Microvascular Network Remodeling in the Ischemic Mouse Brain Defined by Light Sheet Microscopy.

BACKGROUND: Until now, the analysis of microvascular networks in the reperfused ischemic brain has been limited due to tissue transparency challenges. METHODS: Using light sheet microscopy, we assessed microvascular network remodeling in the striatum from 3 hours to 56 days post-ischemia in 2 mouse models of transient middle cerebral artery occlusion lasting 20 or 40 minutes, resulting in mild ischemic brain injury or brain infarction, respectively. We also examined the effect of a clinically applicable S1P (sphingosine-1-phosphate) analog, FTY720 (fingolimod), on microvascular network remodeling. RESULTS: Over 56 days, we observed progressive microvascular degeneration in the reperfused striatum, that is, the lesion core, which was followed by robust angiogenesis after mild ischemic injury induced by 20-minute middle cerebral artery occlusion. However, more severe ischemic injury elicited by 40-minute middle cerebral artery occlusion resulted in incomplete microvascular remodeling. In both cases, microvascular networks did not return to their preischemic state but displayed a chronically altered pattern characterized by higher branching point density, shorter branches, higher unconnected branch density, and lower tortuosity, indicating enhanced network connectivity. FTY720 effectively increased microvascular length density, branching point density, and volume density in both models, indicating an angiogenic effect of this drug. CONCLUSIONS: Utilizing light sheet microscopy together with automated image analysis, we characterized microvascular remodeling in the ischemic lesion core in unprecedented detail. This technology will significantly advance our understanding of microvascular restorative processes and pave the way for novel treatment developments in the stroke field.

Asymmetric Ion-Pairing Photoredox Catalysis for Stereoselective Cationic Polymerization under Light Control.

By virtue of noninvasive regulations by light, photocontrolled polymerizations have attracted considerable attention for the precision synthesis of macromolecules. However, a cationic polymerization with simultaneous photocontrol and tacticity-regulation remains elusive so far. Herein, we introduce an asymmetric ion-pairing photoredox catalysis strategy that allows for the development of a stereoselective cationic polymerization with concurrent light regulation for the first time. By employing an ion pair catalyst (PC+/*A-) consisting of a photoredox active cation (PC+) and a sterically confined chiral anion (*A-) to deliver the stereochemical control, the cationic polymerization of vinyl ethers can be achieved with photocontrol and high isotactic selectivity (up to 91% m) at a remarkable low catalyst loading (50 ppm).

Understanding metric-related pitfalls in image analysis validation.

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence research and its translation into practice. However, increasing evidence shows that, particularly in image analysis, metrics are often chosen inadequately. Although taking into account the individual strengths, weaknesses and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multistage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides a reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Although focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation.

MMV_Im2Im: an open-source microscopy machine vision toolbox for image-to-image transformation.

Over the past decade, deep learning (DL) research in computer vision has been growing rapidly, with many advances in DL-based image analysis methods for biomedical problems. In this work, we introduce MMV_Im2Im, a new open-source Python package for image-to-image transformation in bioimaging applications. MMV_Im2Im is designed with a generic image-to-image transformation framework that can be used for a wide range of tasks, including semantic segmentation, instance segmentation, image restoration, image generation, and so on. Our implementation takes advantage of state-of-the-art machine learning engineering techniques, allowing researchers to focus on their research without worrying about engineering details. We demonstrate the effectiveness of MMV_Im2Im on more than 10 different biomedical problems, showcasing its general potentials and applicabilities. For computational biomedical researchers, MMV_Im2Im provides a starting point for developing new biomedical image analysis or machine learning algorithms, where they can either reuse the code in this package or fork and extend this package to facilitate the development of new methods. Experimental biomedical researchers can benefit from this work by gaining a comprehensive view of the image-to-image transformation concept through diversified examples and use cases. We hope this work can give the community inspirations on how DL-based image-to-image transformation can be integrated into the assay development process, enabling new biomedical studies that cannot be done only with traditional experimental assays. To help researchers get started, we have provided source code, documentation, and tutorials for MMV_Im2Im at [https://github.com/MMV-Lab/mmv_im2im] under MIT license.

Understanding metric-related pitfalls in image analysis validation.

Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately in relation to the underlying research problem. This could be attributed to a lack of accessibility of metric-related knowledge: While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Focusing on biomedical image analysis but with the potential of transfer to other fields, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. To facilitate comprehension, illustrations and specific examples accompany each pitfall. As a structured body of information accessible to researchers of all levels of expertise, this work enhances global comprehension of a key topic in image analysis validation.

ComplexEye: a multi-lens array microscope for high-throughput embedded immune cell migration analysis.

Autonomous migration is essential for the function of immune cells such as neutrophils and plays an important role in numerous diseases. The ability to routinely measure or target it would offer a wealth of clinical applications. Video microscopy of live cells is ideal for migration analysis, but cannot be performed at sufficiently high-throughput (HT). Here we introduce ComplexEye, an array microscope with 16 independent aberration-corrected glass lenses spaced at the pitch of a 96-well plate to produce high-resolution movies of migrating cells. With the system, we enable HT migration analysis of immune cells in 96- and 384-well plates with very energy-efficient performance. We demonstrate that the system can measure multiple clinical samples simultaneously. Furthermore, we screen 1000 compounds and identify 17 modifiers of migration in human neutrophils in just 4 days, a task that requires 60-times longer with a conventional video microscope. ComplexEye thus opens the field of phenotypic HT migration screens and enables routine migration analysis for the clinical setting.

B4GALNT1 promotes hepatocellular carcinoma stemness and progression via integrin α2ß1-mediated FAK and AKT activation.

Background & Aims: ß-1,4-N-Acetyl-galactosaminyltransferase 1 (B4GALNT1) has been reported to contribute to the development of human malignancies. However, its role in hepatocellular carcinoma (HCC) remains uncharacterised. In this study, we aimed to elucidate the role of B4GALNT1 in HCC stemness and progression. Methods: Immunohistochemical staining was used to evaluate B4GALNT1 expression in HCC tissues and adjacent normal liver tissues. Flow cytometry analysis and sphere formation analysis were performed to investigate the role of B4GALNT1 in HCC stemness. Colony formation, Incucyte, wound-healing, Transwell migration, and invasion assays, and an animal model were used to study the role of B4GALNT1 in HCC progression. RNA-sequencing and co-immunoprecipitation were used to investigate the downstream targets of B4GALNT1. Results: B4GALNT1 was upregulated in HCC and associated with poor clinical outcome of patients with the disease. Moreover, B4GALNT1 promoted HCC stemness, migration, invasion, and growth. Mechanistically, B4GALNT1 not only promoted the expression of the integrin α2ß1 ligand THBS4, but also directly interacted with the ß subunit of integrin α2ß1 ITGB1 to inhibit its ubiquitin-independent proteasomal degradation, resulting in activation of FAK and AKT. Ophiopogonin D inhibited HCC stemness and progression by reducing ITGB1 and THBS4 expression and inhibiting FAK and AKT activation. Conclusions: Our study suggests the B4GALNT1/integrin α2ß1/FAK/PI3K/AKT axis as a therapeutic target for the inhibition of HCC stemness and tumour progression. Impact and implications: The role and regulatory mechanism of B4GALNT1 in HCC have not been studied previously. Here, we reveal that B4GALNT1 has a crucial role in HCC stemness and progression by activating the integrin α2ß1/FAK/PI3K/AKT axis, providing a potential target for HCC therapy. In addition, we find Ophiopogonin D as a potential therapeutic drug for patients with HCC.

SETD1A drives stemness by reprogramming the epigenetic landscape in hepatocellular carcinoma stem cells.

Cancer stem cells (CSCs) are responsible for tumor progression and recurrence. However, the mechanisms regulating hepatocellular carcinoma (HCC) stemness remain unclear. Applying a genome-scale CRISPR knockout screen, we identified that the H3K4 methyltransferase SETD1A and other members of Trithorax group proteins drive cancer stemness in HCC. SET domain containing 1A (SETD1A) was positively correlated with poor clinical outcome in patients with HCC. Combination of SETD1A and serum alpha fetoprotein substantially improved the accuracy of predicting HCC relapse. Mechanistically, SETD1A mediates transcriptional activation of various histone-modifying enzymes, facilitates deposition of trimethylated H3K4 (H3K4me3) and H3K27me3, and activates oncogenic enhancers and super-enhancers, leading to activation of oncogenes and inactivation of tumor suppressor genes simultaneously in liver CSCs. In addition, SETD1A cooperates with polyadenylate-binding protein cytoplasmic 1 to regulate H3K4me3 modification on oncogenes. Our data pinpoint SETD1A as a key epigenetic regulator driving HCC stemness and progression, highlighting the potential of SETD1A as a candidate target for HCC intervention and therapy.

Rapid and fully automated blood vasculature analysis in 3D light-sheet image volumes of different organs.

Light-sheet fluorescence microscopy (LSFM) can produce high-resolution tomograms of tissue vasculature with high accuracy. However, data processing and analysis is laborious due to the size of the datasets. Here, we introduce VesselExpress, an automated software that reliably analyzes six characteristic vascular network parameters including vessel diameter in LSFM data on average computing hardware. VesselExpress is ∼100 times faster than other existing vessel analysis tools, requires no user interaction, and integrates batch processing and parallelization. Employing an innovative dual Frangi filter approach, we show that obesity induces a large-scale modulation of brain vasculature in mice and that seven other major organs differ strongly in their 3D vascular makeup. Hence, VesselExpress transforms LSFM from an observational to an analytical working tool.

Integrated intracellular organization and its variations in human iPS cells.

Understanding how a subset of expressed genes dictates cellular phenotype is a considerable challenge owing to the large numbers of molecules involved, their combinatorics and the plethora of cellular behaviours that they determine1,2. Here we reduced this complexity by focusing on cellular organization-a key readout and driver of cell behaviour3,4-at the level of major cellular structures that represent distinct organelles and functional machines, and generated the WTC-11 hiPSC Single-Cell Image Dataset v1, which contains more than 200,000 live cells in 3D, spanning 25 key cellular structures. The scale and quality of this dataset permitted the creation of a generalizable analysis framework to convert raw image data of cells and their structures into dimensionally reduced, quantitative measurements that can be interpreted by humans, and to facilitate data exploration. This framework embraces the vast cell-to-cell variability that is observed within a normal population, facilitates the integration of cell-by-cell structural data and allows quantitative analyses of distinct, separable aspects of organization within and across different cell populations. We found that the integrated intracellular organization of interphase cells was robust to the wide range of variation in cell shape in the population; that the average locations of some structures became polarized in cells at the edges of colonies while maintaining the 'wiring' of their interactions with other structures; and that, by contrast, changes in the location of structures during early mitotic reorganization were accompanied by changes in their wiring.

Organocatalytic [2 + 2] Photopolymerization under Visible Light: Accessing Sustainable Polymers from Cinnamic Acids.

Herein, the successful development of a metal-free, solution [2 + 2] photopolymerization of natural cinnamic acid-derived bisolefinic monomers is reported, which is enabled by a strategy based on direct triplet state access via energy transfer catalysis. 2,2'-Methoxythioxanthone has been identified as an effective organic photocatalyst for the [2 + 2] photopolymerization in solution, which can be excited by visible light and activate the biscinnamate monomers via triplet energy transfer. This method features its metal-free conditions, visible light utilization, solution polymerization, and abundant biomass-based feedstock, as well as processable polymer products, which is different from the rigid, insoluble products obtained from solid-state photopolymerization. This solution polymerization method also shows a good compatibility to monomer structures; cinnamic acid-derived bisolefinic monomers with different linkers, including diamine, natural diol, and bisphenol, can all readily undergo [2 + 2] photopolymerization, and be transformed into colorless, sustainable polymers.

Linear Cyclobutane-Containing Polymer Synthesis via [2 + 2] Photopolymerization in an Unconfined Environment under Visible Light.

The [2 + 2] photopolymerization of diolefinic monomers is an appealing approach for the construction of polymeric materials. Herein, we demonstrate that the establishment of an effective donor-acceptor conjugation by introducing electron-donating alkoxy groups at appropriate positions of the benzene ring could activate p-phenylenediacrylate (PDA), thus enabling the development of the first solution [2 + 2] photopolymerization of such monomers under the irradiation of visible light. Variation on the alkoxy groups and the ester parts could allow access to a series of linear cyclobutane-containing polymer products with high molecular weight (up to 140 kDa) and good solubility in common solvents. Further, temporal control and postpolymerization modification with preinstalled pendant C═C bonds via thiol-ene click reaction are also demonstrated with this [2 + 2] photopolymerization system.

Monophosphoniums as Effective Photoredox Organocatalysts for Visible Light-Regulated Cationic RAFT Polymerization.

Visible light-regulated metal-free polymerizations have attracted considerable attention for macromolecular syntheses in recent years. However, few organic photocatalysts show high efficiency and strict photocontrol in cationic polymerizations. Herein, we introduce monophosphonium-doped polycyclic arenes as an organic photocatalyst, which features the high tunability, broad redox window, long excited state lifetime, and excellent temporal control in the cationic reversible addition-fragmentation chain transfer polymerization of vinyl ethers. A correlation of the catalytic performance and the photophysical and electrochemical properties of photocatalysts is also discussed.

H-EMD: A Hierarchical Earth Mover's Distance Method for Instance Segmentation.

Deep learning (DL) based semantic segmentation methods have achieved excellent performance in biomedical image segmentation, producing high quality probability maps to allow extraction of rich instance information to facilitate good instance segmentation. While numerous efforts were put into developing new DL semantic segmentation models, less attention was paid to a key issue of how to effectively explore their probability maps to attain the best possible instance segmentation. We observe that probability maps by DL semantic segmentation models can be used to generate many possible instance candidates, and accurate instance segmentation can be achieved by selecting from them a set of "optimized" candidates as output instances. Further, the generated instance candidates form a well-behaved hierarchical structure (a forest), which allows selecting instances in an optimized manner. Hence, we propose a novel framework, called hierarchical earth mover's distance (H-EMD), for instance segmentation in biomedical 2D+time videos and 3D images, which judiciously incorporates consistent instance selection with semantic-segmentation-generated probability maps. H-EMD contains two main stages: (1) instance candidate generation: capturing instance-structured information in probability maps by generating many instance candidates in a forest structure; (2) instance candidate selection: selecting instances from the candidate set for final instance segmentation. We formulate a key instance selection problem on the instance candidate forest as an optimization problem based on the earth mover's distance (EMD), and solve it by integer linear programming. Extensive experiments on eight biomedical video or 3D datasets demonstrate that H-EMD consistently boosts DL semantic segmentation models and is highly competitive with state-of-the-art methods.

Cooperation of ATF4 and CTCF promotes adipogenesis through transcriptional regulation.

Adipogenesis is a multi-step process orchestrated by activation of numerous TFs, whose cooperation and regulatory network remain elusive. Activating transcription factor 4 (ATF4) is critical for adipogenesis, yet its regulatory network is unclarified. Here, we mapped genome-wide ATF4 binding landscape and its regulatory network by Chip-seq and RNA-seq and found ATF4 directly modulated transcription of genes enriching in fat cell differentiation. Motifs of TFs especially CTCF were found from ATF4 binding sites, suggesting a direct role of ATF4 in regulating adipogenesis associated with CTCF and other TFs. Deletion of CTCF attenuated adipogenesis while overexpression enhanced adipocyte differentiation, indicating CTCF is indispensable for adipogenesis. Intriguingly, combined analysis of Chip-seq data of these two TFs showed that ATF4 co-localized with CTCF in the promoters of key adipogenic genes including Cebpd and PPARg and co-regulated their transactivation. Moreover, ATF4 directly regulated CTCF expression and interacted with CTCF in differentiated 3T3-L1 cells. In vivo, downregulation of ATF4 suppressed the expression of CTCF, Cebpd, and PPARg, leading to reduced adipose tissue expansion in refeeding mice. Consistently, mRNA expression of ATF4 and CTCF was positively correlated with each other in human subcutaneous adipose tissue and inversely associated with BMI, indicating a possible involvement of these two TFs in adipose development. Taken together, our data propose for the first time that ATF4 and CTCF work cooperatively to control adipogenesis and adipose development via orchestrating transcription of adipogenic genes. Our findings reveal novel therapeutic targets in obesity treatment.

Cell states beyond transcriptomics: Integrating structural organization and gene expression in hiPSC-derived cardiomyocytes.

Although some cell types may be defined anatomically or by physiological function, a rigorous definition of cell state remains elusive. Here, we develop a quantitative, imaging-based platform for the systematic and automated classification of subcellular organization in single cells. We use this platform to quantify subcellular organization and gene expression in >30,000 individual human induced pluripotent stem cell-derived cardiomyocytes, producing a publicly available dataset that describes the population distributions of local and global sarcomere organization, mRNA abundance, and correlations between these traits. While the mRNA abundance of some phenotypically important genes correlates with subcellular organization (e.g., the beta-myosin heavy chain, MYH7), these two cellular metrics are heterogeneous and often uncorrelated, which suggests that gene expression alone is not sufficient to classify cell states. Instead, we posit that cell state should be defined by observing full distributions of quantitative, multidimensional traits in single cells that also account for space, time, and function.

C14orf166 Is a Biomarker for Predicting Hepatocellular Carcinoma Recurrence.

Aim: Chromosome 14 open reading frame 166 (C14orf166) acts as a transcriptional repressor and is correlated with centrosome architecture manipulation. Nevertheless, the function of C14orf166 in hepatocellular carcinoma (HCC) progression remains unclear. We aimed to investigate the role C14orf166 plays in HCC and further compared the prognostic value of C14orf166 with that of clinicopathological features. Methods: C14orf166 expression was evaluated in a human liver cell line, HCC cell lines, HCC tissues and adjacent noncancerous liver tissues with qRT-PCR, western blot and immunohistochemistry. Patients were divided into two different groups according to C14orf166 level. The relationship between C14orf166 expression and clinicopathological features was assessed by Pearson chi-squared test and receiver operating characteristic curves. Cumulative disease-free survival (DFS) and overall survival (OS) curves were evaluated using the Kaplan-Meier method. Results: C14orf166 mRNA and protein expression is upregulated in HCC cell lines and tissues. The level of C14orf166 was correlated with serum alpha-fetoprotein level, lymph node metastasis, tumor size and recurrence, with high C14orf166 expression correlating with high HCC recurrence risk. The poor OS and DFS of HCC patients are partly due to the persistently high HCC recurrence risk. When combined with serum alpha-fetoprotein level, the predictive accuracy of C14orf166 for HCC recurrence was enhanced (AUC = 0.712, 95% CI 0.603-0.821; p = 0.001). Conclusions: This study demonstrated that C14orf166 is a high-risk biomarker and predictive factor for HCC recurrence, providing information for the selection of appropriate treatment strategies.

HIF-2α upregulation mediated by hypoxia promotes NAFLD-HCC progression by activating lipid synthesis via the PI3K-AKT-mTOR pathway.

Non-alcoholic fatty liver disease (NAFLD) is a relevant risk factor for developing hepatocellular carcinoma (HCC). Steatohepatitic HCC (SH-HCC), characterized by HCC with steatosis, is influenced by lipid metabolism disorders. A hypoxic microenvironment is common in HCC and affects lipid metabolism. However, whether hypoxia-induced HIF-2α upregulation exacerbates lipid accumulation to contribute to SH-HCC progression remains unclear. In this study, we demonstrated that HIF-2α was elevated in tissues from NAFLD-HCC patients and was associated with survival. Under hypoxic conditions, upregulated HIF-2α was accompanied by lipid accumulation and PI3K-AKT-mTOR pathway activation. HIF-2α knockdown (KD) in steatotic HCC ameliorated triglyceride accumulation and steatosis. HIF-2α-KD steatotic HCC showed minimal lipid synthesis in a hypoxic environment, which contributes to a reduction in malignant behaviours. However, treatment with MHY1485 restored these behaviours. STAM mice, a mouse model that develops NAFLD-HCC, exhibit more rapid tumour progression upon exposure to hypoxia. STAM mice treated with INK-128 presented abrogated mTOR expression and tumour progression under hypoxic conditions with lower triglycerides and steatosis. In conclusion, in a hypoxic microenvironment, HIF-2α upregulation promotes steatotic HCC progression by activating lipid synthesis via the PI3K-AKT-mTOR pathway. Therefore, HIF-2α can be a biomarker and target in developing specific therapeutic measures for NAFLD-HCC patients.