Use of High Throughput Genomic Tools for the Study of Endothelial Cell Biology

Vascular endothelial cells maintain the interface between the systemic circulation and soft tissues and mediate critical processes such as inflammation in a vascular bed-selective fashion. To expand our understanding of the genetic pathways that underlie these specific functions, we have focused on the identification of novel genes that are differentially expressed in all endothelial cells, as well as restricted groups of this cell type. Virtual subtraction was conducted employing gene expression data deposited in public databases and 384 genes identified.11 The microarray data derived through these experiments have been deposited in the GEO expression database at the NCBI and has been given the accession number GPL217 , with others pending. Primary data and supplementary material associated with this manuscript are being deposited at the following website: http://quertermous.stanford.edu . These genes were spotted on custom microarrays, along with 288 genes identified through subtraction cloning from TGF-β-stimulated endothelial cells. Arrays were evaluated with RNA samples representing endothelial cells cultured from four vascular sources and five non-endothelial cell types. These studies identified 64 pan-endothelial markers that were differentially expressed with at least a threefold difference (range 3- to 55-fold). In addition, differences in gene expression profiles among endothelial cells from different vascular beds were identified. Validation of these findings was performed by RNA blot expression studies, and a number of the novel genes were shown to be expressed under angiogenic conditions in the developing mouse embryo. The combined tools of database mining and transcriptional profiling thus provide expanded knowledge of endothelial cell gene expression and endothelial cell biology.

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Effects of the combination of TRC105 and bevacizumab on endothelial cell biology

Investigational New Drugs ◽

10.1007/s10637-014-0129-y ◽

2014 ◽

Vol 32 (5) ◽

pp. 851-859 ◽

Cited By ~ 28

Author(s):

Yingmiao Liu ◽

Hongyu Tian ◽

Gerard C. Blobe ◽

Charles P. Theuer ◽

Herbert I. Hurwitz ◽

...

Keyword(s):

Endothelial Cell ◽

Cell Biology

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Learning from heterogeneous data sources: an application in spatial proteomics

10.1101/022152 ◽

2015 ◽

Cited By ~ 1

Author(s):

Lisa M. Breckels ◽

Sean Holden ◽

David Wojnar ◽

Claire M. Mulvey ◽

Andy Christoforou ◽

...

Keyword(s):

Mass Spectrometry ◽

Support Vector Machine ◽

Transfer Learning ◽

High Throughput ◽

Cell Biology ◽

Heterogeneous Data ◽

Data Sources ◽

Support Vector ◽

Proteomics Data ◽

Heterogeneous Data Sources

AbstractSub-cellular localisation of proteins is an essential post-translational regulatory mechanism that can be assayed using high-throughput mass spectrometry (MS). These MS-based spatial proteomics experiments enable us to pinpoint the sub-cellular distribution of thousands of proteins in a specific system under controlled conditions. Recent advances in high-throughput MS methods have yielded a plethora of experimental spatial proteomics data for the cell biology community. Yet, there are many third-party data sources, such as immunofluorescence microscopy or protein annotations and sequences, which represent a rich and vast source of complementary information. We present a unique transfer learning classification framework that utilises a nearest-neighbour or support vector machine system, to integrate heterogeneous data sources to considerably improve on the quantity and quality of sub-cellular protein assignment. We demonstrate the utility of our algorithms through evaluation of five experimental datasets, from four different species in conjunction with four different auxiliary data sources to classify proteins to tens of sub-cellular compartments with high generalisation accuracy. We further apply the method to an experiment on pluripotent mouse embryonic stem cells to classify a set of previously unknown proteins, and validate our findings against a recent high resolution map of the mouse stem cell proteome. The methodology is distributed as part of the open-source Bioconductor pRoloc suite for spatial proteomics data analysis.AbbreviationsLOPITLocalisation of Organelle Proteins by Isotope TaggingPCPProtein Correlation ProfilingMLMachine learningTLTransfer learningSVMSupport vector machinePCAPrincipal component analysisGOGene OntologyCCCellular compartmentiTRAQIsobaric tags for relative and absolute quantitationTMTTandem mass tagsMSMass spectrometry

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[15] Quantitative high throughput endothelial cell migration and invasion assay system

Methods in Enzymology - Redox Cell Biology and Genetics Part A ◽

10.1016/s0076-6879(02)52017-6 ◽

2002 ◽

pp. 175-182 ◽

Cited By ~ 7

Author(s):

James C. Maliakal

Keyword(s):

Cell Migration ◽

Endothelial Cell ◽

High Throughput ◽

Assay System ◽

Endothelial Cell Migration ◽

Migration And Invasion ◽

Invasion Assay ◽

Cell Migration And Invasion

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Droplet-based microfluidics in biomedical applications

Biofabrication ◽

10.1088/1758-5090/ac39a9 ◽

2021 ◽

Author(s):

Leyla Amirifar ◽

Mohsen Besanjideh ◽

Rohollah Nasiri ◽

Amir Shamloo ◽

Fatemeh Nasrollahi ◽

...

Keyword(s):

Drug Delivery ◽

High Throughput ◽

Paradigm Shift ◽

Cell Biology ◽

Biomedical Applications ◽

Molecular Detection ◽

Droplet Microfluidics ◽

Droplet Generation ◽

Microfluidic Platforms ◽

Standard Methods

Abstract Droplet-based microfluidic systems have been employed to manipulate discrete fluid volumes with immiscible phases. Creating the fluid droplets at microscale has led to a paradigm shift in mixing, sorting, encapsulation, sensing, and designing high throughput devices for biomedical applications. Droplet microfluidics has opened many opportunities in microparticle synthesis, molecular detection, diagnostics, drug delivery, and cell biology. In the present review, we first introduce standard methods for droplet generation (i.e., passive and active methods) and discuss the latest examples of emulsification and particle synthesis approaches enabled by microfluidic platforms. Then, the applications of droplet-based microfluidics in different biomedical applications are detailed. Finally, a general overview of the latest trends along with the perspectives and future potentials in the field are provided.

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