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Multiplexed Evaluation of Nucleic acids (CARMEN) project.

Multiplexed Evaluation of Nucleic acids (CARMEN) project.

My Assignment about Combinatorial Arrayed Reactions for Multiplexed Evaluation of Nucleic acids (CARMEN) project. You can google it and you will find a lot of information about it. Please consider the following:

1- For the report (MS word) should be about 10 pages long; typed, double- spaced, 12 pt. font New Roman. You should cover these components : introduction, quality evaluation, success and failure of the project, key lessons learned, recommendations, and references.

2- For the slides it should be accompanied by a ten-slide power-point, outline the technology and project’s background (slides 1&2), the key issues you focused on (slides 3-5) and the conclusions, recommendations, and lessons learned (slides 6-10).

The great majority of globally moving pathogens go unnoticed, undermining affected person care and hampering outbreak preparedness and response. To enable routine surveillance and comprehensive diagnostic applications, there is a need for detection technologies that can scale to test many samples1,2,3 while simultaneously testing for many pathogens4,5,6. Here, we develop Combinatorial Arrayed Reactions for Multiplexed Evaluation of Nucleic acids (CARMEN), a platform for scalable, multiplexed pathogen detection. In the CARMEN platform, nanolitre droplets containing CRISPR-based nucleic acid detection reagents7 self-organize in a microwell array8 to pair with droplets of amplified samples, testing each sample against each CRISPR RNA (crRNA) in replicate. The combination of CARMEN and Cas13 detection (CARMEN–Cas13) enables robust testing of more than 4,500 crRNA–target pairs on a single array. Using CARMEN–Cas13, we developed a multiplexed assay that simultaneously differentiates all 169 human-associated viruses with at least 10 published genome sequences and rapidly incorporated an additional crRNA to detect the causative agent of the 2020 COVID-19 pandemic. CARMEN–Cas13 further enables comprehensive subtyping of influenza A strains and multiplexed identification of dozens of HIV drug-resistance mutations. The intrinsic multiplexing and throughput capabilities of CARMEN make it practical to scale, as miniaturization decreases reagent cost per test by more than 300-fold. Scalable, highly multiplexed CRISPR-based nucleic acid detection shifts diagnostic and surveillance efforts from targeted testing of high-priority samples to comprehensive testing of large sample sets, greatly benefiting patients and public health.

Infectious diseases are the finest threats to human being health insurance and international safety, but there is not any broadly offered molecular examination for nearly all condition-leading to microbes, reducing their analysis and surveillance. Of the many viral species capable of infecting humans (576 of which had been sequenced and 169 of which had at least 10 published genomes12 by October 2018), only 39 had diagnostics approved by the FDA (US Food and Drug Administration; https://www.fda.gov). While laboratory developed tests have been developed for clinical testing of diverse pathogens at specific facilities, these tests can have long turnaround times and are rarely multiplexed. Routine comprehensive diagnostic testing would provide a previously unavailable data stream to inform patients, healthcare workers and policy makers to suppress and mitigate outbreaks. However, these tools are not widely available owing to the lack of a scalable and multiplexed technology to quickly and inexpensively identify any circulating pathogen (Fig. 1a). Comprehensive disease detection by sequencing or microarray hybridization provides detailed information about pathogen genotypes and evolution, but is difficult to implement on a wide scale owing to the cost and logistical demands of sample preparation. Rapid, low-cost detection methods, such as CRISPR-based approaches, antigen-based tests, PCR or loop-mediated isothermal amplification (LAMP), detect only one or a small number of pathogens in a given reaction. Combining the strengths of these approaches, an ideal diagnostic and surveillance technology would be highly multiplexed and easily scale across hundreds of samples.

To build a testing platform with this capacity, the team turned to microfluidics, adapting and improving on technology developed in 2018 by Blainey’s lab. The researchers created rubber chips, slightly larger than a smartphone, with tens of thousands of “microwells” — small compartments designed to each hold a pair of nanoliter-sized droplets. One droplet contains viral genetic material from a sample, and the other contains virus-detection reagents.

“The microwell potato chips are created similar to a stamp — it’s rubberized put more than a mold,” explained Ackerman. “We’re easily able to replicate and share this technology with collaborators.”

The recognition method utilized on the chips is tailored through the CRISPR-structured diagnostic SHERLOCK, initial described in 2017 and created by crew of experts in the Extensive Institution, the McGovern Institute for Brain Study at MIT, the Institute for Health-related Design & Research at MIT, as well as the Wyss Institution for Biologically Motivated Engineering at Harvard School.

To utilize the CARMEN foundation, research workers initially extract popular RNA from examples making copies of this genetic materials, the same as the planning method for RT-qPCR diagnostics currently useful for believed COVID-19 situations. The researchers then add a unique fluorescent color dye to each prepared sample and divide the mixture into tiny droplets.

The discovery mixtures, on the flip side, include the CRISPR protein Cas13, helpful tips RNA that looks to get a distinct viral sequence, and substances to document the results. These mixtures are also color-coded and separated into droplets.

1000s of droplets in the trial samples and discovery mixtures are then pooled together and filled onto a nick in one pipetting phase. Each microwell in the chip catches two droplets. When a detection droplet finds its target — a specific viral genetic sequence — in a sample droplet in the same microwell, a signal is produced and detected by a fluorescence microscope. The entire protocol, from RNA extraction to results, takes under eight hours.

“Uniting those two systems in just one program provides fascinating new functionality to look into medical and epidemiological questions,” explained co-publisher Gowtham Thakku, an MIT graduate pupil in Broad’s Infectious Condition and Microbiome System.

CARMEN makes it possible for greater than 4,500 exams on one microfluidics scratch, which can affect patient samples in a variety of methods making use of the offered fluorescent rules. For example, a single chip could simultaneously test 1,048 samples for a single virus, or five samples for 169 viruses. The capacity can be easily scaled up further by adding more chips: “We normally run four or five chips in a single day,” noted Ackerman.

Multiplexing capabilities

To showcase the platform’s multi-analysis abilities, they developed a technique for rapidly testing many examples for that 169 human being-related malware that have over 10 printed genome sequences. The researchers tested this detection panel against 58 patient samples, using multiple chips. They additionally applied CARMEN on patient samples to differentiate between subtypes of influenza A strains and to detect drug-resistance mutations in HIV.

The group also included detection mixtures for SARS-CoV-2 — the virus that triggers Covid-19 — and also other respiratory system pathogens to show, using man made popular sequences, the way the assay could be rapidly adapted to detect growing infections.

“CARMEN offers both remarkable throughput and suppleness in diagnostic testing,” explained co-publisher Catherine Freije, a Harvard scholar university student within the Sabeti laboratory.

The researchers report that the platform’s level of sensitivity resembles previously published SHERLOCK assays, and are generally ongoing to enhance and authenticate CARMEN utilizing more specialized medical trial samples. Coupled with the successful testing data from patient samples described in Nature today, this approach could be readily translatable in the clinic, according to the team.