Development and validation of a rapid, aldehyde dehydrogenase bright-based cord blood potency assay.

Banked, unrelated umbilical cord blood provides access to hematopoietic stem cell transplantation for patients lacking matched bone marrow donors, yet 10% to 15% of patients experience graft failure or delayed engraftment. This may be due, at least in part, to inadequate potency of the selected cord blood unit (CBU). CBU potency is typically assessed before cryopreservation, neglecting changes in potency occurring during freezing and thawing. Colony-forming units (CFUs) have been previously shown to predict CBU potency, defined as the ability to engraft in patients by day 42 posttransplant. However, the CFU assay is difficult to standardize and requires 2 weeks to perform. Consequently, we developed a rapid multiparameter flow cytometric CBU potency assay that enumerates cells expressing high levels of the enzyme aldehyde dehydrogenase (ALDH bright [ALDH(br)]), along with viable CD45(+) or CD34(+) cell content. These measurements are made on a segment that was attached to a cryopreserved CBU. We validated the assay with prespecified criteria testing accuracy, specificity, repeatability, intermediate precision, and linearity. We then prospectively examined the correlations among ALDH(br), CD34(+), and CFU content of 3908 segments over a 5-year period. ALDH(br) (r = 0.78; 95% confidence interval [CI], 0.76-0.79), but not CD34(+) (r = 0.25; 95% CI, 0.22-0.28), was strongly correlated with CFU content as well as ALDH(br) content of the CBU. These results suggest that the ALDH(br) segment assay (based on unit characteristics measured before release) is a reliable assessment of potency that allows rapid selection and release of CBUs from the cord blood bank to the transplant center for transplantation.

A Molecular-scale Programmable Stochastic Process Based On Resonance Energy Transfer Networks: Modeling And Applications

While molecular and cellular processes are often modeled as stochastic processes, such as Brownian motion, chemical reaction networks and gene regulatory networks, there are few attempts to program a molecular-scale process to physically implement stochastic processes. DNA has been used as a substrate for programming molecular interactions, but its applications are restricted to deterministic functions and unfavorable properties such as slow processing, thermal annealing, aqueous solvents and difficult readout limit them to proof-of-concept purposes. To date, whether there exists a molecular process that can be programmed to implement stochastic processes for practical applications remains unknown.

In this dissertation, a fully specified Resonance Energy Transfer (RET) network between chromophores is accurately fabricated via DNA self-assembly, and the exciton dynamics in the RET network physically implement a stochastic process, specifically a continuous-time Markov chain (CTMC), which has a direct mapping to the physical geometry of the chromophore network. Excited by a light source, a RET network generates random samples in the temporal domain in the form of fluorescence photons which can be detected by a photon detector. The intrinsic sampling distribution of a RET network is derived as a phase-type distribution configured by its CTMC model. The conclusion is that the exciton dynamics in a RET network implement a general and important class of stochastic processes that can be directly and accurately programmed and used for practical applications of photonics and optoelectronics. Different approaches to using RET networks exist with vast potential applications. As an entropy source that can directly generate samples from virtually arbitrary distributions, RET networks can benefit applications that rely on generating random samples such as 1) fluorescent taggants and 2) stochastic computing.

By using RET networks between chromophores to implement fluorescent taggants with temporally coded signatures, the taggant design is not constrained by resolvable dyes and has a significantly larger coding capacity than spectrally or lifetime coded fluorescent taggants. Meanwhile, the taggant detection process becomes highly efficient, and the Maximum Likelihood Estimation (MLE) based taggant identification guarantees high accuracy even with only a few hundred detected photons.

Meanwhile, RET-based sampling units (RSU) can be constructed to accelerate probabilistic algorithms for wide applications in machine learning and data analytics. Because probabilistic algorithms often rely on iteratively sampling from parameterized distributions, they can be inefficient in practice on the deterministic hardware traditional computers use, especially for high-dimensional and complex problems. As an efficient universal sampling unit, the proposed RSU can be integrated into a processor / GPU as specialized functional units or organized as a discrete accelerator to bring substantial speedups and power savings.