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Open access peer-reviewed chapter

Recent Progress in the Diagnosis of Staphylococcus in Clinical Settings

Written By

Xue-Di Zhang, Bin Gu, Muhammad Usman, Jia-Wei Tang, Zheng-Kang Li, Xin-Qiang Zhang, Jia-Wei Yan and Liang Wang

Reviewed: 10 October 2022 Published: 09 December 2022

DOI: 10.5772/intechopen.108524

Staphylococcal Infections IntechOpen
Staphylococcal Infections Recent Advances and Perspectives Edited by Jaime Bustos-Martínez

From the Edited Volume

Staphylococcal Infections - Recent Advances and Perspectives

Edited by Jaime Bustos-Martínez and Juan José Valdez-Alarcón

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Abstract

Staphylococci are mainly found on the skin or in the nose. These bacteria are typically friendly, causing no harm to healthy individuals or resulting in only minor issues that can go away on their own. However, under certain circumstances, staphylococcal bacteria could invade the bloodstream, affect the entire body, and lead to life-threatening problems like septic shock. In addition, antibiotic-resistant Staphylococcus is another issue because of its difficulty in the treatment of infections, such as the notorious methicillin-resistant Staphylococcus aureus (MRSA) which is resistant to most of the currently known antibiotics. Therefore, rapid and accurate diagnosis of Staphylococcus and characterization of the antibiotic resistance profiles are essential in clinical settings for efficient prevention, control, and treatment of the bacteria. This chapter highlights recent advances in the diagnosis of Staphylococci in clinical settings with a focus on the advanced technique of surface-enhanced Raman spectroscopy (SERS), which will provide a framework for the real-world applications of novel diagnostic techniques in medical laboratories via bench-top instruments and at the bedside through point-of-care devices.

Keywords

  • Staphylococcus
  • rapid diagnosis
  • mass spectrometry
  • Raman spectrometry
  • machine-learning algorithm

1. Introduction

Bacteria belonging to the genus Staphylococcus is widely distributed in nature and is a common pathogen that causes nosocomial and community-acquired infections. It is a facultatively anaerobic Gram-positive coccus that provides a serious threat to human health due to a combination of toxin-mediated virulence, invasiveness, and antibiotic resistance. Staphylococcus is commonly found in the air, water, dust, and human and animal excretions. Every year, Staphylococcus aureus (S. aureus) causes almost half a million hospitalizations and 50,000 deaths in the United States [1]. This chapter reviewed the recent progress in the diagnosis of staphylococcal bacteria in clinical settings, including the variety of commonly used techniques ranging from traditional culture to emerging molecular methods. Conventionally, the accurate identification of clinical isolates of Staphylococcus needs a battery of tests, which is costly in resource-limited settings, though biochemical tests and drug susceptibility methods have the advantages of low cost and easy operation. However, these methods are limited to phenotypic detection only. The nucleic acid amplification methods such as PCR, real-time fluorescence quantification of nucleic acids and ring-mediated isothermal amplification are sensitive and can detect genes for strain typing. In addition, new technologies such as matrix-assisted laser desorption ionization time-of-flight mass spectrometry, gene sequencing, and SERS are ideal for phenotypic abnormalities, slow growth, and culture-negative infections, etc. The principles, characteristics, and applications of which are therefore reviewed, with an emphasis on the use of SERS as an emerging technique for the detection of bacterial pathogens more efficiently.

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2. Clinical significance of Staphylococcus infections

2.1 Staphylococcal species

The genus Staphylococcus belongs to a diverse group of Micrococcaceae bacteria that can cause many diseases. They have the capacity to produce a wide range of extracellular toxins and cell surface virulence factors. There are currently 85 species and 30 subspecies in the genus [2]. Although most people have antibodies with bodies to staphylococcal infection, these are usually ineffective, and the disease can reoccur multiple times [3] . Staphylococci can cause a variety of infections: (1) S. aureus causes localized abscesses in different places and superficial skin diseases (boils, styes) [4]; (2) S. aureus causes deep-seated infections like osteomyelitis, endocarditis, and potentially fatal skin infections [5]; (3) S. aureus, along with Staphylococcus epidermidis, is a leading cause of hospital-acquired (nosocomial) surgical wound infection and infections caused by indwelling medical device [6]; (4) S. aureus releases enterotoxins into food, which causes food poisoning [7]; (5) S. aureus releases superantigens into the bloodstream, which results in toxic shock syndrome [8]; and (6) urinary tract infections are caused by Staphylococcus saprophyticus, particularly in females [9]. Other Staphylococci species, e.g., Staphylococcus lugdunensis, Staphylococcus haemolyticus, Staphylococcus warneri, Staphylococcus schleiferi, and S. intermedius, are uncommon pathogens. Staphylococcus parasites in humans and primates mainly include the following: S. aureus, S. epidermidis, Staphylococcus capitis, Staphylococcus caprae, S. saccharolyticus, S. warneri, S. haemolyticus, Staphylococcus hominis, S. saprophyticus, Staphylococcus pasteuri, and Staphylococcus xylosus, etc., among which S. aureus colonizes the nasal canals, axillae, and pharynx [10, 11, 12], while S. epidermidis is a widespread human skin commensal [13]. In addition, Staphylococcus species are usually divided into coagulase-positive Staphylococcus (CPS) represented by S. aureus, and coagulase-negative Staphylococcus (CNS) represented by S. epidermidis. The most common type is S. aureus subsp. aureus, among the clonal populations followed by S. epidermidis, S. haemolyticus, and S. saprophyticus subsp. saprophyticus, etc.

2.2 Staphylococcal biological properties

Staphylococcus is spherical or oval in shape, 0.5–1.5 μm in diameter, forming single, paired, quadruple, short-chain, and irregular grape bunches or clusters. Staphylococcus has no flagella and no spores except for a few strains which generally do not form capsules [14]. S. aureus produces a wide range of extracellular proteins and polysaccharides, a number of which are associated with virulence [15]. Except for S. aureus subsp. anaerobius and S. saccharolyticus, most Staphylococci are facultative anaerobes. The nutritional requirements for cultivation are not stringent, and the optimum pH is 7.4–7.6.

Staphylococcus can grow well on blood agar, brain heart infusion agar, tryptone soy agar, and mannitol salt agar [16]. After 24 h of incubation in the atmospheric environment at 34–37°C, they can form round, smooth, neat-edged, raised, moist, opaque, creamy, porcelain white, pale yellow, or orange-yellow colonies. S. aureus subsp. anaerobius, S. saccharolyticus, Staphylococcus auricularis, Staphylococcus vitulinus, S. lentus, and Staphylococcus equorum grow slowly and gradually, and their colonies are commonly seen after 36 h of incubation. Small colony variants (SCVs) of Staphylococcus grow extremely slowly on conventional medium and the colony color is lighter with less pigment [17]. Staphylococci are generally salt-tolerant and grow well on agar with 6.5% NaCl. Some Staphylococcal species like S. aureus can produce hemolysin, and an apparent β-hemolytic ring can be seen after 24 h of incubation on sheep blood or rabbit blood agar [18]. When routinely cultured, many Staphylococci produce fat-soluble carotenoids visible to the naked eye, making the colonies yellow, orange-yellow, or orange and not spread into the agar medium. Staphylococcus does not form pigment in liquid medium, grows uniformly and turbidly, slightly precipitates at the bottom of the tube, and is easy to disperse when shaken. S. aureus colonies are yellow on Mannitol Salt Medium (MSM). White precipitation rings can be formed around S. aureus colonies on Egg-Yolk Salt Agar Medium. Moreover, S. aureus colonies are black on Baird-Parker agar, surrounded by turbid bands and transparent rings. The surface antigens of Staphylococcus are mainly Staphylococcus Protein A (SPA) and polysaccharide antigens. SPA is a surface protein on the cell wall with species and genus specificity while polysaccharide antigens are type-specific. Staphylococcus is one of the most resistant non-spore-forming bacteria, which is resistant to dryness and high salinity and can grow in a medium containing 100–150 g/l NaCl.

2.3 Distribution and epidemiology

Staphylococcus is widely distributed in nature, mainly parasitic on mammals and birds’ skin, sebaceous glands and mucous membranes. Some Staphylococcus and its subspecies are parasitic in selected parts of the host. S. capitis subsp. capitis mainly exists in great amounts in the sebaceous glands on the top of the head and forehead of humans, while S. capitis subsp. ureolyticus is present in abundance in the armpits of humans. S. aureus has the dual characteristics of a colonized and pathogenic bacterium, mainly distributed in the nasal vestibule. About 20% of people have persistent nasal cavity colonization by S. aureus, and 30% have intermittent colonization. In addition, S. aureus is also colonized in the axilla, pharynx, groin and gastrointestinal tract, etc. [13]. It has been shown in the study that S. aureus strains isolated from the blood of 82% of patients with bacteremia are identical to those isolated from the nasal cavity [3, 19]. More than 50 million people are expected to be infected with methicillin-resistant S. aureus (MRSA), which is easily transmitted through skin contact. However, MRSA infection is difficult to cure due to its resistance to most antibiotics, while children, elderly people, and sick patients in hospitals and nursing homes are particularly susceptible. While the number of MRSA bloodstream infections in the US has declined in recent years, the infection still resulted in 20,000 fatalities in 2017. In addition, MRSA was responsible for more than 100,000 deaths worldwide in 2019, highlighting the importance of improved surveillance to prevent and manage the spread of this potentially dangerous bacterium [20]. S. epidermidisis the most common Staphylococcus on the human body surface, especially in moist areas such as armpits, groin, perineum, anterior nostrils, and toes [21]. S. haemolyticus is easily isolated from the apocrine glands in the axilla [16]. In addition, S. saprophyticus subsp. saprophyticus is easily isolated from the female rectum and urinary system [22].

2.4 Staphylococcal infections

S. aureus bacteremia, which often leads to metastatic foci of infection, can occur at any site, but it is especially common with infections associated with intravascular catheters. S. epidermidis and other coagulase-negative Staphylococci are gradually causing hospital-acquired bacteremia because they can form biofilms on intravascular catheters and other foreign objects. Staphylococcal bacteremia is a substantial reason for disease and death in debilitated people [23]. Many Staphylococci are opportunistic pathogens of the skin and mucous membranes. S. aureus is the primary clinic pathogenic bacteria of humans [24]. The diseases caused by the bacterium can be roughly divided into purulent infections and toxin-causing diseases. The former includes superficial infections (boils, carbuncles, folliculitis, paronychia, styes, wound suppuration, abscesses), deep tissue infections (mastitis, cellulitis, necrotizing fasciitis, osteomyelitis, arthritis), and systemic infections like bacteremia. Toxin-related diseases caused by S. aureus mainly include staphylococcal scalded skin syndrome (SSSS) caused by an exfoliative toxin, also known as exfoliative dermatitis; toxic shock syndrome (TSS) caused by toxic shock syndrome toxin-1 (TSST-1) and S. aureus food poisoning (SFP) caused by staphylococcal enterotoxins (SEs). Lymphangitis is caused by bacterial infection of the lymphatic vessels. The organisms that cause the disease enter the body through a skin wound and are either Streptococcus or Staphylococcus. The inflamed lymph vessels appear as red streaks under the skin that extend from the infection site to the groin or armpit. The other symptoms may include fever, chills, headache, and appetite loss. The most typical manifestation of staphylococcal disease is skin infections. Superficial infections can be generalized with vesicular pustules and crusting (impetigo) or focal with nodular abscesses (furuncles and carbuncles). Deeper cutaneous abscesses are relatively common. There could be severe necrotizing skin infections. Staphylococcal newborn infections, which can cause skin lesions with or without exfoliation, bacteremia, meningitis, and pneumonia, typically appear within 6 weeks of delivery [25].

Coagulase-negative staphylococci (CNS) represented by S. epidermidis have become the leading pathogen of nosocomial infection in recent years. They can cause prosthetic valve endocarditis, urinary system infection, central nervous system infection, and bacteremia. S. lugdunensis can cause endocarditis, arthritis, urinary tract infections and bacteremia. In addition, S. saprophyticus can often cause urinary tract infection, prostatitis, wound infections, bacteremia, and so on. Chronic infection or intracellular parasitism of S. aureus often appears in the form of SCVs during in vitro culture [26]. SCVs are now defined as a subgroup of microorganisms that grow slowly on agar medium, form small colonies, have reduced or absent pigment production, and have altered expression of virulence factors (e.g., reduced production of α-hemolysin). This is quite different from typical S. aureus colonies, so it is easy to miss its detection in routine microbial identification. However, SCVs are closely related to chronic and recurrent infections [27, 28]. Typical S. aureus colonies and SCVs often coexist on agar medium. Therefore, in-depth study of SCVs is critical to the treatment and control of clinical infections.

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3. Traditional identification of staphylococcal bacteria

3.1 Microscopic inspection and culture

Microscopic inspection is based on performing morphological tests on colonies. Clinical specimens were smeared, Gram-stained, and the morphology was observed under a microscope. S. aureus is typically identified using tests for clumping factor, coagulase, hemolysins, and thermostable deoxyribonuclease. There are currently available latex agglutination tests. The identity of S. epidermidis is established by using commercial bio-typing kits. Staphylococci are catalase positive and can withstand quite high sodium chloride concentrations (7.5–10%). This feature is often used in the preparation of Staphylococci-specific media. A rapid and efficient method for classifying Gram-positive bacteria species was developed using hyperspectral microscope images. Traditional bacteria detection and identification procedures using specific agar media remain the “gold standard” to differentiate the microorganisms. Furthermore, traditional serotyping approaches based on antibodies or genetic matching, such as plasmid fingerprinting, have been developed [29].

3.2 Staphylococcal biochemical identification

The majority of staphylococcal oxidase tests are negative. Staphylococcus sciuri, S. vitulinus, S. lentus, Staphylococcus fleurettii, and Staphylococcus caseolyticus are positive for oxidase tests due to the presence of Cytochrome c oxidase. Staphylococcus catalase tests are usually positive, while S. aureus subsp. anaerobius and S. saccharolyticus are negative. Most Staphylococcus species can decompose a variety of carbohydrates and deoxidize nitrates, as they are sensitive to lysostaphin and furazolidone, and are resistant to bacitracin and vibriostatic agent O/129 (2,4-diamino-6,7-diisopropylpteridine). The plasma coagulase test and the thermostable nuclease test for S. aureus are positive. S. aureus is sensitive to novobiocin. The biochemical identification of Staphylococcus and other Gram-positive cocci is shown in Table 1. It can be known from the table that Staphylococcus catalase is positive, which is different from Enterococcus and Streptococcus. The identification of biochemical reactions within the Staphylococcus species is shown in Table 2. Mature commercial biochemical identification systems include API Staph (bioMérieux), ID32 Staph (bioMérieux), Vitek (bioMérieux), MicroScan Product Pos ID family (Siemens Health-care Diagnostics), BD BBL Crystal (BD Diagnostics Systems), Sensitire GPID (TREK Diagnostic Systems), etc. Most laboratories use commercial identification systems or automated identification instruments. These methods are simple, convenient, and accurate. However, uncommon strains or strains with phenotypic variants (such as SCVs) require molecular identification due to altered biochemical response patterns.

CharacteristicStaphylococcusMicrococcusEnterococcusBalloon fungusMegacoccusStreptococcusKineococcus
Colony pigmentWhite/yellowYellow/milk whiteWhite/yellowWhiteWhiteColorless/grayND
G + C(%)30–3966–7534–4235–4038–4534–4639–52
Strictly aerobic−+−−±−+
Quadruple arrangementd+−+d−+
Motility−−d−−−+
6.5% NaCl+++++d+
Catalase++−−+−+
Oxidase (modified method)−+−−+−ND
Glucose anaerobic acid productiond−+(±)−+−
Glycerol aerobic acid production+−dNDNDd−
Benzidine test++−−ND−+
Erythromycin (0.4 ug/ml)RSRNDRSND
Bacitracin (0.04 U/disk)RSRSRdND
Furazolidone (100 ug/disk)SRSSSSS
Glucolysin (200 ug/disk)SRRRRRR

Table 1.

Main biochemical identifications of Staphylococcus and other gram-positive cocci.

Note: Abbreviations: +, more than 90% of the strains are positive; ±, more than 90% of the strains are weakly positive; −, more than 90% of the strains are negative; d, 11–89% of the strains are positive; ND, uncertain; (+−), Delayed response; S, sensitive; R, resistant.

SpeciesColony sizeColony PigmentAnaerobic growthaerobic growthHemolysisCoagulaseAgglutination factorCatalaseOxidaseThermo-nucleaseALPPRY
S. aureus subsp. aureus++++++++−++−
S. aureus subsp. anaerobius−−(+)(+)++−−−++ND
Staphylococcus hyicus+−++−d−+−++−
S. intermedius+−(+)+d+d+−+++
Staphylococcus lugdunensisdd++(+)−(+)+−−−+
Staphylococcus schleiferi subsp. coagulans−−++(+)−++−+++
S. schleiferi subsp. schleiferid−++(+)+−+−++ND
Staphylococcus delphini+−(+)+++−+−−+ND
S. hutrae−−++++−+−(±)+ND
Staphylococcus sciuri subsp. carnaticus−d(d)+(±)−d++−d−
S. sciuri subsp. rodentiumdd(d)+(±)−+++−d−
Staphylococcus pseudintermedius+−ND+++−+ND+++
Staphylococcus epidermidis−−++(d)−−+−−+−
SpeciesODCUreaseArginine utilizationNitrate reductionV-PAescin hydrolysisGalactosidaseNovobiocin resistancePolymyxin B resistanceα-LactoseMaltoseSucrose
S. aureus subsp. aureus−d+++−−−++++
S. aureus subsp. anaerobiusNDNDND−−−−−−ND−++
Staphylococcus hyicus−d++−−−−++−+
S. intermedius−+d+−−+−−d(±)+
Staphylococcus lugdunensis+d−++−−−d+++
Staphylococcus schleiferi subsp. coagulans−−+++−(+)−−−−−
S. schleiferi subsp. schleiferiND++++NDND−NDd−d
Staphylococcus delphiniND+++−NDND−ND+++
S. hutraeND+−+−ND+−ND++ND
Staphylococcus sciuri subsp. carnaticus−−−+−+−+−(d)(d)+
S. sciuri subsp. rodentium−−−+−+−+−(d)(d)+
Staphylococcus pseudintermediusND+ND+NDND+−ND+++
Staphylococcus epidermidis(d)+d++−−−+d++
SpeciesColony sizeColony PigmentAnaerobic growthaerobic growthHemolysisCoagulaseAgglutination factorCatalaseOxidaseThermonucleaseALPPRY
Staphylococcus haemolyticus+d(+)+(+)−−+−−−+
Staphylococcus saprophyticus subsp. saprophyticus+d(+)+−−−+−−−+
S. saprophyticus subsp. bovis−+++−−−+−−−−
Staphylococcus warneridd++(d)−−+−−−−
Staphylococcus hominis subsp. hominis−d−+−−−+−−−−
S. hominis subsp. novobiosepticus−−−+−−−+−−−+
S. simulans+−++(d)−−+−−(d)+
Staphylococcus capitis subsp. capitis−−(+)+(d)−−+−−−−
S. capitis subsp. ureolyticus−(d)(+)+(d)−−+−−−(d)
Staphylococcus cohnii subsp. cohniid−d+(d)−−+−−−−
S. cohnii subsp. urealyticus+d(+)+(d)−−+−−+d
Staphylococcus xylosus+dd+−−−+−−dd
Staphylococcus capraed−(+)+(d)−−+−−(+)d
SpeciesODCUreaseArginine utilizationNitrate reductionV-PAescin hydrolysisGalactosidaseNovobiocin resistancePolymyxin B resistanceα-LactoseMaltoseSucrose
Staphylococcus haemolyticus−−+++−−−−d++
Staphylococcus saprophyticus subsp. saprophyticus−+−−+−++−d++
S. saprophyticus subsp. bovis−+−+d−d+ND−++
Staphylococcus warneri−+dd+−−−−d(+)+
Staphylococcus hominis subsp. hominis−+ddd−−−−d+(+)
S. hominis subsp. novobiosepticus−+−dd+−+NDd+(+)
S. simulans−+++d−+−d+(±)+
Staphylococcus capitis subsp. capitis−−ddd−−−−(+)−(+)
S. capitis subsp. ureolyticus−+++d−−−ND+++
Staphylococcus cohnii subsp. cohnii−−−−d−−+−−(d)−
S. cohnii subsp. urealyticus−+−−d−++−+(+)−
Staphylococcus xylosus−+−ddd++−d++
Staphylococcus caprae−++++−−−−+(d)−

Table 2.

Identification of biochemical reactions within the species of Staphylococcus.

Notes: Abbreviations: +, more than 90% of strains are positive; ±, above 90% of strains are weakly positive; −, above 90% of strains are negative; d, 11–89% of positive strains; ND, uncertain; (+−), delayed response. ALP stands for alkaline phosphatase; PRY stands for pyrrolidone amino amidase test; and ODC stands for ornithine decarboxylase.

3.3 Antibiotic resistance

The conventional approaches for antibiotic susceptibility testing of Staphylococci include disk diffusion and broth dilution, which can be operated following the American Clinical and Laboratory Standards Institute (CLSI) and the European Committee for Antimicrobial Susceptibility Testing (EUCAST). The disc diffusion method, also known as the Kirby-Bauer (K-B) method, is based on the principle of sticking a disc containing anti-bacteial drugs onto an agar plate inoculated with the test bacteria. The medicine in the disc absorbs the water in the agar and dissolves continuously to spread around the disc. The growth of bacteria is inhibited within the range of inhibitory concentration around the disc, thus forming a transparent antibacterial ring. Its size reflects the susceptibility of the test bacteria to the drug and is negatively correlated with the test bacteria’s minimum inhibitory concentration (MIC). The principle of the broth dilution method is to use Mueller Hinton Broth (MHB) to dilute the antibacterial drugs to different concentrations and then culture the bacteria. The MIC or the minimal bactericidal concentration (MBC) is tested by observing the growth of the bacteria.

Commercial detection systems for the broth dilution method for drug susceptibility mainly include bioMérieux (http://www.biomerieuxusa.com), Siemens Health-care Diagnostics (http://www.siemens.com), Becton Dickinson Diagnostics (http://www.bd.com) and Thermo Scientific (http://www.thermoscientific.com). S. aureus and S. epidermidis have no natural resistance, while S. saprophyticus, Staphylococcus cohnii, and S. xylosus are naturally resistant to novobiocin, and S. saprophyticus and Staphylococcus kloosii are naturally resistant to fosfomycin. The common resistant phenotypes and screening methods of Staphylococcus are shown in Table 3. S. aureus is a serious danger to worldwide public health security, especially methicillin-resistant S. aureus (MRSA), which has become the leading pathogen of nosocomial infections worldwide. Besides that, drug-resistant genes of multidrug-resistant S. aureus strains can be spread among humans, animals, and the environment through horizontal transfer [30], making the problem of bacterial drug resistance increasingly serious. Turner et al. [31] reported that S. aureus had developed different degrees of resistance to almost all antibiotics in the past 10 years. MRSA refers to S. aureus carrying the mecA gene and (or) S. aureus with a MIC of Oxacillin >4 mg/l, which can be divided into hospital-acquired (HA-MRSA) and community-acquired (CA-MRSA) strains. The drug resistance mechanism of MASA is complex and mainly related to the mecA gene encoding penicillin-binding protein PBP2a [32], the mecC gene encoding penicillin-binding protein PBP2c [33, 34], exogenous acquisition of staphylococcal chromosome mec gene [35], fem gene [36, 37] and other factors.

SpeciesResistant phenotypeTest methodMediumDrugIncubation conditionsResultsQuality controlWhether or not to be confirmed
S. aureusOxacillin ResistanceaOxacillin-Salt Agar ScreenMHA + 4%NaCl6 ug/ml Oxacillin33–35°C, ambient air, 24 h≥1 colonyS. aureus ATCC®29213 S. aureus ATCC® 43300No
mecA-Mediated Oxacillin ResistanceaCefoxitin Broth MicrodilutionCAMHBCefoxitin33–35°C, ambient air, 16–20 h>4 ug/ml = mecA Positive, ≤4 ug/ml = mecA negativeS. aureus ATCC®25,923No
Cefoxitin Disk DiffusionMHA30 μg Cefoxitin disk35± 2 °C, ambient air, 16–18 h≤21 mm = mecA Positive, ≥22 mm = mecA negativeS. aureus ATCC®25,923No
Vancomycin MIC ≥ 8 ug/mlBHI agar dilutionBHI agar6 ug/ml Vancomycin35 ± 2 °C, aerobic, 24 h≥1 colony, presumptive susceptibility reducedEnterococcus faecalis ATCC® 29,212Yes
Disc DiffusionMHA30 ug Vancomycin disk35 ± 2°C, 16–18 h6 mm, presumptive resistantS. aureus ATCC®25,923Yes
Inducible Clindamycin ResistanceClindamycin-Erythromycin Broth MicrodilutionCAMHB4 ug Erythromycin and 0.5 ug Clindamycin in the same well35 ± 2 °C,ambient air, 18–24 hAny growth = positive, no growth = non-inducible clindamycin resistanceS. aureus ATCC®BAA-976, S. aureus ATCC®BAA-977No
D test (Disc Diffusion)MHA or BAP15 ug Erythromycin disk and 2 fug Clindamycin disk are placed 15–26 mm apart35 ± 2 °C, ambient air, 16–18 hzone edge appears “truncated” (similar to the English letter D) = positive; blurred zone edge (beach-like) = Clindamycin resistanceS. aureus ATCC®BAA-976, S. aureus ATCC®BAA-977No
High-Level Mupirocin ResistanceBroth MicrodilutionCAMHB256 ug/ml Mupirocin35 ± 2°C, ambient air, 24 hgrow = mup ApositiveS. aureus ATCC®29,213, S. aureus ATCC®BAA-1708No
Disc DiffusionMHA200 ug Mupirocin disk35 ± 2 °C, ambient air, 24 hno inhibition zone = mupA positive, any inhibition zone = mupA negativeS. aureus ATCC®25,923, S. aureus ATCC®BAA-1708No
CoNSmecA-Mediated Oxacillin ResistancebCefoxitin Disk DiffusionMHA30 μg Cefoxitin disk33–35°C, ambient air, 24 h≤24 mm = mecAPositive, ≥25 mm = mecA negativeS. aureus ATCC® 43,300No
Inducible Clindamycin ResistanceClindamycin-Erythromycin Broth MicrodilutionCAMHB4 ug Erythromycin and 0.5 ug Clindamycin in the same well35 ± 2 °C, ambient air, 18–24 hAny growth = positive, no growth = non-inducible clindamycin resistanceS. aureus ATCC®BAA-976, S. aureus ATCC®BAA-977No
β-Lactamase ProductionDisk diffusion
(Penicillin zone-edge test)		MHA10 units penicillin disk35 ± 2 °C, ambient air, 16–20 hSharp zone edge = β-Lactamase positive, blurred zone edge = β-Lactamase negativeS. aureus ATCC®25,923No
Nitrocefin-based testN/AN/A<1 h or according to the manufacturer’s instructions for useFrom yellow to red or pink = β-lactamase positiveS. aureus ATCC®29,213 S. aureus ATCC® 25,923Yes
D test (Disc Diffusion)MHA or BAP15 ug Erythromycin disk and 2 ug Clindamycin disk are placed 15–26 mm apart35 ± 2 °C, ambient air, 16–18 hzone edge appears “truncated” (similar to the English letter D) = positive; blurred zone edge (beach-like) = clindamycin resistanceS. aureus ATCC®BAA-976, S. aureus ATCC®BAA-977No
β-lactamase productionNitrocefin-based testN/AN/A<1 h or according to the manufacturer’s instructions for useFrom yellow to red or pink = β-lactamase positiveS. aureus ATCC®29,213 S. aureus ATCC® 25,923Yes

Table 3.

The main resistant phenotypes and screening methods of Staphylococcus.

For S. aureus and S. lugdunensis, although S. lugdunensis is a CNS, some resistant phenotypes are more consistent with S. aureus.


For CoNS, except for S. lugdunensis and Staphylococcus pseudintermedius.


Note: Abbreviations: BHI, brain heart infusion; CAMHB, calcium-adjusted MH broth; MHA, MH agar plate; BAP, blood plate; ATCC, American Type Culture Collection.

The cefoxitin disk diffusion assay of mecA-mediated oxacillin resistance for CoNS in Table 3 does not apply to S. lugdunensis and S. pseudintermedius. The detection method of S. lugdunensis is the same as that of S. aureus. The oxacillin resistance of S. pseudintermedius was detected by 1 μg oxacillin disk, while the cefoxitin disc and the MIC methods were both unreliable. When using vancomycin to treat S. aureus, S. aureus is easy to develop from sensitivity to an intermediate or resistance phenotype. Attention should be paid to the detection of vancomycin sensitivity to S. aureus. The detection of vancomycin-intermediate S. aureus (VISA) and vancomycin-resistant S. aureus (VRSA) by automated drug susceptibility systems or disc diffusion methods is complex and the results are unreliable. Therefore, further confirmation is required. Biochemical identification and routine drug susceptibility testing require the acquisition of pure cultured colonies, which is time-consuming for slow-growing staphylococci.

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4. Rapid diagnosis of Staphylococcal infections

4.1 PCR and its derived technologies

4.1.1 Polymerase chain reaction (PCR)

Polymerase chain reaction (PCR) is the most extensively used nucleic acid amplification method for bacterial serotyping and confirmation. RT-PCR (Real-time quantitative PCR) has high sensitivity, high specificity, low pollution, and a high degree of automation [38]. Its reaction is monitored in real-time and can quantitatively detect target genes. The detection time of clinical samples can even be shortened to 1 h. Recent literature reports show that RT-PCR technology is currently the most accurate, reproducible and internationally recognized standard method for the quantitative and qualitative detection of nucleic acid molecules. For example, Okolie et al. [39] simultaneously detected marker genes of Coagulase-negative Staphylococcus (CoNS), staphylococcal protein A (SPA), Panton-Valentine leukocidin (PVL) and methicillin-resistant S. aureus (MRSA) by applying real-time PCR polymorphism analysis. Yang et al. [40] also found that the effect of real-time RT-PCR in detecting methicillin-resistant S. aureus (MRSA) was better than drug susceptibility testing. The enterotoxin produced by S. aureus in food can cause food poisoning, so S. aureus is also a critical detected bacteria in the food industry. Huang et al. [41] found that the TaqMan-MGB probe RT-PCR method established for the coa (encoding coagulase) gene of S. aureus can enhance the speed and sensitivity of food detection. Multiplex PCR is a PCR reaction that simultaneously amplifies two or more DNA sequences from the same sample [42]. In a study by Schmitz et al. [43] a multiplex PCR on bacteria colonies chosen directly from agar plates without prior DNA preparation is described. In parallel, specific primers were used to detect staphylococcal genes coa and mecA. Tsai et al. [44] applied multiple PCR technology to detect Staphylococcus and Vibrio vulnificus in blood and tissue samples of 99 patients with surgically confirmed necrotizing fasciitis (NF) of the extremities. These techniques can be time-consuming and require trained operators who are familiar with the procedure. Therefore, it is interesting to develop a fast, simple, and consistent technology to identify and distinguish between different bacterial species and serotypes.

4.1.2 Isothermal nucleic acid amplification technology

Classical nucleic acid amplification technology has multiple thermal cycling steps, requires strict laboratory conditions, and relies on the use of high-precision instruments that are difficult to miniaturize. The isothermal amplification technology can perform accurate and rapid analysis on site, and is more suitable for integration into miniaturized systems [45]. Loop-mediated isothermal amplification (LAMP) technology was created by Notomi et al. in 2000 [46]. It is a nucleic acid amplification technology that can perform rapid, specific and sensitive detection of target sequences under isothermal conditions. Yin et al. [47] utilize LAMP technology combined with lateral flow assay (LFA) to simultaneously detect S. aureus sea and seb genes. Strand displacement amplification (SDA) is an enzymatic reaction-based DNA in vitro amplification technology [48]. After the initial thermal denaturation of the dsDNA target, it only needs to reach 37°C for the reaction. Cai et al. [49] reported an SDA-based biosensor for the detection of S. aureus. The aptamer was immobilized on streptavidin-modified magnetic beads as a biorecognition molecule, and then bound to its complementary ssDNA. When S. aureus is present, the aptamer binds to it, releasing complementary DNA into the solution and detecting pathogenic microorganisms by SDA amplification. The limit of detection (LOD) of the sensor was 8 CFU/ml, and the recovery rate was more than 93.9%. The time-consuming amplification step was optimized from 2 h to 45 min. Although the reaction time was longer compared to other amplification reactions, it had high sensitivity and easy-to-reach reaction temperature advantages. In addition, there are room temperature amplification technologies such as recombinase polymerase amplification (RPA), rolling circle amplification (RCA), simultaneous amplification and testing (SAT), etc.

4.2 Immunoassay

Immunology-based rapid detection technologies for microorganisms include Immune Fluorescence Assay (IFA), Enzyme-linked Immunosorbent Assay (ELISA), Chemiluminescence Immunoassay (CLIA), Radio Immunoassay (RIA), Immunomagnetic Separation (IMS), and Immune Colloidal Gold (ICG) technique, etc. Among them, IMS is a technology that uses the magnetic responsiveness of the magnetic beads to enrich and separate the target substances by coating the recognition substances such as antigens and antibodies on the superparamagnetic nanomagnetic beads with a specific particle size range. The technical operation is simple and fast, with high specificity and sensitivity. Currently, it has been extensively used in protein and nucleic acid purification, cell separation and pathogen detection, such as Multiple Polymerase Chain Reaction (MPCR), Recombinase Polymerase Amplification (RPA), and Loop-Mediated Isothermal Amplification (LAMP). Zhou et al. [50] use avidin-labeled magnetic beads and biotin-labeled SPA monoclonal antibodies to prepare immunomagnetic beads to enrich S. aureus from sputum, which is then combined with MPCR to detect the mecA gene and femA gene of MRSA strains in sputum samples. The detection of MRSA strains has advantages in terms of detection rate, sensitivity and specificity, especially because the detection time can be shortened from 48–72 h to 4–6 h. The most common application of immunoassay techniques is in the detection of Staphylococcus toxin. Based on the existing ELISA method, Chang et al. successfully constructed a new staphylococcal enterotoxin A (SEA) detection method for microscale solid phase extraction MSPE-ELISA on magnetic microspheres modified with staphylococcal enterotoxin A (SEA) as an aptamer and introduced solid magnetic phase extraction technology. The sensitivity of magnitude is higher as compared to ELISA kits, enabling the high-sensitivity detection of SEA trace amounts in actual samples. Shan et al. [51] used a carboxyl-modified fluorescent microsphere (PSA-R6G) to immobilize a monoclonal antibody against S. aureus as a capture probe. A fluorescein isothiocyanate (FITC)-labeled S. aureus secondary fluorescein antibody served as a sensitive reporter antibody. After double labeling with R6G and FITC, multiparameter flow cytometry analysis observed the enriched S. aureus. Zhao et al. [52] use vancomycin-immobilized gold nanoparticles (VAN-Au NPs) as the first recognition factor to capture S. aureus, and use the second recognition agent of porcine immunoglobulin G (IgG) to ensure its specificity. A novel sandwich-based lateral flow assay (LFA) for highly sensitive and selective detection of S. aureus. Tarisse et al. [53] developed an immunoassay that detects the staphylococcal enterotoxins SEA, SEG, SEH, and SEI with high sensitivity and specificity.

4.3 Mass spectrometry

The molecular weight and structure of different biomolecules, such as proteins, nucleic acids, and polysaccharides, can be analyzed using matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) technology. The basic principle behind matrix-assisted laser desorption is as follows: after the matrix and the sample form an eutectic, the matrix and the sample absorb laser energy to desorb the sample, and charge transfer between the matrix and the sample occurs to ionize the sample molecules. The mass-to-charge ratio of ions is proportional, and the mass-to-charge ratio can be measured according to the flight time to the detector, and a characteristic fingerprint can be obtained through software processing [54]. MALDI-TOF MS technology has the characteristics of rapidity, accuracy, sensitivity and automation [55], and gradually occupies an important position in the identification of microbiology laboratories [56]. Rychert et al. [57] conducted a multicenter study on Gram-positive aerobic bacteria, and the results showed that in 1146 Gram-positive bacteria samples, the accuracy rate at the species level was 92.8%, and the accuracy at the genus level could reach 95.5%. The time required for MALDI-TOF MS to obtain identification results has been shortened from 5 to 48 h or even longer via traditional biochemical methods to less than 6 min per sample, and the cost of reagents for single-sample detection has been reduced to less than 1/4 of traditional methods. The overall identification accuracy of MALDI-TOF MS is >90%, which is higher than that of conventional methods (80–85%); in addition, MALDI-TOF MS is easy to operate, which significantly shortens the time for professional and technical training of personnel [58, 59]. MALDI-TOF MS can also be used to analyze the antibiotic resistance of bacteria. The advantages of MALDI-TOF MS are good specificity and short experimental time as compared with conventional antibiotic susceptibility testing (AST) [60, 61]. MOLDI-TOF MS can also quickly differentiate between MRSA and MSSA [62, 63]. The most essential characteristic peaks for distinguishing MRSA and methicillin-sensitive S. aureus (MSSA) are at the mass spectrum peaks of 3279, 6485, 6555, and 3299 m/z [64].

4.4 Genome sequencing

In 1977, Sanger et al. [65] invented the dideoxyribonucleotide end termination method, and Maxam and Gilbert [66] developed the chemical degradation method, which marked the birth of the next generation of sequencing technology. Sanger sequencing is the standard technology and its length can be up to 1000 bp and the accuracy is almost 100%, but it has the disadvantages of low throughput, high cost, and long time. Next-generation sequencing (NGS) came into being. Next-generation sequencing platforms mainly include the Roche 454 sequencing platform based on microemulsion PCR and pyrosequencing technology, the Illumina sequencing platform based on bridge PCR and fluorescent reversible terminator sequencing-by-synthesis, the SOLID sequencing platform based on microemulsion PCR and oligonucleotide ligation sequencing, and the Ion Torrent PGM and Proton semiconductor sequencing platforms [67].

In 2014, Wilson et al. [68] reported the world’s first case of an infectious disease diagnosed by next-generation sequencing technology. Since then, NGS technology has been gradually recognized and promoted, providing ideas for the diagnosis of unknown pathogens in clinical practice [69] NGS is the most widely used method for high-throughput, massively parallel sequencing of thousands to billions of DNA fragments simultaneously [70]. The third-generation sequencing technology is divided into single-molecule real-time (SMRT) sequencing and nanopore single-molecule sequencing according to different sequencing principles. Gene sequencing can obtain the genomic information of pure colonies and the genomic information of mixed specimens so that highly related lineages can be distinguished with the resolution and precision that other methods lack. Gene sequencing can obtain nearly complete bacterial DNA information, including species, drug-resistance genes, virulence factors, mobile elements, etc. The molecular epidemiology and transmission mechanisms of strains are critical to understanding the occurrence and development of various diseases [71]. The widespread availability of genetic sequencing technology has enabled more detailed studies of MRSA transmission patterns, including analysis of past undocumented transmission and comprehensive, complicated strain evolution [72, 73, 74]. In addition, gene sequencing plays a significant role in the study of MRSA colonization and infection [75].

Moore et al. [76] demonstrated that Whole Genome Sequencing (WGS) has a high resolution for strains that other methods cannot distinguish in MRSA colonization and infection studies. WGS is a comprehensive method that analyzes the entire genomic DNA of a cell at once by using sequencing technology. At present, NGS technology still lacks unified laboratory testing operation specifications, and exogenous nucleic acid contamination will likely lead to false positive results, which will seriously affect clinical diagnosis. NGS can detect two or more non-pathogenic bacteria in the same specimen. The analysis may be because NGS has high sensitivity and the nucleic acid residues of non-specimen pathogens with low sequence numbers or dead pathogens are detected together, which is very likely to lead to misjudgment by clinicians, though NGS results lack recognized interpretation. However, the relationship between sequencing results and treatment is unclear, and drug-resistance genes are difficult to detect, so it still needs to be supplemented with drug susceptibility testing. In summary, NGS technology plays an essential role in identifying pathogens and guiding clinical treatment. With the continuous improvement of NGS detection platforms and the proposal of relevant interpretation, NGS technology will be widely used on standards to guide clinical diagnosis and treatment.

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5. Raman spectroscopy in Staphylococcus identifications

5.1 Principles of Raman scattering effects

Raman scattering is an inelastic scattering phenomenon caused by light striking the surface of a material, revealed by Indian scientist Chandrasekhara Venkata Raman in 1928 [77]. When the molecules of the detected object interact with the incident light photons of the monochromatic beam, elastic and inelastic collisions can occur simultaneously. The scattering mode in which the optical frequency does not change is called Rayleigh scattering. The photon transfers energy to the molecule during an inelastic collision; after it changes direction, some of this energy is transferred to the molecule (Stokes scattering), or the vibration and rotational energy of the molecule is transferred to the photon (Anti-Stokes scattering), changing the frequency of the photon (Raman scattering) [78]. Because Raman scattering can reflect the molecular vibration and vibration-rotation energy level of substances, it is used in molecular structure analysis. However, due to the extremely low scattering efficiency of inelastic scattering, the scattered light intensity is one millionth to one billionth of the incident light intensity, which greatly limits the application of Raman spectroscopy in material analysis and detection, and surface-enhanced Raman spectroscopy was then discovered and developed.

5.2 Surface-enhanced Raman spectroscopy

In 1974, Fleischmann et al. [79] found that the pyridine molecules adsorbed on the rough silver electrode surface had a significant Raman scattering effect. In 1977, after extensive experimental research and theoretical calculation, Jeanmarie et al. [80] named this enhancement effect related to rough metal surfaces such as silver (Ag), gold (Au), and copper (Cu) as the surface-enhanced Raman scattering effect, and the corresponding technology was called surface-enhanced Raman spectroscopy (SERS). The Raman scattering signal of pyridine molecules adsorbed on the rough metal silver surface is enhanced by about 6 orders of magnitude compared to the Raman scattering signal of pyridine molecules in solution, which provides the possibility for the detection of biological macromolecules. The principle of SERS is explained mainly through two mechanisms: chemical enhancement and electromagnetic enhancement. The chemical mechanism (CM) describes the electronic interaction between substrates and adsorbed molecules and offers a small enhancement magnitude 102–103. The electromagnetic enhancement (EM) mechanism contributes by increasing the electromagnetic field near plasmonic structures caused by incident light excitation of a localized surface plasmon resonance (LSPR). Plasmonic nanomaterials are those in which incident electromagnetic radiation from light can coherently excite conduction electrons to oscillate collectively at metal/dielectric interfaces. The large SERS enhancement factor (EF) generated from EM contribution to plasmonic nanomaterials is in the magnitude of 1010–1014 [81] which is significant for the detection of single molecules [82]. Among them, electromagnetic enhancement receives more attention and acknowledges extensive research work. Label-free SERS detection technology has developed into a research hotspot in the field of microbiology due to its advantages of no need for too much preliminary preparation, non-invasive and short detection time, and excellent application prospects in bacterial detection.

5.3 SERS spectra of staphylococcal bacteria

The complex biological meaning and structural information contained in Raman spectra result from the vibrational and rotational frequencies of molecules in the sample. The vibration frequencies of biomolecules such as nucleic acids, proteins, lipids, and carbohydrates in bacteria are different, and they appear as unique peaks in Raman spectra. “Full biometric fingerprints” can be used as a basis for distinguishing different bacteria. Efrima et al. [83] used SERS for bacterial detection, distinguishing Gram-positive and Gram-negative bacteria through the difference in SERS profiles on the cell membrane surface. Since then, the application of SERS in bacterial detection, identification, and classification has received rapid attention. Rebrošová et al. [84] detected 54 S. epidermidis and 51 Candida parapsilosis strains from Mueller-Hinton agar plates using Raman spectroscopy with an accuracy of 96.1% and 98.9%, respectively. Tang et al. [85] applied two deep learning methods, Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM), for SERS detection of 117 staphylococcal strains belonging to 9 species of Staphylococcus, with an accuracy of 98.21% and 94.33%, and Area Under Curve (AUC) values of 99.93% and 99.83%, respectively [85]. In addition, Staphylococcus wornerii, S. hominis, and Staphylococcus korea have unique peaks at 1003 cm−1. Staphylococcus xylinum and Staphylococcus squirrels have special peaks at 1089 and 1093 cm−1 [85]. Rebrošová et al. [86] reported that Raman spectroscopy analysis of 277 staphylococcus strains of 16 species, including S. aureus and S. epidermidis, revealed that the total accuracy of inputting a spectrum was over 99%, and even reached 100% for a few strains, indicating that SERS is a reliable tool for the identification of Staphylococci. The most common S. aureus Raman peaks are primarily at 731 cm−1 [87], which is produced by glycosidic linkages and originates from the abundant peptidoglycan in the cell wall. The other two main Raman peaks at 958 and 1050 cm−1 are protein C-O groups. The typical peak of saturated lipids at 1458 cm−1 comes from the lipids on the cell wall, while the characteristic peaks of the C-N group are from proteins, peptides, and amino acids on the cell wall [88].

In addition to achieving bacterial classification, SERS technology offers the potential to discriminate various bacterial species that belong to the same family. You et al. [89] used 30 cases of S. aureus ATCC25923 and MRSA as the sample training set and 6 cases of ATCC25923 and MRSA as the test set, based on the Principal Component Analysis (PCA) combined linear discriminant analysis (LDA) model for SERS detection. The classification accuracies of ATCC25923 and MRSA on the training and test sets are 76.67% and 75%, respectively according to the PCA-LDA model [89]. In another work, the Ayala team [90] used the SERS technique to differentiate wild-type S. aureus and mutant strains lacking carotenoid production. These results confirm the great potential of SERS in identifying S. aureus. The feasibility of Raman microscopy has been demonstrated to be able to discriminate various genetically distinct forms of a single bacterial species in situ. The rapid differentiation of resistant and susceptible bacteria can be achieved by collecting the Raman spectral signals of the two and combining them with chemometric methods. In the work of Potluri et al. [91] the PCR and SERS technologies were combined to detect the MRSA-specific genes mecA and femA, and had good identification of MRSA and MSSA. In identifying MRSA and MSSA, Ciloglu et al. [92] used SERS combined with machine learning techniques for analysis, and the classification accuracy was achieved at 97.8%. In their other work [93], a Sparse Autoencoder (SAE)-based Deep Neural Network (DNN) algorithm was used to analyze and extract features from raw spectral data and classify MRSA and MSSA bacteria with 97.66% accuracy. SERS can be used to analyze the target of drug action and explore the mechanism of antibiotic-resistant, bacteriostatic and bactericidal actions. After the bacteria are treated with drugs, the bacteria are freeze-dried, and the Raman spectrum information of single cells is collected by Raman microscopy. Microscopic imaging can detect the number of drugs entering cells and drug targets, as well as measure the kinetics of drug uptake in cells and point out interactions [94].

5.4 Raman spectroscopy preprocessing

Raman spectral signals inevitably receive external interference during the acquisition process, such as the mechanical vibration of the instrument itself, cosmic noise, and autofluorescence to a certain extent, which prevents the rapid and accurate analysis of spectral data [95]. Therefore, before formal data analysis, the original Raman signal needs to be preprocessed to eliminate unfavorable factors in the analysis process. Preprocessing can be regarded as a key step in spectral data analysis and is mainly divided into spike removal, smoothing denoising, baseline correction, and vector normalization. For peak removal, when collecting Raman spectra, random, narrow and strong spectral signals appear in the spectral fingerprint due to the random appearance of electronic signals from cosmic particles on CCD or complementary metal-oxide-semiconductor detectors. The existence of spikes will mask other useful information to a great extent; therefore, spike removal is necessary. In general, spikes rarely appear at the same shift in the Raman spectrum of the same sample [96]. In this regard, we can judge whether there is a spike by visually inspecting and comparing the difference in abnormal intensity between different spectral curves [97]. In addition, setting the signal intensity threshold and deriving the spectral data can also achieve the purpose of removing spikes [98]. For the electronic noise composed of cosmic noise, flicker noise, and thermal noise, it will randomly appear in multiple positions of the spectral curve in an unpredictable form, which has a large impact on the quality of Raman spectroscopy data. Savitzky–Golay (S-G) filtering is one of the most commonly used preprocessing methods in the process of smoothing and denoising Raman spectra [99, 100]. This method can keep the shape and width of the signal unchanged while filtering the noise, so as to meet the processing requirements of Raman spectral data in different situations [101]. As one of the recognized best processing steps in Raman spectrum analysis preprocessing [96], baseline correction is used to deal with the continuous distortion caused by uncontrollable factors during Raman spectrum acquisition, such as removing substrate-related Raman signals [99] and fluorescence signals generated by the sample itself [102]. Commonly used methods are asymmetric weighted penalized least squares (arPLS) algorithm [103], adaptive iterative weighted penalized least squares (airPLS) algorithm and polynomial fitting [104]. Normalization is the last step of preprocessing [105]. It is used to deal with the situation of large signal strength caused by uneven sample distribution, laser power difference, experimental environment interference and other factors [104]. Vector normalization is one of the most commonly used normalization methods in Raman spectral analysis [97, 106], It is used to control the difference in Raman signal intensity levels by mapping the data to a range of 0 to 1 for processing [107]. It is worth noting that the order of preprocessing is not fixed and each step does not necessarily need to be performed. When applying to our own experimental data, we need to observe the interaction between each step of preprocessing, and choose the best combination of preprocessing according to the feedback between different preprocessing methods.

5.5 Machine learning analysis of SERS spectra

Data learning aims to convert Raman spectral signals into computer-recognizable abstract feature information. For previously preprocessed spectral data, we need to use more advanced methods based on machine learning algorithms. Machine Learning (ML) is a method of observing existing data, extracting the rules, and then applying them to unknown samples [98]. Traditional Raman spectrum classification and recognition usually use machine-learning algorithms to model and analyze, but the analysis process of this method is relatively complicated, and it needs to go through operations such as preprocessing and feature extraction. In recent years, deep learning has become a hot research topic. Deep learning is to learn features from large-scale raw datasets and to build predictive models directly. There are many deep learning algorithms, including convolutional neural networks (CNN), fully connected networks, and residual neural networks (ResNet), etc. It has decent performance in mining local features of data and extracting international training highlights [108], and its ability to classify and identify data far exceeds that of traditional multivariate statistical analysis algorithms. Wang et al. [109] prepared positively charged nano-silver-based SERS samples combined with the CNN algorithm for rapid identification of drug resistance in S. aureus. Several classifications have achieved good results for the high-intensity SERS fingerprints collected in 107 cells/ml bacterial solution, among which shallow CNN, ResNet25, SVM and Logistic regression all achieved 100% classification accuracy [109]. When the traditional machine learning algorithms SVM, Logistic regression, RF and KNN were used to analyze low-intensity SERS fingerprints collected from low-concentration bacterial solutions of 105 cells/ml and 102 cells/ml, the average recognition accuracy dropped below 80% [109] whereas the shallow layer created by the study CNN achieves 94.5% recognition accuracy, which is more than 25% higher than other ordinary methods [109]. In addition, the SERS combined CNN detection method also achieved good results in identifying MRSA and MSSA. Ho et al. [110] apply deep learning methods to identify 30 common bacterial pathogens. The average separation level was more than 82% accurate at low SNR spectra, and an antibiotic treatment identification accuracy of 97.0 ± 0.3% was achieved [110]. The deep learning method distinguishes between MRSA and MSSA isolates with an accuracy of 89 ± 0.1% [110]. Additionally, Tang et al. studied 9 different Staphylococci, and constructed 8 different machine learning algorithms and 2 deep learning algorithms for the classification and prediction of all the staphylococcal strains [94]. By calculating and comparing the evaluation indicators of different models, it is found that the deep learning algorithm CNN has the best performance (ACC = 98.21%), and the AUC is also the largest (99.93%) [94]. The results show that the deep learning algorithm has strong classification and prediction capabilities in the detection of bacterial pathogens through surface enhanced Raman spectroscopy.

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6. Conclusion and perspectives

With the continuous development of science and technology, the detection methods of Staphylococcus have become more and more diverse, but they all have their advantages and disadvantages. Although the traditional cultivation method is the gold standard, the cultivation time is long, the steps are cumbersome and the technical requirements of the testing personnel are high. Molecular-level identification methods such as PCR, mass spectrometry, and whole-genome sequencing have high sensitivity and specificity with short turn-around time, and can directly detect clinical samples but these techniques have steep learning curves and are expensive. In order to better make up for the shortcomings of various methods, this paper introduces surface-enhanced Raman technology, which has the advantages of low cost, simple operation, label-free, non-invasiveness, high sensitivity, and high specificity in bacterial identification and drug resistance detection, which has great application potential in the near future.

GlossaryAbbreviations
MRSAmethicillin-resistant Staphylococcus aureus
SERSsurface enhanced Raman spectroscopy
CPScoagulase-positive Staphylococcus
CNScoagulase-negative Staphylococcus
SCVsSmall colony variants
MSMmannitol salt medium
SPAStaphylococcus Protein A
SSSSstaphylococcal scalded skin syndrome
TSStoxic shock syndrome
SFPS. aureus food poisoning
SEsstaphylococcal enterotoxins
CLSIClinical and Laboratory Standards Institute
EUCASTEuropean Committee for Antimicrobial Susceptibility Testing
K-BKirby-Bauer
MICminimum inhibitory concentration
MHBMueller Hinton Broth
MBCminimal bactericidal concentration
VISAvancomycin-intermediate S. aureus
VRSAvancomycin-resistant S. aureus
PCRPolymerase chain reaction
NFnecrotizing fasciitis
LAMPLoop-mediated isothermal amplification
LFAlateral flow assay
SDAStrand displacement amplification
RPArecombinase polymerase amplification
RCArolling circle amplification
SATsimultaneous amplification and testing
IFAimmune fluorescence assay
ELISAenzyme-linked immunosorbent assay
CLIAchemiluminescence immunoassay
RIAradioimmunoassay
IMSimmunomagnetic separation
ICGimmune colloidal gold
SEAstaphylococcal enterotoxin A
MSPEmicroscale solid phase extraction
FITCfluorescein isothiocyanate
MALDI-TOF MSmatrix-assisted laser desorption ionization-time of flight mass spectrometry
ASTantibiotic susceptibility testing
MSSAmethicillin-sensitive Staphylococcus aureus
NGSnext-generation sequencing
SMRTsingle-molecule real-time
CMchemical mechanism
EMelectromagnetic enhancement
LSPRlocalized surface plasmon resonance
EFenhancement factor
CNNconvolutional neural networks
LSTMlong short-term memory
AUCarea under curve
PCAprincipal component analysis
LDAlinear discriminant analysis
SAEsparse autoencoder
DNNdeep neural network

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Written By

Xue-Di Zhang, Bin Gu, Muhammad Usman, Jia-Wei Tang, Zheng-Kang Li, Xin-Qiang Zhang, Jia-Wei Yan and Liang Wang

Reviewed: 10 October 2022 Published: 09 December 2022

© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution 3.0 License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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