Dataset Information

Automated detection of whole-cell mitochondrial motility and its dependence on cytoarchitectural integrity.

ABSTRACT: Current methodologies used for mitochondrial motility analysis tend to either overlook individual mitochondrial tracks or analyze only peripheral mitochondria instead of mitochondria in all regions of the cell. Furthermore, motility analysis of an individual mitochondrion is usually quantified by establishing an arbitrary threshold for "directed" motion. In this work, we created a custom, publicly available computational algorithm based on a previously published approach (Giedt et al., 2012. Ann Biomed Eng 40:1903-1916) in order to characterize the distribution of mitochondrial movements at the whole-cell level, while still preserving information about single mitochondria. Our technique is easy to use, robust, and computationally inexpensive. Images are first pre-processed for increased resolution, and then individual mitochondria are tracked based on object connectivity in space and time. When our method is applied to microscopy fields encompassing entire cells, we reveal that the mitochondrial net distances in fibroblasts follow a lognormal distribution within a given cell or group of cells. The ability to model whole-cell mitochondrial motility as a lognormal distribution provides a new quantitative paradigm for comparing mitochondrial motility in naïve and treated cells. We further demonstrate that microtubule and microfilament depolymerization shift the lognormal distribution in directions which indicate decreased and increased mitochondrial movement, respectively. These findings advance earlier work on neuronal axons (Morris and Hollenbeck, 1993. J Cell Sci 104:917-927) by relating them to a different cell type, applying them on a global scale, and automating measurement of mitochondrial motility in general.

SUBMITTER: Kandel J

PROVIDER: S-EPMC4452426 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature

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Automated detection of whole-cell mitochondrial motility and its dependence on cytoarchitectural integrity.

Kandel Judith J Chou Philip P Eckmann David M DM

Biotechnology and bioengineering 20150313 7

Current methodologies used for mitochondrial motility analysis tend to either overlook individual mitochondrial tracks or analyze only peripheral mitochondria instead of mitochondria in all regions of the cell. Furthermore, motility analysis of an individual mitochondrion is usually quantified by establishing an arbitrary threshold for "directed" motion. In this work, we created a custom, publicly available computational algorithm based on a previously published approach (Giedt et al., 2012. Ann ...[more]

PMID: 25678368

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Project description:BackgroundTime-lapse microscopic imaging provides a powerful approach for following changes in cell phenotype over time. Visible responses of whole cells can yield insight into functional changes that underlie physiological processes in health and disease. For example, features of cell motility accompany molecular changes that are central to the immune response, to carcinogenesis and metastasis, to wound healing and tissue regeneration, and to the myriad developmental processes that generate an organism. Previously reported image processing methods for motility analysis required custom viewing devices and manual interactions that may introduce bias, that slow throughput, and that constrain the scope of experiments in terms of the number of treatment variables, time period of observation, replication and statistical options. Here we describe a fully automated system in which images are acquired 24/7 from 384 well plates and are automatically processed to yield high-content motility and morphological data.ResultsWe have applied this technology to study the effects of different extracellular matrix compounds on human osteoblast-like cell lines to explore functional changes that may underlie processes involved in bone formation and maintenance. We show dose-response and kinetic data for induction of increased motility by laminin and collagen type I without significant effects on growth rate. Differential motility response was evident within 4 hours of plating cells; long-term responses differed depending upon cell type and surface coating. Average velocities were increased approximately 0.1 microm/min by ten-fold increases in laminin coating concentration in some cases. Comparison with manual tracking demonstrated the accuracy of the automated method and highlighted the comparative imprecision of human tracking for analysis of cell motility data. Quality statistics are reported that associate with stage noise, interference by non-cell objects, and uncertainty in the outlining and positioning of cells by automated image analysis. Exponential growth, as monitored by total cell area, did not linearly correlate with absolute cell number, but proved valuable for selection of reliable tracking data and for disclosing between-experiment variations in cell growth.ConclusionThese results demonstrate the applicability of a system that uses fully automated image acquisition and analysis to study cell motility and growth. Cellular motility response is determined in an unbiased and comparatively high throughput manner. Abundant ancillary data provide opportunities for uniform filtering according to criteria that select for biological relevance and for providing insight into features of system performance. Data quality measures have been developed that can serve as a basis for the design and quality control of experiments that are facilitated by automation and the 384 well plate format. This system is applicable to large-scale studies such as drug screening and research into effects of complex combinations of factors and matrices on cell phenotype.

Project description:Breast magnetic resonance imaging (MRI) plays an important role in the clinical management of breast cancer. Computerized analysis is increasingly used to quantify breast MRI features in applications such as computer-aided lesion detection and fibroglandular tissue estimation for breast cancer risk assessment. Automated segmentation of the whole-breast as an organ from the other parts imaged is an important step in aiding lesion localization and fibroglandular tissue quantification. For this task, identifying the chest wall line (CWL) is most challenging due to image contrast variations, intensity discontinuity, and bias field.In this work, the authors develop and validate a fully automated image processing algorithm for accurate delineation of the CWL in sagittal breast MRI. The CWL detection is based on an integrated scheme of edge extraction and CWL candidate evaluation. The edge extraction consists of applying edge-enhancing filters and an edge linking algorithm. Increased accuracy is achieved by the synergistic use of multiple image inputs for edge extraction, where multiple CWL candidates are evaluated by the dynamic time warping algorithm coupled with the construction of a CWL reference. Their method is quantitatively validated by a dataset of 60 3D bilateral sagittal breast MRI scans (in total 3360 2D MR slices) that span the full American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) breast density range. Agreement with manual segmentation obtained by an experienced breast imaging radiologist is assessed by both volumetric and boundary-based metrics, including four quantitative measures.In terms of breast volume agreement with manual segmentation, the overlay percentage expressed by the Dice's similarity coefficient is 95.0% and the difference percentage is 10.1%. More specifically, for the segmentation accuracy of the CWL boundary, the CWL overlay percentage is 92.7% and averaged deviation distance is 2.3 mm. Their method requires ≈ 4.5 min for segmenting each 3D breast MRI scan (56 slices) in comparison to ≈ 35 min required for manual segmentation. Further analysis indicates that the segmentation performance of their method is relatively stable across the different BI-RADS density categories and breast volume, and also robust with respect to a varying range of the major parameters of the algorithm.Their fully automated method achieves high segmentation accuracy in a time-efficient manner. It could support large scale quantitative breast MRI analysis and holds the potential to become integrated into the clinical workflow for breast cancer clinical applications in the future.

Dataset Information

Automated detection of whole-cell mitochondrial motility and its dependence on cytoarchitectural integrity.

Publications

Automated detection of whole-cell mitochondrial motility and its dependence on cytoarchitectural integrity.

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