Bacterial single-cell RNA sequencing captures biofilm transcriptional heterogeneity and differential responses to immune pressure.

Biofilm formation is an important mechanism of survival and persistence for many bacterial pathogens. These multicellular communities contain subpopulations of cells that display vast metabolic and transcriptional diversity along with high recalcitrance to antibiotics and host immune defenses. Investigating the complex heterogeneity within biofilm has been hindered by the lack of a sensitive and high-throughput method to assess stochastic transcriptional activity and regulation between bacterial subpopulations, which requires single-cell resolution. We have developed an optimized bacterial single-cell RNA sequencing method, BaSSSh-seq, to study Staphylococcus aureus diversity during biofilm growth and transcriptional adaptations following immune cell exposure. We validated the ability of BaSSSh-seq to capture extensive transcriptional heterogeneity during biofilm compared to planktonic growth. Application of new computational tools revealed transcriptional regulatory networks across the heterogeneous biofilm subpopulations and identification of gene sets that were associated with a trajectory from planktonic to biofilm growth. BaSSSh-seq also detected alterations in biofilm metabolism, stress response, and virulence that were tailored to distinct immune cell populations. This work provides an innovative platform to explore biofilm dynamics at single-cell resolution, unlocking the potential for identifying biofilm adaptations to environmental signals and immune pressure.

Transcriptional Profiling of Phagocytic Leukocytes and Microglia Reveals a Critical Role for Reactive Oxygen Species in Biofilm Containment during Staphylococcus aureus Craniotomy Infection.

Craniotomies are performed to treat a variety of intracranial pathology. Surgical site infection remains a complication of craniotomy despite the use of prophylactic antibiotics and universal sterile precautions. Infections occur in 1-3% of procedures, with approximately half caused by Staphylococcus aureus that forms a biofilm on the bone flap and is recalcitrant to systemic antibiotic therapy. We used an S. aureus-dsRed construct to compare the phagocytic capacity of leukocytes and microglia in vitro and in vivo using a mouse model of craniotomy infection. In addition, single-cell RNA sequencing (scRNA-seq) was applied to determine whether a transcriptional signature could be identified for phagocytic versus nonphagocytic cells in vivo. S. aureus was phagocytosed to equivalent extents in microglia, macrophages, neutrophils, and granulocytic myeloid-derived suppressor cells in vitro; however, microglial uptake of S. aureus was limited in vivo, whereas the other leukocyte populations exhibited phagocytic activity. scRNA-seq comparing the transcriptional signatures of phagocytic (S. aureus-dsRed+) versus nonphagocytic (S. aureus-dsRed-) leukocytes identified classical pathways enriched in phagocytic cells (i.e., reactive oxygen species [ROS]/reactive nitrogen species, lysosome, iron uptake, and transport), whereas nonphagocytic populations had increased ribosomal, IFN, and hypoxia signatures. scRNA-seq also revealed a robust ROS profile, which led to the exploration of craniotomy infection in NADPH oxidase 2 knockout mice. S. aureus burden, leukocyte recruitment, and intracellular bacterial load were significantly increased in NADPH oxidase 2 KO compared with wild-type animals. Collectively, these results highlight the importance of ROS generation in phagocytes for S. aureus biofilm containment, but not clearance, during craniotomy infection.

The Prospect of Nanoparticle Systems for Modulating Immune Cell Polarization During Central Nervous System Infection.

The blood-brain barrier (BBB) selectively restricts the entry of molecules from peripheral circulation into the central nervous system (CNS) parenchyma. Despite this protective barrier, bacteria and other pathogens can still invade the CNS, often as a consequence of immune deficiencies or complications following neurosurgical procedures. These infections are difficult to treat since many bacteria, such as Staphylococcus aureus, encode a repertoire of virulence factors, can acquire antibiotic resistance, and form biofilm. Additionally, pathogens can leverage virulence factor production to polarize host immune cells towards an anti-inflammatory phenotype, leading to chronic infection. The difficulty of pathogen clearance is magnified by the fact that antibiotics and other treatments cannot easily penetrate the BBB, which requires extended regimens to achieve therapeutic concentrations. Nanoparticle systems are rapidly emerging as a promising platform to treat a range of CNS disorders. Nanoparticles have several advantages, as they can be engineered to cross the BBB with specific functionality to increase cellular and molecular targeting, have controlled release of therapeutic agents, and superior bioavailability and circulation compared to traditional therapies. Within the CNS environment, therapeutic actions are not limited to directly targeting the pathogen, but can also be tailored to modulate immune cell activation to promote infection resolution. This perspective highlights the factors leading to infection persistence in the CNS and discusses how novel nanoparticle therapies can be engineered to provide enhanced treatment, specifically through modulation of immune cell polarization.

Neuroimmune metabolism: Uncovering the role of metabolic reprogramming in central nervous system disease.

The rapidly expanding field of immunometabolism has highlighted an intricate association between the metabolic pathways that program cellular pro-inflammatory versus anti-inflammatory activity. This Special Issue on Neuroimmune Metabolism showcases a growing body of work characterizing the metabolic profiles of the major CNS-resident and peripheral immune cell players in neuroinflammation, neurodegeneration, and brain injury. The review articles address the roles of glycolytic, oxidative, and lipid metabolism that are associated with beneficial or detrimental properties in various neurological conditions, address unanswered questions in the field, and discuss promising avenues for future therapeutics. Cover Image for this issue: https://doi.org/10.1111/jnc.15069.

Transcriptional Diversity and Niche-Specific Distribution of Leukocyte Populations during Staphylococcus aureus Craniotomy-Associated Biofilm Infection.

Neurosurgery for brain tumor resection or epilepsy treatment requires a craniotomy to gain access to the brain. Despite prophylactic measures, infectious complications occur at a frequency of 1-3%, with approximately half caused by Staphylococcus aureus (S. aureus) that forms a biofilm on the bone flap and is recalcitrant to antibiotics. Using single-cell RNA sequencing in a mouse model of S. aureus craniotomy infection, this study revealed the complex transcriptional heterogeneity of resident microglia and infiltrating monocytes in the brain, in addition to transcriptionally diverse granulocyte subsets in the s.c. galea and bone flap. In the brain, trajectory analysis identified the transition of microglia from a homeostatic/anti-inflammatory to proinflammatory and proliferative populations, whereas granulocytes in the brain demonstrated a trajectory from a granulocyte myeloid-derived suppressor cell (MDSC)-like phenotype to a small population of mature polymorphonuclear neutrophils (PMNs). In the galea, trajectory analysis identified the progression from two distinct granulocyte-MDSC-like populations to PMN clusters enriched for IFN signaling and cell cycle genes. Based on their abundance in the galea and bone flap, PMNs and MDSCs were depleted using anti-Ly6G, which resulted in increased bacterial burden. This revealed a critical role for PMNs in S. aureus containment because MDSCs were found to attenuate PMN antibacterial activity, which may explain, in part, why craniotomy infection persists in the presence of PMN infiltrates. These results demonstrate the existence of a transcriptionally diverse leukocyte response that likely influences the chronicity of S. aureus craniotomy infection.

Diagnostic Optical Sequencing.

Advances in precision medicine require high-throughput, inexpensive, point-of-care diagnostic methods with multiomics capability for detecting a wide range of biomolecules and their molecular variants. Optical techniques have offered many promising advances toward such diagnostics. However, the inability to squeeze light with several hundred nanometer wavelengths into angstrom-scale volume for single-nucleotide measurements has hindered further progress. This limitation has been circumvented by analyzing the relative nucleobase content with Raman spectroscopy, in an optical sequencing method. Here, we performed optical sequencing measurements on positively charged silver nanoparticles to achieve 93.3% accuracy for predicting nucleobase content in label-free DNA k-mer blocks (where k = 10) as well as measurements on RNA and chemically modified nucleobases for extensions to transcriptomic and epigenetic studies. Our high-accuracy measurements were then used with a content-scoring database searching algorithm to correctly identify a ß-lactamase gene from the MEGARes antibiotic resistance database and confirm the Pseudomonas aeruginosa pathogen of origin from <12 block content measurements (<15% coverage) of the gene. These results prove the feasibility of an optical sequencing platform as a diagnostic method. With the versatile range of available plasmonic substrates offering simple data acquisition, varying resolution (single-molecule to the ensemble), and multiplexing, this optical sequencing platform has potential as the rapid, cost-effective method needed for broad-spectrum biomarker detection.

BOCS: DNA k-mer content and scoring for rapid genetic biomarker identification at low coverage.

A single, inexpensive diagnostic test capable of rapidly identifying a wide range of genetic biomarkers would prove invaluable in precision medicine. Previous work has demonstrated the potential for high-throughput, label-free detection of A-G-C-T content in DNA k-mers, providing an alternative to single-letter sequencing while also having inherent lossy data compression and massively parallel data acquisition. Here, we apply a new bioinformatics algorithm - block optical content scoring (BOCS) - capable of using the high-throughput content k-mers for rapid, broad-spectrum identification of genetic biomarkers. BOCS uses content-based sequence alignment for probabilistic mapping of k-mer contents to gene sequences within a biomarker database, resulting in a probability ranking of genes on a content score. Simulations of the BOCS algorithm reveal high accuracy for identification of single antibiotic resistance genes, even in the presence of significant sequencing errors (100% accuracy for no sequencing errors, and >90% accuracy for sequencing errors at 20%), and at well below full coverage of the genes. Simulations for detecting multiple resistance genes within a methicillin-resistant Staphylococcus aureus (MRSA) strain showed 100% accuracy at an average gene coverage of merely 0.515, when the k-mer lengths were variable and with 4% sequencing error within the k-mer blocks. Extension of BOCS to cancer and other genetic diseases met or exceeded the results for resistance genes. Combined with a high-throughput content-based sequencing technique, the BOCS algorithm potentiates a test capable of rapid diagnosis and profiling of genetic biomarkers ranging from antibiotic resistance to cancer and other genetic diseases.

Nucleotide and structural label identification in single RNA molecules with quantum tunneling spectroscopy.

Although a number of advances have been made in RNA sequencing and structural characterization, the lack of a method for directly determining the sequence and structure of single RNA molecules has limited our ability to probe heterogeneity in gene expression at the level of single cells. Here we present a method for direct nucleotide identification and structural label mapping of single RNA molecules via Quantum Molecular Sequencing (QMSeq). The method combines non-perturbative quantum tunneling spectroscopy to probe the molecular orbitals of ribonucleotides, new experimental biophysical parameters that fingerprint these molecular orbitals, and a machine learning classification algorithm to distinguish between the ribonucleotides. The algorithm uses tunneling spectroscopy measurements on an unknown ribonucleotide to determine its chemical identity and the presence of local chemical modifications. Combining this with structure-dependent chemical labeling presents the possibility of mapping both the sequence and local structure of individual RNA molecules. By optimizing the base-calling algorithm, we show a high accuracy for both ribonucleotide discrimination (>99.8%) and chemical label identification (>98%) with a relatively modest molecular coverage (35 repeat measurements). This lays the groundwork for simultaneous sequencing and structural mapping of single unknown RNA molecules, and paves the way for probing the sequence-structure-function relationship within the transcriptome at an unprecedented level of detail.

Quantum Point Contact Single-Nucleotide Conductance for DNA and RNA Sequence Identification.

Several nanoscale electronic methods have been proposed for high-throughput single-molecule nucleic acid sequence identification. While many studies display a large ensemble of measurements as "electronic fingerprints" with some promise for distinguishing the DNA and RNA nucleobases (adenine, guanine, cytosine, thymine, and uracil), important metrics such as accuracy and confidence of base calling fall well below the current genomic methods. Issues such as unreliable metal-molecule junction formation, variation of nucleotide conformations, insufficient differences between the molecular orbitals responsible for single-nucleotide conduction, and lack of rigorous base calling algorithms lead to overlapping nanoelectronic measurements and poor nucleotide discrimination, especially at low coverage on single molecules. Here, we demonstrate a technique for reproducible conductance measurements on conformation-constrained single nucleotides and an advanced algorithmic approach for distinguishing the nucleobases. Our quantum point contact single-nucleotide conductance sequencing (QPICS) method uses combed and electrostatically bound single DNA and RNA nucleotides on a self-assembled monolayer of cysteamine molecules. We demonstrate that by varying the applied bias and pH conditions, molecular conductance can be switched ON and OFF, leading to reversible nucleotide perturbation for electronic recognition (NPER). We utilize NPER as a method to achieve >99.7% accuracy for DNA and RNA base calling at low molecular coverage (∼12×) using unbiased single measurements on DNA/RNA nucleotides, which represents a significant advance compared to existing sequencing methods. These results demonstrate the potential for utilizing simple surface modifications and existing biochemical moieties in individual nucleobases for a reliable, direct, single-molecule, nanoelectronic DNA and RNA nucleotide identification method for sequencing.

Conformational Smear Characterization and Binning of Single-Molecule Conductance Measurements for Enhanced Molecular Recognition.

Electronic conduction or charge transport through single molecules depends primarily on molecular structure and anchoring groups and forms the basis for a wide range of studies from molecular electronics to DNA sequencing. Several high-throughput nanoelectronic methods such as mechanical break junctions, nanopores, conductive atomic force microscopy, scanning tunneling break junctions, and static nanoscale electrodes are often used for measuring single-molecule conductance. In these measurements, "smearing" due to conformational changes and other entropic factors leads to large variances in the observed molecular conductance, especially in individual measurements. Here, we show a method for characterizing smear in single-molecule conductance measurements and demonstrate how binning measurements according to smear can significantly enhance the use of individual conductance measurements for molecular recognition. Using quantum point contact measurements on single nucleotides within DNA macromolecules, we demonstrate that the distance over which molecular junctions are maintained is a measure of smear, and the resulting variance in unbiased single measurements depends on this smear parameter. Our ability to identify individual DNA nucleotides at 20× coverage increases from 81.3% accuracy without smear analysis to 93.9% with smear characterization and binning (SCRIB). Furthermore, merely 7 conductance measurements (7× coverage) are needed to achieve 97.8% accuracy for DNA nucleotide recognition when only low molecular smear measurements are used, which represents a significant improvement over contemporary sequencing methods. These results have important implications in a broad range of molecular electronics applications from designing robust molecular switches to nanoelectronic DNA sequencing.

Single Nucleobase Identification Using Biophysical Signatures from Nanoelectronic Quantum Tunneling.

Nanoelectronic DNA sequencing can provide an important alternative to sequencing-by-synthesis by reducing sample preparation time, cost, and complexity as a high-throughput next-generation technique with accurate single-molecule identification. However, sample noise and signature overlap continue to prevent high-resolution and accurate sequencing results. Probing the molecular orbitals of chemically distinct DNA nucleobases offers a path for facile sequence identification, but molecular entropy (from nucleotide conformations) makes such identification difficult when relying only on the energies of lowest-unoccupied and highest-occupied molecular orbitals (LUMO and HOMO). Here, nine biophysical parameters are developed to better characterize molecular orbitals of individual nucleobases, intended for single-molecule DNA sequencing using quantum tunneling of charges. For this analysis, theoretical models for quantum tunneling are combined with transition voltage spectroscopy to obtain measurable parameters unique to the molecule within an electronic junction. Scanning tunneling spectroscopy is then used to measure these nine biophysical parameters for DNA nucleotides, and a modified machine learning algorithm identified nucleobases. The new parameters significantly improve base calling over merely using LUMO and HOMO frontier orbital energies. Furthermore, high accuracies for identifying DNA nucleobases were observed at different pH conditions. These results have significant implications for developing a robust and accurate high-throughput nanoelectronic DNA sequencing technique.

Methylene blue-mediated electrocatalytic detection of hexavalent chromium.

We report, for the first time, the design and fabrication of an electrochemical ion (E-ION) sensor for highly specific detection of hexavalent chromium (Cr(VI)). Unlike previously developed electrochemical Cr(VI) sensors, the sensing mechanism relies on the previously unexplored electrocatalytic reaction between Cr(VI) and surface-immobilized methylene blue (MB). The sensor is sensitive, specific, and selective enough to be used in a synthetic aquifer sample. Like many sensors of this class, it is also reagentless, reusable, and compatible with gold-plated screen-printed carbon electrodes. Despite the difference in the sensing mechanism, this E-ION Cr(VI) sensor possesses attributes similar to other MB-based electrochemical sensors, sensors with potential for real world applications.