Somatic Gene Identification
Somatic Gene Identification - Related Videos
08:12
Assessing Somatic Hypermutation in Ramos B Cells after Overexpression or Knockdown of Specific Genes
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Cited by 5 •
2011
We describe how to perform retroviral or lentiviral infections of overexpression or shRNA-containing constructs in the human Ramos B-cell line and how to measure somatic hypermutation in these cells.
07:06
Tissue Regeneration with Somatic Stem Cells
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2023
Somatic or adult stem cells, like embryonic stem cells, are capable of self-renewal but demonstrate a restricted differentiation potential. Nonetheless, these cells are crucial to homeostatic processes and play an important role in tissue repair. By studying and manipulating this cell population, scientist may be able to develop new regenerative therapies for injuries and diseases. This video first defines somatic stem cells, and then explores the role these cells play in tissue regeneration.
09:54
The Use of Induced Somatic Sector Analysis (ISSA) for Studying Genes and Promoters Involved in Wood Formation and Secondary Stem Development
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Cited by 5 •
2016
Here we present a protocol that facilitates the medium to high throughput functional characterization of gene and promoter constructs in tree secondary stem tissue within comparatively short time frames. It is efficient, easy to use and widely applicable to a range of tree species.
08:56
Kinetic Measurement and Real Time Visualization of Somatic Reprogramming
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Cited by 5 •
2016
The protocol presented in this study describes methods for the real-time monitoring of reprogramming progression via the kinetic measurement of positive and negative pluripotent stem cell markers using flow cytometry analysis. The protocol also includes the imaging-based assessment of morphology, and marker or reporter expression during iPSC generation.
08:17
VIGS-Mediated Forward Genetics Screening for Identification of Genes Involved in Nonhost Resistance
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Cited by 22 •
2013
Virus-induced gene silencing is an useful tool for identifying genes involved in nonhost resistance of plants. We demonstrate the use of bacterial pathogens expressing GFPuv in identifying gene silenced plants susceptible to nonhost pathogens. This approach is easy, fast and facilitates large scale screening and similar protocol can be applied to studying various other plant-microbe interactions.
FAQs
Matched normal tissue provides a patient-specific comparison for interpreting sequence differences found in affected tissue. This comparison helps separate acquired changes from variants already present in the germline, normal cellular variation, and technical sequencing errors. As a result, researchers can attribute detected differences more confidently to processes occurring in the affected cells rather than to inherited genetic background.
Variant calling evaluates sequence data to identify candidate genetic differences and distinguish them from sequencing errors or normal cellular variation. In somatic gene identification, the result is interpreted alongside affected and matched normal samples, allowing inherited variants to be excluded. This filtering produces a more focused set of acquired changes for studying disease-related cellular mechanisms.
The detected mutation pattern can provide evidence that genetically distinct groups of cells exist within the same tissue. In cancer research, comparing these changes helps investigate clonal evolution, the process by which cellular groups acquire different genetic features over time. The findings can also illuminate genetic mosaicism, in which an organism contains cells with different genetic compositions.
Somatic analyses may compare DNA or RNA from affected tissue with matched normal tissue, depending on the genetic information being examined. Using these molecular materials supports detection of changes at the sequence level and connects the analysis to the activity of genes in the sampled cells. The comparison strengthens interpretation of disease-associated molecular differences.
A typical workflow begins by obtaining DNA or RNA from affected tissue and a matched normal sample. The materials are then analyzed by sequencing, after which variant calling identifies candidate differences. Researchers interpret the resulting variants by distinguishing acquired changes from inherited variants, sequencing errors, and normal cellular variation, producing findings suitable for genetic and disease-focused investigation.
This approach is especially useful when researchers need to investigate genetic changes associated with cancer or other disorders. It can support disease classification and clarify molecular events involved in tumor development. Because the analysis focuses on differences between affected and normal tissue, it also helps characterize clonal evolution and genetic mosaicism within biological research.
Once acquired variants have been distinguished from inherited variants and technical artifacts, they can provide molecular information about an affected sample. In the context of cancer and other disorders, these findings may support selection of targeted therapies or monitoring of treatment. Their value comes from linking genetic changes in affected cells with clinically relevant disease characterization.