Image Processing in Single Particle Analysis

Transcription

1 Image Processing in Single Particle Analysis EM Meeting 04/06/14

2 Single Particle Analysis P. Thuman-Commike 2001

3 Single Particle Analysis Key steps Particle Selection Alignment- Centering Classification & Averaging Structure Quality Assessment Structure Refinement 3D Reconstruction

4 Single Particle Analysis Key steps Single Particle: isolated, unordered particles with-in principle-identical structure. J. Frank Aim: exploit the random orientation that single particles assume in the vitrified solvent to extract 3D information

5 Different Orientations

6 Initial processing: frame alignment Image- based mo,on correc,on so0ware Gatan cross correla,on Imod cross correla,on Mo,on correc,on so1ware (Li et al., 2013)

7 Graphite test specimen on our Polara (Falcon II) 3.36 Å Frame displacement in Å Structural Ma5eo Biology Department Allegre8, Edoardo D Imprima Janet Matteo Vonck, Allegretti Deryck Mills

8 Initial processing: CTF determination CTFfit graphical interface par,cle based (EMAN2) COfind3 image based (Mindell et al., 2003) Chops up area into boxes (periodogram) and es,mate as,gma,sm

9 Pick particles from the good images Pick the par,cles choosing a suitable box EMAN boxer (~2 diameter, Tang et al., 2007) a XMIPP (supervised and automa,c, Sorzano et al., 2013) Semi- Automa,c picking Template Matching Edge Detec,on Local Comparison of Intensity Values Quan,ta,ve Measure of the Local Image Texture/Sta,s,cs Neural Networks

10 The correct box-size

11 Classification possibilities 2- D classifica,on with XMIPP 3.0 CL2D (Sorzano et al., 2010), Relion ML2D (fast but not robust), Imagic (Van Heel et al., 1996), EMAN2 (fast), Sparx isac (slow, Yang et al., 2012)

12 The initial model Use an exis,ng model: Ø Lower resolu,on cryo- EM map Ø Cryo- EM map of related structure Ø Nega,ve stain map Ø Subtomogram average Ø (Composite) map created from x- ray structure(s) Ø SAXS map Create ini,al model computa,onally: Ø Angular recons,tu,on of class averages Ø Blob (for high symmetry) Ø Make ini,al model from class averages in EMAN Create model from random conical,lt dataset

13 Structure Refinement Projection Matching Orlova et. al, 2010

14 Refinement Run the refinement (projec,on matching) with relion (gold standard, Scheres et al., 2012), EMAN2, Run the sta,s,cal movie- refinement (par,cle- based) with relion averaging between 4 and 11 frames (Bai et al., 2013) Frame- weighted- based refinement (Henderson et al., 2014)

15 Creating Monalisa from Noise

16 Creating Monalisa from Noise

17 Ex Nihilo Structura Fit(s) ~ 6 Å Y. Mao, et al., 2013.

18 Making a Structure Ex Nihilo adapted from R. S. Subramaniam; Henderson;

19 Creating the Gp160 Structure M. van Heel; 2013

20 Creating the Gp160 Structure M. van Heel; 2013

21 From old approaches to RELION

22 Relion: REgularised LIkelihood OptimisatioN (Maximum Likelihood approach) Statistical approach, estimates the likelihood (accuracy) with which every particle is aligned to all the reprojections of the initial model. (Probability weighted integrals over all possible orientations are calculated) Then ML find the value that maximizes the probability of observing the measurement

23 Bayesian approach The reconstruction problem is formulated as finding the model that has the highest probability of being the correct one in the light of both the observed data and available prior information. Bayesian statistical approach impose prior distributions on model parameters (ex. gaussian distribution on the fourier components of the signal) and requires a minimal amount of heuristics of the user because iteratively learns most parameters from the data themselves.

24 Procedure to prevent overfitting: gold standard resolution assessment (relion)

25 3-D Classification (Relion)

26 3D-Classification complex-i

27 Frame-weighted refinement (Henderson et al., 2014) Li et al., 2013

28 Frame-weighted refinement (Henderson et al., 2014) Resolu,on dependent weigh,ng factor for the individual frames is taken from the FSC

29 Validation RCT (Radermacher et al., 1988), Tomography (Baumeister et al., 1999) or,lt pair analysis: collect 2-3 images at 0-15 degrees,lt and use eman2 or Rosenthal web server (Rosenthal et Henderson, 2003; Henderson et al., 2011)

30 Validation RCT (Radermacher et al., 1988), SP tomography (Baumeister et al., 1999) or,lt pair analysis: collect 2-3 images at 0-15 degrees,lt and use eman2 or Rosenthal web server (Rosenthal et Henderson, 2003; Henderson et al., 2011) Use a different so1ware: eman2_refine_easy

31 Validation RCT (Radermacher et al., 1988), SP tomography (Baumeister et al., 1999) or,lt pair analysis: collect 2-3 images at 0-15 degrees,lt and use eman2 or Rosenthal web server (Rosenthal et Henderson, 2003; Henderson et al., 2011) Use a different so1ware: launch eman2_refine_easy Classes must match the reprojec,ons No missing views (check euler angle distribu,on)

32 Validation RCT (Radermacher et al., 1988), SP tomography (Baumeister et al., 1999) or,lt pair analysis: collect 2-3 images at 0-15 degrees,lt and use eman2 or Rosenthal web server (Rosenthal et Henderson, 2003; Henderson et al., 2011) Use a different so1ware: launch eman2_refine_easy (gold standard but less performance and slower than relion) Classes must match the reprojec,ons No missing views (check euler angle distribu,on) Calculate control map with high resolu,on random noise subs,tu,on (Chen et al., 2013)

33 Validation 45 c(8) - ring 40 c(9) - ring Bio- EM (Cossio et Hummer, 2013) only if you have more the one model and you are unsure which fits best your raw data

34 Take home message Hire italians for single particle projects