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.

To obtain accurate, reproducible, and interpretable data when conducting imaging experiments, it is critical to consider external factors affecting data acquisition at various steps of the experimental workflow. Illumination power and stability represent two critical factors, especially when comparing fluorescence intensities between images during a time-lapse experiment or experiments performed at different times or on different microscopes. The fluorescence signal can be generated by different types of light sources. These light sources and their coupling elements (e.g., fibers) can display varying performances over time as they age, move, or as environmental conditions change. Unfortunately, microscope users can often only set illumination power as a percentage of its maximal output and may, therefore, not be aware of potential performance changes. It is important to recognize that a set percentage will not always yield the same illumination power in Watts at the objective over the course of an experiment, not to mention between days or systems. This means that selecting for example 10% output may lead to different experimental results over time or even between two microscopes of the same model. In addition to illumination stability, working within the linear range of the illumination power allows to adjust accurately the illumination power absolute value (in mW) using a fraction (or %) of its maximal value through the imaging software. If you are responsible for system maintenance, routinely measuring the illumination power, stability, and linearity over time can help you detect issues that affect the integrity of the system and thus the reproducibility of an experiment. This protocol describes how to measure the stability and linearity of the illumination power using calibrated external power sensors. This protocol is intended for confocal systems (raster scanning and spinning disks), Multi-photon systems (used for 2P- or 3P-imaging, SHG-imaging, etc.), and widefield systems. It represents the collective experience of over 50 imaging scientists.

Bioimage analysis (BIA), a crucial discipline in biological research, overcomes the limitations of subjective analysis in microscopy through the creation and application of quantitative and reproducible methods. The establishment of dedicated BIA support within academic institutions is vital to improving research quality and efficiency and can significantly advance scientific discovery. However, a lack of training resources, limited career paths and insufficient recognition of the contributions made by bioimage analysts prevent the full realization of this potential. This Perspective - the result of the recent The Company of Biologists Workshop 'Effectively Communicating Bioimage Analysis', which aimed to summarize the global BIA landscape, categorize obstacles and offer possible solutions - proposes strategies to bring about a cultural shift towards recognizing the value of BIA by standardizing tools, improving training and encouraging formal credit for contributions. We also advocate for increased funding, standardized practices and enhanced collaboration, and we conclude with a call to action for all stakeholders to join efforts in advancing BIA.

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.

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.

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.

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.

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.

To obtain accurate, reproducible, and interpretable data when conducting imaging experiments, it is critical to consider external factors affecting data acquisition at various steps of the experimental workflow. Illumination power and stability represent two critical factors, especially when comparing fluorescence intensities between images during a time-lapse experiment or experiments performed at different times or on other microscopes. The fluorescence signal can be generated by different types of light sources. These light sources and their coupling elements (e.g., fibers) can display varying performances over time as they age, move, or as environmental conditions change. Unfortunately, microscope users can often only set illumination power as a percentage of its maximal output and, may therefore, not be aware of potential performance changes. It is important to recognize that a set percentage will not always yield the same illumination power in Watts at the objective over the course of an experiment, not to mention between days or systems. This means that selecting for example 10% output may lead to different experimental results over time and will not necessarily be comparable to outputs obtained from other lasers or microscopes, even those of the very same model. If you are responsible for system maintenance, routinely measuring the illumination power, stability, and linearity over time can help you detect issues that affect the integrity of the system and thus the reproducibility of an experiment. This protocol describes how to measure the stability and linearity of the illumination power using calibrated external power sensors. This protocol is at the moment intended for confocal systems (raster scanning and spinning disks), but will be extended later to other imaging modalities. It represents the collective experience of 60 imaging scientists. Measurements made by our working group with this protocol are available in a public database, which will be updated with further contributions from the community.

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.

Squamous cell carcinomas (SCC) of the lung (LSCC) and head and neck (HNSCC) are very prevalent with poor prognosis and limited treatment options. In both cancer types, Ephrin receptor A2 (EPHA2) is known to be overexpressed and exhibit opposing effects via two distinct signaling mechanisms. While it can inhibit cancer cell survival and migration by ligand-dependent signaling through tyrosine kinase phosphorylation at Y588 and Y772, it can promote tumor progression and cell migration in a ligand-independent manner via phosphorylation at S897. Variable ABnormal morphology (VAB-1) is the C. elegans ortholog of the human ephrin receptor (EPHR) that interacts genetically and biochemically in a dose-dependent manner with the axon guidance receptor, SAX3, the worm ortholog of ROBO. Double mutants of vab-1 (EPHR)/ sαx-3 (ROBO) are synthetic lethal, underscoring the interaction between the two signaling pathways which prompted us to investigate their role in SCC. Using biochemical and biophysical techniques, we show that EPHA2 and ROBO1 reside in the same complex and interact physically to form a functional heterodimer in LSCC and HNSCC. Furthermore, we show that treating squamous cells with the SLIT2, ligand of ROBO1, hinders phosphorylation of EPHA2 at S897, and thereby, attenuates cell proliferation. Interestingly, SLIT2 can interact with EPHA2 and attenuate the proliferation of cells that have low ROBO1 expression. Additionally, SLIT2 can act synergistically with the EPHA2 inhibitor, Ensartinib to attenuate cell growth in LSCC and HNSCC cells. Taken together, the data suggest that SLIT2 may serve as a novel therapeutic for LSCC and HNSCC. Here, we propose to stratify patients for treatment with SLIT2 and/or Ensartinib, based on their EPHA2 and ROBO1 expression levels in the diseased tissue. Thus 85% of LSCC cases can be treated with combination of SLIT2+Ensartinib and 55% of HNSCC cases can be treated with either SLIT2 or Ensartinib. Furthermore, EPHA2 and ROBO1 may represent novel theranostics in these two diseases.One sentence summary Heterodimerization of EPHA2 and ROBO1 receptors attenuates growth of squamous cell carcinomas of the lung and head and neck.### Competing Interest StatementThe authors have declared no competing interest.

Abstract 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) oligos (SSO), we successfully redirected splicing of the AR 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), 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 employs the use of 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.

Introduction: Non-small cell lung cancer (NSCLC) accounts for 85% of the diagnosed lung cancers, of which 40% are lung adenocarcinoma (LUAD). These adenocarcinomas are often diagnosed with advanced or metastatic disease and are typically treated with a combination of cisplatin and immunotherapy. In most cases, malignant cells exposed to cisplatin adapt and become less susceptible to the anti-proliferative and cytotoxic effects of the drugs. Thus, many cisplatin-treated patients experience therapeutic failure and tumor recurrence. Therefore, there is a dire need for a deeper understanding of chemoresistance and the identification of prognostic and predictive markers to discern responders from non-responders. Here, we interrogate the role of Integrinβ4 (ITGB4) and Paxillin (PXN) in developing cisplatin resistance that are components of the focal adhesion complex. Experimental Procedures: Validated lung adenocarcinoma cancer cell lines were engineered to express nuclear specific red fluorescence protein for live tracking of cell proliferation, wound healing and cell death using InCucyte Live Cell Imaging System. Cell line-derived spheroids were generated to recapitulate in vivo conditions and spheroid integrity was monitored over time. Changes in gene expression were checked using western blotting or qPCR. Mitochondrial activity was analyzed using Seahorse and genomic stability was measured using γH2AX staining. Immunohistochemical analysis of the stained tissue section was done using the QuPath software. Summary: Cells with higher expression of PXN and ITGB4 are ~50% less sensitive to cisplatin as compared to low expressing cells. Silencing both PXN and ITGB4 in resistant cells inhibited their proliferation by 6-fold, migration by 30 - 40% and increase cisplatin sensitivity by ~ 60%, in 2D and 3D cultures. Differential gene expression analysis revealed significant enrichment of down regulated genes belonging to pathways like TargetsV1, G2-M checkpoint and E2F targets. VDAC1 and USP1 of MYC target V1 pathway were downregulated ~70% and ~ 50% respectively. Knocking down USP1 increased number of γH2AX foci counts, suggesting increased susceptibility to DNA damage. Knocking down VDAC1 increase mitochondrial respiration ~ 30% and increase ROS production by 3-fold. H3K27 acetylation dropped by ~70% at USP1 promoter site in double knocked down cells, inhibiting its transcription. Cisplatin treatment on a heterogeneous population enriched a fraction of cells expressing higher ITGB4 by 2-fold. Immunohistochemical analysis of tumor tissue sections revealed heterogeneity in expression of PXN, ITGB4 or both. Similar, data was also observed in exosomes isolated from patient serum. Conclusion: ITGB4 and PXN co-expression helps cancer cells to maintain their genomic stability and mitochondrial function, thus helping it to evade drug toxicity. NSCLC adenocarcinomas harboring high expression of them are likely to develop drug resistance. Citation Format: Atish Mohanty, Arin Nam, Alex Pozhitkov, Lu Yang, Michael Nelson, Xiwei Wu, Prakash Kulkarni, Erminia Massarelli, Ravi Salgia. Integrin Beta4/Paxillin mediated chemoresistance in lung adenocarcinoma [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2607.

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.

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.

Transcription initiation factor 90 (TIF-90), an alternatively spliced variant of TIF-IA, differs by a 90 base pair deletion of exon 6. TIF-90 has been shown to regulate ribosomal RNA (rRNA) synthesis by interacting with polymerase I (Pol I) during the initiation of ribosomal DNA (rDNA) transcription in the nucleolus. Recently, we showed that TIF-90-mediated rRNA synthesis can play an important role in driving tumorigenesis in human colon cancer cells. Here we show that TIF-90 binds GTP at threonine 310, and that GTP binding is required for TIF-90-enhanced rRNA synthesis. Overexpression of activated AKT induces TIF-90 T310, but not a GTP-binding site (TIF-90 T310N) mutant, to translocate into the nucleolus and increase rRNA synthesis. Complementing this result, treatment with mycophenolic acid (MPA), an inhibitor of GTP production, dissociates TIF-90 from Pol I and hence abolishes AKT-increased rRNA synthesis by way of TIF-90 activation. Thus, TIF-90 requires bound GTP to fulfill its function as an enhancer of rRNA synthesis. Both TIF variants are highly expressed in colon cancer cells, and depletion of TIF-IA expression in these cells results in significant sensitivity to MPA-inhibited rRNA synthesis and reduced cell proliferation. Finally, a combination of MPA and AZD8055 (an inhibitor of both AKT and mTOR) synergistically inhibits rRNA synthesis, in vivo tumor growth, and other oncogenic activities of primary human colon cancer cells, suggesting a potential avenue for the development of therapeutic treatments by targeting the regulation of rRNA synthesis by TIF proteins.