Improving label-free quantitative proteomics strategies by distributing shared peptides and stabilizing variance

Metaproteomic analysis decodes trophic interactions of microorganisms in the dark ocean

Proteins in the open ocean represent a significant source of organic matter, and their profiles reflect the metabolic activities of marine microorganisms. Here, by analyzing metaproteomic samples collected from the Pacific, Atlantic and Southern Ocean, we reveal size-fractionated patterns of the structure and function of the marine microbiota protein pool in the water column, particularly in the dark ocean (>200 m). Zooplankton proteins contributed three times more than algal proteins to the deep-sea community metaproteome. Gammaproteobacteria exhibited high metabolic activity in the deep-sea, contributing up to 30% of bacterial proteins. Close virus-host interactions of this taxon might explain the dominance of gammaproteobacterial proteins in the dissolved fraction. A high urease expression in nitrifiers suggested links between their dark carbon fixation and zooplankton urea production. In summary, our results uncover the taxonomic contribution of the microbiota to the oceanic protein pool, revealing protein fluxes from particles to the dissolved organic matter pool.

Substrate uptake patterns shape niche separation in marine prokaryotic microbiome

Marine heterotrophic prokaryotes primarily take up ambient substrates using transporters. The patterns of transporters targeting particular substrates shape the ecological role of heterotrophic prokaryotes in marine organic matter cycles. Here, we report a size-fractionated pattern in the expression of prokaryotic transporters throughout the oceanic water column due to taxonomic variations, revealed by a multi-"omics" approach targeting ATP-binding cassette (ABC) transporters and TonB-dependent transporters (TBDTs). Substrate specificity analyses showed that marine SAR11, Rhodobacterales, and Oceanospirillales use ABC transporters to take up organic nitrogenous compounds in the free-living fraction, while Alteromonadales, Bacteroidetes, and Sphingomonadales use TBDTs for carbon-rich organic matter and metal chelates on particles. The expression of transporter proteins also supports distinct lifestyles of deep-sea prokaryotes. Our results suggest that transporter divergency in organic matter assimilation reflects a pronounced niche separation in the prokaryote-mediated organic matter cycles.

Unraveling multifunction of low-temperature Daqu in simultaneous saccharification and fermentation of Chinese light aroma type liquor

Chinese liquor is produced by a representative simultaneous saccharification and fermentation process. Daqu, as a starter of Chinese liquor fermentation, affects both saccharification and fermentation. However, it is still unclear how Daqu contributed to the simultaneous saccharification and fermentation process. Here, using Chinese light aroma type liquor as a case, we identified low-temperature Daqu-originated enzymes and microorganisms that contributed to the simultaneous saccharification and fermentation using metaproteomic analysis combined with amplicon sequencing analysis. α-Amylase and glucoamylase accounted for 95 % of total saccharifying enzymes and were identified as key saccharifying enzymes. Lichtheimia was the key producer of these two enzymes (> 90 %) in low-temperature Daqu. Daqu contributed 90 % α-amylase and 99 % glucoamylase to the initial liquor fermentation. These two enzymes decreased by 35 % and 49 % until day 15 in liquor fermentation. In addition, Daqu contributed key microbial genera (91 % Saccharomyces, 6.5 % Companilactobacillus) and key enzymes (37 % alcohol dehydrogenase, 40 % lactic acid dehydrogenase, 56 % aldehyde dehydrogenase) related with formations of ethanol, lactic acid and flavour compounds to the initial liquor fermentation. The average relative abundances of these fermentation-related key microorganisms and enzymes increased by 2.78 times and 1.29 times till day 15 in liquor fermentation, respectively. It indicated that Daqu provided saccharifying enzymes for starch hydrolysis, and provided both enzymes and microorganisms associated with formations of ethanol, lactic acid and flavour compounds for liquor fermentation. This work illustrated the multifunction of low-temperature Daqu in the simultaneous saccharification and fermentation of Chinese light aroma type liquor. It would facilitate improving liquor fermentation by producing high-quality Daqu.

Application of Machine Learning in Spatial Proteomics

Spatial proteomics is an interdisciplinary field that investigates the localization and dynamics of proteins, and it has gained extensive attention in recent years, especially the subcellular proteomics. Numerous evidence indicate that the subcellular localization of proteins is associated with various cellular processes and disease progression. Mass spectrometry (MS)-based and imaging-based experimental approaches have been developed to acquire large-scale spatial proteomic data. To allow the reliable analysis of increasingly complex spatial proteomics data, machine learning (ML) methods have been widely used in both MS-based and imaging-based spatial proteomic data analysis pipelines. Here, we comprehensively survey the applications of ML in spatial proteomics from following aspects: (1) data resources for spatial proteome are comprehensively introduced; (2) the roles of different ML algorithms in data analysis pipelines are elaborated; (3) successful applications of spatial proteomics and several analytical tools integrating ML methods are presented; (4) challenges existing in modern ML-based spatial proteomics studies are discussed. This review provides guidelines for researchers seeking to apply ML methods to analyze spatial proteomic data and can facilitate insightful understanding of cell biology as well as the future research in medical and drug discovery communities.

Characterization of peptide-protein relationships in protein ambiguity groups via bipartite graphs

In bottom-up proteomics, proteins are enzymatically digested into peptides before measurement with mass spectrometry. The relationship between proteins and their corresponding peptides can be represented by bipartite graphs. We conduct a comprehensive analysis of bipartite graphs using quantified peptides from measured data sets as well as theoretical peptides from an in silico digestion of the corresponding complete taxonomic protein sequence databases. The aim of this study is to characterize and structure the different types of graphs that occur and to compare them between data sets. We observed a large influence of the accepted minimum peptide length during in silico digestion. When changing from theoretical peptides to measured ones, the graph structures are subject to two opposite effects. On the one hand, the graphs based on measured peptides are on average smaller and less complex compared to graphs using theoretical peptides. On the other hand, the proportion of protein nodes without unique peptides, which are a complicated case for protein inference and quantification, is considerably larger for measured data. Additionally, the proportion of graphs containing at least one protein node without unique peptides rises when going from database to quantitative level. The fraction of shared peptides and proteins without unique peptides as well as the complexity and size of the graphs highly depends on the data set and organism. Large differences between the structures of bipartite peptide-protein graphs have been observed between database and quantitative level as well as between analyzed species. In the analyzed measured data sets, the proportion of protein nodes without unique peptides ranged from 6.4% to 55.0%. This highlights the need for novel methods that can quantify proteins without unique peptides. The knowledge about the structure of the bipartite peptide-protein graphs gained in this study will be useful for the development of such algorithms.

Quantitative high-confidence human mitochondrial proteome and its dynamics in cellular context

Mitochondria are key organelles for cellular energetics, metabolism, signaling, and quality control and have been linked to various diseases. Different views exist on the composition of the human mitochondrial proteome. We classified >8,000 proteins in mitochondrial preparations of human cells and defined a mitochondrial high-confidence proteome of >1,100 proteins (MitoCoP). We identified interactors of translocases, respiratory chain, and ATP synthase assembly factors. The abundance of MitoCoP proteins covers six orders of magnitude and amounts to 7% of the cellular proteome with the chaperones HSP60-HSP10 being the most abundant mitochondrial proteins. MitoCoP dynamics spans three orders of magnitudes, with half-lives from hours to months, and suggests a rapid regulation of biosynthesis and assembly processes. 460 MitoCoP genes are linked to human diseases with a strong prevalence for the central nervous system and metabolism. MitoCoP will provide a high-confidence resource for placing dynamics, functions, and dysfunctions of mitochondria into the cellular context.

Bioinformatic Analysis of Temporal and Spatial Proteome Alternations During Infections

Microbial pathogens have evolved numerous mechanisms to hijack host's systems, thus causing disease. This is mediated by alterations in the combined host-pathogen proteome in time and space. Mass spectrometry-based proteomics approaches have been developed and tailored to map disease progression. The result is complex multidimensional data that pose numerous analytic challenges for downstream interpretation. However, a systematic review of approaches for the downstream analysis of such data has been lacking in the field. In this review, we detail the steps of a typical temporal and spatial analysis, including data pre-processing steps (i.e., quality control, data normalization, the imputation of missing values, and dimensionality reduction), different statistical and machine learning approaches, validation, interpretation, and the extraction of biological information from mass spectrometry data. We also discuss current best practices for these steps based on a collection of independent studies to guide users in selecting the most suitable strategies for their dataset and analysis objectives. Moreover, we also compiled the list of commonly used R software packages for each step of the analysis. These could be easily integrated into one's analysis pipeline. Furthermore, we guide readers through various analysis steps by applying these workflows to mock and host-pathogen interaction data from public datasets. The workflows presented in this review will serve as an introduction for data analysis novices, while also helping established users update their data analysis pipelines. We conclude the review by discussing future directions and developments in temporal and spatial proteomics and data analysis approaches. Data analysis codes, prepared for this review are available from https://github.com/BabuLab-UofR/TempSpac, where guidelines and sample datasets are also offered for testing purposes.

Low expression of GSTP1 in the aqueous humour of patients with primary open-angle glaucoma

Primary open‐angle glaucoma (POAG) is characterized by irreversible neurodegeneration accompanied by visual field defects and high intraocular pressure. Currently, an effective treatment is not available to prevent the progression of POAG, other than treatments to decrease the high intraocular pressure. We performed proteomic analysis of aqueous humour (AH) samples from patients with POAG combined with cataract and patients with cataract to obtain a better understanding of the pathogenesis of POAG and explore potential treatment targets for this condition. Samples were collected from 10 patients with POAG combined with cataract and 10 patients with cataract. Samples from each group were pooled. A high‐resolution, label‐free, liquid chromatography‐tandem mass spectrometry‐based quantitative proteomic analysis was performed. In total, 610 proteins were identified in human AH samples from the two groups. A total of 48 up‐regulated proteins and 49 down‐regulated proteins were identified in the POAG combined with cataract group compared with the control group. Gene Ontology (GO) analysis revealed key roles for these proteins in inflammation, immune responses, growth and development, cellular movement and vesicle‐mediated transport in the biological process category. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis indicated the down‐regulated expression of glutathione S‐transferase P (GSTP1) in the glutathione metabolism signalling pathway in the POAG combined with cataract group. Additionally, certain significantly differentially expressed proteins in the proteomic profile were verified by enzyme‐linked immunosorbent assay (ELISA). GSTP1 levels were reduced in the human AH samples from the POAG combined with cataract group, based on the results of ELISA and proteomic profiling. Therefore, GSTP1, a redox‐related marker, may be involved in the pathological process of POAG and may become a treatment target in the future.

Microbial Processing of Jellyfish Detritus in the Ocean

When jellyfish blooms decay, sinking jellyfish detrital organic matter (jelly-OM), rich in proteins and characterized by a low C:N ratio, becomes a significant source of OM for marine microorganisms. Yet, the key players and the process of microbial jelly-OM degradation and the consequences for marine ecosystems remain unclear. We simulated the scenario potentially experienced by the coastal pelagic microbiome after the decay of a bloom of the cosmopolitan Aurelia aurita s.l. We show that about half of the jelly-OM is instantly available as dissolved organic matter and thus, exclusively and readily accessible to microbes. During a typical decay of an A. aurita bloom in the northern Adriatic Sea about 100 mg of jelly-OM L^-1 becomes available, about 44 μmol L^-1 as dissolved organic carbon (DOC), 13 μmol L^-1 as total dissolved nitrogen, 11 μmol L^-1 of total hydrolyzable dissolved amino acids (THDAA) and 0.6 μmol L^-1 PO₄ ^3-. The labile jelly-OM was degraded within 1.5 days (>98% of proteins, ∼70% of THDAA, 97% of dissolved free amino acids and the entire jelly-DOC pool) by a consortium of Pseudoalteromonas, Alteromonas, and Vibrio. These bacteria accounted for >90% of all metabolically active jelly-OM degraders, exhibiting high bacterial growth efficiencies. This implies that a major fraction of the detrital jelly-OM is rapidly incorporated into biomass by opportunistic bacteria. Microbial processing of jelly-OM resulted in the accumulation of tryptophan, dissolved combined amino acids and inorganic nutrients, with possible implications for biogeochemical cycles.

PeptideWitch-A Software Package to Produce High-Stringency Proteomics Data Visualizations from Label-Free Shotgun Proteomics Data

PeptideWitch is a python-based web module that introduces several key graphical and technical improvements to the Scrappy software platform, which is designed for label-free quantitative shotgun proteomics analysis using normalised spectral abundance factors. The program inputs are low stringency protein identification lists output from peptide-to-spectrum matching search engines for ‘control’ and ‘treated’ samples. Through a combination of spectral count summation and inner joins, PeptideWitch processes low stringency data, and outputs high stringency data that are suitable for downstream quantitation. Data quality metrics are generated, and a series of statistical analyses and graphical representations are presented, aimed at defining and presenting the difference between the two sample proteomes.

Proteomic Applications in Antimicrobial Resistance and Clinical Microbiology Studies

Sequences of the genomes of all-important bacterial pathogens of man, plants, and animals have been completed. Still, it is not enough to achieve complete information of all the mechanisms controlling the biological processes of an organism. Along with all advances in different proteomics technologies, proteomics has completed our knowledge of biological processes all around the world. Proteomics is a valuable technique to explain the complement of proteins in any organism. One of the fields that has been notably benefited from other systems approaches is bacterial pathogenesis. An emerging field is to use proteomics to examine the infectious agents in terms of, among many, the response the host and pathogen to the infection process, which leads to a deeper knowledge of the mechanisms of bacterial virulence. This trend also enables us to identify quantitative measurements for proteins extracted from microorganisms. The present review study is an attempt to summarize a variety of different proteomic techniques and advances. The significant applications in bacterial pathogenesis studies are also covered. Moreover, the areas where proteomics may lead the future studies are introduced.

Linking extracellular enzymes to phylogeny indicates a predominantly particle-associated lifestyle of deep-sea prokaryotes

Dominance of dissolved extracellular enzymes indicates that deep-sea prokaryotes are associated mainly with particulate matter.

A Systems Chemoproteomic Analysis of Acyl-CoA/Protein Interaction Networks

Acyl-coenzyme A (CoA)/protein interactions are essential for life. Despite this importance, their global scope and selectivity remains undefined. Here, we describe CATNIP (CoA/AcetylTraNsferase Interaction Profiling), a chemoproteomic platform for the high-throughput analysis of acyl-CoA/protein interactions in endogenous proteomes. First, we apply CATNIP to identify acetyl-CoA-binding proteins through unbiased clustering of competitive dose-response data. Next, we use this method to profile the selectivity of acyl-CoA/protein interactions, leading to the identification of specific acyl-CoA engagement signatures. Finally, we apply systems-level analyses to assess the features of protein networks that may interact with acyl-CoAs, and use a strategy for high-confidence proteomic annotation of acetyl-CoA-binding proteins to identify a site of non-enzymatic acylation in the NAT10 acetyltransferase domain that is likely driven by acyl-CoA binding. Overall, our studies illustrate how chemoproteomics and systems biology can be integrated to understand the roles of acyl-CoA metabolism in biology and disease.

Proteomics Profiling of Autologous Blood and Semen Exosomes from HIV-infected and Uninfected Individuals Reveals Compositional and Functional Variabilities

Blood and semen are important body-fluids that carry exosomes for bioinformation transmission. Therefore, characterization of their proteomes is necessary for understanding body-fluid-specific physiologic and pathophysiologic functions. Using systematic multifactorial proteomic profiling, we characterized the proteomes of exosomes and exosome-free fractions from autologous blood and semen from three HIV-uninfected and three HIV-infected participants (total of 24 samples). We identified exosome-based protein signatures specific to blood and semen along with HIV-induced tissue-dependent proteomic perturbations. We validated our findings with samples from 16 additional donors and showed that unlike blood exosomes (BE), semen exosomes (SE) are enriched in clusterin. SE but not BE promote Protein·Nucleic acid binding and increase cell adhesion irrespective of HIV infection. This is the first comparative study of the proteome of autologous BE and SE. The proteins identified may be developed as biomarkers applicable to different fields of medicine, including reproduction and infectious diseases.

Differential proteomic comparison of breast cancer secretome using a quantitative paired analysis workflow

Background

Worldwide, breast cancer is the main cause of cancer mortality in women. Most cases originate in mammary ductal cells that produce the nipple aspirate fluid (NAF). In cancer patients, this secretome contains proteins associated with the tumor microenvironment. NAF studies are challenging because of inter-individual variability. We introduced a paired-proteomic shotgun strategy that relies on NAF analysis from both breasts of patients with unilateral breast cancer and extended PatternLab for Proteomics software to take advantage of this setup.

Methods

The software is based on a peptide-centric approach and uses the binomial distribution to attribute a probability for each peptide as being linked to the disease; these probabilities are propagated to a final protein p-value according to the Stouffer’s Z-score method.

Results

A total of 1227 proteins were identified and quantified, of which 87 were differentially abundant, being mainly involved in glycolysis (Warburg effect) and immune system activation (activated stroma). Additionally, in the estrogen receptor-positive subgroup, proteins related to the regulation of insulin-like growth factor transport and platelet degranulation displayed higher abundance, confirming the presence of a proliferative microenvironment.

Conclusions

We debuted a differential bioinformatics workflow for the proteomic analysis of NAF, validating this secretome as a treasure-trove for studying a paired-organ cancer type.

Electronic supplementary material

The online version of this article (10.1186/s12885-019-5547-y) contains supplementary material, which is available to authorized users.

Evaluating Chromatographic Approaches for the Quantitative Analysis of a Human Proteome on Orbitrap-Based Mass Spectrometry Systems

The Orbitrap is now a core component of several different instruments. However, evaluating the capabilities of each system is lacking in the field. Here, we compared the performance of multidimensional protein identification (MudPIT) on Velos Pro Orbitrap and Velos Orbitrap Elite mass spectrometers to reversed phase liquid chromatography (RPLC) on a Q-Exactive Plus and an Orbitrap Fusion Lumos. Using HeLa cell protein digests, we carried out triplicate analyses of 16 different chromatography conditions on four different instrumentation platforms. We first optimized RPLC conditions by varying column lengths, inner diameters, and particle sizes. We found that smaller particle sizes improve results but only with smaller inner diameter microcapillary columns. We then selected one chromatography condition on each system and varied gradient lengths. We used distributed normalized spectral abundance factor (dNSAF) values to determine quantitative reproducibility. With Pearson product-moment correlation coefficient r values routinely above 0.96, single RPLC on both the QE+ and Orbitrap Lumos outperformed MudPIT on the Orbitrap Elite mass spectrometer. In addition, when comparing dNSAF values measured for the same proteins across the different platforms, RPLC on the Orbitrap Lumos had greater sensitivity than MudPIT, as demonstrated by the detection and quantification of histone deacetylase complex components. Data are available via ProteomeXchange with identifier 10.6019/PXD009875.

Molecules and elements for quantitative bioanalysis: The allure of using electrospray, MALDI, and ICP mass spectrometry side-by-side

To understand biological processes, not only reliable identification, but quantification of constituents in biological processes play a pivotal role. This is especially true for the proteome: protein quantification must follow protein identification, since sometimes minute changes in abundance tell the real tale. To obtain quantitative data, many sophisticated strategies using electrospray and MALDI mass spectrometry (MS) have been developed in recent years. All of them have advantages and limitations. Several years ago, we started to work on strategies, which are principally capable to overcome some of these limits. The fundamental idea is to use elemental signals as a measure for quantities. We began by replacing the radioactive ³² P with the "cold" natural ³¹ P to quantify modified nucleotides and phosphorylated peptides and proteins and later used tagging strategies for quantification of proteins more generally. To do this, we introduced Inductively Coupled Plasma Mass Spectrometry (ICP-MS) into the bioanalytical workflows, allowing not only reliable and sensitive detection but also quantification based on isotope dilution absolute measurements using poly-isotopic elements. The detection capability of ICP-MS becomes particularly attractive with heavy metals. The covalently bound proteins tags developed in our group are based on the well-known DOTA chelate complex (1,4,7,10-tetraazacyclododecane-N,N',N″,N‴-tetraacetic acid) carrying ions of lanthanoides as metal core. In this review, I will outline the development of this mutual assistance between molecular and elemental mass spectrometry and discuss the scope and limitations particularly of peptide and protein quantification. The lanthanoide tags provide low detection limits, but offer multiplexing capabilities due to the number of very similar lanthanoides and their isotopes. With isotope dilution comes previously unknown accuracy. Separation techniques such as electrophoresis and HPLC were used and just slightly adapted workflows, already in use for quantification in bioanalysis. Imaging mass spectrometry (MSI) with MALDI and laser ablation ICP-MS complemented the range of application in recent years.

Elucidation of the Mechanism of Action for Metal Based Anticancer Drugs by Mass Spectrometry-Based Quantitative Proteomics

The discovery of the anticancer activity of cisplatin and its clinical application has opened a new field for studying metal-coordinated anticancer drugs. Metal-based anticancer drugs, such as cisplatin, can be transported to cells after entering into the human body and form metal–DNA or metal–protein adducts. Then, responding proteins will recognize adducts and form stable complexes. The proteins that were binding with metal-based anticancer drugs were relevant to their mechanism of action. Herein, investigation of the recognition between metal-based anticancer drugs and its binding partners will further our understanding about the pharmacology of cytotoxic anticancer drugs and help optimize the structure of anticancer drugs. The “soft” ionization mass spectrometric methods have many advantages such as high sensitivity and low sample consumption, which are suitable for the analyses of complex biological samples. Thus, MS has become a powerful tool for the identification of proteins binding or responding to metal-based anticancer drugs. In this review, we focused on the mass spectrometry-based quantitative strategy for the identification of proteins specifically responding or binding to metal-based anticancer drugs, ultimately elucidating their mechanism of action.

Corneocyte proteomics: Applications to skin biology and dermatology

Advances in mass spectrometry-based proteomics now permit analysis of complex cellular structures. Application to epidermis and its appendages (nail plate, hair shaft) has revealed a wealth of information about their protein profiles. The results confirm known site-specific differences in levels of certain keratins and add great depth to our knowledge of site specificity of scores of other proteins, thereby connecting anatomy and pathology. An example is the evident overlap in protein profiles of hair shaft and nail plate, helping rationalize their sharing of certain dystrophic syndromes distinct from epidermis. In addition, interindividual differences in protein level are manifest as would be expected. This approach permits characterization of altered profiles as a result of disease, where the magnitude of perturbation can be quantified and monitored during treatment. Proteomic analysis has also clarified the nature of the isopeptide cross-linked residual insoluble material after vigorous extraction with protein denaturants, nearly intractable to analysis without fragmentation. These structures, including the cross-linked envelope of epidermal corneocytes, are comprised of hundreds of protein constituents, evidence for strengthening the terminal structure complementary to disulphide bonding. Along with other developing technologies, proteomic analysis is anticipated to find use in disease risk stratification, detection, diagnosis and prognosis after the discovery phase and clinical validation.

StPeter: Seamless Label-Free Quantification with the Trans-Proteomic Pipeline

Label-free quantification has grown in popularity as a means of obtaining relative abundance measures for proteomics experiments. However, easily accessible and integrated tools to perform label-free quantification have been lacking. We describe StPeter, an implementation of Normalized Spectral Index quantification for wide availability through integration into the widely used Trans-Proteomic Pipeline. This implementation has been specifically designed for reproducibility and ease of use. We demonstrate that StPeter outperforms other state-of-the art packages using a recently reported benchmark data set over the range of false discovery rates relevant to shotgun proteomics results. We also demonstrate that the software is computationally efficient and supports data from a variety of instrument platforms and experimental designs. Results can be viewed within the Trans-Proteomic Pipeline graphical user interfaces and exported in standard formats for downstream statistical analysis. By integrating StPeter into the freely available Trans-Proteomic Pipeline, users can now obtain high-quality label-free quantification of any data set in seconds by adding a single command to the workflow.

Ratiometric Mass Spectrometry for Cell Identification and Quantitation Using Intracellular "Dual-Biomarkers"

This study proposed an easy-to-use method for cell identification and quantitation by ratiometric matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). Two pairs of MS peaks in the molecular fingerprint of cells were selected as intracellular dual-biomarkers due to the stability and specificity of their ratio values in different types of hepatocellular cancer (HCC) cell lines. Five types of HCC cells can be thereafter differentiated based on these two pairs of intracellular peptides/proteins. Two types of HCC cells, Huh7 and LM3 were co-cultured as a model to test whether the method is feasible for cell quantitation. The results indicated that the ratiometric peak intensity of the two pair biomarkers exhibits linear relationship with the proportion of Huh7 cells. Furthermore, tumor heterogeneity was simulated by subcutaneously injecting the co-cultured cells into nude mice. The cell type and proportion in the section of grown tumor tissue can be discriminated using the ratiometric MALDI imaging approach. LC-MS/MS detection revealed that one of the biomarker pairs belongs to thymosin family, β4 and β10. The ratiometric MS spectral approach using intracellular dual-biomarkers might become a pervasive strategy for high-throughput cell identification and quantitation, which is vital in tumor heterogeneity study, clinical diagnosis and drug screening.

Unravelling the biology of chromatin in health and cancer using proteomic approaches

INTRODUCTION

Chromatin remodeling complexes play important roles in the control of genome regulation in both normal and diseased states, and are therefore critical components for the regulation of epigenetic states in cells. Given the role epigenetics plays in cancer, for example, chromatin remodeling complexes are routinely targeted for therapeutic intervention. Areas covered: Protein mass spectrometry and proteomics are powerful technologies used to study and understand chromatin remodeling. While impressive progress has been made in this area, there remain significant challenges in the application of proteomic technologies to the study of chromatin remodeling. As parts of large multi-subunit complexes that can be heavily modified with dynamic post-translational modifications, challenges in the study of chromatin remodeling complexes include defining the content, determining the regulation, and studying the dynamics of the complexes under different cellular states. Expert commentary: Impwortant considerations in the study of chromatin remodeling complexes include the complexity of sample preparation, the choice of proteomic methods for the analysis of samples, and data analysis challenges. Continued research in these three areas promise to yield even greater insights into the biology of chromatin remodeling and epigenetics and the dynamics of these systems in human health and cancer.

Multiple testing corrections in quantitative proteomics: A useful but blunt tool

Multiple testing corrections are a useful tool for restricting the FDR, but can be blunt in the context of low power, as we demonstrate by a series of simple simulations. Unfortunately, in proteomics experiments low power can be common, driven by proteomics-specific issues like small effects due to ratio compression, and few replicates due to reagent high cost, instrument time availability and other issues; in such situations, most multiple testing corrections methods, if used with conventional thresholds, will fail to detect any true positives even when many exist. In this low power, medium scale situation, other methods such as effect size considerations or peptide-level calculations may be a more effective option, even if they do not offer the same theoretical guarantee of a low FDR. Thus, we aim to highlight in this article that proteomics presents some specific challenges to the standard multiple testing corrections methods, which should be employed as a useful tool but not be regarded as a required rubber stamp.

The human cytoplasmic dynein interactome reveals novel activators of motility

In human cells, cytoplasmic dynein-1 is essential for long-distance transport of many cargos, including organelles, RNAs, proteins, and viruses, towards microtubule minus ends. To understand how a single motor achieves cargo specificity, we identified the human dynein interactome by attaching a promiscuous biotin ligase (‘BioID’) to seven components of the dynein machinery, including a subunit of the essential cofactor dynactin. This method reported spatial information about the large cytosolic dynein/dynactin complex in living cells. To achieve maximal motile activity and to bind its cargos, human dynein/dynactin requires ‘activators’, of which only five have been described. We developed methods to identify new activators in our BioID data, and discovered that ninein and ninein-like are a new family of dynein activators. Analysis of the protein interactomes for six activators, including ninein and ninein-like, suggests that each dynein activator has multiple cargos.

DOI:http://dx.doi.org/10.7554/eLife.28257.001

Dynamic and Combinatorial Landscape of Histone Modifications during the Intraerythrocytic Developmental Cycle of the Malaria Parasite

A major obstacle in understanding the complex biology of the malaria parasite remains to discover how gene transcription is controlled during its life cycle. Accumulating evidence indicates that the parasite's epigenetic state plays a fundamental role in gene expression and virulence. Using a comprehensive and quantitative mass spectrometry approach, we determined the global and dynamic abundance of histones and their covalent post-transcriptional modifications throughout the intraerythrocytic developmental cycle of Plasmodium falciparum. We detected a total of 232 distinct modifications, of which 160 had never been detected in Plasmodium and 88 had never been identified in any other species. We further validated over 10% of the detected modifications and their expression patterns by multiple reaction monitoring assays. In addition, we uncovered an unusual chromatin organization with parasite-specific histone modifications and combinatorial dynamics that may be directly related to transcriptional activity, DNA replication, and cell cycle progression. Overall, our data suggest that the malaria parasite has a unique histone modification signature that correlates with parasite virulence.