Unveiling Microbial Diversity: Raman Spectroscopy's Discrimination of Clostridium and Related Genera.

In the clinical environment, the identification of phylogenetic closely related genera and species like Clostridium and Bacillus spp. is challenging. Both genera contain representatives of pathogenic and nonpathogenic species that need to be distinguished for a proper diagnostic read-out. Therefore, reliable and accurate detection methods must be employed for the correct identification of these genera and species. Despite their high pathogenicity, clostridial infections and food contaminations present significant challenges due to their unique cultivation conditions and developmental needs. Therefore, in many diagnostic protocols, the toxins are used for microbiological documentation. However, the applied laboratory methods suffer in accuracy, sometimes require large bacterial loads to provide reliable results, and cannot differentiate pathogenic from nonpathogenic strains. Here, Raman spectroscopy was employed to create an extensive Raman database consisting of pathogenic and nonpathogenic Bacillus and Clostridium species. These genera, as well as representatives of Paraclostridium and Clostridioides were specifically selected for their phylogenetic relation, cultivation conditions, and metabolic activity. A chemometric evaluation of the Raman spectra of single vegetative cells revealed a high discriminating power at the genus level. However, bacilli are considerably easier to classify at the species level than clostridia. The discrimination between the genera and species was based on their phylogeny and not their aerobic and anaerobic cultivation conditions. These encouraging results demonstrated that Raman spectroscopy coupled with chemometrics is a robust and helpful method for differentiating Clostridium species from Bacillus, Clostridioides, and Paraclostridium species. This approach has the potential to be a valuable tool in clinical diagnostics.

Comprehensive multi-gas study by means of fiber-enhanced Raman spectroscopy for the investigation of nitrogen cycle processes.

The extensive use of synthetic fertilizers has led to a considerable increase in reactive nitrogen input into agricultural and natural systems, resulting in negative effects in multiple ecosystems, the so-called nitrogen cascade. Since the global population relies on fertilization for food production, synthetic fertilizer use needs to be optimized by balancing crop yield and reactive nitrogen losses. Fiber-enhanced Raman spectroscopy (FERS) is introduced as a unique method for the simultaneous quantification of multiple gases to the study processes related to the nitrogen cycle. By monitoring changes in the headspace gas concentrations, processes such as denitrification, nitrification, respiration, and nitrogen fixation, as well as fertilizer addition were studied. The differences in concentration between the ambient and prepared process samples were evident in the Raman spectra, allowing for differentiation of process-specific spectra. Gas mixture concentrations were quantified within a range of low ppm to 100% for the gases N2, O2, CO2, N2O, and NH3. Compositional changes were attributed to processes of the nitrogen cycle. With help of multivariate curve resolution, it was possible to quantify N2O and CO2 simultaneously. The impact of fertilizers on N-cycle processes in soil was simulated and analyzed for identifying active processes. Thus, FERS was proven to be a suitable technique to optimize fertilizer composition and to quantify N2O and NH3 emissions, all with a single device and without further sample preparation.

Advancing cerumen analysis: exploring innovative vibrational spectroscopy techniques with respect to their potential as new point-of-care diagnostic tools.

Cerumen, commonly known as earwax, is a complex mixture composed of secretions from ceruminous glands. These secretions are heterogeneous mixtures mainly composed of lipids and proteins. Despite its prevalence, the potential diagnostic value of cerumen remains largely unexplored. Here, we present an in-depth analysis of cerumen utilizing well-known vibrational approaches such as conventional Raman spectroscopy or surface-enhanced Raman spectroscopy (SERS) together with advanced vibrational spectroscopy techniques such as coherent Raman scattering (CRS), i.e. broadband coherent anti-Stokes Raman scattering (CARS) or stimulated Raman scattering (SRS), as well as optical photothermal infrared (OPTIR) spectroscopy. Through the integration of these vibrational spectroscopic methods, lipids and proteins can be identified as the main components of cerumen; however, they contribute to the final spectral information to various extents depending on the vibrational detection scheme applied. The inherently weak Raman signal could be enhanced by linear (SERS) and non-linear (CRS) processes, resulting in efficient acquisition of fingerprint information and allowing for the detection of marker modes, which cannot be addressed by conventional Raman spectroscopy. OPTIR spectroscopy provides complementary information to Raman spectroscopy, however, without the contribution of a fluorescence background. Our findings underscore the utility of these cutting-edge techniques in unveiling the intricate molecular landscape of cerumen, paving the way for novel point-of-care diagnostic methodologies and therapeutic interventions.

Isotope labeled 3D-Raman confocal imaging and atomic force microscopy study on epithelial cells interacting with the fungus Candida albicans.

The human pathogenic fungus Candida albicans damages epithelial cells during superficial infections. Here we use three-dimensional-sequential-confocal Raman spectroscopic imaging and atomic force microscopy to investigate the interaction of C. albicans wild type cells, the secreted C. albicans peptide toxin candidalysin and mutant cells lacking candidalysin with epithelial cells. The candidalysin is responsible for epithelial cell damage and exhibits in its deuterated form an identifiable Raman signal in a frequency region distinct from the cellular frequency region. Vibration modes at 2100-2200 cm-1 attributed to carbon­deuterium bending and at 477 cm-1, attributed to the nitrogen­deuterium out-of-plane bending, found around the nucleus, can be assigned to deuterated candidalysin. Atomic force microscopy visualized 100 nm deep lesions on the cell and force-distance curves indicate the higher adhesion on pore surrounding after incubation with candidalysin. Candidalysin targets the plasma membrane, but is also found inside of the cytosol of epithelial cells during C. albicans infection.

A polyyne toxin produced by an antagonistic bacterium blinds and lyses a Chlamydomonad alga.

Algae are key contributors to global carbon fixation and form the basis of many food webs. In nature, their growth is often supported or suppressed by microorganisms. The bacterium Pseudomonas protegens Pf-5 arrests the growth of the green unicellular alga Chlamydomonas reinhardtii, deflagellates the alga by the cyclic lipopeptide orfamide A, and alters its morphology [P. Aiyar et al., Nat. Commun. 8, 1756 (2017)]. Using a combination of Raman microspectroscopy, genome mining, and mutational analysis, we discovered a polyyne toxin, protegencin, which is secreted by P. protegens, penetrates the algal cells, and causes destruction of the carotenoids of their primitive visual system, the eyespot. Together with secreted orfamide A, protegencin thus prevents the phototactic behavior of C. reinhardtii A mutant of P. protegens deficient in protegencin production does not affect growth or eyespot carotenoids of C. reinhardtii Protegencin acts in a direct and destructive way by lysing and killing the algal cells. The toxic effect of protegencin is also observed in an eyeless mutant and with the colony-forming Chlorophyte alga Gonium pectorale These data reveal a two-pronged molecular strategy involving a cyclic lipopeptide and a conjugated tetrayne used by bacteria to attack select Chlamydomonad algae. In conjunction with the bloom-forming activity of several chlorophytes and the presence of the protegencin gene cluster in over 50 different Pseudomonas genomes [A. J. Mullins et al., bioRxiv [Preprint] (2021). https://www.biorxiv.org/content/10.1101/2021.03.05.433886v1 (Accessed 17 April 2021)], these data are highly relevant to ecological interactions between Chlorophyte algae and Pseudomonadales bacteria.

Raman-Activated, Interactive Sorting of Isotope-Labeled Bacteria.

Due to its high spatial resolution, Raman microspectroscopy allows for the analysis of single microbial cells. Since Raman spectroscopy analyzes the whole cell content, this method is phenotypic and can therefore be used to evaluate cellular changes. In particular, labeling with stable isotopes (SIPs) enables the versatile use and observation of different metabolic states in microbes. Nevertheless, static measurements can only analyze the present situation and do not allow for further downstream evaluations. Therefore, a combination of Raman analysis and cell sorting is necessary to provide the possibility for further research on selected bacteria in a sample. Here, a new microfluidic approach for Raman-activated continuous-flow sorting of bacteria using an optical setup for image-based particle sorting with synchronous acquisition and analysis of Raman spectra for making the sorting decision is demonstrated, showing that active cells can be successfully sorted by means of this microfluidic chip.

Siamese Networks for Clinically Relevant Bacteria Classification Based on Raman Spectroscopy.

Identifying bacterial strains is essential in microbiology for various practical applications, such as disease diagnosis and quality monitoring of food and water. Classical machine learning algorithms have been utilized to identify bacteria based on their Raman spectra. However, convolutional neural networks (CNNs) offer higher classification accuracy, but they require extensive training sets and retraining of previous untrained class targets can be costly and time-consuming. Siamese networks have emerged as a promising solution. They are composed of two CNNs with the same structure and a final network that acts as a distance metric, converting the classification problem into a similarity problem. Classical machine learning approaches, shallow and deep CNNs, and two Siamese network variants were tailored and tested on Raman spectral datasets of bacteria. The methods were evaluated based on mean sensitivity, training time, prediction time, and the number of parameters. In this comparison, Siamese-model2 achieved the highest mean sensitivity of 83.61 ± 4.73 and demonstrated remarkable performance in handling unbalanced and limited data scenarios, achieving a prediction accuracy of 73%. Therefore, the choice of model depends on the specific trade-off between accuracy, (prediction/training) time, and resources for the particular application. Classical machine learning models and shallow CNN models may be more suitable if time and computational resources are a concern. Siamese networks are a good choice for small datasets and CNN for extensive data.

Illuminating the Tiny World: A Navigation Guide for Proper Raman Studies on Microorganisms.

Raman spectroscopy is an emerging method for the identification of bacteria. Nevertheless, a lot of different parameters need to be considered to establish a reliable database capable of identifying real-world samples such as medical or environmental probes. In this review, the establishment of such reliable databases with the proper design in microbiological Raman studies is demonstrated, shining a light into all the parts that require attention. Aspects such as the strain selection, sample preparation and isolation requirements, the phenotypic influence, measurement strategies, as well as the statistical approaches for discrimination of bacteria, are presented. Furthermore, the influence of these aspects on spectra quality, result accuracy, and read-out are discussed. The aim of this review is to serve as a guide for the design of microbiological Raman studies that can support the establishment of this method in different fields.

Optical photothermal infrared spectroscopy and discrete wavenumber imaging for high content screening of single cells.

Major drawbacks of direct mid-infrared spectroscopic imaging of single cells in an aqueous buffer are strong water absorption, low resolution typically above 10 µm, and Mie scattering effects. This study demonstrates how an indirect detection principle can overcome these drawbacks using the optical photothermal infrared (O-PTIR) technique for high-resolution discrete wavenumber imaging and fingerprint spectroscopy of cultivated cells as a model system in a simple liquid sample chamber. The O-PTIR spectra of six leukemia- and cancer-derived cell lines showed main IR bands near 1648, 1547, 1447, 1400, 1220, and 1088 cm-1. Five spectra of approximately 260 single cells per cell type were averaged, the O-PTIR data set was divided into leukemia-derived cells (THP-1, HL 60, Jurkat, and Raji) and cancer cells (HeLa and HepaRG), and partial least squares linear discriminant analysis (PLS-LDA) was applied in the spectral range 800-1800 cm-1 to train three classification models. A leukemia versus cancer cell model showed an accuracy of 90.0%, the HeLa versus HepaRG cell model had an accuracy of 95.4%, and the model for the distinction of leukemia cells had an accuracy of 75.4%. IR bands in linear discriminants (LDs) of the models were correlated with second derivative spectra that resolved more than 25 subbands. The IR and second derivative spectra of proteins, DNA, RNA and lipids were collected as references to confirm band assignments. O-PTIR images of single cells at a 200 nm step size were acquired at 1086, 1548, and 1746 cm-1 to visualize the nucleic acid, protein, and lipid distribution, respectively. Variations in subcellular features and in the lipid-to-protein and nucleic acid-to-protein ratios were identified that were consistent with biomolecular information in LDs. In conclusion, O-PTIR can provide high-quality spectra and images with submicron resolution of single cells in aqueous buffers that offer prospects in high-content screening applications.

SERS-based detection of 5-S-cysteinyl-dopamine as a novel biomarker of Parkinson's disease in artificial biofluids.

The early detection of Parkinson's disease (PD) can significantly improve treatment and quality of life in patients. 5-S-Cysteinyl-dopamine (CDA) is a key metabolite of high relevance for the early detection of PD. Therefore, its sensitive detection with fast and robust methods can improve its use as a biomarker. In this work we show the potentialities of label-free SERS spectroscopy in detecting CDA in aqueous solutions and artificial biofluids, with a simple, fast and sensitive approach. We present a detailed experimental SERS band assignment of CDA employing silver nanoparticle (AgNP) substrates in aqueous media, which was supported by theoretical calculations and simulated Raman and SERS spectra. The tentative orientation of CDA over the AgNP was also studied, indicating that catechol and carboxylic acid play a key role in the metallic surface adsorption. Moreover, we showed that SERS can allow us to identify CDA in aqueous media at low concentration, leading to the identification of some of its characteristic bands in pure water and in synthetic cerebrospinal fluid (SCSF) below 1 × 10-8 M, while its band identification in simulated urine (SUR) can be reached at 1 × 10-7 M. In conclusion, we show that CDA can be suitably detected by means of label-free SERS spectroscopy, which can significantly improve its sensitive detection for further analytical studies as a novel biomarker and further clinical diagnosis in PD patients.

High-throughput Raman spectroscopy allows ex vivo characterization of murine small intestinal intra-epithelial lymphocytes (IEL).

T cells are considered to be critical drivers of intestinal inflammation in mice and people. The so called intra-epithelial lymphocyte (IEL) compartment largely consist of T cells. Interestingly, the specific regulation and contribution of IELs in the context of inflammatory bowel disease remains poorly understood, in part due to the lack of appropriate analysis tools. Powerful, label-free methods could ultimately provide access to this cell population and hence give valuable insight into IEL biology and even more to their disease-related functionalities. Raman spectroscopy has demonstrated over the last few years its potential for reliable cell characterization and differentiation, but its utility in regard to IEL exploration remains unknown. To address this question experimentally, we utilized a murine, T cell-driven experimental model system which is accepted to model human gut inflammation. Here, we repopulated the small intestinal IEL compartment (SI IELs) of Rag1-deficient mice endogenously lacking T cells by transferring naïve CD4+ T helper cells intraperitoneally. Using multivariate statistical analysis, high-throughput Raman spectroscopy managed to define a cell subpopulation ex vivo within the SI IEL pool of mice previously receiving T cells in vivo that displayed characteristic spectral features of lymphocytes. Raman data sets matched flow cytometry analyses with the latter identifying T cell receptor (TCR)αß+ CD4+ T cell population in SI IELs from T cell-transferred mice, but not from control mice, in an abundance comparable to the one detected by Raman spectroscopy. Hence, in this study, we provide experimental evidence for high-throughput Raman spectroscopy to be a novel, future tool to reliably identify and potentially further characterize the T cell pool of small intestinal IELs ex vivo.

Identification of bacteria in mixed infection from urinary tract of patient's samples using Raman analysis of dried droplets.

Urinary tract infections (UTI) are among the most frequent nosocomial infections. A fast identification of the pathogen and assignment of Gram type could help to prescribe most suitable treatments. Raman spectroscopy holds high potential for fast and reliable bacterial pathogens identification. While most studies so far have focused on individual pathogens or artificial mixtures, this contribution aims to translate the analysis to primary urine samples from patients with suspected UTIs. For this, we have included 59 primary urine samples out of which 29 were diagnosed as mixed infections. For Raman analysis, we first trained two classification models based on principal component analysis - linear discriminant analysis (PCA-LDA) with more than 3500 Raman spectra of 85 clinical isolates from 23 species in order to (1) identify the Gram type of the bacteria and (2) assign family membership to one of the six most abundant bacterial families in urinary tract infections (Enterobacteriaceae, Morganellaceae, Pseudomonadaceae, Enterococcaceae, Staphylococcaceae and Streptococcaceae). The classification models were applied to artificial mixtures of Gram positive and Gram negative bacteria to correctly predict mixed infections with an accuracy of 75%. Raman scans of dried droplets did not yet yield optimal classification results on family level. When translating the method to primary urine samples, we observed a strong bias towards Gram negative bacteria, on family level towards Morganellaceae, which reduced prediction accuracy. Spectral differences were observed between isolates grown on standard growth medium and bacteria of the same strain when characterized directly from the patient. Thus, improvement of the classification accuracy is expected with a larger data base containing also bacteria measured directly from the urine sample.

Investigating biochemical and structural changes of glycated collagen using multimodal multiphoton imaging, Raman spectroscopy, and atomic force microscopy.

Advanced glycation end products (AGEs) form extracellular crosslinking with collagenous proteins, which contributes to the development of diabetic complications. In this study, AGEs-related pentosidine (PENT) crosslinks-induced structural and biochemical changes are studied using multimodal multiphoton imaging, Raman spectroscopy and atomic force microscopy (AFM). Decellularized equine pericardium (EP) was glycated with four ribose concentrations ranging between 5 and 200 mM and monitored for up to 30 days. Two-photon excited fluorescence (TPEF) and second harmonic generation (SHG) microscopic imaging probed elastin and collagen fibers, respectively. The glycated EP showed a decrease in the SHG intensities associated with loss of non-centrosymmetry of collagen and an increase of TPEF intensities associated with PENT crosslinks upon glycation. TPEF signals from elastin fibers were unaffected. A three-dimensional reconstruction with SHG + TPEF z-stack images visualized the distribution of collagen and elastin within the EP volume matrix. In addition, Raman spectroscopy (RS) detected changes in collagen-related bands and discriminated glycated from untreated EP. Furthermore, AFM scans showed that the roughness increases and the D-unit structure of fibers remained unchanged during glycation. The PENT crosslinked-induced changes are discussed in the context of previous studies of glutaraldehyde- and genipin-induced crosslinking and collagenase-induced digestion of collagen. We conclude that TPEF, SHG, RS, and AFM are effective, label-free, and non-destructive methods to investigate glycated tissues, differentiate crosslinking processes, and characterize general collagen-associated and disease-related changes, in particular by their RS fingerprints.

Laser spectroscopic technique for direct identification of a single virus I: FASTER CARS.

From the famous 1918 H1N1 influenza to the present COVID-19 pandemic, the need for improved viral detection techniques is all too apparent. The aim of the present paper is to show that identification of individual virus particles in clinical sample materials quickly and reliably is near at hand. First of all, our team has developed techniques for identification of virions based on a modular atomic force microscopy (AFM). Furthermore, femtosecond adaptive spectroscopic techniques with enhanced resolution via coherent anti-Stokes Raman scattering (FASTER CARS) using tip-enhanced techniques markedly improves the sensitivity [M. O. Scully, et al, Proc. Natl. Acad. Sci. U.S.A. 99, 10994-11001 (2002)].

Nonlinear Optical Signal Generation Mediated by a Plasmonic Azimuthally Chirped Grating.

Plasmonic gratings are simple and effective platforms for nonlinear signal generation since they provide a well-defined momentum for photon-plasmon coupling and local hot spots for frequency conversion. Here, a plasmonic azimuthally chirped grating (ACG), which provides spatially resolved broadband momentum for photon-plasmon coupling, was exploited to investigate the plasmonic enhancement effect in two nonlinear optical processes, namely two-photon photoluminescence (TPPL) and second harmonic generation (SHG). The spatial distributions of the nonlinear signals were determined experimentally by hyperspectral mapping with ultrashort pulsed excitation. The experimental spatial distributions of nonlinear signals agree very well with the analytical prediction based on photon-plasmon coupling with the momentum of the ACG, revealing the "antenna" function of the grating in plasmonic nonlinear signal generation. This work highlights the importance of the antenna effect of the gratings for nonlinear signal generation and provides insight into the enhancement mechanism of plasmonic gratings in addition to local hot spot engineering.

Fiber Probe-Based Raman Spectroscopic Identification of Pathogenic Infection Microorganisms on Agar Plates.

Rapid identification of microorganisms is clinically meaningful, and it helps to decelerate the spread of drug resistance and improve patient treatment. In this study, we present a rapid fiber probe-based Raman technique with an excitation wavelength of 785 nm, which is applied to classify and identify nine different species of microorganisms. The cost-effective fiber probe compresses the dimension of the system and provides a more reliable and stable database. All microorganisms were simply cultivated on Luria-Bertani (LB) agar, and Raman spectra were obtained directly from the microbial colonies with the fiber probe within 30 s. The classification model consists of principal component analysis (PCA) in combination with linear discriminant analysis (LDA) and was examined by applying leave-one-batch-out cross-validation (LOBOCV). This model achieved an accuracy of 98.9%. In addition, the validation and identification processes based on independent replicates achieved accuracies of 99.8% and 100%, respectively. The results demonstrated that fiber probe Raman spectroscopy in combination with chemometric analysis allowed a rapid classification and identification of microorganisms only with a normal culture. Therefore, it is promising especially for medical applications and could moreover be helpful to investigate and identify microorganisms rapidly in further studies.

In Vitro Fiber-Probe-Based Identification of Pathogens in Biofilms by Raman Spectroscopy.

Biofilms are the preferred habitat of microorganisms on living and artificial surfaces. Biofilm-related infections, such as infections of medical implants, are difficult to treat, and due to a reduced cultivability of the included bacteria, difficult to diagnose. Therefore, it is highly important to rapidly identify and investigate biofilms on implant surfaces, e.g., during surgery. In this study, we present fiber-probe-based Raman spectroscopy with an excitation wavelength of 785 nm, which was applied to investigate six different pathogen species involved in biofilm-related infections. Biofilms were cultivated in a drip flow reactor, which can model a biofilm growth environment. The signals collected from a fiber probe allowed us to collect Raman spectra not only from the embedded bacterial and yeast cells but also the surrounding extracellular polymeric substance matrix. This information was used in a classification model. The model consists of a principal component analysis in combination with linear discriminant analysis and was examined by applying a leave-one-batch-out cross-validation. This model achieved a classification accuracy of 93.8%. In addition, the identification accuracy increased up to 97.5% when clinical strains were used for identification. A fiber-probe-based Raman spectroscopy method combined with a chemometric analysis might therefore serve as a fast, accurate, and portable strategy for the species identification of biofilm-related infections, e.g., during surgical procedures.

Hybrid 2D Correlation-Based Loss Function for the Correction of Systematic Errors.

We present the derivation of a new kind of loss function from the symmetry rules of synchronous and asynchronous two-dimensional correlation maps. This loss function, which takes into account correlations that are based on causal relations among the members of a series of spectra, can be employed to solve non-linear inverse problems that are plagued by systematic multiplicative errors. This possibility results from the correlation-based loss function being practically insensitive to such systematic errors, which often arise in spectroscopy because sample spectra are usually ratioed against reference spectra. Using dispersion analysis, a sophisticated method of band fitting, of the spectra of poly(methyl methacrylate) films deposited on gold, we demonstrate the applicability and validity of the new loss function. If gold is used as a substrate, experimental spectra are often unphysical, that is, they display reflectance values larger than unity. In such cases, our correlation-based loss function not only helps to achieve accurate fits but also provides corrections to obtain physically meaningful spectra, which leads to results that are superior to conventional correction methods. The validity of the results is checked and proved with help of the results of dispersion analysis of spectra of films of poly(methyl methacrylate) on calcium fluoride (CaF2) and silicon (Si), which do not suffer from the systematic errors.

Phenotypic Differentiation of Autotrophic and Heterotrophic Bacterial Cells Using Raman-D2O Labeling.

Carbon cycling is one of the major biogeochemical processes driven by bacteria. Autotrophic bacteria convert carbon dioxide (CO2) into organic compounds that are used by heterotrophs. Mixotrophic bacteria can employ both autotrophy and heterotrophy for growth. The characterization of the lifestyle of individual cells is essential to understand the microbial activity and thus reveal the implication of bacteria in the carbon flux. In this study, we used groundwater bacteria to investigate the potential of Raman-D2O labeling in combination with chemometrics to identify the carbon assimilation strategies of bacteria. Classification models were built using principal component analysis (PCA) followed by linear discriminant analysis (LDA). Autotrophs assimilated a significantly higher amount (mean C-D ratio between 16.63 and 21.69%) of deuterium than heterotrophs. The C-D signal only provides information about the activity since it appears in the Raman-silent region, where no interference with the taxonomic information is expected. The classification between autotrophs and heterotrophs achieved an overall accuracy of 96.3%. In the validation step with an independent dataset containing species not included in the model, the PCA-LDA model achieved 100% accuracy. This demonstrated that the C-D signal contributed to the identification of autotrophic and heterotrophic bacterial cells. This work reports a robust, rapid, and nondestructive approach for the identification of single cells based on their carbon acquisition strategies. The present study foresees the potential of Raman-D2O labeling as a promising method for automated discrimination of in situ functional activities of bacteria in environmental systems.

Structural and Biochemical Changes in Pericardium upon Genipin Cross-Linking Investigated Using Nondestructive and Label-Free Imaging Techniques.

Tissue cross-linking represents an important and often used technique to enhance the mechanical properties of biomaterials. For the first time, we investigated biochemical and structural properties of genipin (GE) cross-linked equine pericardium (EP) using optical imaging techniques in tandem with quantitative atomic force microscopy (AFM). EP was cross-linked with GE at 37 °C, and its biochemical and biomechanical properties were observed at various time points up to 24 h. GE cross-linked EP was monitored by the normalized ratio between its second-harmonic generation (SHG) and two-photon autofluorescence emissions and remained unchanged for untreated EP; however, a decreasing ratio due to depleted SHG and elevated autofluorescence and a fluorescence band at 625 nm were found for GE cross-linked EP. The mean autofluorescence lifetime of GE cross-linked EP also decreased. The biochemical signature of GE cross-linker and shift in collagen bands were detected and quantified using shifted excitation Raman difference spectroscopy as an innovative approach for tackling artifacts with high fluorescence backgrounds. AFM images indicated a higher and increasing Young's modulus correlated with cross-linking, as well as collagen structural changes in GE cross-linked EP, qualitatively explaining the observed decrease in the second-harmonic signal. In conclusion, we obtained detailed information about the biochemical, structural, and biomechanical effects of GE cross-linked EP using a unique combination of optical and force microscopy techniques in a nondestructive and label-free manner.