Importance of Block Randomization When Designing Proteomics Experiments

Optimized workflow of EV enrichment from human plasma samples for downstream mass spectrometry analysis

To improve the prognosis of bladder and prostate cancer, highly specific and sensitive biomarkers are needed for early detection, prognosis prediction, and therapeutic stratification. Extracellular vesicles (EV) from plasma could fill this gap due to their potential to serve as cancer biomarkers. However, the enrichment of EV is a major challenge, because the highly abundant plasma proteins are interfering with analytical downstream applications like mass spectrometry (MS). Therefore, the purity requirements of the EV samples must be carefully considered when selecting or developing a suitable EV enrichment method. The aim of this study was to compare a self-designed EV enrichment method based on density cushion centrifugation (DCC) combined with size exclusion chromatography (SEC) and concentration (method 1) with the exoRNeasy midi kit from Qiagen (method 2) and with unprocessed plasma. Furthermore, the single steps of method 1 were evaluated for their effectiveness to enrich EV from plasma. The results showed that the EV samples enriched with method 1 contained the highest levels of EV and exosome markers with simultaneously low levels of highly abundant plasma proteins. In summary, the combination of DCC, SEC and concentration proved to be a promising approach to discover EV-based biomarkers from plasma of cancer patients.

Enabling Lipidomic Biomarker Studies for Protected Populations by Combining Noninvasive Fingerprint Sampling with MS Analysis and Machine Learning

Triacylglycerols and wax esters are two lipid classes that have been linked to diseases, including autism, Alzheimer's disease, dementia, cardiovascular disease, dry eye disease, and diabetes, and thus are molecules worthy of biomarker exploration studies. Since triacylglycerols and wax esters make up the majority of skin-surface lipid secretions, a viable sampling method for these potential biomarkers would be that of groomed latent fingerprints. Currently, however, blood-based sampling protocols predominate in the field. The invasiveness of a blood draw limits its utility to protected populations, including children and the elderly. Herein we describe a noninvasive means for sample collection (from fingerprints) paired with fast MS data-acquisition (MassIVE data set MSV000092742) and efficient data analysis via machine learning. Using both supervised and unsupervised classification, we demonstrate the usefulness of this method in determining whether a variable of interest imparts measurable change within the lipidomic data set. As a proof-of-concept, we show that the method is capable of distinguishing between the fingerprints of different individuals as well as between anatomical sebum collection regions. This noninvasive, high-throughput approach enables future lipidomic biomarker researchers to more easily include underrepresented, protected populations, such as children and the elderly, thus moving the field closer to definitive disease diagnoses that apply to all.

Administration of intratumoral GD2-directed interleukin-2 immunocytokine and local radiation therapy to activate immune rejection of spontaneous canine melanoma

Canine malignant melanoma provides a clinically relevant, large animal parallel patient population to study the GD2-reactive hu14.18-IL-2 immunocytokine as it is similar to human melanoma and expresses GD2. The objectives of this study were to evaluate safety, radiation fractionation, and identify informative biomarkers of an in-situ tumor vaccine involving local radiation therapy plus intratumoral-immunocytokine in melanoma tumor-bearing dogs. Twelve dogs (six dogs/arm) with locally advanced or metastatic melanoma were randomized to receive a single 8 Gy fraction (arm A) or three 8 Gy fractions over 1 week (arm B) to the primary site and regional lymph nodes (when clinically involved) with the single or last fraction 5 days before intratumoral-immunocytokine at 12 mg/m 2 on 3 consecutive days. Serial tumor biopsies were obtained. All 12 dogs completed protocol treatment, and none experienced significant or unexpected adverse events. Evidence of antitumor activity includes one dog with a complete response at day 60, one dog with a partial response at day 60, and four dogs with mixed responses. Histology of serial biopsies shows a variably timed increase in intratumoral lymphocytic inflammation in some dogs. Canine NanoString analyses of serial biopsies identified changes in gene signatures of innate and adaptive cell types versus baseline. There were no significant differences in NanoString results between arm A and arm B. We conclude that intratumoral-immunocytokine in combination with local radiation therapy in canine melanoma is well tolerated and has antitumor activity with the potential to inform clinical development in melanoma patients.

Are We There Yet? Assessing the Readiness of Single-Cell Proteomics to Answer Biological Hypotheses

Single-cell analysis is an active area of research in many fields of biology. Measurements at single-cell resolution allow researchers to study diverse populations without losing biologically meaningful information to sample averages. Many technologies have been used to study single cells, including mass spectrometry-based single-cell proteomics (SCP). SCP has seen a lot of growth over the past couple of years through improvements in data acquisition and analysis, leading to greater proteomic depth. Because method development has been the main focus in SCP, biological applications have been sprinkled in only as proof-of-concept. However, SCP methods now provide significant coverage of the proteome and have been implemented in many laboratories. Thus, a primary question to address in our community is whether the current state of technology is ready for widespread adoption for biological inquiry. In this Perspective, we examine the potential for SCP in three thematic areas of biological investigation: cell annotation, developmental trajectories, and spatial mapping. We identify that the primary limitation of SCP is sample throughput. As proteome depth has been the primary target for method development to date, we advocate for a change in focus to facilitate measuring tens of thousands of single-cell proteomes to enable biological applications beyond proof-of-concept.

Plasma complement and coagulation proteins as prognostic factors of negative symptoms: An analysis of the NAPLS 2 and 3 studies

INTRODUCTION

Negative symptoms impact the quality of life of individuals with psychosis and current treatment options for negative symptoms have limited effectiveness. Previous studies have demonstrated that complement and coagulation pathway protein levels are related to later psychotic experiences, psychotic disorder, and functioning. However, the prognostic relationship between complement and coagulation proteins and negative symptoms is poorly characterised.

METHODS

In the North American Prodrome Longitudinal Studies 2 and 3, negative symptoms in 431 individuals at clinical high-risk for psychosis (mean age: 18.2, SD 3.6; 42.5 % female) were measured at multiple visits over 2 years using the Scale of Psychosis-Risk Symptoms. Plasma proteins were quantified at baseline using mass spectrometry. Four factors were derived to represent levels of proteins involved in the activation or regulation of the complement or coagulation systems. The relationships between standardised protein group factors and serial measurements of negative symptoms over time were modelled using generalised least squares regression. Analyses were adjusted for baseline candidate prognostic factors: negative symptoms, positive symptoms, functioning, depressive symptoms, suicidal ideation, cannabis use, tobacco use, antipsychotic use, antidepressant use, age, and sex.

RESULTS

Clinical and demographic prognostic factors of follow-up negative symptoms included negative, positive, and depressive symptoms, functioning, and age. Adjusting for all candidate prognostic factors, the complement regulators group and the coagulation regulators group were identified as prognostic factors of follow-up negative symptoms (β: 0.501, 95 % CI: 0.160, 0.842; β: 0.430, 95 % CI: 0.080, 0.780 respectively. The relationship between complement regulator levels and negative symptoms was also observed in NAPLS2 alone (β: 0.501, 95 % CI: -0.037, 1.039) and NAPLS3 alone, additionally adjusting for BMI (β: 0.442, 95 % CI: 0.127, 0.757).

CONCLUSION

The results indicate that plasma complement and coagulation regulator levels are prognostic factors of negative symptoms, independent of clinical and demographic prognostic factors. These results suggest complement and coagulation regulator levels could have potential utility in informing treatment decisions for negative symptoms in individuals at risk.

MetaboReport: from metabolomics data analysis to comprehensive reporting

MOTIVATION

Metabolomics, as an essential tool in systems biology, is now widely accessible to researchers of all levels. Yet challenges remain in data analysis and result interpretation. To address these challenges, we introduced MetaboReport, a versatile and interactive web app that simplifies metabolomics experiment design, data preprocessing, exploration, statistical analysis, visualization, and reporting.

RESULTS

MetaboReport produces a comprehensive HTML report, including project details, an introduction, interactive plots and tables, statistical results and an in-depth explanations and interpretation of the results. MetaboReport is particularly tailored for research labs and metabolomics core facilities that provide metabolomics services, allowing them to efficiently manage and document different metabolomics projects, and effectively report the metabolomics results to users.

AVAILABILITY AND IMPLEMENTATION

MetaboReport is freely accessible on https://metaboreport.com, with source code available on GitHub (https://github.com/YonghuiDong/MetReport). Alternatively, users can install MetaboReport as a standalone desktop app (https://metaboreport.sourceforge.io).

Environment modulates protein heterogeneity through transcriptional and translational stop codon readthrough

Stop codon readthrough events give rise to longer proteins, which may alter the protein's function, thereby generating short-lasting phenotypic variability from a single gene. In order to systematically assess the frequency and origin of stop codon readthrough events, we designed a library of reporters. We introduced premature stop codons into mScarlet, which enabled high-throughput quantification of protein synthesis termination errors in E. coli using fluorescent microscopy. We found that under stress conditions, stop codon readthrough may occur at rates as high as 80%, depending on the nucleotide context, suggesting that evolution frequently samples stop codon readthrough events. The analysis of selected reporters by mass spectrometry and RNA-seq showed that not only translation but also transcription errors contribute to stop codon readthrough. The RNA polymerase was more likely to misincorporate a nucleotide at premature stop codons. Proteome-wide detection of stop codon readthrough by mass spectrometry revealed that temperature regulated the expression of cryptic sequences generated by stop codon readthrough in E. coli. Overall, our findings suggest that the environment affects the accuracy of protein production, which increases protein heterogeneity when the organisms need to adapt to new conditions.

A multisite study to develop and validate first trimester, circulating microparticle biomarkers for tiered risk stratification of spontaneous preterm birth in nulliparas

BACKGROUND

Despite much research, advances in early prediction of spontaneous preterm birth (sPTB) has been slow. The evolving field of circulating microparticle (CMP) biology may identify novel blood-based, and clinically useful, biomarkers.

OBJECTIVE

To test the ability of a previously identified, 7-marker set of CMP-derived proteins from the first trimester of pregnancy, in the form of an in vitro diagnostic multivariate index assay (IVDMIA), to stratify pregnant patients according to their risk for sPTB.

STUDY DESIGN

We employed a previously validated set of CMP protein biomarkers, utilizing mass spectrometry assays and a nested case-control design in a subset of participants from the Nulliparous Pregnancy Outcomes Study: monitoring mothers-to-be (nuMoM2b). We evaluated these biomarkers in the form of an IVDMIA to predict risk for sPTB at different gestational ages. Plasma samples collected at 9- to 13-weeks' gestation were analyzed. The IVDMIA assigned subjects to 1 of 3 sPTB risk categories: low risk (LR), moderate risk (MR), or high risk (HR). Independent validation on a set-aside set confirmed the IVDMIA's performance in risk stratification.

RESULTS

Samples from 400 participants from the nuMoM2b cohort were used for the study; of these, 160 delivered<37 weeks and 240 delivered at term. Through Monte Carlo simulation in which the validation results were adjusted based on actual weekly sPTB incidence rates in the nuMoM2b cohort, the IVDMIA stratifications demonstrated statistically significant differences among the risk groups in time-to-event (birth) analysis (P<.0001). The incidence-rate adjusted cumulative risks of sPTB at ≤32 weeks' gestation were 0.4%, 1.6%, and 7.5%, respectively for the LR, MR, and HR groups, respectively. Compared to the LR group, the corresponding risk ratios of the IVDMIA assigned MR and HR group were 4.25 (95% confidence interval [CI] 2.2-7.9) and 19.92 (95% CI 10.4-37.4), respectively.

CONCLUSION

A first trimester CMP protein biomarker panel can be used to stratify risk for sPTB at different gestational ages. Such a multitiered stratification tool could be used to assess risk early in pregnancy to enable timely clinical management and interventions, and, ultimately, to enable the development of tailored care pathways for sPTB prevention.

Proteomic Biomarkers for the Prediction of Transition to Psychosis in Individuals at Clinical High Risk: A Multi-cohort Model Development Study

Psychosis risk prediction is one of the leading challenges in psychiatry. Previous investigations have suggested that plasma proteomic data may be useful in accurately predicting transition to psychosis in individuals at clinical high risk (CHR). We hypothesized that an a priori-specified proteomic prediction model would have strong predictive accuracy for psychosis risk and aimed to replicate longitudinal associations between plasma proteins and transition to psychosis. This study used plasma samples from participants in 3 CHR cohorts: the North American Prodrome Longitudinal Studies 2 and 3, and the NEURAPRO randomized control trial (total n = 754). Plasma proteomic data were quantified using mass spectrometry. The primary outcome was transition to psychosis over the study follow-up period. Logistic regression models were internally validated, and optimism-corrected performance metrics derived with a bootstrap procedure. In the overall sample of CHR participants (age: 18.5, SD: 3.9; 51.9% male), 20.4% (n = 154) developed psychosis within 4.4 years. The a priori-specified model showed poor risk-prediction accuracy for the development of psychosis (C-statistic: 0.51 [95% CI: 0.50, 0.59], calibration slope: 0.45). At a group level, Complement C8B, C4B, C5, and leucine-rich α-2 glycoprotein 1 (LRG1) were associated with transition to psychosis but did not surpass correction for multiple comparisons. This study did not confirm the findings from a previous proteomic prediction model of transition from CHR to psychosis. Certain complement proteins may be weakly associated with transition at a group level. Previous findings, derived from small samples, should be interpreted with caution.

A framework for quality control in quantitative proteomics

A thorough evaluation of the quality, reproducibility, and variability of bottom-up proteomics data is necessary at every stage of a workflow from planning to analysis. We share real-world case studies applying adaptable quality control (QC) measures to assess sample preparation, system function, and quantitative analysis. System suitability samples are repeatedly measured longitudinally with targeted methods, and we share examples where they are used on three instrument platforms to identify severe system failures and track function over months to years. Internal QCs incorporated at protein and peptide-level allow our team to assess sample preparation issues and to differentiate system failures from sample-specific issues. External QC samples prepared alongside our experimental samples are used to verify the consistency and quantitative potential of our results during batch correction and normalization before assessing biological phenotypes. We combine these controls with rapid analysis using Skyline, longitudinal QC metrics using AutoQC, and server-based data deposition using PanoramaWeb. We propose that this integrated approach to QC be used as a starting point for groups to facilitate rapid quality control assessment to ensure that valuable instrument time is used to collect the best quality data possible.

Differential expression of haptoglobin in individuals at clinical high risk of psychosis and its association with global functioning and clinical symptoms

BACKGROUND

Immune dysregulation has been observed in patients with schizophrenia or first-episode psychosis, but few have examined dysregulation in those at clinical high-risk (CHR) for psychosis. The aim of this study was to examine whether the peripheral blood-based proteome was dysregulated in those with CHR. Secondly, we examined whether baseline dysregulation was related to current and future functioning and clinical symptoms.

METHODS

We used data from participants of the North American Prodromal Longitudinal Studies (NAPLS) 2 and 3 (n = 715) who provided blood samples (Unaffected Comparison subjects (UC) n = 223 and CHR n = 483). Baseline proteomic data was quantified from plasma samples using mass spectrometry. Differential expression was examined between CHR and UC using logistic regression. Psychosocial functioning was measured using the Global Assessment of Functioning scale (GAF). Symptoms were measured using the subscale scores from the Scale of Psychosis-risk Symptoms; positive, negative, general, and disorganised. Three measures of each outcome were included: baseline, longest available follow-up (last follow-up) and most severe follow-up (MSF). Associations between the proteomic data, GAF and symptoms were assessed using ordinal regression.

RESULTS

Of the 99 proteins quantified, six were differentially expressed between UC and CHR. However, only haptoglobin (HP) survived FDR-correction (OR:1.45, 95 %CI:1.23-1.69, padj = <0.001). HP was cross-sectionally and longitudinally associated with functioning and symptoms such that higher HP values were associated with poorer functioning and more severe symptoms. Results were evident after stringent adjustment and poorer functioning was observed in both NAPLS cohort separately.

CONCLUSION

We demonstrate that elevated HP is robustly observed in those at CHR for psychosis, irrespective of transition to psychosis. HP is longitudinally associated with poorer functioning and greater symptom severity. These results agree with previous reports of increased HP gene expression in individuals at-risk for psychosis and with the dysfunction of the acute phase inflammatory response seen in psychotic disorders.

Multispecies Benchmark Analysis for LC-MS/MS Validation and Performance Evaluation in Bottom-Up Proteomics

We present an instrument-independent benchmark procedure and software (LFQ_bout) for the validation and comparative evaluation of the performance of LC-MS/MS and data processing workflows in bottom-up proteomics. The procedure enables a back-to-back comparison of common and emerging workflows, e.g., diaPASEF or ScanningSWATH, and evaluates the impact of arbitrary and inadequately documented settings or black-box data processing algorithms. It enhances the overall performance and quantification accuracy by recognizing and reporting common quantification errors.

High-Throughput Lipidomic and Metabolomic Profiling for Brain Tissue and Biofluid Samples in Neurodegenerative Disorders

Recent research has revealed the potential of lipidomics and metabolomics in identifying new biomarkers and mechanistic insights for neurodegenerative disorders. To contribute to this promising area, we present a detailed protocol for conducting an integrated lipidomic and metabolomic profiling of brain tissue and biofluid samples. In this method, a single-phase methanol extraction is employed for extracting both nonpolar and highly polar lipids and metabolites from each biological sample. The extracted samples are then subjected to liquid chromatography-mass spectrometry-based assays to provide relative or semiquantitative measurements for hundreds of selected lipids and metabolites per sample. This high-throughput approach enables the generation of new hypotheses regarding the mechanistic and functional significance of lipid and metabolite alterations in neurodegenerative disorders while also facilitating the discovery of new biomarkers to support drug development.

Injectiondesign: web service of plate design with optimized stratified block randomization for modern GC/LC-MS-based sample preparation

BACKGROUND

Plate design is a necessary and time-consuming operation for GC/LC-MS-based sample preparation. The implementation of the inter-batch balancing algorithm and the intra-batch randomization algorithm can have a significant impact on the final results. For researchers without programming skills, a stable and efficient online service for plate design is necessary.

RESULTS

Here we describe InjectionDesign, a free online plate design service focused on GC/LC-MS-based multi-omics experiment design. It offers the ability to separate the position design from the sequence design, making the output more compatible with the requirements of a modern mass spectrometer-based laboratory. In addition, it has implemented an optimized block randomization algorithm, which can be better applied to sample stratification with block randomization for an unbalanced distribution. It is easy to use, with built-in support for common instrument models and quick export to a worksheet.

CONCLUSIONS

InjectionDesign is an open-source project based on Java. Researchers can get the source code for the project from Github: https://github.com/CSi-Studio/InjectionDesign . A free web service is also provided: http://www.injection.design .

Proteomic comparison between non-purified cerebrospinal fluid and cerebrospinal fluid-derived extracellular vesicles from patients with Alzheimer's, Parkinson's and Lewy body dementia

Dementia is a leading cause of death worldwide, with increasing prevalence as global life expectancy increases. The most common neurodegenerative disorders are Alzheimer's disease (AD), dementia with Lewy bodies (DLB) and Parkinson's disease dementia (PDD). With this study, we took an in-depth look at the proteome of the (non-purified) cerebrospinal fluid (CSF) and the CSF-derived extracellular vesicles (EVs) of AD, PD, PD-MCI (Parkinson's disease with mild cognitive impairment), PDD and DLB patients analysed by label-free mass spectrometry. This has led to the discovery of differentially expressed proteins that may be helpful for differential diagnosis. We observed a greater number of differentially expressed proteins in CSF-derived EV samples (N = 276) compared to non-purified CSF (N = 169), with minimal overlap between both datasets. This finding suggests that CSF-derived EV samples may be more suitable for the discovery phase of a biomarker study, due to the removal of more abundant proteins, resulting in a narrower dynamic range. As disease-specific markers, we selected a total of 39 biomarker candidates identified in non-purified CSF, and 37 biomarker candidates across the different diseases under investigation in the CSF-derived EV data. After further exploration and validation of these proteins, they can be used to further differentiate between the included dementias and may offer new avenues for research into more disease-specific pharmacological therapeutics.

Pain responses in preterm infants and parental stress over repeated painful procedures: a randomized pilot trial

OBJECTIVES

In this pilot study, the aims were to determine the feasibility of whether pain behavior in extremely and very preterm infants and perceived parental stress change when parents are involved in pain reducing measures, either actively, performing facilitated tucking or passively, observing the intervention, in comparison to the involvement of nurses only. In addition, the infant's pain reactivity and parental stress over three time points of measurement was of interest.

METHODS

Extremely and very preterm infants in need of subcutaneous erythropoietin were randomly assigned to the two intervention groups. The intervention encompassed that one parent of each infant was involved during the painful procedure: Either parents executed facilitated tucking themselves or stood by, observing the procedure. Usual care involved that nurse executed facilitated tucking. All infants received 0.5 ml of 30% oral glucose solution via cotton swab before the painful procedure. Infant pain was observed with the Bernese Pain Scale for Neonates (BPSN) and measured with the MedStorm skin conductance algesimeter (SCA) before, during, and after the procedure. Parents' stress levels were measured before and after the painful procedure on the infant, using the Current Strain Short Questionnaire (CSSQ). Feasibility of a subsequent trial was determined by assessing recruitment, measurement and active parental involvement. Quantitative data collection methods (i.e. questionnaires, algesimeter) were employed to determine the number of participants for a larger trial and measurement adequacy. Qualitative data (interviews) was employed to determine parents' perspectives of their involvement.

RESULTS

A total of 13 infants (98% participation rate) were included along with their mothers. Median gestational age was 27 weeks (IQR 26-28 weeks), 62% were female. Two infants (12.5%) dropped out of the study as they were transferred to another hospital. Facilitated tucking turned out to be a good method to actively involve parents in pain reducing measures. No significant differences between the two intervention and control groups were found concerning parental stress and infant pain (p = .927). Power analysis indicated that at least N = 741 infants (power of 81%, α = .05) would be needed to obtain statistically significant results in a larger trial, as effect sizes were smaller than expected. Two of the three measurement tools - i.e. the BPSN and CSSQ) - proved easy to implement and were well accepted. owever, the SCA was challenging in this context. Measurements were also found to be time-consuming and resource-intense (i.e. health professionals as assistants).

CONCLUSIONS

Although the intervention was feasible and was readily accepted by parents, the study design was found to be challenging along with the SCA. In preparation of the larger trial, the study design needs to be revisited and adjusted. Thus, issues of time and resources may be resolved. In addition, national and international collaboration with similar neonatal intensive care units (NICU) needs to be considered. Thus, it will be possible to conduct an appropriately powered larger trial, which will yield important results to improve pain management in extremely and preterm infants in NICU.

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.

Peer-led training improves lifejacket wear among occupational boaters: Evidence from a cluster randomized controlled trial on Lake Albert, Uganda

BACKGROUND

The burden of drowning among occupational boaters in low and middle-income countries is highest globally. In Uganda, over 95% of people who drowned from boating-related activities were not wearing lifejackets at the time of the incident. We implemented and evaluated a peer-led training program to improve lifejacket wear among occupational boaters on Lake Albert, Uganda.

METHODS

We conducted a two-arm cluster randomized controlled trial in which fourteen landing sites were randomized to the intervention and non-intervention arm with a 1:1 allocation ratio. In the intervention arm, a six-month peer-to-peer training program on lifejacket wear was implemented while the non-intervention arm continued to receive the routine Marine Police sensitizations on drowning prevention through its community policing program. The effect of the intervention was assessed on self-reported and observed lifejacket wear using a test of differences in proportions of wear following the intention to treat principle. The effect of contamination was assessed using mixed effect modified Poisson regression following the As Treated analysis principle at 95% CI. Results are reported according to the CONSORT statement-extension for cluster randomized trials.

RESULTS

Self-reported lifejacket wear increased markedly from 30.8% to 65.1% in the intervention arm compared to the non-intervention arm which rose from 29.9% to 43.2%. Observed wear increased from 1.0% to 26.8% in the intervention arm and from 0.6% to 8.8% in the non-intervention arm. The test of differences in proportions of self-reported lifejacket wear (65.1%- 43.2% = 21.9%, p-value <0.001) and observed wear (26.8%- 8.8% = 18%, p-value <0.001) showed statistically significant differences between the intervention and non-intervention arm. Self-reported lifejacket wear was higher among boaters who received peer training than those who did not (Adj. PR 1.78, 95% CI 1.38-2.30).

CONCLUSION

This study demonstrated that peer-led training significantly improves lifejacket wear among occupational boaters. The government of Uganda through the relevant ministries, and the Landing Site Management Committees should embrace and scale up peer-led training programs on lifejacket wear to reduce drowning deaths.

Automated splitting into batches for observational biomedical studies with sequential processing

Experimental design usually focuses on the setting where treatments and/or other aspects of interest can be manipulated. However, in observational biomedical studies with sequential processing, the set of available samples is often fixed, and the problem is thus rather the ordering and allocation of samples to batches such that comparisons between different treatments can be made with similar precision. In certain situations, this allocation can be done by hand, but this rapidly becomes impractical with more challenging cohort setups. Here, we present a fast and intuitive algorithm to generate balanced allocations of samples to batches for any single-variable model where the treatment variable is nominal. This greatly simplifies the grouping of samples into batches, makes the process reproducible, and provides a marked improvement over completely random allocations. The general challenges of allocation and why good solutions can be hard to find are also discussed, as well as potential extensions to multivariable settings.

Advances in proteomics: characterization of the innate immune system after birth and during inflammation

Proteomics is the characterization of the protein composition, the proteome, of a biological sample. It involves the large-scale identification and quantification of proteins, peptides, and post-translational modifications. This review focuses on recent developments in mass spectrometry-based proteomics and provides an overview of available methods for sample preparation to study the innate immune system. Recent advancements in the proteomics workflows, including sample preparation, have significantly improved the sensitivity and proteome coverage of biological samples including the technically difficult blood plasma. Proteomics is often applied in immunology and has been used to characterize the levels of innate immune system components after perturbations such as birth or during chronic inflammatory diseases like rheumatoid arthritis (RA) and inflammatory bowel disease (IBD). In cancers, the tumor microenvironment may generate chronic inflammation and release cytokines to the circulation. In these situations, the innate immune system undergoes profound and long-lasting changes, the large-scale characterization of which may increase our biological understanding and help identify components with translational potential for guiding diagnosis and treatment decisions. With the ongoing technical development, proteomics will likely continue to provide increasing insights into complex biological processes and their implications for health and disease. Integrating proteomics with other omics data and utilizing multi-omics approaches have been demonstrated to give additional valuable insights into biological systems.

Omics approaches for the assessment of biological responses to nanoparticles

Nanotechnology has enabled the development of innovative therapeutics, diagnostics, and drug delivery systems. Nanoparticles (NPs) can influence gene expression, protein synthesis, cell cycle, metabolism, and other subcellular processes. While conventional methods have limitations in characterizing responses to NPs, omics approaches can analyze complete sets of molecular entities that change upon exposure to NPs. This review discusses key omics approaches, namely transcriptomics, proteomics, metabolomics, lipidomics and multi-omics, applied to the assessment of biological responses to NPs. Fundamental concepts and analytical methods used for each approach are presented, as well as good practices for omics experiments. Bioinformatics tools are essential to analyze, interpret and visualize large omics data, and to correlate observations in different molecular layers. The authors envision that conducting interdisciplinary multi-omics analyses in future nanomedicine studies will reveal integrated cell responses to NPs at different omics levels, and the incorporation of omics into the evaluation of targeted delivery, efficacy, and safety will improve the development of nanomedicine therapies.

Comparison of propofol-esketamine versus propofol for anesthesia in gastroscopy: a double-blind, randomized controlled clinical trial

Objective

To compare the effects of propofol-esketamine and propofol in gastroscopy in adults.

Methods

This randomized controlled clinical trial was performed from January 2021 to March 2021. Eighty patients were enrolled and allocated into normal saline group (group N) and esketamine group (group E). The primary outcome was total amount of propofol. Secondary outcomes included incidences of injection pain, involuntary movement, hemodynamic and respiratory adverse events during examination, total examination time, recovery time and postoperative adverse effects.

Results

Total amount of propofol was significantly smaller in group E (101.64 ± 32.64 mg) than in group N (129.55 ± 36.34 mg, p = 0.001). Incidences of injection pain, involuntary movement and hypotension was significantly lower in group E than in group N. Incidences of hypertension and tachycardia was higher in group E than in group N. There was no significant difference in incidences of laryngospasm or hypoxemia, total examination time, recovery time, incidences of postoperative adverse effects between two groups.

Conclusion

Combination of propofol with 0.2 mg/kg esketamine reduced total amount of propofol, provided a more stable hemodynamic status and did not affect recovery time in gastroscopy.

Clinical trial registration

http://www.chictr.org, identifier ChiCTR2100042406.

MS-based proteomics of body fluids: The end of the beginning

Accurate biomarkers are a crucial and necessary precondition for precision medicine, yet existing ones are often unspecific and new ones have been very slow to enter the clinic. Mass spectrometry (MS)-based proteomics excels by its untargeted nature, specificity of identification and quantification making it an ideal technology for biomarker discovery and routine measurement. It has unique attributes compared to affinity binder technologies, such as OLINK Proximity Extension Assay and SOMAscan. In a previous review we described technological and conceptual limitations that had held back success (Geyer et al., 2017). We proposed a 'rectangular strategy' to better separate true biomarkers by minimizing cohort-specific effects. Today, this has converged with advances in MS-based proteomics technology, such as increased sample throughput, depth of identification and quantification. As a result, biomarker discovery studies have become more successful, producing biomarker candidates that withstand independent verification and, in some cases, already outperform state-of-the-art clinical assays. We summarize developments over the last years, including the benefits of large and independent cohorts, which are necessary for clinical acceptance. They are also required for machine learning or deep learning. Shorter gradients, new scan modes and multiplexing are about to drastically increase throughput, cross-study integration, and quantification, including proxies for absolute levels. We have found that multi-protein panels are inherently more robust than current single analyte tests and better capture the complexity of human phenotypes. Routine MS measurement in the clinic is fast becoming a viable option. The full set of proteins in a body fluid (global proteome) is the most important reference and the best process control. Additionally, it increasingly has all the information that could be obtained from targeted analysis although the latter may be the most straightforward way to enter into regular use. Many challenges remain, not least of a regulatory and ethical nature, but the outlook for MS-based clinical applications has never been brighter.

Development and validation of a multiple headspace solid-phase microextraction method for accurate and precise analysis of the aroma of Tawny and White Port wines

An accurate and precise Multiple Headspace Solid-Phase Microextraction (MHS-SPME) method was developed and validated for quantifying the volatile composition of White and Tawny Port wines. SPME extraction conditions were optimised using a four-factor three-level Box-Behnken design with three blocks and two replications. Optimal extraction conditions were similar for both Port wines. The method showed good linearity (0.001-50 mg/L), precision (<5%), and detection limits (<1μg/L), well below the olfactory detection threshold. Recoveries higher than 95 % were obtained. Twenty-three aroma compounds were quantified in Tawny and, for the first time, in White Port wines, including five acids, fourteen esters, the most abundant class, and four norisoprenoids, whose levels apparently increased with age. White Port wines had a lower abundance of aroma compounds. Results show that this MHS-SPME method is suitable for analysing volatile composition of White and Tawny Port wines, with reduced costs, manipulation time and eliminating matrix effects.

Analysis of Golgi Protein Acetylation Using In Vitro Assays and Parallel Reaction Monitoring Mass Spectrometry

Acetylation is one of the most abundant post-translational protein modifications that regulates all cellular compartments ranging from chromatin to cytoskeleton and Golgi. The dynamic acetylation of the Golgi stacking protein GRASP55 was shown to regulate Golgi reassembly after mitosis. Here we provide a detailed protocol for the analysis of Golgi acetylation including in vitro assays to detect protein acetylation and mass spectrometry analysis to identify specific acetylation sites and their relative abundance.

Benchmarking of analysis strategies for data-independent acquisition proteomics using a large-scale dataset comprising inter-patient heterogeneity

Numerous software tools exist for data-independent acquisition (DIA) analysis of clinical samples, necessitating their comprehensive benchmarking. We present a benchmark dataset comprising real-world inter-patient heterogeneity, which we use for in-depth benchmarking of DIA data analysis workflows for clinical settings. Combining spectral libraries, DIA software, sparsity reduction, normalization, and statistical tests results in 1428 distinct data analysis workflows, which we evaluate based on their ability to correctly identify differentially abundant proteins. From our dataset, we derive bootstrap datasets of varying sample sizes and use the whole range of bootstrap datasets to robustly evaluate each workflow. We find that all DIA software suites benefit from using a gas-phase fractionated spectral library, irrespective of the library refinement used. Gas-phase fractionation-based libraries perform best against two out of three reference protein lists. Among all investigated statistical tests non-parametric permutation-based statistical tests consistently perform best.

Data independent acquisition (DIA) has been gaining momentum in clinical proteomics. Here, the authors create a benchmark dataset comprising inter-patient heterogeneity to compare popular DIA data analysis workflows for identifying differentially abundant proteins.

Proteomics of Human Biological Fluids for Biomarker Discoveries: Technical Advances and Recent Applications

INTRODUCTION

Biological fluids are routine samples for diagnostic testing and monitoring. Blood samples are typically measured because of their moderate collection invasiveness and high information content on health and disease. Several body fluids, such as cerebrospinal fluid (CSF), are also studied and suited to specific pathologies. Over the last two decades proteomics has quested to identify protein biomarkers but with limited success. Recent technologies and refined pipelines have accelerated the profiling of human biological fluids.

AREAS COVERED

We review proteomic technologies for the identification of biomarkers. Those are based on antibodies/aptamers arrays or mass spectrometry (MS), but new ones are emerging. Advances in scalability and throughput have allowed to better design studies and cope with the limited sample size that had until now prevailed due to technological constraints. With these enablers, plasma/serum, CSF, saliva, tears, urine, and milk proteomes have been further profiled; we provide a non-exhaustive picture of some recent highlights (mainly covering literature from last five years in the Scopus database) using MS-based proteomics.

EXPERT OPINION

While proteomics has been in the shadow of genomics for years, proteomic tools and methodologies have reached a certain maturity. They are better suited to discover innovative and robust biofluid biomarkers.

HUNTER: Sensitive Automated Characterization of Proteolytic Systems by N Termini Enrichment from Microscale Specimen

Proteolysis occurs at low frequency in the cellular environment. Protein N termini reveal essential mechanisms associated with cellular functions, and are useful indicators to track dysfunctional regulation of proteins and pathways in diseases. N terminomics has so far relied on labor-intensive methods, which require relatively large starting sample amounts rendering it ill-suited for high-throughput systems biology studies. Here, we describe protocols for the first scalable and automatable method for sensitive enrichment and identification of N termini from minute samples.

Tutorial: best practices and considerations for mass-spectrometry-based protein biomarker discovery and validation

Mass-spectrometry-based proteomic analysis is a powerful approach for discovering new disease biomarkers. However, certain critical steps of study design such as cohort selection, evaluation of statistical power, sample blinding and randomization, and sample/data quality control are often neglected or underappreciated during experimental design and execution. This tutorial discusses important steps for designing and implementing a liquid-chromatography-mass-spectrometry-based biomarker discovery study. We describe the rationale, considerations and possible failures in each step of such studies, including experimental design, sample collection and processing, and data collection. We also provide guidance for major steps of data processing and final statistical analysis for meaningful biological interpretations along with highlights of several successful biomarker studies. The provided guidelines from study design to implementation to data interpretation serve as a reference for improving rigor and reproducibility of biomarker development studies.

Well Plate Maker: A user-friendly randomized block design application to limit batch effects in largescale biomedical studies

SUMMARY

Many factors can influence results in clinical research, in particular bias in the distribution of samples prior to biochemical preparation. Well Plate Maker is a user-friendly application to design single- or multiple-well plate assays. It allows multiple group experiments to be randomized and therefore helps to reduce possible batch effects. Although primarily fathered to optimize the design of clinical sample analysis by high throughput mass spectrometry (e.g. proteomics or metabolomics), it includes multiple options to limit edge-of-plate effects, to incorporate control samples, or to limit cross-contamination. It thus fits the constraints of many experimental fields.

AVAILABILITY AND IMPLEMENTATION

Well Plate Maker is implemented in R and available at Bioconductor repository (https://bioconductor.org/packages/wpm) under the open source Artistic 2.0 license. In addition to classical scripting, it can be used through a graphical user interface, developed with Shiny technology.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.