Biology subjects

Zhu, Y. J.

Publications and source records attributed to Zhu, Y. J..

10 recordsLinked to original sources

bioRxiv2025

Systematic evaluation of blood contamination in nanoparticle-based plasma proteomics

Circulating blood proteomics enables minimally invasive biomarker discovery. Nanoparticle (NP)-based circulating plasma proteomics studies have reported varying number of proteins, ranging from ca 2000 to 7000 but its not clear whether higher protein number is more informative. Here, we first develop OmniProt - a silica-NP workflow for plasma proteomics which is optimized through systematic evaluation of NP types and protein corona formation parameters. Next, we present an Astral spectral library for 10,109 protein groups expressed in human plasma. Using Astral with a throughput of 60 sample-per-day, OmniProt identifies ca 3000 to 6000 protein groups from human plasma. Notably, we found that platelet/erythrocyte/coagulation-related contamination artificially elevates protein identifications and compromises quantification accuracy for circulating plasma proteins in NP-enriched samples. Through controlled contamination experiments, we identified and validated protein biomarkers for contaminations of platelet, erythrocyte, and coagulation, respectively, in NP-based plasma proteomics experiments. We further developed an open-access software Baize for accessing plasma proteomics contamination. Finally, we validated our OmniProt workflow and Baize software in a clinical cohort of 193 patients with CT-indistinct benign nodules or early lung cancers. On average, we identified ca 4400 protein groups for each plasma sample without significant contamination. Five samples were flagged out as contaminated. In conclusion, this study reveals that contamination of plasma samples significantly alters the protein identification and quantification in NP-based plasma proteomics experiments, and presents an open-access software Baize to evaluate plasma proteome contamination.

biochemistry↗

bioRxiv2025

A perturbation proteomics-based foundation model for virtual cell construction

Building a virtual cell requires comprehensive understanding of protein network dynamics of a cell which necessitates large-scale perturbation proteome data and intelligent computational models learned from the proteome data corpus. Here, we generate a large-scale dataset of over 38 million perturbed protein measurements in breast cancer cell lines and develop a neural ordinary differential equation-based foundation model, namely ProteinTalks. During pretraining, ProteinTalks gains a fundamental understanding of cellular protein network dynamics. Our model encodes protein networks and exhibits consistently improved predictive accuracy across various downstream tasks, highlighting its generalization capabilities and adaptability. In cancer cells, ProteinTalks robustly predicts drug efficacy and synergy, identifies novel drug combinations, and, through its interpretability, uncovers resistance-associated proteins. When applied to more complex system, patient-derived tumor xenografts, ProteinTalks predicts potential responses to drugs. Its integration with clinical patient data enhances the prognosis prediction of breast cancer patients. Collectively, we present a foundational model based on proteome dynamics, offering potential for various downstream applications, including drug discovery, and providing a basis for developing virtual cells.

systems biology↗

bioRxiv2024

Cancer-associated DAXX mutations reveal a critical role for ATRX localization in ALT suppression

To maintain genome stability, proliferating cells must enact a program of telomere maintenance. While most tumors maintain telomeres through the action of telomerase, a subset of tumors utilize a DNA-templated process termed Alternative Lengthening of Telomeres or ALT. ALT is associated with mutations in the ATRX/DAXX/H3.3 histone chaperone complex, which is responsible for deposition of non-replicative histone variant H3.3 at heterochromatic regions of the genome including telomeres. We wished to better understand the role DAXX plays in ALT suppression, and to determine which disease-associated DAXX mutations are unable to suppress ALT. To answer this question, we have leveraged the G292 cell line, in which ATRX is wild type but DAXX has undergone a fusion event with the non-canonical kinesin KIFC3. Restoration of wild-type DAXX in G292 localizes ATRX and abrogates ALT. Using this model system, we tested the ability of a panel of disease-associated DAXX missense variants to suppress ALT. Missense mutations in the ATRX binding domain, the histone binding domain, and the C-terminal SUMO interaction motif reduce the ability of DAXX to suppress ALT. Unexpectedly, we find that mutations in the DAXX histone binding domain lead to failure of ATRX localization. We conclude that a key function of DAXX in ALT suppression is the localization of ATRX to nuclear foci.

cancer biology↗

bioRxiv2024

Integral-Omics: serial extraction and profiling of metabolome, lipidome, genome, transcriptome, whole proteome and phosphoproteome using biopsy tissue

The integrative multi-omics characterization of minute amount of clinical tissue specimens has become increasingly important. Here, we present an approach called Integral-Omics, which enables sequential extraction of metabolites, lipids, genomic DNA, total RNA, proteins, and phosphopeptides from a single biopsy-level tissue specimen. We benchmarked this method with various samples and applied the workflow to perform multi-omics profiling of tissues from six patients with colorectal cancer and found that tumor tissues exhibited suppressed ferroptosis pathway at multi-omics levels. Together, this study presents a methodology that enables sequential extraction and profiling of metabolomics, lipidomics, genomics, transcriptomics, proteomics and phosphoproteomics using biopsy tissue specimens.

biochemistry↗

bioRxiv2022

Proteomic Dynamics of Breast Cancers Identifies Potential Therapeutic Protein Targets

Treatment and relevant targets for breast cancer (BC) remain limited, especially for triple-negative BC (TNBC). We quantified the proteomes of 76 human BC cell lines using data independent acquisition (DIA) based proteomics, identifying 6091 proteins. We then established a 24-protein panel distinguishing TNBC from other BC types. Integrating prior multi-omics datasets with the present proteomic results to predict the sensitivity of 90 drugs, we found that proteomics data improved drug sensitivity predictions. The sensitivity of the 90 drugs was mainly associated with cell cytoskeleton, signal transduction and mitochondrial function. We next profiled the proteome changes of nine cell lines (five TNBC cell lines, four non-TNBC cell lines) perturbated by EGFR/AKT/mTOR inhibitors. In the TNBC cell lines, metabolism pathways were dysregulated after EGFR/mTOR inhibitors treatment, while RNA modification and cell cycle pathways were dysregulated after AKT inhibitor treatment. Our study presents a systematic multi-omics and in-depth analysis of the proteome of BC cells. This work aims to aid in prioritization of potential therapeutic targets for TNBC as well as to provide insight into adaptive drug resistance in TNBC.

systems biology↗

bioRxiv2021

DIA-based Proteomics Identifies IDH2 as a Targetable Regulator of Acquired Drug Resistance in Chronic Myeloid Leukemia

Drug resistance is a critical obstacle to effective treatment in patients with chronic myeloid leukemia (CML). To understand the underlying resistance mechanisms in response to imatinib (IMA) and adriamycin (ADR), the parental K562 cells were treated with low doses of IMA or ADR for two months to generate derivative cells with mild, intermediate and severe resistance to the drugs as defined by their increasing resistance index (RI). PulseDIA-based quantitative proteomics was then employed to reveal the proteome changes in these resistant cells. In total, 7,082 proteotypic proteins from 98,232 peptides were identified and quantified from the dataset using four DIA software tools including OpenSWATH, Spectronaut, DIA-NN, and EncyclopeDIA. Sirtuin Signaling Pathway was found to be significantly enriched in both ADR- and IMA-resistant K562 cells. In particular, IDH2 was identified as a potential drug target correlated with the drug resistance phenotype, and its inhibition by the antagonist AGI-6780 reversed the acquired resistance in K562 cells to either ADR or IMA. Together, our study has implicated IDH2 as a potential target that can be therapeutically leveraged to alleviate the drug resistance in K562 cells when treated with IMA and ADR.

cancer biology↗

bioRxiv2020

Proteogenomic heterogeneity of localized human prostate cancer progression

BackgroundTumor-specific genomic aberrations are routinely determined by high throughput genomic measurements. It remains unclear though, how complex genome alterations affect molecular networks through changing protein levels, and consequently biochemical states of tumor tissues. ResultsHere, we investigated the propagation of genomic effects along the axis of gene expression during prostate cancer progression. For that, we quantified genomic, transcriptomic and proteomic alterations based on 105 prostate samples, consisting of benign prostatic hyperplasia regions and malignant tumors, from 39 prostate cancer patients. Our analysis revealed convergent effects of distinct copy number alterations impacting on common downstream proteins, which are important for establishing the tumor phenotype. We devised a network-based approach that integrates perturbations across different molecular layers, which identified a sub-network consisting of nine genes whose joint activity positively correlated with increasingly aggressive tumor phenotypes and was predictive of recurrence-free survival. Further, our data revealed a wide spectrum of intra-patient network effects, ranging from similar to very distinct alterations on different molecular layers. ConclusionsThis study uncovered molecular networks with remarkably convergent alterations across tumor sites and patients, but it also exposed a diversity of network effects: we could not identify a single sub-network that was perturbed in all high-grade tumor regions.

systems biology↗

bioRxiv2020

DPHL: A pan-human protein mass spectrometry library for robust biomarker discovery using Data-Independent Acquisition and Parallel Reaction Monitoring

To answer the increasing need for detecting and validating protein biomarkers in clinical specimens, proteomic techniques are required that support the fast, reproducible and quantitative analysis of large clinical sample cohorts. Targeted mass spectrometry techniques, specifically SRM, PRM and the massively parallel SWATH/DIA technique have emerged as a powerful method for biomarker research. For optimal performance, they require prior knowledge about the fragment ion spectra of targeted peptides. In this report, we describe a mass spectrometric (MS) pipeline and spectral resource to support data-independent acquisition (DIA) and parallel reaction monitoring (PRM) based biomarker studies. To build the spectral resource we integrated common open-source MS computational tools to assemble an open source computational workflow based on Docker. It was then applied to generate a comprehensive DIA pan-human library (DPHL) from 1,096 data dependent acquisition (DDA) MS raw files, and it comprises 242,476 unique peptide sequences from 14,782 protein groups and 10,943 SwissProt-annotated proteins expressed in 16 types of cancer samples. In particular, tissue specimens from patients with prostate cancer, cervical cancer, colorectal cancer, hepatocellular carcinoma, gastric cancer, lung adenocarcinoma, squamous cell lung carcinoma, diseased thyroid, glioblastoma multiforme, sarcoma and diffuse large B-cell lymphoma (DLBCL), as well as plasma samples from a range of hematologic malignancies were collected from multiple clinics in China, the Netherlands and Singapore and included in the resource. This extensive spectral resource was then applied to a prostate cancer cohort of 17 patients, consisting of 8 patients with prostate cancer (PCa) and 9 with benign prostate hyperplasia (BPH), respectively. Data analysis of DIA data from these samples identified differential expressions of FASN, TPP1 and SPON2 in prostate tumors. Thereafter, PRM validation was applied to a larger PCa cohort of 57 patients and the differential expressions of FASN, TPP1 and SPON2 in prostate tumors were validated. As a second application, the DPHL spectral resource was applied to a patient cohort consisting of samples from 19 DLBCL patients and 18 healthy individuals. Differential expressions of CRP, CD44 and SAA1 between DLBCL cases and healthy controls were detected by DIA-MS and confirmed by PRM. These data demonstrate that the DPHL supported that DIA-PRM MS pipeline enables robust protein biomarker discoveries.

bioinformatics↗

bioRxiv2019

PulseDIA: in-depth data independent acquisition mass spectrometry using enhanced gas phase fractionation

An inherent bottleneck of data independent acquisition (DIA) analysis by Orbitrap-based mass spectrometers is the relatively large window width due to the relatively slow scanning rate compared to TOF. Here we present a novel gas phase separation and MS acquisition method called PulseDIA-MS, which improves the specificity and sensitivity of Orbitrap-based DIA analysis. This is achieved by dividing the ordinary DIA-MS analysis covering the entire mass range into multiple injections for DIA-MS analyses with complementary windows. Using standard HeLa digests, the PulseDIA method identified 69,530 peptide precursors from 9,337 protein groups with ten MS injections of 30 min LC gradient. The PulseDIA scheme containing two complementary windows led to the highest gain of peptide and protein identifications per time unit compared to the conventional 30 min DIA method. We further applied the method to profile the proteome of 18 cholangiocarcinoma (CCA) tissue samples (benign and malignant) from nine patients. PulseDIA identified 7,796 protein groups in these CCA samples, with 14% increase of protein identifications, compared to the conventional DIA method. The missing value for protein matrix dropped by 7% with PulseDIA acquisition. 681 proteins were significantly dysregulated in tumorous CCA samples. Together, we presented and benchmarked an alternative DIA method with higher sensitivity and lower missing rate.

cancer biology↗

bioRxiv2019

High-throughput proteomic analysis of FFPE tissue samples facilitates tumor stratification

Formalin-fixed, paraffin-embedded (FFPE), biobanked tissue samples offer an invaluable resource for clinical and biomarker research. Here we developed a pressure cycling technology (PCT)-SWATH mass spectrometry workflow to analyze FFPE tissue proteomes and applied it to the stratification of prostate cancer (PCa) and diffuse large B-cell lymphoma (DLBCL) samples. We show that the proteome patterns of FFPE PCa tissue samples and their analogous fresh frozen (FF) counterparts have a high degree of similarity and we confirmed multiple proteins consistently regulated in PCa tissues in an independent sample cohort. We further demonstrate temporal stability of proteome patterns from FFPE samples that were stored between one to 15 years in a biobank and show a high degree of the proteome pattern similarity between two types histological region of small FFPE samples, i.e. punched tissue biopsies and thin tissue sections of micrometer thickness, despite the existence of certain degree of biological variations. Applying the method to two independent DLBCL cohorts we identified myeloperoxidase (MPO), a peroxidase enzyme, as a novel prognostic marker. In summary, this study presents a robust proteomic method to analyze bulk and biopsy FFPE tissues and reports the first systematic comparison of proteome maps generated from FFPE and FF samples. Our data demonstrate the practicality and superiority of FFPE over FF samples for proteome in biomarker discovery. Promising biomarker candidates for PCa and DLBCL have been discovered.

biochemistry↗