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1. Murine interfollicular epidermal differentiation is gradualistic with GRHL3 controlling progression from stem to transition cell states

   https://doi.org/10.1038/s41467-020-19234-6  

   Lin, Ziguang; Jin, Suoqin; Chen, Jefferson; Li, Zhuorui; Lin, Zhongqi; Tang, Li; Nie, Qing; Andersen, Bogi (December 2020, Nature Communications)

   null (Ed.)

   Abstract The interfollicular epidermis (IFE) forms a water-tight barrier that is often disrupted in inflammatory skin diseases. During homeostasis, the IFE is replenished by stem cells in the basal layer that differentiate as they migrate toward the skin surface. Conventionally, IFE differentiation is thought to be stepwise as reflected in sharp boundaries between its basal, spinous, granular and cornified layers. The transcription factor GRHL3 regulates IFE differentiation by transcriptionally activating terminal differentiation genes. Here we use single cell RNA-seq to show that murine IFE differentiation is best described as a single step gradualistic process with a large number of transition cells between the basal and spinous layer. RNA-velocity analysis identifies a commitment point that separates the plastic basal and transition cell state from unidirectionally differentiating cells. We also show that in addition to promoting IFE terminal differentiation, GRHL3 is essential for suppressing epidermal stem cell expansion and the emergence of an abnormal stem cell state by suppressing Wnt signaling in stem cells. 

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2. Label-free, in vivo virtual histology of skin using reflectance confocal microscopy and deep learning

   https://doi.org/10.1117/12.2648150  

   Li, Jingxi; Garfinkel, Jason; Zhang, Xiaoran; Wu, Di; Zhang, Yijie; de Haan, Kevin; Wang, Hongda; Liu, Tairan; Bai, Bijie; Rivenson, Yair; et al (March 2023, SPIE Photonics West)

   Shaked, Natan T.; Hayden, Oliver (Ed.)

   We report label-free, in vivo virtual histology of skin using reflectance confocal microscopy (RCM). We trained a deep neural network to transform in vivo RCM images of unstained skin into virtually stained H&E-like microscopic images with nuclear contrast. This framework successfully generalized to diverse skin conditions, e.g., normal skin, basal cell carcinoma, and melanocytic nevi, as well as distinct skin layers, including the epidermis, dermal-epidermal junction, and superficial dermis layers. This label-free in vivo skin virtual histology framework can be transformative for faster and more accurate diagnosis of malignant skin neoplasms, with the potential to significantly reduce unnecessary skin biopsies. 

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3. Defining mammary basal cell transcriptional states using single-cell RNA-sequencing

   https://doi.org/10.1038/s41598-022-08870-1  

   Gutierrez, Guadalupe; Sun, Peng; Han, Yingying; Dai, Xing (December 2022, Scientific Reports)

   Abstract Breast cancer is a heterogenous disease that can be classified into multiple subtypes including the most aggressive basal-like and triple-negative subtypes. Understanding the heterogeneity within the normal mammary basal epithelial cells holds the key to inform us about basal-like cancer cell differentiation dynamics as well as potential cells of origin. Although it is known that the mammary basal compartment contains small pools of stem cells that fuel normal tissue morphogenesis and regeneration, a comprehensive yet focused analysis of the transcriptional makeup of the basal cells is lacking. We used single-cell RNA-sequencing and multiplexed RNA in-situ hybridization to characterize mammary basal cell heterogeneity. We used bioinformatic and computational pipelines to characterize the molecular features as well as predict differentiation dynamics and cell–cell communications of the newly identified basal cell states. We used genetic cell labeling to map the in vivo fates of cells in one of these states. We identified four major distinct transcriptional states within the mammary basal cells that exhibit gene expression signatures suggestive of different functional activity and metabolic preference. Our in vivo labeling and ex vivo organoid culture data suggest that one of these states, marked by Egr2 expression, represents a dynamic transcriptional state that all basal cells transit through during pubertal mammary morphogenesis. Our study provides a systematic approach to understanding the molecular heterogeneity of mammary basal cells and identifies previously unknown dynamics of basal cell transcriptional states. 

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4. Deep learning-enabled, non-invasive virtual histology of skin using reflectance confocal microscopy

   https://doi.org/10.1117/12.2632602  

   Li, Jingxi; Garfinkel, Jason; Zhang, Xiaoran; Wu, Di; Zhang, Yijie; de Haan, Kevin; Wang, Hongda; Liu, Tairan; Bai, Bijie; Rivenson, Yair; et al (October 2022, SPIE Optics and Photonics Conference)

   Volpe, Giovanni; Pereira, Joana B.; Brunner, Daniel; Ozcan, Aydogan (Ed.)

   Reflectance confocal microscopy (RCM) can provide in vivo images of the skin with cellular-level resolution; however, RCM images are grayscale, lack nuclear features and have a low correlation with histology. We present a deep learning-based virtual staining method to perform non-invasive virtual histology of the skin based on in vivo, label-free RCM images. This virtual histology framework revealed successful inference for various skin conditions, such as basal cell carcinoma, also covering distinct skin layers, including epidermis and dermal-epidermal junction. This method can pave the way for faster and more accurate diagnosis of malignant skin neoplasms while reducing unnecessary biopsies. 

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5. Data-driven image analysis to determine antibody-induced dissociation of cell–cell adhesion and antibody pathogenicity in pemphigus

   https://doi.org/10.1093/pnasnexus/pgaf218  

   Ostadi_Moghaddam, Amir; Jin, Xiaowei; Tajvidi_Safa, Bahareh; Seiffert-Sinha, Kristina; Leiker, Merced; Jones, Elijah; Zhai, Haiwei; Rosenbohm, Jordan; Meng, Fanben; Sinha, Animesh_A; et al (July 2025, PNAS Nexus)

   Abstract Pemphigus vulgaris (PV) is a blistering autoimmune disease that affects the skin and mucous membranes. The mechanisms by which PV antibodies induce loss of cohesion in keratinocytes are not fully understood. It is accepted that the process starts with antibody binding to desmosomal targets, which leads to its disassembly and subsequent structural changes to cell–cell adhesions. In vitro imaging of desmosome molecules has been used to characterize this initial phase. However, there remains an untapped potential of image analysis in providing us with more in-depth knowledge regarding biophysical changes after antibody binding. Currently, there is no quantitative framework from immunofluorescence images in PV pathology. Here, we seek to establish a correlation of biophysical changes with antibody pathogenicity by examining the effects of PV antibodies on adhesion molecules and the cytoskeletal network. Specifically, we introduced a data-driven approach to quantitatively evaluate perturbations in adhesion molecules following antibody treatment. We identify distinct imaging signatures that mark the impact of antibody binding on the remodeling of adhesion molecules and introduce a pathogenicity score to compare the relative effects of different antibodies. From this analysis, we showed that the biophysical response of keratinocytes to distinct PV antibodies is highly specific, allowing for accurate prediction of their pathogenicity. For instance, the high pathogenicity scores of the PVIgG and AK23 antibodies show strong agreement with their reported PV pathology. Our data-driven approach offers a detailed framework for the action of antibodies in pemphigus and paves the way for the development of effective diagnostic and therapeutic strategies. 

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