scATAC-seq：
scATAC-seq (Single-cell Assay for Transposase-Accessible Chromatin using sequencing) is a single-cell genomics technology that can be used to identify the open chromatin region (Accessible Chromatin) of each single cell. It combines two technologies: Transposase-Accessible Chromatin sequencing (ATAC-seq) and single-cell sequencing.
ATAC-seq technology uses an enzyme called Transposase, which recognizes and cuts open chromatin regions. By adding some sequencing adapters, these open regions can be amplified, sequenced, and mapped to the genome. Using ATAC-seq technology, we can identify open chromatin regions in a whole population of cells, but we cannot know which cells have which regions.
scATAC-seq technology overcomes this limitation by combining single-cell sequencing and ATAC-seq technology. In this technique, each single cell is individually encased in a tiny reactor. DNA from each cell was then added individually to the ATAC-seq reaction, cut using Transposase and adapters added. Adapters allow us to amplify, sequence, and localize open chromatin regions in single cells. In this way, we can identify open chromatin regions in each single cell, providing deeper insights into gene expression and cellular function.
In summary, scATAC-seq is a high-throughput single-cell genomics technology that, by combining ATAC-seq and single-cell sequencing technology, can identify the open chromatin regions of each single cell, providing us with a deeper understanding of gene expression. and cellular function provide new tools.
Open chromatin regions in single cells
The open chromatin region (Accessible Chromatin) of a single cell refers to the chromatin region on the cell chromosome that can be bound by transcription factors, nucleases, etc. These regions are usually regulatory elements of gene expression. Compared with tightly packed chromatin, regions of open chromatin are more easily accessible to molecules such as transcription factors, thereby regulating gene expression. Therefore, identifying open chromatin regions in single cells can help us understand how each cell differs in gene expression and cellular function, providing insights into the cell's biology.
In single-cell ATAC-seq (scATAC-seq) technology, the Transposase enzyme cleaves open chromatin regions and adds DNA adapters so that these regions can be amplified, sequenced, and mapped to the genome, thereby identifying the openness of each single cell. Chromatin regions. Because the open chromatin regions of each cell can be different, identifying the open chromatin regions of single cells can help us gain insights into the gene expression and regulatory networks of each cell, as well as their different roles in biological processes.
bulkRNA-seq
BulkRNA-seq is a high-throughput sequencing technology that can detect a large number of RNA molecules simultaneously to understand the overall pattern of gene expression. BulkRNA-seq is based on the sequencing of whole cell or tissue RNA, converting RNA transcripts into sequenceable cDNA, and using high-throughput sequencing technology to sequence and quantify these cDNAs. By comparing gene expression between samples, we can understand the differences in gene expression and thereby understand the biological differences and functions between different cells or tissues.
BulkRNA-seq technology includes the following steps:
RNA extraction: Extract RNA from cells or tissues and perform purification and quality control.
RNA sequencing library preparation: Convert RNA transcripts to cDNA and add sequence tags and adapters.
High-throughput sequencing: Perform high-throughput sequencing on RNA sequencing libraries to obtain millions to billions of reads.
Data analysis: Sequencing reads are compared, transcripts are spliced, and expression levels are calculated to obtain a gene expression matrix, and bioinformatics analysis is performed, such as clustering, differential expression analysis, functional enrichment analysis, etc.
BulkRNA-seq has wide applications in many fields, such as:
Analysis of gene expression profiles: BulkRNA-seq can be used to explore gene expression patterns in biological systems to understand the biological differences of cells and tissues under physiological and pathological conditions.
Identification of gene mutations and fusions: BulkRNA-seq can detect gene mutations and fusions to understand the role of genes in diseases such as tumors.
Drug screening: BulkRNA-seq can be used to evaluate the impact of drugs on gene expression, thereby helping to develop new drug targets.
Although BulkRNA-seq can provide us with overall information about cells and tissues, it cannot provide gene expression information of single cells. In some cases, BulkRNA-seq may mask single-cell heterogeneity because it cannot distinguish between different cell types. In addition, BulkRNA-seq may also be affected by technical factors such as batch effects and RNA degradation, so technical attention and statistical analysis are required.
The difference and connection between BulkRNA-seq and scRNA-seq
BulkRNA-seq and scRNA-seq are both RNA sequencing technologies, but their sequencing objects and analysis methods are different.
Sequencing objects: BulkRNA-seq sequences the RNA of entire cells or tissues, while scRNA-seq sequences the RNA of a single cell.
Analysis method: BulkRNA-seq explores biological differences by comparing gene expression between different samples, while scRNA-seq can be used to analyze the gene expression pattern of individual cells to understand the heterogeneity and function between different cells. .
Detection sensitivity: BulkRNA-seq can detect highly expressed genes, but it is often difficult to detect lowly expressed genes because the number of reads for lowly expressed genes is very small and requires deeper sequencing depth for accurate detection. However, scRNA-seq can detect low-expressed genes and can detect specifically expressed genes in a small number of cells.
Data analysis: BulkRNA-seq needs to consider technical factors such as batch effects and RNA degradation between different samples, so batch effects and regularization are required. However, scRNA-seq needs to consider the heterogeneity and sparsity between individual cells, and requires data reduction and cell type identification.
Although BulkRNA-seq and scRNA-seq have different sequencing objects and analysis methods, they are also related. BulkRNA-seq can provide us with the gene expression pattern of the entire tissue or organ, providing us with macroscopic gene expression data. And scRNA-seq can help us understand the heterogeneity and function of individual cells, thereby gaining insights into cell types and functions in biological systems. Therefore, these two techniques can be used flexibly to explore biological problems in different research questions and application scenarios.
metagenomics
Metagenomics is a genomics method that studies microbial communities (including bacteria, fungi, viruses, archaea, etc.). By collecting the DNA or RNA of microbial communities directly from the environment, it can analyze the genomes of the microorganisms in them without cultivating pure cultures.
The core of metagenomics technology is high-throughput sequencing technology, which can quickly sequence the collected DNA or RNA and identify the genomic information of various bacteria, fungi, viruses, archaea, etc. present in the microbial community. This genomic information can be used to understand the species composition, functional characteristics, population structure, ecological niche distribution and other aspects of microbial communities.
Metagenomics technology has been widely used in environmental microbiology, soil microbiology, human intestinal microbiome, aquaculture microbiome, biogeochemistry and other fields. It can not only provide people with an in-depth understanding of the molecular ecology of microorganisms, but also provide important theoretical foundation and technical support in drug discovery, agricultural production, environmental pollution control, etc.
epigenome
Epigenome refers to heritable modifications at the genome level that are independent of DNA sequence, such as DNA methylation, histone modifications, chromatin remodeling, etc. These epigenetic modifications can affect biological processes such as gene expression, cell differentiation, development, and disease occurrence.
Epigenetic modifications affect the readability and accessibility of DNA by changing DNA structure, histone modifications and chromatin structure, thereby regulating gene expression. For example, DNA methylation usually leads to the silencing of certain genes, while histone modification can regulate the expression of certain genes by changing the histone modification status at certain sites.
In recent years, with the development of high-throughput sequencing technology, researchers can measure and compare differences in the epigenome at the whole-genome level, such as DNA methylation and histone modifications, to understand the relationship between epigenetic modifications and disease occurrence. relationships, and epigenetic regulatory mechanisms in different biological processes.
Epigenomic research has been widely used in various fields, such as cancer research, neuroscience, immunology, etc. It can provide new understanding of the mechanisms of disease and provide a theoretical basis for the development of new diagnostic and therapeutic methods.
Does scATAC-seq measure the epigenome?
Yes, scATAC-seq sequencing technology can be used to study the epigenome, the open and closed states of chromatin within cells. ATAC-seq (Assay for Transposase-Accessible Chromatin using sequencing) is a sequencing technology that uses the action of transposase to combine open chromatin regions with sequencing adapters and sequence them. scATAC-seq performs ATAC-seq sequencing at the single cell level, so the epigenomic status of individual cells can be studied. By analyzing scATAC-seq data, information on chromatin accessibility can be obtained, including the openness of promoters, enhancers and other regions, as well as information on gene expression status and cell type.
10x data
10x data refers to sequencing data generated using 10x Genomics' genomic sequencing technology. This technology is a single-cell sequencing technology that can also be used to generate conventional genome sequencing data.
10x Genomics' technology is based on microfluidic chips and GEM (Gel Bead in Emulsion) technology, which separates single cells or high-molecular DNA molecules in tiny water droplets. The DNA molecules are separated into millions of cells in the water droplets, and Prepare a special DNA barcode for each cell. Next, the cells are placed in parallel on a sequencer for high-throughput sequencing.
Through this technology, 10x Genomics can generate single-cell transcriptome, single-cell DNA sequencing data, etc. at lower cost, faster speed, and higher resolution. This technology has been widely used in single-cell gene expression profiling, DNA variation detection, chromatin structure analysis, spatial transcriptomics and other research fields, and has become one of the mainstream technologies in the field of single-cell sequencing.
It should be noted that 10x data usually requires special data analysis, such as using software such as Cell Ranger for preprocessing, denoising, splicing, and gene expression calculation of single-cell data to improve the quality and reliability of the data.
How Cell Ranger handles single-cell data
Cell Ranger is a software developed by 10x Genomics, which is used to process a series of data analysis operations such as preprocessing, denoising, splicing, and gene expression calculation of 10x Genomics single cell data. The following are the main steps for Cell Ranger to process single-cell data:
Data preprocessing: First, Cell Ranger will preprocess the original sequencing data, including data quality control, removal of low-quality sequences and PCR repeats, etc. This can ensure the accuracy of subsequent analysis results.
Cell identification: Use a cell identification algorithm to identify the reads corresponding to each single cell. For the single cell transcriptome data of 10x Genomics, these reads can be divided into two parts: cell barcode and UMI barcode. Cell barcodes are used to identify different single cells, and UMI barcodes are used to remove duplicates.
Splicing transcripts and genes: In 10x Genomics' single-cell sequencing technology, the reads of the same transcript will be split into different cells. Cell Ranger will stitch these split transcripts by comparing these reads and calculate The expression level of each gene.
Expression calculation: For each single cell, Cell Ranger will calculate the number of expressed genes and transcripts and generate a gene expression matrix. In addition, Cell Ranger can also calculate the expression of each gene in all single cells, as well as the difference in expression between different cell types.
Data visualization: Finally, Cell Ranger can visualize processed single-cell data, such as t-SNE diagrams, UMAP diagrams, heat maps, etc., to study the composition of cell communities and the distribution of cell types.
In short, Cell Ranger is a powerful single-cell data analysis software that can efficiently process and analyze 10x Genomics single-cell sequencing data, providing important help for studying single-cell transcriptomics and genomics.
What is scATAC-seq data clustering based on?
In scATAC-seq data analysis, clustering is a common data analysis method that can separate individual cells into different cell categories, which may correspond to different cell types or states. Clustering methods are usually based on similarities or distances between cells and assign similar cells into the same category. For scATAC-seq data, clustering is usually performed based on the similarity or distance between accessible regions of peaks, that is, clustering is based on the similarity or difference of accessible regions in each cell. ongoing.
Before clustering, the original scATAC-seq data needs to be preprocessed, including removing low-quality peaks, normalizing and removing batch effects, etc. Then, a clustering algorithm can be used to divide all cells into different groups. Commonly used clustering algorithms include hierarchical clustering, k-means clustering, spectral clustering, etc. These clustering algorithms can assign cells into the same category based on similarity or distance between peaks.
Furthermore, clustering results can be inspected and adjusted through visualization methods. For example, the t-SNE algorithm can be used to reduce the dimensionality of high-dimensional scATAC-seq data to two-dimensional or three-dimensional space, and the clustering results can be visualized in two-dimensional or three-dimensional space to better understand the clustering results. The validity of the clustering results can also be evaluated using metrics such as cohesion and separation to determine whether the clustering is accurate.
TF motif
TF motif (transcription factor binding site) refers to a specific DNA sequence pattern that appears in the genome to interact with transcription factors and regulate gene expression. Transcription factors are proteins that bind to DNA and control gene expression by binding to TF motifs in the genome.
TF motifs usually consist of specific base sequences that appear in different arrangements in different genes. Transcription factors can recognize and bind to these TF motifs, thereby playing an important role in regulating gene expression. Different transcription factors may bind to different TF motifs, thereby producing different regulatory effects.
In genomics research, identifying and annotating TF motifs is an important task. Researchers can use a variety of computational tools and databases to predict and analyze the distribution and role of TF motifs in the genome to better understand gene regulatory mechanisms and expression regulatory networks.
What is chromatin accessibility?
Chromatin accessibility refers to the extent to which DNA sequences on chromosomes are accessible to transcription factors and other regulatory proteins. In the nucleus, chromosomes present a highly ordered structure, and different chromatin regions may be in different accessibility states, which will directly affect gene expression.
Generally, chromatin accessibility is divided into two states: open (accessible) and closed (inaccessible). Open chromatin regions are usually rich in epigenetic marks (such as acetylated histones, methylation, etc.), and transcription factors and other regulatory proteins can smoothly bind to the DNA sequences therein, thereby promoting gene transcription and expression. In contrast, closed chromatin regions often lack these epigenetic marks, and DNA sequences are often tightly wrapped in histone proteins, making it difficult to effectively interact with transcription factors and other regulatory proteins, thereby inhibiting gene transcription and expression.
Chromatin accessibility is an important biological characteristic and is of great significance for studying biological issues such as gene regulation mechanisms and cell fate determination. By utilizing chromatin accessibility information, scientists can better understand the regulatory mechanisms of gene transcription and expression, and can also help explain the occurrence and development of some diseases.
Why open chromatin regions are often rich in epigenetic marks
Open chromatin regions are often rich in epigenetic marks because these marks can promote the relaxation and decompression of chromatin, making the DNA sequences in them more easily recognized and bound by transcription factors and other regulatory proteins.
Specifically, these epigenetic marks usually include acetylated histones, demethylation, hypomethylation, etc., which can make chromatin regions become more open and relaxed by changing the structure and function of histone proteins. For example, acetylated histones can promote the relaxation and decompression of histone proteins, making the DNA sequences in them more easily recognized and bound by transcription factors and other regulatory proteins, thereby promoting gene transcription and expression.
Therefore, open chromatin regions are often rich in these epigenetic marks, which is why these regions play an important role in the regulation of gene expression.