What We Do-Center for Bioimage Informatics - Carnegie Mellon University

What We Do

Research Projects

Deformation-Based Nuclear Morphometry: Capturing nuclear shape variation in HeLa cells

Led by: Gustavo Rohde, Kris Noel Dahl and Robert F. Murphy

Information available at: http://www.andrew.cmu.edu/user/gustavor/research.html

The empirical characterization of nuclear shape distributions is an important unsolved problem with many applications in biology and medicine. Numerous genetic diseases and cancers have alterations in nuclear morphology, and methods for characterization of morphology could aid in both diagnoses and fundamental understanding of these disorders. Automated approaches have been used to measure features related to the size and shape of the cell nucleus, and statistical analysis of these features has often been performed assuming an underlying Euclidean (linear) vector space. We discuss the difficulties associated with the analysis of nuclear shape in light of the fact that shape spaces are nonlinear, and demonstrate methods for characterizing nuclear shapes and shape distributions based on spatial transformations that map one nucleus to another. By combining large deformation metric mapping with multidimensional scaling we offer a flexible approach for elucidating the intrinsic nonlinear degrees of freedom of a distribution of nuclear shapes. More specifically, we demonstrate approaches for nuclear shape interpolation and computation of mean nuclear shape. We also provide a method for estimating the number of free parameters that contribute to shape as well as an approach for visualizing most representative shape variations within a distribution of nuclei. The proposed methodology can be completely automated, is independent of the dimensionality of the images, and can handle complex shapes. Results obtained by analyzing two sets of images of HeLa cells are shown. In addition to identifying the modes of variation in normal HeLa nuclei, the effects of lamin A/C on nuclear morphology are quantitatively described.

Location Proteomics

Led by: Robert F. Murphy

Information available at: http://murphylab.web.cmu.edu/

The primary focus of current work in the lab is on automated interpretation of fluorescence microscope images.

Publications

The Center faculty and their groups publish extensively. Below are links to their individual pages.

Core Faculty

Affiliated Faculty 

Data

The following data collections have been collected by Murphy Lab and can be found at Murphy Lab data site . They are also a part of the Protein Subcellular Location Image Database . Additional image collections being used in the NSF-sponsored ITR collaborative project between UCSB and CMU are available at UC Santa Barbara Center for Bio-Image Informatics site.

2D CHO

Available at: http://murphylab.web.cmu.edu/data 

The collection consists of fluorescence microscope images of Chinese Hamster Ovary cells using five different labels (anti-giantin, Hoechst 33258 (DNA), anti-lamp2, anti-nop4, anti-tubulin). The images were collected using wide-field microscopy and corrected to remove out-of-focus fluorescence using the nearest neighbor method.

2D HeLa 

Available at: http://murphylab.web.cmu.edu/data 

This collection consists of fluorescence microscope images of HeLa cells using ten differet labels (DAPI, anti-ER, anti-giantin, anti-gpp130, anti-lamp2, anti-mitochondria, anti-nucleolin, phalloidin, anti-transferrin receptor, anti-tubulin). The images were collected using wide-field microscopy and corrected to remove out-of-focus fluorescence using the nearest neighbor method. Parallel images of DNA distributions are included.

3D HeLa 

Available at: http://murphylab.web.cmu.edu/data 

This collection consists of fluorescence microscope for the same probes as in the 2D HeLa collection (except that Propidium iodide was used in place of DAPI). The images were collected using laser-scanning confocal microscopy. Parallel images of total DNA and total protein are included.

3D HeLa-UCE 

Available at: http://murphylab.web.cmu.edu/data 

This collection was created by Dr. Jack Rohrer's group and consists of fluorescence microscope images of cells expressing GFP-tagged constructs of the mannose-6-phosphate uncovering enzyme (UCE). The images were collected using laser-scanning confocal microscopy following the same protocol as the 3D HeLa collection. A web interface showing typical images of each mutant and how their patterns are automatically grouped is available.

3D 3T3 

Available at: http://murphylab.web.cmu.edu/data

This collection was collected in collaboration with Dr. Jonathan Jarvik and Dr. Peter Berget and consists of fluorescence microscope images of cell lines expressing GFP-tagged proteins. The cell lines were obtained by CD-tagging to produce internal GFP-fusions in random proteins. The images were collected using spinning disk confocal microscopy and only images of GFP fluorescence were collected. A web interface showing typical images of each cell line and how their patterns are automatically grouped is available.

Software

The Center faculty and their groups often make software available. Below are links organized by application and task 

Software by Application

Location Proteomics 

PSLID: Protein Subcellular Location Image Database 
Available at: http://murphylab.web.cmu.edu/services/PSLID/

SLIF: Subcellular Location Image Finder 
Available at: http://murphylab.web.cmu.edu/services/SLIF/

Adaptive multirate acquisition of fluorescence microscopy data sets 
Available at: http://andrew.cmu.edu/user/jelenak/Software/index.html

Intelligent acquisition and learning of fluorescence microscope data models 
Available at: http://andrew.cmu.edu/user/jelenak/Software/index.html

Topology preserving STACS segmentation
Available at: http://andrew.cmu.edu/user/jelenak/Software/index.html

Multiresolution frame classification 
Available at: http://andrew.cmu.edu/user/jelenak/Software/index.html

A Graphical Model Approach to Automated Classification of Protein Subcellular Location Patterns in Multi-Cell Images 
Available at: http://murphylab.web.cmu.edu/software/2006_BMC_bioinformatics/

Approximate Inference Approach To Automated Classification Of Protein Subcellular Location Patterns In Multi-Cell Images 
Available at: http://murphylab.web.cmu.edu/software/2006_ISBI_PU/

Automated learning of generative models for subcellular location: Building blocks for systems biology 
Available at: http://murphylab.web.cmu.edu/software/2007_Cytometry_GenModel/ 

Image Analysis for High-Throughput Drosophila Embryo RNAi Screens 

Multiresolution frame classification 
Available at: http://andrew.cmu.edu/user/jelenak/Software/index.html 

Golgi-Body Segmentation 

Active mask segmentation 
Available at: http://andrew.cmu.edu/user/jelenak/Software/index.html 

Identification of Germ Layer Components in Teratomas Derived from Embryonic Stem Cells 

Multiresolution frame classification 
Available at: http://andrew.cmu.edu/user/jelenak/Software/index.html

Software by Task

Database Services

PSLID: Protein Subcellular Location Image Database 
Available at: http://murphylab.web.cmu.edu/services/PSLID/

SLIF: Subcellular Location Image Finder 
Available at: http://murphylab.web.cmu.edu/services/SLIF/

Acquisition 

Adaptive multirate acquisition of fluorescence microscopy data sets 
Available at: http://andrew.cmu.edu/user/jelenak/Software/index.html 

Intelligent acquisition and learning of fluorescence microscope data models 
Available at: http://andrew.cmu.edu/user/jelenak/Software/index.html 

Segmentation 

Topology preserving STACS segmentation 
Available at: http://andrew.cmu.edu/user/jelenak/Software/index.html 

Active mask segmentation 
Available at: http://andrew.cmu.edu/user/jelenak/Software/index.html 

Classification/Recognition 

Multiresolution frame classification 
Available at: http://andrew.cmu.edu/user/jelenak/Software/index.html 

A Graphical Model Approach to Automated Classification of Protein Subcellular Location Patterns in Multi-Cell Images 
Available at: http://murphylab.web.cmu.edu/software/2006_BMC_bioinformatics/ 

Approximate Inference Approach To Automated Classification Of Protein Subcellular Location Patterns In Multi-Cell Images 
Available at: http://murphylab.web.cmu.edu/software/2006_ISBI_PU/ 

Model Learning 

Automated learning of generative models for subcellular location: Building blocks for systems biology 
Available at: http://murphylab.web.cmu.edu/software/2007_Cytometry_GenModel/