Proteomics: Opportunities and Challenges

2011 ◽  
Vol 3 (4) ◽  
pp. 1165-1172 ◽  
Author(s):  
Parag A Pathade ◽  
Vinod A Bairagi ◽  
Yogesh S. Ahire ◽  
Neela M Bhatia
Keyword(s):  
Biomedical Research ◽  
Therapy Monitoring ◽  
Dynamic State ◽  
Data Sets ◽  
Protein Markers ◽  
Drug Target Discovery ◽  
Proteomic Data ◽  
Cell Tissue ◽  
A Cell ◽  
Analytical Tools

‘‘Proteomics’’, is the emerging technology leading to high-throughput identification and understanding of proteins. Proteomics is the protein equivalent of genomics and has captured the imagination of biomolecular scientists, worldwide. Because proteome reveals more accurately the dynamic state of a cell, tissue, or organism, much is expected from proteomics to indicate better disease markers for diagnosis and therapy monitoring. Proteomics is expected to play a major role in biomedical research, and it will have a significant impact on the development of diagnostics and therapeutics for cancer, heart ailments and infectious diseases, in future. Proteomics research leads to the identification of new protein markers for diagnostic purposes and novel molecular targets for drug discovery.  Though the potential is great, many challenges and issues remain to be solved, such as gene expression, peptides, generation of low abundant proteins, analytical tools, drug target discovery and cost. A systematic and efficient analysis of vast genomic and proteomic data sets is a major challenge for researchers, today. Nevertheless, proteomics is the groundwork for constructing and extracting useful comprehension to biomedical research. This review article covers some opportunities and challenges offered by proteomics.   

Automated cell counting of astrocytes on patterned substrates containing aliphatic and charged properties

1995 ◽  
Vol 53 ◽  
pp. 974-975
Author(s):  
W. Shain ◽  
H. Ancin ◽  
H.C. Craighead ◽  
M. Isaacson ◽  
L. Kam ◽  
...  
Keyword(s):  
Cell Culture ◽  
Cell Attachment ◽  
Culture Method ◽  
Cell Counting ◽  
Data Sets ◽  
Nuclear Staining ◽  
Double Positive ◽  
A Cell ◽  
Wafer Test ◽  
Cell Densities

Neural protheses have potential to restore nervous system functions lost by trauma or disease. Nanofabrication extends this approach to implants for stimulating and recording from single or small groups of neurons in the spinal cord and brain; however, tissue compatibility is a major limitation to their practical application. We are using a cell culture method for quantitatively measuring cell attachment to surfaces designed for nanofabricated neural prostheses.Silicon wafer test surfaces composed of 50-μm bars separated by aliphatic regions were fabricated using methods similar to a procedure described by Kleinfeld et al. Test surfaces contained either a single or double positive charge/residue. Cyanine dyes (diIC18(3)) stained the background and cell membranes (Fig 1); however, identification of individual cells at higher densities was difficult (Fig 2). Nuclear staining with acriflavine allowed discrimination of individual cells and permitted automated counting of nuclei using 3-D data sets from the confocal microscope (Fig 3). For cell attachment assays, LRM5 5 astroglial cells and astrocytes in primary cell culture were plated at increasing cell densities on test substrates, incubated for 24 hr, fixed, stained, mounted on coverslips, and imaged with a 10x objective.

scholarly journals Elastic transformation of histological slices allows precise co-registration with microCT data sets for a refined virtual histology approach

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jonas Albers ◽  
Angelika Svetlove ◽  
Justus Alves ◽  
Alexander Kraupner ◽  
Francesca di Lillo ◽  
...  
Keyword(s):  
Three Dimensional ◽  
High Specificity ◽  
Histopathological Analysis ◽  
Specific Information ◽  
Data Sets ◽  
Virtual Histology ◽  
Novel Approach ◽  
Cell Tissue ◽  
Immuno Histochemistry ◽  
3D Information

AbstractAlthough X-ray based 3D virtual histology is an emerging tool for the analysis of biological tissue, it falls short in terms of specificity when compared to conventional histology. Thus, the aim was to establish a novel approach that combines 3D information provided by microCT with high specificity that only (immuno-)histochemistry can offer. For this purpose, we developed a software frontend, which utilises an elastic transformation technique to accurately co-register various histological and immunohistochemical stainings with free propagation phase contrast synchrotron radiation microCT. We demonstrate that the precision of the overlay of both imaging modalities is significantly improved by performing our elastic registration workflow, as evidenced by calculation of the displacement index. To illustrate the need for an elastic co-registration approach we examined specimens from a mouse model of breast cancer with injected metal-based nanoparticles. Using the elastic transformation pipeline, we were able to co-localise the nanoparticles to specifically stained cells or tissue structures into their three-dimensional anatomical context. Additionally, we performed a semi-automated tissue structure and cell classification. This workflow provides new insights on histopathological analysis by combining CT specific three-dimensional information with cell/tissue specific information provided by classical histology.

scholarly journals Syntaxin 1A Gene Is Negatively Regulated in a Cell/Tissue Specific Manner by YY1 Transcription Factor, Which Binds to the −183 to −137 Promoter Region Together with Gene Silencing Factors Including Histone Deacetylase

Biomolecules ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 146
Author(s):  
Takahiro Nakayama ◽  
Toshiyuki Fukutomi ◽  
Yasuo Terao ◽  
Kimio Akagawa
Keyword(s):  
Transcription Factor ◽  
Gene Silencing ◽  
Protein Complex ◽  
Promoter Region ◽  
Neuronal Cell ◽  
Syntaxin 1A ◽  
Tissue Specific ◽  
Cell Tissue ◽  
Specific Manner ◽  
A Cell

The HPC-1/syntaxin 1A (Stx1a) gene, which is involved in synaptic transmission and neurodevelopmental disorders, is a TATA-less gene with several transcription start sites. It is activated by the binding of Sp1 and acetylated histone H3 to the −204 to +2 core promoter region (CPR) in neuronal cell/tissue. Furthermore, it is depressed by the association of class 1 histone deacetylases (HDACs) to Stx1a–CPR in non-neuronal cell/tissue. To further clarify the factors characterizing Stx1a gene silencing in non-neuronal cell/tissue not expressing Stx1a, we attempted to identify the promoter region forming DNA–protein complex only in non-neuronal cells. Electrophoresis mobility shift assays (EMSA) demonstrated that the −183 to −137 OL2 promoter region forms DNA–protein complex only in non-neuronal fetal rat skin keratinocyte (FRSK) cells which do not express Stx1a. Furthermore, the Yin-Yang 1 (YY1) transcription factor binds to the −183 to −137 promoter region of Stx1a in FRSK cells, as shown by competitive EMSA and supershift assay. Chromatin immunoprecipitation assay revealed that YY1 in vivo associates to Stx1a–CPR in cell/tissue not expressing Stx1a and that trichostatin A treatment in FRSK cells decreases the high-level association of YY1 to Stx1a-CPR in default. Reporter assay indicated that YY1 negatively regulates Stx1a transcription. Finally, mass spectrometry analysis showed that gene silencing factors, including HDAC1, associate onto the −183 to −137 promoter region together with YY1. The current study is the first to report that Stx1a transcription is negatively regulated in a cell/tissue-specific manner by YY1 transcription factor, which binds to the −183 to −137 promoter region together with gene silencing factors, including HDAC.

scholarly journals MMP-14-targeted Nanoprobe for in vivo Fluorescence Imaging of Rupture-prone Carotid Plaques

2021 ◽  
Author(s):  
Mengtao Han ◽  
Kaining Liu ◽  
Hongqiu Xiao ◽  
Tao Sun ◽  
Fei Wang ◽  
...  
Keyword(s):  
Fluorescence Imaging ◽  
Fluorescence Signal ◽  
Plaque Progression ◽  
Carotid Plaques ◽  
In Vivo Fluorescence ◽  
Cell Tissue ◽  
A Cell ◽  
In Vivo Fluorescence Imaging ◽  
Membrane Anchoring

Abstract Background: The identification of rupture-prone carotid plaques for preventing stroke remains a clinical challenge. Macrophage matrix metalloproteinase (MMP)-14, which contributes to plaque progression and destabilisation, could be a promising biomarker for plaque imaging. This study aimed to design and synthesise an MMP-14-targeted nanoprobe to noninvasively visualise the behaviour of M1 macrophages in atherosclerotic plaques.Methods: A fluorescence molecular imaging probe ([email protected]) was constructed by covalently attaching the fluorescent dye cyanine (Cy) 5.5, an MMP-14 substrate, and polyethylene glycol (PEG) 5000-wrapped gold nanoparticles (AuNPs), and then administered via tail vein injection to carotid atherosclerosis models for in vivo fluorescence imaging. Additionally, carotid tissues and cultured macrophages were analysed for nanoprobe binding, and MMP-14 and inflammation-related marker expression was evaluated by polymerase chain reaction, western blotting, and immunohistochemistry.Results: MMP-14 expression significantly increased with plaque progression, along with the upregulation of MMP-2 and inflammatory M1 markers, CD68 and F4/80, and significant downregulation of the M2 marker CD206. All of cell, tissue and in vivo fluorescence imaging exhibited a favourable targeting efficacy of [email protected] for MMP-14.Conclusions: MMP-14, a cell membrane-anchoring enzyme, can serve as a biomarker of vulnerable plaques, and MMP-14 substrate-based [email protected], with an intense fluorescence signal after activation and good biocompatibility, can be applied to screen for and monitor plaque progression in vivo.

scholarly journals Probabilistic data integration identifies reliable gametocyte-specific proteins and transcripts in malaria parasites

2017 ◽  
Author(s):  
Lisette Meerstein-Kessel ◽  
Robin van der Lee ◽  
Will Stone ◽  
Kjerstin Lanke ◽  
David A Baker ◽  
...  
Keyword(s):  
Transcript Abundance ◽  
Data Sets ◽  
Asexual Blood Stage ◽  
Systematic Analysis ◽  
Humoral Immune ◽  
Proteomic Data ◽  
Genome Wide ◽  
Field Samples ◽  
Specific Proteins ◽  
Specific Antigens

AbstractPlasmodium gametocytes are the sexual forms of the malaria parasite essential for transmission to mosquitoes. To better understand how gametocytes differ from asexual blood-stage parasites, we performed a systematic analysis of available ‘omics data for P. falciparum and other Plasmodium species. 18 transcriptomic and proteomic data sets were evaluated for the presence of curated “gold standards” of 41 gametocyte-specific versus 46 non-gametocyte genes and integrated using Bayesian probabilities, resulting in gametocyte-specificity scores for all P. falciparum genes.To illustrate the utility of the gametocyte score, we explored newly predicted gametocyte-specific genes as potential biomarkers of gametocyte carriage and exposure. We analyzed the humoral immune response in field samples against 30 novel gametocyte-specific antigens and found five antigens to be differentially recognized by gametocyte carriers as compared to malaria-infected individuals without detectable gametocytes. We also validated the gametocyte-specificity of 15 identified gametocyte transcripts on culture material and samples from naturally infected individuals, resulting in eight transcripts that were >1000-fold higher expressed in gametocytes compared to asexual parasites and whose transcript abundance allowed gametocyte detection in naturally infected individuals. Our integrated genome-wide gametocyte-specificity scores provide a comprehensive resource to identify targets and monitor P. falciparum gametocytemia.

Social data collection and analyses

2021 ◽  
pp. 53-76
Author(s):  
Marie J. E. Charpentier ◽  
Marie Pelé ◽  
Julien P. Renoult ◽  
Cédric Sueur
Keyword(s):  
Social Relationships ◽  
Information Diffusion ◽  
Social Systems ◽  
Large Data ◽  
Natural Environments ◽  
Data Sets ◽  
Large Array ◽  
Fine Grained ◽  
Network Analyses ◽  
Analytical Tools

Sampling accurate and quantitative behavioural data requires the description of fine-grained patterns of social relationships and/or spatial associations, which is highly challenging, especially in natural environments. Although behavioural ecologists have tackled systematic studies on animals’ societies since the nineteenth century, new biologging technologies have the potential to revolutionise the sampling of animals’ social relationships. However, the tremendous quantity of data sampled and the diversity of biologgers (such as proximity loggers) currently available that allow the sampling of a large array of biological and physiological data bring new analytical challenges. The high spatiotemporal resolution of data needed when studying social processes, such as disease or information diffusion, requires new analytical tools, such as social network analyses, developed to analyse large data sets. The quantity and quality of the data now available on a large array of social systems bring undiscovered outputs, consistently opening new and exciting research avenues.

Normalization for Affymetrix GeneChips

2005 ◽  
Vol 44 (03) ◽  
pp. 414-417 ◽  
Author(s):  
M. Neuhäuser ◽  
T. Boes
Keyword(s):  
Biomedical Research ◽  
Computing Time ◽  
Normalization Method ◽  
Point Of View ◽  
Data Sets ◽  
Quantile Normalization ◽  
Biological Variability ◽  
Biological Origin ◽  
Affymetrix Genechips ◽  
Normalization Methods

Summary Objectives: The high density oligonucleotide micro-arrays from Affymetrix (Affymetrix GeneChips) are very popular in biomedical research. They enable to study the expression of thousands of genes simultaneously. In experiments with multiple arrays, normalization techniques are used to reduce the so-called obscuring variation, i.e. the technical variation that is of non-biological origin. Several different normalization methods have been proposed during the last years. Methods: We review published results about the comparison of normalization methods proposed for Affymetrix GeneChips. Results: The quantile normalization seems to perform favorably regarding precision (low variance), accuracy (low bias), and practicability (low computing time). However, according to very recent results [1], this normalization method can have an impact on the biological variability and, therefore, appears to be less than optimal from this point of view. Conclusion: Although the quantile normalization may be recommendable, more investigations based on more data sets are needed so that the different normalization methods can be evaluated on widely differing data.

scholarly journals Gene Therapy: The Molecular Bandage for Treating Genetic Disorders

1970 ◽  
Vol 3 (1) ◽  
pp. 24-27
Author(s):  
Md Manjurul Karim
Keyword(s):  
Gene Therapy ◽  
Viral Vectors ◽  
Genetic Disorders ◽  
Clinical Status ◽  
Genetic Material ◽  
Review Article ◽  
Cell Tissue ◽  
Efficient Gene ◽  
Effective Delivery ◽  
A Cell

The concept of gene therapy involves the transfer of genetic material into a cell, tissue, or whole organ, with a view to curing a disease or at least improving the clinical status of a patient. Much of its success relies heavily on the development of an effective delivery system that is capable of efficient gene transfer in a variety of tissues, without causing any associated pathogenic effects. Viral vectors currently offer the best choice for efficient gene delivery, what has been discussed in this review article. Their performance and pathogenecity has been evaluated in animal models, and encouraging results form the basis for clinical trials to treat genetic disorders and acquired diseases. Despite some initial success in these trials, vector development remains a seminal concern for improved gene therapy technologies. DOI: http://dx.doi.org/10.3329/akmmcj.v3i1.10110 AKMMCJ 2012; 3(1): 24-27

Bio-Inspired Algorithms in Bioinformatics I

2011 ◽  
pp. 236-240
Author(s):  
José Antonio Seoane Fernández ◽  
Mónica Miguélez Rico
Keyword(s):  
Human Genome ◽  
Heterogeneous Data ◽  
Genome Project ◽  
Biological Data ◽  
Organizational Level ◽  
Data Types ◽  
Proteomic Data ◽  
New Information ◽  
Cell Tissue ◽  
The Human Genome Project

Large worldwide projects like the Human Genome Project, which in 2003 successfully concluded the sequencing of the human genome, and the recently terminated Hapmap Project, have opened new perspectives in the study of complex multigene illnesses: they have provided us with new information to tackle the complex mechanisms and relationships between genes and environmental factors that generate complex illnesses (Lopez, 2004; Dominguez, 2006). Thanks to these new genomic and proteomic data, it becomes increasingly possible to develop new medicines and therapies, establish early diagnoses, and even discover new solutions for old problems. These tasks however inevitably require the analysis, filtration, and comparison of a large amount of data generated in a laboratory with an enormous amount of data stored in public databases, such as the NCBI and the EBI. Computer sciences equip biomedicine with an environment that simplifies our understanding of the biological processes that take place in each and every organizational level of live matter (molecular level, genetic level, cell, tissue, organ, individual, and population) and the intrinsic relationships between them. Bioinformatics can be described as the application of computational methods to biological discoveries (Baldi, 1998). It is a multidisciplinary area that includes computer sciences, biology, chemistry, mathematics, and statistics. The three main tasks of bioinformatics are the following: develop algorithms and mathematical models to test the relationships between the members of large biological datasets, analyze and interpret heterogeneous data types, and implement tools that allow the storage, retrieve, and management of large amounts of biological data.

scholarly journals A Dynamic, Cost-Aware, Optimized Maintenance Policy for Interactive Exploration of Linked Data

2019 ◽  
Vol 9 (22) ◽  
pp. 4818
Author(s):  
Usman Akhtar ◽  
Anita Sant’Anna ◽  
Sungyoung Lee
Keyword(s):  
Decision Support ◽  
Biomedical Research ◽  
Linked Data ◽  
Database Systems ◽  
Data Sets ◽  
Limited Capacity ◽  
Maintenance Policy ◽  
Maintenance Costs ◽  
Overhead Cost ◽  
New Knowledge

Vast amounts of data, especially in biomedical research, are being published as Linked Data. Being able to analyze these data sets is essential for creating new knowledge and better decision support solutions. Many of the current analytics solutions require continuous access to these data sets. However, accessing Linked Data at query time is prohibitive due to high latency in searching the content and the limited capacity of current tools to connect to these databases. To reduce this overhead cost, modern database systems maintain a cache of previously searched content. The challenge with Linked Data is that databases are constantly evolving and cached content quickly becomes outdated. To overcome this challenge, we propose a Change-Aware Maintenance Policy (CAMP) for updating cached content. We propose a Change Metric that quantifies the evolution of a Linked Dataset and determines when to update cached content. We evaluate our approach on two datasets and show that CAMP can reduce maintenance costs, improve maintenance quality and increase cache hit rates compared to standard approaches.

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