A data analysis framework for combining multiple batches increases the power of isobaric proteomics experiments.

We present a framework for the analysis of multiplexed mass spectrometry proteomics data that reduces estimation error when combining multiple isobaric batches. Variations in the number and quality of observations have long complicated the analysis of isobaric proteomics data. Here we show that the power to detect statistical associations is substantially improved by utilizing models that directly account for known sources of variation in the number and quality of observations that occur across batches.In a multibatch benchmarking experiment, our open-source software (msTrawler) increases the power to detect changes, especially in the range of less than twofold changes, while simultaneously increasing quantitative proteome coverage by utilizing more low-signal observations. Further analyses of previously published multiplexed datasets of 4 and 23 batches highlight both increased power and the ability to navigate complex missing data patterns without relying on unverifiable imputations or discarding reliable measurements.

Conditional Fragment Ion Probabilities Improve Database Searching for Nonmonoisotopic Precursors.

Stochastic, intensity-based precursor isolation can result in isotopically enriched fragment ions. This problem is exacerbated for large peptides and stable isotope labeling experiments using deuterium or 15N. For stable isotope labeling experiments, incomplete and ubiquitous labeling strategies result in the isolation of peptide ions composed of many distinct structural isomers. Unfortunately, existing proteomics search algorithms do not account for this variability in isotopic incorporation, and thus often yield poor peptide and protein identification rates. We sought to resolve this shortcoming by deriving the expected isotopic distributions of each fragment ion and incorporating them into the theoretical mass spectra used for peptide-spectrum-matching. We adapted the Comet search platform to integrate a modified spectral prediction algorithm we term Conditional fragment Ion Distribution Search (CIDS). Comet-CIDS uses a traditional database searching strategy, but for each candidate peptide we compute the isotopic distribution of each fragment to better match the observed m/z distributions. Evaluating previously generated D2O and 15N labeled data sets, we found that Comet-CIDS identified more confident peptide spectral matches and higher protein sequence coverage compared to traditional theoretical spectra generation, with the magnitude of improvement largely determined by the amount of labeling in the sample.

Triple-threat quantitative multiplexed plasma proteomics analysis on immune complex disease MRL-lpr mice.

Systemic lupus erythematosus is a common autoimmune inflammatory disease which is associated with increases in autoantibodies and immune complexes that deposit in the kidney. The MRL-lpr mouse is a common mouse model used for the study of lupus and immune complex glomerulonephritis but very little is known about the plasma proteome changes in this model. We performed in-depth quantitative proteome profiling on MRL-lpr and control (strain MpJ) mice to investigate the changes in the proteome, immunoglobulins and their glycoproteome as well as protein and immune complexes. Methodologies used included immunohistochemistry, immunoglobulin isotyping, multiplexed proteome profiling, immunoglobulin immunoprecipitation with glycoproteome profiling, and size exclusion chromatography (SEC) profiling to enable a comprehensive proteome profiling of proteins and protein complexes. We also used a novel native multiplexed plasma proteome profiling (NativeMP3) method that relies on native enrichment of plasma proteins enabling ultra-deep single shot profiling where we identified 922 plasma proteins at 1% false discovery rate (FDR) in a single shot mass spectrometry run. We observed many large plasma protein differences between the MRL-lpr and control strain including differences in the immunoglobulins, immunoglobulins against specific antigens, chemokines, and proteases as well as changes in protein complexes such as the immunoproteasome.

Automated 16-Plex Plasma Proteomics with Real-Time Search and Ion Mobility Mass Spectrometry Enables Large-Scale Profiling in Naked Mole-Rats and Mice.

Performing large-scale plasma proteome profiling is challenging due to limitations imposed by lengthy preparation and instrument time. We present a fully automated multiplexed proteome profiling platform (AutoMP3) using the Hamilton Vantage liquid handling robot capable of preparing hundreds to thousands of samples. To maximize protein depth in single-shot runs, we combined 16-plex Tandem Mass Tags (TMTpro) with high-field asymmetric waveform ion mobility spectrometry (FAIMS Pro) and real-time search (RTS). We quantified over 40 proteins/min/sample, doubling the previously published rates. We applied AutoMP3 to investigate the naked mole-rat plasma proteome both as a function of the circadian cycle and in response to ultraviolet (UV) treatment. In keeping with the lack of synchronized circadian rhythms in naked mole-rats, we find few circadian patterns in plasma proteins over the course of 48 h. Furthermore, we quantify many disparate changes between mice and naked mole-rats at both 48 h and one week after UV exposure. These species differences in plasma protein temporal responses could contribute to the pronounced cancer resistance observed in naked mole-rats. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE [1] partner repository with the dataset identifier PXD022891.

Insights into the Molecular Basis of Genome Stability and Pristine Proteostasis in Naked Mole-Rats.

The naked mole-rat (Heterocephalus glaber) is the longest-lived rodent, with a maximal reported lifespan of 37 years. In addition to its long lifespan - which is much greater than predicted based on its small body size (longevity quotient of ~4.2) - naked mole-rats are also remarkably healthy well into old age. This is reflected in a striking resistance to tumorigenesis and minimal declines in cardiovascular, neurological and reproductive function in older animals. Over the past two decades, researchers have been investigating the molecular mechanisms regulating the extended life- and health- span of this animal, and since the sequencing and assembly of the naked mole-rat genome in 2011, progress has been rapid. Here, we summarize findings from published studies exploring the unique molecular biology of the naked mole-rat, with a focus on mechanisms and pathways contributing to genome stability and maintenance of proteostasis during aging. We also present new data from our laboratory relevant to the topic and discuss our findings in the context of the published literature.

Row versus column correlations: avoiding the ecological fallacy in RNA/protein expression studies.

Biomedical researchers are often interested in computing the correlation between RNA and protein abundance. However, correlations can be computed between rows of a data matrix or between columns, and the results are not the same. The belief that these two types of correlation are estimating the same phenomenon is a special case of a well-known logical error called the ecological fallacy. In this article, we review different uses of correlation found in the literature, explain the differences between row and column correlations and argue that one of them has an undesirable interpretation in most applications. Through simulation studies and theoretical derivations, we show that the commonly used Pearson's coefficient, computed from protein and transcript data from a single sample, is only loosely related to the biological correlation that most researchers will be interested in studying. Beyond our basic exploration of the ecological fallacy, we examine how correlations are affected by relative quantification proteomics data and common normalization procedures, finding that double normalization is capable of completely masking true correlative relationships. We conclude with guidelines for properly identifying and computing consistent correlation coefficients.

Future directions in advanced penile cancer - mechanisms of carcinogenesis and a search for targeted therapy.

Penile squamous cell carcinoma (SCC) is a rare and aggressive urological malignancy. Advanced penile SCC requires multimodal management, including surgery and systemic therapy. Given its rarity, there have been few substantial advances in our understanding of the molecular and genomic drivers of penile SCC, especially for patients with relapsed or advanced disease. In this review, we discuss the molecular and genomic landscape of penile SCC, clinical trials in progress and implications for novel therapeutic targets. Future work should focus on preclinical models to provide a platform for investigation and validation of new molecular pathways for testing of therapeutics.

Proteome-Wide Evaluation of Two Common Protein Quantification Methods.

Proteomics experiments commonly aim to estimate and detect differential abundance across all expressed proteins. Within this experimental design, some of the most challenging measurements are small fold changes for lower abundance proteins. While bottom-up proteomics methods are approaching comprehensive coverage of even complex eukaryotic proteomes, failing to reliably quantify lower abundance proteins can limit the precision and reach of experiments to much less than the identified-let alone total-proteome. Here we test the ability of two common methods, a tandem mass tagging (TMT) method and a label-free quantitation method (LFQ), to achieve comprehensive quantitative coverage by benchmarking their capacity to measure 3 different levels of change (3-, 2-, and 1.5-fold) across an entire data set. Both methods achieved comparably accurate estimates for all 3-fold-changes. However, the TMT method detected changes that reached statistical significance three times more often due to higher precision and fewer missing values. These findings highlight the importance of refining proteome quantitation methods to bring the number of usefully quantified proteins into closer agreement with the number of total quantified proteins.

Compositional Proteomics: Effects of Spatial Constraints on Protein Quantification Utilizing Isobaric Tags.

Mass spectrometry (MS) has become an accessible tool for whole proteome quantitation with the ability to characterize protein expression across thousands of proteins within a single experiment. A subset of MS quantification methods (e.g., SILAC and label-free) monitor the relative intensity of intact peptides, where thousands of measurements can be made from a single mass spectrum. An alternative approach, isobaric labeling, enables precise quantification of multiple samples simultaneously through unique and sample specific mass reporter ions. Consequently, in a single scan, the quantitative signal comes from a limited number of spectral features (≤11). The signal observed for these features is constrained by automatic gain control, forcing codependence of concurrent signals. The study of constrained outcomes primarily belongs to the field of compositional data analysis. We show experimentally that isobaric tag proteomics data are inherently compositional and highlight the implications for data analysis and interpretation. We present a new statistical model and accompanying software that improves estimation accuracy and the ability to detect changes in protein abundance. Finally, we demonstrate a unique compositional effect on proteins with infinite changes. We conclude that many infinite changes will appear small and that the magnitude of these estimates is highly dependent on experimental design.

Accelerating high-dimensional clustering with lossless data reduction.

MOTIVATION: For cluster analysis, high-dimensional data are associated with instability, decreased classification accuracy and high-computational burden. The latter challenge can be eliminated as a serious concern. For applications where dimension reduction techniques are not implemented, we propose a temporary transformation which accelerates computations with no loss of information. The algorithm can be applied for any statistical procedure depending only on Euclidean distances and can be implemented sequentially to enable analyses of data that would otherwise exceed memory limitations. RESULTS: The method is easily implemented in common statistical software as a standard pre-processing step. The benefit of our algorithm grows with the dimensionality of the problem and the complexity of the analysis. Consequently, our simple algorithm not only decreases the computation time for routine analyses, it opens the door to performing calculations that may have otherwise been too burdensome to attempt. AVAILABILITY AND IMPLEMENTATION: R, Matlab and SAS/IML code for implementing lossless data reduction is freely available in the Appendix. CONTACT: obrienj@hms.harvard.edu.

QuantFusion: Novel Unified Methodology for Enhanced Coverage and Precision in Quantifying Global Proteomic Changes in Whole Tissues.

Single quantitative platforms such as label-based or label-free quantitation (LFQ) present compromises in accuracy, precision, protein sequence coverage, and speed of quantifiable proteomic measurements. To maximize the quantitative precision and the number of quantifiable proteins or the quantifiable coverage of tissue proteomes, we have developed a unified approach, termed QuantFusion, that combines the quantitative ratios of all peptides measured by both LFQ and label-based methodologies. Here, we demonstrate the use of QuantFusion in determining the proteins differentially expressed in a pair of patient-derived tumor xenografts (PDXs) representing two major breast cancer (BC) subtypes, basal and luminal. Label-based in-spectra quantitative peptides derived from amino acid-coded tagging (AACT, also known as SILAC) of a non-malignant mammary cell line were uniformly added to each xenograft with a constant predefined ratio, from which Ratio-of-Ratio estimates were obtained for the label-free peptides paired with AACT peptides in each PDX tumor. A mixed model statistical analysis was used to determine global differential protein expression by combining complementary quantifiable peptide ratios measured by LFQ and Ratio-of-Ratios, respectively. With minimum number of replicates required for obtaining the statistically significant ratios, QuantFusion uses the distinct mechanisms to "rescue" the missing data inherent to both LFQ and label-based quantitation. Combined quantifiable peptide data from both quantitative schemes increased the overall number of peptide level measurements and protein level estimates. In our analysis of the PDX tumor proteomes, QuantFusion increased the number of distinct peptide ratios by 65%, representing differentially expressed proteins between the BC subtypes. This quantifiable coverage improvement, in turn, not only increased the number of measurable protein fold-changes by 8% but also increased the average precision of quantitative estimates by 181% so that some BC subtypically expressed proteins were rescued by QuantFusion. Thus, incorporating data from multiple quantitative approaches while accounting for measurement variability at both the peptide and global protein levels make QuantFusion unique for obtaining increased coverage and quantitative precision for tissue proteomes.

Improved Method for Determining Absolute Phosphorylation Stoichiometry Using Bayesian Statistics and Isobaric Labeling.

Phosphorylation stoichiometry, or occupancy, is one element of phosphoproteomics that can add useful biological context (Gerber et al. Proc. Natl. Acad. Sci. U. S. A. 2003, 100, 6940-5). We previously developed a method to assess phosphorylation stoichiometry on a proteome-wide scale (Wu et al. Nat. Methods 2011, 8, 677-83). The stoichiometry calculation relies on identifying and measuring the levels of each nonphosphorylated counterpart peptide with and without phosphatase treatment. The method, however, is problematic in that low stoichiometry phosphopeptides can return negative stoichiometry values if measurement error is larger than the percent stoichiometry. Here, we have improved the stoichiometry method through the use of isobaric labeling with 10-plex TMT reagents. In this way, five phosphatase treated and five untreated samples are compared simultaneously so that each stoichiometry is represented by five ratio measurements with no missing values. We applied the method to determine basal stoichiometries of HCT116 cells growing in culture. With this method, we analyzed five biological replicates simultaneously with no need for phosphopeptide enrichment. Additionally, we developed a Bayesian model to estimate phosphorylation stoichiometry as a parameter confined to an interval between 0 and 1 implemented as an R/Stan script. Consequently, both point and interval estimates are consistent with the plausible range of values for stoichiometry. Finally, we report absolute stoichiometry measurements with credible intervals for 6772 phosphopeptides containing at least a single phosphorylation site.

Novel insights from a multiomics dissection of the Hayflick limit.

The process wherein dividing cells exhaust proliferative capacity and enter into replicative senescence has become a prominent model for cellular aging in vitro. Despite decades of study, this cellular state is not fully understood in culture and even much less so during aging. Here, we revisit Leonard Hayflick's original observation of replicative senescence in WI-38 human lung fibroblasts equipped with a battery of modern techniques including RNA-seq, single-cell RNA-seq, proteomics, metabolomics, and ATAC-seq. We find evidence that the transition to a senescent state manifests early, increases gradually, and corresponds to a concomitant global increase in DNA accessibility in nucleolar and lamin associated domains. Furthermore, we demonstrate that senescent WI-38 cells acquire a striking resemblance to myofibroblasts in a process similar to the epithelial to mesenchymal transition (EMT) that is regulated by t YAP1/TEAD1 and TGF-ß2. Lastly, we show that verteporfin inhibition of YAP1/TEAD1 activity in aged WI-38 cells robustly attenuates this gene expression program.

The Influence of Sound-Based Interventions on Motor Behavior After Stroke: A Systematic Review.

Objective: To investigate the effects of sound-based interventions (SBIs) on biomechanical parameters in stroke patients. Methods: PubMed/Medline, Web of Science, the Physiotherapy Evidence Database (PEDro), and the Cochrane Library were searched until September 2019. Studies examining the effect of SBIs on kinematic, kinetic, and electromyographic outcome measures were included. Two independent reviewers performed the screening, and data extraction and risk-of-bias assessment were conducted with the PEDro and Newcastle-Ottawa scale. Disagreements were resolved by a third independent reviewer. Results: Of the 858 studies obtained from all databases, 12 studies and 240 participants met the inclusion and exclusion criteria. Six studies investigated the effect of SBI on upper limb motor tasks, while six examined walking. Concerning quality assessment (Newcastle-Ottawa Quality Assessment Scale and PEDro), the nine cross-sectional studies had a median score of seven, while the randomized controlled trials had a median score of five (fair to good quality). In relation to upper limb motor tasks, only one study found improvements in cortical reorganization and increased central excitability and motor control during reaching after SBI (results of the other five studies were too diverse and lacked quality to substantiate their findings). In relation to walking, results were clearer: SBI led to improvements in knee flexion and gastrocnemius muscle activity. Conclusion: Despite of the heterogeneity of the included studies, evidence was found demonstrating that SBI can induce biomechanical changes in motor behavior during walking in stroke patients. No conclusions could be formulated regarding reaching tasks. Additionally, directions for future research for understanding the underlying mechanism of the clinical improvements after SBI are: (1) using actual music pieces instead of rhythmic sound sequences and (2) examining sub-acute stroke rather than chronic stroke patients.

Effect of music listening on hypertonia in neurologically impaired patients-systematic review.

BACKGROUND: As music listening is able to induce self-perceived and physiological signs of relaxation, it might be an interesting tool to induce muscle relaxation in patients with hypertonia. To this date effective non-pharmacological rehabilitation strategies to treat hypertonia in neurologically impaired patients are lacking. Therefore the aim is to investigate the effectiveness of music listening on muscle activity and relaxation. METHODOLOGY: The search strategy was performed by the PRISMA guidelines and registered in the PROSPERO database (no. 42019128511). Seven databases were systematically searched until March 2019. Six of the 1,684 studies met the eligibility criteria and were included in this review. Risk of bias was assessed by the PEDro scale. In total 171 patients with a variety of neurological conditions were included assessing hypertonia with both clinicall and biomechanical measures. RESULTS: The analysis showed that there was a large treatment effect of music listening on muscle performance (SMD 0.96, 95% CI [0.29-1.63], I 2 = 10%, Z = 2.82, p = 0.005). Music can be used as either background music during rehabilitation (dual-task) or during rest (single-task) and musical preferences seem to play a major role in the observed treatment effect. CONCLUSIONS: Although music listening is able to induce muscle relaxation, several gaps in the available literature were acknowledged. Future research is in need of an accurate and objective assessment of hypertonia.

Systematic identification of cancer cell vulnerabilities to natural killer cell-mediated immune surveillance.

Only a subset of cancer patients respond to T-cell checkpoint inhibitors, highlighting the need for alternative immunotherapeutics. We performed CRISPR-Cas9 screens in a leukemia cell line to identify perturbations that enhance natural killer effector functions. Our screens defined critical components of the tumor-immune synapse and highlighted the importance of cancer cell interferon-γ signaling in modulating NK activity. Surprisingly, disrupting the ubiquitin ligase substrate adaptor DCAF15 strongly sensitized cancer cells to NK-mediated clearance. DCAF15 disruption induced an inflamed state in leukemic cells, including increased expression of lymphocyte costimulatory molecules. Proteomic and biochemical analysis revealed that cohesin complex members were endogenous client substrates of DCAF15. Genetic disruption of DCAF15 was phenocopied by treatment with indisulam, an anticancer drug that functions through DCAF15 engagement. In AML patients, reduced DCAF15 expression was associated with improved survival. These findings suggest that DCAF15 inhibition may have useful immunomodulatory properties in the treatment of myeloid neoplasms.

eIF2B activator prevents neurological defects caused by a chronic integrated stress response.

The integrated stress response (ISR) attenuates the rate of protein synthesis while inducing expression of stress proteins in cells. Various insults activate kinases that phosphorylate the GTPase eIF2 leading to inhibition of its exchange factor eIF2B. Vanishing White Matter (VWM) is a neurological disease caused by eIF2B mutations that, like phosphorylated eIF2, reduce its activity. We show that introduction of a human VWM mutation into mice leads to persistent ISR induction in the central nervous system. ISR activation precedes myelin loss and development of motor deficits. Remarkably, long-term treatment with a small molecule eIF2B activator, 2BAct, prevents all measures of pathology and normalizes the transcriptome and proteome of VWM mice. 2BAct stimulates the remaining activity of mutant eIF2B complex in vivo, abrogating the maladaptive stress response. Thus, 2BAct-like molecules may provide a promising therapeutic approach for VWM and provide relief from chronic ISR induction in a variety of disease contexts.

Differentiating the dementias. Revisiting synucleinopathies and tauopathies.

Dementia is a common, chronic and progressive illness. Many different types of dementia exist. It is important to have knowledge of the various dementia presentations so that the clinician can differentiate one type from another. Past and current approaches of classifying dementias are reviewed in this paper. The past cortical/subcortical scheme is reviewed as well as the current synucleinopathy/tauopathy scheme. This paper focuses on the most common synucleinopathies and tauopathies including Alzheimer's Dementia, Dementia with Lewy Bodies, Parkinson's Disease, Frontotemporal Dementia, Progressive Supranuclear Palsy, Multiple System Atrophy and Corticobasal Ganglionic Degeneration. We systematically approach each dementia and review cognitive, psychiatry and neurological features of each. We also compare and contrast each dementia and the synucleinopathies and taupoathies alike. Our goal is to provide the clinician with sufficient knowledge to competently and confidently diagnose a patient who presents with progressive cognitive decline and deterioration in functioning.

The effects of nonignorable missing data on label-free mass spectrometry proteomics experiments.

An idealized version of a label-free discovery mass spectrometry proteomics experiment would provide absolute abundance measurements for a whole proteome, across varying conditions. Unfortunately, this ideal is not realized. Measurements are made on peptides requiring an inferential step to obtain protein level estimates. The inference is complicated by experimental factors that necessitate relative abundance estimation and result in widespread non-ignorable missing data. Relative abundance on the log scale takes the form of parameter contrasts. In a complete-case analysis, contrast estimates may be biased by missing data and a substantial amount of useful information will often go unused. To avoid problems with missing data, many analysts have turned to single imputation solutions. Unfortunately, these methods often create further difficulties by hiding inestimable contrasts, preventing the recovery of interblock information and failing to account for imputation uncertainty. To mitigate many of the problems caused by missing values, we propose the use of a Bayesian selection model. Our model is tested on simulated data, real data with simulated missing values, and on a ground truth dilution experiment where all of the true relative changes are known. The analysis suggests that our model, compared with various imputation strategies and complete-case analyses, can increase accuracy and provide substantial improvements to interval coverage.

Quantitative mass spectrometry-based multiplexing compares the abundance of 5000 S. cerevisiae proteins across 10 carbon sources.

UNLABELLED: The budding yeast Saccharomyces cerevisiae is a model system for investigating biological processes. Cellular events are known to be dysregulated due to shifts in carbon sources. However, the comprehensive proteomic alterations thereof have not been fully investigated. Here we examined proteomic alterations in S. cerevisiae due to the adaptation of yeast from glucose to nine different carbon sources - maltose, trehalose, fructose, sucrose, glycerol, acetate, pyruvate, lactic acid, and oleate. Isobaric tag-based mass spectrometry techniques are at the forefront of global proteomic investigations. As such, we used a TMT10-plex strategy to study multiple growth conditions in a single experiment. The SPS-MS3 method on an Orbitrap Fusion Lumos mass spectrometer enabled the quantification of over 5000 yeast proteins across ten carbon sources at a 1% protein-level FDR. On average, the proteomes of yeast cultured in fructose and sucrose deviated the least from those cultured in glucose. As expected, gene ontology classification revealed the major alteration in protein abundances occurred in metabolic pathways and mitochondrial proteins. Our protocol lays the groundwork for further investigation of carbon source-induced protein alterations. Additionally, these data offer a hypothesis-generating resource for future studies aiming to investigate both characterized and uncharacterized genes. BIOLOGICAL SIGNIFICANCE: We investigate the proteomic alterations in S. cerevisiae resulting from adaptation of yeast from glucose to nine different carbon sources - maltose, trehalose, fructose, sucrose, glycerol, acetate, pyruvate, lactic acid, and oleate. SPS-MS3 TMT10plex analysis is used for quantitative proteomic analysis. We showcase a technique that allows the quantification of over 5000 yeast proteins, the highest number to date in S. cerevisiae, across 10 growth conditions in a single experiment. As expected, gene ontology classification of proteins with the major alterations in abundances occurred in metabolic pathways and mitochondrial proteins, reflecting the degree of metabolic stress when cellular machinery shifts from growth on glucose to an alternative carbon source. Our protocol lays the groundwork for further investigation of carbon source-induced protein alterations. Improving depth of coverage - measuring abundance changes of over 5000 proteins - increases our understanding of difficult-to-study genes in the model system S. cerevisiae and by homology human cell biology. We submit this highly comprehensive dataset as a hypothesis generating resource for targeted studies on uncharacterized genes.