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Cell Therapy pp 609–625Cite as

The Role of the National Institute of Standards in Measurement Assurance for Cell Therapies

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Abstract

Our work at NIST is focused on improving confidence in measurement. In this chapter we present examples of how NIST interacts with industry and other stakeholders involved with cell therapies to achieve this goal. NIST plays a special role in the development of reference materials and documentary standards and works closely with our stakeholders to carry out these activities. NIST also has a very active technical program in several areas that impinge on cellular therapy development. Some of these activities are designed to address immediate measurement challenges; other aspects of our technical program are focused on developing advanced measurement technologies. Our research programs include working with established measurement methods to make them more reliable, developing orthogonal measurements to provide assurance to more routine methods, and enabling the development of reference materials and reference data.

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References

  1. National Cell Manufacturing Consortium. (2017). Achieving large-scale, cost-effective, reproducible manufacturing of high-quality cells [Online]. Available: http://cellmanufacturingusa.org/ncmc. Accessed 17 July 2020.

  2. Alliance for Regenerative Medicine. https://alliancerm.org/manufacturing/. Accessed 17 July 2020.

  3. U.S. CONGRESS. (2016). 21st Century Cures Act H.R.34. Washington DC. Federal Register. https://www.federalregister.gov/documents/2020/05/01/2020-07419/21st-century-cures-act-interoperability-information-blocking-and-the-onc-health-it-certification. Accessed 17 July 2020.

  4. U.S. Food and Drug Administration. (2019). Standards development and the use of standards in regulatory submissions reviewed in the center for biologics evaluation and research. Guidance for industry. March 2019. https://www.fda.gov/media/124694/download. Accessed 17 July 2020.

  5. Rosslein, M., Elliott, J. T., Salit, M., Petersen, E., et al. (2015). Use of cause-and-effect analysis to design a high-quality nanocytotoxicology assay. Chemical Research in Toxicology, 28, 21–30.

    Article  CAS  Google Scholar 

  6. Elliot, J. T., Tonya, A., & Plant, A. L. (2003). Comparison of reagents for shape analysis of fixed cells by automated fluorescence microscopy. Cytometry, A52, 90–100.

    Article  Google Scholar 

  7. Elliot, J. T., Rosslein, J. T., Song, N., Toman, B., et al. (2017). Toward achieving harmonization in a nano-cytotoxicity assay measurement through an interlaboratory comparison study. ALTEX, 34, 201–218.

    Article  Google Scholar 

  8. Sarkar, S., Lund, S. P., Vyzsatya, R., Vanguri, P., et al. (2017). Evaluating the quality of a cell counting measurement process via a dilution series experimental design. Cytotherapy, 19, 1509–1521.

    Article  Google Scholar 

  9. ISO. (2019). Cell counting – Part 2: Experimental design and statistical analysis to quantify counting method performance. ISO 20391-2:2019(en).

    Google Scholar 

  10. Sarkar S. Pierce L, Lin-Gibson S. Lund SP (2019) Standards landscape in cell counting: Implications for cell & gene therapy. Cell & Gene Therapy Insights 5, pp 117-131

    Google Scholar 

  11. ASTM. (2018). New test method for measuring cell viability in a scaffold. In ASTM Subcommittee: ASTM 2018. F04.43 (Ed.), WK62115.

    Google Scholar 

  12. Wang, L., Derose, P., & Gaigalas, A. K. (2016). Assignment of the number of equivalent reference fluorophores to dyed microspheres. Journal of Research of the National Institute of Standards and Technology, 121.

    Google Scholar 

  13. Wang, L., & Gaigalas, A. K. (2011). Development of multicolor flow cytometry standards: Assignment of ERF units. Journal of Research of the National Institute of Standards and Technology, 116.

    Google Scholar 

  14. Federal Register. (2016). Flow cytometry quantitation consortium. Office of the Federal Register, 46054–46055. https://www.govinfo.gov/app/details/FR-2016-07-15/2016-16761. Accessed 17 July 2020

  15. Degheidy, H., Abbasi, F., Mostowski, H., Gaigalas, A. K., et al. (2016). Consistent, multi-instrument single tube quantification of CD20 in antibody bound per cell based on CD4 reference. Cytometry Part B: Clinical Cytometry, 90, 159–167.

    Article  CAS  Google Scholar 

  16. Wang, L., Degheidy, H., Abbasi, F., Mostowski, H., et al. (2016). Quantitative flow cytometry measurements in antibodies bound per cell based on a CD4 reference. Current Protocols in Cytometry, 75, 1.29. 1-1 29.14.

    Article  Google Scholar 

  17. Wang, L., Abbasi, F., Ornatsky, O., Cole, K. D., et al. (2012). Human CD4+ lymphocytes for antigen quantification: Characterization using conventional flow cytometry and mass cytometry. Cytometry, A81, 567–575.

    Article  Google Scholar 

  18. Wang, M., Misakian, M., He, H. J., Bajcsy, P., et al. (2014). Quantifying CD4 receptor protein in two human CD4+ lymphocyte preparations for quantitative flow cytometry. Clinical Proteomics, 11(p), 43.

    Article  CAS  Google Scholar 

  19. Halter, M., Sisan, D. R., Chalfoun, J., Stottrup, B., et al. (2011). Cell cycle dependent TN-C promoter activity determined by live cell imaging. Cytometry A, 79A, 192–202.

    Article  CAS  Google Scholar 

  20. Halter, M., Tona, A., Bhadriraju, K., Plant, A. L., et al. (2007). Automated live cell imaging of green fluorescent protein degradation in individual fibroblasts. Cytometry A, 71, 827–834.

    Article  Google Scholar 

  21. Dima, A. A., Elliott, J. T., Filiben, J. J., Halter, M., et al. (2011). Comparison of segmentation algorithms for fluorescence microscopy images of cells. Cytometry A, 79, 545–559.

    Article  Google Scholar 

  22. ASTM. (2014). Guide for using fluorescence microscopy to quantify the spread area of fixed cells. In ASTM (Ed.), ASTM 2014 (pp. F2998–F2914)

    Google Scholar 

  23. Edelstein, A. D., Tsuchida, M. A., Amodaj, N., Pinkard, H., et al. (2014). Advanced methods of microscope control using muManager software. Journal of Biological Methods, 1, 2, p e10.

    Article  Google Scholar 

  24. Halter, M., Bier, E., Derose, P. C., Cooksey, G. A., et al. (2014). An automated protocol for performance benchmarking a widefield fluorescence microscope. Cytometry A, 85, 978–985.

    Article  Google Scholar 

  25. National Institute of Standards and Technology (NIST). (2017). Fluorescence microscopy benchmarking [Online]. NIST. https://www.nist.gov/programs-projects/fluorescence-microscopy-benchmarking-overview. Accessed 17 July 2020.

  26. ASTM. (2018). Standard guide for performing quantitative fluorescence intensity measurements in cell-based assays with widefield epifluorescence microscopy. In ASTM (Ed.), Book of standards (Vol. 13.02).

    Google Scholar 

  27. Sisan, D. R., Halter, M., Hubbard, J. B., & Plant, A. L. (2012). Predicting rates of cell state change caused by stochastic fluctuations using a data-driven landscape model. Proceedings of the National Academy of Sciences of the United States of America, 109, 19262–19267.

    Article  CAS  Google Scholar 

  28. Lund, S. P., Hunnard, J. B., & Halter, M. (2014). Nonparametric estimates of drift and diffusion profiles via Fokker-Planck algebra. The Journal of Physical Chemistry B, 118, 12743–12749.

    Article  CAS  Google Scholar 

  29. Hubbard, J. B., Halter, M., & Plant, A. L. (2019). Properties of a multidimensional landscape model for determining cellular network thermodynamics. bioRxiv. https://doi.org/10.1101/682690. Accessed 17 July 2020.

  30. Chalfoun, J., Majurski, M., Dima, A., Halter, M., et al. (2016). Lineage mapper: A versatile cell and particle tracker. Scientific Reports, 6, 36984.

    Article  CAS  Google Scholar 

  31. Bhadiraju, K., Halter, M., Amelot, J., Bajcsy, P., et al. (2016). Large-scale time-lapse microscopy of Oct4 expression in human embryonic stem cell colonies. Stem Cell Research, 17, 122–129.

    Article  Google Scholar 

  32. Bajcsy, P., Chalfoun, J., & Simon, M. (2018). Web microanalysis of big image data. Springer.

    Book  Google Scholar 

  33. Camp, C. H., & Cicerone, M. T. (2015). Chemically sensitive bioimaging with coherent Raman scattering. Nature Photonics, 9, 295–305.

    Article  CAS  Google Scholar 

  34. Camp, C. H., Lee, Y. J., Heddlestone, J. M., Hartshorn, C. M., et al. (2014). High-speed coherent Raman fingerprint imaging of biological tissues. Nature Photonics, 8, 627–634.

    Article  Google Scholar 

  35. Camp, C. H., Lee, Y. J., & Cicerone, M. T. (2016). Quantitative, comparable coherent anti-stokes Raman scattering (CARS) spectroscopy: Correcting errors in phase retrieval. Journal of Raman Spectroscopy, 47, 408–415.

    Article  CAS  Google Scholar 

  36. Leey, Y. J., Vega, S. L., Patel, P., Aamer, K. A., et al. (2014). Quantitative, label-free characterization of stem cell differentiation at the single-cell level by broadband coherent anti-Stokes Raman scattering microscopy. Tissue Engineering Methods (Part C), 20, 562–569.

    Article  Google Scholar 

  37. Mir, M., Bhaduri, N., Wang, R., Zu, R., et al. (2012). Chapter 3: Quantitative phase imaging. In E. Wolf (Ed.), Progress in optics. Elsevier.

    Google Scholar 

  38. Kwee, E., Peterson, A., Stinson, J., Halter, M., et al. (2018). Large field of view quantitative phase imaging of induced pluripotent stem cells and optical pathlength reference materials. SPIE.

    Book  Google Scholar 

  39. Paganin, D., & Nugent, K. A. (1998). Noninterferometric phase imaging with partially coherent light. Physical Review Letters, 80, 2586–2589.

    Article  CAS  Google Scholar 

  40. Peterson, A. W., Halter, M., Tona, A., Bhadriraju, K., et al. (2009). Surface plasmon resonance imaging of cells and surface-associated fibronectin. BMC Cell Biology, 10(p), 16.

    Article  Google Scholar 

  41. Bhadriraju, K., et al. (2010). Using surface plasmon resonance imaging to probe dynamic interactions between cells and extracellular matrix. Cytometry A, 77, 895–903.

    PubMed  Google Scholar 

  42. Peterson, A. W., Halter, M., Tona, A., & Plant, A. L. (2014). High resolution surface plasmon resonance imaging for single cells. BMC Cell Biology, 15(p), 35.

    Article  Google Scholar 

  43. Peterson, A. W., Halter, M., Tona, A., Plant, A. L., & Elliott, J. (2018). Mass measurements of focal adhesions in single cells using high resolution surface plasmon resonance microscopy. SPIE.

    Book  Google Scholar 

  44. Bajcsy, P., Cardone, A., Chalfoun, J., Halter, M., et al. (2015). Survey statistics of automated segmentations applied to optical imaging of mammalian cells. BMC Bioinformatics, 16(p), 330.

    Article  Google Scholar 

  45. Schaub, N. J., Hotaling, N. A., Mabescu, P., Padi, S., et al. (2020). Deep learning predicts function of live retinal pigment epithelium from quantitative microscopy. Journal of Clinical Investigation, 130, 1010–1023.

    Article  CAS  Google Scholar 

  46. Padi, S., Manescu, P., Schaub, N., Hotaling, N., et al. (2020). Comparison of Artificial Intelligence based approaches to cell function prediction. Informatics in Medicine Unlocked, 18, 100270.

    Article  Google Scholar 

  47. NIST. (2020). NIST genome editing consortium. https://www.nist.gov/programs-projects/nist-genome-editing-consortium. Accessed 17 July 2020.

  48. NIST. (2020). Genome in a bottle. https://www.nist.gov/programs-projects/genome-bottle. Accessed 17 July 2020.

  49. Goldfender, R. L., Priest, J. R., Zook, J. M., Grove, M. E., et al. (2016). Medical implications of technical accuracy in genome sequencing. Genome Medicine, 8(p), 24.

    Article  Google Scholar 

  50. Zook, J. M., McDaniel, J., Olson, N. D., Wagner, J., et al. (2019). An open resource for accurately benchmarking small variant and reference calls. Nature Biotechnology, 37, 561–566.

    Article  CAS  Google Scholar 

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Acknowledgments

Certain commercial equipment, instruments, or materials are identified in this chapter to foster understanding. Such identification does not imply recommendation or endorsement by the National Institute of Standards and Technology nor does it imply that the materials or equipment identified are necessarily the best available for the purpose

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Authors and Affiliations

  1. Biosystems and Biomaterials Division, National Institute of Standards and Technology, Gaithersburg, MD, USA

    Anne L. Plant, Charles Camp, John T. Elliott, Tara Eskandari, Michael Halter, Edward Kwee, Samantha Maragh, Alexander Peterson, Laura Pierce, Sumona Sarkar, Carl Simon, Lili Wang, Justin Zook & Sheng Lin-Gibson

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  1. Anne L. Plant
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  2. Charles Camp
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  3. John T. Elliott
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  4. Tara Eskandari
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  7. Samantha Maragh
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  8. Alexander Peterson
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  9. Laura Pierce
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  10. Sumona Sarkar
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  11. Carl Simon
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  12. Lili Wang
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  13. Justin Zook
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  14. Sheng Lin-Gibson
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Corresponding author

Correspondence to Anne L. Plant .

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Editors and Affiliations

  1. Texas Children’s Cancer Center, Center for Cell and Gene Therapy, Houston, TX, USA

    Adrian P. Gee

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Plant, A.L. et al. (2022). The Role of the National Institute of Standards in Measurement Assurance for Cell Therapies. In: Gee, A.P. (eds) Cell Therapy. Springer, Cham. https://doi.org/10.1007/978-3-030-75537-9_38

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