scholarly journals Surface protein imputation from single cell transcriptomes by deep neural networks

2019 ◽  
Author(s):  
Zilu Zhou ◽  
Chengzhong Ye ◽  
Jingshu Wang ◽  
Nancy R. Zhang
Keyword(s):  
Neural Networks ◽  
Cell Surface ◽  
Single Cell ◽  
Deep Neural Networks ◽  
Surface Protein ◽  
Surface Proteins ◽  
Cell Populations ◽  
Cell Surface Proteins ◽  
Learning Framework ◽  
Single Cell Rna Sequencing

While single cell RNA sequencing (scRNA-seq) is invaluable for studying cell populations, cell-surface proteins are often integral markers of cellular function and serve as primary targets for therapeutic intervention. Here we propose a transfer learning framework, single cell Transcriptome to Protein prediction with deep neural network (cTP-net), to impute surface protein abundances from scRNA-seq data by learning from existing single-cell multi-omic resources.

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 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 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 Modular transcriptional programs separately define axon and dendrite connectivity

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Yerbol Z Kurmangaliyev ◽  
Juyoun Yoo ◽  
Samuel A LoCascio ◽  
S Lawrence Zipursky
Keyword(s):  
Transcription Factors ◽  
Single Cell ◽  
Transcriptional Control ◽  
Surface Proteins ◽  
Synaptic Connectivity ◽  
Loss Of Function ◽  
Neuronal Connectivity ◽  
Cell Surface Proteins ◽  
Single Cell Rna Sequencing ◽  
Single Cell Profiling

Patterns of synaptic connectivity are remarkably precise and complex. Single-cell RNA sequencing has revealed a vast transcriptional diversity of neurons. Nevertheless, a clear logic underlying the transcriptional control of neuronal connectivity has yet to emerge. Here, we focused on Drosophila T4/T5 neurons, a class of closely related neuronal subtypes with different wiring patterns. Eight subtypes of T4/T5 neurons are defined by combinations of two patterns of dendritic inputs and four patterns of axonal outputs. Single-cell profiling during development revealed distinct transcriptional programs defining each dendrite and axon wiring pattern. These programs were defined by the expression of a few transcription factors and different combinations of cell surface proteins. Gain and loss of function studies provide evidence for independent control of different wiring features. We propose that modular transcriptional programs for distinct wiring features are assembled in different combinations to generate diverse patterns of neuronal connectivity.

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.

scholarly journals Surface protein imputation from single cell transcriptomes by deep neural networks

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Zilu Zhou ◽  
Chengzhong Ye ◽  
Jingshu Wang ◽  
Nancy R. Zhang
Keyword(s):  
Neural Networks ◽  
Single Cell ◽  
Deep Neural Networks ◽  
Surface Protein

scholarly journals Riemannian geometry and statistical modeling correct for batch effects and control false discoveries in single-cell surface protein count data from CITE-seq

2020 ◽  
Author(s):  
Shuyi Zhang ◽  
Jacob R. Leistico ◽  
Christopher Cook ◽  
Yale Liu ◽  
Raymond J. Cho ◽  
...  
Keyword(s):  
Cell Surface ◽  
Single Cell ◽  
Count Data ◽  
Riemannian Geometry ◽  
Surface Protein ◽  
Surface Proteins ◽  
Quantitative Detection ◽  
Data Sets ◽  
Batch Effects ◽  
Experimental Conditions

Recent advances in next generation sequencing-based single-cell technologies have allowed high-throughput quantitative detection of cell-surface proteins along with the transcriptome in individual cells, extending our understanding of the heterogeneity of cell populations in diverse tissues that are in different diseased states or under different experimental conditions. Count data of surface proteins from the cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) technology pose new computational challenges, and there is currently a dearth of rigorous mathematical tools for analyzing the data. This work utilizes concepts and ideas from Riemannian geometry to remove batch effects between samples and develops a statistical framework for distinguishing positive signals from background noise. The strengths of these approaches are demonstrated on two independent CITE-seq data sets in mouse and human. Python source code implementing the algorithms is available at https://github.com/jssong-lab/SAGACITE.

scholarly journals Interactions with presynaptic photoreceptors mediated by the Dpr11 and DIP-γ cell surface proteins control selection and survival of Drosophila amacrine neurons

2019 ◽  
Author(s):  
Kaushiki P. Menon ◽  
Vivek Kulkarni ◽  
Shin-ya Takemura ◽  
Michael Anaya ◽  
Kai Zinn
Keyword(s):  
Cell Surface ◽  
Color Vision ◽  
Expression Patterns ◽  
Surface Protein ◽  
Higher Order ◽  
Surface Proteins ◽  
Cell Surface Protein ◽  
Cell Surface Proteins ◽  
Binding Partner ◽  
Do So

ABSTRACTDrosophila R7 UV photoreceptors (PRs) are divided into yellow (y) and pale (p) subtypes with different wavelength sensitivities. yR7 PRs express the Dpr11 cell surface protein and are presynaptic to Dm8 amacrine neurons (yDm8) that express Dpr11’s binding partner DIP-γ, while pR7 PRs synapse onto DIP-γ-negative pDm8 neurons. Dpr11 and DIP-γ expression patterns define yellow and pale medulla color vision circuits that project to higher-order areas. DIP- γ and dpr11 mutations affect the morphology of yDm8 arbors in the yellow circuit. yDm8 neurons are generated in excess during development and compete for presynaptic yR7 partners. Transsynaptic interactions between Dpr11 and DIP-γ are required for generation of neurotrophic signals that allow yDm8 neurons to survive. yDm8 and pDm8 neurons do not normally compete for neurotrophic support, but can be forced to do so by manipulating R7 subtype fates. DIP-γ-Dpr11 interactions allow yDm8 neurons to select yR7 PRs as their home column partners.

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