synapsis: A Bioconductor Package to Automate the Analysis of Meiotic Double-Strand Break Repair and Crossover Formation.

Immunofluorescent staining is commonly used to generate images to characterize cytological phenotypes. The manual quantification of DNA double-strand breaks and their repair intermediates during meiosis using image data requires a series of subjective steps, from image selection to the counting of particular events per nucleus. Here we describe "synapsis," a bioconductor package, which includes a set of functions to automate the process of identifying meiotic nuclei and quantifying key double-strand break formation and repair events in a rapid, scalable, and reproducible workflow, and compare it to manual user quantification. The software can be extended for other applications in meiosis research, such as incorporating machine learning approaches to categorize meiotic substages.

Robustness assessment of an automated AI-based white blood cell morphometric analysis system using different smear preparation methods.

INTRODUCTION: Numerous AI-based systems are being developed to evaluate peripheral blood (PB) smears, but the feasibility of these systems on different smear preparation methods has not been fully understood. In this study, we assessed the impact of different smear preparation methods on the robustness of the deep learning system (DLS). METHODS: We collected 193 PB samples from patients, preparing a pair of smears for each sample using two systems: (1) SP50 smears, prepared by the DLS recommended fully automated slide preparation with double fan drying and staining (May-Grunwald Giemsa, M-G) system using SP50 (Sysmex) and (2) SP1000i smears, prepared by automated smear preparation with single fan drying by SP1000i (Sysmex) and manually stained with M-G. Digital images of PB cells were captured using DI-60 (Sysmex), and the DLS performed cell classification. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were used to evaluate the performance of the DLS. RESULTS: The specificity and NPV for all cell types were 97.4%-100% in both smear sets. The average sensitivity and PPV were 88.9% and 90.1% on SP50 smears, and 87.0% and 83.2% on SP1000i smears, respectively. The lower performance on SP1000i smears was attributed to the intra-lineage misclassification of neutrophil precursors and inter-lineage misclassification of lymphocytes. CONCLUSION: The DLS demonstrated consistent performance in specificity and NPV for smears prepared by a system different from the recommended method. Our results suggest that applying an automated smear preparation system optimized for the DLS system may be important.

Imaging Characteristics and Clinical Utility of Half-Dose versus Full-Dose Ultrawidefield Fundus Fluorescein Angiography.

PURPOSE: In early 2022, a fluorescein shortage occurred in the United States. To meet the standard of care for patients who required ultrawidefield fundus fluorescein angiography (UWFFA), a regimen of half-dose (250 mg) sodium fluorescein (10%) was adopted instead of the full dose (500 mg) at the Cole Eye Institute (CEI). In this paper, we compare the image quality, clinical utility, and the side-effect profile of half-dose versus full-dose fluorescein in UWFFA for a cohort of stable patients. DESIGN: Retrospective chart review. PARTICIPANTS: Patients with retinal vascular disease were included if they received half-dose and full-dose UWFFA (Optos California) within 6 months at the CEI. Eyes were excluded if they received intraocular injections, laser procedures, new immunosuppression, and worsened or improved inflammation on clinical examination. METHODS: Quantitative assessment of vascular leakage was performed using a machine learning-enhanced automated segmentation platform. Leakage from late-phase UWFFA images was compared between half-dose and full-dose images. Qualitative assessment of image quality and relative vascular leakage was performed by 2 masked independent reviewers. Side effects after fluorescein administration were recorded for each patient. MAIN OUTCOME MEASURES: Masked leakage grading and automated leakage scores. RESULTS: There were 52 eyes of 35 patients, 42 (81%) uveitic, 5 (9%) diabetic, and 4 (8%) normal controls. Patients had no change to their visual acuity (logarithm of the minimum angle of resolution mean, 0.3 ± 0.6), anterior chamber and vitreous cell between UFFWA's. UWFFA images were deemed of equal quality and leakage by both masked reviewers (78%-87% agreement; κ, 0.642). Automated leakage analysis showed mildly increased leakage in half-dose images overall (3.8% vs. 2.8%; P = 0.01) and in the macula (1.5% vs. 0.6%; P = 0.01). Side effects included nausea (half [n = 3, 9%] vs. full [n = 2, 6%]; P = 0.69) and urticaria (n = 0, 0% vs. n = 1, 2%; P = 0.99) and were not different between doses. CONCLUSIONS: In this cohort, half-dose UWFFA produced images that were of similar quality, clinical utility and with a similar side effect profile compared with full dose. Half-dose UWFFA can be used to accurately assess the retinal vasculature and could be used primarily as a method to save cost and prevent waste. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Regional Brain Net Water Uptake in Computed Tomography after Cardiac Arrest - A Novel Biomarker for Neuroprognostication.

BACKGROUND: Selective water uptake by neurons and glial cells and subsequent brain tissue oedema are key pathophysiological processes of hypoxic-ischemic encephalopathy (HIE) after cardiac arrest (CA). Although brain computed tomography (CT) is widely used to assess the severity of HIE, changes of brain radiodensity over time have not been investigated. These could be used to quantify regional brain net water uptake (NWU), a potential prognostic biomarker. METHODS: We conducted an observational prognostic accuracy study including a derivation (single center cardiac arrest registry) and a validation (international multicenter TTM2 trial) cohort. Early (<6 h) and follow-up (>24 h) head CTs of CA patients were used to determine regional NWU for grey and white matter regions after co-registration with a brain atlas. Neurological outcome was dichotomized as good versus poor using the Cerebral Performance Category Scale (CPC) in the derivation cohort and Modified Rankin Scale (mRS) in the validation cohort. RESULTS: We included 115 patients (81 derivation, 34 validation) with out-of-hospital (OHCA) and in-hospital cardiac arrest (IHCA). Regional brain water content remained unchanged in patients with good outcome. In patients with poor neurological outcome, we found considerable regional water uptake with the strongest effect in the basal ganglia. NWU >8% in the putamen and caudate nucleus predicted poor outcome with 100% specificity (95%-CI: 86-100%) and 43% (moderate) sensitivity (95%-CI: 31-56%). CONCLUSION: This pilot study indicates that NWU derived from serial head CTs is a promising novel biomarker for outcome prediction after CA. NWU >8% in basal ganglia grey matter regions predicted poor outcome while absence of NWU indicated good outcome. NWU and follow-up CTs should be investigated in larger, prospective trials with standardized CT acquisition protocols.

An Automated Imaging-Based Screen for Genetic Modulators of ER Organisation in Cultured Human Cells.

Hereditary spastic paraplegias (HSPs) are a heterogeneous group of mono-genetic inherited neurological disorders, whose primary manifestation is the disruption of the pyramidal system, observed as a progressive impaired gait and leg spasticity in patients. Despite the large list of genes linked to this group, which exceeds 80 loci, the number of cellular functions which the gene products engage is relatively limited, among which endoplasmic reticulum (ER) morphogenesis appears central. Mutations in genes encoding ER-shaping proteins are the most common cause of HSP, highlighting the importance of correct ER organisation for long motor neuron survival. However, a major bottleneck in the study of ER morphology is the current lack of quantitative methods, with most studies to date reporting, instead, on qualitative changes. Here, we describe and apply a quantitative image-based screen to identify genetic modifiers of ER organisation using a mammalian cell culture system. An analysis reveals significant quantitative changes in tubular ER and dense sheet ER organisation caused by the siRNA-mediated knockdown of HSP-causing genes ATL1 and RTN2. This screen constitutes the first attempt to examine ER distribution in cells in an automated and high-content manner and to detect genes which impact ER organisation.

Precision and Speed at Your Fingertips: An Automated Intracranial Hematoma Volume Calculation.

BACKGROUND: Intracranial hemorrhage (ICH) is a severe condition that requires rapid diagnosis and treatment. Automated methods for calculating ICH volumes can reduce human error and improve clinical decisioPlease provide professional degrees (e.g., PhD, MD) for the corresponding author.n-making. A novel automated method has been developed that is comparable to the ABC/2 method in terms of speed and accuracy while providing more accurate volumetric data. METHODS: We developed a novel automated algorithm for calculating intracranial blood volume from computed tomography (CT) scans. The algorithm consists of a Python script that processes Digital Imaging and Communications in Medicine images and determines the blood volume and ratio. The algorithm was validated against manual calculations performed by neurosurgeons. RESULTS: Our novel automated algorithm for calculating intracranial blood volume from CT scans demonstrated excellent agreement with the ABC/2 method, with a median overall difference of just 1.46 mL. The algorithm was also validated in patient groups with ICH, epidural hematoma (EDH), and SDH, with agreement coefficients of 0.992, 0.983, and 0.997, respectively. CONCLUSIONS: The study introduces a novel automated algorithm for calculating the volumes of various ICHs (EDH, and SDH) within CT scans. The algorithm showed excellent agreement with manual calculations and outperformed the commonly used ABC/2 method, which tends to overestimate ICH volume. The automated algorithm offers a more accurate, efficient, and time-saving approach to quantifying ICH, EDH, and SDH volumes, making it a valuable tool for clinical evaluation and decision-making.

Proteome-scale movements and compartment connectivity during the eukaryotic cell cycle.

Cell cycle progression relies on coordinated changes in the composition and subcellular localization of the proteome. By applying two distinct convolutional neural networks on images of millions of live yeast cells, we resolved proteome-level dynamics in both concentration and localization during the cell cycle, with resolution of ∼20 subcellular localization classes. We show that a quarter of the proteome displays cell cycle periodicity, with proteins tending to be controlled either at the level of localization or concentration, but not both. Distinct levels of protein regulation are preferentially utilized for different aspects of the cell cycle, with changes in protein concentration being mostly involved in cell cycle control and changes in protein localization in the biophysical implementation of the cell cycle program. We present a resource for exploring global proteome dynamics during the cell cycle, which will aid in understanding a fundamental biological process at a systems level.

[Short paths to diagnosis with artificial intelligence: systematic literature review on diagnostic decision support systems]. / Kurze Wege zur Diagnose mit künstlicher Intelligenz ­ systematische Literaturrecherche zu "diagnostic decision support systems".

BACKGROUND: Rare diseases are often recognized late. Their diagnosis is particularly challenging due to the diversity, complexity and heterogeneity of clinical symptoms. Computer-aided diagnostic aids, often referred to as diagnostic decision support systems (DDSS), are promising tools for shortening the time to diagnosis. Despite initial positive evaluations, DDSS are not yet widely used, partly due to a lack of integration with existing clinical or practice information systems. OBJECTIVE: This article provides an insight into currently existing diagnostic support systems that function without access to electronic patient records and only require information that is easily obtainable. MATERIALS AND METHODS: A systematic literature search identified eight articles on DDSS that can assist in the diagnosis of rare diseases with no need for access to electronic patient records or other information systems in practices and hospitals. The main advantages and disadvantages of the identified rare disease diagnostic support systems were extracted and summarized. RESULTS: Symptom checkers and DDSS based on portrait photos and pain drawings already exist. The degree of maturity of these applications varies. CONCLUSION: DDSS currently still face a number of challenges, such as concerns about data protection and accuracy, and acceptance and awareness continue to be rather low. On the other hand, there is great potential for faster diagnosis, especially for rare diseases, which are easily overlooked due to their large number and the low awareness of them. The use of DDSS should therefore be carefully considered by doctors on a case-by-case basis.

Deep learning algorithm for fully automated measurement of sagittal balance in adult spinal deformity.

AIM: Deep learning (DL) algorithms can be used for automated analysis of medical imaging. The aim of this study was to assess the accuracy of an innovative, fully automated DL algorithm for analysis of sagittal balance in adult spinal deformity (ASD). MATERIAL AND METHODS: Sagittal balance (sacral slope, pelvic tilt, pelvic incidence, lumbar lordosis and sagittal vertical axis) was evaluated in 141 preoperative and postoperative radiographs of patients with ASD. The DL, landmark-based measurements, were compared with the ground truth values from validated manual measurements. RESULTS: The DL algorithm showed an excellent consistency with the ground truth measurements. The intra-class correlation coefficient between the DL and ground truth measurements was 0.71-0.99 for preoperative and 0.72-0.96 for postoperative measurements. The DL detection rate was 91.5% and 84% for preoperative and postoperative images, respectively. CONCLUSION: This is the first study evaluating a complete automated DL algorithm for analysis of sagittal balance with high accuracy for all evaluated parameters. The excellent accuracy in the challenging pathology of ASD with long construct instrumentation demonstrates the eligibility and possibility for implementation in clinical routine.

Automated analysis of gray matter damage in aged mice reveals impaired remyelination in the cuprizone model.

Multiple sclerosis is a chronic autoimmune disease of the central nervous system characterized by myelin loss, axonal damage, and glial scar formation. Still, the underlying processes remain unclear, as numerous pathways and factors have been found to be involved in the development and progression of the disease. Therefore, it is of great importance to find suitable animal models as well as reliable methods for their precise and reproducible analysis. Here, we describe the impact of demyelination on clinically relevant gray matter regions of the hippocampus and cerebral cortex, using the previously established cuprizone model for aged mice. We could show that bioinformatic image analysis methods are not only suitable for quantification of cell populations, but also for the assessment of de- and remyelination processes, as numerous objective parameters can be considered for reproducible measurements. After cuprizone-induced demyelination, subsequent remyelination proceeded slowly and remained incomplete in all gray matter areas studied. There were regional differences in the number of mature oligodendrocytes during remyelination suggesting region-specific differences in the factors accounting for remyelination failure, as, even in the presence of oligodendrocytes, remyelination in the cortex was found to be impaired. Upon cuprizone administration, synaptic density and dendritic volume in the gray matter of aged mice decreased. The intensity of synaptophysin staining gradually restored during the subsequent remyelination phase, however the expression of MAP2 did not fully recover. Microgliosis persisted in the gray matter of aged animals throughout the remyelination period, whereas extensive astrogliosis was of short duration as compared to white matter structures. In conclusion, we demonstrate that the application of the cuprizone model in aged mice mimics the impaired regeneration ability seen in human pathogenesis more accurately than commonly used protocols with young mice and therefore provides an urgently needed animal model for the investigation of remyelination failure and remyelination-enhancing therapies.

SPIRO - the automated Petri plate imaging platform designed by biologists, for biologists.

Phenotyping of model organisms grown on Petri plates is often carried out manually, despite the procedures being time-consuming and laborious. The main reason for this is the limited availability of automated phenotyping facilities, whereas constructing a custom automated solution can be a daunting task for biologists. Here, we describe SPIRO, the Smart Plate Imaging Robot, an automated platform that acquires time-lapse photographs of up to four vertically oriented Petri plates in a single experiment, corresponding to 192 seedlings for a typical root growth assay and up to 2500 seeds for a germination assay. SPIRO is catered specifically to biologists' needs, requiring no engineering or programming expertise for assembly and operation. Its small footprint is optimized for standard incubators, the inbuilt green LED enables imaging under dark conditions, and remote control provides access to the data without interfering with sample growth. SPIRO's excellent image quality is suitable for automated image processing, which we demonstrate on the example of seed germination and root growth assays. Furthermore, the robot can be easily customized for specific uses, as all information about SPIRO is released under open-source licenses. Importantly, uninterrupted imaging allows considerably more precise assessment of seed germination parameters and root growth rates compared with manual assays. Moreover, SPIRO enables previously technically challenging assays such as phenotyping in the dark. We illustrate the benefits of SPIRO in proof-of-concept experiments which yielded a novel insight on the interplay between autophagy, nitrogen sensing, and photoblastic response.

Automated high-content image-based characterization of microorganism behavioral diversity and distribution.

Microorganisms have evolved complex systems to respond to environmental signals. Gradients of particular molecules and elemental ions alter the behavior of microbes and their distribution within their environment. Microdevices coupled with automated image-based methods are now employed to analyze the instantaneous distribution and motion behaviors of microbial species in controlled environments at small temporal scales, mimicking, to some extent, macro conditions. Such technologies have so far been adopted for investigations mainly on individual species. Similar versatile approaches must now be developed for the characterization of multiple and complex interactions between a microbial community and its environment. Here, we provide a comprehensive step-by-step method for the characterization of species-specific behavior in a synthetic mixed microbial suspension in response to an environmental driver. By coupling accessible microfluidic devices with automated image analysis approaches, we evaluated the behavioral response of three morphologically different telluric species (Phytophthora parasitica, Vorticella microstoma, Enterobacter aerogenes) to a potassium gradient driver. Using the TrackMate plug-in algorithm, we performed morphometric and then motion analyses to characterize the response of each microbial species to the driver. Such an approach enabled to confirm the different morphological features of the three species and simultaneously characterize their specific motion in reaction to the driver and their co-interaction dynamics. By increasing the complexity of suspensions, this approach could be integrated in a framework for phenotypic analysis in microbial ecology research, helping to characterize how key drivers influence microbiota assembly at microbiota host-environment interfaces.

Can Artificial Intelligence Aid Diagnosis by Teleguided Point-of-Care Ultrasound? A Pilot Study for Evaluating a Novel Computer Algorithm for COVID-19 Diagnosis Using Lung Ultrasound.

With the 2019 coronavirus disease (COVID-19) pandemic, there is an increasing demand for remote monitoring technologies to reduce patient and provider exposure. One field that has an increasing potential is teleguided ultrasound, where telemedicine and point-of-care ultrasound (POCUS) merge to create this new scope. Teleguided POCUS can minimize staff exposure while preserving patient safety and oversight during bedside procedures. In this paper, we propose the use of teleguided POCUS supported by AI technologies for the remote monitoring of COVID-19 patients by non-experienced personnel including self-monitoring by the patients themselves. Our hypothesis is that AI technologies can facilitate the remote monitoring of COVID-19 patients through the utilization of POCUS devices, even when operated by individuals without formal medical training. In pursuit of this goal, we performed a pilot analysis to evaluate the performance of users with different clinical backgrounds using a computer-based system for COVID-19 detection using lung ultrasound. The purpose of the analysis was to emphasize the potential of the proposed AI technology for improving diagnostic performance, especially for users with less experience.

Automated Image Analysis of Transmission Electron Micrographs: Nanoscale Evaluation of Radiation-Induced DNA Damage in the Context of Chromatin.

BACKGROUND: Heavy ion irradiation (IR) with high-linear energy transfer (LET) is characterized by a unique depth dose distribution and increased biological effectiveness. Following high-LET IR, localized energy deposition along the particle trajectories induces clustered DNA lesions, leading to low electron density domains (LEDDs). To investigate the spatiotemporal dynamics of DNA repair and chromatin remodeling, we established the automated image analysis of transmission electron micrographs. METHODS: Human fibroblasts were irradiated with high-LET carbon ions or low-LET photons. At 0.1 h, 0.5 h, 5 h, and 24 h post-IR, nanoparticle-labeled repair factors (53BP1, pKu70, pKu80, DNA-PKcs) were visualized using transmission electron microscopy in interphase nuclei to monitor the formation and repair of DNA damage in the chromatin ultrastructure. Using AI-based software tools, advanced image analysis techniques were established to assess the DNA damage pattern following low-LET versus high-LET IR. RESULTS: Low-LET IR induced single DNA lesions throughout the nucleus, and most DNA double-strand breaks (DSBs) were efficiently rejoined with no visible chromatin decondensation. High-LET IR induced clustered DNA damage concentrated along the particle trajectories, resulting in circumscribed LEDDs. Automated image analysis was used to determine the exact number of differently sized nanoparticles, their distance from one another, and their precise location within the micrographs (based on size, shape, and density). Chromatin densities were determined from grayscale features, and nanoparticles were automatically assigned to euchromatin or heterochromatin. High-LET IR-induced LEDDs were delineated using automated segmentation, and the spatial distribution of nanoparticles in relation to segmented LEDDs was determined. CONCLUSIONS: The results of our image analysis suggest that high-LET IR induces chromatin relaxation along particle trajectories, enabling the critical repair of successive DNA damage. Following exposure to different radiation qualities, automated image analysis of nanoparticle-labeled DNA repair proteins in the chromatin ultrastructure enables precise characterization of specific DNA damage patterns.

Quantitative Muscle Fascicle Tractography Using Brightness-Mode Ultrasound.

A muscle's architecture, defined as the geometric arrangement of its fibers with respect to its mechanical line of action, impacts its abilities to produce force and shorten or lengthen under load. Ultrasound and other noninvasive imaging methods have contributed significantly to our understanding of these structure-function relationships. The goal of this work was to develop a MATLAB toolbox for tracking and mathematically representing muscle architecture at the fascicle scale, based on brightness-mode ultrasound imaging data. The MuscleUS_Toolbox allows user-performed segmentation of a region of interest and automated modeling of local fascicle orientation; calculation of streamlines between aponeuroses of origin and insertion; and quantification of fascicle length, pennation angle, and curvature. A method is described for optimizing the fascicle orientation modeling process, and the capabilities of the toolbox for quantifying and visualizing fascicle architecture are illustrated in the human tibialis anterior muscle. The toolbox is freely available.

Automated stereological image analysis approach of the human placenta: Surface areas and vascularization.

Detecting and quantifying surface densities of placental villi and their vasculature adds important information on the development of the placenta under different exposures and pathological conditions. Today, a larger number of samples and tissue areas can be examined using automated Artificial Intelligence-based approaches. Although each image series calls for a particular approach, sharing the methods will help in facilitating reproducibility and comparability. Here we show the protocol of a software-based quantification of vessels (number and area) in villous tissues of human placentas, based on scanned images of full-size placental sections.

Invention of automated numerical algorithm adopting binarization for the evaluation of scalp hair coverage: An image analysis providing a substitute for phototrichogram and global photography assessment for hair diseases.

BACKGROUND: The efficacy of therapeutic modalities for hair disease can be evaluated globally by photo assessment and more precisely by phototrichogram (PTG). However, the latter procedure is laborious, time consuming, subject to inter-observer variation, and requires hair clipping. OBJECTIVE: To establish an automated and patient/investigator friendly methodology enabling quantitative hair amount evaluation for daily clinical practice. METHODS: A novel automated numerical algorithm (aNA) adopting digital image binarization (i.e., black and white color conversion) was invented to evaluate hair coverage and measure PTG parameters in scalp images. Step-by-step improvement of aNA was attempted through comparative analyses of the data obtained respectively by the novel approach and conventional PTG/global photography assessment (GPA). RESULTS: For measuring scalp hair coverage, the initial version of aNA generally agreed with the cumulative hair diameter as assessed using PTG, showing a coefficient of 0.60. However, these outcomes were influenced by the angle of hair near the parting line. By integrating an angle compensation formula, the standard deviation of aNA data decreased from 5.7% to 1.2%. Consequently, the coefficient of determination for hair coverage calculated using the modified aNA and cumulative hair diameter assessed by PTG increased to 0.90. Furthermore, the change in hair coverage as determined by the modified aNA protocol correlated well with changes in the GPA score of images obtained using clinical trials. CONCLUSION: The novel aNA method provides a valuable tool for enabling simple and accurate evaluation of hair growth and volume for clinical trials and for treatment of hair disease.

Automated human induced pluripotent stem cell colony segmentation for use in cell culture automation applications.

Human induced pluripotent stem cells (hiPSCs) have demonstrated great promise for a variety of applications that include cell therapy and regenerative medicine. Production of clinical grade hiPSCs requires reproducible manufacturing methods with stringent quality-controls such as those provided by image-controlled robotic processing systems. In this paper we present an automated image analysis method for identifying and picking hiPSC colonies for clonal expansion using the CellXTM robotic cell processing system. This method couples a light weight deep learning segmentation approach based on the U-Net architecture to automatically segment the hiPSC colonies in full field of view (FOV) high resolution phase contrast images with a standardized approach for suggesting pick locations. The utility of this method is demonstrated using images and data obtained from the CellXTM system where clinical grade hiPSCs were reprogrammed, clonally expanded, and differentiated into retinal organoids for use in treatment of patients with inherited retinal degenerative blindness.

Enabling oxygen-controlled microfluidic cultures for spatiotemporal microbial single-cell analysis.

Microfluidic cultivation devices that facilitate O2 control enable unique studies of the complex interplay between environmental O2 availability and microbial physiology at the single-cell level. Therefore, microbial single-cell analysis based on time-lapse microscopy is typically used to resolve microbial behavior at the single-cell level with spatiotemporal resolution. Time-lapse imaging then provides large image-data stacks that can be efficiently analyzed by deep learning analysis techniques, providing new insights into microbiology. This knowledge gain justifies the additional and often laborious microfluidic experiments. Obviously, the integration of on-chip O2 measurement and control during the already complex microfluidic cultivation, and the development of image analysis tools, can be a challenging endeavor. A comprehensive experimental approach to allow spatiotemporal single-cell analysis of living microorganisms under controlled O2 availability is presented here. To this end, a gas-permeable polydimethylsiloxane microfluidic cultivation chip and a low-cost 3D-printed mini-incubator were successfully used to control O2 availability inside microfluidic growth chambers during time-lapse microscopy. Dissolved O2 was monitored by imaging the fluorescence lifetime of the O2-sensitive dye RTDP using FLIM microscopy. The acquired image-data stacks from biological experiments containing phase contrast and fluorescence intensity data were analyzed using in-house developed and open-source image-analysis tools. The resulting oxygen concentration could be dynamically controlled between 0% and 100%. The system was experimentally tested by culturing and analyzing an E. coli strain expressing green fluorescent protein as an indirect intracellular oxygen indicator. The presented system allows for innovative microbiological research on microorganisms and microbial ecology with single-cell resolution.

A Simple Ca2+-Imaging Approach of Network-Activity Analyses for Human Neurons.

Rapid advances in light microscopy and development of all-optical electrophysiological imaging tools have greatly leveraged the speed and the depth of neurobiology studies. Calcium imaging is a common method that is useful for measuring calcium signals in cells and has been used as a functional proxy for neuronal activity. Here I describe a simple, stimulation-free approach that measures neuronal network activity and single-neuron dynamics in human neurons. This protocol provides the experimental workflow that includes step-wise illustrations of sample preparations, data processing, and analyses that can be used for quick phenotypical assessment and serves as a quick functional readout for mutagenesis or screen effort for neurodegenerative studies.