Lecture 8: Tomography (part 1) 1. Principles of Electron Tomography 2. Sample Preparation 3. Data Acquisition 4. Tomogram Reconstruction 5. Tomogram Denoising Principles of EM Tomography Computer Tomography Electron Tomography Workflow in Electron Tomography VITRIFICATION Vitreous Sectioning strictly < -140'C Focused Ion Beam Milling ELECTRON TOMOGRAPHY electron beam Vitrified thin specimens (-100-500 nm) DATA ANALYSIS Segmentation Denoising Pattern Matching Subtomogram Averaging z Principles of Electron Tomography Aligned Tilt Series Reconstructed Tomogram Sample Preparation for CryoET BIOLOGICAL SAMPLE Purified particles Cellular samples VITRIFICATION Plunge-freezing in liquid ethane High-pressure freezing \ r i r Cryo-FIB milling Cryo-ultramicrotomy IMAGING CryoEM Cryo-electron tomography of thin specimen 3D RECONSTRUCTION & INTERPRETATION Acquisition of Tilt Series Goniometer of a side-entry compustage xxxxxxx? ••••••• xxxxxx w ••••••• wwww ••••••• wwww ••••••• \\\\\\\\ wwww XXXXXXXX wwww wwww wwww \\www wwww wwwxx ••••••2! xxxxxxxx Eucentric height Acquisition of Tilt Series Goniometer of a side-entry compustage Tilt axis offset Acquisition of Tilt Series •••••••• f— •••••••• •••••••• ••••»••• •••••••• 0 \\w\w\ wwww vwwvvv wwww xxxxxxxx xxxxx w\ wxwxw 0 <*1* 2 4M* Predictive Method Collect few initial tilt images Determine image shifts Fit shift to a model of tilt geometry Predict and apply beam/image shifts Collect further images, refine model Acquisition of Tilt Series 0 0 •••••••• •••••••• •••••••• •••••••• •••••••• \\\\\\\\ KXXXXXXK \\\\\\\\ XXX XXXX X KX X XX XX \X X XXX X XX X XXX X \\\\\\\\ wxxww x\\\x\\\ \\\\\\\\ \\\\XXXX \\\\\\\\ \v\\\\\\ \ \ \ \ V \ x \ 0 Focus Position Method Move to Focus, focus and center Move to Record, collect image Tilt, move to Focus, focus, center Move to Record, collect image Refine model of beam/image shifts pata •#- Acquisition 4. f m Automated Data Collection Identify the target area of interest Set parameters for data collection: Range of tilt angles: -60° to +60° Angular step: 1° or 2° Dose per image: 0.5-2.0 e/A2 Dose distribution: uniform vs. tilt-dependent Automated Data Collection Automated focusing Automated determination of the Eucentric height lens Magnified image Electron Tomography Data acquisition (tilt series) Reconstructed tomogram Normalize micrographs (bnorm) L T Seed fiducial markers (bshow. btrack) [ T Track fiducial markers (btrack) T Refine alignment (bshow. btrack) T Reconstruct tomogram (bmgft, btomrec, bzfft, bpatch) T Denoise tomogram (bmedian, bbif, bnad) Tomogram reconstruction workflow (IMOD, Bsoft, EMAN2, Xmipp) Image processing in Bsoft fa Bsoft 4- -i C D lsbr.niams.nih.gov/bsoft/# Apps Cl Noviny Q Radia □ TV □ Slovníky D Software □ Journals Q Vyhledavače D D.C. □ Dan Q CEITEC Pi Other bookmarks Bmjt i Home Code Design Usage Developer Bernard's Software Package Bsoft is a collection of programs and a platform for development of software for unage and molecular processmg in structural biology. Problems in structural biology are approached with a highly modular design, allowing fast development of new algorithms without the burden of issues such as file I O. It provides an easily accessible interface, a resource that can be and lias been used in other packages. The evolution of Bsoft is unique in the sense that it started from different aims and intentions than the typical image processing package. In stead of solving a particular image processmg problem. Bsoft developed to deal with the disparities in approaches in other packages, as well as supporting efforts to handle large volumes of data and processmg tasks in heterogeneous environments. As such, the layout and concepts within Bsoft are significantly different from other programs doing the same kind of processmg. In the following sections I'm presentmg the background and philosophies of Bsoft. winch are still evolving, and may continue for some tune. I Images are stored in pif/mrc/tif files as a set of 2D images or as slices of a 3D image. Parameters are stored in ASCII star files with predetermined organization for micrographs, reconstructions and models. Heymann et alv J. Struct. ß/o/.(2008) 161, 232 Preprocessing of collected data bnorm -ver 7 -images -rescale 127,10 -data byte -out output.star input.mrc output.pif btomo -v 7 -sampling 8.05 -axis 78 -tilt -60,2 -gold 5 -out output.star input.star Finding fiducial markers for alignment of tilt series i678 <67*77 ■«77 =658 c504 =477 o474 o397 =39»3 p301 J1M / °3"3 o326 =298 J 11&3170 °158 / « m «069 „208°™ ' 197,7 Ol970 4844 ,„„,„ o1°^S* °1834 ' ".$828 01758175! 01764 / °"61 / °*3**1699 -1"! 0I6O6 01602 °^15*9 °1626 1603 »1608 ftOfl Set the FOM cutoff Choose a FOM cutoff and close this window before doing anything else 0.009 _ Choose an edge width in pixels to exclude markers ........ ....... rcc-aph$ :—— Seed fiducial markers (bshow, btrack) T Track * cue a ] O'Tiocira'Ti DDif ad) I Done Tracking fiducial markers in all tilt images Norrral m rricrocraphs (tro'Tii -arm's « | id) | btrack -ver 1 -reset -axis 78 -exclude none -resol 15,300 -shift 1000 -update -track 5 -refine markers -out FV3tomo9_trk.star FV3tomo9_seed.star >& FV3tomo9_trk.log Refinement of fiducial marker positions OOO FV3tomo9_r>orm.pif: FV3tomo9.reF4.star File image Micrograph Model Window Help OÖO Tomography Tomography Image type •/ Averaging mi x 969 y 820 value FOM Select 1 1 654 0 619 1 ; 6 905 0 362 1 3 1 785 0 493 1 4 1 932 0 564 1 5 2 858 0 490 1 6 6 175 0 332 1 7 1 044 0 729 1 ■ 1 693 0 751 1 9 2 3 6: 0 854 1 10 _ 580 0 721 1 :: 2 057 0 818 1 3 452 0 800 1 :: 2 479 0 605 1 14 0 572 0 619 1 15 1 694 0 806 1 16 2 280 0 360 1 17 4 082 0 533 1 18 2 937 0 771 1 19 3 388 0 508 : 20 3 785 0 638 : 2: 0 482 0 536 : 22 1 589 0 817 : 23 1 860 0 668 : 25 1 364 0 475 l l< < >l FOM |<_ Clear selection Update close Show markers Marker radius Markers Selected marker Residual Tilt axis FOM cutoff <* Show errors 16165 P Show labels Marker table Residual 3.44361591; FOM Update 9 Show Mg Tilt Axis Level OriginX OriginY ScaleX ScaleY 0 -60 21 77. 9 0 0 3 2 5 1030, 3 3 001 3 001 1 -58 18 77. 90 0 34 1023. 5 1018. 8 1 002 1 001 2 -56 22 77. 93 0 3 3: 1015. 2 1030. 2 1 001 1 001 3 -54 26 77. 94 0 3 2 975 . : 1050. 9 : 000 1 001 4 -52 32 77. 92 0 3 3 1008 , 2 1053. i 0 999 : 000 5 -50 23 77. 90 0 34 994 . 0 1059. 1 l 001 l 000 6 -48 26 77. 92 0 35 981. i- 1048. - l 000 : 001 7 -46 18 77 . 95 0 34 979 . 7 1039. 9 l 001 : 001 8 -44 15 77. 92 0 34 982 . 7 1056. E : 002 l 000 9 -42 19 77. 89 0 3 2 993. -1 1026. 3 : 001 l 001 10 -40 25 77. 92 0 3 5 985 . 8 1025. e : 000 l 001 11 -38 19 77. 93 0 36 9b0 . 8 1034 . 2 : 001 i 000 12 -36 2 0 77. 3 2 0 37 972 . 7 1026. 3 : 001 l 001 : :■; -34 29 78. 3 1 : 35 984 . _ 1025. g 0 999 l 000 ;j -32 2 J 78. 3 3 0 36 951. 3 1039. 4 1 000 l 000 15 -30 23 77. 96 0 38 975. 5 1034. 9 1 000 l 000 16 -28. 22 77. 99 0 38 974. 7 1031. 2 3 000 3 000 2" -26 1 e 78 . 0 42 939. 6 1039. 4 1 000 2 000 18 2 4 3d 78. 3 3 0 33 985. 7 1035. 5 0 999 1 000 19 -22 _ • 78. 3 3 0 4 e 991. s 1020. : : 000 1 000 20 -20. 23 78. 14 0 45 965 . 3 1039. 3 0 999 1 000 2. -ib. 21 77. 3 8 0 42 1012 . 6 1029. e l 000 a 999 22 -16. 12 78. 96 0 42 989. 7 1030. 5 l 000 1 000 2 3 -14. _ 1 78. 2 9 ■■: 948 . 2 1038. 7 000 0 999 btrack -ver 1 -reset -refine 10,z,o,v -image FV3tomo9_align.pif -Post FV3tomo9_err.ps -out FV3tomo9_refl.star FV3tomo9_ref0.star >& FV3tomo9_refl.log Refinement of fiducial marker positions btrack -ver 1 -reset -refine 10,z,o,v -image FV3tomo9_align.pif -Post FV3tomo9_err.ps -out FV3tomo9_refl.star FV3tomo9_ref0.star >& FV3tomo9_refl.log Tomogram reconstruction The projection theorem -> alize micrbgrapru 111 Reconstruct tomogram (bmgft, btomrec, bzfft, bpatch) l Denoise tomogram bmedian, bbif. bnad) Tomogram reconstruction (b) J V ^ 4 B < > ■ -.. C f bmark -v 7 FV3tomo7 ref4.star Reconstruct tomogram (bmgft, btomrec, bzfft, bpatch) T ,e !iric>cra*ri tomrec_PBS.tcsh -rec output.pif-resol 20 -size 2048,2048,550 -remove 10 -thick 20 -scale 1 -out output.star input.star Tomogram reconstruction tomrec_PBS.tcsh -rec output.pif-resol 20 -size 2048,2048,550 -remove 10 -thick 20 -scale 1 -out output.star input.star Denoising of reconstructed tomograms tomnad_PBS.tcsh -size 300,300,550 -overlap 100,100,0 -iterations 30 input.pif output.pif