Background: Bracing and spinal fusion surgery have long been the primary methods for idiopathic scoliosis correction; however, there exist multiple limitations with both techniques. Growth modulation techniques have recently been attempted, but are typically performed across multiple vertebral elements. The aim of this study was to quantify the corrective abilities of a dual-angled, wedge shaped, rigid disc implant designed to correct spinal deformity. Methods: The 3D spinal geometry of four patients was reconstructed using calibrated radiographs, from which personal finite element models were created. Coronal and sagittal Cobb angles and axial stress distribution were calculated pre- and post- simulation of device implantation at the apical vertebral element. Findings: Insertion of a rigid wedged implant resulted in up to 90.1% coronal correction with kyphotic normalization, and reduced axial stress differential within adjacent vertebrae by up to 83.3%. This correction in axial stress differential was seen to propagate to subjacent vertebrae in both rostral and caudal directions. Insertion of two implants yielded greater correction with respect to all three measures. Interpretation: Local Cobb angle correction, increased kyphotic angle, and a decrease in axial stress differential with adjacent and subjacent vertebral levels demonstrate a potential for deformity correction from within the disc space. The decrease in axial stress differential demonstrates a capacity for growth modulation and reversal of the Heuter-Volkmann principle. Based on qualitative views of spinal shape following device implantation, the wedged implant proved more efficacious in correcting single thoracic curves than double major curves.

High-dimensional single-cell technologies present new opportunities for biological discovery, but the complex nature of the resulting datasets makes it challenging to perform comprehensive analysis. One particular challenge is the analysis of single-cell time course datasets: how to identify unique cell populations and track how they change across time points. To facilitate this analysis, we developed FLOW-MAP, a graphical user interface (GUI)-based software tool that uses graph layout analysis with sequential time ordering to visualize cellular trajectories in high-dimensional single-cell datasets obtained from flow cytometry, mass cytometry or single-cell RNA sequencing (scRNAseq) experiments. Here we provide a detailed description of the FLOW-MAP algorithm and how to use the open-source R package FLOWMAPR via its GUI or with text-based commands. This approach can be applied to many dynamic processes, including in vitro stem cell differentiation, in vivo development, oncogenesis, the emergence of drug resistance and cell signaling dynamics. To demonstrate our approach, we perform a step-by-step analysis of a single-cell mass cytometry time course dataset from mouse embryonic stem cells differentiating into the three germ layers: endoderm, mesoderm and ectoderm. In addition, we demonstrate FLOW-MAP analysis of a previously published scRNAseq dataset. Using both synthetic and experimental datasets for comparison, we perform FLOW-MAP analysis side by side with other single-cell analysis methods, to illustrate when it is advantageous to use the FLOW-MAP approach. The protocol takes between 30 min and 1.5 h to complete.