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      Wound Healing: A Cellular Perspective

      1 , 1 , 1 , 1
      Physiological Reviews
      American Physiological Society

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          Abstract

          Wound healing is one of the most complex processes in the human body. It involves the spatial and temporal synchronization of a variety of cell types with distinct roles in the phases of hemostasis, inflammation, growth, re-epithelialization, and remodeling. With the evolution of single cell technologies, it has been possible to uncover phenotypic and functional heterogeneity within several of these cell types. There have also been discoveries of rare, stem cell subsets within the skin, which are unipotent in the uninjured state, but become multipotent following skin injury. Unraveling the roles of each of these cell types and their interactions with each other is important in understanding the mechanisms of normal wound closure. Changes in the microenvironment including alterations in mechanical forces, oxygen levels, chemokines, extracellular matrix and growth factor synthesis directly impact cellular recruitment and activation, leading to impaired states of wound healing. Single cell technologies can be used to decipher these cellular alterations in diseased states such as in chronic wounds and hypertrophic scarring so that effective therapeutic solutions for healing wounds can be developed.

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          Bacterial Biofilms: A Common Cause of Persistent Infections

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            Comparative Analysis of Single-Cell RNA Sequencing Methods.

            Single-cell RNA sequencing (scRNA-seq) offers new possibilities to address biological and medical questions. However, systematic comparisons of the performance of diverse scRNA-seq protocols are lacking. We generated data from 583 mouse embryonic stem cells to evaluate six prominent scRNA-seq methods: CEL-seq2, Drop-seq, MARS-seq, SCRB-seq, Smart-seq, and Smart-seq2. While Smart-seq2 detected the most genes per cell and across cells, CEL-seq2, Drop-seq, MARS-seq, and SCRB-seq quantified mRNA levels with less amplification noise due to the use of unique molecular identifiers (UMIs). Power simulations at different sequencing depths showed that Drop-seq is more cost-efficient for transcriptome quantification of large numbers of cells, while MARS-seq, SCRB-seq, and Smart-seq2 are more efficient when analyzing fewer cells. Our quantitative comparison offers the basis for an informed choice among six prominent scRNA-seq methods, and it provides a framework for benchmarking further improvements of scRNA-seq protocols.
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              Fibrotic disease and the T(H)1/T(H)2 paradigm.

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                Author and article information

                Journal
                Physiological Reviews
                Physiological Reviews
                American Physiological Society
                0031-9333
                1522-1210
                January 2019
                January 2019
                : 99
                : 1
                : 665-706
                Affiliations
                [1 ]Department of Surgery, Stanford University School of Medicine, Stanford, California
                Article
                10.1152/physrev.00067.2017
                6442927
                30475656
                56a6bb37-fc90-402a-ac54-af5748023344
                © 2019
                History

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