3 resultados para Colony-Forming Units Assay
em Duke University
Development and validation of a rapid, aldehyde dehydrogenase bright-based cord blood potency assay.
Resumo:
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.
Resumo:
This thesis demonstrates a new way to achieve sparse biological sample detection, which uses magnetic bead manipulation on a digital microfluidic device. Sparse sample detection was made possible through two steps: sparse sample capture and fluorescent signal detection. For the first step, the immunological reaction between antibody and antigen enables the binding between target cells and antibody-‐‑ coated magnetic beads, hence achieving sample capture. For the second step, fluorescent detection is achieved via fluorescent signal measurement and magnetic bead manipulation. In those two steps, a total of three functions need to work together, namely magnetic beads manipulation, fluorescent signal measurement and immunological binding. The first function is magnetic bead manipulation, and it uses the structure of current-‐‑carrying wires embedded in the actuation electrode of an electrowetting-‐‑on-‐‑dielectric (EWD) device. The current wire structure serves as a microelectromagnet, which is capable of segregating and separating magnetic beads. The device can achieve high segregation efficiency when the wire spacing is 50µμm, and it is also capable of separating two kinds of magnetic beads within a 65µμm distance. The device ensures that the magnetic bead manipulation and the EWD function can be operated simultaneously without introducing additional steps in the fabrication process. Half circle shaped current wires were designed in later devices to concentrate magnetic beads in order to increase the SNR of sample detection. The second function is immunological binding. Immunological reaction kits were selected in order to ensure the compatibility of target cells, magnetic bead function and EWD function. The magnetic bead choice ensures the binding efficiency and survivability of target cells. The magnetic bead selection and binding mechanism used in this work can be applied to a wide variety of samples with a simple switch of the type of antibody. The last function is fluorescent measurement. Fluorescent measurement of sparse samples is made possible of using fluorescent stains and a method to increase SNR. The improved SNR is achieved by target cell concentration and reduced sensing area. Theoretical limitations of the entire sparse sample detection system is as low as 1 Colony Forming Unit/mL (CFU/mL).
Resumo:
Assays that assess cellular mediated immune responses performed under Good Clinical Laboratory Practice (GCLP) guidelines are required to provide specific and reproducible results. Defined validation procedures are required to establish the Standard Operating Procedure (SOP), include pass and fail criteria, as well as implement positivity criteria. However, little to no guidance is provided on how to perform longitudinal assessment of the key reagents utilized in the assay. Through the External Quality Assurance Program Oversight Laboratory (EQAPOL), an Interferon-gamma (IFN-γ) Enzyme-linked immunosorbent spot (ELISpot) assay proficiency testing program is administered. A limit of acceptable within site variability was estimated after six rounds of proficiency testing (PT). Previously, a PT send-out specific within site variability limit was calculated based on the dispersion (variance/mean) of the nine replicate wells of data. Now an overall 'dispersion limit' for the ELISpot PT program within site variability has been calculated as a dispersion of 3.3. The utility of this metric was assessed using a control sample to calculate the within (precision) and between (accuracy) experiment variability to determine if the dispersion limit could be applied to bridging studies (studies that assess lot-to-lot variations of key reagents) for comparing the accuracy of results with new lots to results with old lots. Finally, simulations were conducted to explore how this dispersion limit could provide guidance in the number of replicate wells needed for within and between experiment variability and the appropriate donor reactivity (number of antigen-specific cells) to be used for the evaluation of new reagents. Our bridging study simulations indicate using a minimum of six replicate wells of a control donor sample with reactivity of at least 150 spot forming cells per well is optimal. To determine significant lot-to-lot variations use the 3.3 dispersion limit for between and within experiment variability.