Organic and Organometallic Chemistry at the Single-Molecule, -Particle, and -Molecular-Catalyst-Turnover Level by Fluorescence Microscopy

Mechanistic studies have historically played a key role in the discovery and optimization of reactions in organic and organometallic chemistry. However, even apparently simple organic and organometallic transformations may have surprisingly complicated multistep mechanisms, increasing the difficulty of extracting this mechanistic information. The resulting reaction intermediates often constitute a small fraction of the total reaction mixture, for example, creating a long-term analytical challenge of detection. This challenge is particularly pronounced in cases where the positions of intermediates on the reaction energy surface mean that they do not "build up" to the quantities needed for observation by traditional ensemble analytical tools. Thus, their existence and single-step elementary reactivity cannot be studied directly. New approaches for obtaining this otherwise-missing mechanistic information are therefore needed. Single-turnover, single-molecule, single-particle, and other subensemble fluorescence microscopy techniques are ideally suited for this role because of their sensitivity and spatiotemporal resolution. Inspired by the robust development of single-molecule fluorescence microscopy tools for studying enzyme catalysis, our laboratory has developed analogous fluorescence microscopy techniques to overcome mechanistic challenges in synthetic chemistry, with sensitivity as high as the single-complex, single-turnover, and single-molecule level. These techniques free the experimenter from the previous restriction that intermediates must "build up" to quantities needed for detection by ensemble analytical tools and are suited to systems where synchronization through flash photolysis or stopped flow would be inconvenient or inaccessible. In this process, the techniques transform certain previously "unobservable" intermediates and their elementary single-step reactivities into "observable" ones through sensitive and selective spectral handles. Our program has focused on imaging reactions in small-molecule, organic, and polymer synthetic chemistry with an accent on the reactivity of molecular transition metal complexes and catalysts. Our laboratory initiated studies in this area in 2008 with the imaging of individual palladium complexes that were tagged with spectator fluorophores. To enable imaging, we started with fluorophore selection and development, overcame challenges with imaging in organic solvents, and developed strategies compatible with air-sensitive chemistry and concentrations of reagents generally used in small-molecule synthesis. These studies grew to include characterization of previously unknown organometallic intermediates in the synthesis of organozinc reagents and the direct study of their elementary-step reactivity. The ability to directly observe this behavior generated predictive power for selecting salts that accelerated organozinc reagent formation in synthesis, including salts that had not yet been reported synthetically. In 2017 we also developed the first single-turnover imaging of molecular (chemo)catalysts, which through the technique's spatiotemporal resolution revealed abruptly time-variable polymerization kinetics wherein molecular ruthenium ring-opening metathesis polymerization (ROMP) catalysts changed rates independently from other catalysts less than 1 μm away. Individual catalytic turnovers, each corresponding to one single-chain-elongation reaction arising from insertion of single ROMP or enyne monomers at individual Grubbs II molecular ruthenium catalysts, were spatiotemporally resolved as green flashes in growing polymers. In this Account, we discuss the development of this technique from idea to application, including challenges overcome and strategies created to image synthetic organic and organometallic molecular chemistry at the highest levels of detection sensitivity. We also describe challenges not yet solved and provide an outlook for this growing field at the intersection of microscopy and synthetic/molecular chemistry.

Single-cell and spatially resolved interactomics of tooth-associated keratinocytes in periodontitis

Periodontitis affects billions of people worldwide. To address relationships of periodontal niche cell types and microbes in periodontitis, we generated an integrated single-cell RNA sequencing (scRNAseq) atlas of human periodontium (34-sample, 105918-cell), including sulcular and junctional keratinocytes (SK/JKs). SK/JKs displayed altered differentiation states and were enriched for effector cytokines in periodontitis. Single-cell metagenomics revealed 37 bacterial species with cell-specific tropism. Fluorescence in situ hybridization detected intracellular 16 S and mRNA signals of multiple species and correlated with SK/JK proinflammatory phenotypes in situ. Cell-cell communication analysis predicted keratinocyte-specific innate and adaptive immune interactions. Highly multiplexed immunofluorescence (33-antibody) revealed peri-epithelial immune foci, with innate cells often spatially constrained around JKs. Spatial phenotyping revealed immunosuppressed JK-microniches and SK-localized tertiary lymphoid structures in periodontitis. Here, we demonstrate impacts on and predicted interactomics of SK and JK cells in health and periodontitis, which requires further investigation to support precision periodontal interventions in states of chronic inflammation.

Polybacterial Intracellular Macromolecules Shape Single-Cell Epikine Profiles in Upper Airway Mucosa

The upper airway, particularly the nasal and oral mucosal epithelium, serves as a primary barrier for microbial interactions throughout life. Specialized niches like the anterior nares and the tooth are especially susceptible to dysbiosis and chronic inflammatory diseases. To investigate host-microbial interactions in mucosal epithelial cell types, we reanalyzed our single-cell RNA sequencing atlas of human oral mucosa, identifying polybacterial signatures (20% Gram-positive, 80% Gram-negative) within both epithelial- and stromal-resident cells. This analysis revealed unique responses of bacterial-associated epithelia when compared to two inflammatory disease states of mucosa. Single-cell RNA sequencing, in situ hybridization, and immunohistochemistry detected numerous persistent macromolecules from Gram-positive and Gram-negative bacteria within human oral keratinocytes (HOKs), including bacterial rRNA, mRNA and glycolipids. Epithelial cells with higher concentrations of 16S rRNA and glycolipids exhibited enhanced receptor-ligand signaling in vivo. HOKs with a spectrum of polybacterial intracellular macromolecular (PIM) concentrations were challenged with purified exogenous lipopolysaccharide, resulting in the synergistic upregulation of select innate (CXCL8, TNFSF15) and adaptive (CXCL17, CCL28) epikines. Notably, endogenous lipoteichoic acid, rather than lipopolysaccharide, directly correlated with epikine expression in vitro and in vivo. Application of the Drug2Cell algorithm to health and inflammatory disease data suggested altered drug efficacy predictions based on PIM detection. Our findings demonstrate that PIMs persist within mucosal epithelial cells at variable concentrations, linearly driving single-cell effector cytokine expression and influencing drug responses, underscoring the importance of understanding host-microbe interactions and the implications of PIMs on cell behavior in health and disease at single-cell resolution.

Application of Single-Cell Genomics to Animal Models of Periodontitis and Peri-Implantitis

AIMS

This narrative review aims to synthesize current knowledge on integrating single-cell genomics technologies with animal models of periodontitis and peri-implantitis.

REVIEW

Single-cell RNA sequencing (scRNAseq) reveals cellular heterogeneity and specific cell roles in periodontitis and peri-implantitis, overcoming the limitations of bulk RNA sequencing. Under controlled conditions and genetic manipulation, animal models facilitate studying disease progression, gene functions and systemic disease links, aiding targeted therapy development. Knockout models have started to elucidate the impact of genetic mutations on periodontal disease and host responses. scRNAseq in animal models has been used to examine connections between periodontitis and systemic diseases, revealing altered immune environments and cellular interactions. Emerging studies are now applying these methods to animal models of peri-implantitis. Integrating these datasets into single-cell and spatially resolved atlases will enable future meta-analyses, providing deeper insights into disease mechanisms considering factors such as sex, strain, and age.

CONCLUSIONS

Integrating scRNAseq with animal models advances the understanding of periodontitis and peri-implantitis pathogenesis and precision therapies. The combined use of single-cell and spatial genomics and scRNAseq will further enhance data insights significantly for drug discovery and preclinical testing, making these technologies pivotal in validating animal models and translating findings into clinical practice.

Spatial Deconvolution of Cell Types and Cell States at Scale Utilizing TACIT

Identifying cell types and states remains a time-consuming, error-prone challenge for spatial biology. While deep learning is increasingly used, it is difficult to generalize due to variability at the level of cells, neighborhoods, and niches in health and disease. To address this, we developed TACIT, an unsupervised algorithm for cell annotation using predefined signatures that operates without training data. TACIT uses unbiased thresholding to distinguish positive cells from background, focusing on relevant markers to identify ambiguous cells in multiomic assays. Using five datasets (5,000,000-cells; 51-cell types) from three niches (brain, intestine, gland), TACIT outperformed existing unsupervised methods in accuracy and scalability. Integrating TACIT-identified cell types with a novel Shiny app revealed new phenotypes in two inflammatory gland diseases. Finally, using combined spatial transcriptomics and proteomics, we discovered under- and overrepresented immune cell types and states in regions of interest, suggesting multimodality is essential for translating spatial biology to clinical applications.

The Immunoregulatory Architecture of the Adult Oral Cavity

The oral cavity is a critical barrier with immunosurveillance capabilities. A detailed understanding of its cellular, molecular, and spatial architecture is essential for advancing precision medicine across aerodigestive tissues. Here, we present the first integrated atlas of human adult oral and craniofacial tissues, derived from single-cell RNA sequencing of ~250,000 cells from 70 samples across 13 niches, including salivary glands and oral mucosae. Complementary spatial atlases using a 40-plex protein and 300-plex transcriptomics panels were developed, covering >2 million cells. Using our new AstroSuite package, we identified 8 tissue cellular neighborhoods (TCNs) and 17 multicellular interaction modules (MCIMs) across gland and mucosae in health. Diverse fibrovascular TCNs emerged as centralized hubs, coordinating immune cell migration and activation. Mucosal hubs within stroma displayed higher immune cell density, diversity, and activation than glands, underscoring differences in functional architecture. In keratinized and nonkeratinized mucosae, common fibrovascular hubs, and MCIMs revealed a specialized biogeography of immunoregulation. Under chronic inflammation, mucosal TCNs were fragmented, and MCIMs involving fibroblasts and immune cells were reorganized and expanded. Focused analysis of tertiary lymphoid structures (TLS) and fibroblasts identified TLS-associated MCIMs and drug candidates targeting these spatial interactions, offering a foundational framework for broadly dissecting mucosal immunity to guide precision medicine approaches.

GZMK+CD8+ T cells Target A Specific Acinar Cell Type in Sjögren's Disease

Sjögren's Disease (SjD) is a systemic autoimmune disease without a clear etiology or effective therapy. Utilizing unbiased single-cell and spatial transcriptomics to analyze human minor salivary glands in health and disease we developed a comprehensive understanding of the cellular landscape of healthy salivary glands and how that landscape changes in SjD patients. We identified novel seromucous acinar cell types and identified a population of PRR4+CST3+WFDC2- seromucous acinar cells that are particularly targeted in SjD. Notably, GZMK+CD8 T cells, enriched in SjD, exhibited a cytotoxic phenotype and were physically associated with immune-engaged epithelial cells in disease. These findings shed light on the immune response's impact on transitioning acinar cells with high levels of secretion and explain the loss of this specific cell population in SjD. This study explores the complex interplay of varied cell types in the salivary glands and their role in the pathology of Sjögren's Disease.

Spatial Deconvolution of Cell Types and Cell States at Scale Utilizing TACIT

Identifying cell types and states remains a time-consuming and error-prone challenge for spatial biology. While deep learning is increasingly used, it is difficult to generalize due to variability at the level of cells, neighborhoods, and niches in health and disease. To address this, we developed TACIT, an unsupervised algorithm for cell annotation using predefined signatures that operates without training data, using unbiased thresholding to distinguish positive cells from background, focusing on relevant markers to identify ambiguous cells in multiomic assays. Using five datasets (5,000,000-cells; 51-cell types) from three niches (brain, intestine, gland), TACIT outperformed existing unsupervised methods in accuracy and scalability. Integration of TACIT-identified cell with a novel Shiny app revealed new phenotypes in two inflammatory gland diseases. Finally, using combined spatial transcriptomics and proteomics, we discover under- and overrepresented immune cell types and states in regions of interest, suggesting multimodality is essential for translating spatial biology to clinical applications.