Mass spectrometry and the cellular surfaceome.

The collection of exposed plasma membrane proteins, collectively termed the surfaceome, is involved in multiple vital cellular processes, such as the communication of cells with their surroundings and the regulation of transport across the lipid bilayer. The surfaceome also plays key roles in the immune system by recognizing and presenting antigens, with its possible malfunctioning linked to disease. Surface proteins have long been explored as potential cell markers, disease biomarkers, and therapeutic drug targets. Despite its importance, a detailed study of the surfaceome continues to pose major challenges for mass spectrometry-driven proteomics due to the inherent biophysical characteristics of surface proteins. Their inefficient extraction from hydrophobic membranes to an aqueous medium and their lower abundance compared to intracellular proteins hamper the analysis of surface proteins, which are therefore usually underrepresented in proteomic datasets. To tackle such problems, several innovative analytical methodologies have been developed. This review aims at providing an extensive overview of the different methods for surfaceome analysis, with respective considerations for downstream mass spectrometry-based proteomics.

Single-Cell Phenotypic and Molecular Characterization of Circulating Tumor Cells Isolated from Cryopreserved Peripheral Blood Mononuclear Cells of Patients with Lung Cancer and Sarcoma.

BACKGROUND: The isolation of circulating tumor cells (CTCs) requires rapid processing of the collected blood due to their inherent fragility. The ability to recover CTCs from peripheral blood mononuclear cells (PBMCs) preserved from cancer patients could allow for retrospective analyses or multicenter CTC studies. METHODS: We compared the efficacy of CTC recovery and characterization using cryopreserved PMBCs vs fresh whole blood from patients with non-small cell lung cancer (NSCLC; n = 8) and sarcoma (n = 6). Two epithelial cellular adhesion molecule (EpCAM)-independent strategies for CTC enrichment, based on Parsortix® technology or immunomagnetic depletion of blood cells (AutoMACS®) were tested, followed by DEPArray™ single-cell isolation. Phenotype and genotype, assessed by copy number alterations analysis, were evaluated at a single-cell level. Detection of target mutations in CTC-enriched samples from frozen NSCLC PBMCs was also evaluated by digital PCR (dPCR). RESULTS: The use of cryopreserved PBMCs from cancer patients allowed for the retrospective enumeration of CTCs and their molecular characterization, using both EpCAM-independent strategies that performed equally in capturing CTC. Cells isolated from frozen PBMCs were representative of whole blood-derived CTCs in terms of number, phenotype, and copy number aberration profile/target mutations. Long-term storage (≥3 years) did not affect the efficacy of CTC recovery. Detection of target mutations was also feasible by dPCR in CTC-enriched samples derived from stored PBMCs. CONCLUSIONS: Isolating CTCs from longitudinally collected PBMCs using an unbiased selection strategy can offer a wider range of retrospective genomic/phenotypic analyses to guide patients' personalized therapy, paving the way for sample sharing in multicenter studies.

A disease progression model of longitudinal lung function decline in idiopathic pulmonary fibrosis patients.

Pirfenidone and nintedanib are the first two FDA-approved therapies for treatment of idiopathic pulmonary fibrosis (IPF). The clinical programs for pirfenidone and nintedanib included 1132 patients in the placebo arms and 1691 patients in the treatment arms across 6 trials. We developed a disease progression model to characterize the observed variability in lung function decline, measured as percent predicted forced vital capacity (%p-FVC), and its decrease in decline after treatment. The non-linear longitudinal change in %p-FVC was best described by a Weibull function. The median decreased decline in %p-FVC after treatment was estimated to be 1.50% (95% CI [1.12, 1.79]) and 1.96% (95% CI [1.47, 2.36]) at week 26 and week 52, respectively. Smoking status, weight, %p-FVC, %p-DLco and oxygen use at baseline were identified as significant covariates affecting decline in %p-FVC. The decreased decline in %p-FVC were observed among all subgroups of interest, of which the effects were larger at 1 year compared to 6 months. Based on the disease progression model smoking status and oxygen use at baseline may affect the treatment effect size. At week 52, the decreased decline in %p-FVC for current smokers and patients with oxygen use at baseline were 1.56 (90% CI [1.02, 1.99]) and 2.32 (90% CI [1.74, 2.86]), respectively. These prognostic factors may be used to enrich studies with patients who are more likely to respond to treatment, by demonstrating a lesser decline in lung function, and therefore provide the potential to allow for IPF studies with smaller study populations or shorter durations.

Addressing heterogeneity in amyotrophic lateral sclerosis CLINICAL TRIALS.

Amyotrophic lateral sclerosis (ALS) is a debilitating neurodegenerative disorder with complex biology and significant clinical heterogeneity. Many preclinical and early phase ALS clinical trials have yielded promising results that could not be replicated in larger phase 3 confirmatory trials. One reason for the lack of reproducibility may be ALS biological and clinical heterogeneity. Therefore, in this review, we explore sources of ALS heterogeneity that may reduce statistical power to evaluate efficacy in ALS trials. We also review efforts to manage clinical heterogeneity, including use of validated disease outcome measures, predictive biomarkers of disease progression, and individual clinical risk stratification. We propose that personalized prognostic models with use of predictive biomarkers may identify patients with ALS for whom a specific therapeutic strategy may be expected to be more successful. Finally, the rapid application of emerging clinical and biomarker strategies may reduce heterogeneity, increase trial efficiency, and, in turn, accelerate ALS drug development.

Characterizing disease-associated changes in post-translational modifications by mass spectrometry.

INTRODUCTION: Exploring post-translational modifications (PTMs) with the use of mass spectrometry (PTMomics) is a rapidly developing area, with methods for discovery/quantification being developed and advanced on a regular basis. PTMs are highly important for the regulation of protein function, interaction and activity, both in physiological and disease states. Changes in PTMs can either cause, or be the result of a disease, making them central for biomarker studies and studies of disease pathogenesis. Recently, it became possible to study multiple PTMs simultaneously from low amount of sample material, thereby increasing coverage of the PTMome obtainable from a single sample. Thus, quantitative PTMomics holds great potential to discover biomarkers from tissue and body fluids as well as elucidating disease mechanisms through characterization of signaling pathways. Areas covered: Recent mass spectrometry-based methods for assessment of the PTMome, with focus on the most studied PTMs, are highlighted. Furthermore, both data dependent and data independent acquisition methods are evaluated. Finally, current challenges in the field are discussed. Expert commentary: PTMomics holds great potential for clinical and biomedical research, especially with the generation of spectral libraries of peptides and PTMs from individual patients (permanent PTM maps) for use in personalized medicine.

On Enrichment Strategies for Biomarker Stratified Clinical Trials.

In the era of precision medicine, drugs are increasingly developed to target subgroups of patients with certain biomarkers. In large all-comer trials using a biomarker stratified design, the cost of treating and following patients for clinical outcomes may be prohibitive. With a fixed number of randomized patients, the efficiency of testing certain treatments parameters, including the treatment effect among biomarker-positive patients and the interaction between treatment and biomarker, can be improved by increasing the proportion of biomarker positives on study, especially when the prevalence rate of biomarker positives is low in the underlying patient population. When the cost of assessing the true biomarker is prohibitive, one can further improve the study efficiency by oversampling biomarker positives with a cheaper auxiliary variable or a surrogate biomarker that correlates with the true biomarker. To improve efficiency and reduce cost, we can adopt an enrichment strategy for both scenarios by concentrating on testing and treating patient subgroups that contain more information about specific treatment parameters of primary interest to the investigators. In the first scenario, an enriched biomarker stratified design enriches the cohort of randomized patients by directly oversampling the relevant patients with the true biomarker, while in the second scenario, an auxiliary-variable-enriched biomarker stratified design enriches the randomized cohort based on an inexpensive auxiliary variable, thereby avoiding testing the true biomarker on all screened patients and reducing treatment waiting time. For both designs, we discuss how to choose the optimal enrichment proportion when testing a single hypothesis or two hypotheses simultaneously. At a requisite power, we compare the two new designs with the BSD design in terms of the number of randomized patients and the cost of trial under scenarios mimicking real biomarker stratified trials. The new designs are illustrated with hypothetical examples for designing biomarker-driven cancer trials.

Special Enrichment Strategies Greatly Increase the Efficiency of Missing Proteins Identification from Regular Proteome Samples.

As part of the Chromosome-Centric Human Proteome Project (C-HPP) mission, laboratories all over the world have tried to map the entire missing proteins (MPs) since 2012. On the basis of the first and second Chinese Chromosome Proteome Database (CCPD 1.0 and 2.0) studies, we developed systematic enrichment strategies to identify MPs that fell into four classes: (1) low molecular weight (LMW) proteins, (2) membrane proteins, (3) proteins that contained various post-translational modifications (PTMs), and (4) nucleic acid-associated proteins. Of 8845 proteins identified in 7 data sets, 79 proteins were classified as MPs. Among data sets derived from different enrichment strategies, data sets for LMW and PTM yielded the most novel MPs. In addition, we found that some MPs were identified in multiple-data sets, which implied that tandem enrichments methods might improve the ability to identify MPs. Moreover, low expression at the transcription level was the major cause of the "missing" of these MPs; however, MPs with higher expression level also evaded identification, most likely due to other characteristics such as LMW, high hydrophobicity and PTM. By combining a stringent manual check of the MS2 spectra with peptides synthesis verification, we confirmed 30 MPs (neXtProt PE2 ∼ PE4) and 6 potential MPs (neXtProt PE5) with authentic MS evidence. By integrating our large-scale data sets of CCPD 2.0, the number of identified proteins has increased considerably beyond simulation saturation. Here, we show that special enrichment strategies can break through the data saturation bottleneck, which could increase the efficiency of MP identification in future C-HPP studies. All 7 data sets have been uploaded to ProteomeXchange with the identifier PXD002255.

Recent findings and technological advances in phosphoproteomics for cells and tissues.

Site-specific phosphorylation is a fast and reversible covalent post-translational modification that is tightly regulated in cells. The cellular machinery of enzymes that write, erase and read these modifications (kinases, phosphatases and phospho-binding proteins) is frequently deregulated in different diseases, including cancer. Large-scale studies of phosphoproteins - termed phosphoproteomics - strongly rely on the use of high-performance mass spectrometric instrumentation. This powerful technology has been applied to study a great number of phosphorylation-based phenotypes. Nevertheless, many technical and biological challenges have to be overcome to identify biologically relevant phosphorylation sites in cells and tissues. This review describes different technological strategies to identify and quantify phosphorylation sites with high accuracy, without significant loss of analysis speed and reproducibility in tissues and cells. Moreover, computational tools for analysis, integration and biological interpretation of phosphorylation events are discussed.

Factors influencing the performance of G-quadruplex DNAzyme-based sensors.

G-quadruplex DNAzymes are peroxidase-like complexes formed by nucleic acid G-quadruplexes and hemin. Compared with natural enzymes, G-quadruplex DNAzyme offers many advantages, thus making it a promising tool in the design of biosensors and chemical sensors. Many biosensors and chemical sensors based on G-quadruplex DNAzymes have been reported. A number of factors may affect the performance of G-quadruplex DNAzyme-based sensors. Here we focus on some aspects to be taken into account when designing a G-quadruplex DNAzyme-based sensor. These include the G-quadruplex-forming G-rich sequence, solution components, the reaction substrate, and enrichment strategy for G-quadruplex DNAzyme. We also provide an outlook for further research on G-quadruplex DNAzyme-based sensors.

Sex differences in the generalizability of randomized clinical trials in heart failure with reduced ejection fraction.

AIMS: In order to understand how sex differences impact the generalizability of randomized clinical trials (RCTs) in patients with heart failure (HF) and reduced ejection fraction (HFrEF), we sought to compare clinical characteristics and clinical outcomes between RCTs and HF observational registries stratified by sex. METHODS AND RESULTS: Data from two HF registries and five HFrEF RCTs were used to create three subpopulations: one RCT population (n = 16 917; 21.7% females), registry patients eligible for RCT inclusion (n = 26 104; 31.8% females), and registry patients ineligible for RCT inclusion (n = 20 810; 30.2% females). Clinical endpoints included all-cause mortality, cardiovascular mortality, and first HF hospitalization at 1 year. Males and females were equally eligible for trial enrolment (56.9% of females and 55.1% of males in the registries). One-year mortality rates were 5.6%, 14.0%, and 28.6% for females and 6.9%, 10.7%, and 24.6% for males in the RCT, RCT-eligible, and RCT-ineligible groups, respectively. After adjusting for 11 HF prognostic variables, RCT females showed higher survival compared to RCT-eligible females (standardized mortality ratio [SMR] 0.72; 95% confidence interval [CI] 0.62-0.83), while RCT males showed higher adjusted mortality rates compared to RCT-eligible males (SMR 1.16; 95% CI 1.09-1.24). Similar results were also found for cardiovascular mortality (SMR 0.89; 95% CI 0.76-1.03 for females, SMR 1.43; 95% CI 1.33-1.53 for males). CONCLUSION: Generalizability of HFrEF RCTs differed substantially between the sexes, with females having lower trial participation and female trial participants having lower mortality rates compared to similar females in the registries, while males had higher than expected cardiovascular mortality rates in RCTs compared to similar males in registries.

Genomics in Treatment Development.

The Human Genome Project mapped the 3 billion base pairs in the human genome, which ushered in a new generation of genomically focused treatment development. While this has been very successful in other areas, neuroscience has been largely devoid of such developments. This is in large part because there are very few neurological or mental health conditions that are related to single-gene variants. While developments in pharmacogenomics have been somewhat successful, the use of genetic information in practice has to do with drug metabolism and adverse reactions. Studies of drug metabolism related to genetic variations are an important part of drug development. However, outside of cancer biology, the actual translation of genomic information into novel therapies has been limited. Epigenetics, which relates in part to the effects of the environment on DNA, is a promising newer area of relevance to CNS disorders. The environment can induce chemical modifications of DNA (e.g., cytosine methylation), which can be induced by the environment and may represent either shorter- or longer-term changes. Given the importance of environmental influences on CNS disorders, epigenetics may identify important treatment targets in the future.

Advances in enrichment methods for mass spectrometry-based proteomics analysis of post-translational modifications.

Post-translational modifications (PTMs) occur during or after protein biosynthesis and increase the functional diversity of proteome. They comprise phosphorylation, acetylation, methylation, glycosylation, ubiquitination, sumoylation (among many other modifications), and influence all aspects of cell biology. Mass-spectrometry (MS)-based proteomics is the most powerful approach for PTM analysis. Despite this, it is challenging due to low abundance and labile nature of many PTMs. Hence, enrichment of modified peptides is required for MS analysis. This review provides an overview of most common PTMs and a discussion of current enrichment methods for MS-based proteomics analysis. The traditional affinity strategies, including immunoenrichment, chromatography and protein pull-down, are outlined together with their strengths and shortcomings. Moreover, a special attention is paid to chemical enrichment strategies, such as capture by chemoselective probes, metabolic and chemoenzymatic labelling, which are discussed with an emphasis on their recent progress. Finally, the challenges and future trends in the field are discussed.

Exploring the universal healthy human gut microbiota around the World.

The human gut holds a special place in the study of different microbial environments due to growing evidence that the gut microbiota is related to host health. However, despite extensive research, there is still a lack of knowledge about the core taxa forming the gut microbiota and, moreover, available information is biased towards western microbiomes in both genome databases and most core taxa studies. To tackle these limitations, we tested a database enrichment strategy and analyzed public datasets of whole-genome shotgun data, generated from 545 fecal samples, comprising three gradients of westernization. The NT database was selected as a baseline of biological diversity, subsequently being combined with various studies of interest related to the human microbiota. This enrichment strategy made it possible to improve classification capacity, compared to the original unenriched database, regarding the various lifestyles and populations studied. The effects of incomplete-taxonomy metagenome-assembled genomes on genome database enrichment were also examined, revealing that, while they are helpful, they should be used with caution depending on the taxonomic level of interest. Moreover, in terms of high prevalence, the core analysis revealed a conserved set of bacterial taxa in the healthy human gut microbiota worldwide, despite apparent lifestyle differences. Such taxa show a set of traits, metabolic roles, and ancestral status, making them suitable candidates for a hypothetical phylogenetic core of mutualistic microorganisms co-evolving with the human species.

Chemistry-Assisted Proteomic Profiling of O-GlcNAcylation.

The modification on proteins with O-linked N-acetyl-ß-D-glucosamine (O-GlcNAcylation) is essential for normal cell physiology. Dysregulation of O-GlcNAcylation leads to many human diseases, such as cancer, diabetes and neurodegenerative diseases. Recently, the functional role of O-GlcNAcylation in different physiological states has been elucidated due to the booming detection technologies. Chemical approaches for the enrichment of O-GlcNAcylated proteins combined with mass spectrometry-based proteomics enable the profiling of protein O-GlcNAcylation in a system-wide level. In this review, we summarize recent progresses on the enrichment and proteomic profiling of protein O-GlcNAcylation.

Sample size re-estimation in adaptive enrichment design.

Clinical trial participants are often heterogeneous, which is a fundamental problem in the rapidly developing field of precision medicine. Participants heterogeneity causes considerable difficulty in the current phase III trial designs. Adaptive enrichment designs provide a flexible and intuitive solution. At the interim analysis, we enrich the subgroup of trial participants who have a higher likelihood to benefit from the new treatment. However, it is critical to control the level of the test size and maintain adequate power after enrichment of certain subgroup of participants. We develop two adaptive enrichment strategies with sample size re-estimation and verify their feasibility and practicability through extensive simulations and sensitivity analyses. The simulation studies show that the proposed methods can control the overall type I error rate and exhibit competitive improvement in terms of statistical power and expected sample size. The proposed designs are exemplified with a real trial application.