Cellular heterogeneity is a major cause of treatment resistance in cancer. Despite recent advances in single-cell genomic and transcriptomic sequencing, it remains difficult to relate measured molecular profiles to the cellular activities underlying cancer. Here, we present an integrated experimental system that connects single cell gene expression to heterogeneous cancer cell growth, metastasis, and treatment response. Our system integrates single cell transcriptome profiling with DNA barcode based clonal tracking in patient-derived xenograft models. We show that leukemia cells exhibiting unique gene expression respond to different chemotherapies in distinct but consistent manners across multiple mice. In addition, we uncover a form of leukemia expansion that is spatially confined to the bone marrow of single anatomical sites and driven by cells with distinct gene expression. Our integrated experimental system can interrogate the molecular and cellular basis of the intratumoral heterogeneity underlying disease progression and treatment resistance.

The evaluation of the morphology and organization of collagen fibers is critical in understanding wound healing and tissue remodeling after a thermal injury of the skin. However, histological analysis conducted by pathologists is often labor-intensive and limited to qualitative evaluations and scoring within a narrow field of view. In this study, we propose a convolutional neural network (CNN) model to classify Masson's trichrome (MT)-stained histology images of burn-induced scar tissue and to characterize the microstructures of normal tissue and scar tissue in a quantitative manner. The scar tissue is created on in vivo rodent models and prepared for MT-stained histology slides after wound healing. A CNN model is developed, trained, and tested with various sizes of the histology images for classification and characterization. The proposed model classifies both normal tissue (i.e., without burn, as the control) and scar tissue at various scales with over 97% accuracy. The features acquired from the proposed CNN model visually characterizes the density and directional variance of the collagen fibers distributed in the dermal layers from whole histology images. The proposed deep learning technique can provide an objective and reliable method to rapidly assess and quantify wound repair and remodeling.INDEX TERMS Deep learning, histology image, collagen fiber characterization, scar tissue classification.

Background and Objectives Fibrosis is a highly prevalent disease, which is responsible for 45% of deaths through pathological effects in developed countries. Previous studies have reported that low‐level laser therapy (LLLT) can modulate fibrotic activity, but significant enhancement of therapeutic efficacy is still required for clinical translation. The aim of this study is to evaluate the feasible effect of LLLT combined with phloroglucinol (PHL) on the inhibition of fibrosis in vitro. Study Design/Materials and Methods NIH/3T3 murine embryonic fibroblasts cells were cultured and transforming growth factor‐β1 (TGF‐β1) was treated for transition of fibroblasts. After TGF‐β1 treatment, LLLT and PHL were used, respectively, and in combination to suppress fibrosis. 3‐(4,5‐dimethylthiazol‐2‐yl)‐2,5‐diphenyltetrazolium bromide and BrdU assays were performed to estimate the cell viability and proliferation. To evaluate the expression of fibrotic markers, we used confocal immunofluorescence and western blot. Results When compared with respectively treated groups, the group with the combined treatment of LLLT and PHL significantly reduced cell viability and proliferation. Immunofluorescence staining showed that the combined group minimized more α‐smooth muscle actin (α‐SMA) and type I collagen than the other groups. Western blot analysis showed that the combined treatment had significant decreases in α‐SMA, TGF‐β1, and type I collagen. Conclusions PHL‐assisted LLLT may be an effective treatment to inhibit fibrosis due to its additive effects. The combined treatment has a potential to be an alternative treatment for fibrosis. Lasers Surg. Med. © 2019 Wiley Periodicals, Inc.

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An analysis of scar tissue is necessary to understand the pathological tissue conditions during or after the wound healing process. Hematoxylin and eosin (HE) staining has conventionally been applied to understand the morphology of scar tissue. However, the scar lesions cannot be analyzed from a whole slide image. The current study aimed to develop a method for the rapid and automatic characterization of scar lesions in HE-stained scar tissues using a supervised and unsupervised learning algorithm. The supervised learning used a Mask region-based convolutional neural network (RCNN) to train a pattern from a data representation using MMDetection tools. The K-means algorithm characterized the HE-stained tissue and extracted the main features, such as the collagen density and directional variance of the collagen. The Mask RCNN model effectively predicted scar images using various backbone networks (e.g., ResNet50, ResNet101, ResNeSt50, and ResNeSt101) with high accuracy. The K-means clustering method successfully characterized the HE-stained tissue by separating the main features in terms of the collagen fiber and dermal mature components, namely, the glands, hair follicles, and nuclei. A quantitative analysis of the scar tissue in terms of the collagen density and directional variance of the collagen confirmed 50% differences between the normal and scar tissues. The proposed methods were utilized to characterize the pathological features of scar tissue for an objective histological analysis. The trained model is time-efficient when used for detection in place of a manual analysis. Machine learning-assisted analysis is expected to aid in understanding scar conditions, and to help establish an optimal treatment plan.

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