Tumor protein D52 (TPD52): A novel B-cell/plasma-cell molecule with unique expression pattern and Ca2+-dependent association with annexin VI

We generated a murine monoclonal antibody (B28p) detecting an antigenic determinant shared by the immunoglobulin superfamily receptor translocation-associated 1 (IRTA1) receptor (the immunogen used to raise B28p) and an unrelated 28-kDa protein that was subsequently subjected to extensive characterization. The expression of the 28-kDa protein in normal lymphohematopoietic tissues was restricted to B cells and plasma cells and clearly differed from that expected for IRTA1 (selectively expressed by mucosa-associated lymphoid tissue [MALT] marginal zone B cells). Two-dimensional polyacrylamide gel electrophoresis (2D-PAGE)/mass-spectrometry analysis identified the 28-kDa protein as human tumor protein D52 (TPD52), whose expression had been previously described only in normal and neoplastic epithelia. Specific B28p reactivity with TPD52 was confirmed by immunostaining/immunoblotting of TPD52-transfected cells. TPD52 expression pattern in normal and neoplastic B cells was unique. In fact, unlike other B-cell molecules (paired box 5 [PAX5], CD19, CD79a, CD20, CD22), which are down-regulated during differentiation from B cells to plasma cells, TPD52 expression reached its maximum levels at the plasma cell stage. In the Thiel myeloma cell line, TPD52 bound to annexin VI in a Ca2+-dependent manner, suggesting that these molecules may act in concert to regulate secretory processes in plasma cells, similarly to what was observed in pancreatic acinar cells. Finally, the anti-TPD52 monoclonal antibody served as a valuable tool for the diagnosis of B-cell malignancies

Deep learning-based classification of kidney transplant pathology: a retrospective, multicentre, proof-of-concept study

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 [0.50-4.74]). 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. Copyright (C) 2021 The Author(s). Published by Elsevier Ltd.Immunopathology of vascular and renal diseases and of organ and celltransplantationIP

"4D Biology for health and disease" workshop report

The "4D Biology Workshop for Health and Disease", held on 16-17th ofMarch 2010 in Brussels, aimed at finding the best organising principlesfor large-scale proteomics, interactomics and structural genomics/biology initiatives, and setting the vision for future high-throughputresearch and large-scale data gathering in biological and medical science.Major conclusions of the workshop include the following. (i)Development of new technologies and approaches to data analysis iscrucial. Biophysical methods should be developed that span a broadrange of time/spatial resolution and characterise structures andkinetics of interactions. Mathematics, physics, computational andengineering tools need to be used more in biology and new tools needto be developed. (ii) Database efforts need to focus on improveddefinitions of ontologies and standards so that system-scale data andassociated metadata can be understood and shared efficiently. (iii)Research infrastructures should play a key role in fosteringmultidisciplinary research, maximising knowledge exchange betweendisciplines and facilitating access to diverse technologies. (iv)Understanding disease on a molecular level is crucial. Systemapproaches may represent a new paradigm in the search for biomarkersand new targets in human disease. (v) Appropriate education andtraining should be provided to help efficient exchange of knowledgebetween theoreticians, experimental biologists and clinicians. Theseconclusions provide a strong basis for creating major possibilities inadvancing research and clinical applications towards personalisedmedicine.Biophysical Structural Chemistr

Surface Acoustic Wave Nebulisation Mass Spectrometry for the Fast and Highly Sensitive Characterisation of Synthetic Dyes in Textile Samples

Surface acoustic wave nebulisation (SAWN) mass spectrometry (MS) is a method to generate gaseous ions compatible with direct MS of minute samples at femtomole sensitivity. To perform SAWN, acoustic waves are propagated through a LiNbO3 sampling chip, and are conducted to the liquid sample, which ultimately leads to the generation of a fine mist containing droplets of nanometre to micrometre diameter. Through fission and evaporation, the droplets undergo a phase change from liquid to gaseous analyte ions in a non-destructive manner. We have developed SAWN technology for the characterisation of organic colourants in textiles. It generates electrospray-ionisation-like ions in a non-destructive manner during ionisation, as can be observed by the unmodified chemical structure. The sample size is decreased by tenfold to 1000-fold when compared with currently used liquid chromatography-MS methods, with equal or better sensitivity. This work underscores SAWN-MS as an ideal tool for molecular analysis of art objects as it is non-destructive, is rapid, involves minimally invasive sampling and is more sensitive than current MS-based methods

MSiMass list: A public database of identifications for protein MALDI MSI.

The clinical application of mass spectrometry imaging has developed into a sizeable sub-discipline of proteomics and metabolomics because its seamless integration with pathology enables biomarkers and biomarker profiles to be determined that can aid patient and disease stratification (diagnosis, prognosis and response to therapy). Confident identification of the discriminating peaks remains a challenge owing to the presence of non-tryptic protein fragments, large mass-to-charge ratio ions that are not efficiently fragmented via tandem mass spectrometry or a high density of isobaric species. To aid the clinical development and implementation of mass spectrometry imaging a public database of identifications has been initiated. The MSiMass list database (www.maldi-msi.org/mass) enables users to assign identities to the peaks observed in their experiments and provides the methods by which the identifications were obtained. In contrast to existing protein databases, this list is designed as a community effort without a formal review panel. In this concept, authors can freely enter data, and can comment on existing entries. In such, the database itself is an experiment on sharing knowledge and its ability to rapidly provide quality data will be evaluated in the future