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Multiple myeloma (MM) is a highly refractory hematologic cancer. Targeted immunotherapy has shown promise in MM but remains hindered by the challenge of identifying specific yet broadly representative tumor markers. We analyzed 53 bone marrow (BM) aspirates from 41 MM patients using an unbiased, high-throughput pipeline for therapeutic target discovery via single-cell transcriptomic profiling, yielding 38 MM marker genes encoding cell-surface proteins and 15 encoding intracellular proteins. Of these, 20 candidate genes were highlighted that are not yet under clinical study, 11 of which were previously uncharacterized as therapeutic targets. The findings were cross-validated using bulk RNA sequencing, flow cytometry, and proteomic mass spectrometry of MM cell lines and patient BM, demonstrating high overall concordance across data types. Independent discovery using bulk RNA sequencing reiterated top candidates, further affirming the ability of single-cell transcriptomics to accurately capture marker expression despite limitations in sample size or sequencing depth. Target dynamics and heterogeneity were further examined using both transcriptomic and immuno-imaging methods. In summary, this study presents a robust and broadly applicable strategy for identifying tumor markers to better inform the development of targeted cancer therapy. Significance: Single-cell transcriptomic profiling and multiomic cross-validation to uncover therapeutic targets identifies 38 myeloma marker genes, including 11 transcribing surface proteins with previously uncharacterized potential for targeted antitumor therapy.

Despite advances in proteomic technologies, clinical translation of plasma biomarkers remains low, partly due to a major bottleneck between the discovery of candidate biomarkers and costly clinical validation studies. Due to a dearth of multiplexable assays, generally only a few candidate biomarkers are tested, and the validation success rate is accordingly low. Previously, mass spectrometry-based approaches have been used to fill this gap but feature poor quantitative performance and were generally limited to hundreds of proteins. Here, we demonstrate the capability of an internal standard triggered-parallel reaction monitoring (IS-PRM) assay to greatly expand the numbers of candidates that can be tested with improved quantitative performance. The assay couples immunodepletion and fractionation with IS-PRM and was developed and implemented in human plasma to quantify 5176 peptides representing 1314 breast cancer biomarker candidates. Characterization of the IS-PRM assay demonstrated the precision (median % CV of 7.7%), linearity (median

Although the role of RNA binding proteins (RBPs) in extracellular RNA (exRNA) biology is well established, their exRNA cargo and distribution across biofluids are largely unknown. To address this gap, we extend the exRNA Atlas resource by mapping exRNAs carried by extracellular RBPs (exRBPs). This map was developed through an integrative analysis of ENCODE enhanced crosslinking and immunoprecipitation (eCLIP) data (150 RBPs) and human exRNA profiles (6,930 samples). Computational analysis and experimental validation identified exRBPs in plasma, serum, saliva, urine, cerebrospinal fluid, and cell-culture-conditioned medium. exRBPs carry exRNA transcripts from small non-coding RNA biotypes, including microRNA (miRNA), piRNA, tRNA, small nuclear RNA (snRNA), small nucleolar RNA (snoRNA), Y RNA, and lncRNA, as well as protein-coding mRNA fragments. Computational deconvolution of exRBP RNA cargo reveals associations of exRBPs with extracellular vesicles, lipoproteins, and ribonucleoproteins across human biofluids. Overall, we mapped the distribution of exRBPs across human biofluids, presenting a resource for the community.

Multiple myeloma (MM) is a highly refractory hematologic cancer. Targeted immunotherapy has shown promise in MM but remains hindered by the challenge of identifying specific yet broadly representative tumor markers. We analyzed 53 bone marrow (BM) aspirates from 41 MM patients using an unbiased, high-throughput pipeline for therapeutic target discovery via single-cell transcriptomic profiling, yielding 38 MM marker genes encoding cell-surface proteins and 15 encoding intracellular proteins. Of these, 20 candidate genes were highlighted that are not yet under clinical study, 11 of which were previously uncharacterized as therapeutic targets. The findings were cross-validated using bulk RNA sequencing, flow cytometry, and proteomic mass spectrometry of MM cell lines and patient BM, demonstrating high overall concordance across data types. Independent discovery using bulk RNA sequencing reiterated top candidates, further affirming the ability of single-cell transcriptomics to accurately capture marker expression despite limitations in sample size or sequencing depth. Target dynamics and heterogeneity were further examined using both transcriptomic and immuno-imaging methods. In summary, this study presents a robust and broadly applicable strategy for identifying tumor markers to better inform the development of targeted cancer therapy.Significance:Single-cell transcriptomic profiling and multiomic cross-validation to uncover therapeutic targets identifies 38 myeloma marker genes, including 11 transcribing surface proteins with previously uncharacterized potential for targeted antitumor therapy.

Pearson R correlation values between bulk RNA expression and scRNA expression in PCs for target genes.

Metastasis is the cause of over 90% of all deaths associated with breast cancer, yet the strategies to predict cancer spreading based on primary tumor profiles and therefore prevent metastasis are egregiously limited. As rare precursor cells to metastasis, circulating tumor cells (CTCs) in multicellular clusters in the blood are 20-50 times more likely to produce viable metastasis than single CTCs. However, the molecular mechanisms underlying various CTC clusters, such as homotypic tumor cell clusters and heterotypic tumor-immune cell clusters, are yet to be fully elucidated. Combining machine learning-assisted computational ranking with experimental demonstration to assess cell adhesion candidates, we identified a transmembrane protein Plexin- B2 (PB2) as a new therapeutic target that drives the formation of both homotypic and heterotypic CTC clusters. High PB2 expression in human primary tumors predicts an unfavorable distant metastasis-free survival and is enriched in CTC clusters compared to single CTCs in advanced breast cancers. Loss of PB2 reduces formation of homotypic tumor cell clusters as well as heterotypic tumor-myeloid cell clusters in triple-negative breast cancer. Interactions between PB2 and its ligand Sema4C on tumor cells promote homotypic cluster formation, and PB2 binding with Sema4A on myeloid cells (monocytes) drives heterotypic CTC cluster formation, suggesting that metastasizing tumor cells hijack the PB2/Sema family axis to promote lung metastasis in breast cancer. Additionally, using a global proteomic analysis, we identified novel downstream effectors of the PB2 pathway associated with cancer stemness, cell cycling, and tumor cell clustering in breast cancer. Thus, PB2 is a novel therapeutic target for preventing new metastasis.

SUMMARYG protein-coupled receptor (GPCR) biased agonism, the activation of some signaling pathways over others, is thought to largely be due to differential receptor phosphorylation, or “phosphorylation barcodes.” At chemokine receptors, ligands act as “biased agonists” with complex signaling profiles, which contributes to the limited success in pharmacologically targeting these receptors. Here, mass spectrometry-based global phosphoproteomics revealed that CXCR3 chemokines generate different phosphorylation barcodes associated with differential transducer activation. Chemokine stimulation resulted in distinct changes throughout the kinome in global phosphoproteomic studies. Mutation of CXCR3 phosphosites altered β-arrestin conformation in cellular assays and was confirmed by molecular dynamics simulations. T cells expressing phosphorylation-deficient CXCR3 mutants resulted in agonist- and receptor-specific chemotactic profiles. Our results demonstrate that CXCR3 chemokines are non-redundant and act as biased agonists through differential encoding of phosphorylation barcodes and lead to distinct physiological processes.

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) and fibromyalgia have overlapping neurologic symptoms particularly disabling fatigue. This has given rise to the question whether they are distinct central nervous system (CNS) entities or is one an extension of the other. To investigate this, we used unbiased quantitative mass spectrometry-based proteomics to examine the most proximal fluid to the brain, cerebrospinal fluid (CSF). This was to ascertain if the proteome profile of one was the same or different from the other. We examined two separate groups of ME/CFS, one with (n=15) and one without (n=15) fibromyalgia. We quantified a total of 2,083 proteins using immunoaffinity depletion, tandem mass tag isobaric labeling and offline two-dimensional liquid chromatography coupled to tandem mass spectrometry, including 1,789 that were quantified in all the CSF samples. ANOVA analysis did not yield any proteins with an adjusted p-value < 0.05. This supports the notion that ME/CFS and fibromyalgia as currently defined are not distinct entities.

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 (, Phosphoproteomics can provide systematic insights into disease-associated cell signaling changes. Here, the authors present a sensitive workflow integrating library-based and direct data-independent acquisition approaches, and a hybrid spectral library resource for in-depth phosphoproteomic profiling.

Large numbers of cells are generally required for quantitative global proteome profiling due to surface adsorption losses associated with sample processing. Such bulk measurement obscures important cell-to-cell variability (cell heterogeneity) and makes proteomic profiling impossible for rare cell populations (e.g., circulating tumor cells (CTCs)). Here we report a surfactant-assisted one-pot sample preparation coupled with mass spectrometry (MS) method termed SOP-MS for label-free global single-cell proteomics. SOP-MS capitalizes on the combination of a MS-compatible nonionic surfactant, n-Dodecyl-β-D-maltoside, and hydrophobic surface-based low-bind tubes or multi-well plates for ‘all-in-one’ one-pot sample preparation. This ‘all-in-one’ method including elimination of all sample transfer steps maximally reduces surface adsorption losses for effective processing of single cells, thus improving detection sensitivity for single-cell proteomics. This method allows convenient label-free quantification of hundreds of proteins from single human cells and ~1200 proteins from small tissue sections (close to ~20 cells). When applied to a patient CTC-derived xenograft (PCDX) model at the single-cell resolution, SOP-MS can reveal distinct protein signatures between primary tumor cells and early metastatic lung cells, which are related to the selection pressure of anti-tumor immunity during breast cancer metastasis. The approach paves the way for routine, precise, quantitative single-cell proteomics., Tsai, Zhang, Scholten et al. develop a surfactant- assisted one-pot sample preparation coupled with mass spectrometry method (SOP-MS) for label-free global single-cell proteomics. This method allows researchers to measure hundreds of proteins from single human cells, suggesting its utility for quantitative single-cell proteomics.

Effective phosphoproteome of nanoscale sample analysis remains a daunting task, primarily due to significant sample loss associated with non-specific surface adsorption during enrichment of low stoichiometric phosphopeptide. We developed a novel tandem tip phosphoproteomics sample preparation method that is capable of sample cleanup and enrichment without additional sample transfer, and its integration with our recently developed SOP (Surfactant-assisted One-Pot sample preparation) and iBASIL (improved Boosting to Amplify Signal with Isobaric Labeling) approaches provides a streamlined workflow enabling sensitive, high-throughput nanoscale phosphoproteome measurements. This approach significantly reduces both sample loss and processing time, allowing the identification of >3,000 (>9,500) phosphopeptides from 1 (10) µg of cell lysate using the label-free method without a spectral library. It also enabled precise quantification of ∼600 phosphopeptides from 100 cells sorted by FACS (single-cell level input for the enriched phosphopeptides) and ∼700 phosphopeptides from human spleen tissue voxels with a spatial resolution of 200 µm (equivalent to ∼100 cells) in a high-throughput manner. The new workflow opens avenues for phosphoproteome profiling of mass-limited samples at the low nanogram level.

Intestinal stem cells are non-quiescent, dividing epithelial cells that rapidly differentiate into progenitor cells of the absorptive and secretory cell lineages. The kinetics of this process is rapid such that the epithelium is replaced weekly. To determine how the transcriptome and proteome keep pace with rapid differentiation, we developed a new cell sorting method to purify mouse colon epithelial cells. Here we show that alternative mRNA splicing and polyadenylation dominate changes in the transcriptome as stem cells differentiate into progenitors. In contrast, as progenitors differentiate into mature cell types, changes in mRNA levels dominate the transcriptome. RNA processing targets regulators of cell cycle, RNA, cell adhesion, SUMOylation, and Wnt and Notch signaling. Additionally, global proteome profiling detected >2,800 proteins and revealed RNA:protein patterns of abundance and correlation. Paired together, these data highlight new potentials for autocrine and feedback regulation and provide new insights into cell state transitions in the crypt., Habowski et al. develop a sorting protocol for purification and comparative analysis of mouse colon stem cells and their progeny. By proteomic and transcriptomic analysis, they determine that lineage commitment is accompanied by a greater change in mRNA splicing and polyadenylation than in gene expression and they characterise signalling pathways involved in differentiation.

Tumor-initiating cells with reprogramming plasticity are thought to be essential for cancer development and metastatic regeneration in many cancers; however, the molecular mechanisms are not fully understood. This study reports that CD81, a tetraspanin protein marker of small extracellular vesicles (exosomes), functions as a binding partner of CD44 and facilitates self-renewal of tumor initiating cells. Using machine learning-assisted protein structure modeling, co-immunoprecipitation, and mutagenesis approaches, we further demonstrate that CD81 interacts with CD44 on the cellular membrane through their extracellular regions. In-depth global and phosphoproteomic analyses of clustering tumor cells unveils endocytosis-related signature pathways of proteins and phosphorylation patterns regulated by CD81 and CD44 specifically or shared between two. Notably, CRISPR Cas9-mediated depletion of either CD44 or CD81 results in loss of both proteins in cancer cell-secreted exosomes, a state which abolishes exosome-induced self-renewal of recipient cells for mammosphere formation. CD81 is expressed in >80% of human circulating tumor cells (CTCs) and specifically enriched in clustered CTCs along with CD44 isolated from breast cancer patients. Mimicking the phenotypes of CD44 deficiency, loss of CD81 also inhibits tumor cluster aggregation, tumorigenesis, and lung metastasis of triple negative breast cancer (TNBC), supporting the clinical significance of CD81 in association with patient outcomes. Our study highlights the novel role of CD81 and its partnership with CD44 in cancer exosomes, self-renewal, CTC clustering, and metastasis initiation of TNBC.

SummaryMany individual proteins have been identified as having defined positions relative to cell polarity axes, raising the question of what fraction of all proteins may have polarized localizations. We took advantage of the giant ciliate Stentor coeruleus to quantify the extent of polarized localization proteome-wide. This trumpet-shaped unicellular organism shows a clear morphological anterior-posterior axis defined by a circular array of cilia known as a membranellar band at one end, and a holdfast at the other end. Because individual Stentor cells are over a millimeter in length, we were able to cut the cells into three pieces along the anterior-posterior axis, followed by proteomic analysis of proteins enriched in each piece. We find that approximately 30% of all detected proteins show a polarized location relative to the anterior-posterior cell axis. Proteins with polarized enrichment include centrin-like proteins, calcium-regulated kinases, orthologs of SFI1 and GAS2, and proteases. At the organelle level, nuclear and mitochondrial proteins are enriched in the anterior half of the cell body, but not in the membranellar band itself, while ribosome related proteins are apparently uniformly distributed. RNAi of signaling proteins enriched in the membranellar band, which is the anterior-most structure in the cell, revealed a protein phosphatase 2 subunit b ortholog required for closure of the membranellar band into the ring shape characteristic of Stentor. These results suggest that a large fraction of the Stentor proteome has a polarized localization, and provide a protein-level framework for future analysis of pattern formation and regeneration in Stentor as well as defining a general strategy for subcellular spatial proteomics based on physical dissection of cells.

Resins continue to occupy a place in the waterproof building market. Unlike traditional concrete building materials, the polymerization of resins requires initiators to support the required energy to drive the reaction or reduce the polymerization threshold, which shows a high reaction rate and low energy consumption in the polymerization process. Azo compounds (azos) are energetic substances commonly used in polymerization, but they can cause process hazards due to the amount of heat release and accumulation of the resulting heat. To ensure that similar hazards do not occur, the emerging azo initiators 2,2'-azobis(2-methylpropionamide)dihydrochloride (AIBA), 2-cyanopropan-2-yliminourea (CABN), and 2,2'-azodi(2-methylbutyronitrile) (AMBN) are explored. Depending on the process conditions, it is critical to examine how chemical reactions from a laboratory behave at a large scale. Kinetic models can be used to estimate fundamental safety parameters suitable for assessing the reaction hazards and as control measures, such as time to the maximum reaction rate under adiabatic conditions, time to the conversion limit, and runaway determination for process operation. The structure of this study is a combination of adiabatic calorimeter data and a nonlinear adiabatic dynamics model with the goal of helping to fill the void in research on thermal hazard analysis of emerging azo initiators. The adiabatic data is used to analyze the reaction mode characteristics of the azo compounds, and combined with the external environment, the reaction and temperature parameter changes of the azo compounds due to the reaction are discussed in the actual situation.

Despite advances in proteomic technologies, clinical translation of plasma biomarkers remains low, partly due to a major bottleneck between the discovery of candidate biomarkers and downstream costly clinical validation studies. Due to a dearth of multiplexable assays, generally only a few candidate biomarkers are tested, and the validation success rate is accordingly low. Here, we demonstrate the capability of internal standard triggered-parallel reaction monitoring (IS-PRM) to prioritize candidate biomarkers for validation studies. A 5,176-plex assay coupling immunodepletion and fractionation with IS-PRM was developed and implemented in human plasma to quantify peptides representing 1,314 breast cancer biomarker candidates. Compared to prior approaches using data-dependent analysis, IS-PRM showed improved sensitivity (912 vs 295 proteins quantified) and precision (CV 0.1 vs 0.27) enabling rank-ordering of candidate biomarkers for validation studies. The assay greatly expands capabilities for quantification of large numbers of proteins and is well suited for prioritization of viable candidate biomarkers.

Functional implication of stromal heterogeneity in the prostate remains incompletely understood. Using lineage tracing and light-sheet imaging, we show that some fibroblast cells at the mouse proximal prostatic ducts and prostatic urethra highly express Lgr5. Genetic ablation of these anatomically restricted stromal cells, but not nonselective ablation of prostatic stromal cells, rapidly induces prostate epithelial turnover and dedifferentiation that are reversed following spontaneous restoration of the Lgr5

Recent advances in sample preparation enable label-free mass spectrometry (MS)-based proteome profiling of small numbers of mammalian cells. However, specific devices are often required to downscale sample processing volume from the standard 50-200 μL to sub-μL for effective nanoproteomics, which greatly impedes the implementation of current nanoproteomics methods by the proteomics research community. Herein, we report a facile one-pot nanoproteomics method termed SOPs-MS ( s urfactant-assisted o ne- p ot sample processing at the s tandard volume coupled with MS) for convenient robust proteome profiling of 50-1000 mammalian cells. Building upon our recent development of SOPs-MS for label-free single-cell proteomics at a low μL volume, we have systematically evaluated its processing volume at 10-200 μL using 100 human cells. The processing volume of 50 μL that is in the range of volume for standard proteomics sample preparation has been selected for easy sample handling with a benchtop micropipette. SOPs-MS allows for reliable label-free quantification of ∼1200-2700 protein groups from 50 to 1000 MCF10A cells. When applied to small subpopulations of mouse colon crypt cells, SOPs-MS has revealed protein signatures between distinct subpopulation cells with identification of ∼1500-2500 protein groups for each subpopulation. SOPs-MS may pave the way for routine deep proteome profiling of small numbers of cells and low-input samples.

Brown rot fungi release massive amounts of carbon from forest deadwood, particularly at high latitudes. These fungi degrade wood by generating small reactive oxygen species (ROS) to loosen lignocellulose, to then selectively remove carbohydrates. The ROS mechanism has long been considered the key adaptation defining brown rot wood decomposition, but recently, we found preliminary evidence that fungal glycoside hydrolases (GHs) implicated in early cell wall loosening might have been adapted to tolerate ROS stress and to synergize with ROS to loosen woody lignocellulose. In the current study, we found more specifically that side chain hemicellulases that help in the early deconstruction of the lignocellulosic complex are significantly more tolerant of ROS in the brown rot fungus Rhodonia placenta than in a white rot fungus (Trametes versicolor) and a soft rot fungus (Trichoderma reesei). Using proteomics to understand the extent of tolerance, we found that significant oxidation of secreted R. placenta proteins exposed to ROS was less than half of the oxidation observed for T. versicolor or T. reesei. The principal oxidative modifications observed in all cases were monooxidation and dioxidation/trioxidation (mainly in methionine and tryptophan residues), some of which were critical for enzyme activity. At the peptide level, we found that GHs in R. placenta were the least ROS affected among our tested fungi. These results confirm and describe underlying mechanisms of tolerance in early-secreted brown rot fungal hemicellulases. These enzymatic adaptations may have been as important as nonenzymatic ROS pathway adaptations in brown rot fungal evolution. IMPORTANCE Brown rot fungi play a critical role in carbon recycling and are of industrial interest. These fungi typically use reactive oxygen species (ROS) to indiscriminately “loosen” wood cell walls at the outset of decay. Brown rot fungi avoid oxidative stress associated with this ROS step by delaying the expression/secretion of many carbohydrate-active enzymes, but there are exceptions, notably some side chain hemicellulases, implicated in loosening lignocellulose. In this study, we provide enzyme activity and secretomic evidence that these enzymes in the brown rot model Rhodonia placenta are more ROS tolerant than the white and soft rot isolates tested. For R. placenta, and perhaps all brown rot lineages, these ROS tolerance adaptions may have played a long-overshadowed role in enabling brown rot.

We established a workflow for highly sensitive multiplexed quantitative phosphoproteomics using a nanoscale solid-phase tandem mass tag (TMT) labeling reactor. Phosphopeptides were first enriched by titanium oxide chromatography and then labeled with isobaric TMT reagents in a StageTip packed with hydrophobic polymer-based sorbents. We found that TMT-labeled singly phosphorylated peptides tend to flow through the titanium oxide column. Therefore, TMT labeling should be performed after the enrichment step from tryptic peptides, resulting in the need for microscale reactions with small amounts of phosphopeptides. Using an optimized protocol for tens to hundreds of nanograms of phosphopeptides, we obtained a nearly 10-fold increase in sensitivity compared to the conventional solution-based TMT protocol. We demonstrate that this nanoscale phosphoproteomics protocol works for 50 μg of HeLa proteins treated with selumetinib, and we successfully quantified the selumetinib-regulated phosphorylated sites on a proteome scale. The MS raw data files have been deposited with the ProteomeXchange Consortium via the jPOST partner repository (https://jpostdb.org) with the data set identifier PXD025536.

Effective extension of mass spectrometry-based proteomics to single cells remains challenging. Herein we combined microfluidic nanodroplet technology with tandem mass tag (TMT) isobaric labeling to significantly improve analysis throughput and proteome coverage for single mammalian cells. Isobaric labeling facilitated multiplex analysis of single cell-sized protein quantities to a depth of ∼1 600 proteins with a median CV of 10.9% and correlation coefficient of 0.98. To demonstrate in-depth high throughput single cell analysis, the platform was applied to measure protein expression in 72 single cells from three murine cell populations (epithelial, immune, and endothelial cells) in

Two-dimensional reversed-phase capillary liquid chromatography (2D RPLC) separations have enabled comprehensive proteome profiling of biological systems. However, milligram sample quantities of proteins are typically required due to significant losses during offline fractionation. Such a large sample requirement generally precludes the application samples in the nanogram to low-microgram range. To achieve in-depth proteomic analysis of such small-sized samples, we have developed the nanoFAC (nanoflow Fractionation and Automated Concatenation) 2D RPLC platform, in which the first dimension high-pH fractionation was performed on a 75-μm i.d. capillary column at a 300 nL/min flow rate with automated fraction concatenation, instead of on a typically used 2.1 mm column at a 200 μL/min flow rate with manual concatenation. Each fraction was then fully transferred to the second-dimension low-pH nanoLC separation using an autosampler equipped with a custom-machined syringe. We have found that using a polypropylene 96-well plate as collection device as well as the addition of n-Dodecyl β-D-maltoside (0.01%) in the collection buffer can significantly improve sample recovery. We have demonstrated the nanoFAC 2D RPLC platform can achieve confident identifications of ~49,000–94,000 unique peptides, corresponding to ~6,700–8,300 protein groups using only 100–1000 ng of HeLa tryptic digest (equivalent to ~500–5,000 cells). Furthermore, by integrating with phosphopeptide enrichment, the nanoFAC 2D RPLC platform can identify ~20,000 phosphopeptides from 100 μg of MCF-7 cell lysate.

In this paper, we have proposed a novel 6-DOF magnetic levitation (maglev) system to improve the robustness and upgrade positioning precision. The design concept attempts to keep the good performance in the whole journey of moving rather than the point-to-point positioning precision. Furthermore, we endeavor to develop this system with an expectable large moving range. Based on these concepts, we built the force model that considers the variation from the displacement to the magnetic forces first, and avoids the constraint of the attractive levitation in replacing the repulsive levitation. Finally, we adopt the concept of relative place to build the measuring system. All of the performance of the improved framework is demonstrated in the experimental results.

Comprehensive phosphoproteomic analysis of small populations of cells remains a daunting task due primarily to the insufficient MS signal intensity from low concentrations of enriched phosphopeptides. Isobaric labeling has a unique multiplexing feature where the “total” peptide signal from all channels (or samples) triggers MS/MS fragmentation for peptide identification, while the reporter ions provide quantitative information. In light of this feature, we tested the concept of using a “boosting” sample (e.g., a biological sample mimicking the study samples but available in a much larger quantity) in multiplexed analysis to enable sensitive and comprehensive quantitative phosphoproteomic measurements with

mRNA processing related pathways, including spliceosome and ribosome are over-expressed and unregulated in cancer cells. To investigate the mRNA processing pathways in patient samples, we performed a comprehensive, tandem mass tag (TMT) labeling-based proteome and phosphoproteome profiling of the normal and ovarian cancerous tissues from patients treated with different chemotherapy revealed that malignant cells have overexpression of mRNA processing pathways compared to normal cells. For this analysis, equal amount of tissues were homogenized, and tissue lysates were collected and processed for the quantitative proteomics and phosphoproteomics analysis. KEGG pathway enrichment analysis revealed that spliceosome, basal transcription factors, mRNA surveillance, ribosome and ribosome biogenesis in eukaryotes pathways are upregulated in cancer cells compared to control. On other hand, pathways associated with metabolism and fatty acid biosynthesis were downregulated in malignant cells. Moreover, kinase Enrichment Analysis shows that CDK2, ATM, ATR, and MAPK associated kinases are upregulated in cancer cells compared to normal. Based on proteome analysis of patient samples, we investigated the pharmacological inhibition of Y-box binding protein 1 (YB-1). This is an RNA binding protein and key regulator of pre-mRNA alternative splicing and processing. We recently showed that a natural product-derived small molecule (SU056) inhibits YB-1 activity. We found that SU056 binds to the YB-1 and blocks the RNA binding pocket. Proteomics results suggest that treatment with this drug strongly inhibits the spliceosome pathway. SU056 treatment arrests the ovarian cancer cells in G1 phase and inhibits the CDK2 and Cyclin E. SU056 inhibits the tumor progression and metastasis in the ID8 mice model and sensitizes the OVCAR8 NSG mice model for paclitaxel treatment. Our study provides a compelling case for YB-1 inhibition using SU056 as a potential therapy for ovarian cancer. Citation Format: Dhanir Tailor, Chia-Feng Tsai, Alexander Honkala, Wenqi Li, Tao Liu, Tanja Pejovic, Sanjay V. Malhotra. Targeting mRNA processing pathways in ovarian cancer with a small molecule inhibitor [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2645.

Unbiased single-cell proteomics (scProteomics) promises to advance our understanding of cell functions within complex biological systems. However, a major challenge for current methods is their ability to identify and provide accurate quantitative information for low abundance proteins. Herein, we describe an ion mobility-enhanced mass spectrometry acquisition and peptide identification method, TIFF (Transferring Identification based on FAIMS Filtering), designed to improve the sensitivity and accuracy of label-free scProteomics. TIFF significantly extends the ion accumulation times for peptide ions by filtering out singly charged background ions. The peptide identities are then assigned by a 3-dimensional MS1 feature matching approach (retention time, accurate mass, and FAIMS compensation voltage). The TIFF method enabled unbiased proteome analysis to a depth of >1,700 proteins in single HeLa cells with >1,100 proteins consistently quantified. As a demonstration, we applied the TIFF method to obtain temporal proteome profiles of >150 single murine macrophage cells during a lipopolysaccharide stimulation experiment and identified time-dependent proteome profiles.

Tumor-initiating cells with reprogramming plasticity and/or de-differentiation attributes have been thought to initiate primary tumor development as well as to regenerate secondary tumors in metastatic organs; however, the molecular mechanisms are not fully understood. We previously found that breast tumor-initiating cell marker, CD44, directs multicellular aggregation and cluster formation of circulating tumor cells (CTCs), which further enhance stemness and survival of such cells, enabling metastatic colonization to the lungs. To further elucidate the molecular network underlying CTC cluster formation, we performed global proteomic profiling and discovered that the tetraspanin protein CD81, which is normally enriched in exosomes (small extracellular vesicles), is a new driver of cancer initiation and metastasis as a facilitator and target of CD44. Loss of CD81 compromises tumorigenicity and mammosphere formation of triple negative breast cancer (TNBC) cells. Assisted by machine learning-based algorithms and mutagenesis approach, we found that CD81 interacts with CD44 on the cellular membrane through their extracellular regions. Notably, genetic knockout of CD44 or CD81 results in loss of both CD81 and CD44 in secreted exosomes, a state which abolishes exosome-induced self-renewal of recipient cells, such as mammosphere formation. In addition, RNA sequencing analysis showed that CD81 knockdown up-regulates expression of a cell differentiation marker, SEMA7a, whose down-regulation partially rescues mammosphere formation inhibition by CD81 depletion. Clinically, CD81 expression was observed in >80% of CTCs and specifically enriched and co-expressed along with CD44 in clustered CTCs of breast cancer patients. Mimicing the phenotypes of CD44 deficiency, loss of CD81 also inhibited tumor cell aggregation and lung metastasis of TNBC in both human and mouse tumor models, supporting the clinical significance of CD81 in association with patient outcomes. Our study highlights a new driving role of CD81 in cancer exosome-induced stemness, clustered CTCs, and metastasis initiation of TNBC, reported for the first time to our knowledge.

Glioblastoma (GBM) is the most aggressive nervous system cancer. Understanding its molecular pathogenesis is crucial to improving diagnosis and treatment. Integrated analysis of genomic, proteomic, post-translational modification and metabolomic data on 99 treatment-naive GBMs provides insights to GBM biology. We identify key phosphorylation events (e.g., phosphorylated PTPN11 and PLCG1) as potential switches mediating oncogenic pathway activation, as well as potential targets for EGFR-, TP53-, and RB1-altered tumors. Immune subtypes with distinct immune cell types are discovered using bulk omics methodologies, validated by snRNA-seq, and correlated with specific expression and histone acetylation patterns. Histone H2B acetylation in classical-like and immune-low GBM is driven largely by BRDs, CREBBP, and EP300. Integrated metabolomic and proteomic data identify specific lipid distributions across subtypes and distinct global metabolic changes in IDH-mutated tumors. This work highlights biological relationships that could contribute to stratification of GBM patients for more effective treatment. Wang et al. perform integrated proteogenomic analysis of adult glioblastoma (GBM), including metabolomics, lipidomics, and single nuclei RNA-Seq, revealing insights into the immune landscape of GBM, cell-specific nature of EMT signatures, histone acetylation in classical GBM, and the existence of signaling hubs which could provide therapeutic vulnerabilities.

Protein phosphorylation is a critical post-translational modification (PTM), with cell signaling networks being tightly regulated by protein phosphorylation. Despite recent technological advances in reversed-phase liquid chromatography (RPLC)-mass spectrometry (MS)-based proteomics, comprehensive phosphoproteomic coverage in complex biological systems remains challenging, especially for hydrophilic phosphopeptides that often have multiple phosphorylation sites. Herein we describe an MS-based phosphoproteomics protocol for effective quantitative analysis of hydrophilic phosphopeptides. This protocol was built upon a simple tandem mass tag (TMT)-labeling method for significantly increasing peptide hydrophobicity, thus effectively enhancing RPLC-MS analysis of hydrophilic peptides. Through phosphoproteomic analyses of MCF7 cells, this method was demonstrated to greatly increase the number of identified hydrophilic phosphopeptides and improve MS signal detection. With the TMT labeling method, we were able to identify a previously unreported phosphopeptide from the G protein-coupled receptor (GPCR) CXCR3, QPpSSSR, which is thought to be important in regulating receptor signaling. This protocol is easy to adopt and implement, and thus should have broad utility for effective RPLC-MS analysis of the hydrophilic phosphoproteome as well as other highly hydrophilic analytes.

Our study details the stepwise evolution of gilteritinib resistance in FLT3-mutated acute myeloid leukemia (AML). Early resistance is mediated by the bone marrow microenvironment, which protects residual leukemia cells. Removing these supportive extrinsic ligands drives evolution of late, intrinsic resistance. Whole exome sequencing, CRISPR/Cas, metabolomics, proteomics, and pharmacologic approaches were used to mechanistically define both early and late resistance. Early resistant cells undergo metabolic reprogramming, grow more slowly, and are dependent upon Aurora kinase B (AURKB). Late resistant cells are characterized byexpansion of pre-existing NRAS mutant subclones and continued metabolic reprogramming. Our model closely mirrors the timing and mutations of AML patients treated with gilteritinib.Pharmacological inhibition of AURKB resensitized both early resistant cell cultures and primary leukemia cells from gilteritinib-treated AML patients. These findings support a combinatorial strategy to target early resistant AML cells with AURKB inhibitors and gilteritinib before the expansion of pre-existing resistance mutations occurs.