Non-destructive classification of unlabeled cells: Combining an automated benchtop magnetic resonance scanner and artificial intelligence

In order to treat degenerative diseases, the importance of advanced therapy medicinal products has increased in recent years. The newly developed treatment strategies require a rethinking of the appropriate analytical methods. Current standards are missing the complete and sterile analysis of the product of interest to make the drug manufacturing effort worthwhile. They only consider partial areas of the sample or product while also irreversibly damaging the investigated specimen. Two-dimensional T1 / T2 MR relaxometry meets these requirements and is therefore a promising in-process control during the manufacturing and classification process of cell-based treatments. In this study a tabletop MR scanner was used to perform two-dimensional MR relaxometry. Throughput was increased by developing an automation platform based on a low-cost robotic arm, resulting in the acquisition of a large dataset of cell-based measurements. Two-dimensional inverse Laplace transformation was used for post-processing, followed by data classification performed with support vector machines (SVM) as well as optimized artificial neural networks (ANN). The trained networks were able to distinguish non-differentiated from differentiated MSCs with a prediction accuracy of 85%. To increase versatility, an ANN was trained on 354 independent, biological replicates distributed across ten different cell lines, resulting in a prediction accuracy of up to 98% depending on data composition. The present study provides a proof of principle for the application of T1 / T2 relaxometry as a non-destructive cell classification method. It does not require labeling of cells and can perform whole mount analysis of each sample. Since all measurements can be performed under sterile conditions, it can be used as an in-process control for cellular differentiation. This distinguishes it from other characterization techniques, as most are destructive or require some type of cell labeling. These advantages highlight the technique's potential for preclinical screening of patient-specific cell-based transplants and drugs.

Nuclear magnetic relaxation dispersion of murine tissue for development of T1 (R1 ) dispersion contrast imaging

This study quantified the spin-lattice relaxation rate (R₁ ) dispersion of murine tissues from 0.24 mT to 3 T. A combination of ex vivo and in vivo spin-lattice relaxation rate measurements were acquired for murine tissue. Selected brain, liver, kidney, muscle, and fat tissues were excised and R₁ dispersion profiles were acquired from 0.24 mT to 1.0 T at 37 °C, using a fast field-cycling MR (FFC-MR) relaxometer. In vivo R₁ dispersion profiles of mice were acquired from 1.26 T to 1.74 T at 37 °C, using FFC-MRI on a 1.5 T scanner outfitted with a field-cycling insert electromagnet to dynamically control B₀ prior to imaging. Images at five field strengths (1.26, 1.39, 1.5, 1.61, 1.74 T) were acquired using a field-cycling pulse sequence, where B₀ was modulated for varying relaxation durations prior to imaging. R₁ maps and R₁ dispersion (ΔR₁ /ΔB₀ ) were calculated at 1.5 T on a pixel-by-pixel basis. In addition, in vivo R₁ maps of mice were acquired at 3 T. At fields less than 1 T, a large R₁ magnetic field dependence was observed for tissues. ROI analysis of the tissues showed little relaxation dispersion for magnetic fields from 1.26 T to 3 T. Our tissue measurements show strong R₁ dispersion at field strengths less than 1 T and limited R₁ dispersion at field strengths greater than 1 T. These findings emphasize the inherent weak R₁ magnetic field dependence of healthy tissues at clinical field strengths. This characteristic of tissues can be exploited by a combination of FFC-MRI and T₁ contrast agents that exhibit strong relaxivity magnetic field dependences (inherent or by binding to a protein), thereby increasing the agents' specificity and sensitivity. This development can provide potential insights into protein-based biomarkers using FFC-MRI to assess early changes in tumour development, which are not easily measureable with conventional MRI.

Magnetization Transfer MR Imaging to Monitor Muscle Tissue Formation during Myogenic in Vivo Differentiation of Muscle Precursor Cells

Purpose To determine whether magnetization transfer (MT) magnetic resonance (MR) imaging may serve as a quantitative measure of the degree of fiber formation during differentiation of muscle precursor cells into engineered muscle tissue as a potential noninvasive monitoring tool in mice. Materials and Methods The study was approved by the local ethics committee (no. StV 01/2008) and the local Veterinary Office (license no. 99/2013). Human muscle progenitor cells (hMPCs) derived from rectus abdominis muscles were subcutaneously injected into CD-1 nude mice (CD-1 nude mice, Crl:CD1-Foxn1^nu; Charles River Laboratories, Wilmington, Mass) for development of muscle tissue. The mice underwent MR imaging examinations at 4.7 T at days 1, 3, 7, 14, 21, and 28 after cell transplantation by using a gradient-echo sequence with an MT prepulse and systematic variation of the off-resonance frequency (50-37 500 Hz) at an amplitude of 800°. Direct saturation was estimated from a Bloch equation simulation. The MT ratio (MTR) was correlated to immunohistochemistry findings, Western blot results, and results of myography. Data were analyzed by using one-way or two-way analysis of variance with the Sidak or Tukey multiple comparisons test. Results In the reference skeletal muscle, highest MT was found for 2500 Hz off-resonance frequency with an MTR ± standard deviation of 57.5% ± 3.5. The developing muscle tissue exhibited increasing MT values during the 28 days of myogenic in vivo differentiation and did not reach the values of native skeletal muscle. Mean values of MTR (2500 Hz) for hMPCs were 27.6% ± 6.3 (day 1), 24.7% ± 8.7 (day 3), 28.2% ± 5.7 (day 7), 35.9% ± 5.0 (day 14), 37.0% ± 7.9 (day 21), and 39.9% ± 8.1 (day 28). The results from MT MR imaging correlated qualitatively well with muscle tissue expression of specific skeletal markers, as well as muscle contractility. Conclusion MT MR imaging may be used to noninvasively monitor the process of myogenic in vivo differentiation of hMPCs as a biomarker of the quantity and quality of muscle fiber formation. ^© RSNA, 2016 Online supplemental material is available for this article.

Science to Practice: can MR relaxation and diffusion measurements be used to detect in vivo differentiation of transplanted muscle precursor cells?

Tissue magnetic resonance (MR) relaxometry and diffusion measurements were successfully used to detect the differentiation of implanted human muscle precursor cells (MPCs) into mature skeletal muscle. As no contrast agents are required, this label-free imaging approach may be immediately used in patients to evaluate the outcome of stem cell- and progenitor cell-based therapies aimed to restore defunct muscle.