An optimized reporter of the transcription factor hypoxia-inducible factor 1α reveals complex HIF-1α activation dynamics in single cells.

Immune cells adopt a variety of metabolic states to support their many biological functions, which include fighting pathogens, removing tissue debris, and tissue remodeling. One of the key mediators of these metabolic changes is the transcription factor hypoxia-inducible factor 1α (HIF-1α). Single-cell dynamics have been shown to be an important determinant of cell behavior; however, despite the importance of HIF-1α, little is known about its single-cell dynamics or their effect on metabolism. To address this knowledge gap, here we optimized a HIF-1α fluorescent reporter and applied it to study single-cell dynamics. First, we showed that single cells are likely able to differentiate multiple levels of prolyl hydroxylase inhibition, a marker of metabolic change, via HIF-1α activity. We then applied a physiological stimulus known to trigger metabolic change, interferon-γ, and observed heterogeneous, oscillatory HIF-1α responses in single cells. Finally, we input these dynamics into a mathematical model of HIF-1α-regulated metabolism and discovered a profound difference between cells exhibiting high versus low HIF-1α activation. Specifically, we found cells with high HIF-1α activation are able to meaningfully reduce flux through the tricarboxylic acid cycle and show a notable increase in the NAD+/NADH ratio compared with cells displaying low HIF-1α activation. Altogether, this work demonstrates an optimized reporter for studying HIF-1α in single cells and reveals previously unknown principles of HIF-1α activation.

DeepCell Kiosk: scaling deep learning-enabled cellular image analysis with Kubernetes.

Deep learning is transforming the analysis of biological images, but applying these models to large datasets remains challenging. Here we describe the DeepCell Kiosk, cloud-native software that dynamically scales deep learning workflows to accommodate large imaging datasets. To demonstrate the scalability and affordability of this software, we identified cell nuclei in 106 1-megapixel images in ~5.5 h for ~US$250, with a cost below US$100 achievable depending on cluster configuration. The DeepCell Kiosk can be downloaded at https://github.com/vanvalenlab/kiosk-console ; a persistent deployment is available at https://deepcell.org/ .

Competing memories of mitogen and p53 signalling control cell-cycle entry.

Regulation of cell proliferation is necessary for immune responses, tissue repair, and upkeep of organ function to maintain human health. When proliferating cells complete mitosis, a fraction of newly born daughter cells immediately enter the next cell cycle, while the remaining cells in the same population exit to a transient or persistent quiescent state. Whether this choice between two cell-cycle pathways is due to natural variability in mitogen signalling or other underlying causes is unknown. Here we show that human cells make this fundamental cell-cycle entry or exit decision based on competing memories of variable mitogen and stress signals. Rather than erasing their signalling history at cell-cycle checkpoints before mitosis, mother cells transmit DNA damage-induced p53 protein and mitogen-induced cyclin D1 (CCND1) mRNA to newly born daughter cells. After mitosis, the transferred CCND1 mRNA and p53 protein induce variable expression of cyclin D1 and the CDK inhibitor p21 that almost exclusively determines cell-cycle commitment in daughter cells. We find that stoichiometric inhibition of cyclin D1-CDK4 activity by p21 controls the retinoblastoma (Rb) and E2F transcription program in an ultrasensitive manner. Thus, daughter cells control the proliferation-quiescence decision by converting the memories of variable mitogen and stress signals into a competition between cyclin D1 and p21 expression. We propose a cell-cycle control principle based on natural variation, memory and competition that maximizes the health of growing cell populations.

Deep learning for cellular image analysis.

Recent advances in computer vision and machine learning underpin a collection of algorithms with an impressive ability to decipher the content of images. These deep learning algorithms are being applied to biological images and are transforming the analysis and interpretation of imaging data. These advances are positioned to render difficult analyses routine and to enable researchers to carry out new, previously impossible experiments. Here we review the intersection between deep learning and cellular image analysis and provide an overview of both the mathematical mechanics and the programming frameworks of deep learning that are pertinent to life scientists. We survey the field's progress in four key applications: image classification, image segmentation, object tracking, and augmented microscopy. Last, we relay our labs' experience with three key aspects of implementing deep learning in the laboratory: annotating training data, selecting and training a range of neural network architectures, and deploying solutions. We also highlight existing datasets and implementations for each surveyed application.

Deep learning multi-shot 3D localization microscopy using hybrid optical-electronic computing.

Current 3D localization microscopy approaches are fundamentally limited in their ability to image thick, densely labeled specimens. Here, we introduce a hybrid optical-electronic computing approach that jointly optimizes an optical encoder (a set of multiple, simultaneously imaged 3D point spread functions) and an electronic decoder (a neural-network-based localization algorithm) to optimize 3D localization performance under these conditions. With extensive simulations and biological experiments, we demonstrate that our deep-learning-based microscope achieves significantly higher 3D localization accuracy than existing approaches, especially in challenging scenarios with high molecular density over large depth ranges.

Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments.

Live-cell imaging has opened an exciting window into the role cellular heterogeneity plays in dynamic, living systems. A major critical challenge for this class of experiments is the problem of image segmentation, or determining which parts of a microscope image correspond to which individual cells. Current approaches require many hours of manual curation and depend on approaches that are difficult to share between labs. They are also unable to robustly segment the cytoplasms of mammalian cells. Here, we show that deep convolutional neural networks, a supervised machine learning method, can solve this challenge for multiple cell types across the domains of life. We demonstrate that this approach can robustly segment fluorescent images of cell nuclei as well as phase images of the cytoplasms of individual bacterial and mammalian cells from phase contrast images without the need for a fluorescent cytoplasmic marker. These networks also enable the simultaneous segmentation and identification of different mammalian cell types grown in co-culture. A quantitative comparison with prior methods demonstrates that convolutional neural networks have improved accuracy and lead to a significant reduction in curation time. We relay our experience in designing and optimizing deep convolutional neural networks for this task and outline several design rules that we found led to robust performance. We conclude that deep convolutional neural networks are an accurate method that require less curation time, are generalizable to a multiplicity of cell types, from bacteria to mammalian cells, and expand live-cell imaging capabilities to include multi-cell type systems.

Trafficking of the myrosinase-associated protein GLL23 requires NUC/MVP1/GOLD36/ERMO3 and the p24 protein CYB.

Proteins detrimental to endoplasmic reticulum (ER) morphology need to be efficiently exported. Here, we identify two mechanisms that control trafficking of Arabidopsis thalianaGLL23, a 43 kDa GDSL-like lipase implicated in glucosinolate metabolism through its association with the ß-glucosidase myrosinase. Using immunofluorescence, we identified two mutants that showed aberrant accumulation of GLL23: large perinuclear ER aggregates in the nuclear cage (nuc) mutant; and small compartments contiguous with the peripheral ER in the cytoplasmic bodies (cyb) mutant. Live imaging of fluorescently tagged GLL23 confirmed its presence in the nuc and cyb compartments, but lack of fluorescent signals in the wild-type plants suggested that GLL23 is normally post-translationally modified for ER export. NUC encodes the MVP1/GOLD36/ERMO3 myrosinase-associated protein, previously shown to have vacuolar distribution. CYB is an ER and Golgi-localized p24 type I membrane protein component of coat protein complex (COP) vesicles, animal and yeast homologues of which are known to be involved in selective cargo sorting for ER-Golgi export. Without NUC, GLL23 accumulates in the ER this situation suggests that NUC is in fact active in the ER. Without CYB, both GLL23 and NUC were found to accumulate in cyb compartments, consistent with a role for NUC in GLL23 processing and indicated that GLL23 is the likely sorting target of the CYB p24 protein.

Multiplexed, image-based pooled screens in primary cells and tissues with PerturbView.

Optical pooled screening (OPS) is a scalable method for linking image-based phenotypes with cellular perturbations. However, it has thus far been restricted to relatively low-plex phenotypic readouts in cancer cell lines in culture due to limitations associated with in situ sequencing of perturbation barcodes. Here, we develop PerturbView, an OPS technology that leverages in vitro transcription to amplify barcodes before in situ sequencing, enabling screens with highly multiplexed phenotypic readouts across diverse systems, including primary cells and tissues. We demonstrate PerturbView in induced pluripotent stem cell-derived neurons, primary immune cells and tumor tissue sections from animal models. In a screen of immune signaling pathways in primary bone marrow-derived macrophages, PerturbView uncovered both known and novel regulators of NF-κB signaling. Furthermore, we combine PerturbView with spatial transcriptomics in tissue sections from a mouse xenograft model, paving the way to in situ screens with rich optical and transcriptomic phenotypes. PerturbView broadens the scope of OPS to a wide range of models and applications.

An isolated rupture of the flexor digitorum tendon of the index finger following volar locking plate fixation of a distal radius fracture: A case report.

Volar locking plates (VLP) have been widely used recently to treat distal radius fractures and are considered the gold standard. One of the most common complications of distal radius fracture surgery is flexor pollicis longus rupture, which may also occur in other tendons. Here, we report a case of isolated rupture of the flexor digitorum profundus to the index finger after VLP fixation of a distal radial fracture. Only a few cases of this have been reported in the literature. In previously reported cases, the cause of tendon rupture was repetitive mechanical stress due to implant protrusion. In our case, the plate was placed too distally; however, soft tissue completely covered the distal part of the plate. There was obvious synovitis within the carpal tunnel; therefore, pressure within the carpal tunnel may have increased. The cause of rupture in our case was thought to be a combination of direct mechanical stress and poor circulation due to inadequate VLP fixation.

Cell-to-cell variability in Myc dynamics drives transcriptional heterogeneity in cancer cells.

Cell-to-cell heterogeneity is vital for tumor evolution and survival. How cancer cells achieve and exploit this heterogeneity remains an active area of research. Here, we identify c-Myc as a highly heterogeneously expressed transcription factor and an orchestrator of transcriptional and phenotypic diversity in cancer cells. By monitoring endogenous c-Myc protein in individual living cells, we report the surprising pulsatile nature of c-Myc expression and the extensive cell-to-cell variability in its dynamics. We further show that heterogeneity in c-Myc dynamics leads to variable target gene transcription and that timing of c-Myc expression predicts cell-cycle progression rates and drug sensitivities. Together, our data advocate for a model in which cancer cells increase the heterogeneity of functionally diverse transcription factors such as c-Myc to rapidly survey transcriptional landscapes and survive stress.

Potential Relationships between the Median Nerve Cross-Sectional Area and Physical Characteristics in Unilateral Symptomatic Carpal Tunnel Syndrome Patients.

BACKGROUND: The present study investigated the relationships between the median nerve cross-sectional area (CSA) and physical characteristics in patients with unilateral symptomatic carpal tunnel syndrome (CTS). METHODS: Height, weight, body mass index (BMI), disease duration, results of electrodiagnostic testing (EDX), and median nerve CSA at the level of the wrist crease were recorded in 81 patients with CTS who presented with symptoms on only one side. Correlation coefficients between median nerve CSA and physical characteristics, disease duration, and results of EDX were analyzed. RESULTS: Median nerve CSA at the wrist crease (mm2) was significantly larger on the symptomatic side (14.1 ± 3.8) than on the asymptomatic side (11.5 ± 2.9). Median nerve CSA correlated with body weight (correlation coefficient = 0.39) and BMI (correlation coefficient = 0.44) on the asymptomatic side, but not on the symptomatic side. These correlations were slightly stronger in females (correlation coefficient = 0.46) than in males (correlation coefficient = 0.40). No correlations between median nerve CSA and disease duration and the results of EDX were observed in both sides. CONCLUSIONS: In patients with unilateral symptomatic CTS, median nerve CSA correlated with BMI only on the asymptomatic side. The present results suggest that the relationship between median nerve CSA and BMI in CTS is significant until symptom onset but may be masked by edema and pseudoneuroma after its onset. A higher BMI is associated with a larger CSA of the median nerve, which may be a risk factor for the development of CTS.

Context-dependent regulation of lipid accumulation in adipocytes by a HIF1α-PPARγ feedback network.

Hypoxia-induced upregulation of HIF1α triggers adipose tissue dysfunction and insulin resistance in obese patients. HIF1α closely interacts with PPARγ, the master regulator of adipocyte differentiation and lipid accumulation, but there are conflicting results regarding how this interaction controls the excessive lipid accumulation that drives adipocyte dysfunction. To directly address these conflicts, we established a differentiation system that recapitulated prior seemingly opposing observations made across different experimental settings. Using single-cell imaging and coarse-grained mathematical modeling, we show how HIF1α can both promote and repress lipid accumulation during adipogenesis. Our model predicted and our experiments confirmed that the opposing roles of HIF1α are isolated from each other by the positive-feedback-mediated upregulation of PPARγ that drives adipocyte differentiation. Finally, we identify three factors: strength of the differentiation cue, timing of hypoxic perturbation, and strength of HIF1α expression changes that, when considered together, provide an explanation for many of the previous conflicting reports.

A multiplexed epitope barcoding strategy that enables dynamic cellular phenotypic screens.

Pooled genetic libraries have improved screening throughput for mapping genotypes to phenotypes. However, selectable phenotypes are limited, restricting screening to outcomes with a low spatiotemporal resolution. Here, we integrated live-cell imaging with pooled library-based screening. To enable intracellular multiplexing, we developed a method called EPICode that uses a combination of short epitopes, which can also appear in various subcellular locations. EPICode thus enables the use of live-cell microscopy to characterize a phenotype of interest over time, including after sequential stimulatory/inhibitory manipulations, and directly connects behavior to the cellular genotype. To test EPICode's capacity against an important milestone-engineering and optimizing dynamic, live-cell reporters-we developed a live-cell PKA kinase translocation reporter with improved sensitivity and specificity. The use of epitopes as fluorescent barcodes introduces a scalable strategy for high-throughput screening broadly applicable to protein engineering and drug discovery settings where image-based phenotyping is desired.

Clinical Relevance of Ultrasonographic and Electrophysiological Findings of the Median Nerve in Unilateral Carpal Tunnel Syndrome Patients.

Few studies have compared the unaffected and affected sides in the same carpal tunnel syndrome (CTS) patients using ultrasonography and electrophysiological tests. We focused on unilateral idiopathic CTS patients to investigate whether clinical test results differ between the unaffected and affected sides. The bilateral wrist joints of 61 unilateral idiopathic CTS patients were evaluated. The median nerve cross-sectional area of ultrasound image, and latencies of the compound muscle action potential (CMAP) and sensory nerve action potential (SNAP) were measured. The values obtained were compared between the affected and unaffected sides. The diagnostic accuracies of each parameter were assessed, and cut-off values were defined. Significant differences were observed in all parameters between the affected and unaffected sides (p < 0.01). Area under the curve (AUC) values were 0.74, 0.88, and 0.73 for the cross-sectional area, CMAP distal latency, and SNAP distal latency, respectively. Cut-off values were 11.9 mm2, 5.1 ms, and 3.1 ms for the cross-sectional area, CMAP distal latency, and SNAP distal latency, respectively. The most reliable parameter that reflected clinical symptoms was the distal latency of CMAP. Cut-off values for each parameter are considered to be an index for the onset of the clinical symptoms of CTS.

A forecast for large-scale, predictive biology: Lessons from meteorology.

Quantitative systems biology, in which predictive mathematical models are constructed to guide the design of experiments and predict experimental outcomes, is at an exciting transition point, where the foundational scientific principles are becoming established, but the impact is not yet global. The next steps necessary for mathematical modeling to transform biological research and applications, in the same way it has already transformed other fields, is not completely clear. The purpose of this perspective is to forecast possible answers to this question-what needs to happen next-by drawing on the experience gained in another field, specifically meteorology. We review here a number of lessons learned in weather prediction that are directly relevant to biological systems modeling, and that we believe can enable the same kinds of global impact in our field as atmospheric modeling makes today.

Structure-activity mapping of ARHGAP36 reveals regulatory roles for its GAP homology and C-terminal domains.

ARHGAP36 is an atypical Rho GTPase-activating protein (GAP) family member that drives both spinal cord development and tumorigenesis, acting in part through an N-terminal motif that suppresses protein kinase A and activates Gli transcription factors. ARHGAP36 also contains isoform-specific N-terminal sequences, a central GAP-like module, and a unique C-terminal domain, and the functions of these regions remain unknown. Here we have mapped the ARHGAP36 structure-activity landscape using a deep sequencing-based mutagenesis screen and truncation mutant analyses. Using this approach, we have discovered several residues in the GAP homology domain that are essential for Gli activation and a role for the C-terminal domain in counteracting an N-terminal autoinhibitory motif that is present in certain ARHGAP36 isoforms. In addition, each of these sites modulates ARHGAP36 recruitment to the plasma membrane or primary cilium. Through comparative proteomics, we also have identified proteins that preferentially interact with active ARHGAP36, and we demonstrate that one binding partner, prolyl oligopeptidase-like protein, is a novel ARHGAP36 antagonist. Our work reveals multiple modes of ARHGAP36 regulation and establishes an experimental framework that can be applied towards other signaling proteins.

Engineered Fluorescent E. coli Lysogens Allow Live-Cell Imaging of Functional Prophage Induction Triggered inside Macrophages.

Half of the bacteria in the human gut microbiome are lysogens containing integrated prophages, which may activate in stressful immune environments. Although lysogens are likely to be phagocytosed by macrophages, whether prophage activation occurs or influences the outcome of bacterial infection remains unexplored. To study the dynamics of bacteria-phage interactions in living cells-in particular, the macrophage-triggered induction and lysis of dormant prophages in the phagosome-we adopted a tripartite system where murine macrophages engulf E. coli, which are lysogenic with an engineered bacteriophage λ, containing a fluorescent lysis reporter. Pre-induced prophages are capable of lysing the host bacterium and propagating infection to neighboring bacteria in the same phagosome. A non-canonical pathway, mediated by PhoP, is involved with the native λ phage induction inside phagocytosed E. coli. These findings suggest two possible mechanisms by which induced prophages may function to aid the bactericidal activity of macrophages.

Techniques for Studying Decoding of Single Cell Dynamics.

Cells must be able to interpret signals they encounter and reliably generate an appropriate response. It has long been known that the dynamics of transcription factor and kinase activation can play a crucial role in selecting an individual cell's response. The study of cellular dynamics has expanded dramatically in the last few years, with dynamics being discovered in novel pathways, new insights being revealed about the importance of dynamics, and technological improvements increasing the throughput and capabilities of single cell measurements. In this review, we highlight the important developments in this field, with a focus on the methods used to make new discoveries. We also include a discussion on improvements in methods for engineering and measuring single cell dynamics and responses. Finally, we will briefly highlight some of the many challenges and avenues of research that are still open.

Escalating Threat Levels of Bacterial Infection Can Be Discriminated by Distinct MAPK and NF-κB Signaling Dynamics in Single Host Cells.

During an infection, immune cells must identify the specific level of threat posed by a given bacterial input in order to generate an appropriate response. Given that they use a general non-self-recognition system, known as Toll-like receptors (TLRs), to detect bacteria, it remains unclear how they transmit information about a particular threat. To determine whether host cells can use signaling dynamics to transmit contextual information about a bacterial stimulus, we use live-cell imaging to make simultaneous quantitative measurements of host MAPK and NF-κB signaling, two key pathways downstream of TLRs, and bacterial infection and load. This combined, single-cell approach reveals that NF-κB and MAPK signaling dynamics are sufficient to discriminate between (1) pathogen-associated molecular patterns (PAMPs) versus bacteria, (2) extracellular versus intracellular bacteria, (3) pathogenic versus non-pathogenic bacteria, and (4) the presence or absence of features indicating an active intracellular bacterial infection, such as replication and effector secretion.