Gephyrin: does splicing affect its function?

2006 ◽  
Vol 34 (1) ◽  
pp. 45-47 ◽  
Author(s):  
I. Paarmann ◽  
T. Saiyed ◽  
B. Schmitt ◽  
H. Betz
Keyword(s):  
Alternative Splicing ◽  
Ion Channels ◽  
Single Cells ◽  
Protein Structures ◽  
Cell Types ◽  
Binding Partners ◽  
Its Gene ◽  
Substantial Progress ◽  
Different Cell Types ◽  
Made In

Gephyrin is a protein involved in both synaptic anchoring of inhibitory ligand-gated ion channels and molybdenum cofactor synthesis. Substantial progress has been made in understanding its gene and protein structures. Furthermore, numerous binding partners of gephyrin have been identified. The mechanisms by which these interactions occur are unclear at present. Alternative splicing has been proposed to contribute to gephyrin's functional diversity within single cells as well as in different cell types and tissues.

scholarly journals Subcellular Localization of a Novel Alternative Splicing of IIIG9 and Colocalization with PPP1gamma Isoforms

2008 ◽  
Vol 14 (S3) ◽  
pp. 141-143
Author(s):  
C. Sousa ◽  
A.P. Vintém ◽  
M. Fardilha ◽  
O. da Cruz e Silva ◽  
E. da Cruz e Silva
Keyword(s):  
Alternative Splicing ◽  
Male Infertility ◽  
Cell Types ◽  
Sperm Cells ◽  
Regulatory Subunits ◽  
Specific Antibodies ◽  
Fluorescent Microscope ◽  
Bovine Sperm ◽  
Spermatogonia Cells ◽  
Different Cell Types

In testis we find mainly PPP1gamma2 isoform. We hypothesize that in different cell types we can find different regulatory subunits that may constitute targets for therapeutics of diseases such as male infertility, cancer and Alzheimer's disease. We identified a novel alternative splicing isoform of IIIG9 in testis, a known regulator of PPP1, IIIG9sT, and the aim of this study was its further characterization. We used a specific antibody for IIIG9sT in order to characterize its localization in bovine sperm cells. We also transfected IIIG9sT-GFP construct in mouse spermatogonia cells (GC-1 cells) and we used specific antibodies for each PPP1 isoform for the colocalization studies. We observed them under a fluorescent microscope and a LSM and quantified a high co-localization with PPP1gamma1 and 2 isoforms.

scholarly journals Computational approaches towards reducing contamination in single-cell RNA-seq data

2020 ◽  
Author(s):  
Siamak Yousefi ◽  
Hao Chen ◽  
Jesse F. Ingels ◽  
Melinda S. McCarty ◽  
Arthur G. Centeno ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Real Life ◽  
Cell Types ◽  
Cell Capture ◽  
Rna Seq ◽  
Sequence Analyses ◽  
Cell Functions ◽  
Biological Interpretation ◽  
Different Cell Types

SUMMARYSingle cell RNA sequencing has enabled quantification of single cells and identification of different cell types and subtypes as well as cell functions in different tissues. Single cell RNA sequence analyses assume acquired RNAs correspond to cells, however, RNAs from contamination within the input data are also captured by these assays. The sequencing of background contamination as well as unwanted cells making their way to the final assay Potentially confound the correct biological interpretation of single cell transcriptomic data. Here we demonstrate two approaches to deal with background contamination as well as profiling of unwanted cells in the assays. We use three real-life datasets of whole-cell capture and nucleotide single-cell captures generated by Fluidigm and 10x technologies and show that these methods reduce the effect of contamination, strengthen clustering of cells and improves biological interpretation.

scholarly journals Mapping multicellular programs from single-cell profiles

2020 ◽  
Author(s):  
Livnat Jerby-Arnon ◽  
Aviv Regev
Keyword(s):  
Single Cell ◽  
Spatial Data ◽  
Spatial Information ◽  
Single Cells ◽  
Cell Types ◽  
Risk Genes ◽  
Health And Disease ◽  
Different Cell Types ◽  
Cellular Components ◽  
Acting In Concert

ABSTRACTTissue homeostasis relies on orchestrated multicellular circuits, where interactions between different cell types dynamically balance tissue function. While single-cell genomics identifies tissues’ cellular components, deciphering their coordinated action remains a major challenge. Here, we tackle this problem through a new framework of multicellular programs: combinations of distinct cellular programs in different cell types that are coordinated together in the tissue, thus forming a higher order functional unit at the tissue, rather than only cell, level. We develop the open-access DIALOGUE algorithm to systematically uncover such multi-cellular programs not only from spatial data, but even from tissue dissociated and profiled as single cells, e.g., by single-cell RNA-Seq. Tested on spatial transcriptomes from the mouse hypothalamus, DIALOGUE recovered spatial information, predicted the properties of a cell’s environment only based on its transcriptome, and identified multicellular programs that mark animal behavior. Applied to brain samples and colon biopsies profiled by scRNA-Seq, DIALOGUE identified multicellular configurations that mark Alzheimer’s disease and ulcerative colitis (UC), including a program spanning five cell types that is predictive of response to anti-TNF therapy in UC patients and enriched for UC risk genes from GWAS, each acting in different cell types, but all cells acting in concert. Taken together, our study provides a novel conceptual and methodological framework to unravel multicellular regulation in health and disease.

scholarly journals Unfolding and identification of membrane proteins from native cell membranes

2019 ◽  
Author(s):  
Nicola Galvanetto ◽  
Sourav Maity ◽  
Nina Ilieva ◽  
Zhongjie Ye ◽  
Alessandro Laio ◽  
...  
Keyword(s):  
Membrane Proteins ◽  
Single Molecule ◽  
Single Cells ◽  
Total Content ◽  
Cell Types ◽  
Single Cell Level ◽  
Cell Level ◽  
Single Molecule Force Spectroscopy ◽  
Native Cell ◽  
Different Cell Types

AbstractIs the mechanical unfolding of proteins just a technological feat applicable only to synthetic preparations or can it provide useful information even for real biological samples? Here, we describe a pipeline for analyzing native membranes based on high throughput single-molecule force spectroscopy. The protocol includes a technique for the isolation of the plasma membrane of single cells. Afterwards, one harvests hundreds of thousands SMSF traces from the sample. Finally, one characterizes and identifies the embedded membrane proteins. This latter step is the cornerstone of our approach and involves combining, within a Bayesian framework, the information of the shape of the SMFS Force-distance which are observed more frequently, with the information from Mass Spectrometry and from proteomic databases (Uniprot, PDB). We applied this method to four cell types where we classified the unfolding of 5-10% of their total content of membrane proteins. The ability to mechanically probe membrane proteins directly in their native membrane enables the phenotyping of different cell types with almost single-cell level of resolution.

scholarly journals Massively multiplex single-cell Hi-C

2016 ◽  
Author(s):  
Vijay Ramani ◽  
Xinxian Deng ◽  
Kevin L Gunderson ◽  
Frank J Steemers ◽  
Christine M Disteche ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Cell Types ◽  
Cell Heterogeneity ◽  
Proof Of Concept ◽  
Chromosome Conformation ◽  
Large Numbers ◽  
Conformational Properties ◽  
Novel Method ◽  
Different Cell Types

AbstractWe present combinatorial single cell Hi-C, a novel method that leverages combinatorial cellular indexing to measure chromosome conformation in large numbers of single cells. In this proof-of-concept, we generate and sequence combinatorial single cell Hi-C libraries for two mouse and four human cell types, comprising a total of 9,316 single cells across 5 experiments. We demonstrate the utility of single-cell Hi-C data in separating different cell types, identify previously uncharacterized cell-to-cell heterogeneity in the conformational properties of mammalian chromosomes, and demonstrate that combinatorial indexing is a generalizable molecular strategy for single-cell genomics.

scholarly journals Massively parallel single cell lineage tracing using CRISPR/Cas9 induced genetic scars

2017 ◽  
Author(s):  
Bastiaan Spanjaard ◽  
Bo Hu ◽  
Nina Mitic ◽  
Jan Philipp Junker
Keyword(s):  
Single Cell ◽  
Computational Analysis ◽  
Systematic Approach ◽  
Single Cells ◽  
Cell Lineage ◽  
Transcriptome Profiling ◽  
Cell Types ◽  
Lineage Tracing ◽  
Lineage Trees ◽  
Different Cell Types

A key goal of developmental biology is to understand how a single cell transforms into a full-grown organism consisting of many different cell types. Single-cell RNA-sequencing (scRNA-seq) has become a widely-used method due to its ability to identify all cell types in a tissue or organ in a systematic manner 1–3. However, a major challenge is to organize the resulting taxonomy of cell types into lineage trees revealing the developmental origin of cells. Here, we present a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA-seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes, we reconstruct developmental lineage trees in zebrafish larvae and adult fish. In future analyses, LINNAEUS (LINeage tracing by Nuclease-Activated Editing of Ubiquitous Sequences) can be used as a systematic approach for identifying the lineage origin of novel cell types, or of known cell types under different conditions.

scholarly journals Combinatorial Control of Exon Recognition

2007 ◽  
Vol 283 (3) ◽  
pp. 1211-1215 ◽  
Author(s):  
Klemens J. Hertel
Keyword(s):  
Alternative Splicing ◽  
Splice Site ◽  
Cell Types ◽  
Splice Sites ◽  
Splice Site Selection ◽  
Multiple Parameters ◽  
Splicing Regulators ◽  
Eukaryotic Genes ◽  
Different Cell Types ◽  
Mature Mrnas

Pre-mRNA splicing is a fundamental process required for the expression of most metazoan genes. It is carried out by the spliceosome, which catalyzes the removal of noncoding intronic sequences to assemble exons into mature mRNAs prior to export and translation. Given the complexity of higher eukaryotic genes and the relatively low level of splice site conservation, the precision of the splicing machinery in recognizing and pairing splice sites is impressive. Introns ranging in size from <100 up to 100,000 bases are removed efficiently. At the same time, a large number of alternative splicing events are observed between different cell types, during development, or during other biological processes. This extensive alternative splicing implies a significant flexibility of the spliceosome to identify and process exons within a given pre-mRNA. To reach this flexibility, splice site selection in higher eukaryotes has evolved to depend on multiple parameters such as splice site strength, the presence or absence of splicing regulators, RNA secondary structures, the exon/intron architecture, and the process of pre-mRNA synthesis itself. The relative contributions of each of these parameters control how efficiently splice sites are recognized and flanking introns are removed.

scholarly journals The Heterocyst-Specific NsiR1 Small RNA Is an Early Marker of Cell Differentiation in Cyanobacterial Filaments

mBio ◽  
2014 ◽  
Vol 5 (3) ◽  
Author(s):  
Alicia M. Muro-Pastor
Keyword(s):  
Small Rna ◽  
Single Cells ◽  
Nitrogen Deficiency ◽  
Cell Types ◽  
Nitrogen Fixing ◽  
Vegetative Cells ◽  
Early Marker ◽  
Multicellular Organisms ◽  
Pcc 7120 ◽  
Different Cell Types

ABSTRACT Differentiation of single cells along filaments of cyanobacteria constitutes one of the simplest developmental patterns in nature. In response to nitrogen deficiency, certain cells located in a semiregular pattern along filaments differentiate into specialized nitrogen-fixing cells called heterocysts. The process involves the sequential activation of many genes whose expression takes place, either exclusively or at least more strongly, in those cells undergoing differentiation. In the model cyanobacterium Anabaena (Nostoc) sp. strain PCC 7120, increased transcription of hetR, considered the earliest detectable heterocyst-specific transcript, has been reported to occur in pairs or even in clusters of cells, thus making it difficult to identify prospective heterocysts during the early stages of differentiation, before any morphological change is detectable. The promoter of nsiR1 (nitrogen stress inducible RNA1), a heterocyst-specific small RNA, constitutes a minimal sequence promoting heterocyst-specific transcription. Using confocal fluorescence microscopy, I have analyzed expression of a gfp reporter transcriptionally fused to P nsiR1 . The combined analysis of green fluorescence (reporting transcriptional activity from P nsiR1 ) and red fluorescence (an indication of progress in the differentiation of individual cells) shows that expression of P nsiR1 takes place in single cells located in a semiregular pattern before any other morphological or fluorescence signature of differentiation can be observed, thus providing an early marker for cells undergoing differentiation. IMPORTANCE Cyanobacterial filaments containing heterocysts constitute an example of bacterial division of labor. When using atmospheric nitrogen, these filaments behave as multicellular organisms in which two different cell types (vegetative cells and nitrogen-fixing heterocysts) coexist and cooperate to achieve growth of the filament as a whole. The molecular basis governing the differentiation of individual vegetative cells, and thus the establishment of a one-dimensional pattern from cells that are apparently the same, remains one of the most intriguing aspects of this differentiation process. Recent evidence suggests that, at any given time, some cells in the filaments are more likely than others to become heterocysts when nitrogen limitation is encountered. The robust heterocyst-specific nsiR1 promoter, which is induced very early during differentiation, provides a valuable tool to analyze issues such as early candidacy or the possible role of transcriptional noise in determining the fate of specific cells in cyanobacterial filaments.

Cytoplasmic fine-structure

1958 ◽  
Vol 148 (932) ◽  
pp. 290-308 ◽  
Keyword(s):  
Fine Structure ◽  
Electron Microscope ◽  
Large Body ◽  
Cell Types ◽  
General Terms ◽  
Thin Sectioning ◽  
Cytoplasmic Change ◽  
Cellular Specialization ◽  
Different Cell Types ◽  
Made In

In any attempt to reach an integrated conception of the cytoplasm in variation and development, a study of the fine-structure of the cytoplasm and its relation to the nucleus must take its proper place. It is the object of our paper to survey, as adequately as we are able in a limited space, selected data on cytoplasmic fine-structure and we hope that this will provide the Discussion with a background against which to consider a morphological basis for that variation which genetical studies may show to be due to changes in the organization of the cytoplasm. It is possibly too early as yet to hope that examination of the morphology of cells by means of the electron microscope will reveal cytoplasmic differences between organisms which differ from one another in the characteristics studied in genetical experiments; it would be useful to the future study of the problem of Cytoplasmic change, however, to know within what limits speculation must be reasonably confined by the nature of the fine-structure of the cytoplasm. It is now becoming apparent that though cells of organisms widely separated phylogenetically have basic similarities, cellular specialization has led to some diversity in the fine-structure. In the first part of the paper we shall briefly consider the development of electron-microscope methods, e.g. the thin-sectioning procedures, which have made it possible to examine biological material at a resolution which allows comparatively small macromolecular units to be distinguished (10 to 50 Å); at the same time we shall emphasize the danger of overestimating the significance of the observations that have been made. In the second part we shall consider certain selected features of the cell in some detail; in view of the large body of literature on cell fine-structure that is now available (publications numbered over 100 during the last 6 months of 1956) no attempt will be made to review all the findings which have been published during the last few years. Rather we will consider, in general terms, the structure of each component, then compare the variations in structural form noted in different cell types and indicate where there is direct disagreement in the findings of various authorities.

scholarly journals ProtAnno, an Automated Cell Type Annotation Tool for Single Cell Proteomics Data that Integrates Information from Multiple Reference Sources

2021 ◽  
Author(s):  
Wenxuan Deng ◽  
Biqing Zhu ◽  
Seyoung Park ◽  
Tomokazu S. Sumida ◽  
Avraham Unterman ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Earth Metal ◽  
Cell Types ◽  
Specific Cell ◽  
Cell Type ◽  
Proteomics Data ◽  
Data Annotation ◽  
Different Cell Types ◽  
Unique Molecular Identifier

Compared with sequencing-based global genomic profiling, cytometry labels targeted surface markers on millions of cells in parallel either by conjugated rare earth metal particles or Unique Molecular Identifier (UMI) barcodes. Correct annotation of these cells to specific cell types is a key step in the analysis of these data. However, there is no computational tool that automatically annotates single cell proteomics data for cell type inference. In this manuscript, we propose an automated single cell proteomics data annotation approach called ProtAnno to facilitate cell type assignments without laborious manual gating. ProtAnno is designed to incorporate information from annotated single cell RNA-seq (scRNA-seq), CITE-seq, and prior data knowledge (which can be imprecise) on biomarkers for different cell types. We have performed extensive simulations to demonstrate the accuracy and robustness of ProtAnno. For several single cell proteomics datasets that have been manually labeled, ProtAnno was able to correctly label most single cells. In summary, ProtAnno offers an accurate and robust tool to automate cell type annotations for large single cell proteomics datasets, and the analysis of such annotated cell types can offer valuable biological insights.

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