Visualization and correction of automated segmentation, tracking and lineaging from 5-D stem cell image sequences.

BACKGROUND: Neural stem cells are motile and proliferative cells that undergo mitosis, dividing to produce daughter cells and ultimately generating differentiated neurons and glia. Understanding the mechanisms controlling neural stem cell proliferation and differentiation will play a key role in the emerging fields of regenerative medicine and cancer therapeutics. Stem cell studies in vitro from 2-D image data are well established. Visualizing and analyzing large three dimensional images of intact tissue is a challenging task. It becomes more difficult as the dimensionality of the image data increases to include time and additional fluorescence channels. There is a pressing need for 5-D image analysis and visualization tools to study cellular dynamics in the intact niche and to quantify the role that environmental factors play in determining cell fate. RESULTS: We present an application that integrates visualization and quantitative analysis of 5-D (x,y,z,t,channel) and large montage confocal fluorescence microscopy images. The image sequences show stem cells together with blood vessels, enabling quantification of the dynamic behaviors of stem cells in relation to their vascular niche, with applications in developmental and cancer biology. Our application automatically segments, tracks, and lineages the image sequence data and then allows the user to view and edit the results of automated algorithms in a stereoscopic 3-D window while simultaneously viewing the stem cell lineage tree in a 2-D window. Using the GPU to store and render the image sequence data enables a hybrid computational approach. An inference-based approach utilizing user-provided edits to automatically correct related mistakes executes interactively on the system CPU while the GPU handles 3-D visualization tasks. CONCLUSIONS: By exploiting commodity computer gaming hardware, we have developed an application that can be run in the laboratory to facilitate rapid iteration through biological experiments. We combine unsupervised image analysis algorithms with an interactive visualization of the results. Our validation interface allows for each data set to be corrected to 100% accuracy, ensuring that downstream data analysis is accurate and verifiable. Our tool is the first to combine all of these aspects, leveraging the synergies obtained by utilizing validation information from stereo visualization to improve the low level image processing tasks.

NetMets: software for quantifying and visualizing errors in biological network segmentation.

One of the major goals in biomedical image processing is accurate segmentation of networks embedded in volumetric data sets. Biological networks are composed of a meshwork of thin filaments that span large volumes of tissue. Examples of these structures include neurons and microvasculature, which can take the form of both hierarchical trees and fully connected networks, depending on the imaging modality and resolution. Network function depends on both the geometric structure and connectivity. Therefore, there is considerable demand for algorithms that segment biological networks embedded in three-dimensional data. While a large number of tracking and segmentation algorithms have been published, most of these do not generalize well across data sets. One of the major reasons for the lack of general-purpose algorithms is the limited availability of metrics that can be used to quantitatively compare their effectiveness against a pre-constructed ground-truth. In this paper, we propose a robust metric for measuring and visualizing the differences between network models. Our algorithm takes into account both geometry and connectivity to measure network similarity. These metrics are then mapped back onto an explicit model for visualization.

Novel image analysis approach quantifies morphological characteristics of 3D breast culture acini with varying metastatic potentials.

Prognosis of breast cancer is primarily predicted by the histological grading of the tumor, where pathologists manually evaluate microscopic characteristics of the tissue. This labor intensive process suffers from intra- and inter-observer variations; thus, computer-aided systems that accomplish this assessment automatically are in high demand. We address this by developing an image analysis framework for the automated grading of breast cancer in in vitro three-dimensional breast epithelial acini through the characterization of acinar structure morphology. A set of statistically significant features for the characterization of acini morphology are exploited for the automated grading of six (MCF10 series) cell line cultures mimicking three grades of breast cancer along the metastatic cascade. In addition to capturing both expected and visually differentiable changes, we quantify subtle differences that pose a challenge to assess through microscopic inspection. Our method achieves 89.0% accuracy in grading the acinar structures as nonmalignant, noninvasive carcinoma, and invasive carcinoma grades. We further demonstrate that the proposed methodology can be successfully applied for the grading of in vivo tissue samples albeit with additional constraints. These results indicate that the proposed features can be used to describe the relationship between the acini morphology and cellular function along the metastatic cascade.

Design and fabrication of human skin by three-dimensional bioprinting.

Three-dimensional (3D) bioprinting, a flexible automated on-demand platform for the free-form fabrication of complex living architectures, is a novel approach for the design and engineering of human organs and tissues. Here, we demonstrate the potential of 3D bioprinting for tissue engineering using human skin as a prototypical example. Keratinocytes and fibroblasts were used as constituent cells to represent the epidermis and dermis, and collagen was used to represent the dermal matrix of the skin. Preliminary studies were conducted to optimize printing parameters for maximum cell viability as well as for the optimization of cell densities in the epidermis and dermis to mimic physiologically relevant attributes of human skin. Printed 3D constructs were cultured in submerged media conditions followed by exposure of the epidermal layer to the air-liquid interface to promote maturation and stratification. Histology and immunofluorescence characterization demonstrated that 3D printed skin tissue was morphologically and biologically representative of in vivo human skin tissue. In comparison with traditional methods for skin engineering, 3D bioprinting offers several advantages in terms of shape- and form retention, flexibility, reproducibility, and high culture throughput. It has a broad range of applications in transdermal and topical formulation discovery, dermal toxicity studies, and in designing autologous grafts for wound healing. The proof-of-concept studies presented here can be further extended for enhancing the complexity of the skin model via the incorporation of secondary and adnexal structures or the inclusion of diseased cells to serve as a model for studying the pathophysiology of skin diseases.

Multisensory interface for 5D stem cell image volumes.

Biological imaging of live cell and tissue using 3D microscopy is able to capture time-lapse image sequences showing multiple molecular markers labeling different biological structures simultaneously. In order to analyze this complex multi-dimensional image sequence data, there is a need for automated quantitative algorithms, and for methods to visualize and interact with both the data and the analytical results. Traditional computational human input devices such as the keyboard and mouse are no longer adequate for complex tasks such as manipulating and navigating 3+ dimensional volumes. In this paper, we have developed a new interaction system for interfacing with big data sets using the human visual system together with touch, force and audio feedback. This system includes real-time dynamic 3D visualization, haptic interaction via exoskeletal glove, and tonal auditory components that seamlessly create an immersive environment for efficient qualitative analysis.

Renal metallothionein responds rapidly and site specifically to zinc repletion in growing rats.

Metallothionein (MT) is important for heavy metals and free radical protection in the kidney. MT is responsive to zinc and primarily localized within the renal cortex. However, site-specific renal responses to dietary zinc repletion are understudied. The objective of this study was to examine the effects of dietary zinc deficiency and repletion on renal MT concentration and immunolocalization in rats. Weanling male Sprague Dawley rats were randomly assigned to either a zinc-deficient, zinc control, or pair-fed to zinc-deficient group. Half of the zinc-deficient and pair-fed rats were repleted with the control diet ad libitum for an additional 24h. Renal tissue samples were assessed for total zinc, MT concentrations and MT immunostaining. Dietary zinc deficiency reduced renal zinc and MT concentrations, and attenuated intensity and localization of MT. Dietary zinc repletion for 24h restored renal zinc and MT concentrations, the latter primarily in the proximal convoluted tubules of the cortex. Concentrations of renal MT, but not zinc, were elevated by diet restriction and MT (microg/mg protein) and partially normalized by 24h diet repletion. In conclusion, renal MT modification due to zinc deficiency or diet restriction can be rapidly normalized in a site-specific manner with normal dietary zinc intake. The results support a role for MT in kidney homeostasis, in particular at the level of the proximal tubules in the cortex. The speed of MT repletion may have clinical implications for dietary zinc in the treatment of acute and chronic renal pathology due to toxins and free radicals.

Automated image analysis methods for 3-D quantification of the neurovascular unit from multichannel confocal microscope images.

BACKGROUND: There is a need for integrative and quantitative methods to investigate the structural and functional relations among elements of complex systems, such as the neurovascular unit (NVU), that involve multiple cell types, microvasculatures, and various genomic/proteomic/ionic functional entities. METHODS: Vascular casting and selective labeling enabled simultaneous three-dimensional imaging of the microvasculature, cell nuclei, and cytoplasmic stains. Multidimensional segmentation was achieved by (i) bleed-through removal and attenuation correction; (ii) independent segmentation and morphometry for each corrected channel; and (iii) spatially associative feature computation across channels. The combined measurements enabled cell classification based on nuclear morphometry, cytoplasmic signals, and distance from vascular elements. Specific spatial relations among the NVU elements could be quantified. RESULTS: A software system combining nuclear and vessel segmentation codes and associative features was constructed and validated. Biological variability contributed to misidentified nuclei (9.3%), undersegmentation of nuclei (3.7%), hypersegmentation of nuclei (14%), and missed nuclei (4.7%). Microvessel segmentation errors occurred rarely, mainly due to nonuniform lumen staining. CONCLUSIONS: Associative features across fluorescence channels, in combination with standard features, enable integrative structural and functional analysis of the NVU. By labeling additional structural and functional entities, this method can be scaled up to larger-scale systems biology studies that integrate spatial and molecular information.