Abstract Accurate, reproducible interpretation of kidney allograft biopsies is critical for the diagnosis of graft injury and for informing prognosis and clinical management. The international Banff classification is a consensus diagnostic system based on semiquantitative histological lesion scoring according to either lesion extent or severity in kidney transplant biopsies. However, pathologist scoring is limited by interobserver variability, constrained scalability, and the inherent nature of the scoring system itself. Here we present BanffNET, a weakly supervised, probabilistic deep learning framework that combines self-supervised feature extraction with a novel Bayesian multiple-instance learning framework to predict (continuously) the full spectrum of Banff lesion scores directly from whole-slide images (WSIs). Using lesion-specific aggregation functions tailored to localized (modeling severity) and diffuse histological lesions (modeling extent), BanffNET generates interpretable, patch-level probability maps and calibrated slide-level scores. BanffNET’s performance was assessed relative to consensus, biological correlates of rejection and clinical outcome, demonstrating superior consistency, transportability and generalization. Trained on 7,533 WSIs from three cohorts, BanffNET demonstrates consistent performance on 12,687 WSIs across five external validation cohorts, matching or surpassing individual expert pathologists across lesion assessments. BanffNET scores align more closely than pathologist Banff scores with molecular profiles of rejection, offering an objective, transparent, biologically grounded framework for computational pathology with relevance beyond kidney transplantation.

MS2 spectral similarity is fundamental to interpreting LC-HRMS-NTA data. Beyond the commonly used cosine similarity, a wide range of alternative metrics, including distance-, probability-, and machine-learning-based approaches, provide different perspectives on spectral matching. In this study, we extracted 8290 sub-data sets from publicly and commercially available LC-HRMS/MS libraries (MassBank, MoNa, GNPS, and NIST), each containing spectra sharing a precursor m/z within a 5 mDa tolerance. We evaluated 20 similarity metrics by constructing single-generation local molecular networks. Most metrics failed to produce pure networks, achieving complete resolution in only ≈8% of cases at the recommended 0.7 threshold and ≈20% even when individually optimized. The number of fragment ions showed little influence on network resolution. Instead, performance was driven primarily by the similarity metric and thresholding behavior, indicating that library matching alone rarely supports identification confidence above level 3. These findings highlight the need for cumulative strategies that integrate multiple similarity perspectives and orthogonal information, such as retention time or index.

Accurate pathological assessment of tissue samples is key to diagnosis and optimal treatment decisions. Traditional pathology techniques suffer from subjectivity, resulting in interobserver variability and limitations in identifying subtle molecular changes. Omics approaches provide both molecular evidence and unbiased classification, which increases the quality and reliability of final tissue assessment. Here, we focus on mass spectrometry (MS)-based proteomics as a method to reveal biopsy tissue differences. For MS data to be useful, molecular information collected from formalin-fixed paraffin-embedded-biopsy tissues needs to be consistent and quantitatively accurate and contain sufficient clinically relevant molecular information. Therefore, we developed an MS-based workflow and assessed the analytical repeatability on 36 kidney biopsies, ultimately analyzing molecular differences and similarities of over 5000 proteins per biopsy. An additional 301 transplant biopsies were analyzed to understand other physical parameters, including the effects of tissue size, standing time in the autosampler, and the effect on clinical validation. MS data were acquired using data-independent acquisition, which provides gigabytes of data per sample in the form of high proteome representation, at exquisitely high quantitative accuracy. The formalin-fixed paraffin-embedded-based method optimized here provides a coefficient of variation <20%, analyzing >5000 proteins per sample in parallel. We also observed that tissue thickness does affect the outcome of the data quality: 5 μm sections show more variation in the same sample than 10 μm sections. Notably, our data reveal an excellent agreement for the relative abundance of known protein biomarkers with kidney transplantation lesion scores used in clinical pathological diagnostics. The findings presented here demonstrate the ease, speed, and robustness of the MS-based method, where a wealth of molecular data from minute tissue sections can be used to assist and expand pathology, and possibly reduce the interobserver variability.

We introduce a new ionization technique for compact, portable mass spectrometers. It consists of a syringe with sample liquid capped by a self-ionizing spray nozzle containing a microfabricated nozzle chip. Interaction of the sample liquid with the nozzle wall results in electrical charging without the need for electronics. Elaborate cleaning procedures are redundant when disposable syringes and mass-fabricated spray nozzles are used. This self-named electroless spray ionization (ELI) technique shows comparable performance to conventional ionization techniques. In contrast to commonly used electrospray ionization, ELI exhibits excellent ionization efficiency for low-conductive solutions such as water or acetonitrile. Due to its compact size and the absence of high-voltage electronics, it can also be readily integrated in other ionization sources. Besides reviewing the main properties of ELI, we showcase the technique's potential for two on-site, ambient mass spectroscopy applications: perfume fingerprinting and fast screening of fungicides on citrus fruits.

The human exposome is represented by a vast number of chemicals, the fate and behavior of which remain largely unexplored. While modeling approaches are commonly employed to address this challenge, there is a recognized need for alternative molecular representations, such as molecular fingerprints. However, existing algorithms for computing molecular fingerprints may incorporate irrelevant or insufficient information for accurate activity prediction. In this study, we present an algorithm designed to optimize molecular fingerprints. This algorithm combines the relevant bits of information, aiming to enrich the final fingerprint for predicting specific behavioral properties. To achieve this, relevant variables (i.e. bits) for prediction were collected from six non-hashed fingerprints and fused into a master fingerprint. We used fish toxicity as a proof of concept. The RFR model was developed based on the master fingerprint. It demonstrated comparable results to conventional descriptor-based models with R$^2$ $\approx 0.9$ for the training set and R$^2$ $\approx 0.6$ for the test set. The molecular fingerprints have the advantage of being consistent and interpretable. Consequently, we were able to confirm the relevance of variables to the toxicity prediction. The final model outperformed each of the models based on individual fingerprints in the number of chemicals with prediction error, that fell in the range of +/- one standard deviation of residuals. The number of cases with the lower prediction error was on average four times higher for the master fingerprint-based model. The algorithm developed for optimizing molecular fingerprints is universal and can be applied to various case studies.

Supplementary figures S1-5, materials and methods. 1) Supplementary figures and associated legends: - Figure S1. ERK silencing decreases breast cancer cell migration and invasion but does not influence cell proliferation. - Figure S2. ERK2 does not interact with actin or keratin 8 and ERK2 phosphorylation is supported by vimentin but not actin. - Figure S3. Fluorescence recovery of wild-type Slug-GFP after photobleaching (FRAP). - Figure S4. Mass spectrometric identification of ERK1/2-dependent phosphorylation sites on Slug and validation using a newly generated Slug phospho-serine-87 antibody. - Figure S5. The phosphorylation status of Slug does not influence repression of E-Cadherin, Slug nuclear localisation or stability. 2) Supplementary materials and methods 3) Supplementary references

Abstract Epithelial–mesenchymal transition (EMT) in cells is a developmental process adopted during tumorigenesis that promotes metastatic capacity. In this study, we advance understanding of EMT control in cancer cells with the description of a novel vimentin–ERK axis that regulates the transcriptional activity of Slug (SNAI2). Vimentin, ERK, and Slug exhibited overlapping subcellular localization in clinical specimens of triple-negative breast carcinoma. RNAi-mediated ablation of these gene products inhibited cancer cell migration and cell invasion through a laminin-rich matrix. Biochemical analyses demonstrated direct interaction of vimentin and ERK, which promoted ERK activation and enhanced vimentin transcription. Consistent with its role as an intermediate filament, vimentin acted as a scaffold to recruit Slug to ERK and promote Slug phosphorylation at serine-87. Site-directed mutagenesis established a requirement for ERK-mediated Slug phosphorylation in EMT initiation. Together, these findings identified a pivotal step in controlling the ability of Slug to organize hallmarks of EMT. Cancer Res; 75(11); 2349–62. ©2015 AACR.

The application of proteomics to fresh frozen (FF) and formalin-fixed paraffin-embedded (FFPE) human tissues is an important development spurred on by requests from stakeholder groups in clinical fields. One objective is to complement current diagnostic methods with new specific molecular information. An important goal is to achieve adequate and consistent protein recovery across and within large-scale studies. Here, we describe development of several protocols incorporating mass spectrometry compatible detergents, including Rapigest, PPS, and ProteaseMax. Methods were applied on 4 and 15 μm thick FF tissues, and 4 μm thick FFPE tissues. We evaluated sensitivity and repeatability of the methods and found that the protocol containing Rapigest enabled detection of 630 proteins from FF tissue of 1 mm2 and 15 μm thick, whereas 498 and 297 proteins were detected with the protocols containing ProteaseMax and PPS, respectively. Surprisingly, PPS-containing buffer showed good extraction of the proteins from 4 μm thick FFPE tissue with the average of 270 protein identifications (1 mm2), similar to the results on 4 μm thick FF. Moreover, we found that temperature increases during incubation with urea on 4 μm thick FF tissue revealed a decrease in the number of identified proteins and increase in the number of the carbamylated peptides.

BACKGROUND:Histopathological assessment of transplant biopsies is currently the standard method to diagnose allograft rejection and can help guide patient management, but it is one of the most challenging areas of pathology, requiring considerable expertise, time, and effort. We aimed to analyse the utility of deep learning to preclassify histology of kidney allograft biopsies into three main broad categories (ie, normal, rejection, and other diseases) as a potential biopsy triage system focusing on transplant rejection. METHODS:We performed a retrospective, multicentre, proof-of-concept study using 5844 digital whole slide images of kidney allograft biopsies from 1948 patients. Kidney allograft biopsy samples were identified by a database search in the Departments of Pathology of the Amsterdam UMC, Amsterdam, Netherlands (1130 patients) and the University Medical Center Utrecht, Utrecht, Netherlands (717 patients). 101 consecutive kidney transplant biopsies were identified in the archive of the Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany. Convolutional neural networks (CNNs) were trained to classify allograft biopsies as normal, rejection, or other diseases. Three times cross-validation (1847 patients) and deployment on an external real-world cohort (101 patients) were used for validation. Area under the receiver operating characteristic curve (AUROC) was used as the main performance metric (the primary endpoint to assess CNN performance). FINDINGS:Serial CNNs, first classifying kidney allograft biopsies as normal (AUROC 0·87 [ten times bootstrapped CI 0·85-0·88]) and disease (0·87 [0·86-0·88]), followed by a second CNN classifying biopsies classified as disease into rejection (0·75 [0·73-0·76]) and other diseases (0·75 [0·72-0·77]), showed similar AUROC in cross-validation and deployment on independent real-world data (first CNN normal AUROC 0·83 [0·80-0·85], disease 0·83 [0·73-0·91]; second CNN rejection 0·61 [0·51-0·70], other diseases 0·61 [0·50-0·74]). A single CNN classifying biopsies as normal, rejection, or other diseases showed similar performance in cross-validation (normal AUROC 0·80 [0·73-0·84], rejection 0·76 [0·66-0·80], other diseases 0·50 [0·36-0·57]) and generalised well for normal and rejection classes in the real-world data. Visualisation techniques highlighted rejection-relevant areas of biopsies in the tubulointerstitium. INTERPRETATION:This study showed that deep learning-based classification of transplant biopsies could support pathological diagnostics of kidney allograft rejection. FUNDING:European Research Council; German Research Foundation; German Federal Ministries of Education and Research, Health, and Economic Affairs and Energy; Dutch Kidney Foundation; Human(e) AI Research Priority Area of the University of Amsterdam; and Max-Eder Programme of German Cancer Aid.

The sensitive surfaces of many unvarnished 20th century oil paintings are of great concern for conservators and collection keepers. They may show degradation problems such as paint delamination, dripping, and soft and sticky paint and pose challenges for cleaning due to solvent sensitivity. We report for the first time the use of an innovative ambient ionization technique, surface acoustic wave nebulization-mass spectrometry (SAWN-MS), for the identification and characterization of fatty acids, dicarboxylic species and glycerides in water-sensitive modern oil paints. The composition of 10 relevant Winsor and Newton 1964-1965 paint swatches that present different degrees of water sensitivity and two paint samples from a painting by the British artist Francis Bacon were studied. Principal component analysis was used for SAWN-MS data classification. Electrospray ionization (ESI)-MS was used as control method, specifically to compare the obtained ratios of markers of interest by the two ionization techniques. The results obtained by both ESI-MS and SAWN-MS are correlated and discussed in a broader context including the information on the oil media obtained by gas chromatography (GC-MS) and also on the inorganic materials and salts characterized using a combination of methods in previous reports on samples from the same manufacturer. SAWN-MS was found to be a suitable tool for the determination of soluble organic constituents present in the paints. The method provides an indication of the level of oxidation and hydrolysis of the paint film by monitoring specific markers such as free palmitic and stearic acids, azelaic acid, monoacylglycerols, and diacylglycerols. The data showed that a higher level of water sensitivity coupled with a high level of oxidation and hydrolysis is linked to higher dicarboxylic acid, diacyl- and triacylglyceride content and lower levels of short chain fatty acids. The data obtained by SAWN-MS provided a good correlation between the monitored species and the degree of water sensitivity.

Protein phosphatase 2A (PP2A) critically regulates cell signaling and is a human tumor suppressor. PP2A complexes are modulated by proteins such as cancerous inhibitor of protein phosphatase 2A (CIP2A), protein phosphatase methylesterase 1 (PME-1), and SET nuclear proto-oncogene (SET) that often are deregulated in cancers. However, how they impact cellular phosphorylation and how redundant they are in cellular regulation is poorly understood. Here, we conducted a systematic phosphoproteomics screen for phosphotargets modulated by siRNA-mediated depletion of CIP2A, PME-1, and SET (to reactivate PP2A) or the scaffolding A-subunit of PP2A (PPP2R1A) (to inhibit PP2A) in HeLa cells. We identified PP2A-modulated targets in diverse cellular pathways, including kinase signaling, cytoskeleton, RNA splicing, DNA repair, and nuclear lamina. The results indicate nonredundancy among CIP2A, PME-1, and SET in phosphotarget regulation. Notably, PP2A inhibition or reactivation affected largely distinct phosphopeptides, introducing a concept of nonoverlapping phosphatase inhibition- and activation-responsive sites (PIRS and PARS, respectively). This phenomenon is explained by the PPP2R1A inhibition impacting primarily dephosphorylated threonines, whereas PP2A reactivation results in dephosphorylation of clustered and acidophilic sites. Using comprehensive drug-sensitivity screening in PP2A-modulated cells to evaluate the functional impact of PP2A across diverse cellular pathways targeted by these drugs, we found that consistent with global phosphoproteome effects, PP2A modulations broadly affect responses to more than 200 drugs inhibiting a broad spectrum of cancer-relevant targets. These findings advance our understanding of the phosphoproteins, pharmacological responses, and cellular processes regulated by PP2A modulation and may enable the development of combination therapies.

DEEPGRAFT consortium. Introduction: The gold standard and currently only means to specifically classify diseases of renal allografts is a histopathological assessment of a renal transplant biopsies according to the latest Banff criteria. Some disagreements, however, exist among the transplant pathologists on certain lesions and even diagnoses, which might potentially result in differences in treatment and subsequent graft outcome. Deep learning using convolutional neural networks (CNNs), is a highly effective computational approach, able to recognize patterns in histopathological whole slide images (WSIs) with the potential of automated and highly reproducible classification of post-transplant diseases. The aim of the DEEPGRAFT study is to develop, deploy and validate CNNs to improve renal transplant biopsy-based disease classification and prognostication. Materials and Methods: DEEPGRAFT is an international multicenter consortium that includes the protocol and for cause biopsies from three centers in Europe (Amsterdam UMC, UMC Utrecht and RWTH Aachen). For each biopsy, the H&E, Silver and PAS staining was digitized (N = 3159 WSIs in total). Cases were labeled to the following classes according to the pathology reports: 1) T cell-mediated rejection; TCMR, 2) antibody-mediated rejection; ABMR, 3) mixed rejection, 4) viral nephropathy, 5) no specific allograft pathology, and 6) other diseases of the allograft. A shufflenet was trained by transfer learning and was validated by patient-level three-fold cross-validation. Area-under-the receiver operating characteristics (AUROC) was used as the primary endpoint. We trained a model with all three stains and compared it to a model trained on only the PAS staining as input. The complete tissue (cortex and medulla) was used as input. Results: The CNN trained on the PAS stains had the highest discrimination index for stable graft histology (AUROC = 0.83), which improved when all three stains were included (AUROC = 0.88), see Figure.Discrimination indices for TCMR, ABMR and mixed rejection ranged from 0.67 – 0.77, respectively, and improved to 0.69 – 0.82. Noteworthy, the CNN was able to discriminate viral nephropathies from the rejection classes (AUROC = 0.71), even though no additional immunohistochemistry was used as input for the networks. Discussion: We have shown the potential of a deep learning algorithm to discriminate between clinically relevant diagnoses only from histopathological images. The integration of several histological stainings improved the performance. Our study provides the first example of a large multicenter trial examining the potential of artificial intelligence on non-tumor pathology. Next, we will further extend and refine the algorithm in even larger cohorts and using additional data integration. Conclusion: Deep convolutional neural networks have the potential to reproducibly classify transplant histopathology, even without human expert annotation of individual lesions. Dutch Kidney Foundation Kolff Junior Postdoc grant, 17OKG23. University of Amsterdam, Human(e) AI Research Priority Area Seed grant.

In many applications where small, similar-sized droplets are needed, ultrasonic nebulizers are employed. Little is known about the mechanism of nebulization, for example about what determines the median droplet size. Even less understood, is the droplet size distribution, which is often simply fitted with a log-normal distribution or assumed to be very narrow. We perform the first systematic study of droplet size distributions for different nebulizer technologies, showing that these distributions can be very well fitted with distributions found for sprays, where the size distribution is completely determined by the corrugation of ligaments and the distribution of ligament sizes. In our case, breakup is believed to be due to pinch-off of Faraday instabilities. The droplet size distribution is then set by the distribution of wavelengths of the standing capillary waves and the roughness of the pinch-off ligaments. We show that different nebulizer technologies produce different size distributions, which we relate to (variation in) wavelengths of the waves that contribute to the droplet formation. We further show that the median droplet size scales with the capillary wavelength, with a proportionality constant that depends only slightly on the type of nebulizer, despite order-of-magnitude differences in other parameters.

When considering incident investigations and security checks focused on energetic materials, there is an ongoing need for rapid, on-scene chemical identification. Currently applied methods are not capable of meeting all requirements, and hence, portable mass spectrometry is an interesting alternative although many instrumental challenges still exist. To be able to analyze explosives with mass spectrometry outside the traditional laboratory, suitable ambient ionization methods need to be developed. Ideally such methods are also easily implemented in the field requiring limited to no power sources, gas supplies, flow controllers, and heating devices. For this reason, the potential of SAWN (surface acoustic wave nebulization) for the ambient ionization and subsequent mass spectrometric (MS) analysis of organic explosives was investigated in this study. Excellent sensitivity was observed for nitrate-based organic explosives when operating the MS in negative mode. No dominant adduct peaks were observed for the peroxides TATP and HMTD with SAWN-MS in positive mode. The MS spectra indicate extensive fragmentation of the peroxide explosives even under the mild ionization conditions provided by SAWN. The potential of SAWN-MS was demonstrated with the correct identification of nitrate-based organic explosives in pre- and post-explosion case samples in only a fraction of the time and effort required for the regular laboratory analysis. Results show that SAWN-MS can convincingly identify intact organic energetic compounds and mixtures but that sensitivity is not always sufficient to detect traces of explosives in post-explosion residues.

Background Progression of prostate cancer from benign local tumors to metastatic carcinomas is a multistep process. Here we have investigated the signaling pathways that support migration and invasion of prostate cancer cells, focusing on the role of the NFATC1 transcription factor and its post-translational modifications. We have previously identified NFATC1 as a substrate for the PIM1 kinase and shown that PIM1-dependent phosphorylation increases NFATC1 activity without affecting its subcellular localization. Both PIM kinases and NFATC1 have been reported to promote cancer cell migration, invasion and angiogenesis, but it has remained unclear whether the effects of NFATC1 are phosphorylation-dependent and which downstream targets are involved. Methods We used mass spectrometry to identify PIM1 phosphorylation target sites in NFATC1, and analysed their functional roles in three prostate cancer cell lines by comparing phosphodeficient mutants to wild-type NFATC1. We used luciferase assays to determine effects of phosphorylation on NFAT-dependent transcriptional activity, and migration and invasion assays to evaluate effects on cell motility. We also performed a microarray analysis to identify novel PIM1/NFATC1 targets, and validated one of them with both cellular expression analyses and in silico in clinical prostate cancer data sets. Results Here we have identified ten PIM1 target sites in NFATC1 and found that prevention of their phosphorylation significantly decreases the transcriptional activity as well as the pro-migratory and pro-invasive effects of NFATC1 in prostate cancer cells. We observed that also PIM2 and PIM3 can phosphorylate NFATC1, and identified several novel putative PIM1/NFATC1 target genes. These include the ITGA5 integrin, which is differentially expressed in the presence of wild-type versus phosphorylation-deficient NFATC1, and which is coexpressed with PIM1 and NFATC1 in clinical prostate cancer specimens. Conclusions Based on our data, phosphorylation of PIM1 target sites stimulates NFATC1 activity and enhances its ability to promote prostate cancer cell migration and invasion. Therefore, inhibition of the interplay between PIM kinases and NFATC1 may have therapeutic implications for patients with metastatic forms of cancer. Graphical abstract

Melanoma of the skin is the sixth most common type of cancer in Europe and accounts for 3.4% of all diagnosed cancers. More alarming is the degree of recurrence that occurs with approximately 20% of patients lethally relapsing following treatment. Malignant melanoma is a highly aggressive skin cancer and metastases rapidly extend to the regional lymph nodes (stage 3) and to distal organs (stage 4). Targeted oncotherapy is one of the standard treatment for progressive stage 4 melanoma, and BRAF inhibitors (e.g. vemurafenib, dabrafenib) combined with MEK inhibitor (e.g. trametinib) can effectively counter BRAFV600E-mutated melanomas. Compared to conventional chemotherapy, targeted BRAFV600E inhibition achieves a significantly higher response rate. After a period of cancer control, however, most responsive patients develop resistance to the therapy and lethal progression. The many underlying factors potentially causing resistance to BRAF inhibitors have been extensively studied. Nevertheless, the remaining unsolved clinical questions necessitate alternative research approaches to address the molecular mechanisms underlying metastatic and treatment-resistant melanoma. In broader terms, proteomics can address clinical questions far beyond the reach of genomics, by measuring, i.e. the relative abundance of protein products, post-translational modifications (PTMs), protein localisation, turnover, protein interactions and protein function. More specifically, proteomic analysis of body fluids and tissues in a given medical and clinical setting can aid in the identification of cancer biomarkers and novel therapeutic targets. Achieving this goal requires the development of a robust and reproducible clinical proteomic platform that encompasses automated biobanking of patient samples, tissue sectioning and histological examination, efficient protein extraction, enzymatic digestion, mass spectrometry-based quantitative protein analysis by label-free or labelling technologies and/or enrichment of peptides with specific PTMs. By combining data from, e.g. phosphoproteomics and acetylomics, the protein expression profiles of different melanoma stages can provide a solid framework for understanding the biology and progression of the disease. When complemented by proteogenomics, customised protein sequence databases generated from patient-specific genomic and transcriptomic data aid in interpreting clinical proteomic biomarker data to provide a deeper and more comprehensive molecular characterisation of cellular functions underlying disease progression. In parallel to a streamlined, patient-centric, clinical proteomic pipeline, mass spectrometry-based imaging can aid in interrogating the spatial distribution of drugs and drug metabolites within tissues at single-cell resolution. These developments are an important advancement in studying drug action and efficacy in vivo and will aid in the development of more effective and safer strategies for the treatment of melanoma. A collaborative effort of gargantuan proportions between academia and healthcare professionals has led to the initiation, establishment and development of a cutting-edge cancer research centre with a specialisation in melanoma and lung cancer. The primary research focus of the European Cancer Moonshot Lund Center is to understand the impact that drugs have on cancer at an individualised and personalised level. Simultaneously, the centre increases awareness of the relentless battle against cancer and attracts global interest in the exceptional research performed at the centre.

Tissues and biofluids are important sources of information used for the detection of diseases and decisions on patient therapies. There are several accepted methods for preservation of tissues, among which the most popular are fresh-frozen and formalin-fixed paraffin embedded methods. Depending on the preservation method and the amount of sample available, various specific protocols are available for tissue processing for subsequent proteomic analysis. Protocols are tailored to answer various biological questions, and as such vary in lysis and digestion conditions, as well as duration. The existence of diverse tissue-sample protocols has led to confusion in how to choose the best protocol for a given tissue and made it difficult to compare results across sample types. Here, we summarize procedures used for tissue processing for subsequent bottom-up proteomic analysis. Furthermore, we compare protocols for their variations in the composition of lysis buffers, digestion procedures, and purification steps. For example, reports have shown that lysis buffer composition plays an important role in the profile of extracted proteins: the most common are tris(hydroxymethyl)aminomethane, radioimmunoprecipitation assay, and ammonium bicarbonate buffers. Although, trypsin is the most commonly used enzyme for proteolysis, in some protocols it is supplemented with Lys-C and/or chymotrypsin, which will often lead to an increase in proteome coverage. Data show that the selection of the lysis procedure might need to be tissue-specific to produce distinct protocols for individual tissue types. Finally, selection of the procedures is also influenced by the amount of sample available, which range from biopsies or the size of a few dozen of mm2 obtained with laser capture microdissection to much larger amounts that weight several milligrams.

The impact of solvent exposure on oil paintings and the differences between solvent application methods are longstanding topics in cleaning studies. Solvent exposure is ideally kept to a minimum, because solvent swelling can lead to the extraction and displacement of reactive paint components. In particular, important concerns are fatty acids displacement resulting in metal soap formation and embrittlement of paint due to solvent exposure. In this study, the extraction of a saturated fatty acid (SFA) marker and the formation of zinc soaps were monitored to measure the impact of solvent cleaning on tailored bilayer model systems for aged oil paint. Three methods of solvent application were compared: cotton swab, rigid gel and Evolon tissue (with different solvent loading). The samples were analysed by surface acoustic wave nebulization mass spectrometry (SAWN-MS) and thermally-assisted hydrolysis and methylation pyrolysis gas chromatography mass spectrometry (THM-Py-GC/MS) by comparing the calculated margaric:palmitic acid ( C_17:C_16 ) ratio determined in the extracts (taken from the swab, gel or Evolon tissue). We conclude that both swab cleaning and squeezed Evolon tissue application result in comparable SFA extraction. The rigid gel and Evolon with controlled solvent-loading limit the amount of SFA extraction. The distribution of C_17 after solvent application was visualised using static Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) on cross sections, showing that C_17 redistribution took place in all cases where solvent was applied. Crystalline zinc soaps formation was not observed after 5 min of ethanol exposure in the embedded cross-sections with imaging ATR-FTIR, indicating that solvent exposure does not immediately trigger the formation of crystalline metal soaps. However, significant zinc soap formation was found after 30 min of ethanol exposure using Evolon tissue without controlled loading. This study contributes to a better understanding of the impact of different methods of solvent application on oil paintings and highlights important differences between these methods.

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Systemic understanding of protein phosphatase 2A (PP2A)-regulated cellular processes is still at infancy. Here, we present mass-spectrometry analysis of phospho-targets (dephosphorylome) regulated by PP2A modulation. In addition to PP2A-regulated processes and targets, the data reveal important general concepts and rules related to PP2A-mediated phosphoregulation. These include the unidirectionality paradigm of regulation of phosphorylation, and differential spatial distribution of kinase-and phosphatase-dominated phosphotargets. Data also present first systemic analysis of targets of PP2A-modulating oncoproteins, CIP2A, PME-1, and SET; including targets via which PP2A may coordinately regulate activities of cancer drivers and tumor suppressors such as MYC or TP53. To validate functional utility of this dataset, PP2A dephosphorylome activity was correlated with cancer cell responses to over 300 drugs. Notably, we find that cancer therapy responses can be broadly classified based on PP2A dephosphorylome activity, both in quantitative and qualitative manner. In summary, our data characterize rules by which PP2A coordinate cancer cell phosphosignaling and drug responses. The results also may also direct the use of emerging pharmacological approaches for PP2A activity modulation in human diseases.