Andrew R. Cohen

Objective comparison of particle tracking methods

Particle tracking is of key importance for quantitative analysis of intracellular dynamic processes from time-lapse microscopy image data. Because manually detecting and following large numbers of individual particles is not feasible, automated computational methods have been developed for these tasks by many groups. Aiming to perform an objective comparison of methods, we gathered the community and organized an open competition in which participating teams applied their own methods independently to a commonly defined data set including diverse scenarios. Performance was assessed using commonly defined measures. Although no single method performed best across all scenarios, the results revealed clear differences between the various approaches, leading to notable practical conclusions for users and developers.

Computational prediction of neural progenitor cell fates

Understanding how stem and progenitor cells choose between alternative cell fates is a major challenge in developmental biology. Efforts to tackle this problem have been hampered by the scarcity of markers that can be used to predict cell division outcomes. Here we present a computational method, based on algorithmic information theory, to analyze dynamic features of living cells over time. Using this method, we asked whether rat retinal progenitor cells (RPCs) display characteristic phenotypes before undergoing mitosis that could foretell their fate. We predicted whether RPCs will undergo a self-renewing or terminal division with 99% accuracy, or whether they will produce two photoreceptors or another combination of offspring with 87% accuracy. Our implementation can segment, track and generate predictions for 40 cells simultaneously on a standard computer at 5 min per frame. This method could be used to isolate cell populations with specific developmental potential, enabling previously impossible investigations.

Automated tracing and volume measurements of neurons from 3-D confocal fluorescence microscopy data

Three‐dimensional (3‐D) image analysis algorithms and experimental results that demonstrate the feasibility of fully automated tracing of neurons from fluorescence confocal microscopy data are presented. The input to the automated analysis is a set of successive optical slices that have been acquired using a confocal scanning laser microscope. The output of the system is a labelled graph representation of the neuronal topology that is spatially aligned with the 3‐D image data. A variety of topological and metric analyses can be carried out using this representation. For instance, precise measurements of volumes, lengths, diameters and tortuosities can be made over specific portions of the neuron that are specified in terms of the graph representation. The effectiveness of the method is demonstrated for a set of sample fields featuring selectively stained neurons. Additional work will be needed to refine the method for unsupervised use with complex data involving multiple intertwined neurons and extremely fine dendritic structures.

Automated Three-Dimensional Tracing of Neurons in Confocal and Brightfield Images

Automated three-dimensional (3-D) image analysis methods are presented for tracing of dye-injected neurons imaged by fluorescence confocal microscopy and HRP-stained neurons imaged by transmitted-light brightfield microscopy. An improved algorithm for adaptive 3-D skeletonization of noisy images enables the tracing. This algorithm operates by performing connectivity testing over large N x N x N voxel neighborhoods exploiting the sparseness of the structures of interest, robust surface detection that improves upon classical vacant neighbor schemes, improved handling of process ends or tips based on shape collapse prevention, and thickness-adaptive thinning. The confocal image stacks were skeletonized directly. The brightfield stacks required 3-D deconvolution. The results of skeletonization were analyzed to extract a graph representation. Topological and metric analyses can be carried out using this representation. A semiautomatic method was developed for reconnection of dendritic fragments that are disconnected due to insufficient dye penetration, an imaging deficiency, or skeletonization errors.

Applying systems-level spectral imaging and analysis to reveal the organelle interactome

The organization of the eukaryotic cell into discrete membrane-bound organelles allows for the separation of incompatible biochemical processes, but the activities of these organelles must be coordinated. For example, lipid metabolism is distributed between the endoplasmic reticulum for lipid synthesis, lipid droplets for storage and transport, mitochondria and peroxisomes for β-oxidation, and lysosomes for lipid hydrolysis and recycling. It is increasingly recognized that organelle contacts have a vital role in diverse cellular functions. However, the spatial and temporal organization of organelles within the cell remains poorly characterized, as fluorescence imaging approaches are limited in the number of different labels that can be distinguished in a single image. Here we present a systems-level analysis of the organelle interactome using a multispectral image acquisition method that overcomes the challenge of spectral overlap in the fluorescent protein palette. We used confocal and lattice light sheet instrumentation and an imaging informatics pipeline of five steps to achieve mapping of organelle numbers, volumes, speeds, positions and dynamic inter-organelle contacts in live cells from a monkey fibroblast cell line. We describe the frequency and locality of two-, three-, four- and five-way interactions among six different membrane-bound organelles (endoplasmic reticulum, Golgi, lysosome, peroxisome, mitochondria and lipid droplet) and show how these relationships change over time. We demonstrate that each organelle has a characteristic distribution and dispersion pattern in three-dimensional space and that there is a reproducible pattern of contacts among the six organelles, that is affected by microtubule and cell nutrient status. These live-cell confocal and lattice light sheet spectral imaging approaches are applicable to any cell system expressing multiple fluorescent probes, whether in normal conditions or when cells are exposed to disturbances such as drugs, pathogens or stress. This methodology thus offers a powerful descriptive tool and can be used to develop hypotheses about cellular organization and dynamics.

Vertebrate neural stem cell segmentation, tracking and lineaging with validation and editing

This protocol and the accompanying software program called LEVER (lineage editing and validation) enable quantitative automated analysis of phase-contrast time-lapse images of cultured neural stem cells. Images are captured at 5-min intervals over a period of 5–15 d as the cells proliferate and differentiate. LEVER automatically segments, tracks and generates lineage trees of the stem cells from the image sequence. In addition to generating lineage trees capturing the population dynamics of clonal development, LEVER extracts quantitative phenotypic measurements of cell location, shape, movement and size. When available, the system can include biomolecular markers imaged using fluorescence. It then displays the results to the user for highly efficient inspection and editing to correct any errors in the segmentation, tracking or lineaging. To enable high-throughput inspection, LEVER incorporates features for rapid identification of errors and for learning from user-supplied corrections to automatically identify and correct related errors.

Automatic Summarization of Changes in Biological Image Sequences Using Algorithmic Information Theory

An algorithmic information-theoretic method is presented for object-level summarization of meaningful changes in image sequences. Object extraction and tracking data are represented as an attributed tracking graph (ATG). Time courses of object states are compared using an adaptive information distance measure, aided by a closed-form multidimensional quantization. The notion of meaningful summarization is captured by using the gap statistic to estimate the randomness deficiency from algorithmic statistics. The summary is the clustering result and feature subset that maximize the gap statistic. This approach was validated on four bioimaging applications: 1) It was applied to a synthetic data set containing two populations of cells differing in the rate of growth, for which it correctly identified the two populations and the single feature out of 23 that separated them; 2) it was applied to 59 movies of three types of neuroprosthetic devices being inserted in the brain tissue at three speeds each, for which it correctly identified insertion speed as the primary factor affecting tissue strain; 3) when applied to movies of cultured neural progenitor cells, it correctly distinguished neurons from progenitors without requiring the use of a fixative stain; and 4) when analyzing intracellular molecular transport in cultured neurons undergoing axon specification, it automatically confirmed the role of kinesins in axon specification.

Generation of Rab-based transgenic lines for in vivo studies of endosome biology in zebrafish

The Rab family of small GTPases function as molecular switches regulating membrane and protein trafficking. Individual Rab isoforms define and are required for specific endosomal compartments. To facilitate in vivo investigation of specific Rab proteins, and endosome biology in general, we have generated transgenic zebrafish lines to mark and manipulate Rab proteins. We also developed software to track and quantify endosome dynamics within time‐lapse movies. The established transgenic lines ubiquitously express EGFP fusions of Rab5c (early endosomes), Rab11a (recycling endosomes), and Rab7 (late endosomes) to study localization and dynamics during development. Additionally, we generated UAS‐based transgenic lines expressing constitutive active (CA) and dominant‐negative (DN) versions for each of these Rab proteins. Predicted localization and functional consequences for each line were verified through a variety of assays, including lipophilic dye uptake and Crumbs2a localization. In summary, we have established a toolset for in vivo analyses of endosome dynamics and functions. Developmental Dynamics 240:2452–2465, 2011.

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

BackgroundNeural 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.ResultsWe 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.ConclusionsBy 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.

A neuronal network of mitochondrial dynamics regulates metastasis.

The role of mitochondria in cancer is controversial. Using a genome-wide shRNA screen, we now show that tumours reprogram a network of mitochondrial dynamics operative in neurons, including syntaphilin (SNPH), kinesin KIF5B and GTPase Miro1/2 to localize mitochondria to the cortical cytoskeleton and power the membrane machinery of cell movements. When expressed in tumours, SNPH inhibits the speed and distance travelled by individual mitochondria, suppresses organelle dynamics, and blocks chemotaxis and metastasis, in vivo. Tumour progression in humans is associated with downregulation or loss of SNPH, which correlates with shortened patient survival, increased mitochondrial trafficking to the cortical cytoskeleton, greater membrane dynamics and heightened cell invasion. Therefore, a SNPH network regulates metastatic competence and may provide a therapeutic target in cancer.