Murphy Lab

 Cytometry Development Workshop

 Flow Cytometry



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

HeLa Images

Fluorescence microscope images of HeLa cells using ten different labels

  • DAPI to label DNA
  • a monoclonal antibody against an Endoplasmic Reticulum (ER) antigen
  • a monoclonal antibody against giantin, a Golgi protein
  • a monoclonal antibody against gpp130, a Golgi protein
  • a monoclonal antibody against human LAMP2 (primarily found in lysosomes)
  • a monoclonal antibody against an outer membrane protein of mitochondria
  • a monoclonal antibody against nucleolin
  • a monoclonal antibody against transferrin receptor (primarily found in the plasma membranes and endosomes)
  • rhodamine-conjugated phalloidin, which labels F-actin
  • a monoclonal antibody against beta-tubulin

These images were used in our image classification and image set comparison projects, and 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]

K. Huang and R.F. Murphy (2004). Boosting accuracy of automated classification of fluorescence microscope images for location proteomics. BMC Bioinformatics 5:78. [PDF Reprint]

The images are available as tar archives that have been compressed with gzip. Each will expand to 62 MB after extraction and decompression. The images have been computationally deconvolved using the nearest-neighbor algorithm, and have been cropped to include one cell per image. All pixels outside the cropped region have been set to 0.

  16 bit raw (unscaled) images (TIFF format - use this format for loading a PSLID database) 148 MB
  16 bit protein channel images contrast-stretched to full scale (PNG format). Only contains protein images. 61 MB
  16 bit raw (unscaled) protein channel images (PNG format). Only contains protein images. 39 MB
  Text file describing the data set 2 KB
  16 bit images contrast-stretched to full scale (DNA and protein patterns in PNG format and crops in TIF format). DNA and protein channel images per class. 147 MB

The SLF4 features used in the ISMB and Bioinformatics papers are also available. The first line lists the SLF numbers and the second line lists the short names of the features.

  Comma-delimited file 617 KB
  Tab-delimited file 617 KB

The 180 features used in the BMC Bioinformatics paper listed above to create feature set SLF16 are also available as a Matlab .mat file (2 MB).

Last Updated: 12 Dec 2011

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