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HACKSing heterogeneity in cell motility
- 1. HACKSing Heterogeneity in Cell Motility Hee June Choi, Ph. D. (heejune.hailey.choi@gmail.com) Link to full paper Nat. Comms. (2018) Reproduction and distribution of the presentation without written permission are prohibited. © 2019 Hee June Choi
- 3. Protrusion promoted by coupled activities of Arp2/3 complex & VASP Actin network
- 4. Protrusion promoted by coupled activities of Arp2/3 complex & VASP Actin network growth Arp2/3 : actin network nucleation
- 5. Actin network growth VASP : actin network elongation Protrusion promoted by coupled activities of Arp2/3 complex & VASP
- 6. Protrusion promoted by coupled activities of Arp2/3 complex & VASP Actin network growth Arp2/3 VASP : actin network nucleation : actin network elongation Cell membrane protrusion
- 7. Heterogeneity in protrusion activities ; a complex multi-dimensional time-series data with heterogeneous hidden patterns Actin regulator dynamics Ensemble averaged
- 8. HACKS (1) Live Cell Imaging : cells expressing fluorescently tagged actin, Arp2/3 & VASP (2) Pre-processing: Extract local velocity & intensity time series (4) Characterize the molecular dynamics associated with the phenotype (5) Functional validation of the associated molecules by drug tests (3) Time series clustering: Dimensional reduction (SAX) Feature extraction (ACF) Distance calculation Clustering (DP) Identify subcellular protrusion phenotypes Deconvolution of Heterogeneous Activity in Coordination of cytosKeleton at the Subcellular level Chuangqi Wang*, Hee June Choi*, …, Kwonmoo Lee (2018) Nat. Comms. ( * = equal contribution)
- 9. 5μm Leading edge of a cell expressing Fluorescently-tagged Actin Sampling Window Svitkina, T. M. et al., J Cell Biol (1997) Local Sampling from Live Cell Imaging
- 10. Exemplary velocity time series 5μm Calculation of local velocity every 5 sec. Local fluorescence intensity every 5 sec. Protrusion Phenotype Protein Dynamics Velocity time-series Intensity time-series Measurement 1000 sec. (~ 17 min.) From each sampling window Time Series Clustering Local Sampling from Live Cell Imaging Leading edge of a cell expressing Fluorescently-tagged Actin Retraction Protrusion © Hee June Choi, Ph.D.
- 11. Alignment of time-series Membranedistance Membrane distance Time Protrusion onset Event definition Registered time (0)= Protrusion onset t=0
- 12. Alignment of time-series Time Velocity ν=0 Time Velocity ν=0 Protrusion onset Protrusion onset t =0 Input time-series Velocity time-series acquired from each sampling window t =0 Registered time Registered time
- 13. Alignment of time-series Time Velocity ν=0 Time Velocity ν=0 Protrusion onset t =0 Event Registration Protrusion time-series (= 275s) Input time-series Velocity time-series acquired from each sampling window t =0 Registered time Registered time
- 14. Alignment of time-series Total velocity time series
- 15. Alignment of time-series Total aligned velocity time series
- 16. Dimensional reduction (SAX) Feature extraction (ACF) Distance calculation (ED) Density Peak Clustering (1) Dimensional reductionTime-series clustering : Velocity time-series Time Velocity
- 17. Dimensional reduction (SAX) Feature extraction (ACF) Distance calculation (ED) Density Peak Clustering (1) Dimensional reductionTime-series clustering : Velocity time-series Time Velocity Time interval PAA (Piecewise aggregate approximation)
- 18. Dimensional reduction (SAX) Feature extraction (ACF) Distance calculation (ED) Density Peak Clustering (1) Dimensional reductionTime-series clustering : Velocity time-series Time Velocity Time interval PAA (Piecewise aggregate approximation) Gaussian distribution of PAA Equal probability 3 2 1 Symbolic representation
- 19. Dimensional reduction (SAX) Feature extraction (ACF) Distance calculation (ED) Density Peak Clustering (1) Dimensional reductionTime-series clustering : Velocity time-series Time Velocity Time interval PAA (Piecewise aggregate approximation) Gaussian distribution of PAA Equal probability 3 2 1 Symbolic representation Symbolic Aggregate approXimation (SAX) SAXrepresentation SAX time tag 3 2 1 Symbolic Aggregate approXimation (SAX) Keogh E. et al., In Proc. 5th IEEE International Conference on Data Mining (2005)
- 20. Exemplary aligned velocity time series (1) Dimensional reductionTime-series clustering : Dimensional reduction (SAX) Feature extraction (ACF) Distance calculation (ED) Density Peak Clustering
- 21. Symbolic Aggregate approXimation (SAX) 4 3 2 1 2 4 6 8 10 12 14 SAX time interval SAXrepresentation Proposed dissimilarity measure of two velocity time series in SAX = Approximate Euclidean distance of SAX (1) Dimensional reductionTime-series clustering : Dimensional reduction (SAX) Feature extraction (ACF) Distance calculation (ED) Density Peak Clustering
- 22. Exemplary autocorrelation coefficient (ACF) Dimensional reduction (SAX) Feature extraction (ACF) Distance calculation (ED) Density Peak Clustering Proposed dissimilarity measure of two velocity time series in SAX = Approximate Euclidean distance of SAX Dissimilarity measure of two velocity time series = Squared Euclidean distance between Autocorrelation coefficients (2) Feature extractionTime-series clustering :
- 23. Dimensional reduction (SAX) Feature extraction (ACF) Distance calculation (ED) Density Peak Clustering Input values for Density Peak Clustering (3) Distance calculationTime-series clustering : Pair-wise distance map
- 24. Dimensional reduction (SAX) Feature extraction (ACF) Distance calculation (ED) Density Peak Clustering 21 23 24 25 1 2 3 4 5 6 7 8 91112 14 16 17 18 20 26 27 28 10 13 15 19 22 (4) Density peak clusteringTime-series clustering : Sample point distribution Density Peak Clustering The cluster centers =local density maxima that are far away from any points of higher density. Rodriguez A. & Laio A., Science (2014) ρ (Local density) and δ (Distance from points of higher density) depends only on the distances between data points.
- 25. 1 Dimensional reduction (SAX) Feature extraction (ACF) Distance calculation (ED) Density Peak Clustering 10 1 10 (4) Density peak clusteringTime-series clustering : Sample point distribution 0 1 2 3 4 5 6 7 8 1.0 0.8 0.6 0.4 0.2 0.0 Decision graph for density peak clustering ρ (Density) δ(Distance)
- 26. 1 Dimensional reduction (SAX) Feature extraction (ACF) Distance calculation (ED) Density Peak Clustering 10 1 10 (4) Density peak clusteringTime-series clustering : Sample point distribution 0 1 2 3 4 5 6 7 8 1.0 0.8 0.6 0.4 0.2 0.0 Decision graph for density peak clustering ρ (Density) δ(Distance) 3 4 11 9 2 57 8 12 14 16 17 18 20 23 24 25 13 15 19 22 6 21 23456789 11 12131415161718 19 2021222324 25
- 27. 1 Dimensional reduction (SAX) Feature extraction (ACF) Distance calculation (ED) Density Peak Clustering 10 1 10 (4) Density peak clusteringTime-series clustering : Sample point distribution 0 1 2 3 4 5 6 7 8 1.0 0.8 0.6 0.4 0.2 0.0 Decision graph for density peak clustering ρ (Density) δ(Distance) 3 4 11 9 2 57 8 12 14 16 17 18 20 23 24 25 13 15 19 22 6 21 23456789 11 12131415161718 19 2021222324 25 26 27 28 26 27 28
- 28. 1 Dimensional reduction (SAX) Feature extraction (ACF) Distance calculation (ED) Density Peak Clustering 10 1 10 (4) Density peak clusteringTime-series clustering : Sample point distribution 0 1 2 3 4 5 6 7 8 1.0 0.8 0.6 0.4 0.2 0.0 Decision graph for density peak clustering ρ (Density) δ(Distance) 3 4 11 9 2 57 8 12 14 16 17 18 20 23 24 25 13 15 19 22 6 21 23456789 11 12131415161718 19 2021222324 25 26 27 28 26 27 28 Neighborhood to cluster centers Cluster Centers Noise
- 29. (4) Density peak clusteringTime-series clustering : Dimensional reduction (SAX) Feature extraction (ACF) Distance calculation (ED) Density Peak Clustering Pair-wise distance mapPairwise ordered dissimilarity
- 30. (4) Density peak clusteringTime-series clustering : Dimensional reduction (SAX) Feature extraction (ACF) Distance calculation (ED) Density Peak Clustering Pair-wise distance mapPairwise ordered dissimilarity
- 33. After clustering …Identified subcellular protrusion phenotypes “Fluctuating” “Periodic” “Accelerating”
- 34. Before clustering …After clustering …After clustering …Identified subcellular protrusion phenotypes “Fluctuating” “Periodic” “Accelerating”
- 35. Associating protrusion phenotypes with the underlying molecular dynamics Registered time (s) 0 100 200-100 Velocity Profilevelocity(μm/min) -1 0 1 2 Cluster III Registered time (s) 0 100 200-100 Actin 400 500 600 NormalizedIntensity Registered time (s) 0 100 200-100 Arp2/3 300 400 500 600 Registered time (s) 0 100 200-100 VASP 200 300 400 500 600
- 36. VASP promotes “accelerating protrusion” Cluster I & II “Fluctuating” & “Periodic” ;Coupled nucleation & elongation Cluster III “Accelerating” ;Temporally ordered nucleation & elongation Phase I Nucleation Phase II Elongation Nucleation & Elongation
- 37. Inhibition of VASP decreases strongly “accelerating protrusion” Cluster III-1 “Weak” accelerating Cluster III-1 Cluster III-2 “Strong” accelerating Cluster III-2
- 38. Inhibition of VASP decreases strongly “accelerating protrusion”
- 39. VASP Fluorescence Intensity VASP is differentially recruited according to protrusion phenotypes
- 40. 5μm Before HACKS 25 50 75 100 0 Actin Arp3 VASP HaloTag Proportionof clusterspersample Total(%) Registered time (s) 0 100 200-100 velocity(μm/min) -1 0 1 2 n=764 Registered time (s) 0 100 200-100 velocity(μm/min) -1 0 1 2 n=367 Registered time (s) 0 100 200-100 velocity(μm/min) -1 0 1 2 n=625 Registered time (s) 0 100 200-100 velocity(μm/min) -1 0 1 2 n=674 Registered time (s) 0 100 200-100 velocity(μm/min) -1 0 1 2 n=326 Cluster II-3Cluster II-1 Cluster II-2 Cluster IIICluster I 1.5 2 Cluster ICluster II-1 50 100 Cluster II-1 Cluster II-2 Cluster II-3 Cluster I Cluster III Cluster I 100200300 -100 -50 0 50 100 150 200 250 Adjustedsampleindex Registered time (s) Cluster II-1 100200300 -100 -50 0 50 100 150 200 250 Registered time (s) Cluster II-2 100200300 -100 -50 0 50 100 150 200 250 Registered time (s) 100200300 -100 -50 0 50 100 150 200 250 Cluster II-3 Registered time (s) Cluster III -100 -50 0 50 100 150 200 250 100200300 Registered time (s) velocity(μm/min) -4 -2 0 2 4 6 -6 0.8 1 Prob. Cluster II Subcellular protrusion phenotypes “Fluctuating” “Periodic” “Accelerating” Registered time (s) 0 100 200-100 velocity(μm/min) -1 0 1 2 Velocity Profile n=2756 Ensemble Average Registered time (s) 0 100 200-100 Velocity Profile n=326 velocity(μm/min) -1 0 1 2 Cluster III Registered time (s) 0 100 200-100 -1 0 1 2 Actin n=85 Registered time (s) 0 100 200-100 Actin n=934400 500 600 NormalizedIntensity -1 0 1 2 400 500 600 NormalizedIntensity Registered time (s) 0 100 200-100 Arp3 n=102 Registered time (s) 0 100 200-100 Arp3 n=757300 400 500 600 -1 -1 0 1 2 300 400 500 600 0 1 2 Registered time (s) 0 100 200-100 VASP n=101 Registered time (s) 0 100 200-100 VASP n=682 200 300 400 500 600 -1 0 1 2 -1 0 1 2 200 300 400 500 600 -10 -10 200 300 400 500 600 200 300 400 500 600 VASPActin Arp3 0 s) 0 0 0 VASP promotes “accelerating protrusi Associated molecular dynamics Fluctuating Acclerating Periodic ▪︎Kinetics also serves as info!!! Using machine learning approach, differential coordination of actin regulators were found to generate heterogeneity in subcellular motility
- 41. Conclusion 1. We developed an analysis pipeline, HACKS, to dissect protrusion heterogeneity at the subcellular level. 2. HACKS identified hidden patterns from a complex and heterogeneous velocity time-series data. 3. HACKS provided mechanistic details of molecular dynamics associated with protrusion phenotypes. 4. HACKS revealed subtle specificity of the drug target. This can be potentially applied to clinic. 5. HACKS can be potentially applied to other types of time- series data for cell biological studies.
- 42. Further Study: Deep HACKS DeepHACKS dissects the heterogeneity of subcellular time-series datasets by allowing bi-directional LSTM (Long-Short Term Memory) neural networks to extract fine-grained temporal features by integrating autoencoder with traditional machine learning outcomes.
- 43. Acknowledgments Chauncey Wang, M.S.Prof. Kwonmoo Lee Lee Lab Sungjin Kim, Ph.D. (former) Collaborators Pf. Doohoon Kim (UMMS) Namgyu Lee, Ph.D. Pf. Yongho Bae (SUNY, Buffalo) Aesha Desai, Ph.D.
Editor's Notes
- As you may all agree, cell motility is fundamentally important property of biological systems. If cells are not moving at all, at a slightest level, then they are dead. Cell motility is critical for organisms to survive and thrive as they are important for developmental processes or immune responses. If it goes wrong, then we can metastatic cancer. So understanding precise mechanism of cell motility is important. However, as you saw in the background movie from the previous slide, motility of a cell or population of cells exhibit significant level of heterogeneity, stochasticity and plasticity, which is not surprising considering that heterogeneity is a fundamental and prevalent property of biological systems. ◦ But, methods to identify, quantify and characterize heterogeneity have been lacking and mostly limited to isolated single-cell studies. ◦ So far The mechanism of cell protrusion has been understood based on the ensemble average of actin regulator dynamics, which could lead to the loss of critical information. Therefore, we developed a machine elarning approach HACKS
- Local sampling at mesoscopic scale
- Generate clusters of arbitrary shapes. Robust against noise. No K value required in advance. Somewhat similar to human vision.