NIA R01-AG028928

 

Title:

High-content Image Analysis and Modeling for Neuron Assay Based Screening

Description:

The objective of this proposal is to develop NCELLIQ—Neuron Cell Imaging Quantitator—to assess quantitatively neurite loss and outgrowth imaged by high-content screening (HCS). Loss of synaptic connections and neuronal projections has been shown to be a common feature of many neurodegenerative diseases including Alzheimer’s disease (AD). It significantly precedes neuronal cell death suggesting that neurite damage and loss might be the primary cause of the decline in cognitive function. Neurite loss is also observed following traumatic brain and spinal cord injuries. Therefore, chemical compounds targeting neurite loss prevention or neurite outgrowth stimulation could represent a promising novel treatment in AD and following spinal cord trauma. Loss of neuronal projections in AD can be modeled in vitro in primary mouse cortical neurons treated with the amyloid β peptide, the primary cause of neurodegeneration in AD, neurite loss precedes induction of neuronal death in this disease model. It can be easily assessed visually either in live neurons through bright field microscopy or by immunostaining following fixation. Hence, we propose to develop NCELLIQ to process high content images generated from screening neuron-based assays for neuroprotective compounds.

NCELLIQ generates a score that reflects phenotype associated with each compound and has three key technical contributions. First, NCELLIQ will provide an integrated neuron and nuclei image processing pipeline using advanced computational algorithms to extract rich image contents of neuron based screening assay automatically. Second, it will develop an innovative, effective, and fully automatic neurite centerline extraction method using detector of curvilinear structures and dynamic programming. Third, it will develop a rigorous, mathematical representation of the compound vector, as well as an innovative and effective scoring method to allow intuitive comprehension of the HCS results. NCELLIQ will potentially be an important bioinformatics tool to help identify possible drug leads for treatment of Alzheimer’s and other neurodegenerative diseases.

People:

PI: Stephen Wong, Ph.D., P.E., Department of Radiology, The Methodist Hospital Research Institute.
Co-PI: Junying Yuan, Ph.D., Department of Cell Biology, Harvard Medical School.
https://yuan.med.harvard.edu/
Co-PI: Alexei Degterev, Ph.D., Department of Biochemistry, Tufts University.
http://sackler.tufts.edu/Academics/Degree-Programs/PhD-Programs/Faculty-Research-Pages/Alexei-Degterev.aspx?c=129135040432163723
Investigator: Xiaobo Zhou, Ph.D., Department of Radiology, The Methodist Hospital Research Institute.
Investigator: Marta Lipinski, Ph.D., Department of Cell Biology, Harvard Medical School.

Progress:

The current release of software aims to provide an automated pipeline for quantitative, reproducible, and accurate interpretation of automatic fluorescence microscopy images; in particular, for the labeling and measurement of neurites. Currently, the measurement includes cell number, neuronal cell number, total intensity, average intensity, average area, total neurite length, average neurite length, and neurite brightness, and so on.

We also propose the NeuriteIQ pipeline to analyze loss of neuronal projections is modeled in vitro in primary mouse cortical neurons treated with the amyloid beta peptide. Forty-two hits are selected using the integrated pipeline to screen the 1,040 compounds in the National Institute of Neurological Disorders and Stroke (NINDS) custom collection compound library II. Subsequent structure-activity relationship analysis will be published soon, which indicates that structural features of compounds are effective in complementing quantitative analysis of high content screening for AD drug discovery.

NeuriteIQ image

Publications:

1. Xiong G, Zhou, X., A. Degterev, L. Ji, and S.T.C. Wong. Automated neurite labeling and analysis in fluorescence microscopy images, Cytometry A, 2006 May 5; 69A (6):494-505

2. Xiong G, Zhou X., A. Degterev, S.T.C. Wong, and L. Ji. Automated Neurite Labeling and Analysis in Fluorescence Microscopy Images using curvilinear structure detection, 2006 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Washington, D.C.

3. Zhang Y, Zhou X., A. Degterev, M. Lipinski, D. Adjeroh, J. Yuan, S.T.C. Wong. A novel tracing approach for high-throughout screening of neuron based assays, 2006 IEEE International Symposium on Life Science and Multimodality, Washington, D.C.

4. Zhang Y, Zhou, X., A. Degterev, M. Lipinski, D. Adjeroh, J. Yuan, S.T.C. Wong. A novel tracing algorithm for high throughput imaging screening of neuron based assays, Journal of Neuroscience Methods, Vol. 160, pp149-162, 2007.

5. Zhang Y, Zhou, X., Degterev, A., Lipinski, M., Adjeroh, D., Yuan, J., and Wong ST. Automated neurite extraction using dynamic programming for high-throughput screening of neuron-based assays. Neuroimage. 2007 May 1;35(4):1502-15.

6. Srinivasan R, Zhou X, Miller E, Lu J, Lichtman J, Wong STC. Automated Axon Tracking of 3D Confocal Laser Scanning Microscopy Images Using Guided Probabilistic Region Merging. Neuroinformatics, 5: 189-203(15), 2007.

7. Zhang, Y, Zhou, X, Lu, J, Lichtman, J, Adjeroh, D, Wong, STC. Axon Tracking Using 3D Curvilinear Structure Extraction and Dynamic Programming, Neural Computation, in press, 2007.

8. Zhang Y, Zhou, X., Degterev A, Lipinski M, Adjeroh D, Yuan J, Wong,STC. Dendritic Spine Detection Using Curvilinear Structure Detector and LDA Classifier. NeuroImage. 2007 Jun;36(2):346-60.

9. Cheng J, Zhou X, Miller E, Witt RM, Zhu J, Sabatini BL, Wong ST. A novel computational approach for automatic dendrite spines detection in two-photon laser scan microscopy. J Neurosci Methods. 2007 Sep 15;165(1):122-34.

10. Bai W, Zhou X, Ji L, Wong ST. A fully automatic dendritic spine analysis in two photon laser scanning microscopy images. Cytometry A, 2007 Oct;71(10):818-26.

11. Huang Y, Zhou X, Miaoc B, Lipinskid M, Zhang Y, Li F, Hu G, Degterevc A, Yuan Y, Wong STC. A computational approach for studying neuron morphology for Alzheimer's disease, Submitted to Neuroinformatics, 2008.

12. Zhang Y, Zhou X, Lu J, Lichtman J, Adjeroh D, Wong STC. 3-D Axon Structure Extraction and Analysis in Confocal Fluorescence Microscopy Images, Neural Computation, Vol. 20(8), p. 1899-1927, 2008.

13. Zhou X, Wong STC. Computational Systems Bioinformatics and Bioimaging for Pathway Analysis and Drug Screening, Proc. of IEEE, Vol. 96(8): p. 1310-1331, 2008.

14. Huang Y, Zhou X, Lipinski M, Miao B, Xia Z, Hu G, Degterev A, Yuan J, Wong STC. A novel image based system biology approach for Alzheimer's disease, IEEE/NIH Life Science System and Application Workshop (LiSSA 2009).

15. Huang Y, Zhou X, Miao B, Lipinski M, Li F, Hu G, Degterve A, Yuan J, Wong STC. A Computational Approach for Studying Neuron Morphology for Alzheimer’s Disease, Submitted to Neuroscience Methods, 2009.

16. Xia Z, Zhou X, Cui K, Sun Y, Wong STC. AD Functional Modules Identification using network-constraint fused support vector machine, Submitted to Neuroscience Methods, 2009.

17. Janoos F, Mosaliganti K, Xu X, Machiraju R, Huang K, Wong ST. Robust 3D Reconstruction and Identification of Dendritic Spines from Optical Microscopy Imaging. Med Image Anal. 2009 Feb;13(1):167-79.

18. Fan, J., Zhou, X., Dy, J., Zhang, Y., Wong ST. An Automated Pipeline for Dendrite Spine Detection and Tracking of 3D Optical Microscopy Neuron Images of in vivo Mouse Models. Neuroinformatics. 2009 Jun;7(2):113-30.

19. Zhang Y, Chen K, Baron M, Teylan MA, Kim Y, Song ZH, Greengard P, Wong ST. A neurocomputational method for fully automated 3D dendritic spine detection and segmentation of medium-sized spiny neurons. Neuroimage. 2010 May 1;50(4):1472-84.

20. Huang Y, Zhou X, Miao B, Lipinski M, Li F, Zhang Y, Degterev A, Yuan JY, Hu GS, Wong ST. A computational framework for studying neuron morphology from in vitro high content neuron-based screening. J Neurosci Methods. 2010 Jul 15;190(2):299-309.

21. Cheng J, Zhou X, Miller EL, Alvarez VA, Sabatini BL, Wong ST. Oriented markov random field based dendritic spine segmentation for fluorescence microscopy images. Neuroinformatics. 2010 Oct;8(3):157-70.

22. Li Q, Deng Z, Zhang Y, Zhou X, Nägerl UV, Wong ST. A global spatial similarity optimization scheme to track large numbers of dendritic spines in time-lapse confocal microscopy. IEEE Trans Med Imaging. 2011 Mar;30(3):632-41.

Software Link:

http://www.cbi-tmhs.org/NeuriteIQ