Data-independent Acquisition Mass Spectrometry Research Articles

Abstract A067: Identifying and targeting FOLFIRINOX resistance mechanisms to improve outcomes in pancreatic cancer

Abstract Pancreatic ductal adenocarcinoma (PDAC) is a devastating disease with a 5-year survival rate of only 12%. More than 80% of PDAC patients cannot undergo potentially curative surgical resection due to the presentation of advanced, metastatic disease, and are instead offered systemic chemotherapy regimens to which resistance is rapidly acquired. FOLFIRINOX chemotherapy offers an 11.1-month median survival compared to 6.8 months with Gemcitabine. Rapid onset of resistance to FOLFIRINOX, coupled with the lack of alternative targeted therapies, ultimately results in patients succumbing to the disease. My project aims to identify mechanisms by which PDAC cells acquire resistance to FOLFIRINOX treatment and investigate interventions aimed at enhancing and/or reinstating FOLFIRINOX sensitivity. Our lab has propagated distinct patient derived xenografts (PDXs) from the Avner Australian Pancreatic Cancer Matrix Atlas (APMA) and I have exposed them to 11 - 15 rounds of FOLFIRINOX (or vehicle control) in vivo to reflect the clinical management of PDAC. Additionally, intrapancreatic orthotopic tumors derived from cells from the KPC mouse model (LSL-KrasG12D/+;LSL-Trp53R172H/+;Pdx-1-Cre) have received 5 - 6 rounds of FOLFIRINOX (or vehicle control). These tumors will undergo quantitative data-independent acquisition mass spectrometry proteomic analysis and RNA sequencing transcriptomic analysis (RNAseq) to reveal resistance pathways and/or proteins operating in the chronically treated tumors and the surrounding pancreatic tumor microenvironment. Additionally, we have developed and cultured matched treatment naïve and FOLFIRINOX-treated patient-derived cell lines (PDCLs) and mouse cell lines from the PDX and KPC-derived tumors. These lines will allow us to validate future targets via gene knockdown and/or pharmacological intervention. 3D assays, such as our organotypic matrix models, will be performed to assess the metastatic potential of cancer cells and their response to FOLFIRINOX in vitro. Furthermore, subcutaneous and orthotopic (intrapancreatic) models coupled with intravital (in vivo) imaging and biosensor technology will allow us to assess targeting in live tumors and metastatic sites in real time. It is the hope that this research will lead to the identification of proteins or protein pathways which can be manipulated in order to re-sensitize tumors to FOLFIRINOX chemotherapy. Citation Format: Victoria Tyma, Ying-Fei Liew, Alice Tran, Anna Howell, Shona Ritchie, Avner Australian Pancreatic Cancer Matrix Atlas, Australian Pancreatic Genome Initiative, Thomas Cox, Marina Pajic, David Herrmann, Brooke Pereira, Paul Timpson, Katie Gordon. Identifying and targeting FOLFIRINOX resistance mechanisms to improve outcomes in pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr A067.

Comprehensive Proteomic Profiling of Converted Adipocyte-like Cells from Normal Human Dermal Fibroblasts Using Data-Independent Acquisition Mass Spectrometry

Comprehensive Proteomic Profiling of Converted Adipocyte-like Cells from Normal Human Dermal Fibroblasts Using Data-Independent Acquisition Mass Spectrometry

The trajectory of vesicular proteomic signatures from HBV-HCC by chitosan-magnetic bead-based separation and DIA-proteomic analysis.

Hepatocellular carcinoma (HCC) is a prevalent primary liver cancer often associated with chronic hepatitis B virus infection (CHB) and liver cirrhosis (LC), underscoring the critical need for biomarker discovery to improve patient outcomes. Emerging as a promising avenue for biomarker development, proteomic technology leveraging liquid biopsy from small extracellular vesicles (sEV) offers new insights. Here, we evaluated various methods for sEV isolation and identified polysaccharide chitosan (CS) as an optimal approach. Subsequently, we employed optimized CS-based magnetic beads (Mag-CS) for sEV separation from serum samples of healthy controls, CHB, LC, and HBV-HCC patients. Leveraging data-independent acquisition mass spectrometry coupled with machine learning, we uncovered potential vesicular protein biomarker signatures (KNG1, F11, KLKB1, CAPNS1, CDH1, CPN2, NME2) capable of distinguishing HBV-HCC from CHB, LC, and non-HCC conditions. Collectively, our findings highlight the utility of Mag-CS-based sEV isolation for identifying early detection biomarkers in HBV-HCC.

Repair of Mechanical Cartilage Damage Using Exosomes Derived from Deer Antler Stem Cells.

Articular cartilage has limited self-repair capacity, and current clinical treatment options for cartilage defects are inadequate. However, deer antler cartilage possesses unique regenerative properties, with the ability to rapidly repair itself. This rapid self-repair process is closely linked to the paracrine factors released by deer antler stem cells. These findings present potential for the development of cell-free therapies for cartilage defects in clinical settings. The aim of this study was to investigate a novel method for repairing cartilage. A rat model with articular cartilage defects was established through surgery. Hydrogels loaded with exosomes (Exos) derived from antler stem cells (ASC-Exos) were implanted into the rat cartilage defects. The extent of cartilage damage repair was assessed using histological methods. The effects of ASC-Exos on chondrocytes and rat bone marrow mesenchymal stem cells (BMSCs) were evaluated using cell viability assays, proliferation assays, and scratch assays. Additionally, the maintenance of the chondrocyte phenotype by ASC-Exos was assessed using real-time fluorescence quantitative PCR (qPCR) and western blot analysis. The protein components contained of the Exos were identified using data-independent acquisition (DIA) mass spectrometry. ASC-Exos significantly promoted the repair of cartilage tissue damage. The level of cartilage repair in the experimental group (ASC-Exos) was higher than that in the positive control (human adipose-derived stem cells, hADSC-Exos) and negative control (dulbecco's modified eagle medium) groups (p < 0.05). In vitro experiments demonstrated that ASC-Exos significantly enhanced the proliferation abilities of chondrocytes and the proliferation abilities and the migration abilities of BMSCs (p < 0.05). ASC-Exos up-regulated the expression levels of Aggrecan, Collagen II (COLII), and Sox9 mRNA and proteins in chondrocytes. Analysis of ASC-Exos protein components revealed the presence of active components such as Serotransferrin (TF), S100A4, and Insulin-like growth factor-binding protein 1 (IGF1). ASC-Exos have a significant effect on cartilage damage repair, which may be attributed to their promotion of chondrocyte and BMSCs proliferation and migration, as well as the maintenance of chondrocyte phenotype. This effect may be mediated by the presence of TF, S100A4, and IGF1.

Proteomic landscape profiling of primary prostate cancer reveals a 16-protein panel for prognosis prediction

Prostate cancer (PCa) is the most common malignant tumor in men. Currently, there are few prognosis indicators for predicting PCa outcomes and guiding treatments. Here, we perform comprehensive proteomic profiling of 918 tissue specimens from 306 Chinese patients with PCa using data-independent acquisition mass spectrometry (DIA-MS). We identify over 10,000 proteins and define three molecular subtypes of PCa with significant clinical and proteomic differences. We develop a 16-protein panel that effectively predicts biochemical recurrence (BCR) for patients with PCa, which is validated in six published datasets andone additional 99-biopsy-sample cohort by targeted proteomics. Interestingly, this 16-protein panel effectively predicts BCR across different International Society of Urological Pathology (ISUP) grades and pathological stages and outperforms the D'Amico risk classification system in BCR prediction. Furthermore, double knockout of NUDT5 and SEPTIN8, two components from the 16-protein panel, significantly suppresses the PCa cells to proliferate, invade, and migrate, suggesting the combination of NUDT5 and SEPTIN8 may provide new approaches for PCa treatment.

Iron starvation results in up-regulation of a probable Haloferax volcanii siderophore transporter.

The response of the haloarchaeal model organism Haloferax volcanii to iron starvation was analyzed at the proteome level by data-independent acquisition mass spectrometry. Cells grown in minimal medium with normal iron levels were compared to those grown under low iron conditions, with samples being separated into membrane and cytoplasmic fractions in order to focus on import/export processes which are frequently associated with metal homeostasis. Iron starvation not only caused a severe retardation of growth but also altered the levels of many proteins. Using a comprehensive annotated spectral library and data-independent acquisition mass spectrometry (DIA-MS), we found that iron starvation resulted in significant changes to both the membrane and the soluble proteomes of Hfx. volcanii. The most affected protein is the RND family permease HVO_A0467, which is 44-fold enriched in cells grown under iron starvation. The gene HVO_A0467 can be deleted suggesting that it is not essential under standard conditions. Compared to wild type cells the deletion strain shows only slight changes in growth and cell morphologies show no differences. Molecular docking predictions indicated that HVO_A0467 may be an exporter of the siderophore schizokinen for which a potential biosynthesis cluster is encoded in the Hfx. volcanii genome. Together, these findings confirm the importance of iron for archaeal cells and suggest HVO_0467 as a siderophore exporter.

Quantitative Comparison of Deparaffinization, Rehydration, and Extraction Methods for FFPE Tissue Proteomics and Phosphoproteomics.

Formalin-fixed paraffin-embedded (FFPE) tissues are suitable for proteomic and phosphoproteomic biomarker studies by data-independent acquisition mass spectrometry. The choice of the sample preparation method influences the number, intensity, and reproducibility of identifications. By comparing four deparaffinization and rehydration methods, including heptane, histolene, SubX, and xylene, we found that heptane and methanol produced the lowest coefficients of variation (CVs). Using this, five extraction methods from the literature were modified and evaluated for their performance using kidney, leg muscle, lung, and testicular rat organs. All methods performed well, except for SP3 due to insufficient tissue lysis. Heat n' Beat was the fastest and most reproducible method with the highest digestion efficiency and lowest CVs. S-Trap produced the highest peptide yield, while TFE produced the best phosphopeptide enrichment efficiency. The quantitation of FFPE-derived peptides remains an ongoing challenge with bias in UV and fluorescence assays across methods, most notably in SPEED. Functional enrichment analysis demonstrated that each method favored extracting some gene ontology cellular components over others including chromosome, cytoplasmic, cytoskeleton, endoplasmic reticulum, membrane, mitochondrion, and nucleoplasm protein groups. The outcome is a set of recommendations for choosing the most appropriate method for different settings.

Don't die like me: Which proteins are responsible for the selective neuronal vulnerability within the substantia nigra?

A hallmark of Parkinson's disease is the specific degeneration of dopaminergic neurons in the substantia nigra pars compacta. Interestingly, not all of these neurons are affected to the same extent. Studies revealed that neurons located more ventrally within the substantia nigra pars compacta have a higher prevalence to degenerate than those located in the dorsal tier. The underlying reasons for this selective neuronal vulnerability are still unknown. The aim of the present study was to gain a better understanding of molecular differences between these two neuronal subpopulations that may explain the selective neuronal vulnerability within the human substantia nigra. For this purpose, the neurons from the ventral as well as dorsal tier of the substantia nigra were specifically isolated out of neuropathologically unremarkable human substantia nigra sections with laser microdissection. Following, their proteome was analyzed by data independent acquisition mass spectrometry. The samples were analysed donor-specifically and not pooled for this purpose. A total of 5,391 proteins were identified in the substantia nigra. Of these, 2,453 proteins could be quantified in 100% of the dorsal tier samples. 1,629 could be quantified in 100% of the ventral tier samples. Nine proteins were differentially regulated with a log2 value ≥0.5 and a Qvalue ≤0.05. Of these 7 were higher abundant in the dorsal tier and 2 higher in the ventral tier. These proteins are associated with the cytoskeleton, neuronal plasticity, or calcium homeostasis. With these findings a deeper understanding can be gained of the selective neuronal vulnerability within the substantia nigra and of protective mechanisms against neurodegeneration in specific neuronal subpopulations.

A Rapid One-Pot Workflow for Sensitive Microscale Phosphoproteomics.

Compared to advancements in single-cell proteomics, phosphoproteomics sensitivity has lagged behind due to low abundance, complex sample preparation, and substantial sample input requirements. We present a simple and rapid one-pot phosphoproteomics workflow (SOP-Phos) integrated with data-independent acquisition mass spectrometry (DIA-MS) for microscale phosphoproteomic analysis. SOP-Phos adapts sodium deoxycholate based one-step lysis, reduction/alkylation, direct trypsinization, and phosphopeptide enrichment by TiO2 beads in a single-tube format. By reducing surface adsorptive losses via utilizing n-dodecyl β-d-maltoside precoated tubes and shortening the digestion time, SOP-Phos is completed within 3-4 h with a 1.4-fold higher identification coverage. SOP-Phos coupled with DIA demonstrated >90% specificity, enhanced sensitivity, lower missing values (<1%), and improved reproducibility (8%-10% CV). With a sample size-comparable spectral library, SOP-Phos-DIA identified 33,787 ± 670 to 22,070 ± 861 phosphopeptides from 5 to 0.5 μg cell lysate and 30,433 ± 284 to 6,548 ± 21 phosphopeptides from 50,000 to 2,500 cells. Such sensitivity enabled mapping key lung cancer signaling sites, such as EGFR autophosphorylation sites Y1197/Y1172 and drug targets. The feasibility of SOP-Phos-DIA was demonstrated on EGFR-TKI sensitive and resistant cells, revealing the interplay of multipathway Hippo-EGFR-ERBB signaling cascades underlying the mechanistic insight into EGFR-TKI resistance. Overall, SOP-Phos-DIA is an efficient and robust protocol that can be easily adapted in the community for microscale phosphoproteomic analysis.

The mouse multi-organ proteome from infancy to adulthood

The early-life organ development and maturation shape the fundamental blueprint for later-life phenotype. However, a multi-organ proteome atlas from infancy to adulthood is currently not available. Herein, we present a comprehensive proteomic analysis of ten mouse organs (brain, heart, lung, liver, kidney, spleen, stomach, intestine, muscle and skin) at three crucial developmental stages (1-, 4- and 8-weeks after birth) acquired using data-independent acquisition mass spectrometry. We detect and quantify 11,533 protein groups across the ten organs and obtain 115 age-related differentially expressed protein groups that are co-expressed in all organs from infancy to adulthood. We find that spliceosome proteins prevalently play crucial regulatory roles in the early-life development of multiple organs, and detect organ-specific expression patterns and sexual dimorphism. This multi-organ proteome atlas provides a fundamental resource for understanding the molecular mechanisms underlying early-life organ development and maturation.

A multi-biomarker panel for predicting Tocilizumab response in Rheumatoid arthritis patients

Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease characterized by inflammation in the synovial lining of the joints. Key inflammatory cytokines such as interleukin-6 (IL-6), TNF-α, and others play a critical role in the activation of local synovial leukocytes and the induction of chronic inflammation. Tocilizumab (TCZ), a humanized anti-IL-6 receptor monoclonal antibody, has demonstrated significant clinical efficacy in treating RA patients. However, similar to other inflammatory cytokine blockers, such as TNF-alpha inhibitors, Interleukin-1 inhibitors, or CD20 inhibitors, some patients do not respond to treatment. To address this challenge, our study employed a high-precision proteomics approach to identify protein biomarkers capable of predicting clinical responses to Tocilizumab in RA patients. Through the use of data-independent acquisition (DIA) mass spectrometry, we analyzed serum samples from both TCZ responders and non-responders to discover potential biomarker candidates. These candidates were subsequently validated using individual serum samples from two independent cohorts: a training set (N = 70) and a test set (N = 18), allowing for the development of a robust multi-biomarker panel. The constructed multi-biomarker panel demonstrated an average discriminative power of 86 % between response and non-response groups, with a high area under the curve (AUC) value of 0.84. Additionally, the panel exhibited 100 % sensitivity and 60 % specificity. Collectively, our multi-biomarker panel holds promise as a diagnostic tool to predict non-responders to TCZ treatment in RA patients.

Calib-RT: an open source python package for peptide retention time calibration in DIA mass spectrometry data.

The data independent acquisition (DIA) mass spectrometry (MS) method is increasingly popular in the field of proteomics. But the loss of the correspondence between peptide ions and their spectra in DIA makes the identification challenging. One effective approach to reduce false positive identification is to calculate the deviation between the peptide's estimated retention time (RT) and measured RT. During this process, scaling the spectral library RT into the estimated RT, known as the RT calibration, is a prerequisite for calculating the deviation. Currently, within the DIA algorithm ecosystem, there is a lack of engine-independent and readily usable RT calibration toolkits. In this work, we introduce Calib-RT, a RT calibration method tailored to the characteristics of RT data. This method can achieve the nonlinear calibration across various data scales and tolerate a certain level of noise interference. Calib-RT is expected to enrich the open source DIA algorithm toolchain and assist in the development of DIA identification algorithms. Calib-RT is released as an open source software under the MIT license and can be installed from PyPi as a python module. The source code is available on GitHub at https://github.com/chenghui03/Calib_RT.

A proteomic approach supports the clinical relevance of TAT-Cx43266-283 in glioblastoma

Glioblastoma (GBM) is the most frequent and aggressive primary brain cancer. The Src inhibitor, TAT-Cx43266-283, exerts antitumor effects in in vitro and in vivo models of GBM. Because addressing the mechanism of action is essential to translate these results to a clinical setting, in this study we carried out an unbiased proteomic approach. Data-independent acquisition mass spectrometry proteomics allowed the identification of 190 proteins whose abundance was modified by TAT-Cx43266-283. Our results were consistent with the inhibition of Src as the mechanism of action of TAT-Cx43266-283 and unveiled antitumor effectors, such as p120 catenin. Changes in the abundance of several proteins suggested that TAT-Cx43266-283 may also impact the brain microenvironment. Importantly, the proteins whose abundance was reduced by TAT-Cx43266-283 correlated with an improved GBM patient survival in clinical datasets and none of the proteins whose abundance was increased by TAT-Cx43266-283 correlated with shorter survival, supporting its use in clinical trials.

Integrated Platform for Large-Scale Quantitative Profiling of Phosphotyrosine Signaling Complexes Based on Cofractionation/Mass Spectrometry and Complex-Centric Algorithm.

The scarcity and dynamic nature of phosphotyrosine (pTyr)-modified proteins pose a challenge for researching protein complexes with pTyr modification, which are assembled through multiple protein-protein interactions. We developed an integrated complex-centric platform for large-scale quantitative profiling of pTyr signaling complexes based on cofractionation/mass spectrometry (CoFrac-MS) and a complex-centric algorithm. We initially constructed a trifunctional probe based on pTyr superbinder (SH2-S) for specifically binding and isolation of intact pTyr protein complexes. Then, the CoFrac-MS strategy was employed for the identification of pTyr protein complexes by integrating ion exchange chromatography in conjunction with data independent acquisition mass spectrometry. Furthermore, we developed a novel complex-centric algorithm for quantifying protein complexes based on the protein complex elution curve. Utilizing this algorithm, we effectively quantified 216 putative protein complexes. We further screened 21 regulated pTyr protein complexes related to the epidermal growth factor signal. Our study engenders a comprehensive framework for the intricate examination of pTyr protein complexes and presents, for the foremost occasion, a quantitative landscape delineating the composition of pTyr protein complexes in HeLa cells.

Cerebrospinal Fluid Proteome Map Reveals Molecular Signatures of Reversible Cerebral Vasoconstriction Syndrome

Reversible cerebral vasoconstriction syndrome (RCVS) is a complex neurovascular disorder characterized by repetitive thunderclap headaches and reversible cerebral vasoconstriction. The pathophysiological mechanism of this mysterious syndrome remains underexplored and there is no clinically available molecular biomarker. To provide insight into the pathogenesis of RCVS, this study reported the first landscape of dysregulated proteome of cerebrospinal fluid (CSF) in patients with RCVS (n = 21) compared to the age- and sex-matched controls (n = 20) using data-independent acquisition mass spectrometry. Protein–protein interaction and functional enrichment analysis were employed to construct functional protein networks using the RCVS proteome. An RCVS-CSF proteome library resource of 1054 proteins was established, which illuminated large groups of upregulated proteins enriched in the brain and blood-brain barrier (BBB). Personalized RCVS-CSF proteomic profiles from 17 RCVS patients and 20 controls reveal proteomic changes involving the complement system, adhesion molecules, and extracellular matrix, which may contribute to the disruption of BBB and dysregulation of neurovascular units. Moreover, an additional validation cohort validated a panel of biomarker candidates and a two-protein signature predicted by machine learning model to discriminate RCVS patients from controls with an area under the curve of 0.997. This study reveals the first RCVS proteome and a potential pathogenetic mechanism of BBB and neurovascular unit dysfunction. It also nominates potential biomarker candidates that are mechanistically plausible for RCVS, which may offer potential diagnostic and therapeutic opportunities beyond the clinical manifestations.

Proteomics of tumor and serum samples from isocitrate dehydrogenase-wildtype glioblastoma patients: is the detoxification of reactive oxygen species associated with shorter survival?

Proteomics has been little used for the identification of novel prognostic and/or therapeutic markers in isocitrate dehydrogenase (IDH)-wildtype glioblastoma (GB). In this study, we analyzed 50 tumor and 30 serum samples from short- and long-term survivors of IDH-wildtype GB (STS and LTS, respectively) by data-independent acquisition mass spectrometry (DIA-MS)-based proteomics, with the aim of identifying such markers. DIA-MS identified 5422 and 826 normalized proteins in tumor and serum samples, respectively, with only three tumor proteins and 26 serum proteins displaying significant differential expression between the STS and LTS groups. These dysregulated proteins were principally associated with the detoxification of reactive oxygen species (ROS). In particular, GB patients in the STS group had high serum levels of malate dehydrogenase 1 (MDH1) and ribonuclease inhibitor 1 (RNH1) and low tumor levels of fatty acid-binding protein 7 (FABP7), which may have enabled them to maintain low ROS levels, counteracting the effects of the first-line treatment with radiotherapy plus concomitant and adjuvant temozolomide. A blood score built on the levels of MDH1 and RNH1 expression was found to be an independent prognostic factor for survival based on the serum proteome data for a cohort of 96 IDH-wildtype GB patients. This study highlights the utility of circulating MDH1 and RNH1 biomarkers for determining the prognosis of patients with IDH-wildtype GB. Furthermore, the pathways driven by these biomarkers, and the tumor FABP7 pathway, may constitute promising therapeutic targets for blocking ROS detoxification to overcome resistance to chemoradiotherapy in potential GB STS.

Enhanced Proteomic Coverage in Tissue Microenvironment by Immune Cell Subtype Library-Assisted DIA-MS

Immune cells that infiltrate the tumor microenvironment (TME) play crucial roles in shaping cancer development and influencing clinical outcomes and therapeutic responses. However, obtaining a comprehensive proteomic snapshot of tumor-infiltrating immunity in clinical specimens is often hindered by small sample amounts and a low proportion of immune infiltrating cells in the TME. To enable in-depth and highly sensitive profiling of microscale tissues, we established an immune cell-enriched library-assisted strategy for data-independent acquisition mass spectrometry (DIA-MS). Firstly, six immune cell subtype-specific spectral libraries were established from sorted cluster of differentiation markers, CD8+, CD4+ T lymphocytes, B lymphocytes, natural killer cells, dendritic cells, and macrophages in murine mesenteric lymph nodes (MLNs), covering 7815 protein groups with surface markers and immune cell-enriched proteins. The feasibility of microscale immune proteomic profiling was demonstrated on 1 μg tissue protein from the tumor of murine colorectal cancer (CRC) models using single-shot DIA; the immune cell-enriched library increased coverage to quantify 7419 proteins compared to directDIA analysis (6978 proteins). The enhancement enabled the mapping of 841 immune function-related proteins and exclusive identification of many low-abundance immune proteins, such as CD1D1, and CD244, demonstrating high sensitivity for immune landscape profiling. This approach was used to characterize the MLNs in CRC models, aiming to elucidate the mechanism underlying their involvement in cancer development within the TME. Even with a low percentage of immune cell infiltration (0.25–3%) in the tumor, our results illuminate downregulation in the adaptive immune signaling pathways (such as C-type lectin receptor signaling, and chemokine signaling), T cell receptor signaling, and Th1/Th2/Th17 cell differentiation, suggesting an immunosuppressive status in MLNs of CRC model. The DIA approach using the immune cell-enriched libraries showcased deep coverage and high sensitivity that can facilitate illumination of the immune proteomic landscape for microscale samples.

Proteomic identification of exosomes derived from psoriasis cells using data-independent acquisition mass spectrometry.

Psoriasis is renowned for its chronic nature and complex pathophysiology, with exosomes playing a crucial regulatory role within it. However, the proteomic composition of exosomes extracted from psoriasis cells remains largely unexplored. This study aimed to analyze the proteomic makeup of exosomes derived from psoriasis-model keratinocytes and compare it with that of normal controls, with the goal of identifying specific proteins that could aid in understanding the disease's pathology and potentially serve as biomarkers or therapeutic targets. The normal cultured keratinocyte line HaCaT served as the control group, while a concentration of 10 ng/mL of TNF-α was utilized to stimulate HaCaT cells and induce the formation of psoriasis model cells for the test group. Exosomes were extracted and prepared from the culture supernatant using the magnetic bead method, and their identity was confirmed through transmission electron microscopy, nanoparticle tracking analysis, and Western blotting. Data-independent acquisition (DIA) mass spectrometry was employed to detect the protein composition of exosomes, followed by GO, KEGG, Reactome, and PPI analyses. The analysis revealed a total of 2796 proteins within the exosomes, with 131 showing significant differential expression between the test and control groups. Notably, this study identified the proteins ADO, CBX1, and MIF within the exosomes derived from psoriasis model cells for the first time, highlighting their potential roles in angiogenesis, epigenetic regulation, and inflammatory responses in psoriasis. Several differentially expressed proteins identified in the KEGG enrichment analysis were implicated in immune infiltration pathways, keratinocyte-regulating pathways, angiogenesis pathways, and inflammation pathways. The identification of unique proteins within exosomes derived from psoriasis-model cells offers novel insights into the molecular mechanisms underlying psoriasis. These findings pave the way for further research into the biological functions of these exosomal proteins and their potential utility in diagnosing and treating psoriasis.

Improved drug target deconvolution with PISA-DIA using an extended, overlapping temperature gradient.

Thermal proteome profiling (TPP) is a powerful tool for drug target deconvolution. Recently, data-independent acquisition mass spectrometry (DIA-MS) approaches have demonstrated significant improvements to depth and missingness in proteome data, but traditional TPP (a.k.a. CEllular Thermal Shift Assay "CETSA") workflows typically employ multiplexing reagents reliant on data-dependent acquisition (DDA). Herein, we introduce a new experimental design for the Proteome Integral Solubility Alteration via label-free DIA approach (PISA-DIA). We highlight the proteome coverage and sensitivity achieved by using multiple overlapping thermal gradients alongside DIA-MS, which maximizes efficiencies in PISA sample concatenation and safeguards against missing protein targets that exist at high melting temperatures. We demonstrate our extended PISA-DIA design has superior proteome coverage as compared to using tandem-mass tags (TMT) necessitating DDA-MS analysis. Importantly, we demonstrate our PISA-DIA approach has the quantitative and statistical rigor using A-1331852, a specific inhibitor of BCL-xL. Due to the high melt temperature of this protein target, we utilized our extended multiple gradient PISA-DIA workflow to identify BCL-xL. We assert our novel overlapping gradient PISA-DIA-MS approach is ideal for unbiased drug target deconvolution, spanning a large temperature range whilst minimizing target dropout between gradients, increasing the likelihood of resolving the protein targets of novel compounds.

An MSstats workflow for detecting differentially abundant proteins in large-scale data-independent acquisition mass spectrometry experiments with FragPipe processing.

Technological advances in mass spectrometry and proteomics have made it possible to perform larger-scale and more-complex experiments. The volume and complexity of the resulting data create major challenges for downstream analysis. In particular, next-generation data-independent acquisition (DIA) experiments enable wider proteome coverage than more traditional targeted approaches but require computational workflows that can manage much larger datasets and identify peptide sequences from complex and overlapping spectral features. Data-processing tools such as FragPipe, DIA-NN and Spectronaut have undergone substantial improvements to process spectral features in a reasonable time. Statistical analysis tools are needed to draw meaningful comparisons between experimental samples, but these tools were also originally designed with smaller datasets in mind. This protocol describes an updated version of MSstats that has been adapted to be compatible with large-scale DIA experiments. A very large DIA experiment, processed with FragPipe, is used as an example to demonstrate different MSstats workflows. The choice of workflow depends on the user's computational resources. For datasets that are too large to fit into a standard computer's memory, we demonstrate the use of MSstatsBig, a companion R package to MSstats. The protocol also highlights key decisions that have a major effect on both the results and the processing time of the analysis. The MSstats processing can be expected to take 1-3 h depending on the usage of MSstatsBig. The protocol can be run in the point-and-click graphical user interface MSstatsShiny or implemented with minimal coding expertise in R.