288 CHARACTERIZATION OF PORCINE ADIPOSE TISSUE-DERIVED ADULT STEM CELL SURFACE PROTEINS

2009 ◽  
Vol 21 (1) ◽  
pp. 241
Author(s):  
K. J. Williams ◽  
R. A. Godke ◽  
K. R. Bondioli
Keyword(s):  
Tissue Engineering ◽  
Adipose Tissue ◽  
Stem Cell ◽  
Cell Surface ◽  
Cell Lines ◽  
Adult Stem Cell ◽  
Surface Protein ◽  
Surface Proteins ◽  
Early Passage ◽  
Cell Surface Proteins

Human adipose tissue-derived adult stem (ADAS) cells are a self-renewing population of cells with a multilineage plasticity similar to bone marrow-derived mesenchymal stem cells. Human ADAS have promise for use in combination with various biomaterials for reconstructive tissue engineering. The phenotypic profile of human ADAS cell surface proteins has been partially characterized for stem cell-associated cluster differentiation molecules including CD29, CD44, and CD90. Porcine ADAS cells, an animal model for tissue engineering, also have the ability to self-renew and differentiate into multiple tissue lineages. However, the surface protein phenotype has not been described. Because porcine ADAS are isolated from fat depots likely different from human ADAS liposuction aspirates, it is important to characterize these cells. In this study, we have partially characterized the surface protein phenotype of undifferentiated porcine ADAS cells in comparison with the immunophenotype of undifferentiated human ADAS cells as reported in the literature. Flow cytometry and enhanced chemiluminescence Western blot analysis of early passage (passages 0–4) porcine ADAS cell populations demonstrated that the profiles are not similar to the human ADAS cell surface. Immunoblot detection paired with an enhanced chemiluminescence kit revealed a positive expression for CD44 and CD90 in human ADAS cells as indicated by bands present at the expected sizes and a negative expression for CD44 and CD90 in porcine ADAS cells. Flow cytometric analysis also indicated differences between human and early passage porcine ADAS cell surfaces with a relatively low expression of CD29 (5 cell lines with a mean percent positive of 4.5 ± 1.7 and a range of 2.5–7.2%) and CD44 (5 cell lines with a mean percent positive of 0.66 ± 0.67 and a range of 0.0–1.8%) compared with human ADAS values of 98 ± 1 and 60 ± 15, respectively (Gronthos et al. 2001). Other cell surface proteins analyzed at early passages include CD3 (3 cell lines; 0.07 ± 0.06% positive and 0.0–0.1 range), CD8 (3 cell lines; 0.10 ± 0.10% positive and 0–0.2 range), and CD90 [5 cell lines; 12.7 ± 11.9% positive and 2.4–33 range; human ADAS geometric mean 25.96% (Zuk et al. 2002)]. Analysis of late passage (passages 5–11) porcine ADAS cell populations revealed an increased expression of CD29 (3 cell lines; 26.4 ± 7.2% positive and 21.2–34.6 range). The expression level of CD90 at late passages were 21.3 and 26.9% positive for 2 cell lines and CD44 remained low (3 cell lines; 4.1 ± 3.5% positive and 0.2–7.0 range). Later passages were also analyzed for c-Kit (CD117), which was expressed at low levels (2 cell lines; 0.3 and 0.4% positive). The characterization of adipose tissue-derived adult stem cell surface proteins present at different stages of in vitro culture from a model animal, such as the pig, could have valuable impacts on tissue engineering research. These results suggest that care should be taken when interpreting results from animal models of somatic stem cells.

scholarly journals Identification and characterization of adipose surface epitopes

2020 ◽  
Vol 477 (13) ◽  
pp. 2509-2541 ◽  
Author(s):  
Yasuhiro Onogi ◽  
Ahmed Elagamy Mohamed Mahmoud Khalil ◽  
Siegfried Ussar
Keyword(s):  
Adipose Tissue ◽  
Cell Surface ◽  
Protein Interactions ◽  
Posttranslational Modifications ◽  
Surface Protein ◽  
Surface Proteins ◽  
Endocrine Function ◽  
Whole Body ◽  
Cell Surface Proteins ◽  
Adipocyte Cell

Adipose tissue is a central regulator of metabolism and an important pharmacological target to treat the metabolic consequences of obesity, such as insulin resistance and dyslipidemia. Among the various cellular compartments, the adipocyte cell surface is especially appealing as a drug target as it contains various proteins that when activated or inhibited promote adipocyte health, change its endocrine function and eventually maintain or restore whole-body insulin sensitivity. In addition, cell surface proteins are readily accessible by various drug classes. However, targeting individual cell surface proteins in adipocytes has been difficult due to important functions of these proteins outside adipose tissue, raising various safety concerns. Thus, one of the biggest challenges is the lack of adipose selective surface proteins and/or targeting reagents. Here, we discuss several receptor families with an important function in adipogenesis and mature adipocytes to highlight the complexity at the cell surface and illustrate the problems with identifying adipose selective proteins. We then discuss that, while no unique adipocyte surface protein might exist, how splicing, posttranslational modifications as well as protein/protein interactions can create enormous diversity at the cell surface that vastly expands the space of potentially unique epitopes and how these selective epitopes can be identified and targeted.

scholarly journals The in silico human surfaceome

2018 ◽  
Vol 115 (46) ◽  
pp. E10988-E10997 ◽  
Author(s):  
Damaris Bausch-Fluck ◽  
Ulrich Goldmann ◽  
Sebastian Müller ◽  
Marc van Oostrum ◽  
Maik Müller ◽  
...  
Keyword(s):  
Cell Surface ◽  
In Silico ◽  
Embryonic Stem ◽  
Surface Protein ◽  
Surface Proteins ◽  
Cell Surface Protein ◽  
Cell Surface Proteins ◽  
A Cell ◽  
Visualization Tools ◽  
Protein Classes

Cell-surface proteins are of great biomedical importance, as demonstrated by the fact that 66% of approved human drugs listed in the DrugBank database target a cell-surface protein. Despite this biomedical relevance, there has been no comprehensive assessment of the human surfaceome, and only a fraction of the predicted 5,000 human transmembrane proteins have been shown to be located at the plasma membrane. To enable analysis of the human surfaceome, we developed the surfaceome predictor SURFY, based on machine learning. As a training set, we used experimentally verified high-confidence cell-surface proteins from the Cell Surface Protein Atlas (CSPA) and trained a random forest classifier on 131 features per protein and, specifically, per topological domain. SURFY was used to predict a human surfaceome of 2,886 proteins with an accuracy of 93.5%, which shows excellent overlap with known cell-surface protein classes (i.e., receptors). In deposited mRNA data, we found that between 543 and 1,100 surfaceome genes were expressed in cancer cell lines and maximally 1,700 surfaceome genes were expressed in embryonic stem cells and derivative lines. Thus, the surfaceome diversity depends on cell type and appears to be more dynamic than the nonsurface proteome. To make the predicted surfaceome readily accessible to the research community, we provide visualization tools for intuitive interrogation (wlab.ethz.ch/surfaceome). The in silico surfaceome enables the filtering of data generated by multiomics screens and supports the elucidation of the surfaceome nanoscale organization.

scholarly journals Surfaceome Proteomic of Glioblastoma Revealed Potential Targets for Immunotherapy

2021 ◽  
Vol 12 ◽  
Author(s):  
Mélanie Rose ◽  
Tristan Cardon ◽  
Soulaimane Aboulouard ◽  
Nawale Hajjaji ◽  
Firas Kobeissy ◽  
...  
Keyword(s):  
Cell Surface ◽  
Cell Lines ◽  
Immune Therapy ◽  
Shotgun Proteomics ◽  
Maldi Mass Spectrometry ◽  
Surface Proteins ◽  
Cell Surface Proteins ◽  
Mhc I ◽  
Therapy Strategies ◽  
Very High

Glioblastoma (GBM) is the most common and devastating malignant brain tumor in adults. The mortality rate is very high despite different treatments. New therapeutic targets are therefore highly needed. Cell-surface proteins represent attractive targets due to their accessibility, their involvement in essential signaling pathways, and their dysregulated expression in cancer. Moreover, they are potential targets for CAR-based immunotherapy or mRNA vaccine strategies. In this context, we investigated the GBM-associated surfaceome by comparing it to astrocytes cell line surfaceome to identify new specific targets for GBM. For this purpose, biotinylation of cell surface proteins has been carried out in GBM and astrocytes cell lines. Biotinylated proteins were purified on streptavidin beads and analyzed by shotgun proteomics. Cell surface proteins were identified with Cell Surface Proteins Atlas (CSPA) and Gene Ontology enrichment. Among all the surface proteins identified in the different cell lines we have confirmed the expression of 66 of these in patient’s glioblastoma using spatial proteomic guided by MALDI-mass spectrometry. Moreover, 87 surface proteins overexpressed or exclusive in GBM cell lines have been identified. Among these, we found 11 specific potential targets for GBM including 5 mutated proteins such as RELL1, CYBA, EGFR, and MHC I proteins. Matching with drugs and clinical trials databases revealed that 7 proteins were druggable and under evaluation, 3 proteins have no known drug interaction yet and none of them are the mutated form of the identified proteins. Taken together, we discovered potential targets for immune therapy strategies in GBM.

scholarly journals Transcription Factor Control of Dendrite Targeting via Combinatorial Cell-Surface Codes

2021 ◽  
Author(s):  
Liqun Luo ◽  
Qijing Xie ◽  
Jiefu Li ◽  
Hongjie Li ◽  
Namrata Udeshi ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Transcription Factors ◽  
Cell Surface ◽  
Surface Protein ◽  
Surface Proteins ◽  
Projection Neurons ◽  
Cell Surface Protein ◽  
Cell Surface Proteins ◽  
Cellular Environment ◽  
Combinatorial Expression

Abstract Transcription factors are central commanders specifying cell fate, morphology, and physiology while cell-surface proteins execute these commands through interaction with cellular environment. In developing neurons, it is presumed that transcription factors control wiring specificity through regulation of cell-surface protein expression. However, the number and identity of cell-surface protein(s) a transcription factor regulates remain largely unclear1,2. Also unknown is whether a transcription factor regulates the same or different cell-surface proteins in different neuron types to specify their connectivity. Here we use a lineage-defining transcription factor, Acj6 (ref. 3), to investigate how it controls precise dendrite targeting of Drosophila olfactory projection neurons (PNs). Quantitative cell-surface proteomic profiling of wild-type and acj6 mutant PNs in intact developing brains and a proteome-informed genetic screen identified PN surface proteins that execute Acj6-regulated wiring decisions. These include canonical cell adhesion proteins and proteins previously not associated with wiring, such as the mechanosensitive ion channel Piezo—whose channel activity is dispensable for its wiring function. Comprehensive genetic analyses revealed that Acj6 employs unique sets of cell-surface proteins in different PN types for dendrite targeting. Combinatorial expression of Acj6 wiring executors rescued acj6 mutant phenotypes with higher efficacy and breadth than expression of individual executors. Thus, a key transcription factor controls wiring specificity of different neuron types by specifying distinct combinatorial expression of cell-surface executors.

scholarly journals Prediction of the Secretome and the Surfaceome: A Strategy to Decipher the Crosstalk between Adipose Tissue and Muscle during Fetal Growth

2020 ◽  
Vol 21 (12) ◽  
pp. 4375
Author(s):  
Muriel Bonnet ◽  
Nicolas Kaspric ◽  
Kimberly Vonnahme ◽  
Didier Viala ◽  
Christophe Chambon ◽  
...  
Keyword(s):  
Adipose Tissue ◽  
Cell Surface ◽  
Fetal Growth ◽  
Protein Interactions ◽  
Cell Types ◽  
Surface Proteins ◽  
Protein Protein Interactions ◽  
Cell Surface Proteins ◽  
Bovine Fetuses ◽  
Muscular Tissues

Crosstalk between adipose and muscular tissues is hypothesized to regulate the number of muscular and adipose cells during fetal growth, with post-natal consequences on lean and fat masses. Such crosstalk largely remains, however, to be described. We hypothesized that a characterization of the proteomes of adipose and muscular tissues from bovine fetuses may enhance the understanding of the crosstalk between these tissues through the prediction of their secretomes and surfaceomes. Proteomic experiments have identified 751 and 514 proteins in fetal adipose tissue and muscle. These are mainly involved in the regulation of cell proliferation or differentiation, but also in pathways such as apoptosis, Wnt signalling, or cytokine-mediated signalling. Of the identified proteins, 51 adipokines, 11 myokines, and 37 adipomyokines were predicted, together with 26 adipose and 13 muscular cell surface proteins. Analysis of protein–protein interactions suggested 13 links between secreted and cell surface proteins that may contribute to the adipose–muscular crosstalk. Of these, an interaction between the adipokine plasminogen and the muscular cell surface alpha-enolase may regulate the fetal myogenesis. The in silico secretome and surfaceome analyzed herein exemplify a powerful strategy to enhance the elucidation of the crosstalk between cell types or tissues.

Differences in cell surface proteins of drug resistant and sensitive cell lines

Biochimie ◽  
1976 ◽  
Vol 58 (6) ◽  
pp. 731-736 ◽  
Author(s):  
Myriam Bettane ◽  
Brigitte Hermier ◽  
Jean-Marie Dubert ◽  
Alain Paraf
Keyword(s):  
Cell Surface ◽  
Cell Lines ◽  
Surface Proteins ◽  
Drug Resistant ◽  
Sensitive Cell ◽  
Cell Surface Proteins

scholarly journals Functional correlation between cell adhesive properties and some cell surface proteins.

1977 ◽  
Vol 75 (2) ◽  
pp. 464-474 ◽  
Author(s):  
M Takeichi
Keyword(s):  
Cell Surface ◽  
Chinese Hamster ◽  
Surface Protein ◽  
Surface Proteins ◽  
Adhesive Properties ◽  
Cell Surface Proteins ◽  
Morphological Studies ◽  
Chinese Hamster V79 Cells ◽  
A Cell ◽  
In Cells

The adhesive properties of Chinese hamster V79 cells were analyzed and characterized by various cell dissociation treatments. The comparisons of aggregability among cells dissociated with EDTA, trypsin + Ca2+, and trypsin + EDTA, revealed that these cells have two adhesion mechanisms, a Ca2+-independent and a Ca2+-dependent one. The former did not depend on temperature, whereas the latter occurred only at physiological temperatures. Both mechanisms were trypsin sensitive, but the Ca2+-dependent one was protected by Ca2+ against trypsinization. In morphological studies, the Ca2+-independent adhesion appeared to be a simple agglutination or flocculation of cells, whereas the Ca2+-dependent adhesion seemed to be more physiological, being accompanied by cell deformation resulting in the increase of contact area between adjacent cells. Lactoperoxidase-catalyzed iodination of cell surface proteins revealed that several proteins are more intensely labeled in cells with Ca2+-independent adhesiveness than in cells without that property. It was also found that a cell surface protein with a molecular weight of approximately 150,000 is present only in cells with Ca2+-dependent adhesiveness. The iodination and trypsinization of this protein were protected by Ca2+, suggesting its reactivity to Ca2+. Possible mechanisms for each adhesion property are discussed, taking into account the correlation of these proteins with cell adhesiveness.

scholarly journals Transcription factor control of dendrite targeting via combinatorial cell-surface codes

2021 ◽  
Author(s):  
Qijing Xie ◽  
Jiefu Li ◽  
Hongjie Li ◽  
Namrata D Udeshi ◽  
Tanya Svinkina ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Transcription Factors ◽  
Cell Surface ◽  
Surface Protein ◽  
Surface Proteins ◽  
Projection Neurons ◽  
Cell Surface Protein ◽  
Cell Surface Proteins ◽  
Cellular Environment ◽  
Combinatorial Expression

Transcription factors are central commanders specifying cell fate, morphology, and physiology while cell-surface proteins execute these commands through interaction with cellular environment. In developing neurons, it is presumed that transcription factors control wiring specificity through regulation of cell-surface protein expression. However, the number and identity of cell-surface protein(s) a transcription factor regulates remain largely unclear1,2. Also unknown is whether a transcription factor regulates the same or different cell-surface proteins in different neuron types to specify their connectivity. Here we use a lineage-defining transcription factor, Acj6 (ref. 3), to investigate how it controls precise dendrite targeting of Drosophila olfactory projection neurons (PNs). Quantitative cell-surface proteomic profiling of wild-type and acj6 mutant PNs in intact developing brains and a proteome-informed genetic screen identified PN surface proteins that execute Acj6-regulated wiring decisions. These include canonical cell adhesion proteins and proteins previously not associated with wiring, such as the mechanosensitive ion channel Piezo–whose channel activity is dispensable for its wiring function. Comprehensive genetic analyses revealed that Acj6 employs unique sets of cell-surface proteins in different PN types for dendrite targeting. Combinatorial expression of Acj6 wiring executors rescued acj6 mutant phenotypes with higher efficacy and breadth than expression of individual executors. Thus, a key transcription factor controls wiring specificity of different neuron types by specifying distinct combinatorial expression of cell-surface executors.

scholarly journals Detection of subtype-specific Breast Cancer Surface Protein Biomarkers via a novel Transcriptomics approach

2021 ◽  
Author(s):  
Daniele Mercatelli ◽  
Francesco Formaggio ◽  
Marco Caprini ◽  
Andrew Holding ◽  
Federico Manuel Giorgi
Keyword(s):  
Breast Cancer ◽  
Cell Surface ◽  
Surface Activity ◽  
Prognostic Value ◽  
Surface Protein ◽  
Surface Proteins ◽  
Computational Method ◽  
Cell Surface Proteins ◽  
Machine Learning Classification ◽  
Increased Risk

Background: Cell-surface proteins have been widely used as diagnostic and prognostic markers in cancer research, and as targets for the development of anti-cancer agents. So far, very few attempts have been made to characterize the surfaceome of breast cancer patients, particularly in relation with the current molecular breast cancer (BRCA) classification. In this view, we developed a new computational method to infer cell-surface protein activities from transcriptomics data, termed “SURFACER”. Methods: Gene expression data from GTEx were used to build a normal breast network model as input to infer differential cell-surface proteins activity in BRCA tissue samples retrieved from TCGA vs. normal samples. Data were stratified according to the PAM50 transcriptional subtypes (Luminal A, Luminal B, HER2, Basal), while unsupervised clustering techniques were applied to define BRCA subtypes according to cell-surface proteins activity. Results: Our approach led to the identification of 213 PAM50 subtypes-specific deregulated surface genes and the definition of 5 BRCA subtypes, whose prognostic value was assessed by survival analysis, identifying a cell-surface activity configuration at increased risk. The value of the SURFACER method in BRCA genotyping was tested by evaluating the performance of 11 different machine learning classification algorithms.  Conclusions: BRCA patients can be stratified into 5 surface activity-specific groups having the potential to identify subtype-specific actionable targets to design tailored targeted therapies, or for diagnostic purposes. SURFACER-defined subtypes show also a prognostic value, identifying surface-activity profiles at higher risk.

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