Rapid mass spectrometric conversion of tissue biopsy samples into permanent quantitative digital proteome maps

A hot and cold tumor‑related prognostic signature for stage II colorectal cancer

Globally, colorectal cancer (CRC) is one of the most lethal and prevalent malignancies. Based on the presence of immune cell infiltration in the tumor microenvironment, CRC can be divided into immunologically 'hot' or 'cold' tumors, which in turn leads to the differential efficacy of immunotherapy. However, the immune characteristics of hot and cold CRC tumors remain largely elusive, prompting further investigation of their properties regarding the tumor microenvironment. In the present study, a predictive model was developed based on the differential expression of proteins between cold and hot CRC tumors. First, the differentially expressed proteins (DEPs) were identified using digital spatial profiling and mass spectrometry-based proteomics analysis, and the pathway features of the DEPs were analyzed using functional enrichment analysis. A novel eight-gene signature prognostic risk model was developed (IDO1, MAT1A, NPEPL1, NT5C, PTGR2, RPL29, TMEM126A and TUBB4B), which was validated using data obtained from The Cancer Genome Atlas. The results revealed that the risk score of the eight-gene signature acted as an independent prognostic indicator in patients with stage II CRC (T3-4N0M0). It was also found that a high-risk score in the eight-gene signature was associated with high immune cell infiltration in patients with CRC. Taken together, these findings revealed some of the differential immune characteristics of hot and cold CRC tumors, and an eight-gene signature prognostic risk model was developed, which may serve as an independent prognostic indicator for patients with stage II CRC (T3-4N0M0).

Non-targeted mass spectrometry and feature-based molecular networking for determination of branched fatty acid esters of hydroxy fatty acids in milk

Branched fatty acid esters of hydroxy fatty acids (FAHFAs) represent trace lipids with significant natural biological functions. While exogenous FAHFAs have been extensively studied, research on FAHFAs in milk remains limited, constraining our grasp of their nutritional roles. This study introduces a non-targeted mass spectrometry approach combined with chemical networking of spectral fragmentation patterns to uncover FAHFAs. Through meticulous sample handling and comparisons of various data acquisition and processing modes, we validate the method's superiority, identifying twice as many FAHFAs compared to alternative techniques. This validated method was then applied to different milk samples, revealing 45 chemical signals associated with known and potential FAHFAs, alongside findings of 66 ceramide/hexosylceramide (Cer/HexCer), 48 phosphatidyl ethanolamine/lyso phosphatidyl ethanolamine (PE/LPE), 21 phosphatidylcholine/lysophosphatidylcholine (PC/LPC), 16 phosphatidylinositol (PI), 7 phosphatidylserine (PS), and 11 sphingomyelin (SM) compounds. This study expands our understanding of the FAHFA family in milk and provides a fast and convenient method for identifying FAHFAs.

FlexStat: combinatory differentially expressed protein extraction

Motivation

Mass spectrometry-based system proteomics allows identification of dysregulated protein hubs and associated disease-related features. Obtaining differentially expressed proteins (DEPs) is the most important step of downstream bioinformatics analysis. However, the extraction of statistically significant DEPs from datasets with multiple experimental conditions or disease types through currently available tools remains a laborious task. More often such an analysis requires considerable bioinformatics expertise, making it inaccessible to researchers with limited computational analytics experience.

Results

To uncover the differences among the many conditions within the data in a user-friendly manner, here we introduce FlexStat, a web-based interface that extracts DEPs through combinatory analysis. This tool accepts a protein expression matrix as input and systematically generates DEP results for every conceivable combination of various experimental conditions or disease types. FlexStat includes a suite of robust statistical tools for data preprocessing, in addition to DEP extraction, and publication-ready visualization, which are built on established R scientific libraries in an automated manner. This analytics suite was validated in diverse public proteomic datasets to showcase its high performance of rapid and simultaneous pairwise comparisons of comprehensive datasets.

Availability and implementation

FlexStat is implemented in R and is freely available at https://jglab.shinyapps.io/flexstatv1-pipeline-only/. The source code is accessible at https://github.com/kts-desilva/FlexStat/tree/main.

Genetic interactions reveal distinct biological and therapeutic implications in breast cancer

Co-occurrence and mutual exclusivity of genomic alterations may reflect the existence of genetic interactions, potentially shaping distinct biological phenotypes and impacting therapeutic response in breast cancer. However, our understanding of them remains limited. Herein, we investigate a large-scale multi-omics cohort (n = 873) and a real-world clinical sequencing cohort (n = 4,405) including several clinical trials with detailed treatment outcomes and perform functional validation in patient-derived organoids, tumor fragments, and in vivo models. Through this comprehensive approach, we construct a network comprising co-alterations and mutually exclusive events and characterize their therapeutic potential and underlying biological basis. Notably, we identify associations between TP53mut-AURKAamp and endocrine therapy resistance, germline BRCA1mut-MYCamp and improved sensitivity to PARP inhibitors, and TP53mut-MYBamp and immunotherapy resistance. Furthermore, we reveal that precision treatment strategies informed by co-alterations hold promise to improve patient outcomes. Our study highlights the significance of genetic interactions in guiding genome-informed treatment decisions beyond single driver alterations.

Heat 'n Beat: A Universal High-Throughput End-to-End Proteomics Sample Processing Platform in under an Hour

Proteomic analysis by mass spectrometry of small (≤2 mg) solid tissue samples from diverse formats requires high throughput and comprehensive proteome coverage. We developed a nearly universal, rapid, and robust protocol for sample preparation, suitable for high-throughput projects that encompass most cell or tissue types. This end-to-end workflow extends from original sample to loading the mass spectrometer and is centered on a one-tube homogenization and digestion method called Heat 'n Beat (HnB). It is applicable to most tissues, regardless of how they were fixed or embedded. Sample preparation was divided into separate challenges. The initial sample washing and final peptide cleanup steps were adapted to three tissue sources: fresh frozen (FF), optimal cutting temperature (OCT) compound embedded (FF-OCT), and formalin-fixed paraffin embedded (FFPE). Third, for core processing, tissue disruption and lysis were decreased to a 7 min heat and homogenization treatment, and reduction, alkylation, and proteolysis were optimized into a single step. The refinements produced near doubled peptide yield when compared to our earlier method ABLE delivered a consistently high digestion efficiency of 85-90%, reported by ProteinPilot, and required only 38 min for core processing in a single tube, with the total processing time being 53-63 min. The robustness of HnB was demonstrated on six organ types, a cell line, and a cancer biopsy. Its suitability for high-throughput applications was demonstrated on a set of 1171 FF-OCT human cancer biopsies, which were processed for end-to-end completion in 92 h, producing highly consistent peptide yield and quality for over 3513 MS runs.

Proteomic profiling of ovarian clear cell carcinomas identifies prognostic biomarkers for chemotherapy

Clear cell ovarian carcinoma (CCOC) is a relatively rare subtype of ovarian cancer (OC) with high degree of resistance to standard chemotherapy. Little is known about the underlying molecular mechanisms, and it remains a challenge to predict its prognosis after chemotherapy. Here, we first analyzed the proteome of 35 formalin-fixed paraffin-embedded (FFPE) CCOC tissue specimens from a cohort of 32 patients with CCOC (H1 cohort) and characterized 8697 proteins using data-independent acquisition mass spectrometry (DIA-MS). We then performed proteomic analysis of 28 fresh frozen (FF) CCOC tissue specimens from an independent cohort of 24 patients with CCOC (H2 cohort), leading to the identification of 9409 proteins with DIA-MS. After bioinformatics analysis, we narrowed our focus to 15 proteins significantly correlated with the recurrence free survival (RFS) in both cohorts. These proteins are mainly involved in DNA damage response, extracellular matrix (ECM), and mitochondrial metabolism. Parallel reaction monitoring (PRM)-MS was adopted to validate the prognostic potential of the 15 proteins in the H1 cohort and an independent confirmation cohort (H3 cohort). Interferon-inducible transmembrane protein 1 (IFITM1) was observed as a robust prognostic marker for CCOC in both PRM data and immunohistochemistry (IHC) data. Taken together, this study presents a CCOC proteomic data resource and a single promising protein, IFITM1, which could potentially predict the recurrence and survival of CCOC.

Longitudinal plasma proteome profiling reveals the diversity of biomarkers for diagnosis and cetuximab therapy response of colorectal cancer

Cetuximab therapy is the major treatment for colorectal cancer (CRC), but drug resistance limits its effectiveness. Here, we perform longitudinal and deep proteomic profiling of 641 plasma samples originated from 147 CRC patients (CRCs) undergoing cetuximab therapy with multi-course treatment, and 90 healthy controls (HCs). COL12A1, THBS2, S100A8, and S100A9 are screened as potential proteins to distinguish CRCs from HCs both in plasma and tissue validation cohorts. We identify the potential biomarkers (RRAS2, MMP8, FBLN1, RPTOR, and IMPDH2) for the initial response prediction. In a longitudinal setting, we identify two clusters with distinct fluctuations and construct the model with high accuracy to predict the longitudinal response, further validated in the independent cohort. This study reveals the heterogeneity of different biomarkers for tumor diagnosis, the initial and longitudinal response prediction respectively in the first course and multi-course cetuximab treatment, may ultimately be useful in monitoring and intervention strategies for CRC.

Proteomic-based stratification of intermediate-risk prostate cancer patients

Gleason grading is an important prognostic indicator for prostate adenocarcinoma and is crucial for patient treatment decisions. However, intermediate-risk patients diagnosed in the Gleason grade group (GG) 2 and GG3 can harbour either aggressive or non-aggressive disease, resulting in under- or overtreatment of a significant number of patients. Here, we performed proteomic, differential expression, machine learning, and survival analyses for 1,348 matched tumour and benign sample runs from 278 patients. Three proteins (F5, TMEM126B, and EARS2) were identified as candidate biomarkers in patients with biochemical recurrence. Multivariate Cox regression yielded 18 proteins, from which a risk score was constructed to dichotomize prostate cancer patients into low- and high-risk groups. This 18-protein signature is prognostic for the risk of biochemical recurrence and completely independent of the intermediate GG. Our results suggest that markers generated by computational proteomic profiling have the potential for clinical applications including integration into prostate cancer management.

Forager-farmer transition at the crossroads of East and Southeast Asia 4900 years ago

The southward expansion of East Asian farmers profoundly influenced the social evolution of Southeast Asia by introducing cereal agriculture. However, the timing and routes of cereal expansion in key regions are unclear due to limited empirical evidence. Here we report macrofossil, microfossil, multiple isotopic (C/N/Sr/O) and paleoproteomic data directly from radiocarbon-dated human samples, which were unearthed from a site in Xingyi in central Yunnan and which date between 7000 and 3300 a BP. Dietary isotopes reveal the earliest arrival of millet ca. 4900 a BP, and greater reliance on plant and animal agriculture was indicated between 3800 and 3300 a BP. The dietary differences between hunter-gatherer and agricultural groups are also evident in the metabolic and immune system proteins analysed from their skeletal remains. The results of paleoproteomic analysis indicate that humans had divergent biological adaptations, with and without farming. The combined application of isotopes, archaeobotanical data and proteomics provides a new approach to documenting dietary and health changes across major subsistence transitions.

Optimizing the PROTREC network-based missing protein prediction algorithm

This article summarizes the PROTREC method and investigates the impact that the different hyper-parameters have on the task of missing protein prediction using PROTREC. We evaluate missing protein recovery rates using different PROTREC score selection approaches (MAX, MIN, MEDIAN, and MEAN), different PROTREC score thresholds, as well as different complex size thresholds. In addition, we included two additional cancer datasets in our analysis and introduced a new validation method to check both the robustness of the PROTREC method as well as the correctness of our analysis. Our analysis showed that the missing protein recovery rate can be improved by adopting PROTREC score selection operations of MIN, MEDIAN, and MEAN instead of the default MAX. However, this may come at a cost of reduced numbers of proteins predicted and validated. The users should therefore choose their hyper-parameters carefully to find a balance in the accuracy-quantity trade-off. We also explored the possibility of combining PROTREC with a p-value-based method (FCS) and demonstrated that PROTREC is able to perform well independently without any help from a p-value-based method. Furthermore, we conducted a downstream enrichment analysis to understand the biological pathways and protein networks within the cancerous tissues using the recovered proteins. Missing protein recovery rate using PROTREC can be improved by selecting a different PROTREC score selection method. Different PROTREC score selection methods and other hyper-parameters such as PROTREC score threshold and complex size threshold introduce accuracy-quantity trade-off. PROTREC is able to perform well independently of any filtering using a p-value-based method. Verification of the PROTREC method on additional cancer datasets. Downstream Enrichment Analysis to understand the biological pathways and protein networks in cancerous tissues.

Risk stratification of papillary thyroid cancers using multidimensional machine learning

BACKGROUND

Papillary thyroid cancer (PTC) is one of the most common endocrine malignancies with different risk levels. However, preoperative risk assessment of PTC is still a challenge in the worldwide. Here, the authors first report a Preoperative Risk Assessment Classifier for PTC (PRAC-PTC) by multidimensional features including clinical indicators, immune indices, genetic feature, and proteomics.

MATERIALS AND METHODS

The 558 patients collected from June 2013 to November 2020 were allocated to three groups: the discovery set [274 patients, 274 formalin-fixed paraffin-embedded (FFPE)], the retrospective test set (166 patients, 166 FFPE), and the prospective test set (118 patients, 118 fine-needle aspiration). Proteomic profiling was conducted by FFPE and fine-needle aspiration tissues from the patients. Preoperative clinical information and blood immunological indices were collected. The BRAFV600E mutation were detected by the amplification refractory mutation system.

RESULTS

The authors developed a machine learning model of 17 variables based on the multidimensional features of 274 PTC patients from a retrospective cohort. The PRAC-PTC achieved areas under the curve (AUC) of 0.925 in the discovery set and was validated externally by blinded analyses in a retrospective cohort of 166 PTC patients (0.787 AUC) and a prospective cohort of 118 PTC patients (0.799 AUC) from two independent clinical centres. Meanwhile, the preoperative predictive risk effectiveness of clinicians was improved with the assistance of PRAC-PTC, and the accuracies reached at 84.4% (95% CI: 82.9-84.4) and 83.5% (95% CI: 82.2-84.2) in the retrospective and prospective test sets, respectively.

CONCLUSION

This study demonstrated that the PRAC-PTC that integrating clinical data, gene mutation information, immune indices, high-throughput proteomics and machine learning technology in multicentre retrospective and prospective clinical cohorts can effectively stratify the preoperative risk of PTC and may decrease unnecessary surgery or overtreatment.

Trace Sample Proteome Quantification by Data-Dependent Acquisition without Dynamic Exclusion

Despite continuous technological improvements in sample preparation, mass-spectrometry-based proteomics for trace samples faces the challenges of sensitivity, quantification accuracy, and reproducibility. Herein, we explored the applicability of turboDDA (a method that uses data-dependent acquisition without dynamic exclusion) for quantitative proteomics of trace samples. After systematic optimization of acquisition parameters, we compared the performance of turboDDA with that of data-dependent acquisition with dynamic exclusion (DEDDA). By benchmarking the analysis of trace unlabeled human cell digests, turboDDA showed substantially better sensitivity in comparison with DEDDA, whether for unfractionated or high pH fractionated samples. Furthermore, through designing an iTRAQ-labeled three-proteome model (i.e., tryptic digest of protein lysates from yeast, human, and E. coli) to document the interference effect, we evaluated the quantification interference, accuracy, reproducibility of iTRAQ labeled trace samples, and the impact of PIF (precursor intensity fraction) cutoff for different approaches (turboDDA and DEDDA). The results showed that improved quantification accuracy and reproducibility could be achieved by turboDDA, while a more stringent PIF cutoff resulted in more accurate quantification but less peptide identification for both approaches. Finally, the turboDDA strategy was applied to the differential analysis of limited amounts of human lung cancer cell samples, showing great promise in trace proteomics sample analysis.

Multitissue Circadian Proteome Atlas of WT and Per1-/-/Per2-/- Mice

The molecular basis of circadian rhythm, driven by core clock genes such as Per1/2, has been investigated on the transcriptome level, but not comprehensively on the proteome level. Here we quantified over 11,000 proteins expressed in eight types of tissues over 46 h with an interval of 2 h, using WT and Per1/Per2 double knockout mouse models. The multitissue circadian proteome landscape of WT mice shows tissue-specific patterns and reflects circadian anticipatory phenomena, which are less obvious on the transcript level. In most peripheral tissues of double knockout mice, reduced protein cyclers are identified when compared with those in WT mice. In addition, PER1/2 contributes to controlling the anticipation of the circadian rhythm, modulating tissue-specific cyclers as well as key pathways including nucleotide excision repair. Severe intertissue temporal dissonance of circadian proteome has been observed in the absence of Per1 and Per2. The γ-aminobutyric acid might modulate some of these temporally correlated cyclers in WT mice. Our study deepens our understanding of rhythmic proteins across multiple tissues and provides valuable insights into chronochemotherapy. The data are accessible at https://prot-rhythm.prottalks.com/.

α-SMA+ cancer-associated fibroblasts increased tumor enhancement ratio on contrast-enhanced multidetector-row computed tomography in stages I-III colon cancer

BACKGROUND AND AIM

Our prior research revealed that the tumor enhancement ratio (TER) on triphasic abdominal contrast-enhanced MDCT (CE-MDCT) scans was a prognostic factor for patients with stages I-III colon cancer. Building upon this finding, the present study aims to investigate the proteomic changes in colon cancer patients with varying TER values.

METHODS

TER was analyzed on preoperative triphasic CE-MDCT scans of 160 stages I-III colon cancer patients. The survival outcomes of those in the low-TER and high-TER groups were compared. Proteomic analysis on colon cancer tissues was performed by mass spectrometry (MS) and verified by immune-histological chemistry (IHC) assays. In vivo, mouse xenograft models were employed to test the function of target proteins identified through the MS. CE-MDCT scans were conducted on mice xenografts, and the TER values were compared.

RESULTS

Patients in the high-TER group had a significantly worse prognosis than those in the low-TER group. Proteomic analysis of colon cancer tissues revealed 153 differentially expressed proteins between the two groups. A correlation between TER and the abundance of α-SMA protein in tumor tissue was observed. IHC assays further confirmed that α-SMA protein expression was significantly increased in high-TER colon cancer, predominantly in cancer-associated fibroblasts (CAFs) within the cancer stroma. Moreover, CAFs promoted the growth of CRC xenografts in vivo and increased TER.

CONCLUSIONS

Our study identified the distinct protein changes in colon cancer with low and high TER for the first time. The presence of CAFs may promote the growth of colon cancer and contribute to an increased TER.

Utilizing proteomics to identify and optimize microalgae strains for high-quality dietary protein: a review

Algae-derived protein has immense potential to provide high-quality protein foods for the expanding human population. To meet its potential, a broad range of scientific tools are required to identify optimal algal strains from the hundreds of thousands available and identify ideal growing conditions for strains that produce high-quality protein with functional benefits. A research pipeline that includes proteomics can provide a deeper interpretation of microalgal composition and biochemistry in the pursuit of these goals. To date, proteomic investigations have largely focused on pathways that involve lipid production in selected microalgae species. Herein, we report the current state of microalgal proteome measurement and discuss promising approaches for the development of protein-containing food products derived from algae.

Integrative multi-omic profiling of adult mouse brain endothelial cells and potential implications in Alzheimer's disease

The blood-brain barrier (BBB) is primarily manifested by a variety of physiological properties of brain endothelial cells (ECs), but the molecular foundation for these properties remains incompletely clear. Here, we generate a comprehensive molecular atlas of adult brain ECs using acutely purified mouse ECs and integrated multi-omics. Using RNA sequencing (RNA-seq) and proteomics, we identify the transcripts and proteins selectively enriched in brain ECs and demonstrate that they are partially correlated. Using single-cell RNA-seq, we dissect the molecular basis of functional heterogeneity of brain ECs. Using integrative epigenomics and transcriptomics, we determine that TCF/LEF, SOX, and ETS families are top-ranked transcription factors regulating the BBB. We then validate the identified brain-EC-enriched proteins and transcription factors in normal mouse and human brain tissue and assess their expression changes in mice with Alzheimer's disease. Overall, we present a valuable resource with broad implications for regulation of the BBB and treatment of neurological disorders.

A framework for ultra-low-input spatial tissue proteomics

Spatial proteomics combining microscopy-based cell phenotyping with ultrasensitive mass-spectrometry-based proteomics is an emerging and powerful concept to study cell function and heterogeneity in (patho)physiology. However, optimized workflows that preserve morphological information for phenotype discovery and maximize proteome coverage of few or even single cells from laser microdissected tissue are currently lacking. Here, we report a robust and scalable workflow for the proteomic analysis of ultra-low-input archival material. Benchmarking in murine liver resulted in up to 2,000 quantified proteins from single hepatocyte contours and nearly 5,000 proteins from 50-cell regions. Applied to human tonsil, we profiled 146 microregions including T and B lymphocyte niches and quantified cell-type-specific markers, cytokines, and transcription factors. These data also highlighted proteome dynamics within activated germinal centers, illuminating sites undergoing B cell proliferation and somatic hypermutation. This approach has broad implications in biomedicine, including early disease profiling and drug target and biomarker discovery. A record of this paper's transparent peer review process is included in the supplemental information.

A pathway activity-based proteomic classifier stratifies prostate tumors into two subtypes

Prostate cancer (PCa) is the second most common cancer in males worldwide. The risk stratification of PCa is mainly based on morphological examination. Here we analyzed the proteome of 667 tumor samples from 487 Chinese PCa patients and characterized 9576 protein groups by PulseDIA mass spectrometry. Then we developed a pathway activity-based classifier concerning 13 proteins from seven pathways, and dichotomized the PCa patients into two subtypes, namely PPS1 and PPS2. PPS1 is featured with enhanced innate immunity, while PPS2 with suppressed innate immunity. This classifier exhibited a correlation with PCa progression in our cohort and was further validated by two published transcriptome datasets. Notably, PPS2 was significantly correlated with poor biochemical recurrence (BCR)/metastasis-free survival (log-rank P-value < 0.05). The PPS2 was also featured with cell proliferation activation. Together, our study presents a novel pathway activity-based stratification scheme for PCa.

Advances in data-independent acquisition mass spectrometry towards comprehensive digital proteome landscape

The data-independent acquisition mass spectrometry (DIA-MS) has rapidly evolved as a powerful alternative for highly reproducible proteome profiling with a unique strength of generating permanent digital maps for retrospective analysis of biological systems. Recent advancements in data analysis software tools for the complex DIA-MS/MS spectra coupled to fast MS scanning speed and high mass accuracy have greatly expanded the sensitivity and coverage of DIA-based proteomics profiling. Here, we review the evolution of the DIA-MS techniques, from earlier proof-of-principle of parallel fragmentation of all-ions or ions in selected m/z range, the sequential window acquisition of all theoretical mass spectra (SWATH-MS) to latest innovations, recent development in computation algorithms for data informatics, and auxiliary tools and advanced instrumentation to enhance the performance of DIA-MS. We further summarize recent applications of DIA-MS and experimentally-derived as well as in silico spectra library resources for large-scale profiling to facilitate biomarker discovery and drug development in human diseases with emphasis on the proteomic profiling coverage. Toward next-generation DIA-MS for clinical proteomics, we outline the challenges in processing multi-dimensional DIA data set and large-scale clinical proteomics, and continuing need in higher profiling coverage and sensitivity.

Digital Microfluidics for Microproteomic Analysis of Minute Mammalian Tissue Samples Enabled by a Photocleavable Surfactant

Proteome profiles of precious tissue samples have great clinical potential for accelerating disease biomarker discovery and promoting novel strategies for early diagnosis and treatment. However, tiny clinical tissue samples are often difficult to handle and analyze with conventional proteomic methods. Automated digital microfluidic (DMF) workflows facilitate the manipulation of size-limited tissue samples. Here, we report the assessment of a DMF microproteomics workflow enabled by a photocleavable surfactant for proteomic analysis of minute tissue samples. The surfactant 4-hexylphenylazosulfonate (Azo) was found to facilitate fast droplet movement on DMF and enhance the proteomics analysis. Comparisons of Azo and n-Dodecyl β-d-maltoside (DDM) using small samples of HeLa digest standards and MCF-7 cell digests revealed distinct differences at the peptide level despite similar results at the protein level. The DMF microproteomics workflow was applied for the sample preparation of ∼3 μg biopsies from murine brain tissue. A total of 1969 proteins were identified in three samples, including established neural biomarkers and proteins related to synaptic signaling. Going forward, we propose that the Azo-enabled DMF workflow has the potential to advance the practical clinical application of DMF for the analysis of size-limited tissue samples.

Population serum proteomics uncovers a prognostic protein classifier for metabolic syndrome

Metabolic syndrome (MetS) is a complex metabolic disorder with a global prevalence of 20%-25%. Early identification and intervention would help minimize the global burden on healthcare systems. Here, we measured over 400 proteins from ∼20,000 proteomes using data-independent acquisition mass spectrometry for 7,890 serum samples from a longitudinal cohort of 3,840 participants with two follow-up time points over 10 years. We then built a machine-learning model for predicting the risk of developing MetS within 10 years. Our model, composed of 11 proteins and the age of the individuals, achieved an area under the curve of 0.774 in the validation cohort (n = 242). Using linear mixed models, we found that apolipoproteins, immune-related proteins, and coagulation-related proteins best correlated with MetS development. This population-scale proteomics study broadens our understanding of MetS and may guide the development of prevention and targeted therapies for MetS.

A Comparative Analysis of Data Analysis Tools for Data-Independent Acquisition Mass Spectrometry

Data-independent acquisition (DIA) mass spectrometry-based proteomics generates reproducible proteome data. The complex processing of the DIA data has led to the development of multiple data analysis tools. In this study, we assessed the performance of five tools (OpenSWATH, EncyclopeDIA, Skyline, DIA-NN, and Spectronaut) using six DIA datasets obtained from TripleTOF, Orbitrap, and TimsTOF Pro instruments. By comparing identification and quantification metrics and examining shared and unique cross-tool identifications, we evaluated both library-based and library-free approaches. Our findings indicate that library-free approaches outperformed library-based methods when the spectral library had limited comprehensiveness. However, our results also suggest that constructing a comprehensive library still offers benefits for most DIA analyses. This study provides comprehensive guidance for DIA data analysis tools, benefiting both experienced and novice users of DIA-mass spectrometry technology.

Identifying Patients With Rapid Progression From Hormone-Sensitive to Castration-Resistant Prostate Cancer: A Retrospective Study

Prostate cancer (PCa) is the second most prevalent malignancy and the fifth cause of cancer-related deaths in men. A crucial challenge is identifying the population at risk of rapid progression from hormone-sensitive prostate cancer (HSPC) to lethal castration-resistant prostate cancer (CRPC). We collected 78 HSPC biopsies and measured their proteomes using pressure cycling technology and a pulsed data-independent acquisition pipeline. We quantified 7355 proteins using these HSPC biopsies. A total of 251 proteins showed differential expression between patients with a long- or short-term progression to CRPC. Using a random forest model, we identified seven proteins that significantly discriminated long- from short-term progression patients, which were used to classify PCa patients with an area under the curve of 0.873. Next, one clinical feature (Gleason sum) and two proteins (BGN and MAPK11) were found to be significantly associated with rapid disease progression. A nomogram model using these three features was generated for stratifying patients into groups with significant progression differences (p-value = 1.3×10-4). To conclude, we identified proteins associated with a fast progression to CRPC and an unfavorable prognosis. Based on these proteins, our machine learning and nomogram models stratified HSPC into high- and low-risk groups and predicted their prognoses. These models may aid clinicians in predicting the progression of patients, guiding individualized clinical management and decisions.

Proteomic Dynamics of Breast Cancer Cell Lines Identifies Potential Therapeutic Protein Targets

Treatment and relevant targets for breast cancer (BC) remain limited, especially for triple-negative BC (TNBC). We identified 6091 proteins of 76 human BC cell lines using data-independent acquisition (DIA). Integrating our proteomic findings with prior multi-omics datasets, we found that including proteomics data improved drug sensitivity predictions and provided insights into the mechanisms of action. We subsequently profiled the proteomic changes in nine cell lines (five TNBC and four non-TNBC) treated with EGFR/AKT/mTOR inhibitors. In TNBC, metabolism pathways were dysregulated after EGFR/mTOR inhibitor treatment, while RNA modification and cell cycle pathways were affected by AKT inhibitor. This systematic multi-omics and in-depth analysis of the proteome of BC cells can help prioritize potential therapeutic targets and provide insights into adaptive resistance in TNBC.

Proteome Landscapes of Human Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma

Liver cancer is among the top leading causes of cancer mortality worldwide. Particularly, hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (CCA) have been extensively investigated from the aspect of tumor biology. However, a comprehensive and systematic understanding of the molecular characteristics of HCC and CCA remains absent. Here, we characterized the proteome landscapes of HCC and CCA using the data-independent acquisition (DIA) mass spectrometry (MS) method. By comparing the quantitative proteomes of HCC and CCA, we found several differences between the two cancer types. In particular, we found an abnormal lipid metabolism in HCC and activated extracellular matrix-related pathways in CCA. We next developed a three-protein classifier to distinguish CCA from HCC, achieving an area under the curve (AUC) of 0.92, and an accuracy of 90% in an independent validation cohort of 51 patients. The distinct molecular characteristics of HCC and CCA presented in this study provide new insights into the tumor biology of these two major important primary liver cancers. Our findings may help develop more efficient diagnostic approaches and new targeted drug treatments.

ProJect: a powerful mixed-model missing value imputation method

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

FOSL2 promotes intertumoral infiltration of T cells and increases pathological complete response rates in locally advanced rectal cancer patients

The outcome of neoadjuvant chemoradiotherapy (nCRT) remains highly unpredictable for individuals with locally advanced rectal cancer (LARC). We set out to characterize effective biomarkers that promote a pathological complete response (pCR). We quantified the abundances of 6483 high-confidence proteins in pre-nCRT biopsies of 58 LARC patients from two hospitals with pressure cycling technology (PCT)-assisted pulse data-independent acquisition (PulseDIA) mass spectrometry. Compared with non-pCR patients, pCR patients achieved long-term disease-free survival (DFS) and had higher tumor immune infiltration, especially CD8+ T cell infiltration, before nCRT. FOSL2 was selected as the candidate biomarker for predicting pCR and was found to be significantly upregulated in pCR patients, which was verified in another 54 pre-nCRT biopsies of LARC patients by immunohistochemistry. FOSL2 expression was able to predict pCR by multiple reaction monitoring (MRM) with high efficiency (Area under curve (AUC) = 0.939, specificity = 1.000, sensitivity = 0.850), and high FOSL2 expression was associated with long-term DFS (p = 0.044). When treated with simulated nCRT, FOSL2 sufficiency resulted in more significant inhibition of cell proliferation, and more significant promotion of cell cycle arrest and cell apoptosis. Moreover, CXCL10 secretion with abnormal cytosolic dsDNA accumulation was found in FOSL2-wildtype (FOSL2-WT) tumor cells over nCRT, which might elevate CD8+ T-cell infiltration and CD8+ T-cell-mediated cytotoxicity to promote nCRT-induced antitumor immunity. Our study revealed proteomic profiles in LARC patients before nCRT and highlighted immune activation in the tumors of patients who achieved pCR. We identified FOSL2 as a promising biomarker to predict pCR and promote long-term DFS by contributing to CD8+ T-cell infiltration.

Variant biomarker discovery using mass spectrometry-based proteogenomics

Genomic diversity plays critical roles in risk of disease pathogenesis and diagnosis. While genomic variants-including single nucleotide variants, frameshift variants, and mis-splicing isoforms-are commonly detected at the DNA or RNA level, their translated variant protein or polypeptide products are ultimately the functional units of the associated disease. These products are often released in biofluids and could be leveraged for clinical diagnosis and patient stratification. Recent emergence of integrated analysis of genomics with mass spectrometry-based proteomics for biomarker discovery, also known as proteogenomics, have significantly advanced the understanding disease risk variants, precise medicine, and biomarker discovery. In this review, we discuss variant proteins in the context of cancers and neurodegenerative diseases, outline current and emerging proteogenomic approaches for biomarker discovery, and provide a comprehensive proteogenomic strategy for detection of putative biomarker candidates in human biospecimens. This strategy can be implemented for proteogenomic studies in any field of enquiry. Our review timely addresses the need of biomarkers for aging related diseases.

Age-stratified proteomic characteristics and identification of promising precise clinical treatment targets of colorectal cancer

Colorectal cancer (CRC) is an extremely lethal disease worldwide. However, the underlying pathogenesis remains unclear. This study aimed to reveal the distinct characteristics of age-stratified CRC at the protein level and explore precise treatment targets. Patients who underwent surgical removal with pathologically confirmed CRC at China-Japan Friendship Hospital from January 2020 to October 2021 were recruited, cancer and para-carcinoma tissues (> 5 cm) were detected by mass spectrometry. Ninety-six clinical samples were collected and divided into three groups according to age: young (≤ 50 years), middle-aged (51-69 years), and old (≥ 70 years). Quantitative proteomic analysis was performed, as well as comprehensive bioinformatic analysis based on the Human Protein Atlas, Clinical Proteomic Tumor Analysis Consortium and Connectivity Map databases. The numbers of upregulated and downregulated proteins were 1315 and 560 in the young group, 757 and 311 in the old group, and 1052 and 468 in the middle-aged group, respectively. Bioinformatic analysis showed that these differentially expressed proteins had different molecular functions and participated in extensive signaling pathways. We also revealed ADH1B, ARRDC1, GATM, GTF2H4, MGME1, and LILRB2 as possible cancer-promoting molecules, which might serve as potential prognostic biomarkers and precise therapeutic targets for CRC. SIGNIFICANCE: This study comprehensively characterized the proteomic profiles of age-stratified colorectal cancer patients, focusing on the differentially expressed proteins between cancer and paracancerous tissues in different age groups, in an effort to find corresponding potential prognostic biomarkers and therapeutic targets. In addition, this study provides potentially valuable clinical small molecule inhibitory agents.

HNRNPA2B1 as a potential therapeutic target for thymic epithelial tumor recurrence: An integrative network analysis

Thymic epithelial tumors (TETs) are rare malignant tumors, and the molecular mechanisms of both primary and recurrent TETs are poorly understood. Here we established comprehensive proteomic signatures of 15 tumors (5 recurrent and 10 non-recurrent) and 15 pair wised tumor adjacent normal tissues. We then proposed an integrative network approach for studying the proteomics data by constructing protein-protein interaction networks based on differentially expressed proteins and a machine learning-based score, followed by network modular analysis, functional enrichment annotation and shortest path inference analysis. Network modular analysis revealed that primary and recurrent TETs shared certain common molecular mechanisms, including a spliceosome module consisting of RNA splicing and RNA processing, but the recurrent TET was specifically related to the ribosome pathway. Applying the shortest path inference to the collected seed gene module identified that the ribonucleoprotein hnRNPA2B1 probably serves as a potential target for recurrent TET therapy. The drug repositioning combined molecular dynamics simulations suggested that the compound ergotamine could potentially act as a repurposing drug to treat recurrent TETs by targeting hnRNPA2B1. Our study demonstrates the value of integrative network analysis to understand proteotype robustness and its relationships with genotype, and provides hits for further research on cancer therapeutics.

Simulated annealing aided genetic algorithm for gene selection from microarray data

In recent times, microarray gene expression datasets have gained significant popularity due to their usefulness to identify different types of cancer directly through bio-markers. These datasets possess a high gene-to-sample ratio and high dimensionality, with only a few genes functioning as bio-markers. Consequently, a significant amount of data is redundant, and it is essential to filter out important genes carefully. In this paper, we propose the Simulated Annealing aided Genetic Algorithm (SAGA), a meta-heuristic approach to identify informative genes from high-dimensional datasets. SAGA utilizes a two-way mutation-based Simulated Annealing (SA) as well as Genetic Algorithm (GA) to ensure a good trade-off between exploitation and exploration of the search space, respectively. The naive version of GA often gets stuck in a local optimum and depends on the initial population, leading to premature convergence. To address this, we have blended a clustering-based population generation with SA to distribute the initial population of GA over the entire feature space. To further enhance the performance, we reduce the initial search space by a score-based filter approach called the Mutually Informed Correlation Coefficient (MICC). The proposed method is evaluated on 6 microarray and 6 omics datasets. Comparison of SAGA with contemporary algorithms has shown that SAGA performs much better than its peers. Our code is available at https://github.com/shyammarjit/SAGA.

Resistance prediction in high-grade serous ovarian carcinoma with neoadjuvant chemotherapy using data-independent acquisition proteomics and an ovary-specific spectral library

High-grade serous ovarian carcinoma (HGSOC) is the most common subtype of ovarian cancer with 5-year survival rates below 40%. Neoadjuvant chemotherapy (NACT) followed by interval debulking surgery (IDS) is recommended for patients with advanced-stage HGSOC unsuitable for primary debulking surgery (PDS). However, about 40% of patients receiving this treatment exhibited chemoresistance of uncertain molecular mechanisms and predictability. Here, we built a high-quality ovary-specific spectral library containing 130 735 peptides and 10 696 proteins on Orbitrap instruments. Compared to a published DIA pan-human spectral library (DPHL), this spectral library provides 10% more ovary-specific and 3% more ovary-enriched proteins. This library was then applied to analyze data-independent acquisition (DIA) data of tissue samples from an HGSOC cohort treated with NACT, leading to 10 070 quantified proteins, which is 9.73% more than that with DPHL. We further established a six-protein classifier by parallel reaction monitoring (PRM) to effectively predict the resistance to additional chemotherapy after IDS (Log-rank test, P = 0.002). The classifier was validated with 57 patients from an independent clinical center (P = 0.014). Thus, we have developed an ovary-specific spectral library for targeted proteome analysis, and propose a six-protein classifier that could potentially predict chemoresistance in HGSOC patients after NACT-IDS treatment.

Substantial Downregulation of Mitochondrial and Peroxisomal Proteins during Acute Kidney Injury revealed by Data-Independent Acquisition Proteomics

Acute kidney injury (AKI) manifests as a major health concern, particularly for the elderly. Understanding AKI-related proteome changes is critical for prevention and development of novel therapeutics to recover kidney function and to mitigate the susceptibility for recurrent AKI or development of chronic kidney disease. In this study, mouse kidneys were subjected to ischemia-reperfusion injury, and the contralateral kidneys remained uninjured to enable comparison and assess injury-induced changes in the kidney proteome. A fast-acquisition rate ZenoTOF 7600 mass spectrometer was introduced for data-independent acquisition (DIA) for comprehensive protein identification and quantification. Short microflow gradients and the generation of a deep kidney-specific spectral library allowed for high-throughput, comprehensive protein quantification. Upon AKI, the kidney proteome was completely remodeled, and over half of the 3,945 quantified protein groups changed significantly. Downregulated proteins in the injured kidney were involved in energy production, including numerous peroxisomal matrix proteins that function in fatty acid oxidation, such as ACOX1, CAT, EHHADH, ACOT4, ACOT8, and Scp2. Injured mice exhibited severely declined health. The comprehensive and sensitive kidney-specific DIA assays highlighted here feature high-throughput analytical capabilities to achieve deep coverage of the kidney proteome and will serve as useful tools for developing novel therapeutics to remediate kidney function.

Molecular Modes of Action of an Aqueous Nerium oleander Extract in Cancer Cells In Vitro and In Vivo

Cancer drug resistance remains a major obstacle in clinical oncology. As most anticancer drugs are of natural origin, we investigated the anticancer potential of a standardized cold-water leaf extract from Nerium oleander L., termed Breastin. The phytochemical characterization by nuclear magnetic resonance spectroscopy (NMR) and low- and high-resolution mass spectrometry revealed several monoglycosidic cardenolides as major constituents (adynerin, neritaloside, odoroside A, odoroside H, oleandrin, and vanderoside). Breastin inhibited the growth of 14 cell lines from hematopoietic tumors and 5 of 6 carcinomas. Remarkably, the cellular responsiveness of odoroside H and neritaloside was not correlated with all other classical drug resistance mechanisms, i.e., ATP-binding cassette transporters (ABCB1, ABCB5, ABCC1, ABCG2), oncogenes (EGFR, RAS), tumor suppressors (TP53, WT1), and others (GSTP1, HSP90, proliferation rate), in 59 tumor cell lines of the National Cancer Institute (NCI, USA), indicating that Breastin may indeed bypass drug resistance. COMPARE analyses with 153 anticancer agents in 74 tumor cell lines of the Oncotest panel revealed frequent correlations of Breastin with mitosis-inhibiting drugs. Using tubulin-GFP-transfected U2OS cells and confocal microscopy, it was found that the microtubule-disturbing effect of Breastin was comparable to that of the tubulin-depolymerizing drug paclitaxel. This result was verified by a tubulin polymerization assay in vitro and molecular docking in silico. Proteome profiling of 3171 proteins in the NCI panel revealed protein subsets whose expression significantly correlated with cellular responsiveness to odoroside H and neritaloside, indicating that protein expression profiles can be identified to predict the sensitivity or resistance of tumor cells to Breastin constituents. Breastin moderately inhibited breast cancer xenograft tumors in vivo. Remarkably, in contrast to what was observed with paclitaxel monotherapy, the combination of paclitaxel and Breastin prevented tumor relapse, indicating Breastin's potential for drug combination regimens.

Label-free proteome quantification and evaluation

The label-free quantification (LFQ) has emerged as an exceptional technique in proteomics owing to its broad proteome coverage, great dynamic ranges and enhanced analytical reproducibility. Due to the extreme difficulty lying in an in-depth quantification, the LFQ chains incorporating a variety of transformation, pretreatment and imputation methods are required and constructed. However, it remains challenging to determine the well-performing chain, owing to its strong dependence on the studied data and the diverse possibility of integrated chains. In this study, an R package EVALFQ was therefore constructed to enable a performance evaluation on >3000 LFQ chains. This package is unique in (a) automatically evaluating the performance using multiple criteria, (b) exploring the quantification accuracy based on spiking proteins and (c) discovering the well-performing chains by comprehensive assessment. All in all, because of its superiority in assessing from multiple perspectives and scanning among over 3000 chains, this package is expected to attract broad interests from the fields of proteomic quantification. The package is available at https://github.com/idrblab/EVALFQ.

A multiplexed microflow LC-MS/MS-PRM assay for serologic quantification of IgG N- and HPX O- glycoforms in liver fibrosis

Targeted quantification of glycoproteins has not reached its full potential because of limitations of the existing analytical workflows. In this study, we introduce a targeted microflow LC-MS/MS-PRM method for the quantification of multiple glycopeptides in unfractionated serum samples. The entire preparation of 16 samples in a batch is completed within 3 h, and the LC-MS quantification of all the glycoforms in a sample is completed in 15 min in triplicate, including online capture and desalting. We demonstrate applicability of the workflow on a multiplexed quantification of eight N-glycoforms of immunoglobulin G (IgG) together with two O-glycoforms of hemopexin (HPX). We applied the assay to a serologic study of fibrotic liver disease in patients of HCV etiology. The results document that specific IgG- and HPX-glycoforms detect efficiently fibrotic disease of different degree, and suggest that the LC-MS/MS-PRM assays may provide rapid and reproducible biomarker assay targeting simultaneously the N- and O-glycoforms of the peptides. We propose that such high throughput multiplexed methods may advance the clinical use of the LC-MS/MS assays.

Benchmarking commonly used software suites and analysis workflows for DIA proteomics and phosphoproteomics

A plethora of software suites and multiple classes of spectral libraries have been developed to enhance the depth and robustness of data-independent acquisition (DIA) data processing. However, how the combination of a DIA software tool and a spectral library impacts the outcome of DIA proteomics and phosphoproteomics data analysis has been rarely investigated using benchmark data that mimics biological complexity. In this study, we create DIA benchmark data sets simulating the regulation of thousands of proteins in a complex background, which are collected on both an Orbitrap and a timsTOF instruments. We evaluate four commonly used software suites (DIA-NN, Spectronaut, MaxDIA and Skyline) combined with seven different spectral libraries in global proteome analysis. Moreover, we assess their performances in analyzing phosphopeptide standards and TNF-α-induced phosphoproteome regulation. Our study provides a practical guidance on how to construct a robust data analysis pipeline for different proteomics studies implementing the DIA technique.

Degradomics technologies in matrisome exploration

Consisting of a defined set of extracellular proteins secreted from resident cells and with minor contributions from serum proteins, the extracellular matrix (ECM) is an essential component of all tissues. Maintaining tissue homeostasis, structural support and cellular control through cell-ECM communication, the ECM has come to be viewed as not just a passive structural entity but rather as a dynamic signaling conduit between cells and the extracellular compartment. Proteins and their cleavage products mediate this communication, and aberrant signaling, either directly or indirectly distorting the ECM, results in pathological conditions including cancer, inflammation, fibrosis, and neurodegenerative diseases. Characterization of ECM components, the matrisome, the extracellular environment and their changes in disease is therefore of importance to understand and mitigate by developing novel therapeutics. Liquid chromatography-mass spectrometry (LC-MS) proteomics has been integral to protein and proteome research for decades and long superseded the obsolescent gel-based approaches. A continuous effort has ensured progress with increased sensitivity and throughput as more advanced equipment has been developed hand in hand with specialized enrichment, detection, and identification methods. Part of this effort lies in the field of degradomics, a branch of proteomics focused on discovering novel protease substrates by identification of protease-generated neo-N termini, the N-terminome, and characterizing the responsible protease networks. Various methods to do so have been developed, some specialized for specific tissue types, others for particular proteases, throughput, or ease of use. This review aims to provide an overview of the state-of-the-art proteomics techniques that have successfully been recently utilized to characterize proteolytic cleavages in the ECM and thereby guided new research and understanding of the ECM and matrisome biology.

Direct proteomic and high-resolution microscopy biopsy analysis identifies distinct ventricular fates in severe aortic stenosis

The incidence of aortic valve stenosis (AS), the most common reason for aortic valve replacement (AVR), increases with population ageing. While untreated AS is associated with high mortality, different hemodynamic subtypes range from normal left-ventricular function to severe heart failure. However, the molecular nature underlying four different AS subclasses, suggesting vastly different myocardial fates, is unknown. Here, we used direct proteomic analysis of small left-ventricular biopsies to identify unique protein expression profiles and subtype-specific AS mechanisms. Left-ventricular endomyocardial biopsies were harvested from patients during transcatheter AVR, and inclusion criteria were based on echocardiographic diagnosis of severe AS and guideline-defined AS-subtype classification: 1) normal ejection fraction (EF)/high-gradient; 2) low EF/high-gradient; 3) low EF/low-gradient; and 4) paradoxical low-flow/low-gradient AS. Samples from non-failing donor hearts served as control. We analyzed 25 individual left-ventricular biopsies by data-independent acquisition mass spectrometry (DIA-MS), and 26 biopsies by histomorphology and cardiomyocytes by STimulated Emission Depletion (STED) superresolution microscopy. Notably, DIA-MS reliably detected 2273 proteins throughout each individual left-ventricular biopsy, of which 160 proteins showed significant abundance changes between AS-subtype and non-failing samples including the cardiac ryanodine receptor (RyR2). Hierarchical clustering segregated unique proteotypes that identified three hemodynamic AS-subtypes. Additionally, distinct proteotypes were linked with AS-subtype specific differences in cardiomyocyte hypertrophy. Furthermore, superresolution microscopy of immunolabeled biopsy sections showed subcellular RyR2-cluster fragmentation and disruption of the functionally important association with transverse tubules, which occurred specifically in patients with systolic dysfunction and may hence contribute to depressed left-ventricular function in AS.

Spatially resolved proteomics via tissue expansion

Spatially resolved proteomics is an emerging approach for mapping proteome heterogeneity of biological samples, however, it remains technically challenging due to the complexity of the tissue microsampling techniques and mass spectrometry analysis of nanoscale specimen volumes. Here, we describe a spatially resolved proteomics method based on the combination of tissue expansion with mass spectrometry-based proteomics, which we call Expansion Proteomics (ProteomEx). ProteomEx enables quantitative profiling of the spatial variability of the proteome in mammalian tissues at ~160 µm lateral resolution, equivalent to the tissue volume of 0.61 nL, using manual microsampling without the need for custom or special equipment. We validated and demonstrated the utility of ProteomEx for streamlined large-scale proteomics profiling of biological tissues including brain, liver, and breast cancer. We further applied ProteomEx for identifying proteins associated with Alzheimer's disease in a mouse model by comparative proteomic analysis of brain subregions.

Translational proteomics and phosphoproteomics: Tissue to extracellular vesicles

We are currently experiencing a rapidly developing era in terms of translational and clinical medical sciences. The relatively mature state of nucleic acid examination has significantly improved our understanding of disease mechanism and therapeutic potential of personalized treatment, but misses a large portion of phenotypic disease information. Proteins, in particular phosphorylation events that regulates many cellular functions, could provide real-time information for disease onset, progression and treatment efficacy. The technical advances in liquid chromatography and mass spectrometry have realized large-scale and unbiased proteome and phosphoproteome analyses with disease relevant samples such as tissues. However, tissue biopsy still has multiple shortcomings, such as invasiveness of sample collection, potential health risk for patients, difficulty in protein preservation and extreme heterogeneity. Recently, extracellular vesicles (EVs) have offered a great promise as a unique source of protein biomarkers for non-invasive liquid biopsy. Membranous EVs provide stable preservation of internal proteins and especially labile phosphoproteins, which is essential for effective routine biomarker detection. To aid efficient EV proteomic and phosphoproteomic analyses, recent developments showcase clinically-friendly EV techniques, facilitating diagnostic and therapeutic applications. Ultimately, we envision that with streamlined sample preparation from tissues and EVs proteomics and phosphoproteomics analysis will become routine in clinical settings.

Protocol to identify functional doppelgängers and verify biomedical gene expression data using doppelgangerIdentifier

Functional doppelgängers (FDs) are independently derived sample pairs that confound machine learning model (ML) performance when assorted across training and validation sets. Here, we detail the use of doppelgangerIdentifier (DI), providing software installation, data preparation, doppelgänger identification, and functional testing steps. We demonstrate examples with biomedical gene expression data. We also provide guidelines for the selection of user-defined function arguments. For complete details on the use and execution of this protocol, please refer to Wang et al. (2022).

Opportunities for pharmacoproteomics in biomarker discovery

Proteomic data are a uniquely valuable resource for drug response prediction and biomarker discovery because most drugs interact directly with proteins in target cells rather than with DNA or RNA. Recent advances in mass spectrometry and associated processing methods have enabled the generation of large-scale proteomic datasets. Here we review the significant opportunities that currently exist to combine large-scale proteomic data with drug-related research, a field termed pharmacoproteomics. We describe successful applications of drug response prediction using molecular data, with an emphasis on oncology. We focus on technical advances in data-independent acquisition mass spectrometry (DIA-MS) that can facilitate the discovery of protein biomarkers for drug responses, alongside the increased availability of big biomedical data. We spotlight new opportunities for machine learning in pharmacoproteomics, driven by the combination of these large datasets and improved high-performance computing. Finally, we explore the value of pre-clinical models for pharmacoproteomic studies and the accompanying challenges of clinical validation. We propose that pharmacoproteomics offers the potential for novel discovery and innovation within the cancer landscape. This article is protected by copyright. All rights reserved.

Identification of Gedunin from a Phytochemical Depository as a Novel Multidrug Resistance-Bypassing Tubulin Inhibitor of Cancer Cells

The chemotherapy of tumors is frequently limited by the development of resistance and severe side effects. Phytochemicals may offer promising candidates to meet the urgent requirement for new anticancer drugs. We screened 69 phytochemicals, and focused on gedunin to analyze its molecular modes of action. Pearson test-base correlation analyses of the log₁₀IC₅₀ values of 55 tumor cell lines of the National Cancer Institute (NCI), USA, for gedunin with those of 91 standard anticancer agents revealed statistically significant relationships to all 10 tested microtubule inhibitors. Thus, we hypothesized that gedunin may be a novel microtubule inhibitor. Confocal microscopy, cell cycle measurements, and molecular docking in silico substantiated our assumption. Agglomerative cluster analyses and the heat map generation of proteomic data revealed a subset of 40 out of 3171 proteins, the expression of which significantly correlated with sensitivity or resistance for the NCI cell line panel to gedunin. This indicates the complexity of gedunin’s activity against cancer cells, underscoring the value of network pharmacological techniques for the investigation of the molecular modes of drug action. Finally, we correlated the transcriptome-wide mRNA expression of known drug resistance mechanism (ABC transporter, oncogenes, tumor suppressors) log₁₀IC₅₀ values for gedunin. We did not find significant correlations, indicating that gedunin’s anticancer activity might not be hampered by classical drug resistance mechanisms. In conclusion, gedunin is a novel microtubule-inhibiting drug candidate which is not involved in multidrug resistance mechanisms such as other clinically established mitotic spindle poisons.

Artificial intelligence defines protein-based classification of thyroid nodules

Determination of malignancy in thyroid nodules remains a major diagnostic challenge. Here we report the feasibility and clinical utility of developing an AI-defined protein-based biomarker panel for diagnostic classification of thyroid nodules: based initially on formalin-fixed paraffin-embedded (FFPE), and further refined for fine-needle aspiration (FNA) tissue specimens of minute amounts which pose technical challenges for other methods. We first developed a neural network model of 19 protein biomarkers based on the proteomes of 1724 FFPE thyroid tissue samples from a retrospective cohort. This classifier achieved over 91% accuracy in the discovery set for classifying malignant thyroid nodules. The classifier was externally validated by blinded analyses in a retrospective cohort of 288 nodules (89% accuracy; FFPE) and a prospective cohort of 294 FNA biopsies (85% accuracy) from twelve independent clinical centers. This study shows that integrating high-throughput proteomics and AI technology in multi-center retrospective and prospective clinical cohorts facilitates precise disease diagnosis which is otherwise difficult to achieve by other methods.

Tissue-Characteristic Expression of Mouse Proteome

The mouse is a valuable model organism for biomedical research. Here, we established a comprehensive spectral library and the data-independent acquisition-based quantitative proteome maps for 41 mouse organs, including some rarely reported organs such as the cornea, retina, and nine paired organs. The mouse spectral library contained 178,304 peptides from 12,320 proteins, including 1678 proteins not reported in previous mouse spectral libraries. Our data suggested that organs from the nervous system and immune system expressed the most distinct proteome compared with other organs. We also found characteristic protein expression of immune-privileged organs, which may help understanding possible immune rejection after organ transplantation. Each tissue type expressed characteristic high-abundance proteins related to its physiological functions. We also uncovered some tissue-specific proteins which have not been reported previously. The testis expressed highest number of tissue-specific proteins. By comparison of nine paired organs including kidneys, testes, and adrenal glands, we found left organs exhibited higher levels of antioxidant enzymes. We also observed expression asymmetry for proteins related to the apoptotic process, tumor suppression, and organ functions between the left and right sides. This study provides a comprehensive spectral library and a quantitative proteome resource for mouse studies.

HypDB: A functionally annotated web-based database of the proline hydroxylation proteome

Proline hydroxylation (Hyp) regulates protein structure, stability, and protein-protein interaction. It is widely involved in diverse metabolic and physiological pathways in cells and diseases. To reveal functional features of the Hyp proteome, we integrated various data sources for deep proteome profiling of the Hyp proteome in humans and developed HypDB (https://www.HypDB.site), an annotated database and web server for Hyp proteome. HypDB provides site-specific evidence of modification based on extensive LC-MS analysis and literature mining with 14,413 nonredundant Hyp sites on 5,165 human proteins including 3,383 Class I and 4,335 Class II sites. Annotation analysis revealed significant enrichment of Hyp on key functional domains and tissue-specific distribution of Hyp abundance across 26 types of human organs and fluids and 6 cell lines. The network connectivity analysis further revealed a critical role of Hyp in mediating protein-protein interactions. Moreover, the spectral library generated by HypDB enabled data-independent analysis (DIA) of clinical tissues and the identification of novel Hyp biomarkers in lung cancer and kidney cancer. Taken together, our integrated analysis of human proteome with publicly accessible HypDB revealed functional diversity of Hyp substrates and provides a quantitative data source to characterize Hyp in pathways and diseases.

Clinical applications of mass spectrometry-based proteomics in cancer: where are we?

Tumor tissue processing methodologies in combination with data-independent acquisition mass spectrometry (DIA-MS) have emerged that can comprehensively analyze the proteome of multiple tumor samples accurately and reproducibly. Increasing recognition and adoption of these technologies has resulted in a tranche of studies providing novel insights into cancer classification systems, functional tumor biology, cancer biomarkers, treatment response and drug targets. Despite this, with some limited exceptions, MS-based proteomics has not yet been implemented in routine cancer clinical practice. Here, we summarize the use of DIA-MS in studies that may pave the way for future clinical cancer applications, and highlight the role of alternative MS technologies and multi-omic strategies. We discuss limitations and challenges of studies in this field to date and propose steps for integrating proteomic data into the cancer clinic. This article is protected by copyright. All rights reserved.