Optical Detection of Pathogenic Bacteria: Comparison
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Please note this is a comparison between Version 2 by Dean Liu and Version 1 by Andrea Locke.

The optical detection of pathogenic bacteria is a growing area of ongoing research for clinically-focused applications. Different modalities, like vibrational spectroscopy, fluorescence, scattering- and polarization-based systems, have the potential to provide information about the biomolecular and morphological characteristics of a species for sample identification and differentiation. Additionally, growth pattern recognition, single-cell versus biofilm formations, cell motility and viability, cell mutation, and antibiotic resistance status can be studied with various optical modalities, providing great potential for rapid characterization of disease-causing pathogens.

  • Optical detection
  • Raman spectroscopy
  • Infrared spectroscopy
  • Fluorescence spectroscopy
  • bacteria detection
  • Fourier-transform infrared spectroscopy
  • optical coherence tomography
  • Polarization
  • light scattering

1. Introduction

Bacterial and viral infections account for ~70% of all pathogenic diseases in humans [1][1]. Bacterial pathogens can be acquired from food, water, animals, or even clinical environments, including hospitals and other healthcare settings. Once inside their host, these bacteria can exist in two general life forms: planktonic (i.e., free-floating independent cells) or in aggregates (i.e., biofilms). Most bacteria are deemed harmless until they multiply and accumulate in various regions within the body, which can then lead to the development of infection. The immune system is then triggered as an acute infection develops, and the host’s immune response tries to neutralize the threat. In several cases, bacteria can evade the immune system, and the condition progresses into a persistent and chronic state requiring external interventions. Treatment with antibiotics is typically used as a remedy for the problem. However, the emergence of bacterial strains with antibiotic resistance is on the rise and is a growing global challenge. According to the Center for Disease Control and Prevention, in the United States, ~2.8 million people present infections with associated anti-microbial resistance annually [2][2]. This leads to prolonged treatment, extended hospital stays, and increased mortality associated with bacterial infections [3][3]. Though this resistance may be largely due to the overuse of antibiotics, it is also believed that certain bacteria (i.e., persistent bacteria) have innate characteristics and phenotypes that allow them to evade their host from the start of an infection. Bacteria found in biofilms and those that have adapted to intracellular growth are common examples of species that may cause persistent infections [4][4]. This becomes problematic when trying to identify and characterize the type of infection using gold-standard tools, which have only been tested on known susceptible strains, before administering the appropriate treatment. Therefore, there is a need for technologies that can effectively identify these microbes and their mutated strains—both in their planktonic and biofilm form—to treat patients in a timely manner and reduce healthcare and patient burden.

2. Comparison of Optical Techniques

Of the explored optical strategies for bacterial detection, there is a clear distinction between techniques that perform specific strain detection and those capable of performing general species identification. Fluorescence-based detection systems, as well as interferometer biosensors, require the binding of bacteria to antigen-specific targets and represent examples of optical systems that must be designed for the detection of a particular strain. By transducing the binding of pathogens into a direct change in the optical signal, they offer greater design freedom by their simplicity in signal interpretation and therefore are well-suited for miniaturized system development that is more easily deployable. Being culture-free strategies, they can potentially capture bacteria from patient biofluids for rapid detection. Techniques like vibrational spectroscopy, polarimetry, and elastic light scattering (ELS) fall into the second category because they indirectly identify bacterial species by unique features derived from their respective optical signals. Vibrational spectra, Mueller matrix element values, and ELS scatterograms are analyzed to extract quantitative features that aid in species differentiation. Statistical models can then be used to classify an unknown sample's identity by training classification algorithms on these features from representative datasets of isolated bacterial strains. In most cases, these measurements must be conducted on isolated bacterial colonies, although advances in surface-enhanced Raman spectroscopy (SERS) platforms have attempted to bridge this gap. The remaining optical methods do not perform speciation; instead, they utilize optical signal metrics to characterize bacterial growth kinetics. Optical coherence tomography (OCT) provides a label-free measurement of the formation of biofilms that can be used to track biofilm growth for in vitro studies and the in vivo detection of infection status in diseases like otitis media (OM). Similarly, laser speckle contrast imaging (LSCI) has shown merit as a potential tool to monitor bacterial mobility for the purpose of drug sensitivity screening and clinical antibiotic susceptibility testing. A comparison of these methods, along with the explored sample types and technique accuracy or the limit of detection (LOD), is summarized in Table 3.

Table 3. Comparison of application, sample type, and accuracy/limit of detection (LOD) for various optical modalities.

PurposeModalitySample TypesAccuracy (%)/LOD
Species-specific detection via ligand bindingFISHBiopsy tissue [133,134,135]NA
Species-specific detection via ligand bindingFISHBiopsy tissue [5][6][7]NA
Fecal matter [136,137]NA
Fecal matter [8][9]NA
In vivo biofilms [24,138,140,141,142,143]NA
In vivo biofilms [10][11][12][13][14][15]NA
FRETCell suspension [144,145,146]15–300 CFU/mL
FRETCell suspension [16][17][18]15–300 CFU/mL
Blood cultures [156]10 ng/mL
Blood cultures [19]10 ng/mL
Fluorescence polarizationWhole blood [148]1 CFU/mL
Fluorescence polarizationWhole blood [20]1 CFU/mL
Fluorescence biosensorCell suspension [149,158,159,160,
Cell suspension [21][22][23
161,165,166]10
Fluorescence biosensor][24][25][26][27]10
2
–10
8
CFU/mL
Mixed cell suspension [163,164]
Mixed cell suspension [
10
28][29]10
2
–10
3
CFU/mL
InterferometryMixed cell suspension [183,184]10
InterferometryMixed cell suspension [30][31]10
5
–10
6
CFU/mL
Cell suspension/biofilm growth [185]100 CFU/mL
Cell suspension/biofilm growth[32]100 CFU/mL
Species and/or strain identification via machine learningVibrational spectroscopyClinical isolates [38,39,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,59,,88,90]75–100%
Species and/or strain identification via machine learningVibrational spectroscopyClinical isolates [33]
60,
[34][35][36][37][38][39][40][41][42][43][44][45][46
61,62,63,64,71,82,
][47
83,
][48][49][50][51][52][53][54][55][56][57][58][59]
84
[60][61][62][63][64]75–100%
Urine [85,86]10
Urine[65][66]10
7
CFU/mL, 100%
Biofilms [203]NA
Biofilms [67]NA
SERS biosensor
SERS biosensor
Clinical isolates [91,92,93,
Clinical isolates[68][69
94,
]
95,96,
[70]
97,
[71]
98,
[72]
99,
][74
124]
][
84–100%
[7375][76][77]84–100%
Urine [97]10
Urine[74]10
5
CFU/mL, 97%
Whole blood [99,100]11 CFU/mL, 100%
Whole blood[76][78]11 CFU/mL, 100%
Cerebrospinal fluid [98]NA
Cerebrospinal fluid[75]NA
PolarimetryBacterial cultures [187]
Polarimetry
NA
Bacterial cultures[79]NA
ELSBacterial cultures [188,
ELS
189,190,191,192,
Bacterial cultures [
193,
80
194,195,
]
196,197,
[81][82][83
198]
][84][85]
80–98%
[86][87][88][89][90]80–98%
Urine [197]10
Urine[89]10
7
CFU/mL
Bacterial growth kineticsOCTIn vitro biofilms [170,171,174]NA
Bacterial growth kineticsOCTIn vitro biofilms[91][92][93]NA
In vivo biofilms [173,174,175,176,
[93][95][96
177,
]
178,179,
[97]
180,181]NA
In vivo biofilms[94][98][99][100][101]NA
LSCIBacterial cultures [191,194NA
LSCI
,200,201]
Bacterial cultures [83][86][102][103]NA

3. Future Prospective and Current Clinical Limitations

Advancements in the design of new optical systems have demonstrated the immense potential for various optical spectroscopy, imaging, and sensing tools to assess pathogens within biological samples. These techniques offer rapid, and in some cases, culture-free approaches to detect and characterize bacterial species with simplistic measurement protocols.

When aiming to study and characterize bacteria biofilms in their native environments, as in the case of OM, investigators can seek to use portable OCT systems to examine biofilm structures and, where applicable, types of bacterial effusions. Advances in its in vivo applications now involve using machine learning to automate OM classification based on the images acquired to better aid medical personnel ease in interpreting the OCT images [178][98]. Beyond OM utility, researchers are taking advantage of OCT imaging capabilities for other in vivo biofilm assessment relating to rhinitis, dental plaques, and antibiotic therapy monitoring [204][104].

If the goal is to detect and identify bacteria species, strain, or study antibiotic response, Raman spectroscopy and Fourier-transform infrared (FTIR) spectroscopy are the best tools to use—especially if the species is unknown. However, these modalities are best used when coupled with known databases and machine learning algorithms to improve specificity. Moving towards culture-free detection and in cases where culture-negative bacteria are being investigated, SERS-based techniques can prove beneficial and may be best suited for this application. The significant amplification of the intrinsic Raman signal that the nanoparticles provide is advantageous for culture-free methods. In recent years, nanoparticles have been coupled at the tip of fiber optical probes to create a more reproducible means of acquiring the SERS signal [205][105]. Therefore, with improved sensitivity over conventional Raman optical probes, this technique can provide researchers further flexibility in its use, depending on the ex vivo and in vivo environment. One drawback that researchers need to be aware of is that the SERS-based optical probes and label-free assays need to be in direct contact with the sample. Further research needs to be conducted to assess the sensitivity and specificity of the optical probes in identifying a particular species without labels. If the bacterial species is known, fluorescence, SERS, and interference-based platforms can be used for direct targeting using antigen-specific binding. Of the three, fluorescence spectroscopy is the simplest and most inexpensive tool to use. It is also the most versatile because it can be used for both imaging and quantitative analysis.

To assess bacterial growth kinetics, laser speckle contrast imaging and OCT are the most useful tools. However, with simpler optics, laser speckle is more advantageous and provides faster information, especially in measuring antimicrobial susceptibility. Compared to the gold standard and commercial antimicrobial susceptibility test (AST), which can take anywhere between 8 and 20 h for standard methods, laser speckle contrast can accomplish this within two-to-five hours [202][106]. If interested in colony growth patterns, polarization, and laser-scattering offer users the ability to capture growth patterns over time and utilize those patterns to understand how the cells divide and self-organize into colonies.

Towards point-of-care diagnostics is the miniaturization of fluorescence and SERS-based systems into a mobile or handheld platform for field applications. The simplicity in the optics required for fluorescent-based spectroscopy and imaging allows it to be fashioned into compact handheld devices that can be coupled with microfluidic platforms to be used at the point-of-care [164,166,206][29][27][107]. Similarly, with recent advancements in the design of SERS-based probes and compact spectrophotometers, these systems can couple with both paper- and microfluidic-based tools for point-of-care analysis. However, SERS-based systems are more expensive and have a higher degree of complexity than fluorescent ones, but they do provide the possibility for multiplexed detection.

Bacteria identification and the visualization of the distribution within a localized space can be achieved using various multimodal optical systems. These systems involve the merger of two or more optical modalities to benefit from the individual systems and provide users with more comprehensive information for bacteria detection and characterization. For example, coupling the biomolecular sensitivity of Raman with OCT, which provides depth-resolved imaging of a bacterial environment, can allow users to interrogate bacteria within a localized space [181][101]. Another multimodal system involves the coupling of autofluorescence with OCT for dental biofilm assessment. The fluorescence imaging provides information about the maturity of pathogenic plaque and OCT about the total plaque buildup [207][108].

However, while these optical modalities have shown tremendous promise in detecting bacteria, there are still several challenges associated with these techniques that must be addressed. The broader challenge centers around developing analytical software for quick and reliable data processing and real-time interpretation. For some modalities, such as FTIR, this is already being addressed with the creation of large databases containing information on various biological samples that are open access and can be shared and used as standards by which unknown data can be interpreted. Such databases are in their early stages, and unanimous consensus among scholars in each field is needed to ensure the validity of the information.

Another major challenge is the cost and expertise required to construct the various technologies. For fluorescence devices, this is less of an issue, as discussed. The recent acceptance and use of Raman spectroscopy in forensic science and pharmacology have allowed for the commercialization of various RS tools from benchtop to optical probes and handheld devices. This technique still relies on the use of sensitive detection hardware and narrowband laser sources, which are relatively expensive components. Still, the reduction in prices of these tools is inevitable as the market for these kinds of systems matures. In addition, as more clinicians become aware of the recent FDA-cleared OCT handheld device for OM detection, this technology also has the potential to become more widely available for the common use of biofilm characterization. Research studies with other technologies such as laser speckle contrast, inference, and polarization are still in nascent stages. Therefore, these systems are not readily available, as most are designed and built in-house.

The optical detection of pathogenic bacteria is currently an area of tremendous ongoing research because different modalities have the potential to provide information about the biomolecular make-up of a species, growth pattern recognition, single-cell versus biofilm characterization, cell motility and viability, cell mutation, and antibiotic resistance status. However, further research must be pursued to enable the eventual use of these modalities by clinicians.

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