Production of erythrocytes from directly isolated or Delta1 Notch ligand expanded CD34+ hematopoietic progenitor cells: process characterization, monitoring and implications for manufacture.

BACKGROUND AIMS: Economic ex vivo manufacture of erythrocytes at 10(12) cell doses requires an efficiently controlled bio-process capable of extensive proliferation and high terminal density. High-resolution characterization of the process would identify production strategies for increased efficiency, monitoring and control. METHODS: CD34(+) cord blood cells or equivalent cells that had been pre-expanded for 7 days with Delta1 Notch ligand were placed in erythroid expansion and differentiation conditions in a micro-scale ambr suspension bioreactor. Multiple culture parameters were varied, and phenotype markers and metabolites measured to identify conserved trends and robust monitoring markers. RESULTS: The cells exhibited a bi-modal erythroid differentiation pattern with an erythroid marker peak after 2 weeks and 3 weeks of culture; differentiation was comparatively weighted toward the second peak in Delta1 pre-expanded cells. Both differentiation events were strengthened by omission of stem cell factor and dexamethasone. The cumulative cell proliferation and death, or directly measured CD45 expression, enabled monitoring of proliferative rate of the cells. The metabolic activities of the cultures (glucose, glutamine and ammonia consumption or production) were highly variable but exhibited systematic change synchronized with the change in differentiation state. CONCLUSIONS: Erythroid differentiation chronology is partly determined by the heterogeneous CD34(+) progenitor compartment with implications for input control; Delta1 ligand-mediated progenitor culture can alter differentiation profile with control benefits for engineering production strategy. Differentiation correlated changes in cytokine response, markers and metabolic state will enable scientifically designed monitoring and timing of manufacturing process steps.

Impact of scale on the effectiveness of disease control strategies for epidemics with cryptic infection in a dynamical landscape: an example for a crop disease.

We use a spatially explicit, stochastic model to analyse the effectiveness of different scales of local control strategies in containing the long-term, multi-seasonal spread of a crop disease through a dynamically changing population of susceptible crops in which there is cryptic infection. The model distinguishes between susceptible, infested and symptomatic fields. It is motivated by rhizomania disease on sugar beet in the UK as an exemplar of a spatially structured and partially asymptomatic epidemic. Our results show the importance of matching the scales of local control strategies to prevent intensification and regional spread of disease with the inherent temporal and spatial scales of an epidemic. A simple field-scale containment strategy, whereby the susceptible crop is no longer grown on fields showing symptoms, fails for this system with cryptic infection because the locally applied control lags behind the epidemic. A farm-scale strategy, whereby growers respond to the disease status of neighbouring farms by transferring their quota for sugar beet to farmers in regions of reduced risk, succeeds. We conclude that a soil-borne pathogen such as rhizomania could be managed by movement of susceptible crops in the landscape using a strategy that matches the temporal and spatial scales of the epidemic and which take account of risk aversion among growers. We show some parallels and differences in effectiveness between a 'culling' strategy involving crop removal around emerging foci and the local deployment of partially resistant varieties that reduce amplification and transmission of inoculum. Some relationships between the control of plant and livestock diseases are briefly discussed.

A model for the invasion and spread of rhizomania in the United kingdom: implications for disease control strategies.

ABSTRACT Rhizomania disease of sugar beet represents a major economic threat to the sugar industry in the United Kingdom. Here we use the UK rhizomania epidemic as an exemplar of a range of highly infectious spatially heterogeneous diseases. Using a spatially explicit stochastic model, we investigated the efficacy of a spectrum of possible control strategies, both locally reactive and national in character. These include the use of novel cultivars of beet with different responses to infection, changes in cultivation practice, and reactive containment policies at the farm scale. We show that strictly local responses, including a containment policy similar to that initially implemented in the United Kingdom in response to the disease, are largely ineffective in slowing the spread because they fail to match the natural scale of the epidemic. Larger spatial-scale processes are considerably more successful. We conclude that epidemics have intrinsic temporal and spatial scales that must be matched by any control strategy if it is to be both effective and efficient. We have generated probability distributions for the proportion of farms symptomatic. Over the course of the epidemic, such distributions develop a bimodality that we hypothesize to correspond to the matching of spatial heterogeneity in the susceptible population to the intrinsic scales of the epidemic.