APPROACHES IN QUANTITATIVE, MULTI-DIMENSIONAL MICROSCOPY

Transcription

1 APPROACHES IN QUANTITATIVE, MULTI-DIMENSIONAL MICROSCOPY Profs. Zvi Kam and Benjamin Geiger Department of Molecular Cell Biology The Weizmann Institute of Science Rehovot, Israel In this presentation we display representative examples of a series of novel applications of digital microscopy used for the study of cell-cell and cell matrix adhesions. These applications were used for the characterization of focal adhesions, their formation, forces applied to them, their composition and their signaling activity. Additional material related to the different topics presented here can be obtained upon request, from benny.geiger@weizmann.ac.il

2 BASIC CONCEPTS IN DIGITAL IMAGING

3 Basics: intensity, resolution, dynamic range pixel low intensity pixel high intensity pixel E. Zamir

4 E. Zamir Digital Image Features: I. Spatial Resolution pixel area: resolution: big medium small low medium high Effective pixel area: the area in the real world which is covered by a single pixel. Keep the effective pixel area below the optic resolution!

5 Optimize the use of the dynamic-range available! 12 bits camera 12 bits image underexposed optimal overexposed E. Zamir

6 Digital Image Features: II. Intensity Resolution (dynamic range) 1 bit 8 bit 1 bit (2 1 = 2 intensity levels) 2 bits (2 2 = 4 intensity levels) 3 bits (2 3 = 8 intensity levels) 8 bits (2 8 = 256 intensity levels) E. Zamir Scientific standard: 12 bits (2 12 = 4096 intensity levels)

7 BACKGROUND SUBTRACTION Background labeling within cells can be highly non uniform (see example in the next slide) and special processing is needed in order to correct the image and obtain the net signal that is associated with the object of interest. Several processing strategies are listed below

8 BACKGROUND ESTIMATION: Background fluorescence, due to diffuse distribution or just background can greatly affect the level of super-imposed fluorescent structure. To obtain the net fluorescence of the object of interest, one needs to normalize for illumination inhomogeneous intensity and to subtract background fluorescence, in order to obtain optimal contract and correct quantification of fluorescence Raw image Corrected image

9 DIFFERENT APPROACHES FOR BACKGROUND SUBTRACTION see Appendix II Lichtenstein et al. Cytometry 54A:8-18, Rolling-ball filtration: Sternberg, Computer10:22-34, Fast box filtering: Zamir et al. J Cell Sci 112: , 1669, 1999.

10 Background estimation methods for quantification of fluorescent labeling intensities Mean Rolling-Ball Min

11 IMAGE SEGMENTATION Segmentation is a mathematical process whereby the groups of pixels of the image are classifies according to common properties. For example - groups of neighboring pixels with high levels of vinculin labeling are classified as a focal adhesion, or linear grups of tubulin-positive pixels are defined as microtubules. Such segmentation processes are essential for defining the physical properties of the objects of interest (e.g. - dimensions, orientation, amount etc.). Segmentation is usually conducted using algorithms that are especially developed or adapted to best fit and enhance the most distinctive features of image and non-image pixels

12 Generalizing the Water method for recognizing and measuring structures (see: Zamir et al. J Cell Sci 112: , 1999.) adhesion sites vesicles nuclei bacteria and others... The algorithm requires few simple parameters (e.g.,area of the structure) adjusted for the different structures.

13 Image Processing: Filtering Example: High-Pass Filtration original filtered E. Zamir

14 Water segmentation of focal adhesions Example: Measuring adhesion sites structure area total-intensity average-intensity axial ratio µm µm E. Zamir

15 Cell cycle analysis by Double-color quantification of Water segmented nuclei Levenberg,, et al. Oncogene 18: (1999) 876(1999)

16 Segmentation and analysis using FiberScore Lichtenstein et al. Cytometry 54A:8-18 (2003).

17 ACTIN: RAW IMAGE ACTIN: FIBER IMAGE ACTIN: ORIENTATION IMAGE ACTIN: THINNED FIBER IMAGE N. Lichtenstein

18 TUBULIN: RAW IMAGE TUBULIN: FIBER IMAGE TUBULIN: ORIENTATION IMAGE TUBULIN: THINNED FIBER IMAGE N. Lichtenstein

19 MULTISCALE SEGMENTATION REF: Sharon et al. IEEE Proc Computer vision and Patern Recognition I:70-77, 77, 2000.

20 Segmentation of phagokinetic tracks by multiscale methods. Shay et al. A B ORIGINAL IMAGE LOW RESOLUTION SCALE HIGH RESOLUTION SCALE

21 RATIO IMAGING The relationships between different, yet related images can be visualized by dividing the intensity value of each pixel in one image by the value of the corresponding pixal in the second image, and assigning a specific color to the value of the ratio in the ratio image. Thus a complete match (e.g. ration between identical images) will result in a ratio of exactly 1 for all the pixels. This approach is most useful for the comparison of the subcellular differential distributions of two proteins from double-labeled labeled images, or the changes that occur by translocation of a specific molecule from time-lapse movies (FRIT).

22 RATIO IMAGING Cy FITC FRI (Cy3 / FITC) E. Zamir Cy3/FITC

23 Image Processing Ratio Imaging between Time Points (FRIT) FRIT: component A (in t 2 ) log component A (in t 1 ) FRIT image indicates the temporal changes in the intensity and distribution of one component. QuickTime and a Motion JPEG A decompressor are needed to see this picture. E. Zamir

24 E. Zamir

25 IMAGE CORRELATION The degree of colocalization of two proteins can be estimated from pixel-by by-pixel correlation between the two color components of double-labled labled cells. The dynamics of cellular changes can be evaluated from time-correlations.

26 E. Zamir

27 Correlation scale E. Zamir correlation paxillin-py cadherin-β-catenin tensin-α 5 vinculin-actin paxillin-actin PY-actin α v -α 5 β-catenin-dna cadherin-dna overlap partial overlap mutually exclusive

28 Image Correlation Quantifying subcellular localization Zamir et al. E. Zamir

29 E. Zamir

30 Time correlation quantifying dynamics of changes Kam et al. TICB 11:329-34(2001). 34(2001). Lat-A W H-7 W ML-7 W correlation E. Zamir time (minutes)

31 FORCE MEASUREMENT USING DIGITAL MICROSCOPY An approach for measuring cellular forces applied to the cell s surroundings was developed. Cells expressing a fluorescent (GFP) component of focal adhesion (e.g. vinculin) are plated of an elastic patterned surface, and the force applied by the cell at specific sites is calculated based on the deformation of the matrix (See Balaban et al Nature Cell Biol 3: , 472, 2001.

32 N. Balaban UV Patterning Mask Substrate Mask Photoresist Elastomer Embedded fluorescent markers Topographic pattern

33 Use of patterned elastomer for measurement of forces applied at individual focal adhesions The grid is produced on the surface of the elastomer The visualization is immediate N. Balaban

34 N. Balaban Heart cell on a patterned elastomer Labeled for actin and vinculin

35 Measurement of forces at adhesion sites Displacements Forces ~5.5nN/Sqµm 4 µm 4 µm 4 µm N. Balaban

36 Force measurement during cell relaxation Before BDM 2 min after BDM N. Balaban CONSTANT STRESS: 5.5nN/µm 2

37 MULTIPLE-COLOR LABELING AND IMAGE CLUSTERING To define domains within a cell with similar molecular composition, multiple color labeling is used, followed by multiple parameter clustering, whereby the pixels of the image are sorted according to their compositional similarity (Zamir et al, in preparation)

38 Eli Zamir

39 Identify cellular structures by their molecular content: Multi-color component similarity. ZAMIR, et al.

40 SWEEPING FOCUS MICROSCOPY Taking a picture while focusing the microscope, followed by a 2D deconvolution was developed as a new approach for obtaining a non-blared projected image from thick specimens (Kam, In preparation)

41 2.5 dimensional fast imaging of thick samples

42 Projected deconvolution of mitochondria sweeping-focus image