Related Research Articles
Rapid amplification of cDNA ends (RACE) is a technique used in molecular biology to obtain the full length sequence of an RNA transcript found within a cell. RACE results in the production of a cDNA copy of the RNA sequence of interest, produced through reverse transcription, followed by PCR amplification of the cDNA copies. The amplified cDNA copies are then sequenced and, if long enough, should map to a unique genomic region. RACE is commonly followed up by cloning before sequencing of what was originally individual RNA molecules. A more high-throughput alternative which is useful for identification of novel transcript structures, is to sequence the RACE-products by next generation sequencing technologies.
Cross-linking and immunoprecipitation is a method used in molecular biology that combines UV crosslinking with immunoprecipitation in order to identify RNA binding sites of proteins on a transcriptome-wide scale, thereby increasing our understanding of post-transcriptional regulatory networks. CLIP can be used either with antibodies against endogenous proteins, or with common peptide tags or affinity purification, which enables the possibility of profiling model organisms or RBPs otherwise lacking suitable antibodies.
ChIP-sequencing, also known as ChIP-seq, is a method used to analyze protein interactions with DNA. ChIP-seq combines chromatin immunoprecipitation (ChIP) with massively parallel DNA sequencing to identify the binding sites of DNA-associated proteins. It can be used to map global binding sites precisely for any protein of interest. Previously, ChIP-on-chip was the most common technique utilized to study these protein–DNA relations.
RNA-Seq is a sequencing technique that uses next-generation sequencing (NGS) to reveal the presence and quantity of RNA in a biological sample, representing an aggregated snapshot of the cells' dynamic pool of RNAs, also known as transcriptome.
Single-cell sequencing examines the nucleic acid sequence information from individual cells with optimized next-generation sequencing technologies, providing a higher resolution of cellular differences and a better understanding of the function of an individual cell in the context of its microenvironment. For example, in cancer, sequencing the DNA of individual cells can give information about mutations carried by small populations of cells. In development, sequencing the RNAs expressed by individual cells can give insight into the existence and behavior of different cell types. In microbial systems, a population of the same species can appear genetically clonal. Still, single-cell sequencing of RNA or epigenetic modifications can reveal cell-to-cell variability that may help populations rapidly adapt to survive in changing environments.
ATAC-seq is a technique used in molecular biology to assess genome-wide chromatin accessibility. In 2013, the technique was first described as an alternative advanced method for MNase-seq, FAIRE-Seq and DNase-Seq. ATAC-seq is a faster analysis of the epigenome than DNase-seq or MNase-seq.
Translation complex profile sequencing (TCP-seq) is a molecular biology method for obtaining snapshots of momentary distribution of protein synthesis complexes along messenger RNA (mRNA) chains.
Anthony M. Zador is an American neuroscientist and the Alle Davis Harris Professor of Biology and Chair of Neuroscience at Cold Spring Harbor Laboratory. He is a co-founder, in 2004, of the Computational and Systems Neuroscience (COSYNE) conference, and of the NAISYS meeting about the intersection of neuroscience and artificial intelligence. Dr. Zador's research has focused on understanding the circuits of the auditory cortex in rodents. More recently, he has pioneered a new approach to connectome mapping using the methods of molecular biology, which may dramatically decrease the cost and improve the speed of mapping neuronal circuits at the single cell level.
In epitranscriptomic sequencing, most methods focus on either (1) enrichment and purification of the modified RNA molecules before running on the RNA sequencer, or (2) improving or modifying bioinformatics analysis pipelines to call the modification peaks. Most methods have been adapted and optimized for mRNA molecules, except for modified bisulfite sequencing for profiling 5-methylcytidine which was optimized for tRNAs and rRNAs.
Perturb-seq refers to a high-throughput method of performing single cell RNA sequencing (scRNA-seq) on pooled genetic perturbation screens. Perturb-seq combines multiplexed CRISPR mediated gene inactivations with single cell RNA sequencing to assess comprehensive gene expression phenotypes for each perturbation. Inferring a gene’s function by applying genetic perturbations to knock down or knock out a gene and studying the resulting phenotype is known as reverse genetics. Perturb-seq is a reverse genetics approach that allows for the investigation of phenotypes at the level of the transcriptome, to elucidate gene functions in many cells, in a massively parallel fashion.
Single-cell transcriptomics examines the gene expression level of individual cells in a given population by simultaneously measuring the RNA concentration of hundreds to thousands of genes. Single-cell transcriptomics makes it possible to unravel heterogeneous cell populations, reconstruct cellular developmental pathways, and model transcriptional dynamics — all previously masked in bulk RNA sequencing.
Transcriptomics technologies are the techniques used to study an organism's transcriptome, the sum of all of its RNA transcripts. The information content of an organism is recorded in the DNA of its genome and expressed through transcription. Here, mRNA serves as a transient intermediary molecule in the information network, whilst non-coding RNAs perform additional diverse functions. A transcriptome captures a snapshot in time of the total transcripts present in a cell. Transcriptomics technologies provide a broad account of which cellular processes are active and which are dormant. A major challenge in molecular biology is to understand how a single genome gives rise to a variety of cells. Another is how gene expression is regulated.
Spatial transcriptomics is a method for assigning cell types to their locations in the histological sections and can also be used to determine subcellular localization of mRNA molecules. First described in 2016 by Ståhl et al., it has since undergone a variety of improvements and modifications.
CITE-Seq is a method for performing RNA sequencing along with gaining quantitative and qualitative information on surface proteins with available antibodies on a single cell level. So far, the method has been demonstrated to work with only a few proteins per cell. As such, it provides an additional layer of information for the same cell by combining both proteomics and transcriptomics data. For phenotyping, this method has been shown to be as accurate as flow cytometry by the groups that developed it. It is currently one of the main methods, along with REAP-Seq, to evaluate both gene expression and protein levels simultaneously in different species.
snRNA-seq, also known as single nucleus RNA sequencing, single nuclei RNA sequencing or sNuc-seq, is an RNA sequencing method for profiling gene expression in cells which are difficult to isolate, such as those from tissues that are archived or which are hard to be dissociated. It is an alternative to single cell RNA seq (scRNA-seq), as it analyzes nuclei instead of intact cells.
Patch-sequencing (patch-seq) is a method designed for tackling specific problems involved in characterizing neurons. As neural tissues are one of the most transcriptomically diverse populations of cells, classifying neurons into cell types in order to understand the circuits they form is a major challenge for neuroscientists. Combining classical classification methods with single cell RNA-sequencing post-hoc has proved to be difficult and slow. By combining multiple data modalities such as electrophysiology, sequencing and microscopy, Patch-seq allows for neurons to be characterized in multiple ways simultaneously. It currently suffers from low throughput relative to other sequencing methods mainly due to the manual labor involved in achieving a successful patch-clamp recording on a neuron. Investigations are currently underway to automate patch-clamp technology which will improve the throughput of patch-seq as well.
Deterministic Barcoding in Tissue for Spatial Omics Sequencing (DBiT-seq) was developed at Yale University by Rong Fan and colleagues in 2020 to create a multi-omics approach for studying spatial gene expression heterogenicity within a tissue sample. This method can used for the co-mapping mRNA and protein levels at a near single-cell resolution in fresh or frozen formaldehyde-fixed tissue samples. DBiT-seq utilizes next generation sequencing (NGS) and microfluidics. This method allows for simultaneous spatial transcriptomic and proteomic analysis of a tissue sample. DBiT-seq improves upon previous spatial transcriptomics applications such as High-Definition Spatial Transcriptomics (HDST) and Slide-seq by increasing the number of detectable genes per pixel, increased cellular resolution, and ease of implementation.
TCR-Seq is a method used to identify and track specific T cells and their clones... TCR-Seq utilizes the unique nature of a T-cell receptor (TCR) as a ready-made molecular barcode. This technology can apply to both single cell sequencing technologies and high throughput screens
References
- ↑ Kebschull, Justus M.; Silva, Pedro Garcia da; Reid, Ashlan P.; Peikon, Ian D.; Albeanu, Dinu F.; Zador, Anthony M. (2016). "High-Throughput Mapping of Single-Neuron Projections by Sequencing of Barcoded RNA". Neuron. 91 (5): 975–987. doi:10.1016/j.neuron.2016.07.036. PMC 6640135 . PMID 27545715.
- ↑ "Revolutionary method to map brains at single-neuron resolution successfully demonstrated". 2016-09-21. Retrieved 2018-03-29.
