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There is a close relation between membrane receptor dynamics and their behavior. Several microscopy techniques have been developed to study protein dynamics in live cells such as the Fluorescence Recovery After Photobleaching (FRAP) or the Single Particle Tracking (SPT). These methodologies require expensive instruments, are time consuming, allow the analysis of small portion of the cell or an extremely small number of receptors at a time. Here we propose a time-saving approach that allows to visualize the entire receptor pool and its localization in time. This protocol requires an epifluorescence microscope equipped for structured illuminated sectioning and for live cell imaging. It can be applied to characterize membrane receptor and multi protein complex and their response to activators or inhibitors. Image acquisition and analysis can be performed in two days, while cells and substratum preparation require a few minutes a day for three days. [ABSTRACT FROM AUTHOR]

Natural killer-like B (NKB) cells are unique innate immune cells expressing both natural killer (NK) and B cell receptors. As first responders to infection, they secrete IL-18 to induce a critical cascade of innate and adaptive immune cell infiltration and activation. However, limited research exists on the role of NKB cells in homeostasis and infection, largely due to incomplete and erroneous evaluations. To fill this knowledge gap, we investigated the expression of signaling and trafficking proteins, and the in situ localization and transcriptome of naïve NKB cells comparied to conventionally-defined NK and B cells, as well as modulations of these cells in SIV infection. Intracellular signaling proteins and trafficking markers were expressed differentially on naïve NKB cells, with high expression of CD62L and Syk, and low expression of CD69, α4β7, FcRg, Zap70, and CD3z, findings which were more similar to B cells than NK cells. CD20+NKG2a/c+ NKB cells were identified in spleen, mesenteric lymph nodes (MLN), colon, jejunum, and liver of naïve rhesus macaques (RM) via tissue imaging, with NKB cell counts concentrated in spleen and MLN. For the first time, single cell RNA sequencing (scRNAseq), including BCR sequencing, of sorted NKB cells confirmed that NKB cells are unique. Transcriptomic analysis of naïve splenic NKB cells by scRNAseq showed that NKB cells undergo somatic hypermutation and express Ig receptors, similar to B cells. While only 15% of sorted NKB cells showed transcript expression of both KLRC1 (NKG2A) and MS4A1 (CD20) genes, only 5% of cells expressed KLRC1, MS4A1, and IgH/IgL transcripts. We observed expanded NKB frequencies in RM gut and buccal mucosa as early as 14 and 35 days post-SIV infection, respectively. Further, mucosal and peripheral NKB cells were associated with colorectal cytokine mileu and oral microbiome changes, respectively. Our studies indicate that NKB cells gated on CD3-CD14-CD20+NKG2A/C+ cells were inclusive of transcriptomically conventional B and NK cells in addition to true NKB cells, confounding accurate phenotyping and frequency recordings that could only be resolved using genomic techniques. Although NKB cells were clearly elevated during SIV infection and associated with inflammatory changes during infection, further interrogation is necessary to acurately identify the true phenotype and significance of NKB cells in infection and inflammation. Author summary: Recently, our understanding of traditional innate immunity has become reshaped by the discovery of new innate immune cell subsets as well as newly identified functions/programming of previously described innate immune cell subsets. One such new and unique innate immune cell subset are the natural killer-like B (NKB) cells, which express both NK and B cell receptors. These cells have been described as first responder immune cells in infection and inflammation within mice, non-human primates (NHP), and humans. To clarify if the simple definition of CD3-NKG2A/C+CD20+ cells as NKB cells is sufficient to truly identify the unique NKB cell phenotype, we characterized these cells via imaging cytometry and single cell RNA sequencing for the first time in an NHP model. Our data suggests that the NKB cell phenotype and transcriptome, while unique and relevant in SIV infection, remains very infrequent. Therefore, using flow cytometry to identify NKB cells based on the commonly accepted marker expression could be misleading and cause the erroneous classification of conventional NK cells and/or B cells as NKB cells. [ABSTRACT FROM AUTHOR]

Mitofusins are large GTPases that trigger fusion of mitochondrial outer membranes. Similarly to the human mitofusin Mfn2, which also tethers mitochondria to the endoplasmic reticulum (ER), the yeast mitofusin Fzo1 stimulates contacts between Peroxisomes and Mitochondria when overexpressed. Yet, the physiological significance and function of these "PerMit" contacts remain unknown. Here, we demonstrate that Fzo1 naturally localizes to peroxisomes and promotes PerMit contacts in physiological conditions. These contacts are regulated through co-modulation of Fzo1 levels by the ubiquitin–proteasome system (UPS) and by the desaturation status of fatty acids (FAs). Contacts decrease under low FA desaturation but reach a maximum during high FA desaturation. High-throughput genetic screening combined with high-resolution cellular imaging reveal that Fzo1-mediated PerMit contacts favor the transit of peroxisomal citrate into mitochondria. In turn, citrate enters the TCA cycle to stimulate the mitochondrial membrane potential and maintain efficient mitochondrial fusion upon high FA desaturation. These findings thus unravel a mechanism by which inter-organelle contacts safeguard mitochondrial fusion. The yeast mitofusin Fzo1 naturally localizes to peroxisomes to promote their tethering with mitochondria, but the physiological relevance of this contact is unclear. Here, the authors show that these Fzo1-mediated PerMit contacts are required to maintain mitochondrial fusion as fatty acid desaturation increases. [ABSTRACT FROM AUTHOR]

Single-cell transcriptomics has the potential to provide novel insights into poorly studied microbial eukaryotes. Although several such technologies are available and benchmarked on mammalian cells, few have been tested on protists. Here, we applied a microarray single-cell sequencing (MASC-seq) technology, that generates microscope images of cells in parallel with capturing their transcriptomes, on three species representing important plankton groups with different cell structures; the ciliate Tetrahymena thermophila, the diatom Phaeodactylum tricornutum, and the dinoflagellate Heterocapsa sp. Both the cell fixation and permeabilization steps were adjusted. For the ciliate and dinoflagellate, the number of transcripts of microarray spots with single cells were significantly higher than for background spots, and the overall expression patterns were correlated with that of bulk RNA, while for the much smaller diatom cells, it was not possible to separate single-cell transcripts from background. The MASC-seq method holds promise for investigating "microbial dark matter", although further optimizations are necessary to increase the signal-to-noise ratio. [ABSTRACT FROM AUTHOR]

Fungal organisms contribute to significant human morbidity and mortality and Candida auris (C. auris) infections are of utmost concern due to multi-drug resistant strains and persistence in critical care and hospital settings. Pathogenesis and pathology of C. auris is still poorly understood and in this study, we demonstrate how the use of multiplex immunofluorescent imaging (MxIF) and single-cell analysis can contribute to a deeper understanding of fungal infections within organs. We used two different neutrophil depletion murine models (treated with either 1A8—an anti-Ly6G antibody, or RB6-8C5—an anti-Ly6G/Ly6C antibody; both 1A8 and RB6-8C5 antibodies have been shown to deplete neutrophils) and compared to wildtype, non-neutropenic mice. Following pathologist assessment, fixed samples underwent MxIF imaging using a C. albicans antibody (shown to be cross-reactive to C. auris) and immune cell biomarkers—CD3 (T cells), CD68 (macrophages), B220 (B cells), CD45 (monocytes), and Ly6G (neutrophils) to quantify organ specific immune niches. MxIF analysis highlighted the heterogenous distribution of C. auris infection within heart, kidney, and brain 7 days post-infection. Size and number of fungal abscesses was greatest in the heart and lowest in brain. Infected mice had an increased count of CD3+, CD68+, B220+, and CD45+ immune cells, concentrated around C. auris abscesses. CD68+ cells were predominant in wildtype (non-neutropenic mice) and CD3+/CD45+ cells were predominant in neutropenic mice, with B cells being the least abundant. These findings suggest a Th2 driven immune response in neutropenic C. auris infection mice models. This study demonstrates the value of MxIF to broaden understanding of C. auris pathobiology, and mechanistic understanding of fungal infections. [ABSTRACT FROM AUTHOR]

Multiplexed cellular imaging typically relies on the sequential application of detection probes, as antibodies or DNA barcodes, which is complex and time-consuming. To address this, we developed here protein nanobarcodes, composed of combinations of epitopes recognized by specific sets of nanobodies. The nanobarcodes are read in a single imaging step, relying on nanobodies conjugated to distinct fluorophores, which enables a precise analysis of large numbers of protein combinations. Fluorescence images from nanobarcodes were used as input images for a deep neural network, which was able to identify proteins with high precision. We thus present an efficient and straightforward protein identification method, which is applicable to relatively complex biological assays. We demonstrate this by a multicell competition assay, in which we successfully used our nanobarcoded proteins together with neurexin and neuroligin isoforms, thereby testing the preferred binding combinations of multiple isoforms, in parallel. Multiplexed cellular imaging typically relies on the sequential application of detection probes, which is complex and time-consuming. This study describes a novel approach to protein labeling, in which individual proteins are tagged with combinations of multiple epitopes. This allows a normal four-color-channel microscope to reveal identities of up to fifteen different proteins, enabling simple and powerful multiplexing. [ABSTRACT FROM AUTHOR]

Comprehensive analysis of tissue cell type composition using microscopic techniques has primarily been confined to ex vivo approaches. Here, we introduce NuCLear (Nucleus-instructed tissue composition using deep learning), an approach combining in vivo two-photon imaging of histone 2B-eGFP-labeled cell nuclei with subsequent deep learning-based identification of cell types from structural features of the respective cell nuclei. Using NuCLear, we were able to classify almost all cells per imaging volume in the secondary motor cortex of the mouse brain (0.25 mm3 containing approximately 25,000 cells) and to identify their position in 3D space in a noninvasive manner using only a single label throughout multiple imaging sessions. Twelve weeks after baseline, cell numbers did not change yet astrocytic nuclei significantly decreased in size. NuCLear opens a window to study changes in relative density and location of different cell types in the brains of individual mice over extended time periods, enabling comprehensive studies of changes in cell type composition in physiological and pathophysiological conditions. This study presents NuCLear (Nucleus-instructed tissue composition using deep learning), a novel method enabling in vivo analysis of cell types in the mouse brain. It combines two-photon imaging of labeled cell nuclei with deep learning to classify and track cells over time, allowing the study of cellular changes in physiological and pathological conditions. [ABSTRACT FROM AUTHOR]

One of the first steps in protein sequence analysis is comparing sequences to look for similarities. We propose an information theoretical distance to compare cellular automata representing protein sequences, and determine similarities. Our approach relies in a stationary Hamming distance for the evolution of the automata according to a properly chosen rule, and to build a pairwise similarity matrix and determine common ancestors among different species in a simpler and less computationally demanding computer codes when compared to other methods. [ABSTRACT FROM AUTHOR]

During C. elegans oocyte meiosis I cytokinesis and polar body extrusion, cortical actomyosin is locally remodeled to assemble a contractile ring that forms within and remains part of a much larger and actively contractile cortical actomyosin network. This network both mediates contractile ring dynamics and generates shallow ingressions throughout the oocyte cortex during polar body extrusion. Based on our analysis of requirements for CLS-2, a member of the CLASP family of proteins that stabilize microtubules, we recently proposed that a balance of actomyosin-mediated tension and microtubule-mediated stiffness limits membrane ingression throughout the oocyte during meiosis I polar body extrusion. Here, using live cell imaging and fluorescent protein fusions, we show that CLS-2 is part of a group of kinetochore proteins, including the scaffold KNL-1 and the kinase BUB-1, that also co-localize during meiosis I to structures called linear elements, which are present within the assembling oocyte spindle and also are distributed throughout the oocyte in proximity to, but appearing to underlie, the actomyosin cortex. We further show that KNL-1 and BUB-1, like CLS-2, promote the proper organization of sub-cortical microtubules and also limit membrane ingression throughout the oocyte. Moreover, nocodazole or taxol treatment to destabilize or stabilize oocyte microtubules leads to, respectively, excess or decreased membrane ingression throughout the oocyte. Furthermore, taxol treatment, and genetic backgrounds that elevate the levels of cortically associated microtubules, both suppress excess membrane ingression in cls-2 mutant oocytes. We propose that linear elements influence the organization of sub-cortical microtubules to generate a stiffness that limits cortical actomyosin-driven membrane ingression throughout the oocyte during meiosis I polar body extrusion. We discuss the possibility that this regulation of sub-cortical microtubule dynamics facilitates actomyosin contractile ring dynamics during C. elegans oocyte meiosis I cell division. Author summary: Animal oocytes undergo a sequence of two specialized cell divisions called meiosis I and II in which three quarters of the genome content is expelled into two small, extracellular, membrane-bound compartments called polar bodies. These two meiotic divisions reduce the genome content of the oocyte from a diploid state, with two copies of each chromosome, to a haploid state, with a single copy of each chromosome. As a result, fertilization by a sperm restores the normal diploid genome content. We provide evidence that tubular cytoskeletal polymers called microtubules are enriched near the inner surface the of oocyte cell membrane, where they provide a stiffness that resists membrane ingressions driven by the force-generating cortical actomyosin cytoskeleton. These membrane ingressions occur throughout the oocyte during meiotic polar body extrusion. Our results suggest that a balance of microtubule-mediated stiffness and actomyosin-mediated membrane ingression throughout the oocyte may be important for the proper assembly and function of the contractile ring that pinches off polar bodies at one end of the oocyte during oocyte meiotic cell division. [ABSTRACT FROM AUTHOR]

Spatial heterogeneity in the tumor microenvironment (TME) plays a critical role in gaining insights into tumor development and progression. Conventional metrics typically capture the spatial differential between TME cellular patterns by either exploring the cell distributions in a pairwise fashion or aggregating the heterogeneity across multiple cell distributions without considering the spatial contribution. As such, none of the existing approaches has fully accounted for the simultaneous heterogeneity caused by both cellular diversity and spatial configurations of multiple cell categories. In this article, we propose an approach to leverage spatial entropy measures at multiple distance ranges to account for the spatial heterogeneity across different cellular organizations. Functional principal component analysis (FPCA) is applied to estimate FPC scores which are then served as predictors in a Cox regression model to investigate the impact of spatial heterogeneity in the TME on survival outcome, potentially adjusting for other confounders. Using a non-small cell lung cancer dataset (n = 153) as a case study, we found that the spatial heterogeneity in the TME cellular composition of CD14+ cells, CD19+ B cells, CD4+ and CD8+ T cells, and CK+ tumor cells, had a significant non-zero effect on the overall survival (p = 0.027). Furthermore, using a publicly available multiplexed ion beam imaging (MIBI) triple-negative breast cancer dataset (n = 33), our proposed method identified a significant impact of cellular interactions between tumor and immune cells on the overall survival (p = 0.046). In simulation studies under different spatial configurations, the proposed method demonstrated a high predictive power by accounting for both clinical effect and the impact of spatial heterogeneity. Author summary: Studying spatial heterogeneity in the tumor microenvironment (TME) provides insights into tumor development and progression. In this work, we proposed a novel approach to account for the simultaneous heterogeneity caused by both cellular diversity and spatial organizations of multiple cell types, by leveraging spatial entropy measures. Functional principal component analysis (FPCA) was applied to subject-specific spatial entropy measures calculated at different distance ranges to extract FPC scores which captured the spatial heterogeneity in the TME cellular composition. By applying the proposed approach to the multiplex immunohistochemistry (mIHC) data of patients with non-small cell lung cancer (NSCLC) and a publicly available multiplexed ion beam imaging (MIBI) triple-negative breast cancer (TNBC) dataset, we explored the significant impact of the spatial heterogeneity in the TME cellular composition on patient overall survival while adjusting for other confounders. [ABSTRACT FROM AUTHOR]

Fission yeast is a model organism widely used for studies of eukaryotic cell biology. As such, it is subject to bright-field and fluorescent microscopy. Manual analysis of such data can be laborious and subjective. Therefore, we have developed pomBseen, an image analysis pipeline for the quantitation of fission yeast micrographs containing a bright-field channel and up to two fluorescent channels. It accepts a wide range of image formats and produces a table with the size and total and nuclear fluorescent intensities of the cells in the image. Benchmarking of the pipeline against manually annotated datasets demonstrates that it reliably segments cells and acquires their image parameters. Written in MATLAB, pomBseen is also available as a standalone application. [ABSTRACT FROM AUTHOR]

Protein-protein-interactions play an important role in many cellular functions. Quantitative non-invasive techniques are applied in living cells to evaluate such interactions, thereby providing a broader understanding of complex biological processes. Fluorescence fluctuation spectroscopy describes a group of quantitative microscopy approaches for the characterization of molecular interactions at single cell resolution. Through the obtained molecular brightness, it is possible to determine the oligomeric state of proteins. This is usually achieved by fusing fluorescent proteins (FPs) to the protein of interest. Recently, the number of novel green FPs has increased, with consequent improvements to the quality of fluctuation-based measurements. The photophysical behavior of FPs is influenced by multiple factors (including photobleaching, protonation-induced "blinking" and long-lived dark states). Assessing these factors is critical for selecting the appropriate fluorescent tag for live cell imaging applications. In this work, we focus on novel green FPs that are extensively used in live cell imaging. A systematic performance comparison of several green FPs in living cells under different pH conditions using Number & Brightness (N&B) analysis and scanning fluorescence correlation spectroscopy was performed. Our results show that the new FP Gamillus exhibits higher brightness at the cost of lower photostability and fluorescence probability (pf), especially at lower pH. mGreenLantern, on the other hand, thanks to a very high pf, is best suited for multimerization quantification at neutral pH. At lower pH, mEGFP remains apparently the best choice for multimerization investigation. These guidelines provide the information needed to plan quantitative fluorescence microscopy involving these FPs, both for general imaging or for protein-protein-interactions quantification via fluorescence fluctuation-based methods. [ABSTRACT FROM AUTHOR]

Physiological function is regulated through cellular communication that is facilitated by multiple signaling molecules such as second messengers. Analysis of signal dynamics obtained from cell and tissue imaging is difficult because of intricate spatially and temporally distinct signals. Signal analysis tools based on static region of interest analysis may under- or overestimate signals in relation to region of interest size and location. Therefore, we developed an algorithm for biological signal detection and analysis based on dynamic regions of interest, where time-dependent polygonal regions of interest are automatically assigned to the changing perimeter of detected and segmented signals. This approach allows signal profiles to be rigorously and precisely tracked over time, eliminating the signal distortion observed with static methods. Integration of our approach with state-of-the-art image processing and particle tracking pipelines enabled the isolation of dynamic cellular signaling events and characterization of biological signaling patterns with distinct combinations of parameters including amplitude, duration, and spatial spread. Our algorithm was validated using synthetically generated datasets and compared with other available methods. Application of the algorithm to volumetric time-lapse hyperspectral images of cyclic adenosine monophosphate measurements in rat microvascular endothelial cells revealed distinct signal heterogeneity with respect to cell depth, confirming the utility of our approach for analysis of 5-dimensional data. In human tibial arteries, our approach allowed the identification of distinct calcium signal patterns associated with atherosclerosis. Our algorithm for automated detection and analysis of second messenger signals enables the decoding of signaling patterns in diverse tissues and identification of pathologic cellular responses. [ABSTRACT FROM AUTHOR]

The analysis of 3D microscopic cell culture images plays a vital role in the development of new therapeutics. While 3D cell cultures offer a greater similarity to the human organism than adherent cell cultures, they introduce new challenges for automatic evaluation, like increased heterogeneity. Deep learning algorithms are able to outperform conventional analysis methods in such conditions but require a large amount of training data. Due to data size and complexity, the manual annotation of 3D images to generate large datasets is a nearly impossible task. We therefore propose a pipeline that combines conventional simulation methods with deep-learning-based optimization to generate large 3D synthetic images of 3D cell cultures where the labels are known by design. The hybrid procedure helps to keep the generated image structures consistent with the underlying labels. A new approach and an additional measure are introduced to model and evaluate the reduced brightness and quality in deeper image regions. Our analyses show that the deep learning optimization step consistently improves the quality of the generated images. We could also demonstrate that a deep learning segmentation model trained with our synthetic data outperforms a classical segmentation method on real image data. The presented synthesis method allows selecting a segmentation model most suitable for the user's data, providing an ideal basis for further data analysis. [ABSTRACT FROM AUTHOR]

Glucocorticoids inhibit angiogenesis by activating the glucocorticoid receptor. Inhibition of the glucocorticoid-activating enzyme 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1) reduces tissue-specific glucocorticoid action and promotes angiogenesis in murine models of myocardial infarction. Angiogenesis is important in the growth of some solid tumours. This study used murine models of squamous cell carcinoma (SCC) and pancreatic ductal adenocarcinoma (PDAC) to test the hypothesis that 11β-HSD1 inhibition promotes angiogenesis and subsequent tumour growth. SCC or PDAC cells were injected into female FVB/N or C57BL6/J mice fed either standard diet, or diet containing the 11β-HSD1 inhibitor UE2316. SCC tumours grew more rapidly in UE2316-treated mice, reaching a larger (P<0.01) final volume (0.158 ± 0.037 cm3) than in control mice (0.051 ± 0.007 cm3). However, PDAC tumour growth was unaffected. Immunofluorescent analysis of SCC tumours did not show differences in vessel density (CD31/alpha-smooth muscle actin) or cell proliferation (Ki67) after 11β-HSD1 inhibition, and immunohistochemistry of SCC tumours did not show changes in inflammatory cell (CD3- or F4/80-positive) infiltration. In culture, the growth/viability (assessed by live cell imaging) of SCC cells was not affected by UE2316 or corticosterone. Second Harmonic Generation microscopy showed that UE2316 reduced Type I collagen (P<0.001), whilst RNA-sequencing revealed that multiple factors involved in the innate immune/inflammatory response were reduced in UE2316-treated SCC tumours. 11β-HSD1 inhibition increases SCC tumour growth, likely via suppression of inflammatory/immune cell signalling and extracellular matrix deposition, but does not promote tumour angiogenesis or growth of all solid tumours. [ABSTRACT FROM AUTHOR]

Cytoskeletal pattern formation and structural dynamics are key to a variety of biological functions and a detailed and quantitative analysis yields insight into finely tuned and well-balanced homeostasis and potential pathological alterations. High content life cell imaging of fluorescently labeled cytoskeletal elements under physiological conditions is nowadays state-of-the-art and can record time lapse data for detailed experimental studies. However, systematic quantification of structures and in particular the dynamics (i.e. frame-to-frame tracking) are essential. Here, an unbiased, quantitative, and robust analysis workflow that can be highly automatized is needed. For this purpose we upgraded and expanded our fiber detection algorithm FilamentSensor (FS) to the FilamentSensor 2.0 (FS2.0) toolbox, allowing for automatic detection and segmentation of fibrous structures and the extraction of relevant data (center of mass, length, width, orientation, curvature) in real-time as well as tracking of these objects over time and cell event monitoring. [ABSTRACT FROM AUTHOR]

Hypoxic ischemic encephalopathy (HIE) is a major global cause of neonatal death and lifelong disability. Large animal translational studies of hypoxic ischemic brain injury, such as those conducted in fetal sheep, have and continue to play a key role in furthering our understanding of the cellular and molecular mechanisms of injury and developing new treatment strategies for clinical translation. At present, the quantification of neurons in histological images consists of slow, manually intensive morphological assessment, requiring many repeats by an expert, which can prove to be time-consuming and prone to human error. Hence, there is an urgent need to automate the neuron classification and quantification process. In this article, we present a 'Gradient Direction, Grey level Co-occurrence Matrix' (GD-GLCM) image training method which outperforms and simplifies the standard training methodology using texture analysis to cell-classification. This is achieved by determining the Grey level Co-occurrence Matrix of the gradient direction of a cell image followed by direct passing to a classifier in the form of a Multilayer Perceptron (MLP). Hence, avoiding all texture feature computation steps. The proposed MLP is trained on both healthy and dying neurons that are manually identified by an expert and validated on unseen hypoxic-ischemic brain slice images from the fetal sheep in utero model. We compared the performance of our classifier using the gradient magnitude dataset as well as the gradient direction dataset. We also compare the performance of a perceptron, a 1-layer MLP, and a 2-layer MLP to each other. We demonstrate here a way of accurately identifying both healthy and dying cortical neurons obtained from brain slice images of the fetal sheep model under global hypoxia to high precision by identifying the most minimised MLP architecture, minimised input space (GLCM size) and minimised training data (GLCM representations) to achieve the highest performance over the standard methodology. [ABSTRACT FROM AUTHOR]

Time-lapse microscopy plays critical roles in the studies of cellular dynamics. However, setting up a time-lapse movie experiments is not only laborious but also with low output, mainly due to the cell-losing problem (i.e., cells moving out of limited field of view), especially in a long-time recording. To overcome this issue, we have designed a cost-efficient way that enables cell patterning on the imaging surfaces without any physical boundaries. Using mouse embryonic stem cells as an example system, we have demonstrated that our boundary-free patterned surface solves the cell-losing problem without disturbing their cellular phenotype. Statistically, the presented system increases the effective-throughput of time-lapse microscopy experiments by an order of magnitude. [ABSTRACT FROM AUTHOR]

Centrioles are non-membrane-bound organelles that participate in fundamental cellular processes through their ability to form physical contacts with other structures. During interphase, two mature centrioles can associate to form a single centrosome—a phenomenon known as centrosome cohesion. Centrosome cohesion is important for processes such as cell migration, and yet how it is maintained is unclear. Current models indicate that pericentriolar fibres termed rootlets, also known as the centrosome linker, entangle to maintain centriole proximity. Here, I uncover a centriole–centriole contact site and mechanism of centrosome cohesion based on coalescence of the proximal centriole component cNap1. Using live-cell imaging of endogenously tagged cNap1, I show that proximal centrioles form dynamic contacts in response to physical force from the cytoskeleton. Expansion microscopy reveals that cNap1 bridges between these contact sites, physically linking proximal centrioles on the nanoscale. Fluorescence correlation spectroscopy (FCS)-calibrated imaging shows that cNap1 accumulates at nearly micromolar concentrations on proximal centrioles, corresponding to a few hundred protein copy numbers. When ectopically tethered to organelles such as lysosomes, cNap1 forms viscous and cohesive assemblies that promote organelle spatial proximity. These results suggest a mechanism of centrosome cohesion by cNap1 at the proximal centriole and illustrate how a non-membrane-bound organelle forms organelle contact sites. During interphase, two mature centrioles can associate to form a single centrosome; this "centrosome cohesion" is important for processes such as cell migration, but how is it maintained? This study combines live cell quantitative imaging, expansion microscopy and ectopic tethering to provide insights into the mechanisms by which centrioles maintain spatial proximity inside human cells. [ABSTRACT FROM AUTHOR]

Robustness in developing and homeostatic tissues is supported by various types of spatiotemporal cell-to-cell interactions. Although live imaging and cell tracking are powerful in providing direct evidence of cell coordination rules, extracting and comparing these rules across many tissues with potentially different length and timescales of coordination requires a versatile framework of analysis. Here we demonstrate that graph neural network (GNN) models are suited for this purpose, by showing how they can be applied to predict cell fate in tissues and utilized to infer the cell interactions governing the multicellular dynamics. Analyzing the live mammalian epidermis data, where spatiotemporal graphs constructed from cell tracks and cell contacts are given as inputs, GNN discovers distinct neighbor cell fate coordination rules that depend on the region of the body. This approach demonstrates how the GNN framework is powerful in inferring general cell interaction rules from live data without prior knowledge of the signaling involved. Author summary: During development and homeostasis, cells coordinate with each other to grow, deform, and maintain the tissues. Even with the modern high-throughput cell profiling technologies and high-resolution microscopy, it is still challenging to infer how cell coordination affects the dynamics such as cell fate choice, due to the complexity of the problem and the limited methods to perform perturbation experiments. We here propose a versatile framework of analysis utilizing an interpretable machine learning method based on graph neural network (GNN) which infers the cell-to-cell interaction rules from live images of multicellular dynamics. From the spatiotemporal graphs generated from live images of skin stem cells, we identified previously unaddressed neighbor fate coupling as well as rules consistent with past findings. We further found distinct interaction rules in a different skin region of the body, indicating that our method is useful in probing the diverse mechanism behind the robustness and flexibility in multicellular systems. The GNN framework is applicable for interaction rule discovery for general multicellular dynamics as well as in a wide range of systems where modeling by stochastic interacting agents is effective. [ABSTRACT FROM AUTHOR]

The tumor microenvironment (TME), which characterizes the tumor and its surroundings, plays a critical role in understanding cancer development and progression. Recent advances in imaging techniques enable researchers to study spatial structure of the TME at a single-cell level. Investigating spatial patterns and interactions of cell subtypes within the TME provides useful insights into how cells with different biological purposes behave, which may consequentially impact a subject's clinical outcomes. We utilize a class of well-known spatial summary statistics, the K-function and its variants, to explore inter-cell dependence as a function of distances between cells. Using techniques from functional data analysis, we introduce an approach to model the association between these summary spatial functions and subject-level outcomes, while controlling for other clinical scalar predictors such as age and disease stage. In particular, we leverage the additive functional Cox regression model (AFCM) to study the nonlinear impact of spatial interaction between tumor and stromal cells on overall survival in patients with non-small cell lung cancer, using multiplex immunohistochemistry (mIHC) data. The applicability of our approach is further validated using a publicly available multiplexed ion beam imaging (MIBI) triple-negative breast cancer dataset. Author summary: Investigating spatial patterns and interactions of cells in the tumor microenvironment (TME) provides useful insights into cancer development and progression. In this work, we proposed a novel approach which combined established spatial summary functions with functional data analysis to flexibly model the cell-cell interactions with overall survival at different inter-cell distances, in conjunction with other clinical predictors such as age, disease stage. By applying the proposed framework to multiplex immunohistochemistry (mIHC) data of patients with non-small cell lung cancer (NSCLC), we studied the nonlinear impact of spatial interactions between tumor and stromal cells on overall survival. The applicability of our proposed method is further validated using a publicly available multiplexed ion beam imaging (MIBI) triple-negative breast cancer (TNBC) dataset. [ABSTRACT FROM AUTHOR]

Background: The Sysmex DI-60 digital morphology analyzer is a fully automated, cell-locating image analysis system. This study aimed to evaluate the analytical performance of DI-60. Methods: A total of 822 peripheral blood smears were used. The diagnostic performance of DI-60 in terms of red blood cell (RBC) morphology characterization, white blood cell (WBC) differentials, and the total assay time including hands-on time was evaluated. Results: In comparison with manual slide review, DI-60 demonstrated acceptable accuracy in recognizing polychromasia, target cells, and ovalocytes. However, for schistocytes, DI-60 demonstrated low specificity (10.4%) despite the high sensitivity (97.2%). In the precision analysis of RBC morphology characterization, borderline samples harboring specific RBCs showed inconsistencies in the positive results among 20 replicates. Particularly, 6 of 10 samples showed inconsistencies in the precision for schistocytes. For WBC differentials, the overall agreement between pre-classification results and user-verified results was 89.4%. Except for basophils, normal WBCs showed a good correlation between DI-60 (after user verification) and manual counts. The sensitivities in detecting immature granulocytes, blasts, atypical lymphocytes, and normoblasts were 85.9%, 92.0%, 37.5%, and 77.6%, respectively. Although the total assay time of DI-60 was longer than that of manual review, the hands-on time was considerably shorter with a difference of 144.1 s/slide for abnormal samples. Conclusion: DI-60 demonstrated acceptable performance for normal samples. However, for abnormal WBC differentials and RBC morphology characterization, it should be utilized carefully. DI-60 may contribute to an improvement in laboratory efficiency with increased feasibility. [ABSTRACT FROM AUTHOR]

Segmenting three-dimensional (3D) microscopy images is essential for understanding phenomena like morphogenesis, cell division, cellular growth, and genetic expression patterns. Recently, deep learning (DL) pipelines have been developed, which claim to provide high accuracy segmentation of cellular images and are increasingly considered as the state of the art for image segmentation problems. However, it remains difficult to define their relative performances as the concurrent diversity and lack of uniform evaluation strategies makes it difficult to know how their results compare. In this paper, we first made an inventory of the available DL methods for 3D cell segmentation. We next implemented and quantitatively compared a number of representative DL pipelines, alongside a highly efficient non-DL method named MARS. The DL methods were trained on a common dataset of 3D cellular confocal microscopy images. Their segmentation accuracies were also tested in the presence of different image artifacts. A specific method for segmentation quality evaluation was adopted, which isolates segmentation errors due to under- or oversegmentation. This is complemented with a 3D visualization strategy for interactive exploration of segmentation quality. Our analysis shows that the DL pipelines have different levels of accuracy. Two of them, which are end-to-end 3D and were originally designed for cell boundary detection, show high performance and offer clear advantages in terms of adaptability to new data. Author summary: In recent years, a number of deep learning (DL) algorithms based on computational neural networks have been developed, which claim to achieve high accuracy and automatic segmentation of three-dimensional (3D) microscopy images. Although these algorithms have received considerable attention in the literature, it is difficult to evaluate their relative performances, while it remains unclear whether they really perform better than other, more classical segmentation methods. To clarify these issues, we performed a detailed, quantitative analysis of a number of representative DL pipelines for cell instance segmentation from 3D confocal microscopy image datasets. We developed a protocol for benchmarking the performances of such DL-based segmentation pipelines using common training and test datasets, evaluation metrics, and visualizations. Using this protocol, we evaluated and compared 4 different DL pipelines to identify their strengths and limitations. A high performance non-DL method was also included in the evaluation. We show that DL pipelines may show significant differences in their performances depending on their model architecture and pipeline components but overall show excellent adaptability to unseen data. We also show that our benchmarking protocol could be extended to a variety of segmentation pipelines and datasets. [ABSTRACT FROM AUTHOR]

Regeneration is a widely observed phenomenon by which the integrity of an organism is recovered after damage. To date, studies on the molecular and cellular mechanisms of regeneration have been limited to a handful of model multicellular organisms. Here, the regeneration ability of marine macroalgae (Rhodophyta, Phaeophyceae, Chlorophyta) was systematically surveyed after thallus severing. Live cell imaging on severed thalli uncovered the cellular response to the damage. Three types of responses–budding, rhizoid formation, and/or sporulation–were observed in 25 species among 66 examined, proving the high potential of regeneration of macroalgae. The cellular and nuclear dynamics were monitored during cell repair or rhizoid formation of four phylogenetically diverged species, and the tip growth of the cells near the damaged site was observed as a common response. Nuclear translocation followed tip growth, enabling overall distribution of multinuclei or central positioning of the mononucleus. In contrast, the control of cell cycle events, such as nuclear division and septation, varied in these species. These observations showed that marine macroalgae utilise a variety of regeneration pathways, with some common features. This study also provides a novel methodology of live cell imaging in macroalgae. [ABSTRACT FROM AUTHOR]

A variational autoencoder (VAE) is a machine learning algorithm, useful for generating a compressed and interpretable latent space. These representations have been generated from various biomedical data types and can be used to produce realistic-looking simulated data. However, standard vanilla VAEs suffer from entangled and uninformative latent spaces, which can be mitigated using other types of VAEs such as β-VAE and MMD-VAE. In this project, we evaluated the ability of VAEs to learn cell morphology characteristics derived from cell images. We trained and evaluated these three VAE variants—Vanilla VAE, β-VAE, and MMD-VAE—on cell morphology readouts and explored the generative capacity of each model to predict compound polypharmacology (the interactions of a drug with more than one target) using an approach called latent space arithmetic (LSA). To test the generalizability of the strategy, we also trained these VAEs using gene expression data of the same compound perturbations and found that gene expression provides complementary information. We found that the β-VAE and MMD-VAE disentangle morphology signals and reveal a more interpretable latent space. We reliably simulated morphology and gene expression readouts from certain compounds thereby predicting cell states perturbed with compounds of known polypharmacology. Inferring cell state for specific drug mechanisms could aid researchers in developing and identifying targeted therapeutics and categorizing off-target effects in the future. Author summary: We train machine learning algorithms to identify patterns of drug activity from cell morphology readouts. Known as variational autoencoders (VAE), these algorithms are unsupervised, meaning that they do not require any additional information other than the input data to learn. We train and systematically evaluate three different kinds of VAEs, each that learn different patterns, and we document performance and interpretability tradeoffs. In a comprehensive evaluation, we learn that an approach called latent space arithmetic (LSA) can predict cell states of compounds that interact with multiple targets and mechanisms, a well-known phenomenon known as drug polypharmacology. Importantly, we train other VAEs using a gene expression assay known as L1000 and compare performance to cell morphology VAEs. Gene expression and cell morphology are the two most common types of data for modeling cells. We discover that modeling cell morphology requires vastly different VAE parameters and architectures, and, importantly, that the data types are complementary; they predict polypharmacology of different compounds. Our benchmark and publicly available software will enable future VAE modeling improvements, and our drug polypharmacology predictions demonstrate that we can model potential off-target effects in drugs, which is an important step in drug discovery pipelines. [ABSTRACT FROM AUTHOR]

Developments in high-throughput microscopy have made it possible to collect huge amounts of cell image data that are difficult to analyse manually. Machine learning (e.g., deep learning) is often employed to automate the extraction of information from these data, such as cell counting, cell type classification and image segmentation. However, the effects of different imaging methods on the accuracy of image processing have not been examined systematically. We studied the effects of different imaging methods on the performance of machine learning-based cell type classifiers. We observed lymphoid-primed multipotential progenitor (LMPP) and pro-B cells using three imaging methods: differential interference contrast (DIC), phase contrast (Ph) and bright-field (BF). We examined the classification performance of convolutional neural networks (CNNs) with each of them and their combinations. CNNs achieved an area under the receiver operating characteristic (ROC) curve (AUC) of ~0.9, which was significantly better than when the classifier used only cell size or cell contour shape as input. However, no significant differences were found between imaging methods and focal positions. [ABSTRACT FROM AUTHOR]

Trypanosoma brucei, the causative agent of human African trypanosomiasis, is highly motile and must be able to move in all three dimensions for reliable cell division. These characteristics make long-term microscopic imaging of live T. brucei cells challenging, which has limited our understanding of important cellular events. To address this issue, we devised an imaging approach that confines cells in small volumes within cast agarose microwells that can be imaged continuously for up to 24 h. Individual T. brucei cells were imaged through multiple rounds of cell division with high spatial and temporal resolution. We developed a strategy that employs in-well "sentinel" cells to monitor potential imaging toxicity during loss-of-function experiments such as small-molecule inhibition and RNAi. Using our approach, we show that the asymmetric daughter cells produced during T. brucei division subsequently divide at different rates, with the old-flagellum daughter cell dividing first. The flagellar detachment phenotype that appears during inhibition of the Polo-like kinase homolog TbPLK occurs in a stepwise fashion, with the new flagellum initially linked by its tip to the old, attached flagellum. We probe the feasibility of a previously proposed "back-up" cytokinetic mechanism and show that cells that initiate this process do not appear to complete cell division. This live-cell imaging method will provide a novel avenue for studying a wide variety of cellular events in trypanosomatids that have previously been inaccessible. Author summary: Trypanosoma brucei causes severe diseases that affect humans and livestock in Sub-Saharan Africa. Efficient strategies for manipulating the T. brucei genome have provided a wealth of information about protein localization and function in diverse cellular processes. However, employing live-cell imaging for phenotypic analysis in T. brucei remains a significant challenge because immobilization of this highly motile parasite rapidly leads to morphologic defects and cell death. While fixed-cell imaging can provide snapshots of cellular events, it cannot provide the direct causal link or precise timing of events that comes from watching a living cell change over time. Our strategy using agarose microwells now allows long-term live cell T. brucei imaging with a simple apparatus that can be adapted for a wide range of experimental conditions. [ABSTRACT FROM AUTHOR]

We introduce a framework for end-to-end integrative modeling of 3D single-cell multi-channel fluorescent image data of diverse subcellular structures. We employ stacked conditional β-variational autoencoders to first learn a latent representation of cell morphology, and then learn a latent representation of subcellular structure localization which is conditioned on the learned cell morphology. Our model is flexible and can be trained on images of arbitrary subcellular structures and at varying degrees of sparsity and reconstruction fidelity. We train our full model on 3D cell image data and explore design trade-offs in the 2D setting. Once trained, our model can be used to predict plausible locations of structures in cells where these structures were not imaged. The trained model can also be used to quantify the variation in the location of subcellular structures by generating plausible instantiations of each structure in arbitrary cell geometries. We apply our trained model to a small drug perturbation screen to demonstrate its applicability to new data. We show how the latent representations of drugged cells differ from unperturbed cells as expected by on-target effects of the drugs. Author summary: It is impossible to acquire all the information we want about every cell we are interested in in a single experiment. Even just limiting ourselves to imaging, we can only image a small set of subcellular structures in each cell. If we are interested in integrating those images into a holistic picture of cellular organization directly from data, there are a number of approaches one might take. Here, we leverage the fact that of the three channels we image in each cell, two stay the same across the data set; these two channels assess the cell's shape and nuclear morphology. Given these two reference channels, we learn a model of cell and nuclear morphology, and then use this as a reference frame in which to learn a representation of the localization of each subcellular structure as measured by the third channel. We use β-variational autoencoders to learn representations of both the reference channels and representations of each subcellular structure (conditioned on the reference channels of the cell in which it was imaged). Since these models are both probabilistic and generative, we can use them to understand the variation in the data from which they were trained, to generate instantiations of new cell morphologies, and to generate imputations of structures in real cell images to create an integrated model of subcellular organization. [ABSTRACT FROM AUTHOR]

Morphogenesis is a precise and robust dynamic process during metazoan embryogenesis, consisting of both cell proliferation and cell migration. Despite the fact that much is known about specific regulations at molecular level, how cell proliferation and migration together drive the morphogenesis at cellular and organismic levels is not well understood. Using Caenorhabditis elegans as the model animal, we present a phase field model to compute early embryonic morphogenesis within a confined eggshell. With physical information about cell division obtained from three-dimensional time-lapse cellular imaging experiments, the model can precisely reproduce the early morphogenesis process as seen in vivo, including time evolution of location and morphology of each cell. Furthermore, the model can be used to reveal key cell-cell attractions critical to the development of C. elegans embryo. Our work demonstrates how genetic programming and physical forces collaborate to drive morphogenesis and provides a predictive model to decipher the underlying mechanism. Author summary: Embryonic development is a precise process involving cell division, cell-cell interaction, and cell migration. During the process, how each cell reaches its supposed location and be in contact with the right neighbors, and what roles genetic factors and physical forces play are important and fascinating questions. Using the worm Caenorhabditis elegans as a model system, we build a phase field model to simulate early morphogenesis. With a few physical inputs, the model can precisely reproduce the early morphological development of the worm. Such an accurate simulator can not only teach us how physical forces work together with genetic factors to shape up the complex process of development, but also make predictions, such as key cell-cell attractions critical in the process. [ABSTRACT FROM AUTHOR]

Identification of cell phenotypic states within heterogeneous populations, along with elucidation of their switching dynamics, is a central challenge in modern biology. Conventional single-cell analysis methods typically provide only indirect, static phenotypic readouts. Transmitted light images, on the other hand, provide direct morphological readouts and can be acquired over time to provide a rich data source for dynamic cell phenotypic state identification. Here, we describe an end-to-end deep learning platform, UPSIDE (Unsupervised Phenotypic State IDEntification), for discovering cell states and their dynamics from transmitted light movies. UPSIDE uses the variational auto-encoder architecture to learn latent cell representations, which are then clustered for state identification, decoded for feature interpretation, and linked across movie frames for transition rate inference. Using UPSIDE, we identified distinct blood cell types in a heterogeneous dataset. We then analyzed movies of patient-derived acute myeloid leukemia cells, from which we identified stem-cell associated morphological states as well as the transition rates to and from these states. UPSIDE opens up the use of transmitted light movies for systematic exploration of cell state heterogeneity and dynamics in biology and medicine. Author summary: The human body contains hundreds of different cell types, each with distinctive identities and functions. Identifying these cellular identities and functional states is one of the great challenges in contemporary biology. We have addressed this challenge by developing UPSIDE, a machine learning approach for discovering cell types and cell states from time-resolved live cell imaging data, which provides a richly detailed source of information. Key distinguishing features of UPSIDE include the ability to directly learn cell type-defining features from brightfield images without prior knowledge; and an ability to visualize and interpret these machine-learned features to understand why they were chosen. UPSIDE can distinguish different human blood cell types despite their visual similarity and can use morphologic clues to identify and follow cell states. In a disease-relevant example, we used UPSIDE to identify stem-like and more mature cell subpopulations in patient-derived acute myelogenous leukemia cells, and to determine the speed at which these cell types inter-convert. UPSDIDE provides a general-purpose tool for the unbiased identification and analysis of cell types and state transitions in heterogeneous cell populations. [ABSTRACT FROM AUTHOR]

The robust proliferation of cancer cells requires vastly elevated levels of protein synthesis, which relies on a steady supply of aminoacylated tRNAs. Delivery of tRNAs to the cytoplasm is a highly regulated process, but the machinery for tRNA nuclear export is not fully elucidated. In this study, using a live cell imaging strategy that visualizes nascent transcripts from a specific tRNA gene in yeast, we identified the nuclear basket proteins Mlp1 and Mlp2, two homologs of the human TPR protein, as regulators of tRNA export. TPR expression is significantly increased in lung cancer tissues and correlated with poor prognosis. Consistently, knockdown of TPR inhibits tRNA nuclear export, protein synthesis and cell growth in lung cancer cell lines. We further show that NXF1, a well-known mRNA nuclear export factor, associates with tRNAs and mediates their transport through nuclear pores. Collectively, our findings uncover a conserved mechanism that regulates nuclear export of tRNAs, which is a limiting step in protein synthesis in eukaryotes. Author summary: Protein synthesis is a fundamental biological process for cells to grow and proliferate. Amino acids are the building blocks for proteins, and each is carried to ribosomes for protein synthesis by its own specific transport RNAs (tRNAs). An imbalanced or malfunctioning pool of cellular tRNAs may cause uncontrolled cell growth and proliferation, which are hallmarks of cancer. Nuclear pore complexes (NPCs) consist of multiple proteins termed nucleoporins that mediate transport of materials between nucleus and cytoplasm. Our previous study discovered that tRNA genes in yeast associate with NPCs to coordinate tRNA synthesis with nuclear export. Here, we develop a live cell imaging strategy to elucidate the machinery for tRNA nuclear export, and identify the nucleoporin TPR as a regulator of the process. We find that TPR is overexpressed in lung cancer tissues and correlated with poor prognosis, whereas down-regulation of TPR inhibits tRNA nuclear export, protein synthesis and cell growth. We further show that the nuclear export factor NXF1 associates with tRNAs and mediates their transport through NPCs. Our findings uncover a conserved mechanism by which NPCs control the quantity and quality of tRNAs before they reach the cytoplasm to execute their roles in protein synthesis. [ABSTRACT FROM AUTHOR]

Background: Delivery of CRISPR/Cas RNPs to target cells still remains the biggest bottleneck to genome editing. Many efforts are made to develop efficient CRISPR/Cas RNP delivery methods that will not affect viability of target cell dramatically. Popular current methods and protocols of CRISPR/Cas RNP delivery include lipofection and electroporation, transduction by osmocytosis and reversible permeabilization and erythrocyte-based methods. Methods: In this study we will assess the efficiency and optimize current CRISPR/Cas RNP delivery protocols to target cells. We will conduct our work using molecular cloning, protein expression and purification, cell culture, flow cytometry (immunocytochemistry) and cellular imaging techniques. Discussion: This will be the first extensive comparative study of popular current methods and protocols of CRISPR/Cas RNP delivery to human cell lines and primary cells. All protocols will be optimized and characterized using the following criteria i) protein delivery and genome editing efficacy; ii) viability of target cells after delivery (post-transduction recovery); iii) scalability of delivery process; iv) cost-effectiveness of the delivery process and v) intellectual property rights. Some methods will be considered 'research-use only', others will be recommended for scaling and application in the development of cell-based therapies. [ABSTRACT FROM AUTHOR]

Fluorescence microscopy, which visualizes cellular components with fluorescent stains, is an invaluable method in image cytometry. From these images various cellular features can be extracted. Together these features form phenotypes that can be used to determine effective drug therapies, such as those based on nanomedicines. Unfortunately, fluorescence microscopy is time-consuming, expensive, labour intensive, and toxic to the cells. Bright-field images lack these downsides but also lack the clear contrast of the cellular components and hence are difficult to use for downstream analysis. Generating the fluorescence images directly from bright-field images using virtual staining (also known as "label-free prediction" and "in-silico labeling") can get the best of both worlds, but can be very challenging to do for poorly visible cellular structures in the bright-field images. To tackle this problem deep learning models were explored to learn the mapping between bright-field and fluorescence images for adipocyte cell images. The models were tailored for each imaging channel, paying particular attention to the various challenges in each case, and those with the highest fidelity in extracted cell-level features were selected. The solutions included utilizing privileged information for the nuclear channel, and using image gradient information and adversarial training for the lipids channel. The former resulted in better morphological and count features and the latter resulted in more faithfully captured defects in the lipids, which are key features required for downstream analysis of these channels. [ABSTRACT FROM AUTHOR]

Radiologic images provide a way to monitor tumor development and its response to therapies in a longitudinal and minimally invasive fashion. However, they operate on a macroscopic scale (average value per voxel) and are not able to capture microscopic scale (cell-level) phenomena. Nevertheless, to examine the causes of frequent fast fluctuations in tissue oxygenation, models simulating individual cells' behavior are needed. Here, we provide a link between the average data values recorded for radiologic images and the cellular and vascular architecture of the corresponding tissues. Using hybrid agent-based modeling, we generate a set of tissue morphologies capable of reproducing oxygenation levels observed in radiologic images. We then use these in silico tissues to investigate whether oxygen fluctuations can be explained by changes in vascular oxygen supply or by modulations in cellular oxygen absorption. Our studies show that intravascular changes in oxygen supply reproduce the observed fluctuations in tissue oxygenation in all considered regions of interest. However, larger-magnitude fluctuations cannot be recreated by modifications in cellular absorption of oxygen in a biologically feasible manner. Additionally, we develop a procedure to identify plausible tissue morphologies for a given temporal series of average data from radiology images. In future applications, this approach can be used to generate a set of tissues comparable with radiology images and to simulate tumor responses to various anti-cancer treatments at the tissue-scale level. Author summary: Low levels of oxygen, called hypoxia, are observable in many solid tumors. They are associated with more aggressive malignant cells that are resistant to chemo-, radio-, and immunotherapies. Recently developed imaging techniques provide a way to measure the magnitude of frequent short-term oxygen fluctuations, but they operate on a macro-scale voxel level. To examine the possible causes of rapid oxygen fluctuations at the cell level, we developed a hybrid agent-based mathematical model. We tested two different mechanisms that may be responsible for these cyclic effects on tissue oxygenation: temporal variations in vascular influx of oxygen and modulations in cellular oxygen absorption. Additionally, we developed a procedure to identify plausible tissue morphologies from data collected from radiological images. This can provide a bridge between the micro-scale simulations with individual cells and the longitudinal medical images containing average values. In future applications, this approach can be used to generate a set of tissues compatible with radiology images and to simulate tumor responses to various anticancer treatments at the cell-scale level. [ABSTRACT FROM AUTHOR]

The eukaryotic transcription cycle consists of three main steps: initiation, elongation, and cleavage of the nascent RNA transcript. Although each of these steps can be regulated as well as coupled with each other, their in vivo dissection has remained challenging because available experimental readouts lack sufficient spatiotemporal resolution to separate the contributions from each of these steps. Here, we describe a novel application of Bayesian inference techniques to simultaneously infer the effective parameters of the transcription cycle in real time and at the single-cell level using a two-color MS2/PP7 reporter gene and the developing fruit fly embryo as a case study. Our method enables detailed investigations into cell-to-cell variability in transcription-cycle parameters as well as single-cell correlations between these parameters. These measurements, combined with theoretical modeling, suggest a substantial variability in the elongation rate of individual RNA polymerase molecules. We further illustrate the power of this technique by uncovering a novel mechanistic connection between RNA polymerase density and nascent RNA cleavage efficiency. Thus, our approach makes it possible to shed light on the regulatory mechanisms in play during each step of the transcription cycle in individual, living cells at high spatiotemporal resolution. Author summary: Live cell imaging using fluorescence microscopy provides an exciting way to visualize the transcription cycle in living organisms with great amounts of precision. However, the output of these technologies is often complex and can be hard to interpret. We have developed a computational framework for analyzing the transcription cycle that quantifies rates of RNA initiation, elongation, and cleavage, given input datasets from live cell imaging. Using the developing fruit fly embryo as a case study, we demonstrate that our methodology can quantitatively describe the whole transcription cycle at single-cell resolution. These results allow us to investigate a plethora of avenues, from couplings between different aspects of the transcription cycle at the single-cell level to comparisons with theoretical predictions of distributions of elongation rates across cells. We envision our methodology to provide a unified computational framework for the analysis of transcriptional data obtained from live cell imaging. [ABSTRACT FROM AUTHOR]

Electron microscopy (EM) has been employed for decades to analyze cell structure. To also analyze the positions and functions of specific proteins, one typically relies on immuno-EM or on a correlation with fluorescence microscopy, in the form of correlated light and electron microscopy (CLEM). Nevertheless, neither of these procedures is able to also address the isotopic composition of cells. To solve this, a correlation with secondary ion mass spectrometry (SIMS) would be necessary. SIMS has been correlated in the past to EM or to fluorescence microscopy in biological samples, but not to CLEM. We achieved this here, using a protocol based on transmission EM, conventional epifluorescence microscopy and nanoSIMS. The protocol is easily applied, and enables the use of all three technologies at high performance parameters. We suggest that CLEM-SIMS will provide substantial information that is currently beyond the scope of conventional correlative approaches. [ABSTRACT FROM AUTHOR]

Artificial forms of mechanical limb stimulation are used within multiple fields of study to determine the level of cortical excitability and to map the trajectory of neuronal recovery from cortical damage or disease. Square-wave mechanical or electrical stimuli are often used in these studies, but a characterization of sensory-evoked response properties to square-waves with distinct fundamental frequencies but overlapping harmonics has not been performed. To distinguish between somatic stimuli, the primary somatosensory cortex must be able to represent distinct stimuli with unique patterns of activity, even if they have overlapping features. Thus, mechanical square-wave stimulation was used in conjunction with regional and cellular imaging to examine regional and cellular response properties evoked by different frequencies of stimulation. Flavoprotein autofluorescence imaging was used to map the somatosensory cortex of anaesthetized C57BL/6 mice, and in vivo two-photon Ca2+ imaging was used to define patterns of neuronal activation during mechanical square-wave stimulation of the contralateral forelimb or hindlimb at various frequencies (3, 10, 100, 200, and 300 Hz). The data revealed that neurons within the limb associated somatosensory cortex responding to various frequencies of square-wave stimuli exhibit stimulus-specific patterns of activity. Subsets of neurons were found to have sensory-evoked activity that is either primarily responsive to single stimulus frequencies or broadly responsive to multiple frequencies of limb stimulation. High frequency stimuli were shown to elicit more population activity, with a greater percentage of the population responding and greater percentage of cells with high amplitude responses. Stimulus-evoked cell-cell correlations within these neuronal networks varied as a function of frequency of stimulation, such that each stimulus elicited a distinct pattern that was more consistent across multiple trials of the same stimulus compared to trials at different frequencies of stimulation. The variation in cortical response to different square-wave stimuli can thus be represented by the population pattern of supra-threshold Ca2+ transients, the magnitude and temporal properties of the evoked activity, and the structure of the stimulus-evoked correlation between neurons. [ABSTRACT FROM AUTHOR]

Dataset annotation is a time and labor-intensive task and an integral requirement for training and testing deep learning models. The segmentation of images in life science microscopy requires annotated image datasets for object detection tasks such as instance segmentation. Although the amount of annotated image data has been steadily reduced due to methods such as data augmentation, the process of manual or semi-automated data annotation is the most labor and cost intensive task in the process of cell nuclei segmentation with deep neural networks. In this work we propose a system to fully automate the annotation process of a custom fluorescent cell nuclei image dataset. By that we are able to reduce nuclei labelling time by up to 99.5%. The output of our system provides high quality training data for machine learning applications to identify the position of cell nuclei in microscopy images. Our experiments have shown that the automatically annotated dataset provides coequal segmentation performance compared to manual data annotation. In addition, we show that our system enables a single workflow from raw data input to desired nuclei segmentation and tracking results without relying on pre-trained models or third-party training datasets for neural networks. [ABSTRACT FROM AUTHOR]

Microfluidic-based assays have become effective high-throughput approaches to examining replicative aging of budding yeast cells. Deep learning may offer an efficient way to analyze a large number of images collected from microfluidic experiments. Here, we compare three deep learning architectures to classify microfluidic time-lapse images of dividing yeast cells into categories that represent different stages in the yeast replicative aging process. We found that convolutional neural networks outperformed capsule networks in terms of accuracy, precision, and recall. The capsule networks had the most robust performance in detecting one specific category of cell images. An ensemble of three best-fitted single-architecture models achieves the highest overall accuracy, precision, and recall due to complementary performances. In addition, extending classification classes and data augmentation of the training dataset can improve the predictions of the biological categories in our study. This work lays a useful framework for sophisticated deep-learning processing of microfluidic-based assays of yeast replicative aging. [ABSTRACT FROM AUTHOR]

Microscopy is the main technique to visualize and study the structure and function of cells. The impact of optical and electron microscopy techniques is enormous in all fields of biomedical research. It is possible that different research areas rely on microscopy in diverse ways. Here, we analyzed comparatively the use of microscopy in pharmacology and cell biology, among other biomedical sciences fields. We collected data from articles published in several major journals in these fields. We analyzed the frequency of use of different optical and electron microscopy techniques: bright field, phase contrast, differential interference contrast, polarization, conventional fluorescence, confocal, live cell imaging, super resolution, transmission and scanning electron microscopy, and cryoelectron microscopy. Our analysis showed that the use of microscopy has a distinctive pattern in each research area, and that nearly half of the articles from pharmacology journals did not use any microscopy method, compared to the use of microscopy in almost all the articles from cell biology journals. The most frequent microscopy methods in all the journals in all areas were bright field and fluorescence (conventional and confocal). Again, the pattern of use was different: while the most used microscopy methods in pharmacology were bright field and conventional fluorescence, in cell biology the most used methods were conventional and confocal fluorescence, and live cell imaging. We observed that the combination of different microscopy techniques was more frequent in cell biology, with up to 6 methods in the same article. To correlate the use of microscopy with the research theme of each article, we analyzed the proportion of microscopy figures with the use of cell culture. We analyzed comparatively the vocabulary of each biomedical sciences field, by the identification of the most frequent words in the articles. The collection of data described here shows a vast difference in the use of microscopy among different fields of biomedical sciences. The data presented here could be valuable in other scientific and educational contexts. [ABSTRACT FROM AUTHOR]

The Na+/K+ ATPase is a key regulator of the hepatocytes ionic homeostasis, which when altered may lead to many liver disorders. We demonstrated recently, a significant stimulation of the Na+/K+ ATPase in HepG2 cells treated with the S1P analogue FTY 720P, that was mediated through PGE2. The mechanism by which the prostaglandin exerts its effect was not investigated, and is the focus of this work. The type of receptors involved was determined using pharmacological inhibitors, while western blot analysis, fluorescence imaging of GFP-tagged Na+/K+ ATPase, and time-lapse imaging on live cells were used to detect changes in membrane abundance of the Na+/K+ ATPase. The activity of the ATPase was assayed by measuring the amount of inorganic phosphate liberated in the presence and absence of ouabain. The enhanced activity of the ATPase was not observed when EP4 receptors were blocked but still appeared in presence inhibitors of EP1, EP2 and EP3 receptors. The involvement of EP4 was confirmed by the stimulation observed with EP4 agonist. The stimulatory effect of PGE2 did not appear in presence of Rp-cAMP, an inhibitor of PKA, and was imitated by db-cAMP, a PKA activator. Chelating intracellular calcium with BAPTA-AM abrogated the effect of db-cAMP as well as that of PGE2, but PGE2 treatment in a calcium-free PBS medium did not, suggesting an involvement of intracellular calcium, that was confirmed by the results obtained with 2-APB treatment. Live cell imaging showed movement of GFP–Na+/K+ ATPase-positive vesicles to the membrane and increased abundance of the ATPase at the membrane after PGE2 treatment. It was concluded that PGE2 acts via EP4, PKA, and intracellular calcium. [ABSTRACT FROM AUTHOR]

The accurate segmentation and tracking of cells in microscopy image sequences is an important task in biomedical research, e.g., for studying the development of tissues, organs or entire organisms. However, the segmentation of touching cells in images with a low signal-to-noise-ratio is still a challenging problem. In this paper, we present a method for the segmentation of touching cells in microscopy images. By using a novel representation of cell borders, inspired by distance maps, our method is capable to utilize not only touching cells but also close cells in the training process. Furthermore, this representation is notably robust to annotation errors and shows promising results for the segmentation of microscopy images containing in the training data underrepresented or not included cell types. For the prediction of the proposed neighbor distances, an adapted U-Net convolutional neural network (CNN) with two decoder paths is used. In addition, we adapt a graph-based cell tracking algorithm to evaluate our proposed method on the task of cell tracking. The adapted tracking algorithm includes a movement estimation in the cost function to re-link tracks with missing segmentation masks over a short sequence of frames. Our combined tracking by detection method has proven its potential in the IEEE ISBI 2020 Cell Tracking Challenge (http://celltrackingchallenge.net/) where we achieved as team KIT-Sch-GE multiple top three rankings including two top performances using a single segmentation model for the diverse data sets. [ABSTRACT FROM AUTHOR]

Lifeact is a short actin-binding peptide that is used to visualize filamentous actin (F-actin) structures in live eukaryotic cells using fluorescence microscopy. However, this popular probe has been shown to alter cellular morphology by affecting the structure of the cytoskeleton. The molecular basis for such artefacts is poorly understood. Here, we determined the high-resolution structure of the Lifeact–F-actin complex using electron cryo-microscopy (cryo-EM). The structure reveals that Lifeact interacts with a hydrophobic binding pocket on F-actin and stretches over 2 adjacent actin subunits, stabilizing the DNase I-binding loop (D-loop) of actin in the closed conformation. Interestingly, the hydrophobic binding site is also used by actin-binding proteins, such as cofilin and myosin and actin-binding toxins, such as a hypervariable region of TccC3 (TccC3HVR) from Photorhabdus luminescens and ExoY from Pseudomonas aeruginosa. In vitro binding assays and activity measurements demonstrate that Lifeact indeed competes with these proteins, providing an explanation for the altering effects of Lifeact on cell morphology in vivo. Finally, we demonstrate that the affinity of Lifeact to F-actin can be increased by introducing mutations into the peptide, laying the foundation for designing improved actin probes for live cell imaging. Lifeact is a short actin-binding peptide that is widely used to visualize filamentous actin structures in live cell fluorescence microscopy. This cryo-EM study reveals how Lifeact competes with actin-binding proteins such as cofilin and myosin, providing an explanation for how Lifeact alters cell morphology, while structure-guided site directed mutagenesis demonstrates that the affinity of Lifeact can be modulated. [ABSTRACT FROM AUTHOR]

Several previous studies have shown that when a cell that has taken up nanoparticles divides, the nanoparticles are inherited by the two daughter cells in an asymmetrical fashion, with one daughter cell receiving more nanoparticles than the other. This interesting observation is typically demonstrated either indirectly using mathematical modelling of high-throughput experimental data or more directly by imaging individual cells as they divide. Here we suggest that measurements of the coefficient of variation (standard deviation over mean) of the number of nanoparticles per cell over the cell population is another means of assessing the degree of asymmetry. Using simulations of an evolving cell population, we show that the coefficient of variation is sensitive to the degree of asymmetry and note its characteristic evolution in time. As the coefficient of variation is readily measurable using high-throughput techniques, this should allow a more rapid experimental assessment of the degree of asymmetry. [ABSTRACT FROM AUTHOR]

Taste receptor cells use multiple signaling pathways to detect chemicals in potential food items. These cells are functionally grouped into different types: Type I cells act as support cells and have glial-like properties; Type II cells detect bitter, sweet, and umami taste stimuli; and Type III cells detect sour and salty stimuli. We have identified a new population of taste cells that are broadly tuned to multiple taste stimuli including bitter, sweet, sour, and umami. The goal of this study was to characterize these broadly responsive (BR) taste cells. We used an IP3R3-KO mouse (does not release calcium (Ca2+) from internal stores in Type II cells when stimulated with bitter, sweet, or umami stimuli) to characterize the BR cells without any potentially confounding input from Type II cells. Using live cell Ca2+ imaging in isolated taste cells from the IP3R3-KO mouse, we found that BR cells are a subset of Type III cells that respond to sour stimuli but also use a PLCβ signaling pathway to respond to bitter, sweet, and umami stimuli. Unlike Type II cells, individual BR cells are broadly tuned and respond to multiple stimuli across different taste modalities. Live cell imaging in a PLCβ3-KO mouse confirmed that BR cells use this signaling pathway to respond to bitter, sweet, and umami stimuli. Short term behavioral assays revealed that BR cells make significant contributions to taste driven behaviors and found that loss of either PLCβ3 in BR cells or IP3R3 in Type II cells caused similar behavioral deficits to bitter, sweet, and umami stimuli. Analysis of c-Fos activity in the nucleus of the solitary tract (NTS) also demonstrated that functional Type II and BR cells are required for normal stimulus induced expression. Author summary: We use our taste system to decide if we are going to consume or reject a potential food item. This is critical for survival, as we need energy to live but also need to avoid potentially toxic compounds. Therefore, it is important to understand how the taste cells in our mouth detect the chemicals in food and send a message to our brain. Signals from the taste cells form a code that conveys information about the nature of the potential food item to the brain. How this taste coding works is not well understood. Currently, it is thought that taste cells are primarily selective for each taste stimuli and only detect either bitter, sweet, sour, salt, or umami (amino acids) compounds. Our study describes a new population of taste cells that can detect multiple types of stimuli, including chemicals from different taste qualities. Thus, taste cells can be either selective or generally responsive to stimuli which is similar to the cells in the brain that process taste information. The presence of these broadly responsive taste cells provides new insight into how taste information is sent to the brain for processing. [ABSTRACT FROM AUTHOR]

Centrosomes, the main microtubule organizing centers (MTOCs) of metazoan cells, contain an older "mother" and a younger "daughter" centriole. Stem cells either inherit the mother or daughter-centriole-containing centrosome, providing a possible mechanism for biased delivery of cell fate determinants. However, the mechanisms regulating centrosome asymmetry and biased centrosome segregation are unclear. Using 3D-structured illumination microscopy (3D-SIM) and live-cell imaging, we show in fly neural stem cells (neuroblasts) that the mitotic kinase Polo and its centriolar protein substrate Centrobin (Cnb) accumulate on the daughter centriole during mitosis, thereby generating molecularly distinct mother and daughter centrioles before interphase. Cnb's asymmetric localization, potentially involving a direct relocalization mechanism, is regulated by Polo-mediated phosphorylation, whereas Polo's daughter centriole enrichment requires both Wdr62 and Cnb. Based on optogenetic protein mislocalization experiments, we propose that the establishment of centriole asymmetry in mitosis primes biased interphase MTOC activity, necessary for correct spindle orientation. Centrosomes contain an older 'mother' and a younger 'daughter' centriole, but the mechanisms regulating centrosome asymmetry and biased centrosome segregation are unclear. Here, 3D-structured illumination microscopy and live cell imaging show that in fly neural stem cells the centrosomes become molecularly asymmetric during mitosis, aiding in spindle orientation and biased centrosome segregation. [ABSTRACT FROM AUTHOR]

Self-contained imaging systems are versatile instruments that are becoming a staple in cell culture laboratories. Many of these machines possess motorized stages and on-stage incubators that permit programmable imaging of live cells that make them a sensible tool for high-throughput applications. The EVOS imaging system is such a device and is capable of scanning multi-well dishes and stitching together multiple adjacent fields to produce coherent individual images of each well. Automated batch analysis and quantification of these tiled images does however require off-loading files to other software platforms. Our initial attempts to quantify tiled images captured on an EVOS device was plagued by some expected—and other unforeseeable—issues that arose at nearly every stage of analysis. These included: high background, illumination and stitching artifacts, low contrast, noise, focus inconsistencies, and image distortion—all of which negatively impacted processing efficiency. We have since overcome these obstacles and have created a rigorous cell counting pipeline for analyzing images captured by the EVOS scan function. We present development and optimization of this automated pipeline and submit it as an effective and facile tool for accurately counting cells from tiled images. [ABSTRACT FROM AUTHOR]

The NLRP3 inflammasome has emerged as a central immune regulator for sensing virulence factors expressed by microbial pathogens for triggering antimicrobial inflammation. Inflammation can be harmful and therefore this response must be tightly controlled. The mechanisms by which immune cells, such as macrophages, discriminate benign from pathogenic microbes to control the NLRP3 inflammasome remain poorly defined. Here we used live cell imaging coupled with a compendium of diverse clinical isolates to define how macrophages respond and activate NLRP3 when faced with the human yeast commensal and pathogen Candida albicans. We show that metabolic competition by C. albicans, rather than virulence traits such as hyphal formation, activates NLRP3 in macrophages. Inflammasome activation is triggered by glucose starvation in macrophages, which occurs when fungal load increases sufficiently to outcompete macrophages for glucose. Consistently, reducing Candida's ability to compete for glucose or increasing glucose availability for macrophages tames inflammatory responses. We define the mechanistic requirements for glucose starvation-dependent inflammasome activation by Candida and show that it leads to inflammatory cytokine production, but it does not trigger pyroptotic macrophage death. Pyroptosis occurs only with some clinical Candida isolates and only under specific experimental conditions, whereas inflammasome activation by glucose starvation is broadly relevant. In conclusion, macrophages use their metabolic status, specifically glucose metabolism, to sense fungal metabolic activity and increased microbial loads for activating NLRP3. Therefore, a major consequence of Candida-induced glucose starvation in macrophages is activation of inflammatory responses, with implications for understanding how metabolism modulates inflammation in fungal infections. Author summary: Activation of the immune regulator NLRP3 inflammasome by microbial pathogens has been shown to play both protective and destructive roles in infection, underscoring the importance of tight control over NLRP3-driven inflammation to ensure host health. A key microbe recognised by NLRP3 is the human yeast commensal and pathogen Candida albicans, which is responsible for mucosal and invasive infections. We demonstrate that innate immune cells sense their metabolic status to trigger NLRP3 activation only when microbial numbers have reached dangerous levels. This regulation is a consequence of metabolic competition between C. albicans and macrophages for an essential nutrient–glucose. The NLRP3 inflammasome is activated when increased fungal load in the infection microenvironment drives down glucose levels, thereby causing glucose starvation in macrophages. Restoring glucose homeostasis in macrophages reduced NLRP3 activation and production of the proinflammatory cytokine IL-1β, suggesting that metabolism regulates NLRP3 inflammasome activity in fungal infections. [ABSTRACT FROM AUTHOR]

In eukaryotic cells, KDEL receptors (KDELRs) facilitate the retrieval of endoplasmic reticulum (ER) luminal proteins from the Golgi compartment back to the ER. Apart from the well-documented retention function, recent findings reveal that the cellular KDELRs have more complex roles, e.g. in cell signalling, protein secretion, cell adhesion and tumorigenesis. Furthermore, several studies suggest that a sub-population of KDELRs is located at the cell surface, where they could form and internalize KDELR/cargo clusters after K/HDEL-ligand binding. However, so far it has been unclear whether there are species- or cell-type-specific differences in KDELR clustering. By comparing ligand-induced KDELR clustering in different mouse and human cell lines via live cell imaging, we show that macrophage cell lines from both species do not develop any clusters. Using RT-qPCR experiments and numerical analysis, we address the role of KDELR expression as well as endocytosis and exocytosis rates on the receptor clustering at the plasma membrane and discuss how the efficiency of directed transport to preferred docking sites on the membrane influences the exponent of the power-law distribution of the cluster size. [ABSTRACT FROM AUTHOR]

Radioluminescence microscopy (RLM) is an imaging technique that allows quantitative analysis of clinical radiolabeled drugs and probes in single cells. However, the modality suffers from slow data acquisition (15–30 minutes), thus critically affecting experiments with short-lived radioactive drugs. To overcome this issue, we suggest an approach that significantly accelerates data collection. Instead of using a single scintillator to image the decay of radioactive molecules, we sandwiched the radiolabeled cells between two scintillators. As proof of concept, we imaged cells labeled with [18F]FDG, a radioactive glucose popularly used in oncology to image tumors. Results show that the double scintillator configuration increases the microscope sensitivity by two-fold, thus reducing the image acquisition time by half to achieve the same result as the single scintillator approach. The experimental results were also compared with Geant4 Monte Carlo simulation to confirm the two-fold increase in sensitivity with only minor degradation in spatial resolution. Overall, these findings suggest that the double scintillator configuration can be used to perform time-sensitive studies such as cell pharmacokinetics or cell uptake of short-lived radiotracers. [ABSTRACT FROM AUTHOR]