This Article 
 Bibliographic References 
 Add to: 
On the Application of Active Learning and Gaussian Processes in Postcryopreservation Cell Membrane Integrity Experiments
May-June 2012 (vol. 9 no. 3)
pp. 846-856
F. Barry, Nat. Centre for Biomed. Eng. Sci., Nat. Univ. of Ireland Galway, Galway, Ireland
M. J. Murphy, Nat. Centre for Biomed. Eng. Sci., Nat. Univ. of Ireland Galway, Galway, Ireland
D. Fay, Comput. Lab., Univ. of Cambridge, Cambridge, UK
M. Norkus, Electr. & Electron. Eng., Nat. Univ. of Ireland Galway, Galway, Ireland
G. Olaighin, Electr. & Electron. Eng., Nat. Univ. of Ireland Galway, Galway, Ireland
L. Kilmartin, Electr. & Electron. Eng., Nat. Univ. of Ireland Galway, Galway, Ireland
Biological cell cryopreservation permits storage of specimens for future use. Stem cell cryostorage in particular is fast becoming a broadly spread practice due to their potential for use in regenerative medicine. For the optimal cryopreservation process, ultralow temperatures are needed. However, elevated temperatures are often unavoidable in a typical sample handling cycle which in turn negatively affects the postcryopreservation integrity of cells. In this paper, we present an application of active learning using an underlying Gaussian Process (GP) model in an experimental study on postcryopreservation membrane integrity response to a range of elevated temperature conditions. We tailored this technique for the current investigation and developed an algorithm which enabled identification of the sampling locations for the experiments in order to obtain the highest information return about the process from a limited size sample set. We applied this algorithm in the experimental study investigating the effects of severe temperature elevation (ranging from -40 to 20°C) over a short term event (48 hours) on the postcryopreservation membrane integrity of Mesenchymal Stem Cells (MSCs) derived from human bone marrow. The algorithm showed excellent performance by selecting the locations which maximized the reduction of variance of the process response estimate. An approximating GP model developed from this experimental data shows that the elevated temperatures during cryopreservation have an imminent detrimental effect on cell integrity.

[1] E.E. Spurr, N.E. Wiggins, K.A. Marsden, R.M. Lowenthal, and S.J. Ragg, "Cryopreserved Human Haematopoietic Stem Cells Retain Engraftment Potential After Extended (5-14 years) Cryostorage," Cryobiology, vol. 44, no. 3, pp. 210-217, 2002.
[2] N. Kotobuki, M. Hirose, H. Machida, Y. Katou, K. Muraki, Y. Takakura, and H. Ohgushi, "Viability and Osteogenic Potential of Cryopreserved Human Bone Marrow-Derived Mesenchymal Cells," Tissue Eng., vol. 11, nos. 5/6, pp. 663-673, May 2005.
[3] A. Hubel, "Parameters of Cell Freezing: Implications for the Cryopreservation of Stem Cells," Transfusion Medicine Rev., vol. 11, no. 3, pp. 224-233, 1997.
[4] D. Gao and J.K. Critser, "Mechanisms of Cryoinjury in Living Cells," Institute for LaboratoryAnimal Research J., vol. 41, pp. 187-196, 2000.
[5] R. Martinez-Cantin, N. de Freitas, A. Doucet, and J. Castellanos, "Active Policy Learning for Robot Planning and Exploration under Uncertainty," Proc. Robotics: Science and Systems, June 2007.
[6] T.J. Santner, B. Williams, and W. Notz, The Design and Analysis of Computer Experiments. Springer-Verlag, 2003.
[7] R.B. Gramacy, "Bayesian Treed Gaussian Process Models," PhD thesis, UC Santa Cruz, 2005.
[8] S. Seo, M. Wallat, T. Graepel, and K. Obermayer, "Gaussian Process Regression: Active Data Selection and Test Point Rejection," Proc. DAGM-Symp., pp. 27-34, 2000.
[9] F.H. Cocks and W.E. Brower, "Phase Diagram Relationships in Cryobiology," Cryobiology, vol. 11, no. 4, pp. 340-358, 1974.
[10] D. Pegg, "Equations for Obtaining Melting-Points and Eutectic Temperatures for the Ternary-System Dimethylsulfoxide Sodium-Chloride Water," Cryo-Letters, vol. 7, no. 6, pp. 387-394, Nov./Dec. 1986.
[11] K.K. Fleming and A. Hubel, "Cryopreservation of Hematopoietic Stem Cells: Emerging Science, Technology and Issues," Transfusion Medicine and Hemotherapy, vol. 34, no. 4, pp. 268-275, 2007.
[12] A. Tappel, "Effects of Low Temperature and Freezing on Enzymes and Enzyme Systems," Cryobiology, pp. 163-177, Academic Press, 1966.
[13] A. Galmes, J. Besalduch, J. Bargay, A. Novo, M. Morey, J. Guerra, and M. Duran, "Long-Term Storage at -80 Degrees c of Hematopoietic Progenitor Cells with 5-Percent Dimethyl Sulfoxide as the Sole Cryoprotectant," Transfusion, vol. 39, no. 1, pp. 70-73, Jan. 1999.
[14] A. Sputtek, B. Nowicki, A. Rowe, and P. Kuhnl, "Temperatures Lower than -$80^{\circ}$ C Are Required for the Long-Term Storage of Human Peripheral Blood Progenitor Cells," Transfusion, vol. 45, no. 3,Suppl. S, p. 9A, Sept. 2005.
[15] K. Fowke, J. Behnke, C. Hanson, K. Shea, and M. Cosentino, "Apoptosis: A Method for Evaluating the Cryopreservation of Whole Blood and Peripheral Blood Mononuclear Cells," J. Immunological Methods, vol. 244, nos. 1/2, pp. 139-144, Oct. 2000.
[16] E.D. O'Cearbhaill, M.A. Punchard, M. Murphy, F.P. Barry, P.E. McHugh, and V. Barron, "Response of Mesenchymal Stem Cells to the Biomechanical Environment of the Endothelium on a Flexible Tubular Silicone Substrate," Biomaterials, vol. 29, no. 11, pp. 1610-1619, Apr. 2008.
[17] J. Murphy, D. Fink, E. Hunziker, and F. Barry, "Stem cell Therapy in a Caprine Model of Osteoarthritis," Arthritis and Rheumatism, vol. 48, no. 12, pp. 3464-3474, Dec. 2003.
[18] M. Dominici, K. Le Blanc, I. Mueller, I. Slaper-Cortenbach, F.C. Marini, D.S. Krause, R.J. Deans, A. Keating, D.J. Prockop, and E.M. Horwitz, "Minimal Criteria for Defining Multipotent Mesenchymal Stromal Cells. The International Society for Cellular Therapy Position Statement," Cytotherapy, vol. 8, no. 4, pp. 315-317, Aug. 2006.
[19] M.A. Harrison and I.F. Rae, General Techniques of Cell Culture. Cambridge Univ. Press, 1997.
[20] M. Butler, "Cell Counting and Viability Measurements," Animal Cell Biotechnology: Methods and Protocols, pp. 131-144, Humana Press, 1999.
[21] A. Krause, A. Singh, and C. Guestrin, "Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies," The J. Machine Learning Research, vol. 9, pp. 235-284, 2008.
[22] M.A. Osborne, A. Rogers, S. Ramchurn, S.J. Roberts, and N.R. Jennings, "Towards Real-Time Information Processing of Sensor Network Data Using Computationally Efficient Multi-Output Gaussian Processes," Proc. Seventh Int'l Conf. Information Processing in Sensor Networks, 2008.
[23] M. Osborne, "Bayesian Gaussian Processes for Sequential Prediction, Optimisation and Quadrature," PhD thesis, Univ. of Oxford, 2010.
[24] C.E. Rasmussen and C.K.I. Williams, Gaussian Processes for Machine Learning. MIT press, 2006.
[25] P.W. Goldberg, C.K.I. Williams, and C.M. Bishop, "Regression with Input-Dependent Noise: A Gaussian Process Treatment," Proc. Neural Information Processing Systems (NIPS), 1997.
[26] K. Kersting, C. Plagemann, P. Pfaff, and W. Burgard, "Most Likely Heteroscedastic Gaussian Process Regression," Proc. 24th Ann. Int'l Conf. Machine Learning (ICML '07), Z. Ghahramani, ed., pp. 393-400, June 2007.
[27] D.J.C. MacKay, "Information-Based Objective Functions for Active Data Selection," Neural Computation, vol. 4, pp. 589-603, 1992.
[28] D.A. Cohn, "Neural Network Exploration Using Optimal Experimental Design," Proc. Neural Information Processing Systems (NIPS), pp. 679-686, 1996.
[29] D. Finkel, The DIRECT Optimization Algorithm User Guide, North Carolina State Univ., 2003.
[30] H. Levene, "Robust Test for Equality of Variances," Contributions to Probability and Statistics, pp. 278-292, Stanford Univ. Press, 1960.
[31] M.B. Brown and A.B. Forsythe, "Robust Tests for Equality of Variance," Am. Statistical Assoc., vol. 69, pp. 364-367, 1974.
[32] P. Mazur, "The Role of Intacellular Freezing in the Death of Cells Cooled at Supraoptimal Rates," Cryobiology, vol. 14, pp. 251-272, 1977.
[33] J. Lovelock, "The Denaturation of Lipid-protein Complexes as a Cause of Damage by Freezing," Proc. Royal Soc. of London Series B - Biological Sciences, vol. 147, no. 929, pp. 427-433, 1957.

Index Terms:
physiological models,biomembranes,bone,cellular biophysics,Gaussian processes,learning (artificial intelligence),medical computing,orthopaedics,temperature -40 degC to 20 degC,Gaussian process model,active learning,postcryopreservation cell membrane integrity experiments,biological cell cryopreservation,stem cell cryostorage,regenerative medicine,optimal cryopreservation processing,elevated temperature conditions,mesenchymal Stem Cells,human bone marrow,imminent detrimental effect,cell integrity,Temperature distribution,Kernel,Data models,Biomembranes,Vectors,Stem cells,temperature elevation.,Active learning,Gaussian process,postcryopreservation cell integrity,stem cells
F. Barry, M. J. Murphy, D. Fay, M. Norkus, G. Olaighin, L. Kilmartin, "On the Application of Active Learning and Gaussian Processes in Postcryopreservation Cell Membrane Integrity Experiments," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 9, no. 3, pp. 846-856, May-June 2012, doi:10.1109/TCBB.2011.155
Usage of this product signifies your acceptance of the Terms of Use.