Multiple Imputation Approaches Applied to the Missing Value Problem in Bottom-Up Proteomics

Algorithms and tools for data-driven omics integration to achieve multilayer biological insights: a narrative review

Systems biology is a holistic approach to biological sciences that combines experimental and computational strategies, aimed at integrating information from different scales of biological processes to unravel pathophysiological mechanisms and behaviours. In this scenario, high-throughput technologies have been playing a major role in providing huge amounts of omics data, whose integration would offer unprecedented possibilities in gaining insights on diseases and identifying potential biomarkers. In the present review, we focus on strategies that have been applied in literature to integrate genomics, transcriptomics, proteomics, and metabolomics in the year range 2018-2024. Integration approaches were divided into three main categories: statistical-based approaches, multivariate methods, and machine learning/artificial intelligence techniques. Among them, statistical approaches (mainly based on correlation) were the ones with a slightly higher prevalence, followed by multivariate approaches, and machine learning techniques. Integrating multiple biological layers has shown great potential in uncovering molecular mechanisms, identifying putative biomarkers, and aid classification, most of the time resulting in better performances when compared to single omics analyses. However, significant challenges remain. The high-throughput nature of omics platforms introduces issues such as variable data quality, missing values, collinearity, and dimensionality. These challenges further increase when combining multiple omics datasets, as the complexity and heterogeneity of the data increase with integration. We report different strategies that have been found in literature to cope with these challenges, but some open issues still remain and should be addressed to disclose the full potential of omics integration.

Predicting Childhood and Adolescence Hypertension: Analysis of Predictors Using Machine Learning

BACKGROUND

There has been a substantial burden of hypertension in children and adolescents. Given the availability of primary prevention strategies, it is important to determine predictors for early identification of children and adolescents at risk of hypertension. This study aims to attempt and validate machine learning (ML) algorithms for accurately predicting blood pressure (BP) status (normal, prehypertension, and hypertension) over 1- and 3-year periods, identifying key predictors without compromising model performance.

METHODS

We included a population-based cohort of primary 1 to secondary 6 students (typically aged 6 to 18 years) during the academic years of 1995 to 1996 and 2019 to 2020 in Hong Kong. Thirty-six easy-assessed predictors were initially model childhood BP status. Multiple ML algorithms, decision tree, random forest, k-nearest neighbor, eXtreme Gradient Boosting (XGBoost), and multinomial logistic regression (MLR), were used. Model evaluation was performed by various accuracy metrics. The Shapley Additive Explanations (SHAP) was used to identify key features for both predictions.

RESULTS

A total of 923 301 and 602 179 visit pairs were used for the 1- and 3-year predictions, respectively. XGBoost demonstrated the highest prediction accuracies for 1-year (macro-area under the receiver operating characteristic curve [AUROC] = 0.92, micro-AUROC = 0.91) and 3-year (macro-AUROC = 0.91, micro-AUROC = 0.90) periods. The traditional MLR approach had the lowest accuracies for 1- (macro-AUROC = 0.70, micro-AUROC = 0.68) and 3-year (macro-AUROC = 0.70, micro-AUROC = 0.68) predictions. The SHAP values identified 17 key predictors without the need for direct BP measurements or laboratory tests.

CONCLUSION

ML prediction models can accurately predict childhood prehypertension and hypertension at 1 and 3 years, independent of BP and laboratory measurements. The identified key predictors may inform areas for personalized prevention in hypertension.

Tau filaments are tethered within brain extracellular vesicles in Alzheimer's disease

The abnormal assembly of tau protein in neurons is a pathological hallmark of multiple neurodegenerative diseases, including Alzheimer's disease (AD). Assembled tau associates with extracellular vesicles (EVs) in the central nervous system of individuals with AD, which is linked to its clearance and prion-like propagation. However, the identities of the assembled tau species and EVs, as well as how they associate, are not known. Here, we combined quantitative mass spectrometry, cryo-electron tomography and single-particle cryo-electron microscopy to study brain EVs from individuals with AD. We found tau filaments composed mainly of truncated tau that were enclosed within EVs enriched in endo-lysosomal proteins. We observed multiple filament interactions, including with molecules that tethered filaments to the EV limiting membrane, suggesting selective packaging. Our findings will guide studies into the molecular mechanisms of EV-mediated secretion of assembled tau and inform the targeting of EV-associated tau as potential therapeutic and biomarker strategies for AD.

Penalized likelihood optimization for censored missing value imputation in proteomics

Label-free bottom-up proteomics using mass spectrometry and liquid chromatography has long been established as one of the most popular high-throughput analysis workflows for proteome characterization. However, it produces data hindered by complex and heterogeneous missing values, which imputation has long remained problematic. To cope with this, we introduce Pirat, an algorithm that harnesses this challenge using an original likelihood maximization strategy. Notably, it models the instrument limit by learning a global censoring mechanism from the data available. Moreover, it estimates the covariance matrix between enzymatic cleavage products (ie peptides or precursor ions), while offering a natural way to integrate complementary transcriptomic information when multi-omic assays are available. Our benchmarking on several datasets covering a variety of experimental designs (number of samples, acquisition mode, missingness patterns, etc.) and using a variety of metrics (differential analysis ground truth or imputation errors) shows that Pirat outperforms all pre-existing imputation methods. Beyond the interest of Pirat as an imputation tool, these results pinpoint the need for a paradigm change in proteomics imputation, as most pre-existing strategies could be boosted by incorporating similar models to account for the instrument censorship or for the correlation structures, either grounded to the analytical pipeline or arising from a multi-omic approach.

Premature cognitive decline in a mouse model of tuberous sclerosis

Little is known about the influence of (impaired) neurodevelopment on cognitive aging. We here used a mouse model for tuberous sclerosis (TS) carrying a heterozygous deletion of the Tsc2 gene. Loss of Tsc2 function leads to mTOR hyperactivity in mice and patients. In a longitudinal behavioral analysis, we found premature decline of hippocampus-based cognitive functions together with a significant reduction of immediate early gene (IEG) expression. While we did not detect any morphological changes of hippocampal projections and synaptic contacts, molecular markers of neurodegeneration were increased and the mTOR signaling cascade was downregulated in hippocampal synaptosomes. Injection of IGF2, a molecule that induces mTOR signaling, could fully rescue cognitive impairment and IEG expression in aging Tsc2+/- animals. This data suggests that TS is an exhausting disease that causes erosion of the mTOR pathway over time and IGF2 is a promising avenue for treating age-related degeneration in mTORopathies.

The early transition to cold-induced browning in mouse subcutaneous white adipose tissue (scWAT) involves proteins related to nerve remodeling, cytoskeleton, mitochondria, and immune cells

White adipose tissue (WAT) is a dynamic organ capable of remodelling in response to metabolic state. For example, in response to stimuli such as cold exposure, WAT can develop inducible brown adipocytes ('browning') capable of non-shivering thermogenesis, through concurrent changes to mitochondrial content and function. This is aided by increased neurite outgrowth and angiogenesis across the tissue, providing the needed neurovascular supply for uncoupling protein 1 activation. While several RNA-sequencing studies have been performed in WAT, including newer single cell and single nuclei studies, little work has been done to investigate changes to the adipose proteome, particularly during dynamic periods of tissue remodelling such as cold stimulation. Here, we conducted a comprehensive proteomic analysis of inguinal subcutaneous (sc) WAT during the initial 'browning' period of 24 or 72hrs of cold exposure in mice. We identified four significant pathways impacted by cold stimulation that are involved in tissue remodelling, which included mitochondrial function and metabolism, cytoskeletal remodelling, the immune response, and the nervous system. Taken together, we found that early changes in the proteome of WAT with cold stimulation predicted later structural and functional changes in the tissue that are important for tissue and whole-body remodelling to meet energetic and metabolic needs.

DNA repair and anti-cancer mechanisms in the long-lived bowhead whale

At over 200 years, the maximum lifespan of the bowhead whale exceeds that of all other mammals. The bowhead is also the second-largest animal on Earth, reaching over 80,000 kg1. Despite its very large number of cells and long lifespan, the bowhead is not highly cancer-prone, an incongruity termed Peto's Paradox2. This phenomenon has been explained by the evolution of additional tumor suppressor genes in other larger animals, supported by research on elephants demonstrating expansion of the p53 gene3-5. Here we show that bowhead whale fibroblasts undergo oncogenic transformation after disruption of fewer tumor suppressors than required for human fibroblasts. However, analysis of DNA repair revealed that bowhead cells repair double strand breaks (DSBs) and mismatches with uniquely high efficiency and accuracy compared to other mammals. The protein CIRBP, implicated in protection from genotoxic stress, was present in very high abundance in the bowhead whale relative to other mammals. We show that CIRBP and its downstream protein RPA2, also present at high levels in bowhead cells, increase the efficiency and fidelity of DNA repair in human cells. These results indicate that rather than possessing additional tumor suppressor genes as barriers to oncogenesis, the bowhead whale relies on more accurate and efficient DNA repair to preserve genome integrity. This strategy which does not eliminate damaged cells but repairs them may be critical for the long and cancer-free lifespan of the bowhead whale.

Prenatal exposure to per- and polyfluoroalkyl substances, fetoplacental hemodynamics, and fetal growth

INTRODUCTION

The impact of legacy per- and polyfluoroalkyl substances (PFAS) on fetal growth has been well studied, but assessments of next-generation PFAS and PFAS mixtures are sparse and the potential role of fetoplacental hemodynamics has not been studied. We aimed to evaluate associations between prenatal PFAS exposure and fetal growth and fetoplacental hemodynamics.

METHODS

We included 747 pregnant women from the BiSC birth cohort (Barcelona, Spain (2018-2021)). Twenty-three PFAS were measured at 32 weeks of pregnancy in maternal plasma, of which 13 were present above detectable levels. Fetal growth was measured by ultrasound, as estimated fetal weight at 32 and 37 weeks of gestation, and weight at birth. Doppler ultrasound measurements for uterine (UtA), umbilical (UmA), and middle cerebral artery (MCA) pulsatility indices (PI), as well as the cerebroplacental ratio (CPR - ratio MCA to UmA), were obtained at 32 weeks to assess fetoplacental hemodynamics. We applied linear mixed effects models to assess the association between singular PFAS and longitudinal fetal growth and PI, and Bayesian Weighted Quantile Sum models to evaluate associations between the PFAS mixture and the aforementioned outcomes, controlled for the relevant covariates.

RESULTS

Single PFAS and the mixture tended to be associated with reduced fetal growth and CPR PI, but few associations reached statistical significance. Legacy PFAS PFOS, PFHpA, and PFDoDa were associated with statistically significant decreases in fetal weight z-score of 0.13 (95%CI (-0.22, -0.04), 0.06 (-0.10, 0.01), and 0.05 (-0.10, 0.00), respectively, per doubling of concentration. The PFAS mixture was associated with a non-statistically significant 0.09 decrease in birth weight z-score (95%CI -0.22, 0.04) per quartile increase.

CONCLUSION

This study suggests that legacy PFAS may be associated with reduced fetal growth, but associations for next generation PFAS and for the PFAS mixture were less conclusive. Associations between PFAS and fetoplacental hemodynamics warrant further investigation.

Missing Values in Longitudinal Proteome Dynamics Studies: Making a Case for Data Multiple Imputation

Temporal proteomics data sets are often confounded by the challenges of missing values. These missing data points, in a time-series context, can lead to fluctuations in measurements or the omission of critical events, thus hindering the ability to fully comprehend the underlying biomedical processes. We introduce a Data Multiple Imputation (DMI) pipeline designed to address this challenge in temporal data set turnover rate quantifications, enabling robust downstream analysis to gain novel discoveries. To demonstrate its utility and generalizability, we applied this pipeline to two use cases: a murine cardiac temporal proteomics data set and a human plasma temporal proteomics data set, both aimed at examining protein turnover rates. This DMI pipeline significantly enhanced the detection of protein turnover rate in both data sets, and furthermore, the imputed data sets captured new representation of proteins, leading to an augmented view of biological pathways, protein complex dynamics, as well as biomarker-disease associations. Importantly, DMI exhibited superior performance in benchmark data sets compared to single imputation methods (DSI). In summary, we have demonstrated that this DMI pipeline is effective at overcoming challenges introduced by missing values in temporal proteome dynamics studies.

Proteome changes in larval zebrafish (Danio rerio) and fathead minnow (Pimephales promelas) exposed to (±) anatoxin-a

Anatoxin-a and its analogues are potent neurotoxins produced by several genera of cyanobacteria. Due in part to its high toxicity and potential presence in drinking water, these toxins pose threats to public health, companion animals and the environment. It primarily exerts toxicity as a cholinergic agonist, with high affinity at neuromuscular junctions, but molecular mechanisms by which it elicits toxicological responses are not fully understood. To advance understanding of this cyanobacteria, proteomic characterization (DIA shotgun proteomics) of two common fish models (zebrafish and fathead minnow) was performed following  (±) anatoxin-a exposure. Specifically, proteome changes were identified and quantified in larval fish exposed for 96 h (0.01-3 mg/L (±) anatoxin-a and caffeine (a methodological positive control) with environmentally relevant treatment levels examined based on environmental exposure distributions of surface water data. Proteomic concentration - response relationships revealed 48 and 29 proteins with concentration - response relationships curves for zebrafish and fathead minnow, respectively. In contrast, the highest number of differentially expressed proteins (DEPs) varied between zebrafish (n = 145) and fathead minnow (n = 300), with only fatheads displaying DEPs at all treatment levels. For both species, genes associated with reproduction were significantly downregulated, with pathways analysis that broadly clustered genes into groups associated with DNA repair mechanisms. Importantly, significant differences in proteome response between the species was also observed, consistent with prior observations of differences in response using both behavioral assays and gene expression, adding further support to model specific differences in organismal sensitivity and/or response. When DEPs were read across from humans to zebrafish, disease ontology enrichment identified diseases associated with cognition and muscle weakness consistent with the prior literature. Our observations highlight limited knowledge of how (±) anatoxin-a, a commonly used synthetic racemate surrogate, elicits responses at a molecular level and advances its toxicological understanding.

Differential proteome profiling of bacterial culture supernatants reveals candidates for the induction of oral immune priming in the red flour beetle

Most organisms are host to symbionts and pathogens, which led to the evolution of immune strategies to prevent harm. Whilst the immune defences of vertebrates are classically divided into innate and adaptive, insects lack specialized cells involved in adaptive immunity, but have been shown to exhibit immune priming: the enhanced survival upon infection after a first exposure to the same pathogen or pathogen-derived components. An important piece of the puzzle are the pathogen-associated molecules that induce these immune priming responses. Here, we make use of the model system consisting of the red flour beetle (Tribolium castaneum) and its bacterial pathogen Bacillus thuringiensis, to compare the proteomes of culture supernatants of two closely related B. thuringiensis strains that either induce priming via the oral route, or not. Among the proteins that might be immunostimulatory to T. castaneum, we identify the Cry3Aa toxin, an important plasmid-encoded virulence factor of B. thuringiensis. In further priming-infection assays we test the relevance of Cry-carrying plasmids for immune priming. Our findings provide valuable insights for future studies to perform experiments on the mechanisms and evolution of immune priming.

Prognostic models predicting transition to psychotic disorder using blood-based biomarkers: a systematic review and critical appraisal

Accumulating evidence suggests individuals with psychotic disorder show abnormalities in metabolic and inflammatory processes. Recently, several studies have employed blood-based predictors in models predicting transition to psychotic disorder in risk-enriched populations. A systematic review of the performance and methodology of prognostic models using blood-based biomarkers in the prediction of psychotic disorder from risk-enriched populations is warranted. Databases (PubMed, EMBASE and PsycINFO) were searched for eligible texts from 1998 to 15/05/2023, which detailed model development or validation studies. The checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) was used to guide data extraction from eligible texts and the Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess the risk of bias and applicability of the studies. A narrative synthesis of the included studies was performed. Seventeen eligible studies were identified: 16 eligible model development studies and one eligible model validation study. A wide range of biomarkers were assessed, including nucleic acids, proteins, metabolites, and lipids. The range of C-index (area under the curve) estimates reported for the models was 0.67-1.00. No studies assessed model calibration. According to PROBAST criteria, all studies were at high risk of bias in the analysis domain. While a wide range of potentially predictive biomarkers were identified in the included studies, most studies did not account for overfitting in model performance estimates, no studies assessed calibration, and all models were at high risk of bias according to PROBAST criteria. External validation of the models is needed to provide more accurate estimates of their performance. Future studies which follow the latest available methodological and reporting guidelines and adopt strategies to accommodate required sample sizes for model development or validation will clarify the value of including blood-based biomarkers in models predicting psychosis.

Multi-Omic Temporal Landscape of Plasma and Synovial Fluid-Derived Extracellular Vesicles Using an Experimental Model of Equine Osteoarthritis

Extracellular vesicles (EVs) contribute to osteoarthritis pathogenesis through their release into joint tissues and synovial fluid. Synovial fluid-derived EVs have the potential to be direct biomarkers in the causal pathway of disease but also enable understanding of their role in disease progression. Utilizing a temporal model of osteoarthritis, we defined the changes in matched synovial fluid and plasma-derived EV small non-coding RNA and protein cargo using sequencing and mass spectrometry. Data exploration included time series clustering, factor analysis and gene enrichment interrogation. Chondrocyte signalling was analysed using luciferase-based transcription factor activity assays. EV protein cargo appears to be more important during osteoarthritis progression than small non-coding RNAs. Cluster analysis revealed plasma-EVs represented a time-dependent response to osteoarthritis induction associated with supramolecular complexes. Clusters for synovial fluid-derived EVs were associated with initial osteoarthritis response and represented immune/inflammatory pathways. Factor analysis for plasma-derived EVs correlated with day post-induction and were primarily composed of proteins modulating lipid metabolism. Synovial fluid-derived EVs factors represented intermediate filament and supramolecular complexes reflecting tissue repair. There was a significant interaction between time and osteoarthritis for CRE, NFkB, SRE, SRF with a trend for osteoarthritis synovial fluid-derived EVs at later time points to have a more pronounced effect.

Pan-Cancer Proteomics Analysis to Identify Tumor-Enriched and Highly Expressed Cell Surface Antigens as Potential Targets for Cancer Therapeutics

The National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC) provides unique opportunities for cancer target discovery using protein expression. Proteomics data from CPTAC tumor types have been primarily generated using a multiplex tandem mass tag (TMT) approach, which is designed to provide protein quantification relative to reference samples. However, relative protein expression data are suboptimal for prioritization of targets within a tissue type, which requires additional reprocessing of the original proteomics data to derive absolute quantitation estimation. We evaluated the feasibility of using differential protein analysis coupled with intensity-based absolute quantification (iBAQ) to identify tumor-enriched and highly expressed cell surface antigens, employing tandem mass tag (TMT) proteomics data from CPTAC. Absolute quantification derived from TMT proteomics data was highly correlated with that of label-free proteomics data from the CPTAC colon adenocarcinoma cohort, which contains proteomics data measured by both approaches. We validated the TMT-iBAQ approach by comparing the iBAQ value to the receptor density value of HER2 and TROP2 measured by flow cytometry in about 30 selected breast and lung cancer cell lines from the Cancer Cell Line Encyclopedia. Collections of these tumor-enriched and highly expressed cell surface antigens could serve as a valuable resource for the development of cancer therapeutics, including antibody-drug conjugates and immunotherapeutic agents.

ProJect: a powerful mixed-model missing value imputation method

Missing values (MVs) can adversely impact data analysis and machine-learning model development. We propose a novel mixed-model method for missing value imputation (MVI). This method, ProJect (short for Protein inJection), is a powerful and meaningful improvement over existing MVI methods such as Bayesian principal component analysis (PCA), probabilistic PCA, local least squares and quantile regression imputation of left-censored data. We rigorously tested ProJect on various high-throughput data types, including genomics and mass spectrometry (MS)-based proteomics. Specifically, we utilized renal cancer (RC) data acquired using DIA-SWATH, ovarian cancer (OC) data acquired using DIA-MS, bladder (BladderBatch) and glioblastoma (GBM) microarray gene expression dataset. Our results demonstrate that ProJect consistently performs better than other referenced MVI methods. It achieves the lowest normalized root mean square error (on average, scoring 45.92% less error in RC_C, 27.37% in RC_full, 29.22% in OC, 23.65% in BladderBatch and 20.20% in GBM relative to the closest competing method) and the Procrustes sum of squared error (Procrustes SS) (exhibits 79.71% less error in RC_C, 38.36% in RC full, 18.13% in OC, 74.74% in BladderBatch and 30.79% in GBM compared to the next best method). ProJect also leads with the highest correlation coefficient among all types of MV combinations (0.64% higher in RC_C, 0.24% in RC full, 0.55% in OC, 0.39% in BladderBatch and 0.27% in GBM versus the second-best performing method). ProJect's key strength is its ability to handle different types of MVs commonly found in real-world data. Unlike most MVI methods that are designed to handle only one type of MV, ProJect employs a decision-making algorithm that first determines if an MV is missing at random or missing not at random. It then employs targeted imputation strategies for each MV type, resulting in more accurate and reliable imputation outcomes. An R implementation of ProJect is available at https://github.com/miaomiao6606/ProJect.

Tau filaments are tethered within brain extracellular vesicles in Alzheimer's disease

The abnormal assembly of tau protein in neurons is the pathological hallmark of multiple neurodegenerative diseases, including Alzheimer's disease (AD). In addition, assembled tau associates with extracellular vesicles (EVs) in the central nervous system of patients with AD, which is linked to its clearance and prion-like propagation between neurons. However, the identities of the assembled tau species and the EVs, as well as how they associate, are not known. Here, we combined quantitative mass spectrometry, cryo-electron tomography and single-particle cryo-electron microscopy to study brain EVs from AD patients. We found filaments of truncated tau enclosed within EVs enriched in endo-lysosomal proteins. We observed multiple filament interactions, including with molecules that tethered filaments to the EV limiting membrane, suggesting selective packaging. Our findings will guide studies into the molecular mechanisms of EV-mediated secretion of assembled tau and inform the targeting of EV-associated tau as potential therapeutic and biomarker strategies for AD.

Dealing with missing values in proteomics data

Proteomics data are often plagued with missingness issues. These missing values (MVs) threaten the integrity of subsequent statistical analyses by reduction of statistical power, introduction of bias, and failure to represent the true sample. Over the years, several categories of missing value imputation (MVI) methods have been developed and adapted for proteomics data. These MVI methods perform their tasks based on different prior assumptions (e.g., data is normally or independently distributed) and operating principles (e.g., the algorithm is built to address random missingness only), resulting in varying levels of performance even when dealing with the same dataset. Thus, to achieve a satisfactory outcome, a suitable MVI method must be selected. To guide decision making on suitable MVI method, we provide a decision chart which facilitates strategic considerations on datasets presenting different characteristics. We also bring attention to other issues that can impact proper MVI such as the presence of confounders (e.g., batch effects) which can influence MVI performance. Thus, these too, should be considered during or before MVI.

Proteomic characterization of spinal cord synaptoneurosomes from Tg-SOD1/G93A mice supports a role for MNK1 and local translation in the early stages of amyotrophic lateral sclerosis

The isolation of synaptoneurosomes (SNs) represents a useful means to study synaptic events. However, the size and density of synapses varies in different regions of the central nervous system (CNS), and this also depends on the experimental species studied, making it difficult to define a generic protocol for SNs preparation. To characterize synaptic failure in the spinal cord (SC) in the Tg-SOD1/G93A mouse model of amyotrophic lateral sclerosis (ALS), we applied a method we originally designed to isolate cortical and hippocampal SNs to SC tissue. Interestingly, we found that the SC SNs were isolated in a different gradient fraction to the cortical/hippocampal SNs. We compared the relative levels of synaptoneurosomal proteins in wild type (WT) animals, with control (Tg-SOD1) or Tg-SOD1/G93A mice at onset and those that were symptomatic using iTRAQ proteomics. The results obtained suggest that an important regulator of local synaptic translation, MNK1 (MAP kinase interacting serine/threonine kinase 1), might well influence the early stages of ALS.

Accounting for multiple imputation-induced variability for differential analysis in mass spectrometry-based label-free quantitative proteomics

Imputing missing values is common practice in label-free quantitative proteomics. Imputation aims at replacing a missing value with a user-defined one. However, the imputation itself may not be optimally considered downstream of the imputation process, as imputed datasets are often considered as if they had always been complete. Hence, the uncertainty due to the imputation is not adequately taken into account. We provide a rigorous multiple imputation strategy, leading to a less biased estimation of the parameters' variability thanks to Rubin's rules. The imputation-based peptide's intensities' variance estimator is then moderated using Bayesian hierarchical models. This estimator is finally included in moderated t-test statistics to provide differential analyses results. This workflow can be used both at peptide and protein-level in quantification datasets. Indeed, an aggregation step is included for protein-level results based on peptide-level quantification data. Our methodology, named mi4p, was compared to the state-of-the-art limma workflow implemented in the DAPAR R package, both on simulated and real datasets. We observed a trade-off between sensitivity and specificity, while the overall performance of mi4p outperforms DAPAR in terms of F-Score.

Assessment of label-free quantification and missing value imputation for proteomics in non-human primates

BACKGROUND

Reliable and effective label-free quantification (LFQ) analyses are dependent not only on the method of data acquisition in the mass spectrometer, but also on the downstream data processing, including software tools, query database, data normalization and imputation. In non-human primates (NHP), LFQ is challenging because the query databases for NHP are limited since the genomes of these species are not comprehensively annotated. This invariably results in limited discovery of proteins and associated Post Translational Modifications (PTMs) and a higher fraction of missing data points. While identification of fewer proteins and PTMs due to database limitations can negatively impact uncovering important and meaningful biological information, missing data also limits downstream analyses (e.g., multivariate analyses), decreases statistical power, biases statistical inference, and makes biological interpretation of the data more challenging. In this study we attempted to address both issues: first, we used the MetaMorphues proteomics search engine to counter the limits of NHP query databases and maximize the discovery of proteins and associated PTMs, and second, we evaluated different imputation methods for accurate data inference. We used a generic approach for missing data imputation analysis without distinguising the potential source of missing data (either non-assigned m/z or missing values across runs).

RESULTS

Using the MetaMorpheus proteomics search engine we obtained quantitative data for 1622 proteins and 10,634 peptides including 58 different PTMs (biological, metal and artifacts) across a diverse age range of NHP brain frontal cortex. However, among the 1622 proteins identified, only 293 proteins were quantified across all samples with no missing values, emphasizing the importance of implementing an accurate and statiscaly valid imputation method to fill in missing data. In our imputation analysis we demonstrate that Single Imputation methods that borrow information from correlated proteins such as Generalized Ridge Regression (GRR), Random Forest (RF), local least squares (LLS), and a Bayesian Principal Component Analysis methods (BPCA), are able to estimate missing protein abundance values with great accuracy.

CONCLUSIONS

Overall, this study offers a detailed comparative analysis of LFQ data generated in NHP and proposes strategies for improved LFQ in NHP proteomics data.

HarmonizR enables data harmonization across independent proteomic datasets with appropriate handling of missing values

Dataset integration is common practice to overcome limitations in statistically underpowered omics datasets. Proteome datasets display high technical variability and frequent missing values. Sophisticated strategies for batch effect reduction are lacking or rely on error-prone data imputation. Here we introduce HarmonizR, a data harmonization tool with appropriate missing value handling. The method exploits the structure of available data and matrix dissection for minimal data loss, without data imputation. This strategy implements two common batch effect reduction methods—ComBat and limma (removeBatchEffect()). The HarmonizR strategy, evaluated on four exemplarily analyzed datasets with up to 23 batches, demonstrated successful data harmonization for different tissue preservation techniques, LC-MS/MS instrumentation setups, and quantification approaches. Compared to data imputation methods, HarmonizR was more efficient and performed superior regarding the detection of significant proteins. HarmonizR is an efficient tool for missing data tolerant experimental variance reduction and is easily adjustable for individual dataset properties and user preferences.

Dataset integration is common practice to overcome limitations in statistically underpowered omics datasets. Here the authors present “HarmonizR”, a tool for missing data tolerant experimental variance reduction in large, integrated but independently generated datasets without data imputation, adjustable for individual dataset modalities, correction algorithm, and user preferences.

A modified TurboID approach identifies tissue-specific centriolar components in C. elegans

Proximity-dependent labeling approaches such as BioID have been a great boon to studies of protein-protein interactions in the context of cytoskeletal structures such as centrosomes which are poorly amenable to traditional biochemical approaches like immunoprecipitation and tandem affinity purification. Yet, these methods have so far not been applied extensively to invertebrate experimental models such as C. elegans given the long labeling times required for the original promiscuous biotin ligase variant BirA*. Here, we show that the recently developed variant TurboID successfully probes the interactomes of both stably associated (SPD-5) and dynamically localized (PLK-1) centrosomal components. We further develop an indirect proximity labeling method employing a GFP nanobody-TurboID fusion, which allows the identification of protein interactors in a tissue-specific manner in the context of the whole animal. Critically, this approach utilizes available endogenous GFP fusions, avoiding the need to generate multiple additional strains for each target protein and the potential complications associated with overexpressing the protein from transgenes. Using this method, we identify homologs of two highly conserved centriolar components, Cep97 and BLD10/Cep135, which are present in various somatic tissues of the worm. Surprisingly, neither protein is expressed in early embryos, likely explaining why these proteins have escaped attention until now. Our work expands the experimental repertoire for C. elegans and opens the door for further studies of tissue-specific variation in centrosome architecture.