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Draft: Human Cytome Projectformer:
Cell Biochemistry Group
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Substantial progress in human genomics is paired with insufficient knowledge on dynamic processes associating biomolecules within cells and cells to cell systems, tissue and organisms as well as on many disease processes. The enormeous biocomplexity requires a far better understanding of elementary processes like cell cycle regulation, differentiation, tissue formation and apoptosis or of molecular disease mechanisms to provide amongst other things, therapy related, individualized disease course predictions in patients for the application of early preventive therapies as a precondition for the improvement of general health.
With diseases as deviations of molecular processes in cells or cell systems (cytomes) from normal levels, the advances in instrumentation and in fluorescence labeling techniques of biomolecules permit large scale single cell multiparametric analysis by image and flow cytometry of entire cell systems in combination with information collection and exhaustive knowledge extraction from the total heterogeneity of measured cells (cytomics). This experimental progress has led to the predictive medicine by cytomics concept for individualized disease course predictions in patients.
At the given biocomplexity of tissues, it seems presently difficult to shortly understand the enormous molecular complexity of cell systems exclusively by hypothesis-driven systematic perturbations of model systems followed by computer modelling of molecular pathways as suggested by the systems biology concept. The high redundancy of molecular pathways like in cell signalling, cell proliferation or during apoptosis as well as the adequate choice of perturbation conditions requires a high number of investigations for the collection of a multitude of details without certainty to have focused on ultimately relevant disease associated metabolic pathways, molecular hotspots or pharmaceutical targets by this bottom-up approach.
Alternatively, the top-down approach of analyzing differential changes of molecular single cell phenotypes of entire cell systems by data pattern classification reveals differences between diseased and healthy persons following the concept that diseases are caused by molecular changes in cells with cells constituting the basic function units of organisms.
Molecular cell phenotypes result from genotype and exposure influences. Differential molecular cell phenotypes represent a direct or indirect molecular correlate of the disease process depending on whether diseased or disease associated cells are investigated. Individual data patterns may vary to a certain extent between patients due the person to person differences of genotype and exposure conditions.
The genomic information serves in this context as an inventory of the biomolecular capacity of organisms. With this knowledge, the disease induced molecular differential can be directly explored in patient cells.
The hypothesis-driven parameter selection for the differential screens of diseased versus healthy cells in combination with an hypothesis-free multiparametric data pattern classification directly identifies the disease associated molecular hotspots. It is largely independent of the exact a-priori knowledge of the ultimate molecular causes of disease. In particular, it is not necessary to first investigate the molecular effects of systematic perturbations in model cell systems to acquire enough knowledge about the molecular cell systems behavior as a prerequisite for the study of specific molecular disease processes as suggested by the bottom-up concept of systems biology. The analysis of the frequently highly redundant molecular pathways of gene realisation is initially bypassed in this way, simplifying the investigations significantly.
Disease inducing molecular pathways are explored by a molecular reverse engineering strategy of molecular cell phenotype differentials at the cell systems level. The pathways can be mathematically modeled (biomedical cell systems biology) to further increase the predictive capacity. It is likely that new target molecules and lead structures for drug discovery will be detected by the hypothesis-free data pattern classification due to the opening and penetration of unknown molecular knowledge spaces. In this sense cytomics represent an entry to biomedical cell systems biology within the larger framework of molecular cell systems biology.
This top-down analysis concept reaches from the expressed molecular cell phenotype as disease correlate in the periphery down to the molecular coding information at the genome level. The potential of the concept consists in its general applicability for various areas of clinical or ambulant medicine.
As a consequence of these considerations
thoughts
about the challenges of a
human cytome project
have emerged and raised
interest
Considering that fluorescence and light scatter signals are measured on relative scales, that differnt image or flow cytonmetry analysis instruments of the same type do not provide identical signals for the same objects due to tolerances in the multitude of electronical, optical and mechanical parts, and that cell population oriented evaluations in multiparametric data spaces remain to some extent arbitrary, it seems appropriate to express results in a relational way. Errors of accuracy mostly cancel when parameter values are relationally expressed as fraction of the means of results from suitable reference groups. The relational expression conserves the relative individual positions of the parameter means and their coefficients of variation as measure of the dispersion of the parameter value distributions. Furthermore the identity of reference groups from various laboratories can be objectively verified.
A relational classification system for the objective molecular description of diseases and elementary cellular states like differentiation, maturation, division or malignancy at the cellular level can then be established Different cell types will be in a standardised relation to each other in some kind of periodic system of cells. This requires considerations about information collection, data analysis strategies and bioinformatic knowledge extraction. Results can be stored in a relational knowledge system or framework for scientific hypothesis development as well as for directly medicine related purposes like predictive medicine by cytomics that is the prediction on the therapy dependent future disease course in individual patients, furthermore the possibilities of personalized therapy, the search for new pharmaceutical targets and the definition of specific projects.
The establishment of such a system in a human cytome project, using the various single cell oriented molecular technologies in conjunction with specific biomolecule labelling, represents a combined challenge to science, medicine and technological innovation. Such a program should be implemented at an international scale by providing specific focus support from various funding agencies in a concerted way.
| © 2010 G.Valet |