Molecule Screen and Cell Quality Molecule Screen and Cell Quality - - PowerPoint PPT Presentation

▶
molecule screen and cell quality molecule screen and cell
SMART_READER_LITE
LIVE PREVIEW

Molecule Screen and Cell Quality Molecule Screen and Cell Quality - - PowerPoint PPT Presentation

Aug 13, 2022 334 likes •534 views

Molecule Screen and Cell Quality Molecule Screen and Cell Quality Assessment Assessment Assessment Assessment A Robotic and A Robotic and BioMEMS A Robotic and BioMEMS A Robotic and BioMEMS BioMEMS Approach Approach Yu Sun Advanced

slide-1
SLIDE 1

Molecule Screen and Cell Quality Molecule Screen and Cell Quality Assessment Assessment A Robotic and A Robotic and BioMEMS BioMEMS Assessment Assessment – A Robotic and A Robotic and BioMEMS BioMEMS Approach Approach

Yu Sun Advanced Micro and Nanosystems Laboratory

  • Dept. Mechanical and Industrial Engineering
  • Inst. Biomaterials and Biomedical Engineering

University of Toronto University of Toronto

slide-2
SLIDE 2

Microrobotic Cell Manipulation

  • Slow
  • Low success rate
  • Low cell survival rate
  • Long learning curve
  • Inter-operator

variability

  • Skill dependent
  • Skill dependent
  • Molecule testing
  • Molecule testing
  • Enucleation
  • Polar body biopsy

2

  • Polar body biopsy
  • Clinical ICSI
slide-3
SLIDE 3

Cell Microinjection

  • Limited speed and

reproducibility

– Search & immobilize: random – manual switch: slow

  • rient: trial error; slow; poor

– orient: trial-error; slow; poor controllability – speed: 2 cells/min speed: 2 cells/min

  • Targets

– speed: 12 cells/min

Sun and Nelson, Sun and Nelson, IJRR IJRR, Vol. 21 , Vol. 21 (2002) (2002)

p – rapid cell immobilization and

  • rientation

3

– high success/survival rates – grad students can do the work

slide-4
SLIDE 4

Cell Holding Devices

  • Biomed. Microdevices, Vol. 11, 2009

Three devices every two days

4

slide-5
SLIDE 5

Fast Cell Immobilization

  • Pressure: 1.6~2.2kPa (mouse zygotes)
  • Immobilization time: 10~16sec (5×5 array)

5

slide-6
SLIDE 6

Vision-Based Contact Detection

44 46 48 x coordinate (pixel) pixel method sub-pixel method fitted-sub-pixel method 1 2 3 4 5 6 50 100 150 200 42 p P S

6

Wang, Liu, and Sun, Wang, Liu, and Sun, Int. J. Robotics Research

  • Int. J. Robotics Research, Vol. 26 (2007)

, Vol. 26 (2007)

slide-7
SLIDE 7

Microrobotic Cell Injection

7

slide-8
SLIDE 8

Microrobotic Cell Injection

video speed: 1X

8

video speed: 1X

slide-9
SLIDE 9

Cell Orientation Control

9

slide-10
SLIDE 10

Cell Orientation Control

10

slide-11
SLIDE 11

System Control Architecture

  • Image space task planner
  • Looking then moving
  • On-line coordinate transformation

11

slide-12
SLIDE 12

System Performance

  • Mouse embryo preparation

– ICR mice (8~12 weeks) PSMG and hCG injection (48hr) – PSMG and hCG injection (48hr) – timed pregnancy (12hr) – embryo collection: ~20 cells/mouse

mouse dissection

  • System optimization (n=306)

– injection speed: 200m/sec t ti d 500 /

4.5 day post-injection

– retraction speed: 500m/sec – injection pressure: 40~45kPa

  • PBS injection (n=240)
  • PBS injection (n=240)

– speed: 12 cells/min (2 cells/min) – success rate: 98.9% (90%)

12

mouse blastocysts

– survival rate: 89.8% (80%)

slide-13
SLIDE 13

Results – Mitochondrial Protein Testing

100 100

79 8±3 6% 82 8±2 4% 85 6±3 7%

success rates survival rates

60 80 100

e (%)

60 80 (%)

79.8±3.6% 82.8±2.4% ~50% 85.6±3.7% 51 1±4 9% 49±4 8% 74.1±2.5%

20 40 60 n=302 n=307 n=273 survival rate n=273 20 40 n=100 n=400 success rate n=400

~50% 51.1±4.9% 49±4.8%

KSOM control HTF control HTF + buffer HTF + protein

20 n 302 n 307 n 273 n 273 20 manual protein injection automated buffer injection automated protein injection

13

Nature Biotechnology, under review, 2010

slide-14
SLIDE 14

Vision-Based Force Measurement

PDMS ll h ldi d i d b i l

  • PDMS cell holding device and a sub-pixel

visual tracking algorithm Post deflectionsinjection forces

14

  • Post deflectionsinjection forces
slide-15
SLIDE 15

Force Analysis

hi

p

vi

f

i

f ) a ( ) b (

1

d 

 30

hi

f

F

1

d

2

d a

  • Linear elasticity validation: 

<11 1º Linear elasticity validation: max<11.1

3 2 2 3 4

) 6 8 ( 8 ) 2 )( 1 ( 40

i

H H H F 

    

1 4 2 2 3 4 2

3 ) 6 8 ( 8 9 ) 2 )( 1 ( 40

i i i i i i

ED a H a H a ED a H a   

15

slide-16
SLIDE 16

Distinguish Normal/Defective Oocytes

  • ICR young (n=20) and old (n=20) mouse oocytes
  • Stiffness: slope of force-deformation curve
  • Overlap: 4.4-4.8 nN/m

240 160 200 240 II

young oocytes

  • ld oocytes

(nN) I 6 8 urve (nN/m)

 6.4±1.3 nN/m

80 120 ndentation force 4 6 ce-deformation cu

3.3±0.9 nN/m

4 8 12 16 20 24 40 i young oocytes

  • ld oocytes

2 slope of forc

16

cell deformation (m)

Lab Chip, to appear in 2010

slide-17
SLIDE 17

Zona Pellucida (ZP) Structure Analysis

surface morphology (SEM) glycoprotein density (TEM) glycoprotein density

  • ZP1-3: low expression in old
  • ocytes (Hum. Mol. Genet.,

2004)

17

2004)

slide-18
SLIDE 18

F-Actin Content Analysis

3 0

  • Phalloidin-FITC staining of F-actin

F-actin content

2.0 2.5 3.0 t (10

6)

1.0 1.5 e fluorescent unit

Y) 150 180 120 Y) 150 180 120

young oocytes

  • ld oocytes

0.0 0.5 n=10 relative n=12

image coordinate (Y 30 60 90 120 20 40 60 80 100 image coordinate (Y 30 60 90 120 20 40 60 80 100

  • Krt8 and Myo10: low

expression in old oocytes (H m Mol Genet 2004)

18

image coordinate (X) 30 60 90 120 150 180 20 image coordinate (X) 30 60 90 120 150 180 20

(Hum. Mol. Genet., 2004)

slide-19
SLIDE 19

Summary

  • Synergy: microrobotics and bioMEMS
  • Microrobotic cell injection

Microrobotic cell injection

– Enabled molecule testing and MEL gene prioritization prioritization

  • In situ distinguish healthy and aged mouse
  • ocytes to assess cell quality for cell
  • ocytes to assess cell quality for cell

selection

19