Remote fluorescing nanosensors to fingerprint pathogens

flourescent nanosensors identify pathogens

Date: 26th November 2020

As we are all too acutely aware infectious diseases are a major cause of morbidity and mortality worldwide. Fast and accurate detection of pathogens such as bacteria in a non-invasive manner with minimal processing would be hugely advantageous in combating these diseases.  Now, scientists have developed a set of near infrared (NIR) fluorescent nanosensors, for remote fingerprinting of clinically important bacteria, advancing the field of personalised pathogen diagnostics.

The current approaches for microbiological diagnosis of bacteria rely on chromogenic culture, DNA detection or mass spectrometry, all of which require timely processes such as sampling, transport, purification, and/or cultivation. However, direct detection and identification of bacterial pathogens using highly sensitive biosensors would represent a large step forward in simplifying diagnostic tests.  Although, they would have to be selective in order to distinguish between diverse sets of potential pathogens against sample backgrounds.

Now researchers led by Sebastian Kruss, from Ruhr-Universität Bochum, Germany, have developed a new method for detecting bacteria and infections, using fluorescent nanosensors that fluoresce in the NIR spectrum. The highly sensitive nanosensors were integrated into functional hydrogel arrays, and were chemically tailored to detect released bacterial metabolites and specific virulence factors.

Bacteria are known to alter their chemical environment through the release of a range of factors such as signalling molecules, enzymes, and metabolites. Such molecules vary between bacteria, and different combinations of biomarkers can provide information about the nature of the bacterium.

Fluorescence changes in the presence of bacterial molecules

The team started by developing multiple NIR fluorescent nanosensors from single-walled carbon nanotubes (SWCNTs) such that they change their fluorescence signal in response to bacterial metabolites and virulence factors.  These nanosensors were then incorporated into a biocompatible hydrogel (HG) on which the bacteria were plated.  Bacteria growing on top of this hydrogel released molecules that changed the (spatial) sensor array fingerprint, which in turn allowed the team to differentiate important pathogens.

Nine nanosensors were combined in a hydrogel array, with one of those acting as a NIR fluorescent reference, and the system was remotely monitored by NIR stand-off detection. With simple detection apparatus, the system just required an NIR sensitive camera, LED light source, NIR filters and an objective lens. Four sensors were tailored for specific bacterial targets and the other four were generic lower-selectivity sensors.

The sensors were designed to detect different metabolites such as lipopolysaccharides (LPS) which are endotoxins, or virulence factors such as siderophores which capture essentials elements in environment.

Clinically relevant tests

To test the nanosensors, the team used clinical isolates from S. aureus and S. epidermidis – which are responsible for over 50% of all clinical joint infections.  Analysis showed that the two different bacteria populations could be distinguished based on their metabolic fingerprint with ~80% likelihood, despite the two pathogens being closely related.

Next, the team wished to evaluate the timescale on which the sensor array responded. This time they used bacterial culture supernatants added to the sensor array and monitored for P. aeruginosa and S. aureus, the sensor array responded between 15 and 45 min after addition with a specific pattern, suggesting that the arrays could provide a rapid diagnostic test.

Lastly, the team want to evaluate the sensor array performance in the context of smart surface applications such as in implants.  Host-induced responses were tested using human synovial fluid from non-infected patients and those with low/high-grade infections.  The array was able to sense bacterial targets and bacterial growth, even in complex matrixes such as synovia, and detected the presence of the methicillin resistant S. aureus (MRSA).

Conclusion and future applications

The team here, have developed multiple NIR fluorescent sensors to remotely fingerprint important clinical pathogens.  The nanosensors can detect major bacterial virulence factors, enzyme and metabolic activity and can be imaged remotely in the NIR.  This technology offers a new, flexible tool for non-invasive, sensitive diagnostic testing that is much more rapid that the current standard tests and can be used for local identification of bacterial infections and contaminations.

In the future, the team are looking to improve the analysis of the sensor array pattern by introducing machine learning (ML) algorithms.  This will also be complemented by increasing the number of sensors in the array, optimising the gel thickness and sensor size, which would accelerate precise classification and increase time resolution and sensitivity.

One area the team are particularly excited about is applying the technology to create ‘smart’ surfaces such as intelligent implants.  Since the wavelength of NIR penetrates deeper into human tissue than visible light, the bacterial sensors could be used under wound dressing or on implants.  The foundation of optical detection of infections on these intelligent implants, would allow clinicians to perform non-invasive, remote monitoring of possible infections quickly, greatly improving patient care.

In a similar manner, it could also be adapted to produce rapid diagnosis of blood samples, for example to diagnose sepsis.  If this type of rapid diagnostic could be paired with other novel technology, such as the telodendrimer nanotrap that can adsorb multiple sepsis mediators, it would create a hugely powerful combination that could help alleviate the estimated 48.9 million cases of sepsis recorded worldwide (2017) and a staggering 11 million sepsis-related deaths that occur every year.

Overall, NIR remote detection of bacteria could enable faster diagnostics and tailored antibiotic treatment, accelerating personalised pathogen diagnostics, and ultimately resulting in better clinical outcomes and lower mortality rates.


For more information please see the press release from Ruhr-Universität Bochum

Nißler, R., O. Bader, M. Dohmen, S. G. Walter, C. Noll, G. Selvaggio, U. Groß and S. Kruss (2020). “Remote near infrared identification of pathogens with multiplexed nanosensors.” Nature Communications 11(1): 5995.