scholarly journals Transit peptide elements mediate selective protein targeting to two different types of chloroplasts in the single-cell C4 species Bienertia sinuspersici

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Diana Wimmer ◽  
Philipp Bohnhorst ◽  
Vinay Shekhar ◽  
Inhwan Hwang ◽  
Sascha Offermann
Keyword(s):  
Single Cell ◽  
Protein Targeting ◽  
Transit Peptide ◽  
Bienertia Sinuspersici ◽  
C4 Species ◽  
Different Types

scholarly journals Isolation of dimorphic chloroplasts from the single-cell C4 species Bienertia sinuspersici

Plant Methods ◽  
2012 ◽  
Vol 8 (1) ◽  
pp. 8 ◽  
Author(s):  
Shiu-Cheung Lung ◽  
Makoto Yanagisawa ◽  
Simon DX Chuong
Keyword(s):  
Single Cell ◽  
Bienertia Sinuspersici ◽  
C4 Species

scholarly journals Resolving the Compartmentation and Function of C4 Photosynthesis in the Single-Cell C4 Species Bienertia sinuspersici

2011 ◽  
Vol 155 (4) ◽  
pp. 1612-1628 ◽  
Author(s):  
Sascha Offermann ◽  
Thomas W. Okita ◽  
Gerald E. Edwards
Keyword(s):  
Single Cell ◽  
C4 Photosynthesis ◽  
Bienertia Sinuspersici ◽  
C4 Species ◽  
And Function

scholarly journals Exploring mechanisms linked to differentiation and function of dimorphic chloroplasts in the single cell C4 species Bienertia sinuspersici

2014 ◽  
Vol 14 (1) ◽  
pp. 34 ◽  
Author(s):  
Josh Rosnow ◽  
Pradeep Yerramsetty ◽  
James O Berry ◽  
Thomas W Okita ◽  
Gerald E Edwards
Keyword(s):  
Single Cell ◽  
Bienertia Sinuspersici ◽  
C4 Species ◽  
And Function

Expression profiling and proteomic analysis of isolated photosynthetic cells of the non-Kranz C4 species Bienertia sinuspersici

2010 ◽  
Vol 37 (1) ◽  
pp. 1 ◽  
Author(s):  
Joonho Park ◽  
Thomas W. Okita ◽  
Gerald E. Edwards
Keyword(s):  
Single Cell ◽  
Protein Modification ◽  
Protein Identification ◽  
Phylogenetic Analyses ◽  
Protein Profile ◽  
Ion Homeostasis ◽  
Cell Wall Modification ◽  
Kranz Anatomy ◽  
Bienertia Sinuspersici ◽  
C4 Species

Bienertia sinuspersici Akhani represents one form of C4 photosynthesis that occurs without Kranz anatomy in family Chenopodiaceae. Analysis of transcript profiles and proteomics were made to gain information on this single-cell C4 photosynthetic mechanism. Chlorenchyma cells were isolated and purified from mature leaves. From these cells, a cDNA library was made from which sequences were obtained on 2385 clones using conventional methods. To obtain a protein profile, the multi dimensional protein identification technique was used, resulting in identification of 322 unique proteins in chlorenchyma cells. After analysing datasets from the EST library and proteomics, genes and proteins were classified into 23 and 17 categories according to types of biological processes, respectively. These include photosynthesis and photorespiration, other biosynthetic and metabolic processes, cell wall modification, defence response, DNA repair, electron transport, other cellular and developmental processes, protein folding, protein targeting, protein modification, proteolysis, redox and ion homeostasis, response to biotic and abiotic stresses, RNA modification, transcription, translation, transport and unknowns. Sequence and phylogenetic analyses were made of C4 cycle enzymes to characterise the relationship between homologues found in Bienertia with public gene sequences from other chenopods and representative C3 and C4 species from other families. Identified photosynthetic genes and proteins are discussed with respect to the proposed function of an NAD-ME type C4 cycle in this single-cell C4 system.

Thidiazuron induces high-frequency indirect shoot organogenesis of Bienertia sinuspersici: a single-cell C4 species

2016 ◽  
Vol 126 (1) ◽  
pp. 141-151 ◽  
Author(s):  
Jennifer Anne Northmore ◽  
Dustin Sigurdson ◽  
Sarah Schoor ◽  
Amer Rustum ◽  
Simon D. X. Chuong
Keyword(s):  
Single Cell ◽  
High Frequency ◽  
Shoot Organogenesis ◽  
Bienertia Sinuspersici ◽  
C4 Species

The effects of salinity on photosynthesis and growth of the single-cell C4 species Bienertia sinuspersici (Chenopodiaceae)

2010 ◽  
Vol 106 (3) ◽  
pp. 201-214 ◽  
Author(s):  
Courtney P. Leisner ◽  
Asaph B. Cousins ◽  
Sascha Offermann ◽  
Thomas W. Okita ◽  
Gerald E. Edwards
Keyword(s):  
Single Cell ◽  
Bienertia Sinuspersici ◽  
C4 Species

scholarly journals Identifying cell types from single-cell data based on similarities and dissimilarities between cells

2021 ◽  
Vol 22 (S3) ◽  
Author(s):  
Yuanyuan Li ◽  
Ping Luo ◽  
Yi Lu ◽  
Fang-Xiang Wu
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Spectral Clustering ◽  
Incidence Matrix ◽  
Expression Patterns ◽  
Cell Types ◽  
Clustering Method ◽  
Different Types ◽  
Cell Data ◽  
Spectral Clustering Method

Abstract Background With the development of the technology of single-cell sequence, revealing homogeneity and heterogeneity between cells has become a new area of computational systems biology research. However, the clustering of cell types becomes more complex with the mutual penetration between different types of cells and the instability of gene expression. One way of overcoming this problem is to group similar, related single cells together by the means of various clustering analysis methods. Although some methods such as spectral clustering can do well in the identification of cell types, they only consider the similarities between cells and ignore the influence of dissimilarities on clustering results. This methodology may limit the performance of most of the conventional clustering algorithms for the identification of clusters, it needs to develop special methods for high-dimensional sparse categorical data. Results Inspired by the phenomenon that same type cells have similar gene expression patterns, but different types of cells evoke dissimilar gene expression patterns, we improve the existing spectral clustering method for clustering single-cell data that is based on both similarities and dissimilarities between cells. The method first measures the similarity/dissimilarity among cells, then constructs the incidence matrix by fusing similarity matrix with dissimilarity matrix, and, finally, uses the eigenvalues of the incidence matrix to perform dimensionality reduction and employs the K-means algorithm in the low dimensional space to achieve clustering. The proposed improved spectral clustering method is compared with the conventional spectral clustering method in recognizing cell types on several real single-cell RNA-seq datasets. Conclusions In summary, we show that adding intercellular dissimilarity can effectively improve accuracy and achieve robustness and that improved spectral clustering method outperforms the traditional spectral clustering method in grouping cells.

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