Release date: 2018-03-27
Recently, "Nature" published an article entitled "technology to watch in 2018". Experts and scholars from the world's top universities and research institutions have made wonderful achievements in a number of advanced technologies that will have a major impact on the life sciences in 2018. Introduction. These technologies cover everything from genomic re-encoding, transcriptome mapping, tumor vaccine development, to the application of Internet of Things technology in life sciences.
1. Recoding the genome --- George Church, Harvard Medical School, geneticist
A new technology that can simultaneously modify multiple genetic loci is available! Codonrecoding is a versatile gene editing technique that allows various organisms to become resistant to most or all viruses and can precisely change hundreds of sites in each cell at a time. . Any one of the codons is replaced with redundancy in the genetic code, and once these substitutions are completed, the cell will no longer recognize the sequence. When a virus infects a cell with all of these codons, the virus cannot transcribe its messenger RNA into a protein due to the lack of tRNA, and the virus will die.
To make multiple precise changes at a time, the experimenter used a multiplexed automated genome engineering (MAGE) technique to introduce short fragments of genetic material containing targeted base pairs into cells that prevent DNA mismatch repair. After several rounds of cell replication, these genetic material is fully integrated into the bacterial genome.
In fact, what re-encoding technology can do is much more than that. For example, the researchers developed another re-encoding technique to improve the vaccine strain of Salmonella typhimurium; or re-encode an organism to introduce non-standard amino acids into the protein for introduction into the organism that did not exist. Chemical substance: an amino acid that emits light or binds to a nucleic acid or forms an unusual bond. Finally, re-encoding technology also provides a powerful strategy for bio-containment.
2. Transcriptome Mapping - Zhuang Xiaowei, Director, Advanced Imaging Center, Harvard University
The recently launched Human Cell Atlas (HCA) program aims to identify all cell types in the human body and map their spatial distribution. The program's goal is ambitious. Projects of this size will require many assistive technologies.
Single-cell RNA sequencing is an effective method for identifying different cell types and an important tool for mapping HCA. The traditional single-cell separation and extraction of RNA loses the spatial environmental information of cells in tissues - how these cells are organized and interact Related information.
The center is developing an image-based single-cell transcriptomics method, multiplexed error-robust fluorescence in situ hybridization (MERFISH), which uses a barcode with low error rate to identify each cell. Different types of RNA are detected by large-scale composite labeling and continuous imaging to detect these barcodes, thereby enabling transcriptional profiling of cells in intact tissues to study their spatial background.
MERFISH® technology can image 1000 different mRNAs in a single cell. With further development, it is possible for MERFISH to detect all transcriptomes in cells of intact tissues.
3. Advance the application of tumor vaccines--- Elaine Mardis, Co-Executive Director, Institute of Genomic Medicine, National Children's Hospital, Columbus, Ohio
Usually, in cancer patients, there are some abnormal proteins caused by mutations in cancer cell genes, which are called new tumor antigens. Some of them have the potential to elicit an immune response in a given individual and can therefore be used to personalize the development of tumor vaccines or other drugs.
Researchers are beginning to use a new high-throughput method, the CyTOF technology, to identify cells that express specific proteins for new antigen research. Compared to flow cytometry, this technology replaces a limited number of fluorescent tags with metal tags, up to more than 100, and even more in the future.
This technology can transform the field of cancer immunogenomics, helping researchers identify the most highly expressed new antigens in cancer cells that most respond to the immune system. Researchers can then use this information to create a personalized anti-cancer vaccine.
On the other hand, in studying any given new antigenic species predicted by the genome and whether it causes a significant immune response, CyTOF can help by quantifying the binding strength of multiple predictive peptides to human T? cells. We have an in-depth understanding of this issue.
This is not just cancer genomics. As long as you can find antibodies that bind to the protein you are interested in, you can use CyTOF to track the abundance and composition of the protein produced by the cell. This allows us to track proteins with unprecedented precision and multiple dimensions.
4. Link genotype and phenotype---Ruedi Aebersold, System Biology Scientist, Institute of Molecular Systems Biology, ETH Zurich
Combining massive amounts of high-quality genomic information with clinically collected population-test data is an effective way to understand the mechanisms by which disease occurs, develops, and translates genetic variation into treatment.
One of the keys is the analysis of protein complexes. Find out which protein complexes are disturbed from big data and how do they interfere?
One approach is to combine computational and quantitative proteomics and then accurately quantify thousands of proteins in tumor and control samples. Now we can use SWATH-MS (sequential window acquisition of all theoretical mass specra) to generate such data sets. The altered protein complex is studied at the structural level by cryo-electron microscopy single-particle analysis or cryo-electrontomography (CET) to show how the mutation alters the protein. Composition, topology, structure, and functionality.
CET can also reveal how protein structures change with other conditions, such as changes in the structure of protein phosphorylation. Together, these techniques will help us understand how protein complexes are disrupted at the molecular level in disease situations. This will help design targeted drugs to clear, inactivate or activate the protein.
5. Extended Genome Sequence Analysis --- Rebecca CalisiRodrÃguez, University of California, Davis, Reproductive Biologist
To clarify the complete mechanism of chronic stress-damaging reproduction, we recently used RNA sequencing to delve into the activity of every gene that is transcriptionally active in the reproductive axis of the pigeon, the hypothalamus, pituitary gland and gonads in the brain. How the pigeon's reproductive system responds to stress. These will help develop new gene therapies or drugs to treat thousands of infertile men and women.
In addition, we can also sequence all animals in the real world as a powerful model for assessing the effects of harmful substances on the reproductive axis in the environment. At the same time, new technologies can be combined with traditional scientific tools to expand the discovery in an unprecedented way.
6. Building a Scientific Internet of Things - Vivienne Ming, Institute of Theoretical Neuroscientists and Executive Chairman, Socos Labs Laboratory, University of California, Berkeley
The Internet of Things is changing our lives, and it is also likely to revolutionize science. Researchers are working on the Internet of Scientific Things (IoST), an open system that connects distributed sensors and actuators to a powerful machine learning platform that drives experiments around the world. For example, Google's smartphones can detect early symptoms of Parkinson's disease from gait changes detected by cell phone accelerometers and gyroscopes. Using an extended smartphone sensor, Professor Ming's team is able to predict manic episodes in patients with bipolar disorder.
Intelligent transactions in the Internet of Things will increase the ability of scientists to find data related to their domain. “If my neuroimaging software plugs directly into the IoST platform and accesses the data in real time, not only my lab, but everyone inside and outside my domain, I can log in to the platform to access this data.†Professor Ming pointed out: “ If scientists build these systems themselves, they can make publications more equal, data collection more shareable, and science more transparent!†(Bio Valley bioon.com)
Source: Bio Valley
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