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  1. Cai, GuoShuai (Ed.)
    With the rapid advances of various single-cell technologies, an increasing number of single-cell datasets are being generated, and the computational tools for aligning the datasets which make subsequent integration or meta-analysis possible have become critical. Typically, single-cell datasets from different technologies cannot be directly combined or concatenated, due to the innate difference in the data, such as the number of measured parameters and the distributions. Even datasets generated by the same technology are often affected by the batch effect. A computational approach for aligning different datasets and hence identifying related clusters will be useful for data integration and interpretation in large scale single-cell experiments. Our proposed algorithm called JSOM, a variation of the Self-organizing map, aligns two related datasets that contain similar clusters, by constructing two maps—low-dimensional discretized representation of datasets–that jointly evolve according to both datasets. Here we applied the JSOM algorithm to flow cytometry, mass cytometry, and single-cell RNA sequencing datasets. The resulting JSOM maps not only align the related clusters in the two datasets but also preserve the topology of the datasets so that the maps could be used for further analysis, such as clustering. 
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  2. Abstract

    Regeneration of skeletal muscle after volumetric injury is thought to be impaired by a dysregulated immune microenvironment that hinders endogenous repair mechanisms. Such defects result in fatty infiltration, tissue scarring, chronic inflammation, and debilitating functional deficits. Here, we evaluated the key cellular processes driving dysregulation in the injury niche through localized modulation of sphingosine‐1‐phosphate (S1P) receptor signaling. We employ dimensionality reduction and pseudotime analysis on single cell cytometry data to reveal heterogeneous immune cell subsets infiltrating preclinical muscle defects due to S1P receptor inhibition. We show that global knockout of S1P receptor 3 (S1PR3) is marked by an increase of muscle stem cells within injured tissue, a reduction in classically activated relative to alternatively activated macrophages, and increased bridging of regenerating myofibers across the defect. We found that local S1PR3 antagonism via nanofiber delivery of VPC01091 replicated key features of pseudotime immune cell recruitment dynamics and enhanced regeneration characteristic of global S1PR3 knockout. Our results indicate that local S1P receptor modulation may provide an effective immunotherapy for promoting a proreparative environment leading to improved regeneration following muscle injury.

     
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