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B-cell receptor (BCR) signaling plays an important role in the pathogenesis of mantle cell lymphoma (MCL), but the detailed mechanisms are not fully understood. In this study, through a genome-wide loss-of-function screen, we identify carcinoembryonic antigen-related cell adhesion molecule 1 (CEACAM1) as an essential factor in a subset of MCL tumors. Our signal transduction studies reveal that CEACAM1 plays a critical role in BCR activation through involvement in two dynamic processes. First, following BCR engagement, CEACAM1 co-localizes to the membrane microdomains (lipid rafts) by anchoring to the F-actin cytoskeleton through the adaptor protein filamin A. Second, CEACAM1 recruits and increases the abundance of SYK in the BCR complex leading to BCR activation. These activities of CEACAM1 require its cytoplasmic tail and the N-terminal ectodomain. Considering that previous studies have extensively characterized CEACAM1 as an ITIM-bearing inhibitory receptor, our findings regarding its activating role are both surprising and context-dependent, which may have implications for BCR-targeting therapies. Pathological B-cell receptor (BCR) signaling is a key driver of mantle cell lymphoma tumorigenesis. Here, the authors discover that CEACAM1, an immunoglobulin-like transmembrane protein, is essential for a subset of mantle cell lymphoma through activation of the BCR.

Heterogeneity of endothelial cell (EC) populations reflects their diverse functions in maintaining tissue’s homeostasis. However, their phenotypic, molecular, and functional properties are not entirely mapped. We use the Tie2-CreERT2;Rosa26-tdTomato reporter mouse to trace, profile, and cultivate primary ECs from different organs. As paradigm platform, we use this strategy to study bone marrow endothelial cells (BMECs). Single-cell mRNA sequencing of primary BMECs reveals that their diversity and native molecular signatures is transitorily preserved in an ex vivo culture that conserves key cell-to-cell microenvironment interactions. Macrophages sustain BMEC cellular diversity and expansion and preserve sinusoidal-like BMECs ex vivo. Endomucin expression discriminates BMECs in populations exhibiting mutually exclusive properties and distinct sinusoidal/arterial and tip/stalk signatures. In contrast to arterial-like, sinusoidal-like BMECs are short-lived, form 2D-networks, contribute to in vivo angiogenesis, and support hematopoietic stem/progenitor cells in vitro. This platform can be extended to other organs’ ECs to decode mechanistic information and explore therapeutics. Here Kim et al . show that primary BMECs can be maintained ex vivo as distinct sinusoidal- and arterial-like populations and that the presence of macrophages is critical to preserve their native transcriptomic profiles and functional heterogeneity.

Pancreatic islets are three-dimensional cell aggregates consisting of unique cellular composition, cell-to-cell contacts, and interactions with blood vessels. Cell aggregation is essential for islet endocrine function; however, it remains unclear how developing islets establish aggregation. By combining genetic animal models, imaging tools, and gene expression profiling, we demonstrate that islet aggregation is regulated by extracellular matrix signaling and cell-cell adhesion. Islet endocrine cell-specific inactivation of extracellular matrix receptor integrin β1 disrupted blood vessel interactions but promoted cell-cell adhesion and the formation of larger islets. In contrast, ablation of cell-cell adhesion molecule α-catenin promoted blood vessel interactions yet compromised islet clustering. Simultaneous removal of integrin β1 and α-catenin disrupts islet aggregation and the endocrine cell maturation process, demonstrating that establishment of islet aggregates is essential for functional maturation. Our study provides new insights into understanding the fundamental self-organizing mechanism for islet aggregation, architecture, and functional maturation.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal human cancers. However, the symptoms and radiographic appearance of chronic pancreatitis (CP) mimics that of PDAC, and sometimes the 2 entities can also be difficult to differentiate microscopically. The need for accurate differentiation of PDAC and CP has become a major topic in pancreatic pathology. These 2 diseases can present similar histomorphological features, such as excessive deposition of fibrotic stroma in the tissue microenvironment and inflammatory cell infiltration. In this paper, we present a quantitative analysis pipeline empowered by graph neural networks (GNN) capable of automatic detection and differentiation of PDAC and CP in human histological specimens. Modeling histological images as graphs and deploying graph convolutions can enable the capture of histomorphological features at different scales, ranging from nuclear size to the organization of ducts. The analysis pipeline combines image features computed from co-registered hematoxylin and eosin (H&E) images and Second-Harmonic Generation (SHG) microscopy images, with the SHG images enabling the extraction of collagen fiber morphological features. Evaluating the analysis pipeline on a human tissue micro-array dataset consisting of 786 cores and a tissue region dataset consisting of 268 images, it attained 86.4% accuracy with an average area under the curve (AUC) of 0.954 and 88.9% accuracy with an average AUC of 0.957, respectively. Moreover, incorporating topological features of collagen fibers computed from SHG images into the model further increases the classification accuracy on the tissue region dataset to 91.3% with an average AUC of 0.962, suggesting that collagen characteristics are diagnostic features in PDAC and CP detection and differentiation. [Display omitted] •Image analysis-based differentiation of pancreatic cancer and chronic pancreatitis.•Multimodal microscopy for histopathological features and collagen-based features.•Graph neural networks for multi-resolution histomorphological features.

CD8+ tumor-infiltrating lymphocytes (TILs) are associated with improved survival in triple-negative breast cancer (TNBC) yet have no association with survival in estrogen receptor-positive (ER+) BC. The basis for these contrasting findings remains elusive. We identified subsets of BC tumors infiltrated by CD8+ T cells with characteristic features of exhausted T cells (TEX). Tumors with abundant CD8+ TEX exhibited a distinct tumor microenvironment marked by amplified interferon-γ signaling-related pathways and higher programmed death ligand 1 expression. Paradoxically, higher levels of tumor-infiltrating CD8+ TEX associated with decreased overall survival of patients with ER+ BC but not patients with TNBC. Moreover, high tumor expression of a CD8+ TEX signature identified dramatically reduced survival in premenopausal, but not postmenopausal, patients with ER+ BC. Finally, we demonstrated the value of a tumor TEX signature score in identifying high-risk premenopausal ER+ BC patients among those with intermediate Oncotype DX Breast Recurrence Scores. Our data highlight the complex relationship between CD8+ TILs, interferon-γ signaling, and ER status in BC patient survival. This work identifies tumor-infiltrating CD8+ TEX as a key feature of reduced survival outcomes in premenopausal patients with early-stage ER+ BC.

Advanced digital control of microscopes and programmable data acquisition workflows have become increasingly important for improving the throughput and reproducibility of optical imaging experiments. Combinations of imaging modalities have enabled a more comprehensive understanding of tissue biology and tumor microenvironments in histopathological studies. However, insufficient imaging throughput and complicated workflows still limit the scalability of multimodal histopathology imaging. We present a hardware-software co-design of a whole slide scanning system for high-throughput multimodal tissue imaging, including brightfield (BF) and laser scanning microscopy. The system can automatically detect regions of interest using deep neural networks in a low-magnification rapid BF scan of the tissue slide and then conduct high-resolution BF scanning and laser scanning imaging on targeted regions with deep learning-based run-time denoising and resolution enhancement. The acquisition workflow is built using Pycro-Manager, a Python package that bridges hardware control libraries of the Java-based open-source microscopy software Micro-Manager in a Python environment. The system can achieve optimized imaging settings for both modalities with minimized human intervention and speed up the laser scanning by an order of magnitude with run-time image processing. The system integrates the acquisition pipeline and data analysis pipeline into a single workflow that improves the throughput and reproducibility of multimodal histopathological imaging.

Aims/hypothesis Transcriptome analyses revealed insulin-gene-derived transcripts in non-beta endocrine islet cells. We studied alternative splicing of human INS mRNA in pancreatic islets. Methods Alternative splicing of insulin pre-mRNA was determined by PCR analysis performed on human islet RNA and single-cell RNA-seq analysis. Antisera were generated to detect insulin variants in human pancreatic tissue using immunohistochemistry, electron microscopy and single-cell western blot to confirm the expression of insulin variants. Cytotoxic T lymphocyte (CTL) activation was determined by MIP-1β release. Results We identified an alternatively spliced INS product. This variant encodes the complete insulin signal peptide and B chain and an alternative C-terminus that largely overlaps with a previously identified defective ribosomal product of INS . Immunohistochemical analysis revealed that the translation product of this INS -derived splice transcript was detectable in somatostatin-producing delta cells but not in beta cells; this was confirmed by light and electron microscopy. Expression of this alternatively spliced INS product activated preproinsulin-specific CTLs in vitro. The exclusive presence of this alternatively spliced INS product in delta cells may be explained by its clearance from beta cells by insulin-degrading enzyme capturing its insulin B chain fragment and a lack of insulin-degrading enzyme expression in delta cells. Conclusions/interpretation Our data demonstrate that delta cells can express an INS product derived from alternative splicing, containing both the diabetogenic insulin signal peptide and B chain, in their secretory granules. We propose that this alternative INS product may play a role in islet autoimmunity and pathology, as well as endocrine or paracrine function or islet development and endocrine destiny, and transdifferentiation between endocrine cells. INS promoter activity is not confined to beta cells and should be used with care when assigning beta cell identity and selectivity. Data availability The full EM dataset is available via www.nanotomy.org (for review: http://www.nanotomy.org/OA/Tienhoven2021SUB/6126-368/ ). Single-cell RNA-seq data was made available by Segerstolpe et al [ 13 ] and can be found at https://sandberglab.se/pancreas . The RNA and protein sequence of INS-splice was uploaded to GenBank (BankIt2546444 INS-splice OM489474). Graphical abstract

The post-translational modification of proteins by ubiquitinating enzymes plays a central role in a number of cellular functions, such as cell proteolysis, DNA repair, and cell signaling and communication. Deubiquitinating enzymes (DUBs) disassemble ubiquitin chains and remove ubiquitin moieties from proteins. Targeting DUBs in cancer models has revealed an important role for these enzymes in tumorigenesis, and they therefore have emerged as attractive therapeutic targets. In the present study, the effects of three DUB inhibitors, PR-619, RA-9 and LDN-91946, on a non-small cell lung cancer cell line (A549) and a mesothelioma cell line (H2373) were investigated. PR-619 significantly inhibited cell adhesion and the proliferation of both cell lines. RA-9 exerted an inhibitory effect on the adhesion and proliferation of H2373 cells, whereas it had no effect on A549 cells. Notably, however, while PR-619 attenuated the proliferation of both cell lines, it exerted an opposite effect on cell motility; in the case of A549 cells, there was a significant increase in cell motility, while for the H2373 cells, there was a significant decrease. Furthermore, protein phosphorylation kinetic analyses revealed that the effects were cell line-specific. In H2373 cells, the phosphorylation of only one peptide corresponding to the P85A protein was significantly affected, and while LDN-91946 treatment increased phosphorylation, treatment with RA-9 or PR-619 decreased its phosphorylation compared to the DMSO control. By contrast, in the case of A549 cells, the phosphorylation of 21 peptides was significantly affected by the same compounds. In light of the potential for the negative side-effects of DUB inhibition, such as increased cancer cell motility, the data presented herein underscore the dire need for the development of specific DUB inhibitors and to elucidate the individual role of DUB family members in cancer biology before they can be specifically pharmacologically targeted.

Type 1 diabetes (T1D) is a progressive autoimmune condition that culminates in the loss of insulin-producing beta cells. Pancreatic histopathology provides essential insights into disease initiation and progression yet an integrated perspective of pathogenic processes is lacking due to limited sample availability, the dispersed nature of anatomical lesions, and often restricted analytical dimensionality. Here, we combined multiplexed immunostaining, high-magnification whole-slide imaging, digital pathology, and semi-automated image analysis strategies to interrogate pancreatic tail and head regions obtained from organ donors across T1D stages including at-risk and at-onset cases. Deconvolution of architectural features, endocrine cell composition, immune cell burden, and spatial relations of ~25,000 islets revealed a series of novel histopathological correlates especially in the prodromal disease stage preceding clinical T1D. Altogether, our comprehensive \"single-islet\" analyses permit the reconstruction of a revised natural T1D history with implications for further histopathological investigations, considerations of pathogenetic modalities, and therapeutic interventions.

Images document scientific discoveries and are prevalent in modern biomedical research. Microscopy imaging in particular is currently undergoing rapid technological advancements. However, for scientists wishing to publish obtained images and image-analysis results, there are currently no unified guidelines for best practices. Consequently, microscopy images and image data in publications may be unclear or difficult to interpret. Here, we present community-developed checklists for preparing light microscopy images and describing image analyses for publications. These checklists offer authors, readers and publishers key recommendations for image formatting and annotation, color selection, data availability and reporting image-analysis workflows. The goal of our guidelines is to increase the clarity and reproducibility of image figures and thereby to heighten the quality and explanatory power of microscopy data. Community-developed checklists offer best-practice guidance for biologists preparing light microscopy images and describing image analyses for publications.