George Church, Ph.D., who is a Core Faculty member at the Wyss Institute and Professor of Genetics at Harvard Medical School and of Health Sciences and Technology at Harvard and the Massachusetts Institute of Technology (MIT), first pioneered the idea of using short synthetic DNA as a long-term information storage medium. His team first converted a complete book, including 5.27 megabits of text and images, into a binary digital code, which they then encoded in DNA, and finally decoded again using next-generation sequencing technology. It is estimated that 1 gram of DNA can hold up to 215 petabytes (1 petabyte = 1 million gigabytes) of information, although this number fluctuates as different research teams break new grounds in testing the upper storage limit of DNA. Assuming the Avengers Endgame movie in 720p HD takes up 6 gigabytes, you can store 36 million copies of the movie in one gram of DNA. Global digital data is expected to grow to 175 zettabytes (1 zettabyte = 1 million petabytes) by 2025, and because all the digital data in the world can be theoretically stored in 81kg of DNA, DNA storage is being actively pursued as a compelling storage medium for the future.
A Single Drop Of Synthetic DNA Can Store All Of The Data In The World | IFLScience
One obstacle to this kind of data storage is the cost of synthesizing such large amounts of DNA. Currently it would cost $1 trillion to write one petabyte of data (1 million gigabytes). To become competitive with magnetic tape, which is often used to store archival data, Bathe estimates that the cost of DNA synthesis would need to drop by about six orders of magnitude. Bathe says he anticipates that will happen within a decade or two, similar to how the cost of storing information on flash drives has dropped dramatically over the past couple of decades.
Data storage. Synthetic polymers are the most prevalent chemical alternative to DNA data storage. Ideally, polymers could be designed to contain a variety of different monomers, each coding for different information, thus surpassing the potential of encoding information into four nucleotides of DNA. In reality, sequentially reading the information from polymers remains challenging as automated methods are not available for synthetic polymers8. As long as the information contained in a single molecule cannot be read sequentially, the challenge of information retrieval does not scale well with the amount of information to be read.
More recently, droplet-based platforms (for example, Chromium from 10x Genomics, ddSEQ from Bio-Rad Laboratories, InDrop from 1CellBio, and μEncapsulator from Dolomite Bio/Blacktrace Holdings) have become commercially available, in which some of the companies also provide the reagents for the entire wet-lab scRNA-seq procedure. Droplet-based instruments can encapsulate thousands of single cells in individual partitions, each containing all the necessary reagents for cell lysis, reverse transcription and molecular tagging, thus eliminating the need for single-cell isolation through flow-cytometric sorting or micro-dissection [45,46,47]. This approach allows many thousands of cells to be assessed by scRNA-seq. However, a dedicated hardware platform is a prerequisite for such droplet-based methods, which might not be readily available to a researcher considering scRNA-seq for the first time. In summary, generating a robust scRNA-seq dataset is now feasible for wet-lab researchers with little to no prior expertise in single-cell genomics. Careful consideration must be paid, however, to the commercial protocols and platforms to be adopted. We will discuss later which protocols are favoured for particular research questions.
With regard to preserving single-cell transcriptomes before scRNA-seq, most published scRNA-seq studies progressed immediately from single-cell isolation to cell lysis and mRNA capture. This is clearly an important consideration for experimental design as it is not trivial to process multiple samples simultaneously from biological replicate animals or individual patients if labour-intensive single-cell isolation protocols such as FACS-sorting or micro-dissection are employed. Commercial droplet-based platforms might offer a partial solution as a small number of samples (for example, eight samples on the Chromium system) can be processed simultaneously. For samples derived from different individuals, SNP information might allow processing as pools, followed by haplotype-based deconvolution of cells [52]. Another possible solution might be to bank samples until such time as scRNA-seq processing can be conducted. To this end, recent studies have explored the effect of cryopreservation on scRNA-seq profiles and indeed suggest that high-fidelity scRNA-seq data can be recovered from stored cells [47, 53]. Furthermore, over the past few years, protocols compatible with certain cell-fixation methods have started to emerge [34, 35, 38, 54, 55].
Before further analyses, scRNA-seq data typically require a number of bio-informatic QC checks, where poor-quality data from single cells (arising as a result of many possible reasons, including poor cell viability at the time of lysis, poor mRNA recovery and low efficiency of cDNA production) can be justifiably excluded from subsequent analysis. Currently, there is no consensus on exact filtering strategies, but most widely used criteria include relative library size, number of detected genes and fraction of reads mapping to mitochondria-encoded genes or synthetic spike-in RNAs [76, 77]. Recently, sophisticated computational tools for identifying low-quality cells have also been introduced [78,79,80,81]. Other considerations are whether single cells have actually been isolated or whether indeed two or more cells have been mistakenly assessed in a particular sample. This can sometimes be assessed at the time of single-cell isolation, but, depending on the chosen technique, this might not always be possible. 2ff7e9595c
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