Emily Newton, Contributing Writer05.17.24
The COVID-19 pandemic has highlighted the need for higher standards in microbe awareness, sanitation and research, though many conventional methods are inefficient or relatively unreliable. Metagenomic deep sequencing may change that.
Recent advances in metagenomic next-generation sequencing (mNGS) have made this once-niche and unrefined practice more accessible and reliable. Capitalizing on this evolving technology today could help labs drive their microbial analysis into the future.
Streamlined microbial detection
One of the biggest advantages of metagenomic sequencing is it enables much faster analysis. In some cases, it can take just six hours to complete what would take standard methods between one and three days.
Much of this efficiency stems from the fact that mNGS requires less pre- and post-processing. Labs can study microbes in situ instead of isolating specific targets or developing cultures. Metagenomic techniques analyze all genetic material within a sample simultaneously, minimizing the need for repeated tests and individual sampling.
In clinical settings, this streamlined detection means hospitals can determine and administer ideal treatments before patients’ conditions progress too severely. Research use cases can benefit from lower overall costs, as they’ll require less labor and tool time to achieve the same results.
Modelling complex interactions
Another benefit of analyzing an entire sample at once is it uncovers real-world interactions that individual culturing may miss. While isolation is beneficial in many cases, it doesn’t provide a reliable look at how different microbes behave in their natural environments. Because mNGS observes microbes within these environments, it reveals how they actually interact with other genetic material.
In-situ analysis is particularly useful for medical research. Some drug interactions may perform well in a vacuum but fail to achieve the same results in real-world microbiomes, thanks to interference from other microbes. Observing microorganisms in a larger, more diverse environment lets labs detect these real-world complications before moving to clinical trials.
Modelling complex interactions like this may also lead to unintended beneficial discoveries. Isolated experiments can only uncover truths about the targeted sample, whereas metagenomic techniques can reveal truths about microbes a lab wasn’t even trying to analyze.
Detecting unknown microbes
Similarly, applying mNGS in microbe detection removes complications from an overly narrow scope of research. Conventional, primer-based methods can only detect microbes based on their known interactions with primers. However, not all genetic material will respond to these chemicals, so this approach may fail to identify microbes researchers know less about.
As much as 30% of proteins in human cells have unknown structures and functions. Considering how broad these knowledge gaps are, detection methods that leave little room for unknown factors have limited long-term research value.
Metagenomic deep sequencing, by contrast, doesn’t rely on known indicators. Consequently, it can reveal the presence and interaction of microbes researchers don’t have as much data on. By uncovering these details, labs can push genomic research into previously unexplored territories.
Application versatility
Metagenomic sequencing is also more versatile than conventional microbial detection methods. This practice is inherently less focused, as it models an entire microbiome instead of singling out cultures or interactions. Consequently, labs can apply it to a diverse set of use cases.
Medical applications are the most obvious use for mNGS, but this sequencing is also valuable in environmental research. Food processing is another unconventional but promising application for this technology. Cleaning systems in this industry encounter thousands of food particles a day, limiting sanitizers’ efficacy over time. By analyzing these contaminants through mNGS, facilities can get a better understanding of their sanitation needs.
This application versatility opens the door to more potential value from investment in metagenomic sequencing equipment. Multiple value streams like this can be hard to achieve otherwise in an industry so reliant on specialized machinery.
One of mNGS’s greatest strengths also presents a substantial limitation. These techniques’ lack of focus is beneficial in that it makes them versatile, but it also limits their results’ interpretability. It’s hard to determine cause-and-effect relationships with such a broad scope. Consequently, labs may need to perform further testing to identify root causes, counteracting mNGS’s streamlined nature.
The most straightforward solution here is to avoid over-applying mNGS. Metagenomic sequencing is best for general research, observing complex interactions and detecting microbes’ presence. Other methods may be better for determining causality in microbe behavior.
Metagenomic sequencing may also experience higher false positives and negatives in some situations. However, some studies have shown mNGS can be more effective at classifying some infections than conventional methods despite this shortcoming. Tighter quality control steps and finely tuned classification algorithms can help reduce these risks while retaining mNGS’s accuracy.
Challenges remain, but as more labs embrace these methods, best practices will emerge. As that happens, mNGS could alter the future of microbial research.
Emily Newton is the Editor-in-Chief of Revolutionized. She’s always excited to learn how the latest industry trends will improve the world. She has over five years of experience covering stories in the science and tech sectors.
Recent advances in metagenomic next-generation sequencing (mNGS) have made this once-niche and unrefined practice more accessible and reliable. Capitalizing on this evolving technology today could help labs drive their microbial analysis into the future.
Benefits of metagenomic deep sequencing
Conventional microbial detection methods typically rely on primers, culturing or isolation. Metagenomic deep sequencing moves past this need to unlock several important advantages.Streamlined microbial detection
One of the biggest advantages of metagenomic sequencing is it enables much faster analysis. In some cases, it can take just six hours to complete what would take standard methods between one and three days.
Much of this efficiency stems from the fact that mNGS requires less pre- and post-processing. Labs can study microbes in situ instead of isolating specific targets or developing cultures. Metagenomic techniques analyze all genetic material within a sample simultaneously, minimizing the need for repeated tests and individual sampling.
In clinical settings, this streamlined detection means hospitals can determine and administer ideal treatments before patients’ conditions progress too severely. Research use cases can benefit from lower overall costs, as they’ll require less labor and tool time to achieve the same results.
Modelling complex interactions
Another benefit of analyzing an entire sample at once is it uncovers real-world interactions that individual culturing may miss. While isolation is beneficial in many cases, it doesn’t provide a reliable look at how different microbes behave in their natural environments. Because mNGS observes microbes within these environments, it reveals how they actually interact with other genetic material.
In-situ analysis is particularly useful for medical research. Some drug interactions may perform well in a vacuum but fail to achieve the same results in real-world microbiomes, thanks to interference from other microbes. Observing microorganisms in a larger, more diverse environment lets labs detect these real-world complications before moving to clinical trials.
Modelling complex interactions like this may also lead to unintended beneficial discoveries. Isolated experiments can only uncover truths about the targeted sample, whereas metagenomic techniques can reveal truths about microbes a lab wasn’t even trying to analyze.
Detecting unknown microbes
Similarly, applying mNGS in microbe detection removes complications from an overly narrow scope of research. Conventional, primer-based methods can only detect microbes based on their known interactions with primers. However, not all genetic material will respond to these chemicals, so this approach may fail to identify microbes researchers know less about.
As much as 30% of proteins in human cells have unknown structures and functions. Considering how broad these knowledge gaps are, detection methods that leave little room for unknown factors have limited long-term research value.
Metagenomic deep sequencing, by contrast, doesn’t rely on known indicators. Consequently, it can reveal the presence and interaction of microbes researchers don’t have as much data on. By uncovering these details, labs can push genomic research into previously unexplored territories.
Application versatility
Metagenomic sequencing is also more versatile than conventional microbial detection methods. This practice is inherently less focused, as it models an entire microbiome instead of singling out cultures or interactions. Consequently, labs can apply it to a diverse set of use cases.
Medical applications are the most obvious use for mNGS, but this sequencing is also valuable in environmental research. Food processing is another unconventional but promising application for this technology. Cleaning systems in this industry encounter thousands of food particles a day, limiting sanitizers’ efficacy over time. By analyzing these contaminants through mNGS, facilities can get a better understanding of their sanitation needs.
This application versatility opens the door to more potential value from investment in metagenomic sequencing equipment. Multiple value streams like this can be hard to achieve otherwise in an industry so reliant on specialized machinery.
Considerations for implementing metagenomic sequencing
Naturally, metagenomic deep sequencing has some unique challenges. Labs must recognize said obstacles to implement these methods effectively.One of mNGS’s greatest strengths also presents a substantial limitation. These techniques’ lack of focus is beneficial in that it makes them versatile, but it also limits their results’ interpretability. It’s hard to determine cause-and-effect relationships with such a broad scope. Consequently, labs may need to perform further testing to identify root causes, counteracting mNGS’s streamlined nature.
The most straightforward solution here is to avoid over-applying mNGS. Metagenomic sequencing is best for general research, observing complex interactions and detecting microbes’ presence. Other methods may be better for determining causality in microbe behavior.
Metagenomic sequencing may also experience higher false positives and negatives in some situations. However, some studies have shown mNGS can be more effective at classifying some infections than conventional methods despite this shortcoming. Tighter quality control steps and finely tuned classification algorithms can help reduce these risks while retaining mNGS’s accuracy.
Metagenomic techniques take microbial detection further
While it’s still a relatively new field, metagenomic deep sequencing shows significant potential. Labs focusing on microbial detection should take note of these innovations. Capitalizing on these methods can accelerate microbe research across many use cases.Challenges remain, but as more labs embrace these methods, best practices will emerge. As that happens, mNGS could alter the future of microbial research.
Emily Newton is the Editor-in-Chief of Revolutionized. She’s always excited to learn how the latest industry trends will improve the world. She has over five years of experience covering stories in the science and tech sectors.