As an alternative to biochemical testing, matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry (MALDI-TOF MS) has become a revolutionary, widely used tool in numerous clinical diagnostic laboratories. The method is based on the ionization of chemical compounds and measurements of their mass to charge (m/z) ratio. These create a specific microbial fingerprint (a peptide mass fingerprint) allowing identification upon comparison with databases. The whole process takes only minutes and can distinguish even between the most closely related microbial species (Bader et al., 2011; Hendrickx et al., 2011; Lee et al., 2017; Rychert, 2019) and detect antimicrobial resistance (Burckhardt and Zimmermann, 2018; Florio et al., 2020). However, the procedure involves multiple-step-sample preparation and relatively costly consumables. The expensive MALDI-TOF MS device makes the method unaffordable for laboratories in developing countries (Yonetani et al., 2016; Peng et al., 2019).

Direct methods, besides characterization of microbes and screening, can be used for the identification of microbes in mixed samples as well as for identification of non-culturable microbes (Ramamurthy et al., 2014; Franco-Duarte et al., 2019). They are predominantly based on microscopy, serology, or molecular analyses and do not require cultivation.

Microscopy techniques (bright-field microscopy, dark-field microscopy) can give some indication of the presence of microbes in a sample (Franco-Duarte et al., 2019). A combination of microscopy techniques with other tools/procedures can be used to increase the identification power. Examples include fluorescent dyes (Amann and Fuchs, 2008; Sabnis, 2015), scanning electron microscopy (SEM) (Relucenti et al., 2021), transmission electron microscopy (TEM) (McCutcheon and Southam, 2018), confocal microscopy (CLSM) (Cardinale et al., 2017), and atomic force microscopy (ATM) (James et al., 2016). These methods are also considered valuable in biofilm studies. A disadvantage of direct microscopy methods is an unspecific result not allowing accurate identification unless combined with additional procedure/tools, which makes the process costly and time-demanding.

Serological methods used in clinical diagnostics include the detection of antigens and antibodies. They are usually highly specific and must be ordered in a goal-directed manner (e.g., confirmation or exclusion of certain infectious agents). The positivity of antibody tests may be delayed due to the dynamics of antibody production in the human body. Besides, the immune response of the hosts usually has polyclonal nature and is influenced by genetic factors as well as environmental factors. Therefore, the reaction of a patient´s serum with an analytical system is not precisely predictable and there might be some variations. During the detection of antigens, antigenic variations (leading to different serotypes) might cause problems (Fierz, 2003).

The advent of the “genomic era” brought an astounding array of techniques incredibly useful for the characterization of biological materials and organisms, including microbes (Spratt, 2004). The development of polymerase chain reaction (PCR) in 1983 was a real breakthrough in (clinical) microbiology. Later on, real-time PCR (RT-PCR) was developed and brought a new wind into diagnostics improving the speed, sensitivity, and specificity of microbial detection (Mackay, 2004). Also, 16S rRNA, 16S – 23S rRNA (bacteria), and 18S rRNA (eukaryotes) PCR-sequencing can be a useful tool for identifying microorganisms combining PCR amplification of 16S (18S) rRNA gene, which is highly specific to each microbial species, and its subsequent sequencing (Reller et al., 2007; Singhal et al., 2015; Peker et al., 2019). When 16S rRNA gene is identical, species identification could rely on other conserved genes, such as gyrA, gyrB, rpoB, tuf, and heat shock proteins (Franco-Duarte et al., 2019). Although PCR-based methods can detect pathogens at early stages of infection and do not require cultivation, clinical samples often contain low numbers of microbial cells, complicating their capturing. They require preprocessing before the PCR reaction (incl. removal of PCR inhibitors, extraction of maximum microbes from the sample without contamination, isolation of nucleic acids). Furthermore, those methods detect the only presence of nucleic acid (or its part), which can be misleading since the human body contains large amounts of microbial genetic material (Franco-Duarte et al., 2019; Kubina and Dziedzic, 2020).

Modern technologies allow miniaturization and automatization of the methods and building more efficient approaches, such as next-generation sequencing (NGS). NGS allows parallel sequencing of enormous numbers of whole genomes or parts of nucleic acids at once, providing reliable identification of microorganisms at the nucleic acid level (Sabat et al., 2017; Boers et al., 2019). To overcome the low numbers of pathogens present in a sample, PCR amplification can be used. Compared with other widely used methods, disadvantages of using NGS in clinical diagnostics of infections include high costs and lower sensitivity, or more specifically, higher recovery of clinically unimportant microorganisms, and often too general identification (e.g., phylum level) (Lee et al., 2020).

We can conclude that all the methods mentioned above have advantages and disadvantages. Thus, different methods might be suitable or combined for different types of infections/expected causative agents. To shorten the time necessary for identification, many approaches have been proposed. Their main goals include shortening/skipping cultivation, shortening the time and reducing consumables needed for the identification, automatizing the process, and/or adding further information about a sample (e.g., virulence factors). These approaches include but are not limited to certain protocols for MALDI-TOF MS (Drancourt, 2010; Meex et al., 2012; Hoyos-Mallecot et al., 2014; Idelevich et al., 2014; Machen et al., 2014; Verroken et al., 2015; Zhou et al., 2017), infrared spectroscopy (FTIR) (Ojeda and Dittrich, 2012; Zarnowiec et al., 2015; Vogt et al., 2019), nuclear magnetic resonance (NMR) spectroscopy (Romaniuk and Cegelski, 2015; Palama et al., 2016), capillary electrophoresis (incl. capillary isoelectric focusing (Ruzicka et al., 2016; Xu and Sun, 2021), electrical field-flow fractionation (Saenton et al., 2000; Reschiglian et al., 2002), microfluidic devices (Kim et al., 2012; Zhou et al., 2019; Pérez-Rodríguez et al., 2022) and Raman spectroscopy (Franco-Duarte et al., 2019). Recently, Raman spectroscopy has been undergoing a boom in microbiology, as many studies suggest its potential for the identification of microbes, their virulence factors, detection of metabolic changes, and last but not least, single-cell analyses of microbial cells.

Raman spectroscopy appears to be a valuable tool for the identification of microorganisms (Maquelin et al., 2003; Samek et al., 2008; Almarashi et al., 2012; Kastanos et al., 2012; Schie and Huser, 2013; Neugebauer et al., 2015; Pahlow et al., 2015; Read and Whiteley, 2015; Tien et al., 2016; Rebrošová et al., 2017; de Siqueira e Oliveira et al., 2021; Rebrošová et al., 2022), even in mixed samples (Yogesha et al., 2019). The identification can be performed from colonies grown on solid agar plates, microcolonies (Choo-Smith et al., 2001; Mathey et al., 2015), or microorganisms in liquid media (Schuster et al., 2000; Samek et al., 2010; Avci et al., 2015; Kotanen et al., 2016; Nakar et al., 2022; Rebrošová et al., 2022) and microbial spectra are highly reproducible within a device (Mlynáriková et al., 2015). Furthermore, Raman spectroscopy can be used for the characterization of microbial virulence factors, including antimicrobial resistance (Wulf et al., 2012; Bernatová et al., 2013; Dekter et al., 2017; Rousseau et al., 2021; Nakar et al., 2022) and the ability to form a biofilm (Samek et al., 2008; Samek et al., 2014; Liu et al., 2014; Hrubanova et al., 2018; Keleştemur et al., 2018; Rebrošová et al., 2019). There are some Raman studies of phenotypic changes caused by exposure to environmental stimuli, including antibiotics (Athamneh et al., 2014), alcohol (Zu et al., 2016), or metabolic stressors (Tanniche et al., 2020). Raman spectroscopy was successfully used to quantify microbes in a sample, too (Escoriza et al., 2006). To gain a stronger signal, the Raman signal can be amplified using surface-enhanced Raman spectroscopy (SERS), which is widely used in microbiological studies (Samek et al., 2021). Recently, there was significant progress in single-cell analyzes employing Raman spectroscopy and other variations of Raman spectroscopy, allowing to skip the cultivation step. The most frequently used approaches are summarized below.

A commonly used method for separating microbes from liquid media/samples is centrifugation. Published works consider centrifugation+Raman spectroscopy to be promising for identification of microbes from human body fluids, namely ascitic fluid (Kloß et al., 2015b), sputum (Kloß et al., 2015a), artificial bronchoalveolar lavage (Wichmann et al., 2021), and urine (Schröder et al., 2015; Rebrošová et al., 2022). Premasiri et al. showed a possibility to combine centrifugation and SERS to identify pathogens and their antimicrobial susceptibility (Premasiri et al., 2017). Together with filtration lysis and SERS, centrifugation was used to identify pathogens from human serum (Kotanen et al., 2016).

Moreover, centrifugation+SERS showed a possibility of identifying Chlamydia trachomatis and Neisseria gonorrhoeae and characterizing their extra-cellular metabolomics (Chen et al., 2018).

Magnetic beads are widely used for separation and isolation during bioprocessing, especially for the isolation of nucleic acids. However, the mechanism itself allows magnetic separation to be applied on various samples: magnetic separation relies upon forces induced in magnetically susceptible materials by magnetic fields (Schwaminger et al., 2019). In biology, primarily magnetic beads coated with synthetical or biological polymers (including antibodies) capable of capturing the target molecules/cells are used. Target molecules/cells bind to a polymer. Afterward, the whole complex (magnetic carrier with polymer + target molecule/cell) is captured by applying magnetic force (Berensmeier, 2006).

Kusić et al. successfully used magnetic beads coated with Legionella spp. specific polyclonal immunoglobulins for isolating single Legionella sp. cells from biofilm and subsequently identifying them with Raman spectroscopy (Kusić et al., 2014). Kearns et al. developed a bionanosensor based on magnetic separation and SERS, which can be used to identify microbes in concentrations as low as 10¹ CFU/mL in less than one hour (Kearns et al., 2017). Hu et al. showed a possibility of capturing Candida sp. cells from serum and characterizing them using SERS (Hu et al., 2021). Li et al. used polyethyleneimine-modified magnetic microspheres (Fe₃O₄@PEI) and SERS for bacterial identification and antimicrobial resistance determination from 77 blood samples (Li et al., 2019). A combination of SERS and immunomagnetic beads can also be used to detect Clostridium botulinum toxins A and B (Kim et al., 2019). Since magnetic separation is commonly used for nucleic acid isolation, Hwang et al. applied this method to isolate bacterial genomic DNA and identify it using SERS and fluorescent assay favoring SERS by means of sensitivity (Hwang et al., 2021). Detection of bacterial DNA by SERS using streptavidin-coated magnetic particles was also proved by Qun et al. with a limit of detection of 5 pM (Qun et al., 2015).

Dielectrophoresis (DEP) is defined as a movement of dielectric particles through a medium in response to a non-uniform electric field. A particle becomes polarized, and due to the difference in electric field strength on the two sides of the particle, the particle is moved in the electric field gradient region by net dielectrophoretic force (Tay et al., 2009). This effect can be used for the separation of particles (Fernandez et al., 2017) and is widely used for enrichment and isolation of microbial cells before analysis at a single-cell level (Fernandez et al., 2017; Zhang et al., 2019; Sarno et al., 2021; Weber et al., 2021). Some groups recently suggested its possible combination with Raman spectroscopy for single-cell microbial analyses (Neugebauer et al., 2015). Chen et al. showed the efficiency of DEP-based microfluidic chip for Raman detection and measurements of Shewanella oneidensis cells in HEPES buffer (Chen et al., 2018). From clinically relevant applications, Cheng et al. used DEP with SERS to isolate and identify bacteria in isotonic solution with human blood cells, proposing rapid detection of microbes in human blood from 12h blood cultures (Cheng et al., 2014). To identify pathogens causing urinary tract infections faster, Schröder et al. proposed using a combination of DEP and Raman spectroscopy to identify Escherichia coli and Enterococcus faecalis in human urine, providing results in 35 minutes (Schröder et al., 2013).

Optical tweezers—a Nobel price-winning (2018) groundbreaking invention by Alfred Ashkin—use single-beam gradient force to hold particles/micro-objects (incl. microbial cells) in place and manipulate them (Ashkin, 1997; Wu et al., 2017). With an optical trap, living cells suspended in a liquid cultivation medium can be immobilized in a solution using the forces generated by a tightly focused laser beam. Combined with Raman spectroscopy (usually termed Raman tweezers), it has found numerous applications, especially in analytical and physical chemistry. Raman tweezers is currently starting to be applied in cell and molecular biology. It allows single-cell analyses of microbes, for example, direct identification of microbes from liquid samples, including wastewater (Cui et al., 2021) and human urine (Rebrošová et al., 2022), in less than 10 minutes. This combination can also be used to detect antibiotic resistance (Bernatová et. al 2014; Pilát et al., 2018; Bernatová et al., 2021) and an ability to form a biofilm (Samek et al., 2015). Moreover, it was used to describe metabolic changes in microbes (Singh et al., 2006; Huang et al., 2007) and bacterial lysis (Chen et al., 2009).

Current advantages and disadvantages of the Raman spectroscopy for microbiology are summarized in the Table 1 .

Unambiguously, Raman spectroscopy offers a streamlined, fast, non-destructive, and non-invasive approach for identifying microbes and their virulence factors suitable for clinical diagnostics. Compared to conventional and molecular methods, sample preparation is easy and does not necessarily require expensive consumables. Moreover, multiple methods were proposed to isolate or enrich microbes, allowing their single-cell analyses and non-culture-based detection and identification.

However, getting Raman spectroscopy to clinical laboratories might have a long-time coming. At the moment, there are no commercial and/or well-annotated databases of microbial Raman fingerprints, as discussed by Wang et al. (Wang et al., 2021). To make comparisons of acquired Raman spectra across various instruments, one should consider that the quantum efficiency of the given detector and optical elements depends on a wavelength. Therefore, acquired data should be corrected according to an instrument response profile. Ideally, a spectral sensitivity curve should be used (Rebrošová et al., 2017). Thus, Raman spectra presented in different studies are group-specific and custom-tailored, which makes data standardization complicated (Lorenz et al., 2017). Nonetheless, no large-scale studies were performed to compare microbial Raman spectra from different groups.

Microbial studies often employ the SERS technique. Again, there is a problem with the standardization of SERS microbial detection protocols (Witkowska et al., 2020; Samek et al., 2021). Although SERS provides a stronger signal than conventional Raman spectroscopy, several factors influence the signal enhancement and, consequently, the final spectrum. These may include types of culture media, culturing conditions, sample preparation method, and interactions between SERS substrate and individual microbial cells (Witkowska et al., 2020; Samek et al., 2021).

Other problems are (currently) relatively high input costs, the need for complicated instrumentation, and specialized operators. Automatization and miniaturization would be beneficial for potential future clinical use.

If those problems are solved, Raman spectroscopy-based point-of-care device could start a revolution in clinical diagnostic microbiology in the future. Assuming its broad spectrum of applications in medicine, one device equipped with various diagnostic databases might become a complex diagnostic tool. From a microbiological point of view, Raman spectroscopy could be used for rapid pathogen identification and characterization of its virulence factors, which would, in turn, result in tailored antimicrobial treatment, reduced financial burdens associated with healthcare, and above all, improved patient management and reduced mortality from infections.