Molecular weight assessment of proteins in total proteome profiles using 1D-PAGE and LC/MS/MS

BACKGROUND: The observed molecular weight of a protein on a 1D polyacrylamide gel can provide meaningful insight into its biological function. Differences between a protein's observed molecular weight and that predicted by its full length amino acid sequence can be the result of different types of post-translational events, such as alternative splicing (AS), endoproteolytic processing (EPP), and post-translational modifications (PTMs). The characterization of these events is one of the important goals of total proteome profiling (TPP). LC/MS/MS has emerged as one of the primary tools for TPP, but since this method identifies tryptic fragments of proteins, it has not generally been used for large-scale determination of the molecular weight of intact proteins in complex mixtures. RESULTS: We have developed a set of computational tools for extracting molecular weight information of intact proteins from total proteome profiles in a high throughput manner using 1D-PAGE and LC/MS/MS. We have applied this technology to the proteome profile of a human lymphoblastoid cell line under standard culture conditions. From a total of 1 × 10(7 )cells, we identified 821 proteins by at least two tryptic peptides. Additionally, these 821 proteins are well-localized on the 1D-SDS gel. 656 proteins (80%) occur in gel slices in which the observed molecular weight of the protein is consistent with its predicted full-length sequence. A total of 165 proteins (20%) are observed to have molecular weights that differ from their predicted full-length sequence. We explore these molecular-weight differences based on existing protein annotation. CONCLUSION: We demonstrate that the determination of intact protein molecular weight can be achieved in a high-throughput manner using 1D-PAGE and LC/MS/MS. The ability to determine the molecular weight of intact proteins represents a further step in our ability to characterize gene expression at the protein level. The identification of 165 proteins whose observed molecular weight differs from the molecular weight of the predicted full-length sequence provides another entry point into the high-throughput characterization of protein modification

Beyond Traditional Approaches: Multi-Task Network for Breast Ultrasound Diagnosis

Breast Ultrasound plays a vital role in cancer diagnosis as a non-invasive approach with cost-effective. In recent years, with the development of deep learning, many CNN-based approaches have been widely researched in both tumor localization and cancer classification tasks. Even though previous single models achieved great performance in both tasks, these methods have some limitations in inference time, GPU requirement, and separate fine-tuning for each model. In this study, we aim to redesign and build end-to-end multi-task architecture to conduct both segmentation and classification. With our proposed approach, we achieved outstanding performance and time efficiency, with 79.8% and 86.4% in DeepLabV3+ architecture in the segmentation task.Comment: 7 pages, 3 figure

Slice-level Detection of Intracranial Hemorrhage on CT Using Deep Descriptors of Adjacent Slices

The rapid development in representation learning techniques such as deep neural networks and the availability of large-scale, well-annotated medical imaging datasets have to a rapid increase in the use of supervised machine learning in the 3D medical image analysis and diagnosis. In particular, deep convolutional neural networks (D-CNNs) have been key players and were adopted by the medical imaging community to assist clinicians and medical experts in disease diagnosis and treatment. However, training and inferencing deep neural networks such as D-CNN on high-resolution 3D volumes of Computed Tomography (CT) scans for diagnostic tasks pose formidable computational challenges. This challenge raises the need of developing deep learning-based approaches that are robust in learning representations in 2D images, instead 3D scans. In this work, we propose for the first time a new strategy to train \emph{slice-level} classifiers on CT scans based on the descriptors of the adjacent slices along the axis. In particular, each of which is extracted through a convolutional neural network (CNN). This method is applicable to CT datasets with per-slice labels such as the RSNA Intracranial Hemorrhage (ICH) dataset, which aims to predict the presence of ICH and classify it into 5 different sub-types. We obtain a single model in the top 4% best-performing solutions of the RSNA ICH challenge, where model ensembles are allowed. Experiments also show that the proposed method significantly outperforms the baseline model on CQ500. The proposed method is general and can be applied to other 3D medical diagnosis tasks such as MRI imaging. To encourage new advances in the field, we will make our codes and pre-trained model available upon acceptance of the paper.Comment: Accepted for presentation at the 22nd IEEE Statistical Signal Processing (SSP) worksho

CD4+FoxP3+ regulatory T cells confer infectious tolerance in a TGF-β–dependent manner

CD4+FoxP3+ regulatory T (T reg) cells comprise a separate lineage of T cells that are essential for maintaining immunological tolerance to self. The molecular mechanism(s) by which T reg cells mediate their suppressive effects remains poorly understood. One molecule that has been extensively studied in T reg cell suppression is transforming growth factor (TGF)-β, but its importance remains controversial. We found that TGF-β complexed to latency-associated peptide (LAP) is expressed on the cell surface of activated but not resting T reg cells. T reg cell LAP–TGF-β plays an important role in the suppression of the proliferation of activated T cells, but it is not required for the suppression of naive T cell activation. More importantly, T reg cell–derived TGF-β could generate de novo CD4+FoxP3+ T cells in vitro from naive precursors in a cell contact–dependent, antigen-presenting cell–independent and αV integrin–independent manner. The newly induced CD4+FoxP3+ T cells are suppressive both in vitro and in vivo. Transfer of activated antigen-specific T reg cells with naive antigen-specific responder T cells to normal recipients, followed by immunization, also results in induction of FoxP3 expression in the responder cells. T reg cell–mediated generation of functional CD4+FoxP3+ cells via this TGF-β–dependent pathway may represent a major mechanism as to how T reg cells maintain tolerance and expand their suppressive abilities

Expression and Function of Ccbe1 in the Chick Early Cardiogenic Regions Are Required for Correct Heart Development

During the course of a differential screen to identify transcripts specific for chick heart/hemangioblast precursor cells, we have identified Ccbe1 (Collagen and calcium-binding EGF-like domain 1). While the importance of Ccbe1 for the development of the lymphatic system is now well demonstrated, its role in cardiac formation remained unknown. Here we show by whole-mount in situ hybridization analysis that cCcbe1 mRNA is initially detected in early cardiac progenitors of the two bilateral cardiogenic fields (HH4), and at later stages on the second heart field (HH9-18). Furthermore, cCcbe1 is expressed in multipotent and highly proliferative cardiac progenitors. We characterized the role of cCcbe1 during early cardiogenesis by performing functional studies. Upon morpholino-induced cCcbe1 knockdown, the chick embryos displayed heart malformations, which include aberrant fusion of the heart fields, leading to incomplete terminal differentiation of the cardiomyocytes. cCcbe1 overexpression also resulted in severe heart defects, including cardia bifida. Altogether, our data demonstrate that although cardiac progenitors cells are specified in cCcbe1 morphants, the migration and proliferation of cardiac precursors cells are impaired, suggesting that cCcbe1 is a key gene during early heart development.FCT [SFRH/BD/65628/2009, SFRH/BPD/86497/2012, SFRH/BPD/41081/2007]; F.C.T.B.I. fellowship [PTDC/SAU-BID/114902/ 2009]; FCT; Institute for Biotechnology Bioengineering (Centro Biomedicina Molecular e Celular (IBB/CBME), Laboratorio Associado (LA) in the frame of Project [PestOE/EQB/LA0023/2013]info:eu-repo/semantics/publishedVersio

Electron effective mass in GaN revisited: New insights from terahertz and mid-infrared optical Hall effect

Electron effective mass is a fundamental material parameter defining the free charge carrier transport properties, but it is very challenging to be experimentally determined at high temperatures relevant to device operation. In this work, we obtain the electron effective mass parameters in a Si-doped GaN bulk substrate and epitaxial layers from terahertz (THz) and mid-infrared (MIR) optical Hall effect (OHE) measurements in the temperature range of 38–340 K. The OHE data are analyzed using the well-accepted Drude model to account for the free charge carrier contributions. A strong temperature dependence of the electron effective mass parameter in both bulk and epitaxial GaN with values ranging from (0.18 ± 0.02) m0 to (0.34 ± 0.01) m0 at a low temperature (38 K) and room temperature, respectively, is obtained from the THz OHE analysis. The observed effective mass enhancement with temperature is evaluated and discussed in view of conduction band nonparabolicity, polaron effect, strain, and deviations from the classical Drude behavior. On the other hand, the electron effective mass parameter determined by MIR OHE is found to be temperature independent with a value of (0.200 ± 0.002) m0. A possible explanation for the different findings from THz OHE and MIR OHE is proposed