Phase contrast time-lapse microscopy datasets with automated and manual cell tracking annotations.

Phase contrast time-lapse microscopy is a non-destructive technique that generates large volumes of image-based information to quantify the behaviour of individual cells or cell populations. To guide the development of algorithms for computer-aided cell tracking and analysis, 48 time-lapse image sequences, each spanning approximately 3.5 days, were generated with accompanying ground truths for C2C12 myoblast cells cultured under 4 different media conditions, including with fibroblast growth factor 2 (FGF2), bone morphogenetic protein 2 (BMP2), FGF2 + BMP2, and control (no growth factor). The ground truths generated contain information for tracking at least 3 parent cells and their descendants within these datasets and were validated using a two-tier system of manual curation. This comprehensive, validated dataset will be useful in advancing the development of computer-aided cell tracking algorithms and function as a benchmark, providing an invaluable opportunity to deepen our understanding of individual and population-based cell dynamics for biomedical research.

Determining the subcellular location of new proteins from microscope images using local features.

MOTIVATION: Evaluation of previous systems for automated determination of subcellular location from microscope images has been done using datasets in which each location class consisted of multiple images of the same representative protein. Here, we frame a more challenging and useful problem where previously unseen proteins are to be classified. RESULTS: Using CD-tagging, we generated two new image datasets for evaluation of this problem, which contain several different proteins for each location class. Evaluation of previous methods on these new datasets showed that it is much harder to train a classifier that generalizes across different proteins than one that simply recognizes a protein it was trained on. We therefore developed and evaluated additional approaches, incorporating novel modifications of local features techniques. These extended the notion of local features to exploit both the protein image and any reference markers that were imaged in parallel. With these, we obtained a large accuracy improvement in our new datasets over existing methods. Additionally, these features help achieve classification improvements for other previously studied datasets. AVAILABILITY: The datasets are available for download at http://murphylab.web.cmu.edu/data/. The software was written in Python and C++ and is available under an open-source license at http://murphylab.web.cmu.edu/software/. The code is split into a library, which can be easily reused for other data and a small driver script for reproducing all results presented here. A step-by-step tutorial on applying the methods to new datasets is also available at that address. CONTACT: murphy@cmu.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Nanoparticle transport from mouse vagina to adjacent lymph nodes.

To test the feasibility of localized intravaginal therapy directed to neighboring lymph nodes, the transport of quantum dots across the vaginal wall was investigated. Quantum dots instilled into the mouse vagina were transported across the vaginal mucosa into draining lymph nodes, but not into distant nodes. Most of the particles were transported to the lumbar nodes; far fewer were transported to the inguinal nodes. A low level of transport was evident at 4 hr after intravaginal instillation, and transport peaked at about 36 hr after instillation. Transport was greatly enhanced by prior vaginal instillation of Nonoxynol-9. Hundreds of micrograms of nanoparticles/kg tissue (ppb) were found in the lumbar lymph nodes at 36 hr post-instillation. Our results imply that targeted transport of microbicides or immunogens from the vagina to local lymph organs is feasible. They also offer an in vivo model for assessing the toxicity of compounds intended for intravaginal use.

An engineered approach to stem cell culture: automating the decision process for real-time adaptive subculture of stem cells.

Current cell culture practices are dependent upon human operators and remain laborious and highly subjective, resulting in large variations and inconsistent outcomes, especially when using visual assessments of cell confluency to determine the appropriate time to subculture cells. Although efforts to automate cell culture with robotic systems are underway, the majority of such systems still require human intervention to determine when to subculture. Thus, it is necessary to accurately and objectively determine the appropriate time for cell passaging. Optimal stem cell culturing that maintains cell pluripotency while maximizing cell yields will be especially important for efficient, cost-effective stem cell-based therapies. Toward this goal we developed a real-time computer vision-based system that monitors the degree of cell confluency with a precision of 0.791±0.031 and recall of 0.559±0.043. The system consists of an automated phase-contrast time-lapse microscope and a server. Multiple dishes are sequentially imaged and the data is uploaded to the server that performs computer vision processing, predicts when cells will exceed a pre-defined threshold for optimal cell confluency, and provides a Web-based interface for remote cell culture monitoring. Human operators are also notified via text messaging and e-mail 4 hours prior to reaching this threshold and immediately upon reaching this threshold. This system was successfully used to direct the expansion of a paradigm stem cell population, C2C12 cells. Computer-directed and human-directed control subcultures required 3 serial cultures to achieve the theoretical target cell yield of 50 million C2C12 cells and showed no difference for myogenic and osteogenic differentiation. This automated vision-based system has potential as a tool toward adaptive real-time control of subculturing, cell culture optimization and quality assurance/quality control, and it could be integrated with current and developing robotic cell cultures systems to achieve complete automation.

Automated analysis of protein subcellular location in time series images.

MOTIVATION: Image analysis, machine learning and statistical modeling have become well established for the automatic recognition and comparison of the subcellular locations of proteins in microscope images. By using a comprehensive set of features describing static images, major subcellular patterns can be distinguished with near perfect accuracy. We now extend this work to time series images, which contain both spatial and temporal information. The goal is to use temporal features to improve recognition of protein patterns that are not fully distinguishable by their static features alone. RESULTS: We have adopted and designed five sets of features for capturing temporal behavior in 2D time series images, based on object tracking, temporal texture, normal flow, Fourier transforms and autoregression. Classification accuracy on an image collection for 12 fluorescently tagged proteins was increased when temporal features were used in addition to static features. Temporal texture, normal flow and Fourier transform features were most effective at increasing classification accuracy. We therefore extended these three feature sets to 3D time series images, but observed no significant improvement over results for 2D images. The methods for 2D and 3D temporal pattern analysis do not require segmentation of images into single cell regions, and are suitable for automated high-throughput microscopy applications. AVAILABILITY: Images, source code and results will be available upon publication at http://murphylab.web.cmu.edu/software CONTACT: murphy@cmu.edu.

A Graphical Model to Determine the Subcellular Protein Location in Artificial Tissues.

Location proteomics is concerned with the systematic analysis of the subcellular location of proteins. In order to perform comprehensive analysis of all protein location patterns, automated methods are needed. With the goal of extending automated subcellular location pattern analysis methods to high resolution images of tissues, 3D confocal microscope images of polarized CaCo2 cells immunostained for various proteins were collected. A three-color staining protocol was developed that permits parallel imaging of proteins of interest as well as DNA and the actin cytoskeleton. The collection is composed of 11 to 21 images for each of the 9 proteins that depict major subcellular patterns. A classifier was trained to recognize the subcellular location pattern of segmented cells with an accuracy of 89.2%. Using the Prior Updating method allowed improvement of this accuracy to 99.6%. This study demonstrates the benefit of using a graphical model approach for improving the pattern classification in tissue images.