UV-Raman Spectroscopic Identification of Fungal Spores Important for Respiratory Diseases

Leveraging single-cell Raman spectroscopy and single-cell sorting for the detection and identification of yeast infections

Invasive fungal infection serves as a great threat to human health. Discrimination between fungal and bacterial infections at the earliest stage is vital for effective clinic practice; however, traditional culture-dependent microscopic diagnosis of fungal infection usually requires several days, meanwhile, culture-independent immunological and molecular methods are limited by the detectable type of pathogens and the issues with high false-positive rates. In this study, we proposed a novel culture-independent phenotyping method based on single-cell Raman spectroscopy for the rapid discrimination between fungal and bacterial infections. Three Raman biomarkers, including cytochrome c, peptidoglycan, and nucleic acid, were identified through hierarchical clustering analysis of Raman spectra across 12 types of most common yeast and bacterial pathogens. Compared to those of bacterial pathogens, the single cells of yeast pathogens demonstrated significantly stronger Raman peaks for cytochrome c, but weaker signals for peptidoglycan and nucleic acid. A two-step protocol combining the three biomarkers was established and able to differentiate fungal infections from bacterial infections with an overall accuracy of 94.9%. Our approach was also used to detect ten raw urinary tract infection samples. Successful identification of fungi was achieved within half an hour after sample obtainment. We further demonstrated the accurate fungal species taxonomy achieved with Raman-assisted cell ejection. Our findings demonstrate that Raman-based fungal identification is a novel, facile, reliable, and with a breadth of coverage approach, that has a great potential to be adopted in routine clinical practice to reduce the turn-around time of invasive fungal disease (IFD) diagnostics.

Artificial intelligence-aided rapid and accurate identification of clinical fungal infections by single-cell Raman spectroscopy

Integrating artificial intelligence and new diagnostic platforms into routine clinical microbiology laboratory procedures has grown increasingly intriguing, holding promises of reducing turnaround time and cost and maximizing efficiency. At least one billion people are suffering from fungal infections, leading to over 1.6 million mortality every year. Despite the increasing demand for fungal diagnosis, current approaches suffer from manual bias, long cultivation time (from days to months), and low sensitivity (only 50% produce positive fungal cultures). Delayed and inaccurate treatments consequently lead to higher hospital costs, mobility and mortality rates. Here, we developed single-cell Raman spectroscopy and artificial intelligence to achieve rapid identification of infectious fungi. The classification between fungi and bacteria infections was initially achieved with 100% sensitivity and specificity using single-cell Raman spectra (SCRS). Then, we constructed a Raman dataset from clinical fungal isolates obtained from 94 patients, consisting of 115,129 SCRS. By training a classification model with an optimized clinical feedback loop, just 5 cells per patient (acquisition time 2 s per cell) made the most accurate classification. This protocol has achieved 100% accuracies for fungal identification at the species level. This protocol was transformed to assessing clinical samples of urinary tract infection, obtaining the correct diagnosis from raw sample-to-result within 1 h.

Riverine microplastics derived from mulch film in Hainan Island: Occurrence, source and fate

Mulch film (MF) residues is an important source of microplastics (MPs) in farmland, but its transportation risk to the wider environment was still unknown. Some researches have pursued the sources of MPs found in exorheic rivers. Even so, a systematic study depicting the occurrence, source and fate of microplastics derived from mulch films (MPMF), the crucial component of MPs in farmlands, in exorheic rivers still lacking. Here, the combination of UV-Vis Raman spectroscopy and X-ray photoelectron spectroscopy (XPS) was used to identify the full-size MPMF (1-5000 μm) in field sediment samples collected by single-diagonal systematic sampling. This study verified that MPMF, a polyethylene-matrix composite doped with additives, contributed a considerable part of MPs detected in upstream farmland soil and riverine sediments, and even had an abundance of 38 ± 11 items/kg to 82 ± 15 items/kg, accounting for 9.0%-13.7% of the total MPs in estuary sediments. Notably, upstream farmland was identified to the main source of the riverine MPMF by partial least square path modeling (PLS-PM), contributing to 94.7% of MPMF in riverside sediments and 85.0% of MPMF in estuary sediments. Our study first demonstrates that MPMF constitutes a non-negligible component of MPs in estuarine sediments and underlines the urgency of strengthening the management of MPs pollution in drainage areas with a high agricultural intensity.

Detection of multi-resistant clinical strains of E. coli with Raman spectroscopy

In recent years, we have seen a steady rise in the prevalence of antibiotic-resistant bacteria. This creates many challenges in treating patients who carry these infections, as well as stopping and preventing outbreaks. Identifying these resistant bacteria is critical for treatment decisions and epidemiological studies. However, current methods for identification of resistance either require long cultivation steps or expensive reagents. Raman spectroscopy has been shown in the past to enable the rapid identification of bacterial strains from single cells and cultures. In this study, Raman spectroscopy was applied for the differentiation of resistant and sensitive strains of Escherichia coli. Our focus was on clinical multi-resistant (extended-spectrum β-lactam and carbapenem-resistant) bacteria from hospital patients. The spectra were collected using both UV resonance Raman spectroscopy in bulk and single-cell Raman microspectroscopy, without exposure to antibiotics. We found resistant strains have a higher nucleic acid/protein ratio, and used the spectra to train a machine learning model that differentiates resistant and sensitive strains. In addition, we applied a majority of voting system to both improve the accuracy of our models and make them more applicable for a clinical setting. This method could allow rapid and accurate identification of antibiotic resistant bacteria, and thus improve public health.

Multimodal Molecular Imaging and Identification of Bacterial Toxins Causing Mushroom Soft Rot and Cavity Disease

Soft rot disease of edible mushrooms leads to rapid degeneration of fungal tissue and thus severely affects farming productivity worldwide. The bacterial mushroom pathogen Burkholderia gladioli pv. agaricicola has been identified as the cause. Yet, little is known about the molecular basis of the infection, the spatial distribution and the biological role of antifungal agents and toxins involved in this infectious disease. We combine genome mining, metabolic profiling, MALDI-Imaging and UV Raman spectroscopy, to detect, identify and visualize a complex of chemical mediators and toxins produced by the pathogen during the infection process, including toxoflavin, caryoynencin, and sinapigladioside. Furthermore, targeted gene knockouts and in vitro assays link antifungal agents to prevalent symptoms of soft rot, mushroom browning, and impaired mycelium growth. Comparisons of related pathogenic, mutualistic and environmental Burkholderia spp. indicate that the arsenal of antifungal agents may have paved the way for ancestral bacteria to colonize niches where frequent, antagonistic interactions with fungi occur. Our findings not only demonstrate the power of label-free, in vivo detection of polyyne virulence factors by Raman imaging, but may also inspire new approaches to disease control.

Raman Stable Isotope Probing of Bacteria in Visible and Deep UV-Ranges

Raman stable isotope probing (Raman-SIP) is an excellent technique that can be used to access the overall metabolism of microorganisms. Recent studies have mainly used an excitation wavelength in the visible range to characterize isotopically labeled bacteria. In this work, we used UV resonance Raman spectroscopy (UVRR) to evaluate the spectral red-shifts caused by the uptake of isotopes (¹³C, ¹⁵N, ²H(D) and ¹⁸O) in E. coli cells. Moreover, we present a new approach based on the extraction of labeled DNA in combination with UVRR to identify metabolically active cells. The proof-of-principle study on E. coli revealed heterogeneities in the Raman features of both the bacterial cells and the extracted DNA after labeling with ¹³C, ¹⁵N, and D. The wavelength of choice for studying ¹⁸O- and deuterium-labeled cells is 532 nm is, while ¹³C-labeled cells can be investigated with visible and deep UV wavelengths. However, ¹⁵N-labeled cells are best studied at the excitation wavelength of 244 nm since nucleic acids are in resonance at this wavelength. These results highlight the potential of the presented approach to identify active bacterial cells. This work can serve as a basis for the development of new techniques for the rapid and efficient detection of active bacteria cells without the need for a cultivation step.

pH-Dependent Flavin Adenine Dinucleotide and Nicotinamide Adenine Dinucleotide Ultraviolet Resonance Raman (UVRR) Spectra at Intracellular Concentration

The ultraviolet resonance Raman spectra of the adenine-containing enzymatic redox cofactors nicotinamide adenine dinucleotide and flavin adenine dinucleotide in aqueous solution of physiological concentration are compared with the aim of distinguishing between them and their building block adenine in potential co-occurrence in biological materials. At an excitation wavelength of 266 nm, the spectra are dominated by the strong resonant contribution from adenine; nevertheless, bands assigned to vibrational modes of the nicotinamide and the flavin unit are found to appear at similar signal strength. Comparison of spectra measured at pH 7 with data obtained pH 10 and pH 3 shows characteristic changes when pH is increased or lowered, mainly due to deprotonation of the flavin and nicotinamide moieties, and protonation of the adenine, respectively.

Raman 18 O-labeling of bacteria in visible and deep UV-ranges

Raman stable isotope labeling with ² H, ¹³ C or ¹⁵ N has been reported as an elegant approach to investigate cellular metabolic activity, which is of great importance to reveal the functions of microorganisms in native environments. A new strategy termed Raman ¹⁸ O-labeling was developed to probe the metabolic activity of bacteria. Raman ¹⁸ O-labeling refers to the combination of Raman microspectroscopy with ¹⁸ O-labeling using H₂¹⁸ O. At an excitation wavelength of 532 nm, the incorporation of ¹⁸ O into the amide I group of proteins and DNA/RNA bases was observed in Escherichia coli cells, while for an excitation wavelength electronically resonant with DNA or aromatic amino acid absorption at 244 nm ¹⁸ O assimilation was detected using chemometric tools rather than visual inspection. Raman ¹⁸ O-labeling at 532 nm combined with 2D correlation analysis confirmed the assimilation of ¹⁸ O in proteins and nucleic acids and revealed the growth strategy of E. coli cells; they underwent protein synthesis followed by nucleic acid synthesis. Independent cultural replicates at different incubation times corroborated the reproducibility of these results. The variations in spectral features of ¹⁸ O-labeled cells revealed changes in physiological information of cells. Hence, Raman ¹⁸ O-labeling could provide a powerful tool to identify metabolically active bacterial cells.

Determining the stages of cellular differentiation using deep ultraviolet resonance Raman spectroscopy

Cellular differentiation is a fundamental process in which one cell type changes into one or more specialized cell types. Cellular differentiation starts at the beginning of embryonic development when a simple zygote begins to transform into a complex multicellular organism composed of various cell and tissue types. This process continues into adulthood when adult stem cells differentiate into more specialized cells for normal growth, regeneration, repair, and cellular turnover. Any abnormalities associated with this fundamental process of cellular differentiation are linked to life-threatening conditions, including degenerative diseases and cancers. Detection of undifferentiated and different stages of differentiated cells can be used for disease diagnosis but is often challenging due to the laborious procedures, expensive tools, and specialized technical skills which are required. Here, a novel approach, called deep ultraviolet resonance Raman spectroscopy, is used to study various stages of cellular differentiation using a well-known myoblast cell line as a model system. These cells proliferate in the growth medium and spontaneously differentiate in differentiation medium into myocytes and later into myotubes. The cellular and molecular characteristics of these cells mimic very well actual muscle tissue in vivo. We have found that undifferentiated myoblast cells and myoblast cells differentiated at three different stages are able to be easily separated using deep ultraviolet resonance Raman spectroscopy in combination with chemometric techniques. Our study has a great potential to study cellular differentiation during normal development as well as to detect abnormal cellular differentiation in human pathological conditions in future studies.

Towards development of a novel universal medical diagnostic method: Raman spectroscopy and machine learning

This review summarizes recent progress made using Raman spectroscopy and machine learning for potential universal medical diagnostic applications.

Klarite as a label-free SERS-based assay: a promising approach for atmospheric bioaerosol detection

We present a SERS-based Klarite interface for the rapid and culture-free detection and quantification of atmospheric bioaerosols in the real-world environment.