RNA sequencing of the next generation

RNA sequencing (often abbreviated as RNA-Seq) is a technique that uses next-generation sequencing (NGS) technology to quantify and catalog the number and sequences of RNA molecules in a biological sample. Sequencing the RNA of a cell provides insights into the cell's transcriptome, which includes all RNA molecules present in the cell at a given time.

The special feature of RNA sequencing is that it not only determines the structure and quantity of RNA in a sample, but can also reveal changes in gene expression under different conditions and/or at different times. Furthermore, special methods can also be used to detect transcriptional changes at the cellular level. This opens up new aspects in biological research and medical diagnostics. In contrast to older methods such as DNA sequencing, which looks at the entire genome of a cell, RNA sequencing focuses on the active parts of the genome. These active parts are the parts that are transcribed into RNA and ultimately produce the proteins that are important for the function or dysfunction of the cell.

Two techniques in particular have proven to be especially valuable:

• the general RNA sequencing technique and
• single-cell RNA sequencing.

Der Ablauf der RNA-Sequenzierung: Die RNA-Sequenzierung ist ein komplexer Prozess, der in mehrere Schritte unterteilt ist. Jeder Schritt spielt eine entscheidende Rolle im Gesamtablauf. Grob orientierend lassen sich drei Hauptphasen unterscheiden:

• die Vorbereitung der RNA-Probe
• das Sequenzierungsverfahren selbst und
• die Datenanalyse.

In der ersten Phase wird die RNA isoliert und für die Sequenzierung vorbereitet. Anschließend folgt die Sequenzierung, bei der die RNA-Sequenzen erfasst werden. Schließlich werden diese Daten analysiert, um die Genexpression, d.h. die Aktivität bestimmter Gene zu verschiedenen Zeitpunkten oder unter verschiedenen Bedingungen zu bestimmen.

• Probenvorbereitung: Zu Beginn wird die RNA aus der Probe isoliert. Dabei werden oft Methoden eingesetzt, die sicherstellen, dass nur hochqualitative und intakte RNA-Moleküle für die Sequenzierung verwendet werden. Die Qualität der RNA ist entscheidend für zuverlässige Ergebnisse.
• Library Preparation: Die isolierte RNA wird dann in eine sogenannte cDNA-Bibliothek eingestellt. Das Erstellen einer cDNA-Bibliothek ist ein notwendiger Schritt, da die meisten Sequenzierungsplattformen DNA und nicht RNA sequenzieren.
• Sequenzierung: Nachdem die cDNA-Bibliothek erstellt wurde, folgt die eigentliche Sequenzierung. Technologien der nächsten Generation (NGS) ermöglichen die parallele Sequenzierung von Millionen von DNA-Fragmenten. Die resultierenden Sequenzen - sogenannte Reads - werden dann mithilfe von Software rekonstruiert, um die RNA-Sequenzen darzustellen.
• Datenanalyse: Die generierten Daten werden im Anschluss analysiert. Dabei werden die Reads den Referenzgenomen oder Transkriptomen zugeordnet, um festzustellen, welche Gene exprimiert werden und in welchem Ausmaß. Diese Daten erlauben es Interessierten  Muster der Genexpression unter verschiedenen Bedingungen zu untersuchen.

The practical applications of RNA sequencing are already impressively diverse and range from the discovery of new drugs and the improvement of agricultural technologies to the diagnosis and treatment of diseases.

RNA sequencing, a key technology in modern genome research, has revolutionized the understanding of gene expression and cellular functions. It offers unique insights into the complex system of genetic regulation. Two techniques in particular have proven to be especially valuable:

• the general RNA sequencing technique and
• single-cell RNA sequencing.

The standard RNA sequencing technique makes it possible to comprehensively analyze the transcriptomes of cells or tissue samples. This method provides information about which genes are active in the samples at a given time and how strongly they are expressed. The process involves the extraction of total RNA from the sample, the conversion of this RNA into cDNA (complementary DNA) and the subsequent sequencing of the cDNA using a high-throughput sequencing platform (cDNA is easier to sequence than RNA and is more stable, leading to more accurate results). The resulting data provides information about gene expression and helps to understand biological processes and disease mechanisms.

For example, the RNA sequencing technique could be used to investigate how gene expression in human liver cells responds to a specific treatment with a new drug. By analyzing the changes in gene expression, potential target genes for further therapeutic interventions can be identified.

In addition to identifying expressed genes, RNA sequencing also enables the discovery of new genes that may have been overlooked in previous studies, as well as alternative splice variants of existing genes. This information expands our understanding of genomic complexity and functional diversity within cells.

Single-cell RNA sequencing: While the general RNA sequencing technique provides insights into the average gene expression within a sample, single-cell RNA sequencing (scRNA-Seq) enables the study of gene expression at the individual cell level. This makes it possible to analyze the heterogeneity within cell populations that exists in tissue samples, even in seemingly homogeneous cell cultures. In single-cell RNA sequencing, individual cells are isolated, their RNA is converted into cDNA and sequenced. The resulting data provide detailed insights into the cellular state and the respective activation status of individual cells and enable precise analysis of cellular diversity and function.

Cancer research: An exemplary application of single-cell RNA sequencing could be in cancer research, where scientists use the technique to study the differences in gene expression between cancer cells and normal cells within a tumor. Through this analysis, potential therapeutic targets can be discovered that specifically target cancer cells while leaving normal cells untouched.

1. Böhner A et al. (2023) The neglected twin: Nummular eczema is a variant of atopic dermatitis with codominant TH2/TH17 immune response. J Allergy Clin Immunol 152:408-419.
2. He H et al. (2021) Tape strips detect distinct immune and barrier profiles in atopic dermatitis and psoriasis. J Allergy Clin Immunol 147:199-212.
3. Jovic D et al. (2022) Single-cell RNA sequencing technologies and applications: A brief overview. Clin Transl Med 12:e694.
4. Liu Y et al. (2022) Classification of human chronic inflammatory skin disease based on single-cell immune profiling. Sci Immunol 7:eabl9165.
5. Petegrosso R et al. (2020) Machine learning and statistical methods for clustering single-cell RNA-sequencing data. Brief Bioinform 21:1209-1223.
6. Slovin S et al. (2021) Single-Cell RNA Sequencing Analysis: A Step-by-Step Overview. Methods Mol Biol 2284:343-365.
7. Zhang H et al. (2018) A multitask clustering approach for single-cell RNA-seq analysis in Recessive Dystrophic Epidermolysis Bullosa. PLoS Comput Biol 14:e1006053.
8. Zhang B et al. (2023) Single-cell profiles reveal distinctive immune response in atopic dermatitis in contrast to psoriasis. Allergy 78:439-453.
9. Zhao K et al. (2023) Multi-Objective Genetic Algorithm for Cluster Analysis of Single-Cell Transcriptomes. J Pers Med 13:183