Understanding Gene Expression

All the genetic information of an organism is stored in DNA packaged as chromosomes and further partitioned into gene sequences that are expressed via cellular machinery under specific conditions. Gene expression refers to the process by which information stored in a gene is used as the template to produce a functional protein or RNA molecule. This process is essential for the proper functioning of cells and overall health of organisms.

Gene expression involves two main steps: transcription and translation. During transcription, the information in a gene is copied into a messenger RNA (mRNA) molecule by RNA polymerase. The mRNA carries the genetic information from the nucleus to the cytoplasm, where it serves as a template for protein synthesis¹. In translation, the mRNA is read by ribosomes in the cytoplasm, which use the information to assemble a specific sequence of amino acids into a protein.

Gene expression is a tightly regulated process influenced by many factors, including environmental cues, cellular signaling pathways and epigenetic modifications. These regulatory mechanisms ensure that genes are expressed at the right time and in the right amount to meet the needs of the cell or organism. Alterations in gene expression can have significant consequences for health and disease, and understanding these processes is an important area of research in molecular biology and genetics.

Gene Expression Profiling and Quantitation: Methods and Techniques

The goal of gene expression analysis is to understand the function and regulation of genes by measuring the mRNA level in different cell types, tissues or developmental stages. Gene expression analysis has a long history that dates back to the 1970s, when the first methods for studying gene expression were developed. Two early methods for gene expression analysis were Northern blotting and in situ hybridization. Northern blotting was developed to detect specific RNA molecules². This method involves isolating RNA from a sample, separating the RNA molecules by size using gel electrophoresis, transferring the RNA to a membrane and then hybridizing the RNA with a labeled DNA probe. The resulting signal can be detected using autoradiography or other imaging methods. In 1986, in situ hybridization was developed as a way to detect localized gene expression in tissues or cells³. This method involves hybridizing a labeled RNA or DNA probe to a specific RNA molecule within a tissue section or cell. The resulting signal can be visualized using microscopy.

Over time, new methods for gene expression analysis were developed that built on these early techniques. For example, microarrays were developed in the 1990s as a way to simultaneously measure the expression of thousands of genes in a single experiment. Nowadays, the most popular methods for transcriptome analysis include expression microarrays, RNA sequencing (RNA-seq), quantitative polymerase chain reaction (qPCR) and in situ hybridization.

Microarray analysis is a technique used in molecular biology to study gene expression on a large scale. It involves using a small chip containing many DNA probes that can detect and measure the abundance of specific genes in a biological sample.

In a typical microarray experiment, RNA is extracted from cells or tissues and converted into complementary DNA (cDNA) using reverse transcription. The cDNA is then labeled with a fluorescent or radioactive tag and hybridized into the microarray. The microarray chip is then scanned to detect the signals emitted by the labeled cDNA, which reflect the abundance of each gene in the sample⁴.

Microarray analysis can identify genes that are differentially expressed between different biological samples, such as normal and diseased tissue, or investigate how gene expression changes in response to a specific stimulus or treatment. It can also be used to identify genetic mutations or variations in DNA sequences⁵.

Microarray analysis has many applications in biomedical research, including discovering new drug targets, developing diagnostic tools for diseases and identifying genetic markers for disease susceptibility.

RNA-seq is a high-throughput technique used to analyze gene expression at the transcriptome level. It involves sequencing the RNA molecules present in a biological sample to provide information on the types and abundance of transcripts present in the sample.

In a typical RNA-seq experiment, RNA is extracted from cells or tissues and converted into cDNA using reverse transcription. The cDNA is then fragmented and sequenced using high-throughput sequencing technologies. The resulting sequence reads are then aligned to a reference genome or transcriptome to identify the transcripts that are present in the sample⁶.

RNA-seq can be used to quantify gene expression levels, identify mutations or alternative splicing events, detect novel transcripts and gene fusions and analyze the expression of non-coding RNAs⁷.

There are many applications of RNA-seq in biomedical research:

• Discovering new drug targets.
• Identifying biomarkers for disease diagnosis and prognosis.
• Characterizing disease mechanisms.
• Conducting basic research to study gene regulation and function.

qPCR is the most widely used technique for quantifying gene expression levels. It involves amplifying a specific target sequence of RNA or DNA using PCR and then measuring the amount of amplification product generated in real-time during the amplification process⁸.

In a qPCR experiment, RNA is first reverse transcribed into cDNA using reverse transcriptase. The cDNA is then mixed with specific primers that bind to the target gene sequence of interest and with fluorescent dyes or probes that bind to the amplification product. The mixture is then subjected to a series of temperature cycles that allow the primers to anneal to the target sequence and for the polymerase to synthesize new strands of DNA. As new copies of the target sequence are synthesized, the fluorescence emitted by the probes or dyes is measured and recorded by a detection system⁹.

The amount of fluorescence produced is proportional to the amount of amplification product present in the reaction. The relative expression level of the target gene can be quantified by comparing the fluorescence of the target gene to a reference gene.

qPCR has several advantages over other techniques. It is more sensitive, requires less starting material and has a shorter turnaround time. It is also relatively inexpensive and can be used to analyze any number of genes or samples.

The many applications of qPCR in biomedical research include:

• The identification of disease biomarkers.
• The validation of gene expression changes detected by other techniques.
• The quantification of gene expression levels in response to different treatments or conditions¹⁰.

In situ hybridization (ISH) is a technique used to localize and visualize specific RNA molecules within cells or tissues. It is commonly used to study gene expression patterns in a spatially resolved manner.

In a typical ISH experiment, a specific RNA probe is designed to bind to a complementary RNA sequence expressed in the target cell or tissue. The probe is labeled with a detectable fluorescent or radioactive tag that allows it to be visualized under a microscope¹¹.

ISH can be used to visualize the spatial distribution of specific RNA molecules and to detect changes in gene expression patterns in response to different treatments or conditions¹². It is particularly useful for studying gene expression in fixed tissues because it allows the detection of gene expression at the single-cell level. This capability can reveal insights like spatially resolved cell-specific gene expression patterns.

ISH has many applications in biomedical research:

• The identification of novel genes and their functions.
• The characterization of gene expression patterns during development and disease progression.
• The identification of biomarkers for disease diagnosis and prognosis.
• Drug discovery and development studies where cellular localization information is needed.

Conclusion

Gene expression analysis facilitates studies into regulation and expression patterns contributing to various biological processes. By understanding the mechanisms that control gene expression, we can gain insights into the underlying causes of disease, identify potential drug targets and develop new therapies.

However, gene expression analysis also has some limitations. One major limitation is that it can be difficult to accurately quantify gene expression levels, particularly when working with complex biological samples such as tissues or blood. There is also the potential for technical variability between different experiments, which can affect the reproducibility of results. Another limitation is that gene expression analysis is often limited to the genes that are included in the assay and may not provide a comprehensive view of the entire transcriptome. Additionally, some RNA molecules, such as non-coding RNAs, can be difficult to detect using standard gene expression analysis techniques. Finally, some methods of gene expression analysis can be expensive and time-consuming, requiring specialized equipment and expertise. Despite these limitations, gene expression analysis remains a powerful tool for studying gene regulation and its impact on biological processes. It continues to be a major focus of research in many fields of biology and medicine.

References

1. Feher, J. in Quantitative Human Physiology (Second Edition) (ed Joseph Feher) 120-129 (Academic Press, 2017).
2. Alwine, J. C., Kemp, D. J. & Stark, G. R. Method for detection of specific RNAs in agarose gels by transfer to diazobenzyloxymethyl-paper and hybridization with DNA probes. Proceedings of the National Academy of Sciences of the United States of America 74, 5350-5354, doi:10.1073/pnas.74.12.5350 (1977).
3. Penschow, J. D., Haralambidis, J., Aldred, P., Tregear, G. W. & Coghlan, J. P. Location of gene expression in CNS using hybridization histochemistry. Methods Enzymol 124, 534-548, doi:10.1016/0076-6879(86)24038-0 (1986).
4. Govindarajan, R., Duraiyan, J., Kaliyappan, K. & Palanisamy, M. Microarray and its applications. Journal of pharmacy & bioallied sciences 4, S310-312, doi:10.4103/0975-7406.100283 (2012).
5. Kaushik, S., Kaushik, S. & Sharma, D. in Encyclopedia of Bioinformatics and Computational Biology (eds Shoba Ranganathan, Michael Gribskov, Kenta Nakai, & Christian Schönbach) 118-133 (Academic Press, 2019).
6. Hrdlickova, R., Toloue, M. & Tian, B. RNA-Seq methods for transcriptome analysis. Wiley interdisciplinary reviews. RNA 8, doi:10.1002/wrna.1364 (2017).
7. Corchete, L. A. et al. Systematic comparison and assessment of RNA-seq procedures for gene expression quantitative analysis. Scientific Reports 10, 19737, doi:10.1038/s41598-020-76881-x (2020).
8. Maddocks, S. & Jenkins, R. in Understanding PCR (eds Sarah Maddocks & Rowena Jenkins) 45-52 (Academic Press, 2017).
9. Wong, M. L. & Medrano, J. F. Real-time PCR for mRNA quantitation. BioTechniques 39, 75-85, doi:10.2144/05391RV01 (2005).
10. Bustin, S. A. & Huggett, J. F. Reproducibility of biomedical research - The importance of editorial vigilance. Biomolecular detection and quantification 11, 1-3, doi:10.1016/j.bdq.2017.01.002 (2017).
11. jamalzadeh, S. et al. QuantISH: RNA in situ hybridization image analysis framework for quantifying cell type-specific target RNA expression and variability. Laboratory Investigation 102, 753-761, doi:10.1038/s41374-022-00743-5 (2022).
12. Young, A. P., Jackson, D. J. & Wyeth, R. C. A technical review and guide to RNA fluorescence in situ hybridization. PeerJ 8, e8806, doi:10.7717/peerj.8806 (2020).

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