Quantitative Image Analysis Techniques Research Articles

Quantitative image analysis for assessing extracellular polymeric substances in activated sludge under atrazine exposure

Extracellular polymeric substances (EPS) play a vital role in biological wastewater treatment systems. This study investigates the impact of herbicide atrazine (ATZ) on the overall performance, EPS yield, composition, and sludge morphology in an activated sludge (AS) system operated in a sequencing batch reactor (SBR). Since conventional methods for analyzing EPS are time-consuming and releases residues, a new approach was developed in this work to evaluate the EPS fractions and components, based on the morphological characterization of the biomass using quantitative image analysis (QIA) technique coupled with multivariate statistics.Results showed that exposure to ATZ inhibit biomass activity in terms of organic matter (COD) and nitrogen removal. Moreover, both tightly bound EPS (TB-EPS) and loosely bound EPS (LB-EPS) increased under ATZ, indicating that microorganisms release EPS as a defense mechanism against environmental changes. The PN/PS ratio also increases, indicating likely increased hydrophobicity in ATZ phases. Furthermore, ATZ phases exhibit a predominance of large aggregates compared to intermediate and small ones, indicating a change in aggregate morphological structure associated with EPS production. The new approach using QIA coupled with partial least squares (PLS) modeling provides accurate predictions of EPS content. The increase in TB-EPS is closely related to the rise of large aggregates in phases exposed to higher ATZ concentrations. The PLS models demonstrate high accuracy for EPS prediction (coefficients of determination, R2 above 0.86), showcasing the feasibility of using QIA for EPS assessment in AS systems. This approach offers significant potential for regular process monitoring and management, providing a more environmentally friendly methodology by eliminating the need for chemical usage.

Grain Shape Evolution of Sand‐Sized Sediments During Transport From Mountains to Dune Fields

AbstractGrain shape is used less frequently in identifying the origin of sediments than grain size distributions and/or surface microtextures because of technological limitations. In addition, systematic studies on grain shape change of sand‐sized sediments from a source‐to‐sink perspective are lacking, and yet the evolutionary mechanism of grain shape along the sediment transport pathway remains poorly understood. Here, we investigated grain shape changes in sand‐sized sediments ranging from mountains to dune fields using an efficient and quantitative dynamic image analysis technique. By means of the detailed hydro‐geomorphic analysis and field sampling in a quasi‐closed sedimentary system, we collected 41 samples from the Hulunbeier Sandy Land (HSL) in northern China and its potential source region. Also, 21 typical fluvial deposit samples from different river systems in China were collected for comparison. We identify that the sphericity, symmetry, aspect ratio, and mineralogical maturity of the fluvial deposits from mountains are significantly lower than the eolian sand in dune fields. Despite the apparent inheritance of sediments from the mountains to the dune fields, the abrupt changes in the sedimentological and geochemical properties along their pathway suggest that multiple cycles of eolian abrasion within the HSL modify the sediments significantly after they are transported away from the fluvial environments. The well‐defined separation between eolian sand and fluvial deposits by grain shape parameters such as sphericity and symmetry underscores that grain shape can be a powerful tool for tracing sediment transport history in paleoenvironmental or extraterrestrial studies.

External validation of an MR-based radiomic model predictive of locoregional control in oropharyngeal cancer.

To externally validate a pre-treatment MR-based radiomics model predictive of locoregional control in oropharyngeal squamous cell carcinoma (OPSCC) and to assess the impact of differences between datasets on the predictive performance. Radiomic features, as defined in our previously published radiomics model, were extracted from the primary tumor volumes of 157 OPSCC patients in a different institute. The developed radiomics model was validated using this cohort. Additionally, parameters influencing performance, such as patient subgroups, MRI acquisition, and post-processing steps on prediction performance will be investigated. For this analysis, matched subgroups (based on human papillomavirus (HPV) status of the tumor, T-stage, and tumor subsite) and a subgroup with only patients with 4-mm slice thickness were studied. Also the influence of harmonization techniques (ComBat harmonization, quantile normalization) and the impact of feature stability across observers and centers were studied. Model performances were assessed by area under the curve (AUC), sensitivity, and specificity. Performance of the published model (AUC/sensitivity/specificity: 0.74/0.75/0.60) drops when applied on the validation cohort (AUC/sensitivity/specificity: 0.64/0.68/0.60). The performance of the full validation cohort improves slightly when the model is validated using a patient group with comparable HPV status of the tumor (AUC/sensitivity/specificity: 0.68/0.74/0.60), using patients acquired with a slice thickness of 4 mm (AUC/sensitivity/specificity: 0.67/0.73/0.57), or when quantile harmonization was performed (AUC/sensitivity/specificity: 0.66/0.69/0.60). The previously published model shows its generalizability and can be applied on data acquired from different vendors and protocols. Harmonization techniques and subgroup definition influence performance of predictive radiomics models. • Radiomics, a noninvasive quantitative image analysis technique, can support the radiologist by enhancing diagnostic accuracy and/or treatment decision-making. • A previously published model shows its generalizability and could be applied on data acquired from different vendors and protocols.

Quantitative ultrasound image analysis of axillary lymph nodes to differentiate malignancy from reactive benign changes due to COVID-19 vaccination

PurposeThe aim of this study is to assess the potential of quantitative image analysis and machine learning techniques to differentiate between malignant lymph nodes and benign lymph nodes affected by reactive changes due to COVID-19 vaccination. MethodIn this institutional review board–approved retrospective study, we improved our previously published artificial intelligence model, by retraining it with newly collected images and testing its performance on images containing benign lymph nodes affected by COVID-19 vaccination. All the images were acquired and selected by specialized breast-imaging radiologists and the nature of each node (benign or malignant) was assessed through a strict clinical protocol using ultrasound-guided biopsies. ResultsA total of 180 new images from 154 different patients were recruited: 71 images (10 cases and 61 controls) were used to retrain the old model and 109 images (36 cases and 73 controls) were used to evaluate its performance. The achieved accuracy of the proposed method was 92.7% with 77.8% sensitivity and 100% specificity at the optimal cut-off point. In comparison, the visual node inspection made by radiologists from ultrasound images reached 69.7% accuracy with 41.7% sensitivity and 83.6% specificity. ConclusionsThe results obtained in this study show the potential of the proposed techniques to differentiate between malignant lymph nodes and benign nodes affected by reactive changes due to COVID-19 vaccination. These techniques could be useful to non-invasively diagnose lymph node status in patients with suspicious reactive nodes, although larger multicenter studies are needed to confirm and validate the results.

A real-time monitoring system for automatic morphology analysis of yeast cultivation in a jar fermenter.

The monitoring of microbial cultivation in real time and controlling their cultivation aid in increasing the production yield of useful material in a jar fermenter. Common sensors such as dissolved oxygen (DO) and pH can easily provide general-purpose indexes but do not reveal the physiological states of microbes because of the complexity of measuring them in culture conditions. It is well known from microscopic observations that the microbial morphology changes in response to the intracellular state or extracellular environment. Recently, studies have focused on rapid and quantitative image analysis techniques using machine learning or deep learning for gleaning insights into the morphological, physiological or gene expression information in microbes. During image analysis, it is necessary to retrieve high-definition images to analyze the microbial morphology in detail. In this study, we have developed a microfluidic device with a high-speed camera for the microscopic observation of yeast, and have constructed a system capable of generating their morphological information in real-time and at high definition. This system was connected to a jar fermenter, which enabled the automatic sampling for monitoring the cultivation. We successfully acquired high-definition images of over 10,000 yeast cells in about 2.2s during ethanol fermentation automatically for over 168h. We recorded 33,600 captures containing over 1,680,000 cell images. By analyzing these images, the morphological changes of yeast cells through ethanol fermentation could be captured, suggesting the expansion of the application of this system in controlling microbial fermentation using the morphological information generated. KEY POINTS: • Enables real-time visualization of microbes in a jar fermenter using microscopy. • Microfluidic device for acquiring high-definition images. • Generates a large amount of image data by using a high-speed camera.

Developing a model for estimating infarction onset time based on computed tomography radiomics in patients with acute middle cerebral artery occlusion

BackgroundRadiomics analysis is a newly emerging quantitative image analysis technique. The aim of this study was to extract a radiomics signature from the computed tomography (CT) imaging to determine the infarction onset time in patients with acute middle cerebral artery occlusion (MCAO).MethodsA total of 123 patients with acute MCAO in the M1 segment (85 patients in the development cohort and 38 patients in the validation cohort) were enrolled in the present study. Clinicoradiological profiles, including head CT without contrast enhancement and computed tomographic angiography (CTA), were collected. The time from stroke onset (TFS) was classified into two subcategories: ≤ 4.5 h, and > 4.5 h. The middle cerebral artery (MCA) territory on CT images was segmented to extract and score the radiomics features associated with the TFS. In addition, the clinicoradiological factors related to the TFS were identified. Subsequently, a combined model of the radiomics signature and clinicoradiological factors was constructed to distinguish the TFS ≤ 4.5 h. Finally, we evaluated the overall performance of our constructed model in an external validation sample of ischemic stroke patients with acute MCAO in the M1 segment.ResultsThe area under the curve (AUC) of the radiomics signature for discriminating the TFS in the development and validation cohorts was 0.770 (95% confidence interval (CI): 0.665–0.875) and 0.792 (95% CI: 0.633–0.950), respectively. The AUC of the combined model comprised of the radiomics signature, age and ASPECTS on CT in the development and validation cohorts was 0.808 (95% CI: 0.701–0.916) and 0.833 (95% CI: 0.702–0.965), respectively. In the external validation cohort, the AUC of the radiomics signature was 0.755 (95% CI: 0.614–0.897), and the AUC of the combined model was 0.820 (95% CI: 0.712–0.928).ConclusionsThe CT-based radiomics signature is a valuable tool for discriminating the TFS in patients with acute MCAO in the M1 segment, which may guide the use of thrombolysis therapy in patients with indeterminate stroke onset time.

MRI biomarkers in neuro-oncology.

The central role of MRI in neuro-oncology is undisputed. The technique is used, both in clinical practice and in clinical trials, to diagnose and monitor disease activity, support treatment decision-making, guide the use of focused treatments and determine response to treatment. Despite recent substantial advances in imaging technology and image analysis techniques, clinical MRI is still primarily used for the qualitative subjective interpretation of macrostructural features, as opposed to quantitative analyses that take into consideration multiple pathophysiological features. However, the field of quantitative imaging and imaging biomarker development is maturing. The European Imaging Biomarkers Alliance (EIBALL) and Quantitative Imaging Biomarkers Alliance (QIBA) are setting standards for biomarker development, validation and implementation, as well as promoting the use of quantitative imaging and imaging biomarkers by demonstrating their clinical value. In parallel, advanced imaging techniques are reaching the clinical arena, providing quantitative, commonly physiological imaging parameters that are driving the discovery, validation and implementation of quantitative imaging and imaging biomarkers in the clinical routine. Additionally, computational analysis techniques are increasingly being used in the research setting to convert medical images into objective high-dimensional data and define radiomic signatures of disease states. Here, I review the definition and current state of MRI biomarkers in neuro-oncology, and discuss the clinical potential of quantitative image analysis techniques.

Assessment of an aerobic granular sludge system in the presence of pharmaceutically active compounds by quantitative image analysis and chemometric techniques

In this study, a sequencing batch reactor (SBR) with aerobic granular sludge (AGS) was operated with synthetic wastewater containing environmental relevant concentrations of 17β-estradiol (E2), 17α-ethinylestradiol (EE2) and sulfamethoxazole (SMX). Despite the presence of the studied PhAC, the granular fraction clearly predominated (TSSgran/TSS ranging from 0.82 to 0.98) throughout the monitoring period, presenting aggregates with high organic fraction (VSS/TSS above 0.83) and good settling characteristics (SVI5 ranging from 15 to 39 mL/gTSS). A principal component analysis (PCA) with quantitative image analysis (QIA) based data allowed to distinguish the different operational periods, namely with mature granules (CONT), and the E2, EE2, and SMX feeding periods. It further revealed a positive relationship between the biomass density, sludge settling ability, overall and granular biomass contents, granulation properties, granular biomass fraction and large granules fraction and size. Moreover, a discriminant analysis (DA) allowed to successfully discriminate not only the different operational periods, mainly by using the floccular apparent density, granular stratification and contents data, but also the PhAC presence in samples. The filamentous bacteria contents, sludge settling properties, settling properties stability and granular stratification, structure and contents parameters were found to be crucial for that purpose.

Quantitative Molecular Positron Emission Tomography Imaging Using Advanced Deep Learning Techniques.

The widespread availability of high-performance computing and the popularity of artificial intelligence (AI) with machine learning and deep learning (ML/DL) algorithms at the helm have stimulated the development of many applications involving the use of AI-based techniques in molecular imaging research. Applications reported in the literature encompass various areas, including innovative design concepts in positron emission tomography (PET) instrumentation, quantitative image reconstruction and analysis techniques, computer-aided detection and diagnosis, as well as modeling and prediction of outcomes. This review reflects the tremendous interest in quantitative molecular imaging using ML/DL techniques during the past decade, ranging from the basic principles of ML/DL techniques to the various steps required for obtaining quantitatively accurate PET data, including algorithms used to denoise or correct for physical degrading factors as well as to quantify tracer uptake and metabolic tumor volume for treatment monitoring or radiation therapy treatment planning and response prediction.This review also addresses future opportunities and current challenges facing the adoption of ML/DL approaches and their role in multimodality imaging.

Automated feature quantification of Lipiodol as imaging biomarker to predict therapeutic efficacy of conventional transarterial chemoembolization of liver cancer

Conventional transarterial chemoembolization (cTACE) is a guideline-approved image-guided therapy option for liver cancer using the radiopaque drug-carrier and micro-embolic agent Lipiodol, which has been previously established as an imaging biomarker for tumor response. To establish automated quantitative and pattern-based image analysis techniques of Lipiodol deposition on 24 h post-cTACE CT as biomarker for treatment response. The density of Lipiodol deposits in 65 liver lesions was automatically quantified using Hounsfield Unit thresholds. Lipiodol deposition within the tumor was automatically assessed for patterns including homogeneity, sparsity, rim, and peripheral deposition. Lipiodol deposition was correlated with enhancing tumor volume (ETV) on baseline and follow-up MRI. ETV on baseline MRI strongly correlated with Lipiodol deposition on 24 h CT (p < 0.0001), with 8.22% ± 14.59 more Lipiodol in viable than necrotic tumor areas. On follow-up, tumor regions with Lipiodol showed higher rates of ETV reduction than areas without Lipiodol (p = 0.0475) and increasing densities of Lipiodol enhanced this effect. Also, homogeneous (p = 0.0006), non-sparse (p < 0.0001), rim deposition within sparse tumors (p = 0.045), and peripheral deposition (p < 0.0001) of Lipiodol showed improved response. This technical innovation study showed that an automated threshold-based volumetric feature characterization of Lipiodol deposits is feasible and enables practical use of Lipiodol as imaging biomarker for therapeutic efficacy after cTACE.

The Geometry of the Generalized Gamma Manifold and an Application to Medical Imaging

The Fisher information metric provides a smooth family of probability measures with a Riemannian manifold structure, which is an object in information geometry. The information geometry of the gamma manifold associated with the family of gamma distributions has been well studied. However, only a few results are known for the generalized gamma family that adds an extra shape parameter. The present article gives some new results about the generalized gamma manifold. This paper also introduces an application in medical imaging that is the classification of Alzheimer’s disease population. In the medical field, over the past two decades, a growing number of quantitative image analysis techniques have been developed, including histogram analysis, which is widely used to quantify the diffuse pathological changes of some neurological diseases. This method presents several drawbacks. Indeed, all the information included in the histogram is not used and the histogram is an overly simplistic estimate of a probability distribution. Thus, in this study, we present how using information geometry and the generalized gamma manifold improved the performance of the classification of Alzheimer’s disease population.

Surface kinetics analysis by direct area measurement: Laser assisted dehydration of α-FeOOH

A novel method for surface kinetics analysis based solely on quantitative image analysis technique is proposed, and implemented for evaluating the activation energy, 110 (10) meV, of laser assisted dehydration of goethite. Millisecond radiative heat transfer via 785 nm laser beam, allowed us to elucidate a transient surface dehydration below 200 °C. In addition, the proposed method evaluates the laser threshold Irradiance - 1.2(1) μW/μm2 and its temperature dependence. The complementary contributions of radiative (transient) and conductive (equilibrium) heat transfer to the sample provides viable physical elucidation of the observed phenomenon - dehydrated spot surface area, variations with the laser power and the equilibrium temperature. The method allows effectively to isolate and study surface process and can be efficiently applied for any pulse laser assisted surface transformation, when the initial and final products have visual contrast.

Impact of seismic image quality on fault interpretation uncertainty

Uncertainty in the geological interpretation of a seismic image is affected by image quality. Using quantitative image analysis techniques, we have mapped differences in image contrast and reflection continuity for two different representations of the same grayscale seismic image, one in two-way-time (TWT) and one in depth. The contrast and reflection continuity of the depth image is lower than that of the TWT image. We compare the results of 196 interpretations of a single fault with the quality of the seismic image. Low contrast and continuity areas correspond to a greater range of interpreted fault geometries, resulting in a broader spread of fault interpretations in the depth image. Subtle differences in interpreted fault geometries introduce changes in fault characteristics (e.g., throw, heave) that are critical for understanding crustal and lithospheric processes. Seismic image quality impacts interpretation certainty, as evidenced by the increased range in fault interpretations. Quantitative assessments of image quality could inform: (1) whether model-based interpretation (e.g., fault geometry prediction at depth) is more robust than a subjective interpretation; and (2) uncertainty assessments of fault interpretations used to predict tectonic processes such as crustal extension.

Examination of Effects of Low-Frequency Ultrasound on Scleral Permeability and Collagen Network

Delivery of therapeutics to the intraocular space or to targeted tissues in the posterior segment is challenging because of the structural and dynamic barriers surrounding the eye. Previously, we reported the feasibility of using ultrasound (US) irradiation to deliver macromolecules to the posterior segment of the eye via the transscleral route, which consists of sclera as the outermost anatomic barrier. In this study, we found that although ultrasound increases scleral permeability for macromolecules, the scleral collagen arrangement remains undisturbed. In an ex vivo experiment, protein permeation across the sclera was significantly enhanced by ultrasound in the stable cavitation regime. The scleral collagen network was further examined by second harmonic generation imaging. Quantitative image analysis techniques were adopted to examine the density, anisotropy and interlacing pattern of collagen fibers before and after ultrasound irradiation. Repeated ultrasound applications did not induce significant changes in the arrangement of collagen fibrils at 40 kHz with a spatial average temporal average intensity (ISATA) <1.8 W/cm2. These parameters correspond to a mechanical index (MI) below 0.8 in our setting. These data suggested that enhanced permeation of macromolecules across the sclera was achieved without disturbing the collagen network of the sclera. This evidence supports that low-frequency, low-intensity ultrasound is a tolerable approach to transscleral drug delivery.

Quantitative differentiation of normal and scarred tissues using second-harmonic generation microscopy.

The aim of this study was to differentiate normal and scarred hamster cheek pouch samples by applying a quantitative image analysis technique for determining collagen fiber direction and density in second-harmonic generation microscopy images. This paper presents a collagen tissue analysis of scarred cheek pouches of four adult male Golden Syrian hamsters as an animal model for vocal fold scarring. One cheek pouch was scarred using an electrocautery unit and the other cheek was used as a control for each hamster. A home-built upright microscope and a compact ultrafast fiber laser were used to acquire depth resolved epi-collected second-harmonic generation images of collagen fibers. To quantify the average fiber direction and fiber density in each image, we applied two-dimensional Fourier analysis and intensity thresholding at five different locations for each control and scarred tissue sample, respectively. The resultant depth-resolved average fiber direction variance for scarred hamster cheek pouches (0.61 ± 0.03) was significantly lower (p < 0.05) than control tissue (0.73 ± 0.04), indicating increased fiber alignment within the scar. Depth-resolved average voxel density measurements indicated scarred tissues contained greater (p < 0.005) fiber density (0.72 ± 0.09) compared to controls (0.18 ± 0.03). In the present study, image analysis of both fiber alignment and density from depth-resolved second-harmonic generation images in epi-detection mode enabled the quantification of the increased collagen fiber deposition and alignment typically observed in fibrosis. The epi-detection geometry is the only viable method for in vivo imaging as well as imaging thick turbid tissues. These quantitative endpoints, clearly differentiating between control and scarred hamster cheek pouches, provide an objective means to characterize the extent of vocal fold scarring in vivo in preclinical and clinical research. In particular, this non-invasive method offers advantages for monitoring scar treatments in live animals and following the effects of scarring-related treatments such as application of steroids or drugs targeting pathways involved in fibrosis. SCANNING 38:684-693, 2016. © 2016 Wiley Periodicals, Inc.

Automatic cell nuclei segmentation and classification of breast cancer histopathology images

Breast cancer is the leading type of malignant tumor observed in women and the effective treatment depends on its early diagnosis. Diagnosis from histopathological images remains the "gold standard" for breast cancer. The complexity of breast cell histopathology (BCH) images makes reliable segmentation and classification hard. In this paper, an automatic quantitative image analysis technique of BCH images is proposed. For the nuclei segmentation, top-bottom hat transform is applied to enhance image quality. Wavelet decomposition and multi-scale region-growing (WDMR) are combined to obtain regions of interest (ROIs) thereby realizing precise location. A double-strategy splitting model (DSSM) containing adaptive mathematical morphology and Curvature Scale Space (CSS) corner detection method is applied to split overlapped cells for better accuracy and robustness. For the classification of cell nuclei, 4 shape-based features and 138 textural features based on color spaces are extracted. Optimal feature set is obtained by support vector machine (SVM) with chain-like agent genetic algorithm (CAGA). The proposed method was tested on 68 BCH images containing more than 3600 cells. Experimental results show that the mean segmentation sensitivity was 91.53% (±4.05%) and specificity was 91.64% (±4.07%). The classification performance of normal and malignant cell images can achieve 96.19% (±0.31%) for accuracy, 99.05% (±0.27%) for sensitivity and 93.33% (±0.81%) for specificity.

Spatial Intensity Distribution Analysis Reveals Abnormal Oligomerization of Proteins in Single Cells

Knowledge of membrane receptor organization is essential for understanding the initial steps in cell signaling and trafficking mechanisms, but quantitative analysis of receptor interactions at the single-cell level and in different cellular compartments has remained highly challenging. To achieve this, we apply a quantitative image analysis technique—spatial intensity distribution analysis (SpIDA)—that can measure fluorescent particle concentrations and oligomerization states within different subcellular compartments in live cells. An important technical challenge faced by fluorescence microscopy-based measurement of oligomerization is the fidelity of receptor labeling. In practice, imperfect labeling biases the distribution of oligomeric states measured within an aggregated system. We extend SpIDA to enable analysis of high-order oligomers from fluorescence microscopy images, by including a probability weighted correction algorithm for nonemitting labels. We demonstrated that this fraction of nonemitting probes could be estimated in single cells using SpIDA measurements on model systems with known oligomerization state. Previously, this artifact was measured using single-step photobleaching. This approach was validated using computer-simulated data and the imperfect labeling was quantified in cells with ion channels of known oligomer subunit count. It was then applied to quantify the oligomerization states in different cell compartments of the proteolipid protein (PLP) expressed in COS-7 cells. Expression of a mutant PLP linked to impaired trafficking resulted in the detection of PLP tetramers that persist in the endoplasmic reticulum, while no difference was measured at the membrane between the distributions of wild-type and mutated PLPs. Our results demonstrate that SpIDA allows measurement of protein oligomerization in different compartments of intact cells, even when fractional mislabeling occurs as well as photobleaching during the imaging process, and reveals insights into the mechanism underlying impaired trafficking of PLP.

Polyhydroxyalkanoate granules quantification in mixed microbial cultures using image analysis: Sudan Black B versus Nile Blue A staining

Polyhydroxyalkanoates (PHA) can be produced and intracellularly accumulated as inclusions by mixed microbial cultures (MMC) for bioplastic production and in enhanced biological phosphorus removal (EBPR) systems. Classical methods for PHA quantification use a digestion step prior to chromatography analysis, rendering them labor intensive and time-consuming. The present work investigates the use of two quantitative image analysis (QIA) procedures specifically developed for PHA inclusions identification and quantification. MMC obtained from an EBPR system were visualized by bright-field and fluorescence microscopy for PHA inclusions detection, upon Sudan Black B (SBB) and Nile Blue A (NBA) staining, respectively. The captured color images were processed by QIA techniques and the image analysis data were further treated using multivariate statistical analysis. Partial least squares (PLS) regression coefficients of 0.90 and 0.86 were obtained between QIA parameters and PHA concentrations using SBB and NBA, respectively. It was found that both staining procedures might be seen as alternative methodologies to classical PHA determination.

Aroma production by Yarrowia lipolytica in airlift and stirred tank bioreactors: Differences in yeast metabolism and morphology

The production of γ-decalactone from castor oil in batch cultures of Yarrowia lipolytica W29 was compared in stirred tank (STR) and airlift bioreactors. Oxygen mass transfer from air to biphasic medium was characterized in both bioreactors through correlations for kLa with power input and superficial gas velocity. Different conditions of oxygen transfer rate (OTR) were selected to perform biotransformations and for both bioreactors improvement of γ-decalactone productivity was obtained with OTR increase, with the highest value of 75±10mgL−1h−1 being obtained in the STR for a kLa of 113±5h−1. However, a 2-fold increase in γ-decalactone concentration (around 3gL−1) was achieved in the airlift compared to STR. Morphological characterization of the yeast cells by quantitative image analysis techniques showed that pneumatic agitation causes less impact in the cells morphology than mechanical agitation. A predominance of somewhat loose cells and quite irregular structures was observed in the STR.

Decoupling Polarization of the Golgi Apparatus and GM1 in the Plasma Membrane

Cell polarization is a process of coordinated cellular rearrangements that prepare the cell for migration. GM1 is synthesized in the Golgi apparatus and localized in membrane microdomains that appear at the leading edge of polarized cells, but the mechanism by which GM1 accumulates asymmetrically is unknown. The Golgi apparatus itself becomes oriented toward the leading edge during cell polarization, which is thought to contribute to plasma membrane asymmetry. Using quantitative image analysis techniques, we measure the extent of polarization of the Golgi apparatus and GM1 in the plasma membrane simultaneously in individual cells subject to a wound assay. We find that GM1 polarization starts just 10 min after stimulation with growth factors, while Golgi apparatus polarization takes 30 min. Drugs that block Golgi polarization or function have no effect on GM1 polarization, and, conversely, inhibiting GM1 polarization does not affect Golgi apparatus polarization. Evaluation of Golgi apparatus and GM1 polarization in single cells reveals no correlation between the two events. Our results indicate that Golgi apparatus and GM1 polarization are controlled by distinct intracellular cascades involving the Ras/Raf/MEK/ERK and the PI3K/Akt/mTOR pathways, respectively. Analysis of cell migration and invasion suggest that MEK/ERK activation is crucial for two dimensional migration, while PI3K activation drives three dimensional invasion, and no cumulative effect is observed from blocking both simultaneously. The independent biochemical control of GM1 polarity by PI3K and Golgi apparatus polarity by MEK/ERK may act synergistically to regulate and reinforce directional selection in cell migration.