A machine vision system for automated non-invasive assessment of cell viability via dark field microscopy, wavelet feature selection and classification

Tea polyphenols protect bovine intestinal epithelial cells from the adverse effects of heat-stress in vitro

Heat-stress (HS) leads to impaired gut health, adversely affecting milk production of dairy cows. In the present study, we investigated the protective effects of tea polyphenols (TP) against HS-induced damage in bovine intestinal epithelial cells (BIECs) and explored the underlying mechanisms. Primary BIECs were isolated from bovine duodenum, cultured and treated as follows: (1) control cells incubated in complete medium at 37 °C for 12 h, (2) TP group incubated in medium containing 100 μg/mL TP at 37 °C for 12 h, (3) HS group incubated in medium at 37 °C for 6 h followed by 6 h at 42 °C, and (4) HS + TP group incubated with 100 μg/mL TP for 6 h at 37 °C and 6 h at 42 °C. TP improved cell viability and antioxidant capacity, and decreased apoptosis and LDH activity. TP led to upregulation of Nrf2 and its target antioxidant genes HO-1, NQO1 and SOD1 expression. TP significantly decreased the expression of proinflammatory cytokine genes (NF-κB, IL-6 and TNF-α), and increased expression of the anti-inflammatory cytokine gene, IL-10. The above results suggested that TP protected BIECs from HS-induced adverse effects by alleviating oxidative stress and inflammatory responses, indicating that TP can alleviate HS-induced intestinal damage in dairy cows.

Exploring an optimal wavelet-based filter for cryo-ET imaging

Cryo-electron tomography (cryo-ET) is one of the most advanced technologies for the in situ visualization of molecular machines by producing three-dimensional (3D) biological structures. However, cryo-ET imaging has two serious disadvantages—low dose and low image contrast—which result in high-resolution information being obscured by noise and image quality being degraded, and this causes errors in biological interpretation. The purpose of this research is to explore an optimal wavelet denoising technique to reduce noise in cryo-ET images. We perform tests using simulation data and design a filter using the optimum selected wavelet parameters (three-level decomposition, level-1 zeroed out, subband-dependent threshold, a soft-thresholding and spline-based discrete dyadic wavelet transform (DDWT)), which we call a modified wavelet shrinkage filter; this filter is suitable for noisy cryo-ET data. When testing using real cryo-ET experiment data, higher quality images and more accurate measures of a biological structure can be obtained with the modified wavelet shrinkage filter processing compared with conventional processing. Because the proposed method provides an inherent advantage when dealing with cryo-ET images, it can therefore extend the current state-of-the-art technology in assisting all aspects of cryo-ET studies: visualization, reconstruction, structural analysis, and interpretation.

Lifetime Distributions from Tracking Individual BC3H1 Cells Subjected to Yessotoxin

This work shows examples of lifetime distributions for individual BC3H1 cells after start of exposure to the marine toxin yessotoxin (YTX) in an experimental dish. The present tracking of many single cells from time-lapse microscopy data demonstrates the complexity in individual cell fate and which can be masked in aggregate properties. This contribution also demonstrates the general practicality of cell tracking. It can serve as a conceptually simple and non-intrusive method for high throughput early analysis of cytotoxic effects to assess early and late time points relevant for further analyzes or to assess for variability and sub-populations of interest. The present examples of lifetime distributions seem partly to reflect different cell death modalities. Differences between cell lifetime distributions derived from populations in different experimental dishes can potentially provide measures of inter-cellular influence. Such outcomes may help to understand tumor-cell resistance to drug therapy and to predict the probability of metastasis.

Phenotypic signatures arising from unbalanced bacterial growth

Fluctuations in the growth rate of a bacterial culture during unbalanced growth are generally considered undesirable in quantitative studies of bacterial physiology. Under well-controlled experimental conditions, however, these fluctuations are not random but instead reflect the interplay between intra-cellular networks underlying bacterial growth and the growth environment. Therefore, these fluctuations could be considered quantitative phenotypes of the bacteria under a specific growth condition. Here, we present a method to identify “phenotypic signatures” by time-frequency analysis of unbalanced growth curves measured with high temporal resolution. The signatures are then applied to differentiate amongst different bacterial strains or the same strain under different growth conditions, and to identify the essential architecture of the gene network underlying the observed growth dynamics. Our method has implications for both basic understanding of bacterial physiology and for the classification of bacterial strains.

The measurement of bacterial growth in batch cultures is a routine practice in microbiology. In these cultures, bacterial growth rates drastically fluctuate over time due to the continuously changing growth environment: changing population size, accumulation of waste products, and depletion of nutrients. Such “unbalanced” growth is normally considered undesirable, which has led to the design of methods to achieve balanced growth environments (i.e., chemostats). However, we have discovered that unbalanced growth dynamics contain rich information that can be exploited to deduce regulatory functions or to classify cell strains or growth conditions. We further show that this approach is generally applicable to temporal gene expression data. Taken together, our method and results have broad implications for system identification, experimental design, and the study of cellular growth.

Categorization of two-photon microscopy images of human cartilage into states of osteoarthritis

OBJECTIVE

The degeneration of articular cartilage is part of the clinical syndrome of osteoarthritis (OA) and one of the most common causes of pain and disability in middle-aged and older people(1). However, the objective detection of an initial state of OA is still challenging. In order to categorize cartilage into states of OA, an algorithm is presented which offers objective categorization on the basis of two-photon laser-scanning microscopy (TPLSM) images.

METHODS

The algorithm is based on morphological characteristics of the images and results in a topographical visualization. This paper describes the algorithm and shows the result of a categorization of human cartilage samples.

RESULTS

The resulting map of the analysis of TPLSM images can be divided into areas which correspond to the grades of the Outerbridge-Categorization. The algorithm is able to differentiate the samples in coincidence with the macroscopic impression.

CONCLUSION

The method is promising for early OA detection and categorization. In order to achieve a higher benefit for the physician the method must be transferred to an endoscopic setup for an application in surgery.

Calibration of a digital in-line holographic microscopy system: depth of focus and bioprocess analysis

Digital in-line holographic microscopy (DIHM) allows access to both intensity and phase information with conventional microscopic lateral resolutions. Such imaging techniques can, however, be used to increase the depth of focus compared to conventional compound microscopes. We present a simple DIHM capable of imaging weakly scattering 10 μm diameter microspheres as well as Hs578T cells over a depth of 1 mm; i.e., we demonstrate an increase by a factor of 100 over the depth of focus of a conventional microscope.

Label-free detection of neuronal differentiation in cell populations using high-throughput live-cell imaging of PC12 cells

Detection of neuronal cell differentiation is essential to study cell fate decisions under various stimuli and/or environmental conditions. Many tools exist that quantify differentiation by neurite length measurements of single cells. However, quantification of differentiation in whole cell populations remains elusive so far. Because such populations can consist of both proliferating and differentiating cells, the task to assess the overall differentiation status is not trivial and requires a high-throughput, fully automated approach to analyze sufficient data for a statistically significant discrimination to determine cell differentiation. We address the problem of detecting differentiation in a mixed population of proliferating and differentiating cells over time by supervised classification. Using nerve growth factor induced differentiation of PC12 cells, we monitor the changes in cell morphology over days by phase-contrast live-cell imaging. For general applicability, the classification procedure starts out with many features to identify those that maximize discrimination of differentiated and undifferentiated cells and to eliminate features sensitive to systematic measurement artifacts. The resulting image analysis determines the optimal post treatment day for training and achieves a near perfect classification of differentiation, which we confirmed in technically and biologically independent as well as differently designed experiments. Our approach allows to monitor neuronal cell populations repeatedly over days without any interference. It requires only an initial calibration and training step and is thereafter capable to discriminate further experiments. In conclusion, this enables long-term, large-scale studies of cell populations with minimized costs and efforts for detecting effects of external manipulation of neuronal cell differentiation.

Measurement of cell viability

An overview of the methods for assessing cell viability is presented. Different protocols of the most commonly used assays are described in detail so that the readers may be able to determine which assay is suitable for their own projects in plant biotechnology.

Machine vision for digital microfluidics

Machine vision is widely used in an industrial environment today. It can perform various tasks, such as inspecting and controlling production processes, that may require humanlike intelligence. The importance of imaging technology for biological research or medical diagnosis is greater than ever. For example, fluorescent reporter imaging enables scientists to study the dynamics of gene networks with high spatial and temporal resolution. Such high-throughput imaging is increasingly demanding the use of machine vision for real-time analysis and control. Digital microfluidics is a relatively new technology with expectations of becoming a true lab-on-a-chip platform. Utilizing digital microfluidics, only small amounts of biological samples are required and the experimental procedures can be automatically controlled. There is a strong need for the development of a digital microfluidics system integrated with machine vision for innovative biological research today. In this paper, we show how machine vision can be applied to digital microfluidics by demonstrating two applications: machine vision-based measurement of the kinetics of biomolecular interactions and machine vision-based droplet motion control. It is expected that digital microfluidics-based machine vision system will add intelligence and automation to high-throughput biological imaging in the future.