Community-developed checklists for publishing images and image analyses.

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.

Collagen fiber centerline tracking in fibrotic tissue via deep neural networks with variational autoencoder-based synthetic training data generation.

The role of fibrillar collagen in the tissue microenvironment is critical in disease contexts ranging from cancers to chronic inflammations, as evidenced by many studies. Quantifying fibrillar collagen organization has become a powerful approach for characterizing the topology of collagen fibers and studying the role of collagen fibers in disease progression. We present a deep learning-based pipeline to quantify collagen fibers' topological properties in microscopy-based collagen images from pathological tissue samples. Our method leverages deep neural networks to extract collagen fiber centerlines and deep generative models to create synthetic training data, addressing the current shortage of large-scale annotations. As a part of this effort, we have created and annotated a collagen fiber centerline dataset, with the hope of facilitating further research in this field. Quantitative measurements such as fiber orientation, alignment, density, and length can be derived based on the centerline extraction results. Our pipeline comprises three stages. Initially, a variational autoencoder is trained to generate synthetic centerlines possessing controllable topological properties. Subsequently, a conditional generative adversarial network synthesizes realistic collagen fiber images from the synthetic centerlines, yielding a synthetic training set of image-centerline pairs. Finally, we train a collagen fiber centerline extraction network using both the original and synthetic data. Evaluation using collagen fiber images from pancreas, liver, and breast cancer samples collected via second-harmonic generation microscopy demonstrates our pipeline's superiority over several popular fiber centerline extraction tools. Incorporating synthetic data into training further enhances the network's generalizability. Our code is available at https://github.com/uw-loci/collagen-fiber-metrics.

Coordination between ECM and cell-cell adhesion regulates the development of islet aggregation, architecture, and functional maturation.

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 ß1 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 ß1 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.

Risk Factors for Delayed Diagnosis of Positional Plagiocephaly: A Review of 25,322 Patients.

OBJECTIVE: This study identifies risk factors for late positional plagiocephaly (PP) diagnosis and impact on helmet therapy. DESIGN: We conducted a retrospective review of all patients diagnosed with PP over 10 years at five Southern California hospitals. SETTING: Patients diagnosed with PP at an included hospital. PATIENTS: 25,332 patients were diagnosed with PP over 10 years. INTERVENTIONS: Patients diagnosed with PP early (< = 6 months) and late (>6 months) were compared. MAIN OUTCOME MEASURES: Cohorts were evaluated for demographics, gestational history, associated conditions, and hospitalizations through direct comparison, logistic regression, and correlation analyses. Rates of referrals and helmet orders were compared. RESULTS: Of patients reviewed, 4.8% (n = 1216) were diagnosed late. On multivariate analysis, late diagnoses were more likely Hispanic or Black/African-American. Early gestational age, hydrocephalus, and VP shunt were more frequent in late diagnoses. Patients diagnosed late had longer NICU and overall hospital stays. Earlier gestational age, longer NICU or overall hospital stay correlated with later age at PP diagnosis. 8.9% of patients were referred for helmet therapy evaluation. Patients diagnosed late were 2.63 and 1.64 times as likely to be referred and require helmet therapy, respectively. CONCLUSIONS: Patients who are Hispanic or Black/African-American, premature, have hydrocephalus, or VP shunt have higher rates of delayed PP diagnosis. Shorter gestational age or longer NICU or hospital stay correlates with later diagnosis, which increases helmet therapy requirements. Additional interventions are needed for at risk patients to routinely evaluate for and minimize the risk of developing PP.

Presence of immunogenic alternatively spliced insulin gene product in human pancreatic delta cells.

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-1ß 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).

Hardware-software co-design of an open-source automatic multimodal whole slide histopathology imaging system.

Significance: 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. Aim: 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. Approach: 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. Results: 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. Conclusions: The system integrates the acquisition pipeline and data analysis pipeline into a single workflow that improves the throughput and reproducibility of multimodal histopathological imaging.

Community-developed checklists for publishing images and image analyses.

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 the obtained images and image analyses results, there are to date no unified guidelines. 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 image analysis for publications. These checklists offer authors, readers, and publishers key recommendations for image formatting and annotation, color selection, data availability, and for reporting image analysis workflows. The goal of our guidelines is to increase the clarity and reproducibility of image figures and thereby heighten the quality and explanatory power of microscopy data is in publications.

Multiscale Label-Free Imaging of Fibrillar Collagen in the Tumor Microenvironment.

With recent advances in cancer therapeutics, there is a great need for improved imaging methods for characterizing cancer onset and progression in a quantitative and actionable way. Collagen, the most abundant extracellular matrix protein in the tumor microenvironment (and the body in general), plays a multifaceted role, both hindering and promoting cancer invasion and progression. Collagen deposition can defend the tumor with immunosuppressive effects, while aligned collagen fiber structures can enable tumor cell migration, aiding invasion and metastasis. Given the complex role of collagen fiber organization and topology, imaging has been a tool of choice to characterize these changes on multiple spatial scales, from the organ and tumor scale to cellular and subcellular level. Macroscale density already aids in the detection and diagnosis of solid cancers, but progress is being made to integrate finer microscale features into the process. Here we review imaging modalities ranging from optical methods of second harmonic generation (SHG), polarized light microscopy (PLM), and optical coherence tomography (OCT) to the medical imaging approaches of ultrasound and magnetic resonance imaging (MRI). These methods have enabled scientists and clinicians to better understand the impact collagen structure has on the tumor environment, at both the bulk scale (density) and microscale (fibrillar structure) levels. We focus on imaging methods with the potential to both examine the collagen structure in as natural a state as possible and still be clinically amenable, with an emphasis on label-free strategies, exploiting intrinsic optical properties of collagen fibers.

Integration of single-cell transcriptomes and biological function reveals distinct behavioral patterns in bone marrow endothelium.

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.

Tumor-infiltrating exhausted CD8+ T cells dictate reduced survival in premenopausal estrogen receptor-positive breast cancer.

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.

Differentiation of pancreatic ductal adenocarcinoma and chronic pancreatitis using graph neural networks on histopathology and collagen fiber features.

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.

Protein Phosphatase 2A as a Therapeutic Target in Small Cell Lung Cancer.

Protein phosphatase 2A (PP2A), a serine/threonine phosphatase involved in the regulation of apoptosis, proliferation, and DNA-damage response, is overexpressed in many cancers, including small cell lung cancer (SCLC). Here we report that LB100, a small molecule inhibitor of PP2A, when combined with platinum-based chemotherapy, synergistically elicited an antitumor response both in vitro and in vivo with no apparent toxicity. Using inductively coupled plasma mass spectrometry, we determined quantitatively that sensitization via LB100 was mediated by increased uptake of carboplatin in SCLC cells. Treatment with LB100 alone or in combination resulted in inhibition of cell viability in two-dimensional culture and three-dimensional spheroid models of SCLC, reduced glucose uptake, and attenuated mitochondrial and glycolytic ATP production. Combining LB100 with atezolizumab increased the capacity of T cells to infiltrate and kill tumor spheroids, and combining LB100 with carboplatin caused hyperphosphorylation of the DNA repair marker γH2AX and enhanced apoptosis while attenuating MET signaling and invasion through an endothelial cell monolayer. Taken together, these data highlight the translational potential of inhibiting PP2A with LB100 in combination with platinum-based chemotherapy and immunotherapy in SCLC.

A Multifunctional LNA Oligonucleotide-Based Strategy Blocks AR Expression and Transactivation Activity in PCa Cells.

The androgen receptor (AR) plays a critical role in the development of prostate cancer (PCa) through the activation of androgen-induced cellular proliferation genes. Thus, blocking AR-mediated transcriptional activation is expected to inhibit the growth and spread of PCa. Using tailor-made splice-switching locked nucleic acid (LNA) oligonucleotides (SSOs), we successfully redirected splicing of the AR precursor (pre-)mRNA and destabilized the transcripts via the introduction of premature stop codons. Furthermore, the SSOs simultaneously favored production of the AR45 mRNA in lieu of the full-length AR. AR45 is an AR isoform that can attenuate the activity of both full-length and oncogenic forms of AR by binding to their common N-terminal domain (NTD), thereby blocking their transactivation potential. A large screen was subsequently used to identify individual SSOs that could best perform this dual function. The selected SSOs powerfully silence AR expression and modulate the expression of AR-responsive cellular genes. This bi-functional strategy that uses a single therapeutic molecule can be the basis for novel PCa treatments. It might also be customized to other types of therapies that require the silencing of one gene and the simultaneous expression of a different isoform.

Activation of EPHA2-ROBO1 Heterodimer by SLIT2 Attenuates Non-canonical Signaling and Proliferation in Squamous Cell Carcinomas.

The tyrosine kinase receptor ephrin receptor A2 (EPHA2) is overexpressed in lung (LSCC) and head and neck (HNSCC) squamous cell carcinomas. Although EPHA2 can inhibit tumorigenesis in a ligand-dependent fashion via phosphorylation of Y588 and Y772, it can promote tumorigenesis in a ligand-independent manner via phosphorylation of S897. Here, we show that EPHA2 and Roundabout Guidance Receptor 1 (ROBO1) interact to form a functional heterodimer. Furthermore, we show that the ROBO1 ligand Slit Guidance Ligand 2 (SLIT2) and ensartinib, an inhibitor of EPHA2, can attenuate growth of HNSCC cells and act synergistically in LSCC cells. Our results suggest that patients with LSCC and HNSCC may be stratified and treated based on their EPHA2 and ROBO1 expression patterns. Although ~73% of patients with LSCC could benefit from SLIT2+ensartinib treatment, ~41% of patients with HNSCC could be treated with either SLIT2 or ensartinib. Thus, EPHA2 and ROBO1 represent potential LSCC and HNSCC theranostics.

Effects of selected deubiquitinating enzyme inhibitors on the proliferation and motility of lung cancer and mesothelioma cell lines.

The post­translational 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, PR­619, RA­9 and LDN­91946, on a non­small cell lung cancer cell line (A549) and a mesothelioma cell line (H2373) were investigated. PR­619 significantly inhibited cell adhesion and the proliferation of both cell lines. RA­9 exerted an inhibitory effect on the adhesion and proliferation of H2373 cells, whereas it had no effect on A549 cells. Notably, however, while PR­619 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 line­specific. In H2373 cells, the phosphorylation of only one peptide corresponding to the P85A protein was significantly affected, and while LDN­91946 treatment increased phosphorylation, treatment with RA­9 or PR­619 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 side­effects 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.

Resident memory CD8+ T cells within cancer islands mediate survival in breast cancer patients.

CD8+ tumor-infiltrating lymphocytes (TILs) correlate with relapse-free survival (RFS) in most cancer types, including breast cancer. However, subset composition, functional status, and spatial location of CD8+ TILs in relation to RFS in human breast tumors remain unclear. Spatial tissue analysis via quantitative immunofluorescence showed that infiltration of CD8+ T cells into cancer islands was more significantly associated with RFS than CD8+ T cell infiltration into either tumor stroma or total tumor. Localization into cancer islands within tumors is mediated by expression of the integrin CD103, which is a marker for tissue-resident memory T cells (TRMs). Analysis of fresh tumor samples revealed that CD8+ TRMs are functionally similar to other CD8+ TILs, suggesting that the basis of their protective effect is their spatial distribution rather than functional differences. Indeed, CD103+ TRMs, as compared with CD103-CD8+ TILs, are enriched within cancer islands, and CD8+ TRM proximity to cancer cells drives the association of CD8+ TIL densities with RFS. Together, these findings reveal the importance of cancer island-localized CD8+ TRMs in surveillance of the breast tumor microenvironment and as a critical determinant of RFS in patients with breast cancer.

Evaluating the immunogenicity and protective efficacy of a DNA vaccine encoding Lassa virus nucleoprotein.

Several viruses in the Arenavirus genus of the family Arenaviridae cause severe, often fatal, hemorrhagic fever. One such virus, Lassa virus (LV), is a frequent cause of disease in Africa, and survivors often are left with substantial neurological impairment. The feasibility of protective immunization against LV infection, and the associated disease, has been demonstrated in animal models, using recombinant vaccinia viruses to deliver Lassa proteins. Circumstantial evidence implicates cellular immunity in this Lassa-induced protection, but this has not been confirmed. Here, we describe DNA vaccines that encode LV proteins. A single inoculation of a plasmid encoding full-length Lassa nucleoprotein (LNP) can induce CD8(+) T cell responses in mice and can protect against challenge with two arenaviruses, lymphocytic choriomeningitis virus (LCMV) and Pichinde virus (PV). A DNA minigene vaccine encoding a 9 amino acid sequence from LNP also induces CD8(+) T cells and protects against arenavirus challenge, thus confirming prior speculation that protective cellular immunity is induced by LV proteins.