Decreased complement 4d increases poor prognosis in patients with non‑small cell lung cancer combined with gastrointestinal lymph node metastasis

Lung cancer is a common malignancy that is difficult to treat and has a high risk of mortality. Although gastrointestinal lymph node metastasis has long been known to exert major impact on the prognosis of lung cancer, the mechanism of its occurrence and potential biological markers remain elusive. Therefore, the present study retrospectively analyzed data from 132 patients with non-small cell lung cancer (NSCLC) combined with lymph node metastasis between February 2010 and April 2019 from the First Affiliated Hospital of Soochow University (Suzhou, China) and Sichuan Cancer Hospital (Chengdu, China). Overall survival was assessed using Kaplan-Meier analysis and Cox logistic regression model. In addition, a prediction model was constructed based on immune indicators such as complement C3b and C4d (measured by ELISA), before the accuracy of this model was validated using calibration curves for 5-year OS. Among the 132 included patients, a total of 92 (70.0%) succumbed to the disease within 5 years. Multifactorial analysis revealed that complement C3b deficiency increased the risk of mortality by nearly two-fold [hazard ratio (HR)=2.23; 95% CI=1.20-4.14; P=0.017], whilst complement C4d deficiency similarly increased the risk of mortality by two-fold (HR=2.14; 95% CI=1.14-4.00; P=0.012). The variables were subsequently screened using Cox model to construct a prediction model based on complement C3b and C4d levels before a Nomogram plotted. By internal validation for the 132 patients, the Nomogram accurately estimated the risk of mortality, with a corrected C-index of 0.810. External validation of the model in another 50 patients from Sichuan Cancer Hospital revealed an accuracy of 77.0%. Overall, this mortality risk prediction model constructed based on complement levels showed accuracy in assessing the prognosis of patients with metastatic NSCLC. Therefore, complement C3b and C4d have potential for use as biomarkers to predict the risk of mortality in such patients.

Serum Proteomic Analysis Identifies SAA1, FGA, SAP, and CETP as New Biomarkers for Eosinophilic Granulomatosis With Polyangiitis

Background

Eosinophilic granulomatosis with polyangiitis (EGPA) is characterized by asthma-like attacks in its early stage, which is easily misdiagnosed as severe asthma. Therefore, new biomarkers for the early diagnosis of EGPA are needed, especially for differentiating the diagnosis of asthma.

Objectives

To identify serum biomarkers that can be used for early diagnosis of EGPA and to distinguish EGPA from severe asthma.

Method

Data-independent acquisition (DIA) analysis was performed to identify 45 healthy controls (HC), severe asthma (S-A), and EGPA patients in a cohort to screen biomarkers for early diagnosis of EGPA and to differentiate asthma diagnosis. Subsequently, parallel reaction monitoring (PRM) analysis was applied to a validation cohort of 71 HC, S-A, and EGPA patients.

Result

Four candidate biomarkers were identified from DIA and PRM analysis-i.e., serum amyloid A1 (SAA1), fibrinogen-α (FGA), and serum amyloid P component (SAP)-and were upregulated in the EGPA group, while cholesteryl ester transfer protein (CETP) was downregulated in the EGPA group compared with the S-A group. Receiver operating characteristics analysis shows that, as biomarkers for early diagnosis of EGPA, the combination of SAA1, FGA, and SAP has an area under the curve (AUC) of 0.947, a sensitivity of 82.35%, and a specificity of 100%. The combination of SAA1, FGA, SAP, and CETP as biomarkers for differential diagnosis of asthma had an AUC of 0.921, a sensitivity of 78.13%, and a specificity of 100%, which were all larger than single markers. Moreover, SAA1, FGA, and SAP were positively and CETP was negatively correlated with eosinophil count.

Conclusion

DIA-PRM combined analysis screened and validated four previously unexplored but potentially useful biomarkers for early diagnosis of EGPA and differential diagnosis of asthma.

Unbiased spatial proteomics with single-cell resolution in tissues

Mass spectrometry (MS)-based proteomics has become a powerful technology to quantify the entire complement of proteins in cells or tissues. Here, we review challenges and recent advances in the LC-MS-based analysis of minute protein amounts, down to the level of single cells. Application of this technology revealed that single-cell transcriptomes are dominated by stochastic noise due to the very low number of transcripts per cell, whereas the single-cell proteome appears to be complete. The spatial organization of cells in tissues can be studied by emerging technologies, including multiplexed imaging and spatial transcriptomics, which can now be combined with ultra-sensitive proteomics. Combined with high-content imaging, artificial intelligence and single-cell laser microdissection, MS-based proteomics provides an unbiased molecular readout close to the functional level. Potential applications range from basic biological questions to precision medicine.

A streamlined platform for analyzing tera-scale DDA and DIA mass spectrometry data enables highly sensitive immunopeptidomics

Integrating data-dependent acquisition (DDA) and data-independent acquisition (DIA) approaches can enable highly sensitive mass spectrometry, especially for imunnopeptidomics applications. Here we report a streamlined platform for both DDA and DIA data analysis. The platform integrates deep learning-based solutions of spectral library search, database search, and de novo sequencing under a unified framework, which not only boosts the sensitivity but also accurately controls the specificity of peptide identification. Our platform identifies 5-30% more peptide precursors than other state-of-the-art systems on multiple benchmark datasets. When evaluated on immunopeptidomics datasets, we identify 1.7-4.1 and 1.4-2.2 times more peptides from DDA and DIA data, respectively, than previously reported results. We also discover six T-cell epitopes from SARS-CoV-2 immunopeptidome that might represent potential targets for COVID-19 vaccine development. The platform supports data formats from all major instruments and is implemented with the distributed high-performance computing technology, allowing analysis of tera-scale datasets of thousands of samples for clinical applications.

Biomarker Candidates for Tumors Identified from Deep-Profiled Plasma Stem Predominantly from the Low Abundant Area

The plasma proteome has the potential to enable a holistic analysis of the health state of an individual. However, plasma biomarker discovery is difficult due to its high dynamic range and variability. Here, we present a novel automated analytical approach for deep plasma profiling and applied it to a 180-sample cohort of human plasma from lung, breast, colorectal, pancreatic, and prostate cancers. Using a controlled quantitative experiment, we demonstrate a 257% increase in protein identification and a 263% increase in significantly differentially abundant proteins over neat plasma. In the cohort, we identified 2732 proteins. Using machine learning, we discovered biomarker candidates such as STAT3 in colorectal cancer and developed models that classify the diseased state. For pancreatic cancer, a separation by stage was achieved. Importantly, biomarker candidates came predominantly from the low abundance region, demonstrating the necessity to deeply profile because they would have been missed by shallow profiling.

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.

Single-Shot 10K Proteome Approach: Over 10,000 Protein Identifications by Data-Independent Acquisition-Based Single-Shot Proteomics with Ion Mobility Spectrometry

The evolution of mass spectrometry (MS) and analytical techniques has led to the demand for proteome analysis with high proteome coverage in single-shot measurements. Focus has been placed on data-independent acquisition (DIA)-MS and ion mobility spectrometry as techniques for deep proteome analysis. We aimed to expand the proteome coverage by single-shot measurements using optimizing high-field asymmetric waveform ion mobility spectrometry parameters in DIA-MS. With our established proteome analysis system, more than 10,000 protein groups were identified from HEK293 cell digests within 120 min of MS measurement time. Additionally, we applied our approach to the analysis of host proteins in mouse feces and detected as many as 892 host protein groups (771 upregulated/121 downregulated proteins) in a mouse model of repeated social defeat stress (R-SDS) used in studying depression. Interestingly, 285 proteins elevated by R-SDS were related to mental disorders. The fecal host protein profiling by deep proteome analysis may help us understand mental illness pathologies noninvasively. Thus, our approach will be helpful for an in-depth comparison of protein expression levels for biological and medical research because it enables the analysis of highly proteome coverage in a single-shot measurement.

Obtaining Complete Human Proteomes

Proteins are the molecular effectors of the information encoded in the genome. Proteomics aims at understanding the molecular functions of proteins in their biological context. In contrast to transcriptomics and genomics, the study of proteomes provides deeper insight into the dynamic regulatory layers encoded at the protein level, such as posttranslational modifications, subcellular localization, cell signaling, and protein-protein interactions. Currently, mass spectrometry (MS)-based proteomics is the technology of choice for studying proteomes at a system-wide scale, contributing to clinical biomarker discovery and fundamental molecular biology. MS technologies are continuously being developed to fulfill the requirements of speed, resolution, and quantitative accuracy, enabling the acquisition of comprehensive proteomes. In this review, we present how MS technology and acquisition methods have evolved to meet the requirements of cutting-edge proteomics research, which is describing the human proteome and its dynamic posttranslational modifications with unprecedented depth. Finally, we provide a perspective on studying proteomes at single-cell resolution. Expected final online publication date for the Annual Review of Genomics and Human Genetics, Volume 23 is October 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Different syngeneic tumors show distinctive intrinsic tumor-immunity and mechanisms of actions (MOA) of anti-PD-1 treatment

Cancers are immunologically heterogeneous. A range of immunotherapies target abnormal tumor immunity via different mechanisms of actions (MOAs), particularly various tumor-infiltrate leukocytes (TILs). We modeled loss of function (LOF) in four common anti-PD-1 antibody-responsive syngeneic tumors, MC38, Hepa1-6, CT-26 and EMT-6, by systematical depleting a series of TIL lineages to explore the mechanisms of tumor immunity and treatment. CD8⁺-T-cells, CD4⁺-T-cells, T_reg, NK cells and macrophages were individually depleted through either direct administration of anti-marker antibodies/reagents or using DTR (diphtheria toxin receptor) knock-in mice, for some syngeneic tumors, where specific subsets were depleted following diphtheria toxin (DT) administration. These LOF experiments revealed distinctive intrinsic tumor immunity and thus different MOAs in their responses to anti-PD-1 antibody among different syngeneic tumors. Specifically, the intrinsic tumor immunity and the associated anti-PD-1 MOA were predominately driven by CD8⁺ cytotoxic TILs (CTL) in all syngeneic tumors, excluding Hepa1-6 where CD4⁺ T_eff TILs played a key role. TIL-T_reg also played a critical role in supporting tumor growth in all four syngeneic models as well as M₂-macrophages. Pathway analysis using pharmacodynamic readouts of immuno-genomics and proteomics on MC38 and Hepa1-6 also revealed defined, but distinctive, immune pathways of activation and suppression between the two, closely associated with the efficacy and consistent with TIL-pharmacodynamic readouts. Understanding tumor immune-pathogenesis and treatment MOAs in the different syngeneic animal models, not only assists the selection of the right model for evaluating new immunotherapy of a given MOA, but also can potentially help to understand the potential disease mechanisms and strategize optimal immune-therapies in patients.

High-throughput proteomics of nanogram-scale samples with Zeno SWATH MS

The possibility to record proteomes in high throughput and at high quality has opened new avenues for biomedical research, drug discovery, systems biology, and clinical translation. However, high-throughput proteomic experiments often require high sample amounts and can be less sensitive compared to conventional proteomic experiments. Here, we introduce and benchmark Zeno SWATH MS, a data-independent acquisition technique that employs a linear ion trap pulsing (Zeno trap pulsing) to increase the sensitivity in high-throughput proteomic experiments. We demonstrate that when combined with fast micro- or analytical flow-rate chromatography, Zeno SWATH MS increases protein identification with low sample amounts. For instance, using 20 min micro-flow-rate chromatography, Zeno SWATH MS identified more than 5000 proteins consistently, and with a coefficient of variation of 6%, from a 62.5 ng load of human cell line tryptic digest. Using 5 min analytical flow-rate chromatography (800 µl/min), Zeno SWATH MS identified 4907 proteins from a triplicate injection of 2 µg of a human cell lysate, or more than 3000 proteins from a 250 ng tryptic digest. Zeno SWATH MS hence facilitates sensitive high-throughput proteomic experiments with low sample amounts, mitigating the current bottlenecks of high-throughput proteomics.

Review of the Real and Sometimes Hidden Costs in Proteomics Experimental Workflows

A typical proteomics workflow covers all the steps from growing or collecting the cells/tissues/organism, protein extraction, digestion and cleanup, mass spectrometric analysis, and, finally, extensive bioinformatics to derive biological insight from the data. The details of the procedures employed for this can vary widely by laboratory and by sample type: e.g., hard tissues or cells with walls require much more mechanical disruption to extract proteins than do soft tissues, biological fluids, or wall-less cells. Everything then converges on the mass spectrometer, where there are further choices to be made about how to do the analysis. There is one commonality, however, virtually every group around the world now uses liquid chromatography on-line coupled to tandem mass spectrometry, which means that significant amounts of instrument time are dedicated to every sample. There are many other reviews or methods papers, including in this volume, that cover the details of the various procedures involved in proteomic analyses of all types of samples. Our focus here will be on the cost considerations for such analyses, including considerations to ensure that useful data can be obtained the first time a sample is analyzed. Some of these costs are often overlooked, particularly for those groups who operate their own mass spectrometer(s) and do not have to go to a fee-for-service facility to have something analyzed. The chapter presents several challenges and key suggestions in proving hypotheses in proteomics experimental workflow in different biological systems with specific regard to the costs involved, both real and hidden. The detailed methodology for cost-based studies reported in this chapter can help researchers to set up their laboratory with appropriate equipment as well as to identify potential collaborations based on their analytical instrumentation.

Investigation of Effects of the Spectral Library on Analysis of diaPASEF Data

Targeted analysis of data-independent acquisition (DIA) data needs a spectral library, which is generated by data-dependent acquisition (DDA) experiments or directly from DIA data. A comparison of the DDA library and DIA library in analyzing DIA data has been reported. However, the effects of different spectral libraries on the analysis of diaPASEF data have not been investigated. Here, we generate different spectral libraries with varying proteome coverage to analyze parallel accumulation-serial fragmentation (diaPASEF) data. Besides, we also employ the library-free strategy. The library, constructed by extensive fractionation DDA experiments, produces the highest numbers of precursors and proteins but with a high percentage of missing values. The library-free strategy identifies 10-20% fewer proteins than the library-based method but with a high degree of data completeness. A further study shows that the library-free strategy, although it identifies fewer proteins than the library-based method, leads to similar biological conclusions as the library-based method.

Sample Size-Comparable Spectral Library Enhances Data-Independent Acquisition-Based Proteome Coverage of Low-Input Cells

Despite advancements of data-independent acquisition mass spectrometry (DIA-MS) to provide comprehensive and reproducible proteome profiling, its utility in very low-input samples is limited. Due to different proteome complexities and corresponding peptide ion abundances, the conventional LC-MS/MS acquisition and widely used large-scale DIA libraries may not be suitable for the micro-nanogram samples. In this study, we report a sample size-comparable library-based DIA approach to enhance the proteome coverage of low-input nanoscale samples (i.e., nanogram cells, ∼5-50 cells). By constructing sample size-comparable libraries, 2380 and 3586 protein groups were identified from as low as 0.75 (∼5 cells) and 1.5 ng (∼10 cells), respectively, highlighting one of the highest proteome coverage with good reproducibility (86%-99% in triplicate results). For the 0.75 ng sample (∼5 cells), significantly superior identification (2380 proteins) was achieved by small-size library-based DIA, compared to 1908, 1749, and 107 proteins identified from medium-size and large-size libraries and a lung cancer resource spectral library, respectively. A similar trend was observed using a different instrument and data analysis pipeline, indicating the generalized conclusion of the approach. Furthermore, the small-size library uniquely identified 518 (22%) proteins in the low-abundant region and spans over a 5-order dynamic range. Spectral similarity analysis revealed that the fragmentation ion pattern in the DIA-MS/MS spectra of the dataset and spectral library play crucial roles for mapping low abundant proteins. With these spectral libraries made freely available, the optimized library-based DIA strategy and DIA digital map will advance quantitative proteomics applications for mass-limited samples.

Identification of novel proteins and mechanistic pathways associated with early-onset hypertension by deep proteomic mapping of resistance arteries

Resistance arteries are small blood vessels that create resistance to blood flow. In hypertension, resistance arteries undergo remodeling, affecting their ability to contract and relax appropriately. To date, no study has mapped the hypertension-related proteomic changes in resistance arteries. Using a novel data-independent acquisition–mass spectrometry (DIA-MS) approach, we determined the proteomic changes in small mesenteric and renal arteries in pre- and early-onset hypertension from the spontaneously hypertensive rat (SHR) model, which represents human primary hypertension. Compared with normotensive controls, mesenteric arteries from 12-week-old SHRs had 286 proteins that were significantly up- or downregulated, whereas 52 proteins were identified as up- or downregulated in mesenteric arteries from 6-week-old SHRs. Of these proteins, 18 were also similarly regulated in SHR renal arteries. Our pathway analyses reveal several novel pathways in the pathogenesis of hypertension. Finally, using a matrisome database, we identified 38 altered extracellular-matrix-associated proteins, many of which have never previously been associated with hypertension. Taken together, this study reveals novel proteins and mechanisms that are associated with early-onset hypertension, thereby providing novel insights into disease progression.

Computational Optimization of Spectral Library Size Improves DIA-MS Proteome Coverage and Applications to 15 Tumors

Efficient peptide and protein identifications from data-independent acquisition mass spectrometric (DIA-MS) data typically rely on a project-specific spectral library with a suitable size. Here, we describe subLib, a computational strategy for optimizing the spectral library for a specific DIA data set based on a comprehensive spectral library, requiring the preliminary analysis of the DIA data set. Compared with the pan-human library strategy, subLib achieved a 41.2% increase in peptide precursor identifications and a 35.6% increase in protein group identifications in a test data set of six colorectal tumor samples. We also applied this strategy to 389 carcinoma samples from 15 tumor data sets: up to a 39.2% increase in peptide precursor identifications and a 19.0% increase in protein group identifications were observed. Our strategy for spectral library size optimization thus successfully proved to deepen the proteome coverages of DIA-MS data.

Identification of tumor antigens with immunopeptidomics

The identification of actionable tumor antigens is indispensable for the development of several cancer immunotherapies, including T cell receptor-transduced T cells and patient-specific mRNA or peptide vaccines. Most known tumor antigens have been identified through extensive molecular characterization and are considered canonical if they derive from protein-coding regions of the genome. By eluting human leukocyte antigen-bound peptides from tumors and subjecting these to mass spectrometry analysis, the peptides can be identified by matching the resulting spectra against reference databases. Recently, mass-spectrometry-based immunopeptidomics has enabled the discovery of noncanonical antigens-antigens derived from sequences outside protein-coding regions or generated by noncanonical antigen-processing mechanisms. Coupled with transcriptomics and ribosome profiling, this method enables the identification of thousands of noncanonical peptides, of which a substantial fraction may be detected exclusively in tumors. Spectral matching against the immense noncanonical reference may generate false positives. However, sensitive mass spectrometry, analytical validation and advanced bioinformatics solutions are expected to uncover the full landscape of presented antigens and clinically relevant targets.

AlphaTims: Indexing Trapped Ion Mobility Spectrometry-TOF Data for Fast and Easy Accession and Visualization

High-resolution MS-based proteomics generates large amounts of data, even in the standard LC-tandem MS configuration. Adding an ion mobility dimension vastly increases the acquired data volume, challenging both analytical processing pipelines and especially data exploration by scientists. This has necessitated data aggregation, effectively discarding much of the information present in these rich datasets. Taking trapped ion mobility spectrometry (TIMS) on a quadrupole TOF (Q-TOF) platform as an example, we developed an efficient indexing scheme that represents all data points as detector arrival times on scales of minutes (LC), milliseconds (TIMS), and microseconds (TOF). In our open-source AlphaTims package, data are indexed, accessed, and visualized by a combination of tools of the scientific Python ecosystem. We interpret unprocessed data as a sparse four-dimensional matrix and use just-in-time compilation to machine code with Numba, accelerating our computational procedures by several orders of magnitude while keeping to familiar indexing and slicing notations. For samples with more than six billion detector events, a modern laptop can load and index raw data in about a minute. Loading is even faster when AlphaTims has already saved indexed data in an HDF5 file, a portable scientific standard used in extremely large-scale data acquisition. Subsequently, data accession along any dimension and interactive visualization happens in milliseconds. We have found AlphaTims to be a key enabling tool to explore high-dimensional LC-TIMS-Q-TOF data and have made it freely available as an open-source Python package with a stand-alone graphical user interface at https://github.com/MannLabs/alphatims or as part of the AlphaPept 'ecosystem'.

CsoDIAq Software for Direct Infusion Shotgun Proteome Analysis

Direct infusion shotgun proteome analysis (DISPA) is a new paradigm for expedited mass spectrometry-based proteomics, but the original data analysis workflow was onerous. Here, we introduce CsoDIAq, a user-friendly software package for the identification and quantification of peptides and proteins from DISPA data. In addition to establishing a complete and automated analysis workflow with a graphical user interface, CsoDIAq introduces algorithmic concepts to spectrum-spectrum matching to improve peptide identification speed and sensitivity. These include spectra pooling to reduce search time complexity and a new spectrum-spectrum match score called match count and cosine, which improves target discrimination in a target-decoy analysis. Fragment mass tolerance correction also increased the number of peptide identifications. Finally, we adapt CsoDIAq to standard LC-MS DIA and show that it outperforms other spectrum-spectrum matching software.

Time-resolved in vivo ubiquitinome profiling by DIA-MS reveals USP7 targets on a proteome-wide scale

Mass spectrometry (MS)-based ubiquitinomics provides system-level understanding of ubiquitin signaling. Here we present a scalable workflow for deep and precise in vivo ubiquitinome profiling, coupling an improved sample preparation protocol with data-independent acquisition (DIA)-MS and neural network-based data processing specifically optimized for ubiquitinomics. Compared to data-dependent acquisition (DDA), our method more than triples identification numbers to 70,000 ubiquitinated peptides in single MS runs, while significantly improving robustness and quantification precision. Upon inhibition of the oncology target USP7, we simultaneously record ubiquitination and consequent changes in abundance of more than 8,000 proteins at high temporal resolution. While ubiquitination of hundreds of proteins increases within minutes of USP7 inhibition, we find that only a small fraction of those are ever degraded, thereby dissecting the scope of USP7 action. Our method enables rapid mode-of-action profiling of candidate drugs targeting DUBs or ubiquitin ligases at high precision and throughput.

Combining improved sample preparation, data-independent acquisition mass spectrometry and deep learning, the authors develop a workflow for more robust and precise quantitative ubiquitinome profiling. They use this method to characterize targets of the deubiquitinase USP7 and effects of USP7 inhibitors.

A proteomic view on lysosomes

Lysosomes are the main degradative organelles of almost all eukaryotic cells. They fulfil a crucial function in cellular homeostasis, and impairments in lysosomal function are connected to a continuously increasing number of pathological conditions. In recent years, lysosomes are furthermore emerging as control centers of cellular metabolism, and major regulators of cellular signaling were shown to be activated at the lysosomal surface. To date, >300 proteins were demonstrated to be located in/at the lysosome, and the lysosomal proteome and interactome is constantly growing. For the identification of these proteins, and their involvement in cellular mechanisms or disease progression, mass spectrometry (MS)-based proteomics has proven its worth in a large number of studies. In this review, we are recapitulating the application of MS-based approaches for the investigation of the lysosomal proteome, and their application to a diverse set of research questions. Numerous strategies were applied for the enrichment of lysosomes or lysosomal proteins and their identification by MS-based methods. This allowed for the characterization of the lysosomal proteome, the investigation of lysosome-related disorders, the utilization of lysosomal proteins as biomarkers for diseases, and the characterization of lysosome-related cellular mechanisms. While these >60 studies provide a comprehensive picture of the lysosomal proteome across several model organisms and pathological conditions, various proteomics approaches have not been applied to lysosomes yet, and a large number of questions are still left unanswered.

Mass spectrometry-based proteomic analysis of the effect of storage temperature on non-invasively collected samples of human stratum corneum

PURPOSE

The collection of human stratum corneum (SC) samples by tape-stripping promises to deliver a non-invasive method for biomarker analysis of skin diseases. The current study examines the effect of storage temperature and SC layer depth on the proteome profile of SC samples.

EXPERIMENTAL DESIGN

The samples were collected from healthy volunteers (n = 5) using 10 sequential adhesive discs. Discs were pooled by five (discs 1-5, 6-10) and stored at various temperatures for 10 days before their analysis by mass spectrometry.

RESULTS

No statistically significant difference was seen in the protein yield between discs 1-5 (14.8 mg) and 6-10 (14.4 mg), or between discs stored at -80°C (14.7 mg), -20°C (15.8 mg), 4°C (14.9 mg) or room temperature (13.2 mg). Mass spectrometry analysis revealed that the storage of SC samples at higher temperatures does not affect their proteome profile considerably (< 4.7% peptide precursor loss at lower temperatures vs. -80°C). On the other hand, while 95.3% of the identified peptide precursors were shared between discs 1-5 and 6-10, the level of 17 proteins was significantly changed between these conditions.

CONCLUSIONS

The findings of this study will likely have major implications on the conduct of proteomic studies involving SC sample collection, storage, and transportation.

High-throughput proteomics and AI for cancer biomarker discovery

Biomarkers are assayed to assess biological and pathological status. Recent advances in high-throughput proteomic technology provide opportunities for developing next generation biomarkers for clinical practice aided by artificial intelligence (AI) based techniques. We summarize the advances and limitations of cancer biomarkers based on genomic and transcriptomic analysis, as well as classical antibody-based methodologies. Then we review recent progresses in mass spectrometry (MS)-based proteomics in terms of sample preparation, peptide fractionation by liquid chromatography (LC) and mass spectrometric data acquisition. We highlight applications of AI techniques in high-throughput clinical studies as compared with clinical decisions based on singular features. This review sets out our approach for discovering clinical biomarkers in studies using proteomic big data technology conjoined with computational and statistical methods.

Trapped Ion Mobility Spectrometry and Parallel Accumulation-Serial Fragmentation in Proteomics

Recent advances in efficiency and ease of implementation have rekindled interest in ion mobility spectrometry, a technique that separates gas phase ions by their size and shape and that can be hybridized with conventional LC and MS. Here, we review the recent development of trapped ion mobility spectrometry (TIMS) coupled to TOF mass analysis. In particular, the parallel accumulation-serial fragmentation (PASEF) operation mode offers unique advantages in terms of sequencing speed and sensitivity. Its defining feature is that it synchronizes the release of ions from the TIMS device with the downstream selection of precursors for fragmentation in a TIMS quadrupole TOF configuration. As ions are compressed into narrow ion mobility peaks, the number of peptide fragment ion spectra obtained in data-dependent or targeted analyses can be increased by an order of magnitude without compromising sensitivity. Taking advantage of the correlation between ion mobility and mass, the PASEF principle also multiplies the efficiency of data-independent acquisition. This makes the technology well suited for rapid proteome profiling, an increasingly important attribute in clinical proteomics, as well as for ultrasensitive measurements down to single cells. The speed and accuracy of TIMS and PASEF also enable precise measurements of collisional cross section values at the scale of more than a million data points and the development of neural networks capable of predicting them based only on peptide sequences. Peptide collisional cross section values can differ for isobaric sequences or positional isomers of post-translational modifications. This additional information may be leveraged in real time to direct data acquisition or in postprocessing to increase confidence in peptide identifications. These developments make TIMS quadrupole TOF PASEF a powerful and expandable platform for proteomics and beyond.

Aircraft noise exposure drives the activation of white blood cells and induces microvascular dysfunction in mice

Epidemiological studies showed that traffic noise has a dose-dependent association with increased cardiovascular morbidity and mortality. Whether microvascular dysfunction contributes significantly to the cardiovascular health effects by noise exposure remains to be established. The connection of inflammation and immune cell interaction with microvascular damage and functional impairment is also not well characterized. Male C57BL/6J mice or gp91phox^−/y mice with genetic deletion of the phagocytic NADPH oxidase catalytic subunit (gp91phox or NOX-2) were used at the age of 8 weeks, randomly instrumented with dorsal skinfold chambers and exposed or not exposed to aircraft noise for 4 days. Proteomic analysis (using mass spectrometry) revealed a pro-inflammatory phenotype induced by noise exposure that was less pronounced in noise-exposed gp91phox^−/y mice. Using in vivo fluorescence microscopy, we found a higher number of adhesive leukocytes in noise-exposed wild type mice. Dorsal microvascular diameter (by trend), red blood cell velocity, and segmental blood flow were also decreased by noise exposure indicating microvascular constriction. All adverse effects on functional parameters were normalized or improved at least by trend in noise-exposed gp91phox^−/y mice. Noise exposure also induced endothelial dysfunction in cerebral microvessels, which was associated with higher oxidative stress burden and inflammation, as measured using video microscopy. We here establish a link between a pro-inflammatory phenotype of plasma, activation of circulating leukocytes and microvascular dysfunction in mice exposed to aircraft noise. The phagocytic NADPH oxidase was identified as a central player in the underlying pathophysiological mechanisms.

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Noise exposure induces a pro-thrombo-inflammatory phenotype in mouse plasma.

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Aircraft noise increases leukocyte-endothelium interactions in dorsal microvessels.

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Noise decreases segmental blood flow/red blood cell velocity in dorsal microvessels.

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Noise increases cerebral microvascular dysfunction and oxidative stress.

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Nox2 deficiency (gp91phox^-/y) improves noise-induced adverse effects.

Ultra-fast proteomics with Scanning SWATH

Accurate quantification of the proteome remains challenging for large sample series and longitudinal experiments. We report a data-independent acquisition method, Scanning SWATH, that accelerates mass spectrometric (MS) duty cycles, yielding quantitative proteomes in combination with short gradients and high-flow (800 µl min-1) chromatography. Exploiting a continuous movement of the precursor isolation window to assign precursor masses to tandem mass spectrometry (MS/MS) fragment traces, Scanning SWATH increases precursor identifications by ~70% compared to conventional data-independent acquisition (DIA) methods on 0.5-5-min chromatographic gradients. We demonstrate the application of ultra-fast proteomics in drug mode-of-action screening and plasma proteomics. Scanning SWATH proteomes capture the mode of action of fungistatic azoles and statins. Moreover, we confirm 43 and identify 11 new plasma proteome biomarkers of COVID-19 severity, advancing patient classification and biomarker discovery. Thus, our results demonstrate a substantial acceleration and increased depth in fast proteomic experiments that facilitate proteomic drug screens and clinical studies.

The Isotopic Ac-IP Tag Enables Multiplexed Proteome Quantification in Data-Independent Acquisition Mode

Data-independent acquisition (DIA) is an increasingly used approach for quantitative proteomics. However, most current isotope labeling strategies are not suitable for DIA as they lead to more complex MS2 spectra or severe ratio distortion. As a result, DIA suffers from a lower throughput than data-dependent acquisition (DDA) due to a lower level of multiplexing. Herein, we synthesized an isotopically labeled acetyl-isoleucine-proline (Ac-IP) tag for multiplexed quantification in DIA. Differentially labeled peptides have distinct precursor ions carrying the quantitative information but identical MS2 spectra since the isotopically labeled Ac-Ile part leaves as a neutral loss upon collision-induced dissociation, while fragmentation of the peptide backbone generates regular fragment ions for identification. The Ac-IP-labeled samples can be analyzed using general DIA liquid chromatography–mass spectrometry settings, and the data obtained can be processed with established approaches. Relative quantification requires deconvolution of the isotope envelope of the respective precursor ions. Suitability of the Ac-IP tag is demonstrated with a triplex-labeled yeast proteome spiked with bovine serum albumin that was mixed at 10:5:1 ratios, resulting in measured ratios of 9.7:5.3:1.1.

Sanchez-Garcia M, Morrison T, Howden AJM, Watts ER, Arienti S, Sadiku P, Coelho P, Mirchandani AS, Zhang A, Hope D, Clark SK, Singleton J, Johnston S, Grecian R, Poon A, McNamara S, Harper I, Fourman MH, Brenes AJ, Pathak S, Lloyd A, Blanco GR, von Kriegsheim A, Ghesquiere B, Vermaelen W, Cologna CT, Dhaliwal K, Hirani N, Dockrell DH, Whyte MKB, Griffith D, Cantrell DA, Walmsley SR. -------A type I IFN, prothrombotic hyperinflammatory neutrophil signature is distinct for COVID-19 ARDS--

Background: Acute respiratory distress syndrome (ARDS) is a severe critical condition with a high mortality that is currently in focus given that it is associated with mortality caused by coronavirus disease 2019 (COVID-19). Neutrophils play a key role in the lung injury characteristic of non-COVID-19 ARDS and there is also accumulating evidence of neutrophil mediated lung injury in patients who succumb to infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Methods: We undertook a functional proteomic and metabolomic survey of circulating neutrophil populations, comparing patients with COVID-19 ARDS and non-COVID-19 ARDS to understand the molecular basis of neutrophil dysregulation. Results: Expansion of the circulating neutrophil compartment and the presence of activated low and normal density mature and immature neutrophil populations occurs in ARDS, irrespective of cause. Release of neutrophil granule proteins, neutrophil activation of the clotting cascade and upregulation of the Mac-1 platelet binding complex with formation of neutrophil platelet aggregates is exaggerated in COVID-19 ARDS. Importantly, activation of components of the neutrophil type I interferon responses is seen in ARDS following infection with SARS-CoV-2, with associated rewiring of neutrophil metabolism, and the upregulation of antigen processing and presentation. Whilst dexamethasone treatment constricts the immature low density neutrophil population, it does not impact upon prothrombotic hyperinflammatory neutrophil signatures. Conclusions: Given the crucial role of neutrophils in ARDS and the evidence of a disordered myeloid response observed in COVID-19 patients, this work maps the molecular basis for neutrophil reprogramming in the distinct clinical entities of COVID-19 and non-COVID-19 ARDS.

Sanchez-Garcia M, Morrison T, Howden AJM, Watts ER, Arienti S, Sadiku P, Coelho P, Mirchandani AS, Zhang A, Hope D, Clark SK, Singleton J, Johnston S, Grecian R, Poon A, McNamara S, Harper I, Fourman MH, Brenes AJ, Pathak S, Lloyd A, Blanco GR, von Kriegsheim A, Ghesquiere B, Vermaelen W, Cologna CT, Dhaliwal K, Hirani N, Dockrell DH, Whyte MKB, Griffith D, Cantrell DA, Walmsley SR. -------A type I IFN, prothrombotic hyperinflammatory neutrophil signature is distinct for COVID-19 ARDS--

Background: Acute respiratory distress syndrome (ARDS) is a severe critical condition with a high mortality that is currently in focus given that it is associated with mortality caused by coronavirus disease 2019 (COVID-19). Neutrophils play a key role in the lung injury characteristic of non-COVID-19 ARDS and there is also accumulating evidence of neutrophil mediated lung injury in patients who succumb to infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Methods: We undertook a functional proteomic and metabolomic survey of circulating neutrophil populations, comparing patients with COVID-19 ARDS and non-COVID-19 ARDS to understand the molecular basis of neutrophil dysregulation. Results: Expansion of the circulating neutrophil compartment and the presence of activated low and normal density mature and immature neutrophil populations occurs in ARDS, irrespective of cause. Release of neutrophil granule proteins, neutrophil activation of the clotting cascade and upregulation of the Mac-1 platelet binding complex with formation of neutrophil platelet aggregates is exaggerated in COVID-19 ARDS. Importantly, activation of components of the neutrophil type I interferon responses is seen in ARDS following infection with SARS-CoV-2, with associated rewiring of neutrophil metabolism, and the upregulation of antigen processing and presentation. Whilst dexamethasone treatment constricts the immature low density neutrophil population, it does not impact upon prothrombotic hyperinflammatory neutrophil signatures. Conclusions: Given the crucial role of neutrophils in ARDS and the evidence of a disordered myeloid response observed in COVID-19 patients, this work maps the molecular basis for neutrophil reprogramming in the distinct clinical entities of COVID-19 and non-COVID-19 ARDS.

A data-independent acquisition-based global phosphoproteomics system enables deep profiling

Phosphoproteomics can provide insights into cellular signaling dynamics. To achieve deep and robust quantitative phosphoproteomics profiling for minute amounts of sample, we here develop a global phosphoproteomics strategy based on data-independent acquisition (DIA) mass spectrometry and hybrid spectral libraries derived from data-dependent acquisition (DDA) and DIA data. Benchmarking the method using 166 synthetic phosphopeptides shows high sensitivity (<0.1 ng), accurate site localization and reproducible quantification (~5% median coefficient of variation). As a proof-of-concept, we use lung cancer cell lines and patient-derived tissue to construct a hybrid phosphoproteome spectral library covering 159,524 phosphopeptides (88,107 phosphosites). Based on this library, our single-shot streamlined DIA workflow quantifies 36,350 phosphosites (19,755 class 1) in cell line samples within two hours. Application to drug-resistant cells and patient-derived lung cancer tissues delineates site-specific phosphorylation events associated with resistance and tumor progression, showing that our workflow enables the characterization of phosphorylation signaling with deep coverage, high sensitivity and low between-run missing values.

Elucidation of host-virus surfaceome interactions using spatial proteotyping

The cellular surfaceome and its residing extracellularly exposed proteins are involved in a multitude of molecular signaling processes across the viral infection cycle. Successful viral propagation, including viral entry, immune evasion, virion release and viral spread rely on dynamic molecular interactions with the surfaceome. Decoding of these viral-host surfaceome interactions using advanced technologies enabled the discovery of fundamental new functional insights into cellular and viral biology. In this review, we highlight recently developed experimental strategies, with a focus on spatial proteotyping technologies, aiding in the rational design of theranostic strategies to combat viral infections.

High-throughput, in-depth and estimated absolute quantification of plasma proteome using data-independent acquisition/mass spectrometry ("HIAP-DIA")

Mass spectrometry-based plasma proteomics has been demonstrated to be a useful tool capable of quantifying hundreds of proteins in a single LC-MS/MS experiment, for biomarker discovery or elucidation of disease mechanisms. We developed a novel data-independent acquisition (DIA)/MS-based workflow for high-throughput, in-depth and estimated absolute quantification of plasma proteins (termed HIAP-DIA), without depleting high-abundant proteins, in a single-shot experiment. In HIAP-DIA workflow, we generated an ultra-deep cumulative undepleted and depleted spectral library which contained 55,157 peptides and 5,328 proteins, optimized column length (50 cm) and gradient (90 min) of liquid chromatography instrumentation, optimized 50 DIA segments with average isolation window 17 Th, and selected reference proteins for estimated absolute quantification of all plasma proteins. A total of 606 proteins were quantified in triplicate, and 427 proteins were quantified with CV <20% in plasma proteome. R-squared value of overlapped 208 endogenous PQ500 estimated protein amounts from HIAP-DIA and absolute quantification with internal standards was 0.82, indicating high quantification accuracy of HIAP-DIA. As a pilot study, the HIAP-DIA approach described here was applied to a myelodysplastic syndromes (MDS) disease cohort. We achieved absolute quantification of 789 plasma proteins in 22 clinical plasma samples, spanning less than six orders of magnitude with quantification limit 10-20 ng/mL, and discovered 95 differentially expressed proteins providing insights into MDS pathophysiology.

Use of Hybrid Data-Dependent and -Independent Acquisition Spectral Libraries Empowers Dual-Proteome Profiling

In the context of bacterial infections, it is imperative that physiological responses can be studied in an integrated manner, meaning a simultaneous analysis of both the host and the pathogen responses. To improve the sensitivity of detection, data-independent acquisition (DIA)-based proteomics was found to outperform data-dependent acquisition (DDA) workflows in identifying and quantifying low-abundant proteins. Here, by making use of representative bacterial pathogen/host proteome samples, we report an optimized hybrid library generation workflow for DIA mass spectrometry relying on the use of data-dependent and in silico-predicted spectral libraries. When compared to searching DDA experiment-specific libraries only, the use of hybrid libraries significantly improved peptide detection to an extent suggesting that infection-relevant host-pathogen conditions could be profiled in sufficient depth without the need of a priori bacterial pathogen enrichment when studying the bacterial proteome. Proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifiers PXD017904 and PXD017945.

Targeted Quantification of the Lysosomal Proteome in Complex Samples

In eukaryotic cells, lysosomes play a crucial role in the breakdown of a variety of components ranging from small molecules to complex structures, ascertaining the continuous turnover of cellular building blocks. Furthermore, they act as a regulatory hub for metabolism, being crucially involved in the regulation of major signaling pathways. Currently, ~450 lysosomal proteins can be reproducibly identified in a single cell line by mass spectrometry, most of which are low-abundant, restricting their unbiased proteomic analysis to lysosome-enriched fractions. In the current study, we applied two strategies for the targeted investigation of the lysosomal proteome in complex samples: data-independent acquisition (DIA) and parallel reaction monitoring (PRM). Using a lysosome-enriched fraction, mouse embryonic fibroblast whole cell lysate, and mouse liver whole tissue lysate, we investigated the capabilities of DIA and PRM to investigate the lysosomal proteome. While both approaches identified and quantified lysosomal proteins in all sample types, and their data largely correlated, DIA identified on average more proteins, especially for lower complex samples and longer chromatographic gradients. For the highly complex tissue sample and shorter gradients, however, PRM delivered a better performance regarding both identification and quantification of lysosomal proteins. All data are available via ProteomeXchange with identifier PXDD023278.

Misincorporation Proteomics Technologies: A Review

Proteinopathies are diseases caused by factors that affect proteoform conformation. As such, a prevalent hypothesis is that the misincorporation of noncanonical amino acids into a proteoform results in detrimental structures. However, this hypothesis is missing proteomic evidence, specifically the detection of a noncanonical amino acid in a peptide sequence. This review aims to outline the current state of technology that can be used to investigate mistranslations and misincorporations whilst framing the pursuit as Misincorporation Proteomics (MiP). The current availability of technologies explored herein is mass spectrometry, sample enrichment/preparation, data analysis techniques, and the hyphenation of approaches. While many of these technologies show potential, our review reveals a need for further development and refinement of approaches is still required.

Proteome-wide Systems Genetics to Identify Functional Regulators of Complex Traits

Proteomic technologies now enable the rapid quantification of thousands of proteins across genetically diverse samples. Integration of these data with systems-genetics analyses is a powerful approach to identify new regulators of economically important or disease-relevant phenotypes in various populations. In this review, we summarize the latest proteomic technologies and discuss technical challenges for their use in population studies. We demonstrate how the analysis of correlation structure and loci mapping can be used to identify genetic factors regulating functional protein networks and complex traits. Finally, we provide an extensive summary of the use of proteome-wide systems genetics throughout fungi, plant, and animal kingdoms and discuss the power of this approach to identify candidate regulators and drug targets in large human consortium studies.

Qualitative and Quantitative Shotgun Proteomics Data Analysis from Data-Dependent Acquisition Mass Spectrometry

Shotgun proteomics is the inferential analysis of proteoforms using peptide proxies produced by enzyme-catalyzed hydrolysis of entire proteomes. Such peptides are usually identified by nanoflow liquid chromatography coupled to tandem mass spectrometry analysis (nLC-MS/MS). Traditionally, MS/MS analysis is performed in data-dependent acquisition (DDA) mode, which usually produces a pattern of fragment masses unique to a single peptide's fragmentation. Here, I describe a statistically rigorous qualitative and quantitative computational analysis for shotgun proteomics DDA analysis using free open-source software tools. MS/MS data are used to identify peptides, and the area of peptide mass/charge over chromatographic elution is used to quantify peptides. All peptides that uniquely map to a protein sequence predicted from the genome are combined into a single protein quantity, which can then be compared across experimental conditions. Statistically significant protein changes can be summarized using gene ontology or pathway term enrichment analysis.

A critical comparison of three MS-based approaches for quantitative proteomics analysis

MS-based proteomics is expanding its role as a routine tool for biological discovery. Nevertheless, the task of accurately and precisely quantifying thousands of analytes in a single experiment remains challenging. In this study, the diagnostic accuracy of three popular data-dependent methods for protein relative quantification (label-free [LF], dimethyl labelling [DML] and tandem mass tags [TMT]) has been assessed using a mixed species proteome (three species) and five experimental replicates per condition. Data were produced using a quadrupole-Orbitrap mass spectrometer and analysed using a single platform (the MaxQuant/Perseus software suite). The whole comparative analysis was repeated three times over a period of 6 months, in order to assess the consistency of the reported findings. As expected, label-based methods reproducibly provided a lower false positives rate, whereas TMT and LF performed similarly, and significantly better than DML, in terms of proteome coverage using the same instrument time. Although parameters like proteome coverage and precision were consistent in between replicates, other parameters like sensitivity, intended as the capacity of correctly classifying true positives (regulated proteins), were found to be less reproducible, especially at challenging fold-changes (1.5). Collectively, data suggest that an increased interest in data reproducibility would be desirable in the quantitative proteomics field.

Recent Developments in Data Independent Acquisition (DIA) Mass Spectrometry: Application of Quantitative Analysis of the Brain Proteome

Mass spectrometry is the driving force behind current brain proteome analysis. In a typical proteomics approach, a protein isolate is digested into tryptic peptides and then analyzed by liquid chromatography–mass spectrometry. The recent advancements in data independent acquisition (DIA) mass spectrometry provide higher sensitivity and protein coverage than the classic data dependent acquisition. DIA cycles through a pre-defined set of peptide precursor isolation windows stepping through 400–1,200 m/z across the whole liquid chromatography gradient. All peptides within an isolation window are fragmented simultaneously and detected by tandem mass spectrometry. Peptides are identified by matching the ion peaks in a mass spectrum to a spectral library that contains information of the peptide fragment ions' pattern and its chromatography elution time. Currently, there are several reports on DIA in brain research, in particular the quantitative analysis of cellular and synaptic proteomes to reveal the spatial and/or temporal changes of proteins that underlie neuronal plasticity and disease mechanisms. Protocols in DIA are continuously improving in both acquisition and data analysis. The depth of analysis is currently approaching proteome-wide coverage, while maintaining high reproducibility in a stable and standardisable MS environment. DIA can be positioned as the method of choice for routine proteome analysis in basic brain research and clinical applications.

Roadmap to functional characterization of the human intestinal microbiota in its interaction with the host

It is known for more than 100 years that the intestinal microbes are important for the host's health and the last decade this is being intensely studied with a focus on the mechanistic aspects. Among the fundamental functions of the intestinal microbiome are the priming of the immune system, the production of essential vitamins and the energy harvest from foods. By now, several dozens of diseases, both intestinal and non-intestinal related, have been associated with the intestinal microbiome. Initially, this was based on the description of the composition between groups of different health status or treatment arms based on phylogenetic approaches based on the 16S rRNA gene sequences. This way of analysis has mostly moved to the analysis of all the genes or transcripts of the microbiome i.e. metagenomics and meta-transcriptomics. Differences are regularly found but these have to be taken with caution as we still do not know what the majority of genes of the intestinal microbiome are capable of doing. To circumvent this caveat researchers are studying the proteins and the metabolites of the microbiome and the host via metaproteomics and metabolomics approaches. However, also here the complexity is high and only a fraction of signals obtained with high throughput instruments can be identified and assigned to a known protein or molecule. Therefore, modern microbiome research needs advancement of existing and development of new analytical techniques. The usage of model systems like intestinal organoids where samples can be taken and processed rapidly as well as microfluidics systems may help. This review aims to elucidate what we know about the functionality of the human intestinal microbiome, what technologies are advancing this knowledge, and what innovations are still required to further evolve this actively developing field.

Complement sC5b-9 and CH50 increase the risk of cancer-related mortality in patients with non-small cell lung cancer

Objectives: Immunologic dysfunction occurred in most of patients with non-small cell lung cancer (NSCLC), which worsened the overall survival (OS) of patients. Complement activation plays a significant role in abnormal activation of immune system. However, the prognostic value of complement components such as CH50 and sC5b-9 in NSCLC patients remains unclear. This study evaluated the risk factors of NSCLC and created a prediction model.

Methods: A real-world study was conducted including data from 928 patients with NSCLC between April 1, 2005 and June 1, 2015. CH50 and sC5b-9 were recorded during the admission. Cox proportional hazard model was applied for survival analyses and for assessing risk factors of cancer-related mortality and to create a nomogram for prediction. The accuracy of the model was evaluated by C-index and calibration curve.

Results: In this study, the mortality in group with high CH50 level (≥ 480.56 umol/L) was 92.0%. Based on univariate analysis, we put factors (P <0.05) into a multivariate regression model, patients with high CH50 level (P <0.001, HR=1.59) and sC5b-9 >1422.18 μmol/L (P <0.001, HR=2.28) remained statistically factors for worsened OS and regarded as independent risk factors. These independently associated risk factors were applied to establish an OS estimation nomogram. Nomogram revealed good accuracy in estimating the risk, with a bootstrap-corrected C index of 0.741.

Conclusion: sC5b-9 and CH50 increased the risk of cancer-related mortality in patients with NSCLC. Nomogram based on multivariate analysis demonstrated good accuracy in estimating the risk of overall mortality.

Optimizing Accuracy and Depth of Protein Quantification in Experiments Using Isobaric Carriers

The isobaric carrier approach, which combines small isobarically labeled samples with a larger isobarically labeled carrier sample, finds diverse applications in ultrasensitive mass spectrometry analysis of very small samples, such as single cells. To enhance the growing use of isobaric carriers, we characterized the trade-offs of using isobaric carriers in controlled experiments with complex human proteomes. The data indicate that isobaric carriers directly enhance peptide sequence identification without simultaneously increasing the number of protein copies sampled from small samples. The results also indicate strategies for optimizing the amount of isobaric carrier and analytical parameters, such as ion accumulation time, for different priorities such as improved quantification or an increased number of identified proteins. Balancing these trade-offs enables adapting isobaric carrier experiments to different applications, such as quantifying proteins from limited biopsies or organoids, building single-cell atlases, or modeling protein networks in single cells. In all cases, the reliability of protein quantification should be estimated and incorporated in all subsequent analyses. We expect that these guidelines will aid in explicit incorporation of the characterized trade-offs in experimental designs and transparent error propagation in data analysis.

Sense and Nonsense of Elevated Column Temperature in Proteomic Bottom-up LC-MS Analyses

Elevated column temperature represents a simple means for improving chromatographic separation of peptides. Here, we demonstrated the advantages of the column temperature in peptide separation using state-of-the-art columns. More importantly, we also determined how temperature can impair proteomic bottom-up analyses. We found that an elevated temperature in combination with the acidic pH of the mobile phase induced in-column peptide hydrolysis with high specificity to Asp and accelerated five modification reactions of amino acids. The positive effects of temperature dominated in the 30 min long gradients since the column operated at 90 °C provided the largest number of identified peptides and proteins. However, the adverse effects of temperature on peptide integrity in longer liquid chromatography-mass spectrometry (LC-MS) analyses required its reduction to obtain optimum results. The largest number of peptides was identified using the column maintained at 75 °C in 60 min long gradients, at 60 °C in 120 min long gradients, and at 45 °C in 240 min long gradients. Our results indicate that no universal column temperature exists for bottom-up LC-MS analyses. Quite the contrary, the temperature setting must be selected rationally to exploit the full capabilities of the state-of-the-art mass spectrometers in proteomic LC-MS analyses, with the gradient time being a critical factor.

PASS-DIA: A Data-Independent Acquisition Approach for Discovery Studies

A data-independent acquisition (DIA) approach is being increasingly adopted as a promising strategy for identification and quantitation of proteomes. As most DIA data sets are acquired with wide isolation windows, highly complex MS/MS spectra are generated, which negatively impacts obtaining peptide information through classical protein database searches. Therefore, the analysis of DIA data mainly relies on the evidence of the existence of peptides from prebuilt spectral libraries. Consequently, one major weakness of this method is that it does not account for peptides that are not included in the spectral library, precluding the use of DIA for discovery studies. Here, we present a strategy termed Precursor ion And Small Slice-DIA (PASS-DIA) in which MS/MS spectra are acquired with small isolation windows (slices) and MS/MS spectra are interpreted with accurately determined precursor ion masses. This method enables the direct application of conventional spectrum-centric analysis pipelines for peptide identification and precursor ion-based quantitation. The performance of PASS-DIA was observed to be superior to both data-dependent acquisition (DDA) and conventional DIA experiments with 69 and 48% additional protein identifications, respectively. Application of PASS-DIA for the analysis of post-translationally modified peptides again highlighted its superior performance in characterizing phosphopeptides (77% more), N-terminal acetylated peptides (56% more), and N-glycopeptides (83% more) as compared to DDA alone. Finally, the use of PASS-DIA to characterize a rare proteome of human fallopian tube organoids enabled 34% additional protein identifications than DDA alone and revealed biologically relevant pathways including low abundance proteins. Overall, PASS-DIA is a novel DIA approach for use as a discovery tool that outperforms both conventional DDA and DIA experiments to provide additional protein information. We believe that the PASS-DIA method is an important strategy for discovery-type studies when deeper proteome characterization is required.

Proteomics in Non-model Organisms: A New Analytical Frontier

For the last century we have relied on model organisms to help understand fundamental biological processes. Now, with advancements in genome sequencing, assembly, and annotation, non-model organisms may be studied with the same advanced bioanalytical toolkit as model organisms. Proteomics is one such technique, which classically relies on predicted protein sequences to catalog and measure complex proteomes across tissues and biofluids. Applying proteomics to non-model organisms can advance and accelerate biomimicry studies, biomedical advancements, veterinary medicine, agricultural research, behavioral ecology, and food safety. In this postmodel organism era, we can study almost any species, meaning that many non-model organisms are, in fact, important emerging model organisms. Herein we specifically focus on eukaryotic organisms and discuss the steps to generate sequence databases, analyze proteomic data with or without a database, and interpret results as well as future research opportunities. Proteomics is more accessible than ever before and will continue to rapidly advance in the coming years, enabling critical research and discoveries in non-model organisms that were hitherto impossible.

Spatially resolved analysis of FFPE tissue proteomes by quantitative mass spectrometry

Bottom-up mass spectrometry-based proteomics relies on protein digestion and peptide purification. The application of such methods to broadly available clinical samples such as formalin-fixed and paraffin-embedded (FFPE) tissues requires reversal of chemical crosslinking and the removal of reagents that are incompatible with mass spectrometry. Here, we describe in detail a protocol that combines tissue disruption by ultrasonication, heat-induced antigen retrieval and two alternative methods for efficient detergent removal to enable quantitative proteomic analysis of limited amounts of FFPE material. To show the applicability of our approach, we used hepatocellular carcinoma (HCC) as a model system. By combining the described protocol with laser-capture microdissection, we were able to quantify the intra-tumor heterogeneity of a tumor specimen on the proteome level using a single slide with tissue of 10-µm thickness. We also demonstrate broader applicability to other tissues, including human gallbladder and heart. The procedure described in this protocol can be completed within 8 d.