Machine learning enabled multiplex detection of periodontal pathogens by surface-enhanced Raman spectroscopy.

Periodontitis is a chronic inflammation of the periodontium caused by a persistent bacterial infection, resulting in destruction of the supporting structures of teeth. Analysis of microbial composition in saliva can inform periodontal status. Actinobacillus actinomycetemcomitans (Aa), Porphyromonas gingivalis (Pg), and Streptococcus mutans (Sm) are among reported periodontal pathogens, and were used as model systems in this study. Our atomic force microscopic (AFM) study revealed that these pathogens are biological nanorods with dimensions of 0.6-1.1 µm in length and 500-700 nm in width. Current bacterial detection methods often involve complex preparation steps and require labeled reporting motifs. Employing surface-enhanced Raman spectroscopy (SERS), we revealed cell-type specific Raman signatures of these pathogens for label-free detection. It overcame the complexity associated with spectral overlaps among different bacterial species, relying on high signal-to-noise ratio (SNR) spectra carefully collected from pure species samples. To enable simple, rapid, and multiplexed detection, we harnessed advanced machine learning techniques to establish predictive models based on a large set of raw spectra of each bacterial species and their mixtures. Using these models, given a raw spectrum collected from a bacterial suspension, simultaneous identification of all three species in the test sample was achieved at 95.6 % accuracy. This sensing modality can be applied to multiplex detection of a broader range and a larger set of periodontal pathogens, paving the way for hassle-free detection of oral bacteria in saliva with little to no sample preparation.

Distinctive structure, composition and biomechanics of collagen fibrils in vaginal wall connective tissues associated with pelvic organ prolapse.

Collagen is the predominant structural protein within connective tissues. Pelvic organ prolapse (POP) is characterized by weakening of the pelvic floor connective tissues and loss of support for pelvic organs. In this study, we examined the multiscale structure, molecular composition and biomechanics of native collagen fibrils in connective tissues of the posterior vaginal fornix collected from healthy women and POP patients, and established the correlation of these properties with clinical POP quantification (POP-Q) scores. The collagen characteristics, including collagen amount, ratio of Collagen I and Collagen III, collagen fibril d-period, alignment and stiffness, were found to change progressively with the increase of the clinical measurement of Point C, a measure of uterine descent and apical prolapse. The results imply that a severe prolapse is associated with stiffer collagen fibrils, reduced collagen d-period, increased fibril alignment and imbalanced collagen synthesis, degradation and deposition. Additionally, prolapse progression appears to be synchronized with deterioration of the collagen matrix, suggesting that a POP-Q score obtained via a non-invasive clinical test can be potentially used to quantitatively assess collagen abnormality of a patient's local tissue. STATEMENT OF SIGNIFICANCE: Abnormal collagen metabolism and deposition are known to associate with connective tissue disorders, such as pelvic organ prolapse. Quantitative correlation of the biochemical and biophysical characteristics of collagen in a prolapse patient's tissue with the clinical diagnostic measurements is unexplored and unestablished. This study fills the knowledge gap between clinical prolapse quantification and the individual's cellular and molecular disorders leading to connective tissue failure, thus, provides the basis for clinicians to employ personalized treatment that can best manage the patient's condition and to alert pre-symptomatic patients for early management to avoid unwanted surgery.

Electrospun Silk Fibroin-CNT Composite Fibers: Characterization and Application in Mediating Fibroblast Stimulation.

Electrospinning is a simple, low-cost, and highly efficient technique to generate desirable nano/microfibers from polymer solutions. Silk fibroin (SF), a biopolymer found in Bombyx mori cocoons, has attracted attention for various biomedical applications. In this study, functionalized CNT was incorporated in SF to generate biocomposite fibers by electrospinning. The electrospun (E-spun) fibers were well aligned with morphology mimicking the locally oriented ECM proteins in connective tissues. While as-spun fibers dissolved in water in just two minutes, ethanol vapor post-treatment promoted ß-sheet formation leading to improved fiber stability in an aqueous environment (>14 days). The addition of a minute amount of CNT effectively improved the E-spun fiber alignment and mechanical strength while retained high biocompatibility and biodegradability. The fibers' electrical conductivity increased by 13.7 folds and 21.8 folds, respectively, in the presence of 0.1 w% and 0.2 w% CNT in SF fibers. With aligned SF-CNT 0.1 % fibers as a cell culture matrix, we found electrical stimulation effectively activated fibroblasts from patients of pelvic organ prolapse (POP), a connective tissue disorder. The stimulation boosted the fibroblasts' productivity of collagen III (COLIII) and collagen I (COLI) by 74 folds and 58 folds, respectively, and reduced the COLI to COLIII ratio favorable for tissue repair. The developed material and method offer a simple, direct, and effective way to remedy the dysfunctional fibroblasts of patients for personalized cell therapeutic treatment of diseases and health conditions associated with collagen disorder.

Distinctive roles of fibrillar collagen I and collagen III in mediating fibroblast-matrix interaction: A nanoscopic study.

One major goal in tissue engineering is to create functional materials, mimicking scaffolds in native tissues, to modulate cell function for tissue repair. Collagen is the most abundant structural protein in human body. Though collagen I (COLI) and collagen III (COLIII) are the predominant collagen types in connective tissues and they form stable hybrid fibrils at varied ratios, cell responses to the hybrid matrices are underinvestigated. In this work, we aim to explicate the distinctive roles of COLI and COLIII in fibroblast activation. Unidirectionally aligned COLI, COLIII and COLI-COLIII hybrid nanofibrils were generated via epitaxial growth of collagen on mica. AFM analyses revealed that, with the increase of COLI/COLIII ratio, the fibril width and stiffness increased and the binding affinity of cells to the matrix decreased. A hybrid matrix was found to activate fibroblasts the most effectively, characterized by extensive cell polarization with rigid stress fiber bundles and high α-SMA expression, and by the highest-level of collagen synthesis. It is ascribed to the fine balance between biochemical and biophysical cues achieved on the hybrid matrix. Thus, matrices of aligned COLI-COLIII hybrid fibrils and their derived multifunctional composites can be good candidates of implantation scaffolds for tissue regeneration.

Visualizing Cholesterol in the Brain by On-Tissue Derivatization and Quantitative Mass Spectrometry Imaging.

Despite being a critical molecule in the brain, mass spectrometry imaging (MSI) of cholesterol has been under-reported compared to other lipids due to the difficulty in ionizing the sterol molecule. In the present work, we have employed an on-tissue enzyme-assisted derivatization strategy to improve detection of cholesterol in brain tissue sections. We report distribution and levels of cholesterol across specific structures of the mouse brain, in a model of Niemann-Pick type C1 disease, and during brain development. MSI revealed that in the adult mouse, cholesterol is the highest in the pons and medulla and how its distribution changes during development. Cholesterol was significantly reduced in the corpus callosum and other brain regions in the Npc1 null mouse, confirming hypomyelination at the molecular level. Our study demonstrates the potential of MSI to the study of sterols in neuroscience.

Differential Proteomics Reveals miR-155 as a Novel Indicator of Liver and Spleen Pathology in the Symptomatic Niemann-Pick Disease, Type C1 Mouse Model.

Niemann-Pick disease, type C1 (NPC1) is a rare, autosomal recessive, lipid storage disorder caused by mutations in NPC1. As a result, there is accumulation of unesterified cholesterol and sphingolipids in the late endosomal/lysosomal system. Clinically, patients can present with splenomegaly and hepatomegaly. In the current study, we analyzed the differential proteome of the spleen in symptomatic Npc1-/- mice to complement previous studies focused on the differential proteome of the liver, and then evaluated biomolecules that may serve as tissue biomarkers. The proteomic analysis revealed altered pathways in NPC1 representing different functional categories including heme synthesis, cellular regulation and phosphoinositide metabolism in both tissues. Differential proteins included several activators of the ubiquitous and critical protein, Akt, a major kinase involved in multiple cellular processes. Evaluation of Akt revealed decreased expression in both the liver and spleen tissues of symptomatic Npc1-/- mice. Upstream regulation analysis also suggested that miR-155 may modulate the differences of known downstream protein targets observed in our dataset. Upon evaluation of miR-155, we observed an increased expression in the liver and decreased expression in the spleen of symptomatic Npc1-/- mice. Here, we propose that miR-155 may be a novel indicator of spleen and liver pathology in NPC1.