Murphy Lab

Home
Information
 People
 Addresses
 Cytometry Development Workshop
 FCS API

Research
 Projects
 Publications
 Software
 Presentations
 Flow Cytometry

Services
 PSLID
 SLIF
 Waldo

Data
 Download

Affiliations
 Carnegie Mellon University
 Lane Center for Computational Biology
 Center for Bioimage Informatics
 Biological Sciences Department
 Biomedical Engineering Department
 Machine Learning Department
 MBIC






Murphy Lab - Data


Most important

2D HeLa
3D HeLa
2D 3T3 RT Set3
2D 3T3 RT Set4
3D 3T3

Other Datasets

Raw and processed image collections
2D CHO
2D 3T3 RT Set1
2D 3T3 RT Set2
3D UCE
Hand-labeled image collections
2D 3T3 and U20S segmented nuclei
Other collections
UCSF yeast GFP images

Supplementary Data

ISBI 2006 SImEC2
ISMB 2007 Yeast Image Classification
Cytometry 2007 Generative Models

2D images of 3T3 and U20S cells hand-segmented to show nuclear boundaries

L.P. Coelho, A. Shariff and R. F. Murphy (2009). Nuclear segmentation in microsope cell images: A hand-segmented dataset and comparison of algorithms. Proceedings of the 2009 IEEE International Symposium on Biomedical Imaging (ISBI 2009), p. 518-521.

Supplementary Data for Cytometry generative models paper

T. Zhao and R.F. Murphy (2007). Automated learning of generative models for subcellular location: Building blocks for systems biology. Cytometry 71A:978-990.

Supplementary Data for ISMB/ECCB 2007 Yeast Image Classification paper

S.-C. Chen, T. Zhao, G. J. Gordon, and R. F. Murphy (2007). Automated Image Analysis of Protein Localization in Budding Yeast. Bioinformatics 23:i66-i71.

2D 3T3 Randomly CD-Tagged Images

The 2D 3T3 Randomly CD-Tagged collections were created by generating randomly CD-tagged cell clones in collaboration with Dr. Peter Berget and Jonathan Jarvik and then imaging them by automated microscopy.

2D 3T3 Randomly CD-Tagged Images: Set 1 - (download)

The Set 1 collection is described in

E. Garcia Osuna, J. Hua, N.W. Bateman, T. Zhao, P.B. Berget and R.F. Murphy (2007). Large-Scale Automated Analysis of Protein Subcellular Location Patterns in Randomly-Tagged 3T3 Cells. Annals Biomed. Eng. 35:1081-1087.

2D 3T3 Randomly CD-Tagged Images: Set 2 - (download)

The Set 2 collection comprises all images included in the RandTag database, release 1.0

2D 3T3 Randomly CD-Tagged Images: Set 3 - (download)

The Set 3 contains the widefield images from

Luis Pedro Coelho, Joshua D. Kangas, Armaghan W. Naik, Elvira Osuna-Highley, Estelle Glory-Afshar, Margaret Fuhrman, Ramanuja Simha, Peter B. Berget, Jonathan W. Jarvik, and Robert F. Murphy. (2013) Determining the subcellular location of new proteins from microscope images using local features. Bioinformatics doi:10.1093/bioinformatics/btt392.

2D 3T3 Randomly CD-Tagged Images: Set 4 - (download)

The Set 4 contains the confocal images from

Luis Pedro Coelho, Joshua D. Kangas, Armaghan W. Naik, Elvira Osuna-Highley, Estelle Glory-Afshar, Margaret Fuhrman, Ramanuja Simha, Peter B. Berget, Jonathan W. Jarvik, and Robert F. Murphy. (2013) Determining the subcellular location of new proteins from microscope images using local features. Bioinformatics doi:10.1093/bioinformatics/btt392.

3D 3T3 Images - (download)

The 3D 3T3 collection was collected in collaboration with Dr. Jonathan Jarvik and 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. The collection is described in:
X. Chen, M. Velliste, S. Weinstein, J.W. Jarvik and R.F. Murphy (2003). Location proteomics - Building subcellular location trees from high resolution 3D fluorescence microscope images of randomly-tagged proteins. Proc. SPIE 4962: 298-306.
A. Shariff, R.F. Murphy, and G. Rohde (2011) Automated Estimation of Microtubule Model Parameters from 3-D Live Cell Microscopy Images. Proceedings of the 2011 IEEE International Symposium on Biomedical Imaging (ISBI 2011), pp. 1330-1333.

3D UCE Images - (download)

The 3D HeLa-UCE 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. The collection is described in:
P. Nair, B.E. Schaub, K. Huang, X. Chen, R.F. Murphy, J.M. Griffith, H.J. Geuze, and J. Rohrer (2005). Characterization of the TGN Exit Signal of the human Mannose 6-Phosphate Uncovering Enzyme. J. Cell Sci. 118:2949-2956.

3D HeLa Images - (download)

The 3D HeLa 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. The collection is described in:
M. Velliste and R.F. Murphy (2002). Automated Determination of Protein Subcellular Locations from 3D Fluorescence Microscope Images. Proceedings of the 2002 IEEE International Symposium on Biomedical Imaging (ISBI 2002), pp. 867-870.
T. Peng and R.F. Murphy (2011) Image-derived, Three-dimensional Generative Models of Cellular Organization. Cytometry Part A 79A:383-391.

Supplementary Data for ISBI 2006 SImEC2 paper

T. Zhao, S. Soto, and R.F. Murphy (2006). Improved Comparison of Protein Subcellular Location Patterns. Proceedings of the 2006 IEEE International Symposium on Biomedical Imaging (ISBI 2006), pp. 562-565.

2D HeLa Images - (download)

Fluorescence microscope images of HeLa cells using ten different labels (DAPI, anti-ER, anti-giantin, anti-gpp130, anti-lamp2, anti-mitochondria, anti-nucleolin, phalloidin, anti-transferrin receptor, anti-tubulin).

These images have been referenced in the following publications:

R. F. Murphy, M. V. Boland and M. Velliste (2000). Towards a Systematics for Protein Subcellular Location: Quantitative Description of Protein Localization Patterns and Automated Analysis of Fluorescence Microscope Images. Proc Int Conf Intell Syst Mol Biol (ISMB 2000) 8: 251-259. [PDF Reprint with high res images (3.8 MB)] [PDF Reprint with compressed images (176 K)]

M. V. Boland and R. F. Murphy (2001). A Neural Network Classifier Capable of Recognizing the Patterns of all Major Subcellular Structures in Fluorescence Microscope Images of HeLa Cells. Bioinformatics 17:1213-1223. [PDF Reprint] [Screen PDF]

E.J.S. Roques and R.F. Murphy (2002). Objective evaluation of differences in protein subcellular distribution. Traffic 3: 61-65. [PDF Reprint]

2D CHO Images - (download)

Fluorescence microscope images of CHO cells using five different labels (anti-giantin, Hoechst 33258 (DNA), anti-lamp2, anti-nop4, anti-tubulin).

These images were used in our image classification and typical image selection projects, and have been referenced in the following publications:

M. V. Boland, M. K. Markey and R. F. Murphy (1998) Automated Recognition of Patterns Characteristic of Subcellular Structures in Fluorescence Microscopy Images. Cytometry 33: 366-375. [PDF reprint]

M. K. Markey, M. V. Boland and R. F. Murphy (1999). Towards Objective Selection of Representative Microscope Images. Biophys. J. 76:2230-2237. [PDF Reprint]




Last Updated: 16 Jul 2013




Copyright © 1996-2013 by the Murphy Lab, Carnegie Mellon University