CHAI: consensus clustering through similarity matrix integration for cell-type identification.

Several methods have been developed to computationally predict cell-types for single cell RNA sequencing (scRNAseq) data. As methods are developed, a common problem for investigators has been identifying the best method they should apply to their specific use-case. To address this challenge, we present CHAI (consensus Clustering tHrough similArIty matrix integratIon for single cell-type identification), a wisdom of crowds approach for scRNAseq clustering. CHAI presents two competing methods which aggregate the clustering results from seven state-of-the-art clustering methods: CHAI-AvgSim and CHAI-SNF. CHAI-AvgSim and CHAI-SNF demonstrate superior performance across several benchmarking datasets. Furthermore, both CHAI methods outperform the most recent consensus clustering method, SAME-clustering. We demonstrate CHAI's practical use case by identifying a leader tumor cell cluster enriched with CDH3. CHAI provides a platform for multiomic integration, and we demonstrate CHAI-SNF to have improved performance when including spatial transcriptomics data. CHAI overcomes previous limitations by incorporating the most recent and top performing scRNAseq clustering algorithms into the aggregation framework. It is also an intuitive and easily customizable R package where users may add their own clustering methods to the pipeline, or down-select just the ones they want to use for the clustering aggregation. This ensures that as more advanced clustering algorithms are developed, CHAI will remain useful to the community as a generalized framework. CHAI is available as an open source R package on GitHub: https://github.com/lodimk2/chai.

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.

CHAI: Consensus Clustering Through Similarity Matrix Integration for Cell-Type Identification.

Several methods have been developed to computationally predict cell-types for single cell RNA sequencing (scRNAseq) data. As methods are developed, a common problem for investigators has been identifying the best method they should apply to their specific use-case. To address this challenge, we present CHAI (consensus Clustering tHrough similArIty matrix integratIon for single cell type identification), a wisdom of crowds approach for scRNAseq clustering. CHAI presents two competing methods which aggregate the clustering results from seven state of the art clustering methods: CHAI-AvgSim and CHAI-SNF. Both methods demonstrate improved performance on a diverse selection of benchmarking datasets, besides also outperforming a previous consensus clustering method. We demonstrate CHAI's practical use case by identifying a leader tumor cell cluster enriched with CDH3. CHAI provides a platform for multiomic integration, and we demonstrate CHAI-SNF to have improved performance when including spatial transcriptomics data. CHAI is intuitive and easily customizable; it provides a way for users to add their own clustering methods to the pipeline, or down-select just the ones they want to use for the clustering aggregation. CHAI is available as an open source R package on GitHub: https://github.com/lodimk2/chai.

Brain-Computer-Spinal Interface Restores Upper Limb Function After Spinal Cord Injury.

Brain-computer interfaces (BCIs) are an emerging strategy for spinal cord injury (SCI) intervention that may be used to reanimate paralyzed limbs. This approach requires decoding movement intention from the brain to control movement-evoking stimulation. Common decoding methods use spike-sorting and require frequent calibration and high computational complexity. Furthermore, most applications of closed-loop stimulation act on peripheral nerves or muscles, resulting in rapid muscle fatigue. Here we show that a local field potential-based BCI can control spinal stimulation and improve forelimb function in rats with cervical SCI. We decoded forelimb movement via multi-channel local field potentials in the sensorimotor cortex using a canonical correlation analysis algorithm. We then used this decoded signal to trigger epidural spinal stimulation and restore forelimb movement. Finally, we implemented this closed-loop algorithm in a miniaturized onboard computing platform. This Brain-Computer-Spinal Interface (BCSI) utilized recording and stimulation approaches already used in separate human applications. Our goal was to demonstrate a potential neuroprosthetic intervention to improve function after upper extremity paralysis.

Implantable ultrasonic imaging assembly for automated monitoring of internal organs.

An implantable miniaturized imaging device can be attractive in many clinical applications. They include automated, periodic, high-resolution monitoring of susceptible organs for early detection of an anomalous growth. In this paper, we propose an implantable ultrasonic imager capable of online high-resolution imaging of a region inside the body. A feasibility analysis is presented, with respect to design of such a system and its application to online monitoring of tumor growth in deep internal organs. We use ultrasound (US) imaging technology, as it is safe, low-cost, can be easily miniaturized, and amenable for long-term, point-of-care (POC) monitoring. The design space of the proposed system has been explored including form factor, transducer specifications and power/energy requirements. We have analyzed the effectiveness of the system in timely detection of anomalous growth in a case study through software simulations using a widely-accepted ultrasonic platform (Field II). Finally, through experimental studies using medical grade phantoms and an ultrasound scanner, we have evaluated the system with respect to its major imaging characteristics. It is observed that interstitial imaging under area/power constraints would achieve significantly better imaging quality in terms of contrast sensitivity and spatial resolution than existing techniques in deep, internal body parts, while maintaining the automated monitoring advantages.

Implantable ultrasonic dual functional assembly for detection and treatment of anomalous growth.

High Intensity Focused Ultrasound (HIFU) is emerging as an accurate, noninvasive method for ablation of certain primary and metastatic tumors. Typically, ablation is performed with an external therapeutic transducer. However, external HIFU treatment suffers from limitations of low therapeutic efficiency for ablation of tumors, deep in internal organs such as liver, kidney and brain. Interstitial HIFU through an internal transducer, implanted locally near the organ of interest, could alleviate some of these limitations. Furthermore, it can be attractive for point-of-care (POC) treatment. In this paper, we propose the design of a dual-functional implantable assembly for image-guided HIFU treatment of anomalous growth. It is realized by effective integration of a central HIFU array with two ultrasonic imaging arrays for high-resolution online monitoring and efficient treatment. We explore the design space for the implant and identify the major design parameters including the power requirement. Using a widely used simulation platform, we show that the proposed implant, besides providing a potential POC solution, achieves a better therapeutic performance for certain tumor positions in internal organs, than the extracorporeal HIFU treatment.