similar between two strains. Transcriptomic samples were analyzed for four time points and revealed that the response of Z. mobilis to sodium acetate stress is dynamic, complex, and involved about one-fifth of the total predicted genes from all different functional categories. The modest correlations between proteomic and transcriptomic data may suggest the involvement of posttranscriptional control. In addition, the transcriptomic data of forty-four microarrays from four experiments for ZM4 and AcR under different conditions were combined to identify strain-specific, media-responsive, growth phase-dependent, and treatment-responsive gene expression profiles. Together this study indicates that minimal medium has the most dramatic effect on gene expression compared to rich medium followed by growth phase, inhibitor, and strain background. Genes involved in protein biosynthesis, glycolysis and fermentation as well as ATP synthesis and stress response play key roles in Z. mobilis metabolism with consistently strong expression levels under different conditions.

Mechanisms of hexabromocyclododecanes induced developmental toxicity in marine medaka (Oryzias melastigma) embryos

Hexabromocyclododecanes (HBCDs) are widely used as additive brominated flame retardants, and are now ubiquitous contaminants in the environmental media and biota, including the marine environment and marine organisms. However, the impacts of HBCDs on marine fish are not well known. In this study the embryos of marine medaka (Oryzias melastigma) were used to assess the developmental toxicity of HBCDs. Freshly fertilized marine medaka embryos were exposed to various concentrations of technical HBCD (tHBCD, 0, 5, 20 and 50μg/L) until the first fry stage, and hatch success, morphology and cardiac function were examined. In all the exposure groups (5, 20 and 50μg/L) tHBCD significantly increased the embryo heart beats. The measurement of sinus venosus–bulbus arteriosus (SV–BA) distance indicated that tHBCD significantly enlarged the SV–BA distance at exposure concentrations of 20 and 50μg/L. The malformation rate at the first fry stage was also induced by tHBCD in a dose dependent manner, with the formation of pericardial edema and yolk sac edema as the most frequently observed malformation. In addition, the concentrations of total HBCD isomers (ΣHBCDs) in embryos in the current study were comparative with environmental levels and increased with increasing exposure duration. Furthermore, exposure to tHBCD also induced the level of 8-oxodG, a representative oxidative DNA damage. The mechanisms of HBCD-induced developmental toxicity were further explored by TUNEL assay, gel-based quantitative proteomic approach and measurement of the expression of several stress responsive genes, such as p53, TNF-α, IL-1β, CYP1A, COX-1 and COX-2, together with the activities of caspases. The results suggested that HBCDs exposure at environmentally realistic concentrations induced oxidative stress and apoptosis, and suppressed nucleotide and protein synthesis, which all together resulted in developmental toxicity, particularly in the cardiovascular system, in the embryos of O. melastigma.

Cross-Correlation of Spectral Count Ranking to Validate Quantitative Proteome Measurements

The measurement of change in biological systems through protein quantification is a central theme in modern biosciences and medicine. Label-free MS-based methods have greatly increased the ease and throughput in performing this task. Spectral counting is one such method that uses detected MS2 peptide fragmentation ions as a measure of the protein amount. The method is straightforward to use and has gained widespread interest. Additionally reports on new statistical methods for analyzing spectral count data appear at regular intervals, but a systematic evaluation of these is rarely seen. In this work, we studied how similar the results are from different spectral count data analysis methods, given the same biological input data. For this, we chose the algorithms Beta Binomial, PLGEM, QSpec, and PepC to analyze three biological data sets of varying complexity. For analyzing the capability of the methods to detect differences in protein abundance, we also performed controlled experiments by spiking a mixture of 48 human proteins in varying concentrations into a yeast protein digest to mimic biological fold changes. In general, the agreement of the analysis methods was not particularly good on the proteome-wide scale, as considerable differences were found between the different algorithms. However, we observed good agreements between the methods for the top abundance changed proteins, indicating that for a smaller fraction of the proteome changes are measurable, and the methods may be used as valuable tools in the discovery-validation pipeline when applying a cross-validation approach as described here. Performance ranking of the algorithms using samples of known composition showed PLGEM to be superior, followed by Beta Binomial, PepC, and QSpec. Similarly, the normalized versions of the same method, when available, generally outperformed the standard ones. Statistical detection of protein abundance differences was strongly influenced by the number of spectra acquired for the protein and, correspondingly, its molecular mass.

Abstract P6-05-01: Basal and induced receptor profiles cluster cell lines into subtypes and predict drug response in a panel of breast cancer lines

Abstract Molecular subtype is a critical determinant of therapeutic approaches in breast cancer. The subtype is based on the expression of three receptors: The Her2/ErbB2 receptor tyrosine kinase (RTK), whose over-expression defines the HER2amp subtype, and the estrogen or progesterone nuclear hormone receptors, whose over-expression defines the HR+ subtype. Triple negative breast cancers (TNBCs) express low levels of all three receptors. HER2amp status serves as a biomarker for therapy with anti-Her2/ErbB2 antibodies such as trastuzumab or pertuzumab and HR+ status is a biomarker for therapy with hormone receptor antagonists such as tamoxifen. TNBCs are usually treated with cytotoxic chemotherapy. However, breast cancer subtypes are heterogeneous, clinical and mRNA subtypes are not identical, and even the best available biomarker, HER2amp status, correctly predicts response to trastuzumab in only a subset of patients. Projects such as the Cancer Cell Line Encyclopedia aim to identify drug response determinants by relating dose-response profiles for multiple compounds to genomic features across cancer cell collections. Recent studies have suggested that measures of target activity such as phosphorylation are more correlated with drug response than mutation status or gene expression, and steady-state protein data is becoming available for tumors and cell lines. However, few studies have tried to relate the biochemistry of signal transduction to drug response on a large scale, mainly because the measurements are demanding and the utility of the approach is unproven. In this work, we ask whether measurement of the basal and perturbed states of immediate-to-early signaling proteins in serum-starved cells, a robust measurement that can be collected with relatively little biological variability, is predictive of tumor subtype as well as of drug response. We measured the abundance and basal phosphorylation state of nuclear and cell surface receptors and of downstream signaling kinases in the standardized NCI-ICBP43 cell line collection. Because perturbation reveals features of signal transduction that are not apparent by steady state profiling, and because biological ligands present in the microenvironment can alter drug sensitivity, we also collected response profiles by exposing cells to a diverse set of growth factors and cytokines and measuring the activities of downstream signaling kinases before and after ligand addition. We present how the resulting set of ∼3×105 receptor and cell response measurements segregated with clinical subtype and how these proteomic measurements predict the sensitivity of cells to a range of targeted anti-cancer drugs. Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P6-05-01.

Pre-Analytical and Analytical Variability in Absolute Quantitative Mrm-Based Plasma Proteomic Studies

Quantitative plasma proteomics, through the use of targeted MRM-MS and isotopically labeled standards, is emerging as a popular technique to address biological- and biomedical-centered queries. High precision and accuracy are essential in such measurements, particularly in protein biomarker research where translation to the clinic is sought. Standardized procedures and routine performance evaluation of all stages of the workflow (both pre-analytical and analytical) are therefore imperative to satisfy these requisites and enable high inter-laboratory reproducibility and transferability. In this review, we first discuss the pre-analytical and analytical variables that can affect the precision and accuracy of 'absolute' quantitative plasma proteomic measurements. Proposed strategies to limit such variability will then be highlighted and unmet needs for future exploration will be noted. Although there is no way to conduct a truly comprehensive review on this broad, rapidly changing topic, we have highlighted key aspects and included references to review articles on various sub-topics.

Understanding the Role of Proteolytic Digestion on Discovery and Targeted Proteomic Measurements Using Liquid Chromatography Tandem Mass Spectrometry and Design of Experiments

Workflows in bottom-up proteomics have traditionally implemented the use of proteolysis during sample preparation; enzymatic digestion is most commonly performed using trypsin. This results in the hydrolysis of peptide bonds forming tryptic peptides, which can then be subjected to LC-MS/MS analysis. While the structure, specificity, and kinetics of trypsin are well characterized, a lack of consensus and understanding has remained regarding fundamental parameters critical to obtaining optimal data from a proteomics experiment. These include the type of trypsin used, pH during digestion, incubation temperature as well as enzyme-to-substrate ratio. Through the use of design of experiments (DOE), we optimized these parameters, resulting in deeper proteome coverage and a greater dynamic range of measurement. The knowledge gained from optimization of a discovery-based proteomics experiment was applied to targeted LC-MS/MS experiments using protein cleavage-isotope dilution mass spectrometry for absolute quantification. We demonstrated the importance of these digest parameters with respect to our limit of detection as well as our ability to acquire more accurate quantitative measurements. Additionally, we were able to quantitatively account for peptide decay observed in previous studies, caused by nonspecific activity of trypsin. The tryptic digest optimization described here has eliminated this previously observed peptide decay as well as provided a greater understanding and standardization for a common but critical sample treatment used across the field of proteomics.

Drug Synergy Screen and Network Modeling in Dedifferentiated Liposarcoma Identifies CDK4 and IGF1R as Synergistic Drug Targets

Dedifferentiated liposarcoma (DDLS) is a rare but aggressive cancer with high recurrence and low response rates to targeted therapies. Increasing treatment efficacy may require combinations of targeted agents that counteract the effects of multiple abnormalities. To identify a possible multicomponent therapy, we performed a combinatorial drug screen in a DDLS-derived cell line and identified cyclin-dependent kinase 4 (CDK4) and insulin-like growth factor 1 receptor (IGF1R) as synergistic drug targets. We measured the phosphorylation of multiple proteins and cell viability in response to systematic drug combinations and derived computational models of the signaling network. These models predict that the observed synergy in reducing cell viability with CDK4 and IGF1R inhibitors depends on the activity of the AKT pathway. Experiments confirmed that combined inhibition of CDK4 and IGF1R cooperatively suppresses the activation of proteins within the AKT pathway. Consistent with these findings, synergistic reductions in cell viability were also found when combining CDK4 inhibition with inhibition of either AKT or epidermal growth factor receptor (EGFR), another receptor similar to IGF1R that activates AKT. Thus, network models derived from context-specific proteomic measurements of systematically perturbed cancer cells may reveal cancer-specific signaling mechanisms and aid in the design of effective combination therapies.

Bridging the gap between transcriptome and proteome measurements identifies post-translationally regulated genes

Despite much dynamical cellular behaviour being achieved by accurate regulation of protein concentrations, messenger RNA abundances, measured by microarray technology, and more recently by deep sequencing techniques, are widely used as proxies for protein measurements. Although for some species and under some conditions, there is good correlation between transcriptome and proteome level measurements, such correlation is by no means universal due to post-transcriptional and post-translational regulation, both of which are highly prevalent in cells. Here, we seek to develop a data-driven machine learning approach to bridging the gap between these two levels of high-throughput omic measurements on Saccharomyces cerevisiae and deploy the model in a novel way to uncover mRNA-protein pairs that are candidates for post-translational regulation. The application of feature selection by sparsity inducing regression (l₁ norm regularization) leads to a stable set of features: i.e. mRNA, ribosomal occupancy, ribosome density, tRNA adaptation index and codon bias while achieving a feature reduction from 37 to 5. A linear predictor used with these features is capable of predicting protein concentrations fairly accurately (R² = 0.86). Proteins whose concentration cannot be predicted accurately, taken as outliers with respect to the predictor, are shown to have annotation evidence of post-translational modification, significantly more than random subsets of similar size P < 0.02. In a data mining sense, this work also shows a wider point that outliers with respect to a learning method can carry meaningful information about a problem domain.

Disruptive by design: a perspective on engineering in analytical chemistry.

Perhaps paradoxically, we argue that the biological sciences are "data-limited". In contrast to the glut of DNA sequencing data available, high-throughput protein analysis is expensive and largely inaccessible. Hence, we posit that access to robust protein-level data is inadequate. Here, we use the framework of the formal engineering design process to both identify and understand the problems facing measurement science in the 21st century. In particular, discussion centers on the notable challenge of realizing protein analyses that are as effective (and transformative) as genomics tools. This Perspective looks through the lens of a case study on protein biomarker validation and verification, to highlight the importance of iterative design in realizing significant advances over currently available measurement capabilities in the candidate or targeted proteomics space. The Perspective follows a podium presentation given by the author at The 16th International Conference on Miniaturized Systems for Chemistry and Life Sciences (μTAS 2012), specifically focusing on novel targeted proteomic measurement tools based in microfluidic design. The role of unmet needs identification, iteration in concept generation and development, and the existing gap in rapid prototyping tools for separations are all discussed.

The Human Eye Proteome Project: perspectives on an emerging proteome.

There are an estimated 285 million people with visual impairment worldwide, of whom 39 million are blind. The pathogenesis of many eye diseases remains poorly understood. The human eye is currently an emerging proteome that may provide key insight into the biological pathways of disease. We review proteomic investigations of the human eye and present a catalogue of 4842 nonredundant proteins identified in human eye tissues and biofluids to date. We highlight the need to identify new biomarkers for eye diseases using proteomics. Recent advances in proteomics do now allow the identification of hundreds to thousands of proteins in tissues and fluids, characterization of various PTMs and simultaneous quantification of multiple proteins. To facilitate proteomic studies of the eye, the Human Eye Proteome Project (HEPP) was organized in September 2012. The HEPP is one of the most recent components of the Biology/Disease-driven Human Proteome Project (B/D-HPP) whose overarching goal is to support the broad application of state-of-the-art measurements of proteins and proteomes by life scientists studying the molecular mechanisms of biological processes and human disease. The large repertoire of investigative proteomic tools has great potential to transform vision science and enhance understanding of physiology and disease processes that affect sight.

Microbial metabolomics: welcome to the real world!

In the post-genomics era there has been a growing emphasis on understanding the metabolic consequences of engineering cells to produce desired chemicals. Proteomics measurements have improved our understanding of gene expression, and metabolomics promises to take us a step further. A consequence of the metabolome being further down the line from gene function is that the interpretation of metabolomics data reflects more closely the activities of a cell at the functional level and potentially leads to the revelation of metabolite function. This latter point is of great importance especially when we consider that a given metabolite may come from more than one metabolic pathway and may impact the cell in multiple ways. Further, by accurately quantifying the metabolome, we can begin to understand the stoichiometric significance of metabolite production. Since metabolomics was first coined, it was received with a great deal of fanfare. Like the earlier omics techniques, it was thought that comprehensive metabolomics data sets would be a staple of functional genomics analyses. That was until it was realized that the structural diversity that exists within the metabolome would make it virtually impossible, with current analytical technologies, to characterize the metabolomes of microorganisms fully. To make matters worse, the diverse chemical and physical properties within the metabolome, together with the structural differences in the cell walls of microorganisms, meant that it would be extremely difficult to establish a single sample extraction procedure that was suitable for all metabolites. Added to the fact that the recovery of metabolites would largely depend on the extraction protocol used, meant that the achievement of global metabolite analysis was not a trivial matter. Questions that should have been asked at the time were, how much of the metabolome was necessary to characterize metabolism and what aspects of metabolomics data were truly meaningful? While the goal of complete holistic metabolomics data sets is still what we aspire to achieve, at present we have softened our stance on metabolomics by focusing on local metabolite profiling. In addition to this, we have concentrated our efforts on looking for specific differences in metabolite levels in response to genetic modification, stress responses and environmental change. Even though this is a far cry from global metabolite profiling, these approaches combined with the latest analytical technologies are being used to successfully unravel gene function and to identify biomarkers and unknown metabolites. The most promising techniques that are currently employed in microbial metabolomics research are nuclear magnetic resonance (NMR), array-based mass spectrometry (MS), gas chromatography–MS (GC–MS), liquid chromatography–MS (LC–MS), and capillary electrophoresis–MS (CE–MS). Though NMR can provide high throughput fingerprinting and structural elucidation, cost, sensitivity and quantification are issues that need to be addressed if it is to compete with the MS-based technologies. While arraybased techniques, such as nanostructure-initiator MS (NIMS), are ideal for screening crude microbial extracts, developments in nano surface technology are essential if E. E. K. Baidoo J. D. Keasling (&) Physical Biosciences Division, Joint Bioenergy Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA e-mail: jdkeasling@lbl.gov

Sipros/ProRata: a versatile informatics system for quantitative community proteomics

Sipros/ProRata is an open-source software package for end-to-end data analysis in a wide variety of community proteomics measurements. A database-searching program, Sipros 3.0, was developed for accurate general-purpose protein identification and broad-range post-translational modification searches. Hybrid Message Passing Interface/OpenMP parallelism of the new Sipros architecture allowed its computation to be scalable from desktops to supercomputers. The upgraded ProRata 3.0 performs label-free quantification and isobaric chemical labeling quantification in addition to metabolic labeling quantification. Sipros/ProRata is a versatile informatics system that enables identification and quantification of proteins and their variants in many types of community proteomics studies. Both programs are freely available under the GNU GPL license at Sipros.omicsbio.org and ProRata.omicsbio.org.