Single-Cell Sequencing: Principle, Steps, Uses, Developments Microbiologystudy

Single-cell sequencing is a method that sequences a single cell at a time to study its genome, transcriptome, epigenome, or other molecular characteristics. It is useful for studying the unique characteristics of cells and understanding how they develop or contribute to diseases. This helps us study the differences between different cells and how they are related to each other.

Traditional sequencing, also called bulk sequencing, looks at the DNA or RNA of a group of cells and gives an average representation of many cells. While this is useful for studying general patterns in large groups of cells, it does not provide information about the unique differences between individual cells. This makes it less suitable for studying complex systems with many different types of cells or finding rare cell types and small differences between cells.

Single-cell sequencing is a newer technology that solves this problem. It was named as the Method of the Year for 2013 by Nature. Unlike traditional sequencing methods, single-cell sequencing looks at one cell at a time which allows the study of the unique genetic makeup of each cell. This helps to study biological processes in detail which is not possible with bulk sequencing methods.

Principle of Single-Cell Sequencing

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The principle of single-cell sequencing involves studying individual cells to understand their unique molecular characteristics and study cell diversity. It works by isolating single cells from a group and analyzing their genetic material.

The process of single-cell sequencing begins by isolating individual cells. Each isolated cell is tagged with a unique nucleotide sequence or cellular barcode which helps to trace back the sequencing data to its original cell. These barcodes also allow simultaneous sequencing of multiple cells in a single run. Short adapters are also added to the template to prepare it for sequencing. The resulting library of fragments is ready for sequencing using next-generation sequencing methods.

Process of Single-Cell Sequencing

Isolation of Single Cells

The first step is isolating individual cells from a group of cells. Different isolation methods are used for different types of cells. It is easier to isolate abundant cells compared to rare cells. For abundant cells, simple methods like serial dilution, mouth pipetting, micromanipulation, and microfluidic platforms can be used. Rare cells like circulating tumor cells (CTCs) are harder to isolate because they are present in small numbers. Methods like MagSweeper and nanofilters use electric fields and size filtering to capture rare cell. Some of the other commonly used isolation methods are:

Fluorescence-Activated Cell Sorting (FACS): In this method, cells are labeled with fluorescent markers that attach to specific proteins. A flow cytometer separates cells based on their fluorescence. This method is effective in isolating specific cell populations but it requires a large number of starting cells and can damage cell viability during sorting.
Magnetic-Activated Cell Sorting (MACS): This technique uses magnetic particles to tag specific cell surface proteins. The tagged cells are isolated using an external magnetic field. This is fast and scalable but can cause damage to cells and has low purity when isolating cells from complex populations.

Laser Capture Microdissection (LCM): This method uses a laser to isolate cells directly from tissue samples on a microscope slide. It is especially useful for capturing cells from intact tissue samples but requires visual inspection to identify the target cells. This process can sometimes cause damage to the cells during isolation.
Manual Cell Picking or Micromanipulation: In this method, microscope and micropipettes are used to manually pick and isolate cells. This method is precise but it is time-consuming and has low throughput.

Extraction and Amplification of Genetic Material

After isolating individual cells, they are lysed to release their genetic material. The DNA is amplified to ensure sufficient material for sequencing.

Library Preparation

The amplified genetic material is prepared into a sequencing library which contains single-stranded DNA fragments that are derived from a single cell. Library preparation includes barcoding each isolated cell and adding adapter sequences. The barcodes help to identify the original source of the data after sequencing and the adapters help bind the templates to the sequencing device.

Sequencing

After library preparation, sequencing can be done using next-generation sequencing (NGS) platforms. The most commonly used method is Illumina which uses sequencing-by-synthesis method. This method involves adding fluorescently labeled nucleotides into the growing DNA strand. Each nucleotide emits a specific fluorescent signal to identify the added base. Proton-based sequencing can also be used for single-cell sequencing. Instead of fluorescent labels, this method detects the release of protons during nucleotide incorporation. The proton-based sequencing method is faster than the fluorescence-based method but it is less accurate. Other NGS methods like Oxford Nanopore and PacBio sequencing can also be used for single-cell sequencing.

Data Analysis

The data analysis of single-cell sequencing is divided into 3 phases: primary, secondary, and tertiary analysis.

Primary Analysis: The raw output of sequencing is initially in binary base call (BCL) files. Primary analysis converts the raw BCL files into FASTQ files for compatibility with downstream tools. These files contain sequence information. During this phase, barcode tags in the data are used to separate sequences from different cells or libraries which helps in accurate cell identification.
Secondary Analysis: In this phase, the sequences in the FASTQ files are aligned to a reference genome or transcriptome. This step helps in variant detection, transcript quantification, and gene expression profiling. Quality control is also done to check the integrity of the data by filtering poor-quality data and removing non-cellular barcodes.
Tertiary Analysis: In this phase, data visualization and biological interpretation are done to extract biological information. The information is simplified and visualized to identify patterns and unique characteristics. It also involves analyzing the differences in gene expression between cells, studying biological pathways, and creating clear visuals to understand the results.

Advantages of Single-Cell Sequencing

Single-cell sequencing helps to study genetic material at the single-cell level. This provides a more detailed understanding of cellular differences.
It can detect rare cell types that are missed in bulk sequencing. This is useful in fields like cancer research and immunology.
It provides detailed information about individual cells within a specific tissue.

It can be used to study complex biological systems by studying the composition and cellular diversity of all cells in a tissue sample.
It helps us to better understand different cell types and complex tissues.

Limitations of Single-Cell Sequencing

Single-cell sequencing is expensive which limits its applications for large-scale studies.

The isolation process can stress cells which causes changes in their characteristics and viability.
Single-cell sequencing data has more noise compared to bulk sequencing which reduces reliability in some applications.
Single-cell sequencing often doesn’t detect all genes in each cell so a lot of data is missing or has zero values. Some genes may be present in a cell but not captured by sequencing. This sparsity in data can make data analysis difficult.

The data can be unreliable as it is collected from just one cell. It is hard to know which measurements are correct and which are just due to technical errors.
The amplification step can introduce errors and biases, making it difficult to detect genetic variations in individual cells accurately.

Applications of Single-Cell Sequencing

Single-cell sequencing can be used in biomedical research in different fields including developmental biology, neurobiology, diabetes research, and cancer studies.

It is useful in cancer research to identify genetic variations within tumor cells. This helps to understand cancer cells and their mechanisms. It is also useful for developing targeted treatments.
It is used in immunology to study different immune cells and their roles in different diseases.
It helps to study cellular differentiation and changes occurring during processes like embryogenesis in developmental biology.

Single-cell sequencing is also useful in studying microorganisms by detecting microbial species, their genetic structure, and their roles in different processes like antibiotic resistance.
It is also useful in clinical diagnostics and to understand disease mechanisms. It helps identify rare or novel cell types. It is also useful for developing therapeutic methods for different diseases.

Video on Single-Cell Sequencing

Developments in Single-Cell Sequencing

Single-cell sequencing has made it easier to study individual cells and understand the differences between cells. In recent years, single-cell sequencing has advanced even more to develop newer methods and tools for studying individual cells efficiently. Some of these advancements in single-cell sequencing are:

Single-cell combinatorial marker sequencing (SCI-seq) can create many single-cell libraries at once. It helps detect changes in cell copy numbers, making it easier and cheaper to study cell differences within tissues.
Single-cell Chromatin Overall Omic-scale Landscape Sequencing (scCOOL-seq) can measure different features of a single cell, such as DNA changes, chromatin states, and DNA methylation patterns. These features help to understand gene regulation and cellular behavior.

Topographic single-cell sequencing (TSCS) gives information about the spatial location of cells. This method is especially useful for cancer research as it helps track the movement and spread of tumor cells.
Microwell-seq is a cost-effective single-cell RNA sequencing that uses microwell array to capture individual cells. This method can detect more effectively and reduces costs compared to older droplet-based methods.
Split-pool ligation-based transcriptome sequencing (SPLiT-seq) is another cost-effective single-cell RNA sequencing method that uses barcoding to label individual cells.

There are newer methods that combine single-cell sequencing with other techniques like CRISPR. For example, in CRISPR droplet sequencing (CROP-seq), CRISPR screening is combined with single-cell RNA sequencing.

Single-Cell Sequencing vs. Bulk Sequencing

Characteristics	Single-cell Sequencing	Bulk Sequencing
Method	It studies one cell at a time and gives detailed information about individual cells.	It looks at all the cells together and produces average data of a population of cells.
Cell Differences	It can detect cellular differences and identify rare cell types.	It represents the overall average of all cells and cannot detect individual cell differences.
Data Complexity	It generates large and complex datasets.	It produces simpler and summarized datasets.
Cost	It is more expensive due to the need of advanced technology and data analysis.	It is less expensive to study large cell populations.
Data Output	It gives a detailed view of individual cells, including unique gene expression or mutations.	It provides a generalized profile of the entire cell population.
Applications	It can be used in fields like developmental biology, cancer research, and immune response studies.	It is suitable for large-scale genomic studies and broader studies where cellular-level detail is not needed.
Limitations	It is technically demanding and more expensive.	It offers a generalized view so it doesn’t reveal the differences between cells.

References

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Jovic, D., Liang, X., Zeng, H., Lin, L., Xu, F., & Luo, Y. (2022). Single‐cell RNA sequencing technologies and applications: A brief overview. Clinical and Translational Medicine, 12(3). https://doi.org/10.1002/ctm2.694
Lähnemann, D., Köster, J., Szczurek, E. et al. (2020). Eleven grand challenges in single-cell data science. Genome Biology 21, 31. https://doi.org/10.1186/s13059-020-1926-6
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Method of the Year 2013. (2013). Nature Methods, 11(1), 1. https://doi.org/10.1038/nmeth.2801
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Sreenivasan, V. K. A., Balachandran, S., & Spielmann, M. (2022). The role of single-cell genomics in human genetics. Journal of medical genetics, 59(9), 827–839. https://doi.org/10.1136/jmedgenet-2022-108588
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Single-Cell Sequencing: Principle, Steps, Uses, Developments microbiologystudy

Principle of Single-Cell Sequencing