Formation of Stress Fibres Huckemann in Adult Stem Cells - PowerPoint PPT Presentation

Stem Cell Stress Fibres Formation of Stress Fibres Huckemann in Adult Stem Cells Introduction Digitizing WiZer Stephan F . Huckemann Persistence Application University of Göttingen, Outlook Felix Bernstein Institute for Mathematical Statistics in the Biosciences References May 22, 2014 supported by the Niedersachsen Vorab of the Volkswagen Foundation

Stem Cell Stress Fibres Contributors Huckemann Introduction • Max Sommerfeld (SAMSI 2013/14) Digitizing • Kwang-Rae Kim (now at the Univ. of Nottingham) WiZer Persistence • Florian Rehfeldt and Carina Wollnik Application (Physics III/Biophysics, Göttingen) Outlook • Carsten Gottschlich, Benjamin Eltzner and Axel Munk References (Univ. Göttingen) • DFG CRC 755 “Nanoscale Photonic Imaging” • SAMSI LDHD

Stem Cell Stress Fibres One Motivation: Stem Cell Huckemann Therapy Introduction Digitizing • Medical condition e.g. post heart attack, WiZer • medical goal e.g. grow new heart muscle tissue, Persistence • intervention strategy: inject stem cells. Application Outlook References

Stem Cell Stress Fibres One Motivation: Stem Cell Huckemann Therapy Introduction Digitizing • Medical condition e.g. post heart attack, WiZer • medical goal e.g. grow new heart muscle tissue, Persistence • intervention strategy: inject stem cells. Application Outlook • Here: adult mesenchymal human stem cells References • e.g. from bone marrow • pluripotent = differentiate e.g. into • myoblasts = muscle precusor cells, • osteoblasts = bone precusor cells, • lipoblasts = fat precursor cells, • etc.

Stem Cell Stress Fibres One Motivation: Stem Cell Huckemann Therapy Introduction Digitizing • Medical condition e.g. post heart attack, WiZer • medical goal e.g. grow new heart muscle tissue, Persistence • intervention strategy: inject stem cells. Application Outlook • Here: adult mesenchymal human stem cells References • e.g. from bone marrow • pluripotent = differentiate e.g. into • myoblasts = muscle precusor cells, • osteoblasts = bone precusor cells, • lipoblasts = fat precursor cells, • etc. • Previous research by Engler et al. (2006) indicates that surrounding tissue elasticity influences the blast – type.

Stem Cell Stress Fibres Problem at Hand: Study Early Huckemann Stem Cell Differentiation Introduction Digitizing • put cells on gel of varying WiZer elasticity (kPa), Persistence • flourescence labeling of Application actin-myosin filaments, Outlook • photograph after 24 hrs. References (before duplication).

Stem Cell Stress Fibres Problem at Hand: Study Early Huckemann Stem Cell Differentiation Introduction Digitizing • put cells on gel of varying WiZer elasticity (kPa), Persistence • flourescence labeling of Application actin-myosin filaments, Outlook • photograph after 24 hrs. References (before duplication). From Zemel et al. (2010)

Stem Cell Stress Fibres Biomechanical Hypotheses Huckemann From Zemel et al. (2010) Introduction Digitizing WiZer Persistence Application Outlook References Orientation detection: elongated Laplacians of a Gaussian

Stem Cell Stress Fibres Biomechanical Hypotheses Huckemann From Zemel et al. (2010) Introduction Digitizing WiZer Persistence Application Outlook References Orientation detection: elongated Laplacians of a Gaussian • Low rigidity (1kPa) ⇒ few short non-oriented filaments. • Resonance rigidity (11 kPa) ⇒ many long aligned filaments. • High rigidity (34 kPa) ⇒ many long filaments with varying directions.

Stem Cell Stress Fibres Challenges Huckemann 1 Good data: reliably digitize filament structure → Introduction filament process Digitizing WiZer ( λ, φ ) z i , i = 1 , . . . , N , λ ∈ R + , φ ∈ [ 0 , π ) . Persistence Application Outlook References

Stem Cell Stress Fibres Challenges Huckemann 1 Good data: reliably digitize filament structure → Introduction filament process Digitizing WiZer ( λ, φ ) z i , i = 1 , . . . , N , λ ∈ R + , φ ∈ [ 0 , π ) . Persistence Application 2 Over time → a process of filament processes indexed Outlook in time. References

Stem Cell Stress Fibres Challenges Huckemann 1 Good data: reliably digitize filament structure → Introduction filament process Digitizing WiZer ( λ, φ ) z i , i = 1 , . . . , N , λ ∈ R + , φ ∈ [ 0 , π ) . Persistence Application 2 Over time → a process of filament processes indexed Outlook in time. References 3 Statistics of (processes of) filament processes or at least of descriptors.

Stem Cell Stress Fibres Challenges Huckemann 1 Good data: reliably digitize filament structure → Introduction filament process Digitizing WiZer ( λ, φ ) z i , i = 1 , . . . , N , λ ∈ R + , φ ∈ [ 0 , π ) . Persistence Application 2 Over time → a process of filament processes indexed Outlook in time. References 3 Statistics of (processes of) filament processes or at least of descriptors. 4 Today: total pixel number of filaments in direction φ : f ( φ ) := E [ λ | φ ] E [ ♯ z i | φ ] , φ ∈ [ 0 , π ) .

Stem Cell Stress Fibres Challenges Huckemann 1 Good data: reliably digitize filament structure → Introduction filament process Digitizing WiZer ( λ, φ ) z i , i = 1 , . . . , N , λ ∈ R + , φ ∈ [ 0 , π ) . Persistence Application 2 Over time → a process of filament processes indexed Outlook in time. References 3 Statistics of (processes of) filament processes or at least of descriptors. 4 Today: total pixel number of filaments in direction φ : f ( φ ) := E [ λ | φ ] E [ ♯ z i | φ ] , φ ∈ [ 0 , π ) . 5 Infer on the number of modes of f ( φ ) :

Stem Cell Stress Fibres Challenges Huckemann 1 Good data: reliably digitize filament structure → Introduction filament process Digitizing WiZer ( λ, φ ) z i , i = 1 , . . . , N , λ ∈ R + , φ ∈ [ 0 , π ) . Persistence Application 2 Over time → a process of filament processes indexed Outlook in time. References 3 Statistics of (processes of) filament processes or at least of descriptors. 4 Today: total pixel number of filaments in direction φ : f ( φ ) := E [ λ | φ ] E [ ♯ z i | φ ] , φ ∈ [ 0 , π ) . 5 Infer on the number of modes of f ( φ ) : 1 kPa ⇒ many modes? •

Stem Cell Stress Fibres Challenges Huckemann 1 Good data: reliably digitize filament structure → Introduction filament process Digitizing WiZer ( λ, φ ) z i , i = 1 , . . . , N , λ ∈ R + , φ ∈ [ 0 , π ) . Persistence Application 2 Over time → a process of filament processes indexed Outlook in time. References 3 Statistics of (processes of) filament processes or at least of descriptors. 4 Today: total pixel number of filaments in direction φ : f ( φ ) := E [ λ | φ ] E [ ♯ z i | φ ] , φ ∈ [ 0 , π ) . 5 Infer on the number of modes of f ( φ ) : 1 kPa ⇒ many modes? • • 11 kPa ⇒ one mode?

Stem Cell Stress Fibres Challenges Huckemann 1 Good data: reliably digitize filament structure → Introduction filament process Digitizing WiZer ( λ, φ ) z i , i = 1 , . . . , N , λ ∈ R + , φ ∈ [ 0 , π ) . Persistence Application 2 Over time → a process of filament processes indexed Outlook in time. References 3 Statistics of (processes of) filament processes or at least of descriptors. 4 Today: total pixel number of filaments in direction φ : f ( φ ) := E [ λ | φ ] E [ ♯ z i | φ ] , φ ∈ [ 0 , π ) . 5 Infer on the number of modes of f ( φ ) : 1 kPa ⇒ many modes? • • 11 kPa ⇒ one mode? • 34 kPa ⇒ more than one but not many modes?

Stem Cell Stress Fibres Good Data: Reliably Digitize Huckemann Filament Structure Introduction Digitizing WiZer Persistence Application Outlook References Good quality image

Stem Cell Stress Fibres Good Data: Reliably Digitize Huckemann Filament Structure Introduction Digitizing WiZer Persistence Application Outlook References Elongated Laplacian Good quality image of a Gaussian following Zemel et al. (2010) filament pixel �→ orientation

Stem Cell Stress Fibres Good Data: Reliably Digitize Huckemann Filament Structure Introduction Digitizing WiZer Persistence Application Outlook References Constrained reverse Elongated Laplacian Good quality image diffusion by Basu of a Gaussian et al. (2013) following Zemel et al. (2010) filament pixel �→ filament pixel �→ yes/no orientation

Stem Cell Stress Fibres Good Data: Reliably Digitize Huckemann Filament Structure Introduction Digitizing WiZer Persistence Application Outlook References Constrained reverse Elongated Laplacian Good quality image diffusion by Basu of a Gaussian et al. (2013) following Zemel et al. (2010) filament pixel �→ filament pixel �→ yes/no orientation Ground truth?

Stem Cell Stress Fibres Methods Against Ground Truth Huckemann Introduction Digitizing WiZer Persistence Application Outlook References Manually expert marked ground truth database

Stem Cell Stress Fibres Methods Against Ground Truth Huckemann Introduction Digitizing WiZer Persistence Application Outlook References eLoGs CRD Manually expert marked ground truth database • Yellow: correctly traced • Green: false detects • Red: not detected