Single-shot differential phase contrast microscopy using ring-shaped polarisation multiplexing illumination.

We propose a differential phase contrast microscopy that enables single-shot phase imaging for unstained biological samples. The proposed approach employs a ring-shaped LED array for polarisation multiplexing illumination and a polarisation camera for image acquisition. As such, multiple images of different polarisation angles can be simultaneously captured with a single shot. Through polarisation demultiplexing, the sample phase can therefore be recovered from the single-shot measurement. Both simulations and experiments demonstrate the effectiveness of the approach. We also demonstrate that ring-shaped illumination enables higher contrast and lower-distortion imaging results than disk-shaped illumination does. The proposed single-shot approach potentially enables phase contrast imaging for live cell samples in vitro. Lay Description: We propose a microscopy that enables imaging of transparent samples, unstained cells, etc. We demonstrate that the proposed method enables higher contrast and lower-distortion imaging results than conventional methods, and significantly improves imaging efficiency. The proposed method potentially enables dynamic imaging for live cell samples in vitro.

An updated approach to the evaluation of the urinary sediment.

Examination of the urinary sediment (U-sed) is an important non-invasive, rapid, and inexpensive tool for the diagnosis and surveillance over time of renal diseases. In this Educational Review, we describe first how to collect, prepare, and examine urine samples in order to obtain reliable results. Then, we describe the U-sed findings in isolated microscopic hematuria, glomerular diseases, acute interstitial nephritis, acute kidney injury, reactivation of the BK virus in kidney transplant recipients, and crystalluric genetic diseases.

Reflective Phase-Contrast for High-Contrast Imaging of van der Waals Heterostructure.

Optical microscopy plays a critical role in the fabrication of two-dimensional (2D) van der Waals heterostructures. An outstanding challenge in conventional microscopy is to visualize transparent 2D layers as well as embedded monolayers in a stacked heterostructure with high optical contrast. Phase-contrast microscopy, first developed by Frits Zernike in the 1930s, leverages the interference effect between specimen scattered light and background light to increase the contrast of transparent specimens. Such phase-contrast microscopy, always in a transmission configuration, revolutionized the study of transparent cellular structures in biology. Here, we develop a versatile reflective phase-contrast microscopy for imaging 2D heterostructures. We employ two spatial light modulators to flexibly control the intensity and phase of the illumination and the reflected light. This reflective phase-contrast microscopy achieves unprecedented high contrast for imaging a transparent 2D monolayer. It also enables direct observation of 2D monolayers embedded inside a thick heterostructure that are "invisible" in conventional microscopy.

Microbial Acoustical Analyzer for Antibiotic Indication.

In this study, a compact acoustic analyzer for express analysis of antibiotics based on a piezoelectric resonator with a lateral electric field and combined with a computer was developed. The possibility of determining chloramphenicol in aqueous solutions in the concentration range of 0.5-15 µg/mL was shown. Bacterial cells that are sensitive to this antibiotic were used as a sensory element. The change in the electrical impedance modulus of the resonator upon addition of the antibiotic to the cell suspension served as an analytical signal. The analysis time did not exceed 4 min. The correlation of the experimental results of an acoustic sensor with the results obtained using the light phase-contrast microscopy and standard microbiological analysis was established. The compact biological analyzer demonstrated stability, reproducibility, and repeatability of results.

Learning Diatoms Classification from a Dry Test Slide by Holographic Microscopy.

Diatoms are among the dominant phytoplankters in marine and freshwater habitats, and important biomarkers of water quality, making their identification and classification one of the current challenges for environmental monitoring. To date, taxonomy of the species populating a water column is still conducted by marine biologists on the basis of their own experience. On the other hand, deep learning is recognized as the elective technique for solving image classification problems. However, a large amount of training data is usually needed, thus requiring the synthetic enlargement of the dataset through data augmentation. In the case of microalgae, the large variety of species that populate the marine environments makes it arduous to perform an exhaustive training that considers all the possible classes. However, commercial test slides containing one diatom element per class fixed in between two glasses are available on the market. These are usually prepared by expert diatomists for taxonomy purposes, thus constituting libraries of the populations that can be found in oceans. Here we show that such test slides are very useful for training accurate deep Convolutional Neural Networks (CNNs). We demonstrate the successful classification of diatoms based on a proper CNNs ensemble and a fully augmented dataset, i.e., creation starting from one single image per class available from a commercial glass slide containing 50 fixed species in a dry setting. This approach avoids the time-consuming steps of water sampling and labeling by skilled marine biologists. To accomplish this goal, we exploit the holographic imaging modality, which permits the accessing of a quantitative phase-contrast maps and a posteriori flexible refocusing due to its intrinsic 3D imaging capability. The network model is then validated by using holographic recordings of live diatoms imaged in water samples i.e., in their natural wet environmental condition.

Automated noninvasive epithelial cell counting in phase contrast microscopy images with automated parameter selection.

Cell counting is commonly used to determine proliferation rates in cell cultures and for adherent cells it is often a 'destructive' process requiring disruption of the cell monolayer resulting in the inability to follow cell growth longitudinally. This process is time consuming and utilises significant resource. In this study a relatively inexpensive, rapid and widely applicable phase contrast microscopy-based technique has been developed that emulates the contrast changes taking place when bright field microscope images of epithelial cell cultures are defocused. Processing of the resulting images produces an image that can be segmented using a global threshold; the number of cells is then deduced from the number of segmented regions and these cell counts can be used to generate growth curves. The parameters of this method were tuned using the discrete mereotopological relations between ground truth and processed images. Cell count accuracy was improved using linear discriminant analysis to identify spurious noise regions for removal. The proposed cell counting technique was validated by comparing the results with a manual count of cells in images, and subsequently applied to generate growth curves for oral keratinocyte cultures supplemented with a range of concentrations of foetal calf serum. The approach developed has broad applicability and utility for researchers with standard laboratory imaging equipment.

Mathematical imaging methods for mitosis analysis in live-cell phase contrast microscopy.

In this paper we propose a workflow to detect and track mitotic cells in time-lapse microscopy image sequences. In order to avoid the requirement for cell lines expressing fluorescent markers and the associated phototoxicity, phase contrast microscopy is often preferred over fluorescence microscopy in live-cell imaging. However, common specific image characteristics complicate image processing and impede use of standard methods. Nevertheless, automated analysis is desirable due to manual analysis being subjective, biased and extremely time-consuming for large data sets. Here, we present the following workflow based on mathematical imaging methods. In the first step, mitosis detection is performed by means of the circular Hough transform. The obtained circular contour subsequently serves as an initialisation for the tracking algorithm based on variational methods. It is sub-divided into two parts: in order to determine the beginning of the whole mitosis cycle, a backwards tracking procedure is performed. After that, the cell is tracked forwards in time until the end of mitosis. As a result, the average of mitosis duration and ratios of different cell fates (cell death, no division, division into two or more daughter cells) can be measured and statistics on cell morphologies can be obtained. All of the tools are featured in the user-friendly MATLAB®Graphical User Interface MitosisAnalyser.

Assessment of occupational exposure to asbestos fibers: Contribution of analytical transmission electron microscopy analysis and comparison with phase-contrast microscopy.

From November 2009 to October 2010, the French general directorate for labor organized a large field-study using analytical transmission electron microscopy (ATEM) to characterize occupational exposure to asbestos fibers during work on asbestos containing materials (ACM). The primary objective of this study was to establish a method and to validate the feasibility of using ATEM for the analysis of airborne asbestos of individual filters sampled in various occupational environments. For each sampling event, ATEM data were compared to those obtained by phase-contrast optical microscopy (PCOM), the WHO-recommended reference technique. A total of 265 results were obtained from 29 construction sites where workers were in contact with ACM. Data were sorted depending on the combination of the ACM type and the removal technique. For each "ACM-removal technique" combination, ATEM data were used to compute statistical indicators on short, fine and WHO asbestos fibers. Moreover, exposure was assessed taking into account the use of respiratory protective devices (RPD). As in previous studies, no simple relationship was found between results by PCOM and ATEM counting methods. Some ACM, such as asbestos-containing plasters, generated very high dust levels, and some techniques generated considerable levels of dust whatever the ACM treated. On the basis of these observations, recommendations were made to measure and control the occupational exposure limit. General prevention measures to be taken during work with ACM are also suggested. Finally, it is necessary to continue acquiring knowledge, in particular regarding RPD and the dust levels measured by ATEM for the activities not evaluated during this study.

Monitoring of adherent live cells morphology using the undecimated wavelet transform multivariate image analysis (UWT-MIA).

Cell morphology is an important macroscopic indicator of cellular physiology and is increasingly used as a mean of probing culture state in vitro. Phase contrast microscopy (PCM) is a valuable tool for observing live cells morphology over long periods of time with minimal culture artifact. Two general approaches are commonly used to analyze images: individual object segmentation and characterization by pattern recognition. Single-cell segmentation is difficult to achieve in PCM images of adherent cells since their contour is often irregular and blurry, and the cells bundle together when the culture reaches confluence. Alternatively, pattern recognition approaches such as the undecimated wavelet transform multivariate image analysis (UWT-MIA), allow extracting textural features from PCM images that are correlated with cellular morphology. A partial least squares (PLS) regression model built using textural features from a set of 200 ground truth images was shown to predict the distribution of cellular morphological features (major and minor axes length, orientation, and roundness) with good accuracy for most images. The PLS models were then applied on a large dataset of 631,136 images collected from live myoblast cell cultures acquired under different conditions and grown in two different culture media. The method was found sensitive to morphological changes due to cell growth (culture time) and those introduced by the use of different culture media, and was able to distinguish both sources of variations. The proposed approach is promising for application on large datasets of PCM live-cell images to assess cellular morphology and growth kinetics in real-time which could be beneficial for high-throughput screening as well as automated cell culture kinetics assessment and control applications. Biotechnol. Bioeng. 2017;114: 141-153. © 2016 Wiley Periodicals, Inc.

Segmentation Approach Towards Phase-Contrast Microscopic Images of Activated Sludge to Monitor the Wastewater Treatment.

Image processing and analysis is an effective tool for monitoring and fault diagnosis of activated sludge (AS) wastewater treatment plants. The AS image comprise of flocs (microbial aggregates) and filamentous bacteria. In this paper, nine different approaches are proposed for image segmentation of phase-contrast microscopic (PCM) images of AS samples. The proposed strategies are assessed for their effectiveness from the perspective of microscopic artifacts associated with PCM. The first approach uses an algorithm that is based on the idea that different color space representation of images other than red-green-blue may have better contrast. The second uses an edge detection approach. The third strategy, employs a clustering algorithm for the segmentation and the fourth applies local adaptive thresholding. The fifth technique is based on texture-based segmentation and the sixth uses watershed algorithm. The seventh adopts a split-and-merge approach. The eighth employs Kittler's thresholding. Finally, the ninth uses a top-hat and bottom-hat filtering-based technique. The approaches are assessed, and analyzed critically with reference to the artifacts of PCM. Gold approximations of ground truth images are prepared to assess the segmentations. Overall, the edge detection-based approach exhibits the best results in terms of accuracy, and the texture-based algorithm in terms of false negative ratio. The respective scenarios are explained for suitability of edge detection and texture-based algorithms.

Highly specific detection of Cryptosporidium spp. oocysts in human stool samples by undemanding and inexpensive phase contrast microscopy.

To compare phase contrast microscopy (PCM) of unstained slides for the detection of Cryptosporidium spp. oocysts with a commercially available enzyme immunoassay (EIA) for the detection of cryptosporidial antigen in human stool samples, we prospectively analysed by both methods 463 fresh human stool samples obtained from diarrhoeic patients between July and October 2014. Compared with the EIA, the sensitivity, specificity, positive and negative predictive value of PCM were 88.9 % (95 % confidence interval (CI), 66.0-98.1 %), 100 % (95 % CI, 99.0-100 %), 100 % (95 % CI, 77.3-100 %) and 99.6 % (95 % CI, 98.3-100 %), respectively. Additionally, we retrospectively examined with PCM 65 fixed stool samples that had been collected in 2010 from mostly asymptomatic Rwandan children <5 years of age; 14 of these samples had previously yielded positive results with a highly sensitive real-time (RT)-PCR. PCM detected cryptosporidia in 5/14 RT-PCR-positive samples, and notably, also in one of 51 RT-PCR-negative samples, which was subsequently confirmed by acid-fast staining. Positive and negative percent agreement of PCM with RT-PCR were 35.7 % (95 % CI, 16.2-61.4 %) and 98.0 % (95 % CI, 88.7-100 %), respectively. Positive PCM results were associated with higher RT-PCR cycle threshold values (p = 0.044). In conclusion, PCM offers a highly specific, undemanding and inexpensive method for the laboratory diagnosis of acute human cryptosporidiosis independent of the causative Cryptosporidium species.

Segmentation Method of Time-Lapse Microscopy Images with the Focus on Biocompatibility Assessment.

Biocompatibility testing of new materials is often performed in vitro by measuring the growth rate of mammalian cancer cells in time-lapse images acquired by phase contrast microscopes. The growth rate is measured by tracking cell coverage, which requires an accurate automatic segmentation method. However, cancer cells have irregular shapes that change over time, the mottled background pattern is partially visible through the cells and the images contain artifacts such as halos. We developed a novel algorithm for cell segmentation that copes with the mentioned challenges. It is based on temporal differences of consecutive images and a combination of thresholding, blurring, and morphological operations. We tested the algorithm on images of four cell types acquired by two different microscopes, evaluated the precision of segmentation against manual segmentation performed by a human operator, and finally provided comparison with other freely available methods. We propose a new, fully automated method for measuring the cell growth rate based on fitting a coverage curve with the Verhulst population model. The algorithm is fast and shows accuracy comparable with manual segmentation. Most notably it can correctly separate live from dead cells.

Condenser-free contrast methods for transmitted-light microscopy.

Phase contrast microscopy allows the study of highly transparent yet detail-rich specimens by producing intensity contrast from phase objects within the sample. Presented here is a generalized phase contrast illumination schema in which condenser optics are entirely abrogated, yielding a condenser-free yet highly effective method of obtaining phase contrast in transmitted-light microscopy. A ring of light emitting diodes (LEDs) is positioned within the light-path such that observation of the objective back focal plane places the illuminating ring in appropriate conjunction with the phase ring. It is demonstrated that true Zernike phase contrast is obtained, whose geometry can be flexibly manipulated to provide an arbitrary working distance between illuminator and sample. Condenser-free phase contrast is demonstrated across a range of magnifications (4-100×), numerical apertures (0.13-1.65NA) and conventional phase positions. Also demonstrated is condenser-free darkfield microscopy as well as combinatorial contrast including Rheinberg illumination and simultaneous, colour-contrasted, brightfield, darkfield and Zernike phase contrast. By providing enhanced and arbitrary working space above the preparation, a range of concurrent imaging and electrophysiological techniques will be technically facilitated. Condenser-free phase contrast is demonstrated in conjunction with scanning ion conductance microscopy (SICM), using a notched ring to admit the scanned probe. The compact, versatile LED illumination schema will further lend itself to novel next-generation transmitted-light microscopy designs. The condenser-free illumination method, using rings of independent or radially-scanned emitters, may be exploited in future in other electromagnetic wavebands, including X-rays or the infrared.

Evaluation of the dark-medium objective lens in counting asbestos fibers by phase-contrast microscopy.

A Japanese round-robin study revealed that analysts who used a dark-medium (DM) objective lens reported higher fiber counts from American Industrial Hygiene Association (AIHA) Proficiency Analytical Testing (PAT) chrysotile samples than those using a standard objective lens, but the cause of this difference was not investigated at that time. The purpose of this study is to determine any major source of this difference by performing two sets of round-robin studies. For the first round-robin study, 15 AIHA PAT samples (five each of chrysotile and amosite generated by water-suspended method, and five chrysotile generated by aerosolization method) were prepared with relocatable cover slips and examined by nine laboratories. A second round-robin study was then performed with six chrysotile field sample slides by six out of nine laboratories who participated in the first round-robin study. In addition, two phase-shift test slides to check analysts' visibility and an eight-form diatom test plate to compare resolution between the two objectives were examined. For the AIHA PAT chrysotile reference slides, use of the DM objective resulted in consistently higher fiber counts (1.45 times for all data) than the standard objective (P-value < 0.05), regardless of the filter generation (water-suspension or aerosol) method. For the AIHA PAT amosite reference and chrysotile field sample slides, the fiber counts between the two objectives were not significantly different. No statistically significant differences were observed in the visibility of blocks of the test slides between the two objectives. Also, the DM and standard objectives showed no pattern of differences in viewing the fine lines and/or dots of each species images on the eight-form diatom test plate. Among various potential factors that might affect the analysts' performance of fiber counts, this study supports the greater contrast caused by the different phase plate absorptions as the main cause of high counts for the AIHA PAT chrysotile slides using the DM objective. The comparison of fiber count ratios (DM/standard) between the AIHA PAT chrysotile samples and chrysotile field samples indicates that there is a fraction of fibers in the PAT samples approaching the theoretical limit of visibility of the phase-contrast microscope with 3-degree phase-shift. These fibers become more clearly visible through the greater contrast from the phase plate absorption of the DM objective. However, as such fibers are not present in field samples, no difference in counts between the two objectives was observed in this study. The DM objective, therefore, could be allowed for routine fiber counting as it will maintain continuity with risk assessments based on earlier phase-contrast microscopy fiber counts from field samples. Published standard methods would need to be modified to allow a higher aperture specification for the objective.

Automated method for the rapid and precise estimation of adherent cell culture characteristics from phase contrast microscopy images.

The quantitative determination of key adherent cell culture characteristics such as confluency, morphology, and cell density is necessary for the evaluation of experimental outcomes and to provide a suitable basis for the establishment of robust cell culture protocols. Automated processing of images acquired using phase contrast microscopy (PCM), an imaging modality widely used for the visual inspection of adherent cell cultures, could enable the non-invasive determination of these characteristics. We present an image-processing approach that accurately detects cellular objects in PCM images through a combination of local contrast thresholding and post hoc correction of halo artifacts. The method was thoroughly validated using a variety of cell lines, microscope models and imaging conditions, demonstrating consistently high segmentation performance in all cases and very short processing times (<1 s per 1,208 × 960 pixels image). Based on the high segmentation performance, it was possible to precisely determine culture confluency, cell density, and the morphology of cellular objects, demonstrating the wide applicability of our algorithm for typical microscopy image processing pipelines. Furthermore, PCM image segmentation was used to facilitate the interpretation and analysis of fluorescence microscopy data, enabling the determination of temporal and spatial expression patterns of a fluorescent reporter. We created a software toolbox (PHANTAST) that bundles all the algorithms and provides an easy to use graphical user interface. Source-code for MATLAB and ImageJ is freely available under a permissive open-source license.

Automatic detection and analysis of cell motility in phase-contrast time-lapse images using a combination of maximally stable extremal regions and Kalman filter approaches.

Phase-contrast illumination is simple and most commonly used microscopic method to observe nonstained living cells. Automatic cell segmentation and motion analysis provide tools to analyze single cell motility in large cell populations. However, the challenge is to find a sophisticated method that is sufficiently accurate to generate reliable results, robust to function under the wide range of illumination conditions encountered in phase-contrast microscopy, and also computationally light for efficient analysis of large number of cells and image frames. To develop better automatic tools for analysis of low magnification phase-contrast images in time-lapse cell migration movies, we investigated the performance of cell segmentation method that is based on the intrinsic properties of maximally stable extremal regions (MSER). MSER was found to be reliable and effective in a wide range of experimental conditions. When compared to the commonly used segmentation approaches, MSER required negligible preoptimization steps thus dramatically reducing the computation time. To analyze cell migration characteristics in time-lapse movies, the MSER-based automatic cell detection was accompanied by a Kalman filter multiobject tracker that efficiently tracked individual cells even in confluent cell populations. This allowed quantitative cell motion analysis resulting in accurate measurements of the migration magnitude and direction of individual cells, as well as characteristics of collective migration of cell groups. Our results demonstrate that MSER accompanied by temporal data association is a powerful tool for accurate and reliable analysis of the dynamic behaviour of cells in phase-contrast image sequences. These techniques tolerate varying and nonoptimal imaging conditions and due to their relatively light computational requirements they should help to resolve problems in computationally demanding and often time-consuming large-scale dynamical analysis of cultured cells.

Rapid fiber-detection technique by artificial intelligence in phase-contrast microscope images of simulated atmospheric samples.

Since the manufacture, import, and use of asbestos products have been completely abolished in Japan, the main cause of asbestos emissions into the atmosphere is the demolition and removal of buildings built with asbestos-containing materials. To detect and correct asbestos emissions from inappropriate demolition and removal operations at an early stage, a rapid method to measure atmospheric asbestos fibers is required. The current rapid measurement method is a combination of short-term atmospheric sampling and phase-contrast microscopy counting. However, visual counting takes a considerable amount of time and is not sufficiently fast. Using artificial intelligence (AI) to analyze microscope images to detect fibers may greatly reduce the time required for counting. Therefore, in this study, we investigated the use of AI image analysis for detecting fibers in phase-contrast microscope images. A series of simulated atmospheric samples prepared from standard samples of amosite and chrysotile were observed using a phase-contrast microscope. Images were captured, and training datasets were created from the counting results of expert analysts. We adopted 2 types of AI models-an instance segmentation model, namely the mask region-based convolutional neural network (Mask R-CNN), and a semantic segmentation model, namely the multi-level aggregation network (MA-Net)-that were trained to detect asbestos fibers. The accuracy of fiber detection achieved with the Mask R-CNN model was 57% for recall and 46% for precision, whereas the accuracy achieved with the MA-Net model was 95% for recall and 91% for precision. Therefore, satisfactory results were obtained with the MA-Net model. The time required for fiber detection was less than 1 s per image in both AI models, which was faster than the time required for counting by an expert analyst.

Uncovering hidden treasures: Mapping morphological changes in the differentiation of human mesenchymal stem cells to osteoblasts using deep learning.

Deep Learning (DL) is becoming an increasingly popular technology being employed in life sciences research due to its ability to perform complex and time-consuming tasks with significantly greater speed, accuracy, and reproducibility than human researchers - allowing them to dedicate their time to more complex tasks. One potential application of DL is to analyze cell images taken by microscopes. Quantitative analysis of cell microscopy images remain a challenge - with manual cell characterization requiring excessive amounts of time and effort. DL can address these issues, by quickly extracting such data and enabling rigorous, empirical analysis of images. Here, DL is used to quantitively analyze images of Mesenchymal Stem Cells (MSCs) differentiating into Osteoblasts (OBs), tracking morphological changes throughout this transition. The changes in morphology throughout the differentiation protocol provide evidence for a distinct path of morphological transformations that the cells undergo in their transition, with changes in perimeter being observable before changes in eceentricity. Subsequent differentiation experiments can be quantitatively compared with our dataset to concretely evaluate how different conditions affect differentiation and this paper can also be used as a guide for researchers on how to utilize DL workflows in their own labs.

Quantitative microbiology with widefield microscopy: navigating optical artefacts for accurate interpretations.

Time-resolved live-cell imaging using widefield microscopy is instrumental in quantitative microbiology research. It allows researchers to track and measure the size, shape, and content of individual microbial cells over time. However, the small size of microbial cells poses a significant challenge in interpreting image data, as their dimensions approache that of the microscope's depth of field, and they begin to experience significant diffraction effects. As a result, 2D widefield images of microbial cells contain projected 3D information, blurred by the 3D point spread function. In this study, we employed simulations and targeted experiments to investigate the impact of diffraction and projection on our ability to quantify the size and content of microbial cells from 2D microscopic images. This study points to some new and often unconsidered artefacts resulting from the interplay of projection and diffraction effects, within the context of quantitative microbiology. These artefacts introduce substantial errors and biases in size, fluorescence quantification, and even single-molecule counting, making the elimination of these errors a complex task. Awareness of these artefacts is crucial for designing strategies to accurately interpret micrographs of microbes. To address this, we present new experimental designs and machine learning-based analysis methods that account for these effects, resulting in accurate quantification of microbiological processes.

Compaction and Segregation of DNA in Escherichia coli.

Theoretical and experimental approaches have been applied to study the polymer physics underlying the compaction of DNA in the bacterial nucleoid. Knowledge of the compaction mechanism is necessary to obtain a mechanistic understanding of the segregation process of replicating chromosome arms (replichores) during the cell cycle. The first part of this review discusses light microscope observations demonstrating that the nucleoid has a lower refractive index and thus, a lower density than the cytoplasm. A polymer physics explanation for this phenomenon was given by a theory discussed at length in this review. By assuming a phase separation between the nucleoid and the cytoplasm and by imposing equal osmotic pressure and chemical potential between the two phases, a minimal energy situation is obtained, in which soluble proteins are depleted from the nucleoid, thus explaining its lower density. This theory is compared to recent views on DNA compaction that are based on the exclusion of polyribosomes from the nucleoid or on the transcriptional activity of the cell. These new views prompt the question of whether they can still explain the lower refractive index or density of the nucleoid. In the second part of this review, we discuss the question of how DNA segregation occurs in Escherichia coli in the absence of the so-called active ParABS system, which is present in the majority of bacteria. How is the entanglement of nascent chromosome arms generated at the origin in the parental DNA network of the E. coli nucleoid prevented? Microscopic observations of the position of fluorescently-labeled genetic loci have indicated that the four nascent chromosome arms synthesized in the initial replication bubble segregate to opposite halves of the sister nucleoids. This implies that extensive intermingling of daughter strands does not occur. Based on the hypothesis that leading and lagging replichores synthesized in the replication bubble fold into microdomains that do not intermingle, a passive four-excluding-arms model for segregation is proposed. This model suggests that the key for segregation already exists in the structure of the replication bubble at the very start of DNA replication; it explains the different patterns of chromosome arms as well as the segregation distances between replicated loci, as experimentally observed.