[PPT] - Tracking endosomes in hippocampal neurons Yannis Kalaidzidis PowerPoint Presentation

SLIDE 1

Tracking endosomes in hippocampal neurons

Yannis Kalaidzidis 2019-01-09 QBI-2019 Rennes

SLIDE 2

Major challenge - low SNR

SLIDE 3

Major challenge - low SNR

Livia Goto-Silva

SLIDE 4

Major challenge - low SN

SLIDE 5

Intensity time course

0.0 20 40 60 80 100 150 155 160 165 170 175 180

t (sec) Intensity (a.u.)

SLIDE 6

Frog Eye Filter: Probabilistic model

 

   

2 2 2 2

2 2

1 1 1 , | , , 2 2

I F b I b F

I F B P I b e e e

  

       

     

    

SLIDE 7

Hyperparameter estimation

 

2 2 2 2 2

1 2 2 2

1 1 2 1 erfc erfc 1 2 2 2 2 1 1 erfc 1 erfc 2 2 2

t t t t

t t e e t e m t e e t

       

            

  

                                                        

from equation we found β and μ.

SLIDE 8

Background estimation

i

I F B B at b    

 

2 2

1 2

1 | , , , , | , 2

I F at b

P I a b e P F dF



     

    

 

       

1 1

1 1 1 1 1 1 1 1 1

i i i N i i i i i i i i i N i i i i i

R d t L a d R d L b d            

 

                                             

 

   

2

1 2 1 1 2

1 log erfc 1 2 2 2 where 1 erfc 2

i i i

R N i i i i x

d e L d e x x

 

    

        

                                

  

2

1 1 2 2

1 | , , , , erfc 2 2 2

R R

e d P I a b e

 

      

       

        

where ; ; at b I R d R           

SLIDE 9

Background estimation

0.0 5.0 10 15 20 25 200 400 600 800 1000 1200

SLIDE 10

Estimation of foreground intensity

       

, , | , , , , , | , , , | , | ,

b b

P I F B B I P I F B I P F P B B         

   

0 0

| , , , , , , , | , , , , ,

b b

P F b I P I F B b I dB dI        

 

 



 

2 2 2 2 2 2

1 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2

1 erfc , 2 2 2 | , , , , , 1 erfc , 2 2 2

b b

I B F F b b b b b I B b b b b

I F B e e F P F B I I B e F F

    

                       

       

                                         

SLIDE 11

Estimation of foreground intensity

 

| , , , , ,

b

F F P F B I dF    



 



 

2 2 2

1 2 2 1 2 2 2 2 2 2

1 erfc 1 2 2 1 1 1 erfc erfc 2 2 1 1 where ; ; ; ; 1 1 ;

W b b

e T I B s G G U F G I B s s I s G T s s B s W s

 

                    

 

                                                            

2

I B U s    

SLIDE 12

De-noised movie

SLIDE 13

De-noising Evaluation

Raw Images Frog Eye Filter

0.0 1.0 2.0 3.0 4.0 120 140 160 180 200 220 0.0 1.0 2.0 3.0 4.0 120 140 160 180 200 220

SLIDE 14

De-noising Evaluation

PURE-LET Raw Images

0.0 1.0 2.0 3.0 4.0 120 140 160 180 200 220 0.0 1.0 2.0 3.0 4.0 120 140 160 180 200 220

SLIDE 15

De-noising Evaluation

Frog Eye Filter

0.0 1.0 2.0 3.0 4.0 120 140 160 180 200 220

PURE-LET

0.0 1.0 2.0 3.0 4.0 120 140 160 180 200 220

SLIDE 16

De-noising Evaluation

Raw Frog Eye CTV (ICY) PURE-LET (Fiji)

SLIDE 17

Intensity time course in ROI

0.0 20 40 60 80 100 150 155 160 165 170 175 180 185

Intensity (a.u.)

Original Image

time (sec)

0.0 20 40 60 80 100 150 155 160 165 170 175 180 185

Frog Eye

Endosomes cross through ROI Intensity (a.u.) time (sec)

0.0 20 40 60 80 100 150 155 160 165 170 175 180 185

PURE-LET

Intensity (a.u.) time (sec)

SLIDE 18

High background challenge

SLIDE 19

High background challenge

SLIDE 20

Frog Eye Filter

t (sec) Intensity (a.u.)

66 68 70 72 74 76 78 80 82 155 160 165 170 175

SLIDE 21

66 68 70 72 74 76 78 80 82 155 160 165 170 175

Frog Eye Filter

t (sec) Intensity (a.u.)

SLIDE 22

66 68 70 72 74 76 78 80 82 155 160 165 170 175

Frog Eye Filter

t (sec) Intensity (a.u.)

SLIDE 23

Frog Eye Filter

SLIDE 24

Frog Eye Filter

SLIDE 25

Frog Eye Filter

SLIDE 26

Frog Eye filter (APPL1)

retrograde retrograde

SLIDE 27

0.01 0.1 1.0 0.0 500 1000 1500 2000

Speed distribution

Speed (µm/sec) # movements Retrograde Anterograde

0.358 0.055 0.331 0.056

r a

m m    

Student_t 0.73

value

p 

SLIDE 28

Speed distribution

Speed (µm/sec) # movements

0.01 0.1 1.0 0.0 500 1000 1500 2000

SLIDE 29

Speed distribution

Speed (µm/sec) # movements Retrograde

0.01 0.1 1.0 0.0 500 1000 1500 2000

µ δµ σ δσ A δA m δm 0.936 0.052 0.69 0.035 7 494 543 1.18 0.072 0.087 0.003 1.388 0.023 45 583 554 0.22 0.01

SLIDE 30

Speed distribution

Speed (µm/sec) # movements Retrograde

0.01 0.1 1.0 0.0 500 1000 1500 2000

µ δµ σ δσ A δA m δm 0.936 0.052 0.69 0.035 7 494 543 1.18 0.072 0.087 0.003 1.388 0.023 45 583 554 0.22 0.01 Anterograde µ δµ σ δσ A δA m δm 1.674 0.099 0.44 0.043 2 881 392 1.84 0.11 0.085 0.002 1.42 0.027 43 662 596 0.24 0.01

SLIDE 31

Speed distribution

Speed (µm/sec) # movements Retrograde

0.01 0.1 1.0 0.0 500 1000 1500 2000

µ δµ σ δσ A δA m δm 0.936 0.052 0.69 0.035 7 494 543 1.18 0.072 0.087 0.003 1.388 0.023 45 583 554 0.22 0.01 Anterograde µ δµ σ δσ A δA m δm 1.674 0.099 0.44 0.043 2 881 392 1.84 0.11 0.085 0.002 1.42 0.027 43 662 596 0.24 0.01

7

Fast Movement: Student_t 9.6 10 Slow Movement: Student_t 0.157

value value

p p



  

SLIDE 32

Conclusion

De-noising allow to compensate low SNR and

image in low phototoxicity/low bleaching mode.

Frog Eye filter allow track dim endosomes in

axons with high background fluorescence

Sped distribution de-convolusion allows to

reveal significantly different components, which are not distinguishable in average values

SLIDE 33

Acknowledgements

Marino Zerial Lab

Hernán Andrés Morales Navarrete Alexander Kalaidzidis

http://motintracking.mpi-cbg.de

SLIDE 34