Time-lapse Data Research Articles

Highly repeatable time-lapse seismic with distributed compressive sensing — Mitigating effects of calibration errors

Recently, we demonstrated that combining joint recovery with low-cost nonreplicated randomized sampling tailored to time-lapse seismic can give us access to high-fidelity, highly repeatable, dense prestack vintages, and high-grade time lapse. To arrive at this result, we assumed well-calibrated surveys — i.e., we presumed accurate postplot source/receiver positions. Unfortunately, in practice, seismic surveys are prone to calibration errors, which are unknown deviations between actual and postplot acquisition geometry. By means of synthetic experiments, we analyze the possible impact of these errors on vintages and on time-lapse data obtained with our joint-recovery model from compressively sampled surveys. Supported by these experiments, we demonstrate that highly repeatable time-lapse vintages are attainable despite the presence of unknown calibration errors in the positions of the shots. We assess the repeatability quantitatively for two scenarios by studying the impact of calibration errors on conventional dense but irregularly sampled surveys and on low-cost compressed surveys. To separate time-lapse effects from calibration issues, we consider the idealized case in which the subsurface remains unchanged and the practical situation in which time-lapse changes are restricted to a subset of the data. In both cases, the quality of the recovered vintages and time lapse decreases gracefully for low-cost compressed surveys with increasing calibration errors. Conversely, the quality of vintages from expensive densely periodically sampled surveys decreases more rapidly as unknown and difficult-to-control calibration errors increase.

CO2 Plume Migration and Fate at Sleipner, Norway: Calibration of Numerical Models, Uncertainty Analysis, and Reactive Transport Modelling of CO2 Trapping to 10,000 Years

The Sleipner Project in Norway is the world's first industrial-scale geological carbon dioxide storage project. Time-lapse seismic monitoring data have been collected, tracing CO2 plume development from 1996 to 2010. Therefore, the Sleipner Project provides a somewhat unique opportunity to simulate the dynamics of CO2 in a real geological system. The purpose of this study is to simulate CO2 plume migration dynamics and assess the impact of uncertain factors on short and long term migration and fate of CO2 for the uppermost layer (Layer 9) of the Utsira Sand.First, we applied a multi-phase compositional simulator to the Sleipner Benchmark model for Layer 9 and calibrated our model against the time-lapsed seismic monitoring data at the site from 1999 to 2008. By adjusting lateral permeability anisotropy, CH4 in the CO2 stream, and reservoir temperature, approximate match with the observed plume was achieved. Model-predicted gas saturation, thickness of the CO2 accumulation, and CO2 solubility in brine (none of them used as calibration metrics) were all comparable with interpretations of the seismic data in the literature.Second, hundreds of simulations of parameter sensitivity (pressure, temperature, feeders, spill rates, relative permeability curves, and CH4 content) were conducted for the plume migration, based on the calibrated model. The results showed that simulated plume extents are sensitive to permeability anisotropy, temperature, and CH4 content, but not sensitive to the other parameters. However, adjusting a single parameter within the reported range of values in the literature would not reproduce the north-south trending CO2 plume; it took a combination of permeability, CH4, and temperature adjustments to match simulated CO2 plume with seismic monitoring data. Although there is a range of uncertain parameters, the predicted fate of CO2 fell within a narrow band, ∼ 93±2% structural trapping and ∼ 7±2% solubility trapping. The calibrated model is not unique. Many combinations of permeability anisotropy, temperature, and CH4 would produce similar matches. Other possibilities that would have improved the development of an N–S elongated CO2 plume, such as a slight tilting of the surface of Utsira top to the south, were not experimented in this study, but are worthy of exploration for future studies.Finally, we used coupled reactive mass transport model to investigate the effects of rate laws and regional groundwater flow on long-term CO2 fate in Layer 9. The mineral composition and brine chemistry for the Utsira sand were adopted from the literature, and we modelled 100 year injection and continued water-rock interaction to 10,000 years. The results indicated that: (1) The predicted fraction of CO2 mineral trapping when using the linear rate law for feldspar dissolution is twice as much as when using the non-linear rate law. (2) Mineral trapping is more significant when regional groundwater flow is taken into consideration. Under the influence of regional groundwater flow, the replenishment of fresh brine from upstream continuously dissolves CO2 at the tail of CO2 plume, generating a larger acidified area where mineral trapping takes place. In a Sleipner like aquifer, the upstream replenishment of groundwater results in ∼ 22% mineral trapping at year 10,000, compared to the ∼ 4% when the effects of regional groundwater are ignored.

NucleiNet: A convolutional encoder-decoder network for bio-image denoising.

Generic and scalable data analysis procedures are highly demanded by the increasing number of multi-dimensional biomedical data. However, especially for time-lapse biological data, the high level of noise prevents for automated high-throughput analysis methods. The rapid developing of machine-learning methods and particularly deep-learning methods provide new tools and methodologies that can help in the denoising of such data. Using a convolutional encoder-decoder network, one can provide a scalable bio-image platform, called NucleiNet, to automatically segment, classify and track cell nuclei. The proposed method can achieve 0.99 F-score and 0.99 pixel-wise accuracy on C. elegans dataset, which means that over 99% of nuclei can be successfully detected with no merging nuclei found.

Prediction of CO2 Saturation by Using Well Logging Data, in the Process of CO2-EOR and Geological Storage of CO2

CO2 saturation monitoring is one of the key parameters to determine the geological storage of CO2. Because of the CO2-EOR or CO2 injection, the CO2-brine-rock interactions in reservoirs will lead to a series of complex changes in the mineral composition of rocks, porosity, pore structure, properties of formation water and residual oil saturation. At the same time, the difference of the CO2 spread range in reservoirs will cause the different CO2 saturation in different regions; in the same region, the reservoir CO2 saturation will also be different at the different time. To predict the complex variation of CO2 saturation in reservoirs, the processing and interpretation of the time-lapse logging data and the comparative analysis are needed. The steps for determining the CO2 saturation are as follows. Before CO2 injection, the background values of the formation water, rock mineral composition, porosity, pore structure and oil saturation are determined by the implementation of well logging. Firstly, the formation water parameters were monitored and determined at a specified time in the process of CO2 injection or CO2-EOR. Secondly, the time-lapse well logging was carried out to determine the rock mineral composition, porosity, pore structure, oil saturation and saturation CO2. The case-hole formation resistivity, pulsed neutron logging series and C/O logging series are often used to process and interpret the results of well logging data and to predict the residual oil and CO2 saturation. In addition, if the variety of porosity would be ignored, CO2 saturation is based on the prediction. CO2 saturation is equal to the parameter such as: ((the background value of the acoustic logging porosity) – (the time-lapse neutron logging porosity))/ (the background value of the acoustic logging porosity).

Unscented Kalman filter assimilation of time-lapse self-potential data for monitoring solute transport

Monitoring the extent and evolution of contaminant plumes in local and regional groundwater systems from existing landfills is critical in contamination control and remediation. The self-potential survey is an efficient and economical nondestructive geophysical technique that can be used to investigate underground contaminant plumes. Based on the unscented transform, we have built a Kalman filtering cycle to conduct time-lapse data assimilation for monitoring the transport of solute based on the solute transport experiment using a bench-scale physical model. The data assimilation was formed by modeling the evolution based on the random walk model and observation correcting based on the self-potential forward. Thus, monitoring self-potential data can be inverted by the data assimilation technique. As a result, we can reconstruct the dynamic process of the contaminant plume instead of using traditional frame-to-frame static inversion, which may cause inversion artifacts. The data assimilation inversion algorithm was evaluated through noise-added synthetic time-lapse self-potential data. The result of the numerical experiment shows validity, accuracy and tolerance to the noise of the dynamic inversion. To validate the proposed algorithm, we conducted a scaled-down sandbox self-potential observation experiment to generate time-lapse data that closely mimics the real-world contaminant monitoring setup. The results of physical experiments support the idea that the data assimilation method is a potentially useful approach for characterizing the transport of contamination plumes using the unscented Kalman filter (UKF) data assimilation technique applied to field time-lapse self-potential data.

A BaSiC tool for background and shading correction of optical microscopy images

Quantitative analysis of bioimaging data is often skewed by both shading in space and background variation in time. We introduce BaSiC, an image correction method based on low-rank and sparse decomposition which solves both issues. In comparison to existing shading correction tools, BaSiC achieves high-accuracy with significantly fewer input images, works for diverse imaging conditions and is robust against artefacts. Moreover, it can correct temporal drift in time-lapse microscopy data and thus improve continuous single-cell quantification. BaSiC requires no manual parameter setting and is available as a Fiji/ImageJ plugin.

Estimating saturation and density changes caused by CO2 injection at Sleipner — Using time-lapse seismic amplitude-variation-with-offset and time-lapse gravity

We have developed a calibrated, simple time-lapse seismic method for estimating saturation changes from the [Formula: see text]-storage project at Sleipner offshore Norway. This seismic method works well to map changes when [Formula: see text] is migrating laterally away from the injection point. However, it is challenging to detect changes occurring below [Formula: see text] layers that have already been charged by some [Formula: see text]. Not only is this partly caused by the seismic shadow effects, but also by the fact that the velocity sensitivity for [Formula: see text] change in saturation from 0.3 to 1.0 is significantly less than saturation changes from zero to 0.3. To circumvent the seismic shadow zone problem, we combine the time-lapse seismic method with time-lapse gravity measurements. This is done by a simple forward modeling of gravity changes based on the seismically derived saturation changes, letting these saturation changes be scaled by an arbitrary constant and then by minimizing the least-squares error to obtain the best fit between the scaled saturation changes and the measured time-lapse gravity data. In this way, we are able to exploit the complementary properties of time-lapse seismic and gravity data.

Low-cost time-lapse seismic with distributed compressive sensing — Part 1: Exploiting common information among the vintages

Time-lapse seismic is a powerful technology for monitoring a variety of subsurface changes due to reservoir fluid flow. However, the practice can be technically challenging when one seeks to acquire colocated time-lapse surveys with high degrees of replicability among the shot locations. We have determined that under “ideal” circumstances, in which we ignore errors related to taking measurements off the grid, high-quality prestack data can be obtained from randomized subsampled measurements that are observed from surveys in which we choose not to revisit the same randomly subsampled on-the-grid shot locations. Our acquisition is low cost because our measurements are subsampled. We have found that the recovered finely sampled prestack baseline and monitor data actually improve significantly when the same on-the-grid shot locations are not revisited. We achieve this result by using the fact that different time-lapse data share information and that nonreplicated (on-the-grid) acquisitions can add information when prestack data are recovered jointly. Whenever the time-lapse data exhibit joint structure — i.e., they are compressible in some transform domain and share information — sparsity-promoting recovery of the “common component” and “innovations,” with respect to this common component, outperforms independent recovery of the prestack baseline and monitor data. The recovered time-lapse data are of high enough quality to serve as the input to extract poststack attributes used to compute time-lapse differences. Without joint recovery, artifacts — due to the randomized subsampling — lead to deterioration of the degree of repeatability of the time-lapse data. We tested this method by carrying out experiments with reliable statistics from thousands of repeated experiments. We also confirmed that high degrees of repeatability are achievable for an ocean-bottom cable survey acquired with time-jittered continuous recording.

MDia1 promotes T cell extravasation and enables induction of Experimental Autoimmune Encephalitis

Abstract Self-reactive T cells play a central role in Multiple Sclerosis (MS) and invade the central nervous system (CNS) to drive neurological damage. To enter tissues, T cells extravasate through the vascular endothelial cell wall via a process known as transendothelial migration (TEM). While many of the adhesion molecules and chemokine signaling pathways required for TEM are well characterized, little is known about how downstream cytoskeletal effectors mediate the mechanical forces and shape changes needed for T cell TEM. Mouse Diaphanous Homolog 1 (mDia1) is a terminal cytoskeletal effector involved directly in actin network remodeling. It has been associated with the formation of membrane protrusions that likely function in migration and cell-cell interactions and is highly expressed by activated T cells. The goal of our study was to examine the role of mDia1 in T cell extravasation into the CNS and subsequent induction of neuroinflammation. mDia1 knock-out (KO) mice were partially protected from Experimental Autoimmune Encephalomyelitis (EAE), a mouse model of MS. mDia1 deficient ex-vivo activated auto-reactive T cells displayed similar activation kinetics compared to control T cells. However, when these activated mDia1 deficient T cells were transferred into wild-type mice, they were impaired in their ability to induce EAE. In vitro time-lapse microscopy data suggest that mDia1 deficient T cells have a reduced ability to complete TEM across brain endothelium. This defect may explain the reduced ability of mDia1 deficient T cells to induce EAE. Collectively our data suggest that mDia1 may be a promising therapeutic target for regulating T cell trafficking in MS.

Trajectory energy minimization for cell growth tracking and genealogy analysis.

Cell growth experiments with a microfluidic device produce large-scale time-lapse image data, which contain important information on cell growth and patterns in their genealogy. To extract such information, we propose a scheme to segment and track bacterial cells automatically. In contrast with most published approaches, which often split segmentation and tracking into two independent procedures, we focus on designing an algorithm that describes cell properties evolving between consecutive frames by feeding segmentation and tracking results from one frame to the next one. The cell boundaries are extracted by minimizing the distance regularized level set evolution (DRLSE) model. Each individual cell was identified and tracked by identifying cell septum and membrane as well as developing a trajectory energy minimization function along time-lapse series. Experiments show that by applying this scheme, cell growth and division can be measured automatically. The results show the efficiency of the approach when testing on different datasets while comparing with other existing algorithms. The proposed approach demonstrates great potential for large-scale bacterial cell growth analysis.

Low-cost time-lapse seismic with distributed compressive sensing — Part 2: Impact on repeatability

Irregular or off-the-grid spatial sampling of sources and receivers is inevitable in field seismic acquisitions. Consequently, time-lapse surveys become particularly expensive because current practices aim to replicate densely sampled surveys for monitoring changes occurring in the reservoir due to hydrocarbon production. We have determined that under certain circumstances, high-quality prestack data can be obtained from cheap randomized subsampled measurements that are observed from nonreplicated surveys. We extend our time-jittered marine acquisition to time-lapse surveys by designing acquisition on irregular spatial grids that render simultaneous, subsampled, and irregular measurements. Using the fact that different time-lapse data share information and that nonreplicated surveys add information when prestack data are recovered jointly, we recover periodic densely sampled and colocated prestack data by adapting the recovery method to incorporate a regularization operator that maps traces from an irregular spatial grid to a regular periodic grid. The recovery method is, therefore, a combined operation of regularization, interpolation (estimating missing fine-grid traces from subsampled coarse-grid data), and source separation (unraveling overlapping shot records). By relaxing the insistence on replicability between surveys, we find that recovery of the time-lapse difference shows little variability for realistic field scenarios of slightly nonreplicated surveys that suffer from unavoidable natural deviations in spatial sampling of shots (or receivers) and pragmatic compressed-sensing-based nonreplicated surveys when compared with the “ideal” scenario of exact replicability between surveys. Moreover, the recovered densely sampled prestack baseline and monitor data improve significantly when the acquisitions are not replicated, and hence they can serve as input to extract poststack attributes used to compute time-lapse differences. Our observations are based on experiments conducted for an ocean-bottom cable survey acquired with time-jittered continuous recording assuming source equalization (or the same source signature) for the time-lapse surveys and no changes in wave heights, water column velocities or temperature, and salinity profiles.

Parameter inference for stochastic single-cell dynamics from lineage tree data

BackgroundWith the advance of experimental techniques such as time-lapse fluorescence microscopy, the availability of single-cell trajectory data has vastly increased, and so has the demand for computational methods suitable for parameter inference with this type of data. Most of currently available methods treat single-cell trajectories independently, ignoring the mother-daughter relationships and the information provided by the population structure. However, this information is essential if a process of interest happens at cell division, or if it evolves slowly compared to the duration of the cell cycle.ResultsIn this work, we propose a Bayesian framework for parameter inference on single-cell time-lapse data from lineage trees. Our method relies on a combination of Sequential Monte Carlo for approximating the parameter likelihood function and Markov Chain Monte Carlo for parameter exploration. We demonstrate our inference framework on two simple examples in which the lineage tree information is crucial: one in which the cell phenotype can only switch at cell division and another where the cell state fluctuates slowly over timescales that extend well beyond the cell-cycle duration.ConclusionThere exist several examples of biological processes, such as stem cell fate decisions or epigenetically controlled phase variation in bacteria, where the cell ancestry is expected to contain important information about the underlying system dynamics. Parameter inference methods that discard this information are expected to perform poorly for such type of processes. Our method provides a simple and computationally efficient way to take into account single-cell lineage tree data for the purpose of parameter inference and serves as a starting point for the development of more sophisticated and powerful approaches in the future.

4D inversion of time-lapse magnetotelluric data sets for monitoring geothermal reservoir

The productivity of a geothermal reservoir, which is a function of the pore-space and fluid-flow path of the reservoir, varies since the properties of the reservoir changes with geothermal reservoir production. Because the variation in the reservoir properties causes changes in electrical resistivity, time-lapse (TL) three-dimensional (3D) magnetotelluric (MT) methods can be applied to monitor the productivity variation of a geothermal reservoir thanks to not only its sensitivity to the electrical resistivity but also its deep depth of survey penetration. For an accurate interpretation of TL MT-data sets, a four-dimensional (4D) MT inversion algorithm has been developed to simultaneously invert all vintage data considering time-coupling between vintages. However, the changes in electrical resistivity of deep geothermal reservoirs are usually small generating minimum variation in TL MT responses. Maximizing the sensitivity of inversion to the changes in resistivity is critical in the success of 4D MT inversion. Thus, we further developed a focused 4D MT inversion method by considering not only the location of a reservoir but also the distribution of newly-generated fractures during the production. For the evaluation of the 4D MT algorithm, we tested our 4D inversion algorithms using synthetic TL MT-data sets.

Ground penetrating radar for fracture mapping in underground hazardous waste disposal sites: A case study from an underground research tunnel, South Korea

Secure disposal or storage of nuclear waste within stable geologic environments hinges on the effectiveness of artificial and natural radiation barriers. Fractures in the bedrock are viewed as the most likely passage for the transport of radioactive waste away from a disposal site. We utilize ground penetrating radar (GPR) to map fractures in the tunnel walls of an underground research tunnel at the Korea Atomic Energy Research Institute (KAERI). GPR experiments within the KAERI Underground Research Tunnel (KURT) were carried out by using 200MHz, 500MHz, and 1000MHz antennas. By using the high-frequency antennas, we were able to identify small-scale fractures, which were previously unidentified during the tunnel excavation process. Then, through 3-D visualization of the grid survey data, we reconstructed the spatial distribution and interconnectivity of the multi-scale fractures within the wall. We found that a multi-frequency GPR approach provided more details of the complex fracture network, including deep structures. Furthermore, temporal changes in reflection polarity between the GPR surveys enabled us to infer the hydraulic characteristics of the discrete fracture network developed behind the surveyed wall. We hypothesized that the fractures exhibiting polarity change may be due to a combination of air-filled and mineralogical boundaries. Simulated GPR scans for the considered case were consistent with the observed GPR data. If our assumption is correct, the groundwater flow into these near-surface fractures may form the water-filled fractures along the existing air-filled ones and hence cause the changes in reflection polarity over the given time interval (i.e., 7days). Our results show that the GPR survey is an efficient tool to determine fractures at various scales. Time-lapse GPR data may be essential to characterize the hydraulic behavior of discrete fracture networks in underground disposal facilities.

Time-lapse microgravity data acquisition in baseline stage of CO2 injection Gundih pilot project

Indonesia is conducting research on CO2 sequestration and monitoring in Gundih area. Geophysical methods are applied in the study area as monitoring technologies of CO2 storage in the subsurface. In this paper, we will describe data quality and baseline geophysical survey (2014 and 2016) as part of time-lapse microgravity (TLM) data acquisition. The same set of two relative gravimeters (Scintrex CG5) were used in 2014 and 2016 gravity data acquisition to provide TLM map. TLM can be useful to monitor the mass increase and decrease due to significant activities in the reservoir, but for this project TLM signal (caused by the CO2 injection in Gundih’s reservoir) estimated very small. Considering current project status, no injection yet, the data acquisition in 2014 and 2016 will be analysed to help us understand non-target anomalies in near surface. Preliminary analysis of gravity differences between 2014 and 2016 shows correlation pattern of time-lapse microgravity with land use in the study area. Rice fields and villages in the Western part of the study area correlate with negative TLM anomalies and forest area in the Eastern part of the study area correlate with positive TLM anomalies.

Visualization of reticulophagy in living cells using an endoplasmic reticulum-targeted p62 mutant.

Reticulophagy is a type of selective autophagy in which protein aggregate-containing and/or damaged endoplasmic reticulum (ER) fragments are engulfed for lysosomal degradation, which is important for ER homeostasis. Several chemical drugs and mutant proteins that promote protein aggregate formation within the ER lumen can efficiently induce reticulophagy in mammalian cells. However, the exact mechanism and cellular localization of reticulophagy remain unclear. In this report, we took advantage of the self-oligomerization property of p62/SQSTM1, an adaptor for selective autophagy, and developed a novel reticulophagy system based on an ER-targeted p62 mutant to investigate the process of reticulophagy in living cells. LC3 conversion analysis via western blot suggested that p62 mutant aggregate-induced ER stress triggered a cellular autophagic response. Confocal imaging showed that in cells with moderate aggregation conditions, the aggregates of ER-targeted p62 mutants were efficiently sequestered by autophagosomes, which was characterized by colocalization with the autophagosome precursor marker ATG16L1, the omegasome marker DFCP1, and the late autophagosomal marker LC3/GATE-16. Moreover, time-lapse imaging data demonstrated that the LC3- or DFCP1-positive protein aggregates are tightly associated with the reticular structures of the ER, thereby suggesting that reticulophagy occurs at the ER and that omegasomes may be involved in this process.

Fabrication of all-transparent polymer-based and encapsulated nanofluidic devices using nano-indentation lithography

In this paper, we describe a novel and simple process for the fabrication of all-transparent and encapsulated polymeric nanofluidic devices using nano-indentation lithography. First, a nanomechanical probe is used to ‘scratch’ nanoscale channels on polymethylmethacrylate (PMMA) substrates with sufficiently high hardness. Next, polydimethylsiloxane (PDMS) is used twice to duplicate the nanochannels onto PDMS substrates from the ‘nano-scratched’ PMMA substrates. A number of experiments are conducted to explore the relationships between the nano-indentation parameters and the nanochannel dimensions and to control the aspect ratio of the fabricated nanochannels. In addition, traditional photolithography combined with soft lithography is employed to fabricate microchannels on another PDMS ‘cap’ substrate. After manually aligning the substrates, all uncovered channels on two separate PDMS substrates are bonded to achieve a sealed and transparent nanofluidic device, which makes the dimensional transition from microscale to nanoscale feasible. The smallest dimensions of the achievable nanochannels that we have demonstrated thus far are of ~20 nm depth and ~800 nm width, with lengths extendable beyond 100 μm. Fluid flow experiments are performed to verify the reliability of the device. Two types of colloidal solution are used to visualize the fluid flow through the nanochannels, that is, ethanol is mixed with gold colloid or fluorescent dye (fluorescein isothiocyanate), and the flow rate and filling time of liquid in the nanochannels are estimated based on time-lapsed image data. The simplicity of the fabrication process, bio-compatibility of the polymer substrates, and optical transparency of the nanochannels for flow visualization are key characteristics of this approach that will be very useful for nanofluidic and biomolecular research applications in the future.

Inferring fitness landscapes and selection on phenotypic states from single-cell genealogical data.

Recent advances in single-cell time-lapse microscopy have revealed non-genetic heterogeneity and temporal fluctuations of cellular phenotypes. While different phenotypic traits such as abundance of growth-related proteins in single cells may have differential effects on the reproductive success of cells, rigorous experimental quantification of this process has remained elusive due to the complexity of single cell physiology within the context of a proliferating population. We introduce and apply a practical empirical method to quantify the fitness landscapes of arbitrary phenotypic traits, using genealogical data in the form of population lineage trees which can include phenotypic data of various kinds. Our inference methodology for fitness landscapes determines how reproductivity is correlated to cellular phenotypes, and provides a natural generalization of bulk growth rate measures for single-cell histories. Using this technique, we quantify the strength of selection acting on different cellular phenotypic traits within populations, which allows us to determine whether a change in population growth is caused by individual cells’ response, selection within a population, or by a mixture of these two processes. By applying these methods to single-cell time-lapse data of growing bacterial populations that express a resistance-conferring protein under antibiotic stress, we show how the distributions, fitness landscapes, and selection strength of single-cell phenotypes are affected by the drug. Our work provides a unified and practical framework for quantitative measurements of fitness landscapes and selection strength for any statistical quantities definable on lineages, and thus elucidates the adaptive significance of phenotypic states in time series data. The method is applicable in diverse fields, from single cell biology to stem cell differentiation and viral evolution.

Quantifying receptor trafficking and colocalization with confocal microscopy.

Confocal microscopy is a powerful tool for the study of cellular receptor trafficking and endocytosis. Unbiased and robust image analysis workflows are required for the identification, and study, of aberrant trafficking. After a brief review of related strategies, identifying both good and bad practice, custom workflows for the analysis of live cell 3D time-lapse data are presented. Strategies for data pre-processing, including denoising and background subtraction are considered. We use a 3D level set protocol to accurately segment cells using only the signal from fluorescently labelled receptor. A protocol for the quantification of changes to subcellular receptor distribution over time is then presented. As an example, ligand stimulated trafficking of epidermal growth factor receptor (EGFR) is shown to be significantly reduced in both AG1478 and Dynasore treated cells. Protocols for the quantitative analysis of colocalization between receptor and endosomes are also introduced, including strategies for signal isolation and statistical testing. By calculating the Manders and Pearson coefficients, both co-occurrence and correlation can be assessed. A statistically significant decrease in the level of ligand induced co-occurrence between EGFR and rab5 positive endosomes is demonstrated for both the AG1478 and Dynasore treated cells relative to a control. Finally, a strategy for the visualisation of co-occurrence is presented, which provides an unbiased alternative to colour overlays.

生产测井时间推移测井资料确定生产层剩余油饱和度

生产测井时间推移测井资料确定生产层剩余油饱和度