Single-cell-based models in biology and medicine

Single-Cell-Based Models in Biology and Medicine

Gersbach, Ph. Abstract The study of cell lineage commitment is critical to improving our understanding of tissue development and regeneration and to enhancing stem cell-based therapies and engineered tissue replacements. Recently, the discovery of an unanticipated degree of variability in fundamental biological processes, including divergent responses of genetically identical cells to various stimuli, has provided mechanistic insight into cellular decision making and the collective behavior of cell populations.

Therefore the study of lineage commitment with single-cell resolution could provide greater knowledge of cellular differentiation mechanisms and the influence of noise on cell processes.

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This will require the adoption of new technologies singlewandern kassel single-cell analysis, in contrast to traditional methods that typically measure average values of bulk population behavior. This review discusses the recent development of methods for analyzing the behavior of individual cells and single-cell-based models in biology and medicine these approaches are leading to deeper understanding and better control of cellular decision making.

Keywords: single-cell, stochastic, single-molecule, lineage commitment, cellular heterogeneity, noise, gene expression, cell differentiation Introduction The goal of regenerative medicine is to develop cell-based approaches to restoring the function of diseased or damaged tissues [ 1 ]. This unique approach to repairing tissues is accompanied by a diverse set of challenges. Regenerative medicine strategies often rely on specific lineage-committed cell types that perform all the appropriate functions for target tissue regeneration.

Alternatively, progenitor cells, such as stem cells, may be used such that native or external cues guide their differentiation into the appropriate cell type. For both approaches, it is essential to have a thorough understanding of cell lineage commitment and precise control over this process for these cell-based therapies to be safe and effective.

The Role of Single-Cell Analyses in Understanding Cell Lineage Commitment

A large toolkit exists for directed cell lineage commitment in regenerative applications. Soluble growth factors can be used to maintain pluripotency or direct stem cells to defined lineages by activating natural signaling pathways [ 23 ]. Additionally, the composition and properties of the extracellular matrix and intercellular contact can control these pathways [ 45 ]. Various biomaterials have been engineered to serve as scaffolds that guide cell fate for christian dating physical attraction in vitro and in vivo applications [ 67 ].

New therapies being released to market show the single-cell-based models in biology and medicine of regenerative medicine using techniques such as these [ 8 ].

The field is being further refined by single-cell-based models in biology and medicine development of gene therapies and genetic reprogramming, as discussed in more detail below. An increased understanding of cell lineage commitment has the potential to catalyze advances in all of these areas. Long-term changes in cell behavior, including cell lineage commitment, are almost exclusively guided by changes in gene expression.

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Transcription factors are the main components of the cellular machinery that interact with DNA and modulate gene expression. The delivery of specific factors associated with particular cell states can reprogram the cell by activating the corresponding gene networks [ 9 - 13 ].

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  • At Helmholtz Zentrum München, a method of targeted RNA sequencing transcriptome analysis has now been developed, which precisely detects the smallest amounts of gene transcripts in single cells.
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The prototypical example of transcription factor-driven differentiation in mammalian cells is the induction of myogenesis by the muscle-specific transcription factor MyoD [ 1415 ]. Forced expression of MyoD robustly converts various cell types to a skeletal myoblast-like phenotype [ 1617 ].

Master transcription factors that induce several other cell lineages have also been identified.

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For example, Runx2 drives osteoblast differentiation and skeletogenesis [ dating app bremen - 22 ], Sox9 regulates cartilage development and chondrogenic gene expression [ 23 - 25 ], and Ascl1 in conjunction with other factors induces the development of a neuronal phenotype [ 26 - 30 ].

Furthermore, the delivery of Pdx1 transdifferentiates liver and exocrine cells into an insulin-producing phenotype similar to pancreatic beta-islet cells [ 31 - 35 ] and GATA4 with a cocktail of other factors can drive cells to become functionally similar to cardiomyocytes both in single-cell-based models in biology and medicine [ 36 ] and single-cell-based models in biology and medicine vivo [ 3738 ].

These are only a few examples of the different factors found to induce transdifferentiation.

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The landmark discovery that the transcription factors Oct4, Sox2, Klf4, and c-Myc can create a pluripotent state in terminally-differentiated adult cells [ 39 - 41 ] has created numerous possibilities for directing cells towards a desired phenotype for applications in regenerative medicine [ 13 ]. Importantly, all of these examples of transcription factor-driven genetic reprogramming are inefficient processes.

However, there is scant knowledge about the bias of different microcultivation technologies on cellular functions. We therefore performed a systematic cross-platform comparison of three different microscale cultivation systems commonly harnessed in single-cell analysis: microfluidic non-contact cell traps driven by negative dielectrophoresis, microfluidic monolayer growth chambers, and semi-solid agarose pads.

Production of induced pluripotent stem cells iPSCs results in reprogramming frequencies that range from 0. Early iterations of iPSC production methods were unable to meet some hallmarks of pluripotency such as chimera generation or germline-competency [ 3943 ].

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These results suggested that cells can exist in a partially reprogrammed state. In this state, cells are not able to revert to their original phenotype but also are not completely reprogrammed to the intended phenotype [ 44 ].

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Similarly, individual cells show variable responses to the same reprogramming stimuli, possibly because of stochastic variability in the population [ 45 ]. Furthermore, reprogrammed iPSCs that have not differentiated are capable of forming tumors after implantation, and therefore it must be ensured that all cells used therapeutically have been completely directed to a nontumorigenic phenotype.

A Novel Method to Characterize Genes with High-Precision in Single Cells

A thorough understanding of decision making at the single-cell level is necessary to address these issues. Additionally, the observation of single-cell behavior and heterogeneity within a cell population can provide deeper insight into the mechanisms of natural differentiation and lineage commitment.

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This review focuses on cellular heterogeneity in the context of cell differentiation and genetic reprogramming and discusses methods for analyzing single-cell behavior that can expand our understanding of cellular lineage commitment. Origins of Heterogeneity in Cell Populations The value of a biochemical measurement averaged across a large cell population does not necessarily describe the value for any one cell within that population Fig.

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This misrepresentation is exacerbated in data sets that consist of dissimilar binary states, such as distinct cell phenotypes.