Age of red blood cells is not associated with in-hospital mortality in massively transfused patients.

BACKGROUND: Studies comparing mortality following massive transfusion (MT) with fresher versus longer-stored red blood cells (RBCs) have focused on trauma patients. The Australian and New Zealand Massive Transfusion Registry collects data on all adult MT cases (≥5 RBCs within 4 hours, any bleeding context, ≥18 years) at participating hospitals. METHODS: Years 2007 to 2018 data from 29 hospitals were analyzed to quantify the association between mortality and RBC storage time in adult MT cases. We ran three logistic regression models separately on each of seven bleeding contexts, with in-hospital mortality as the outcome and, in turn, (1) mean storage time (STmean) quartiles, (2) proportion of RBCs ≥30 days old (propOLD), and (3) scalar age of blood index as predictors. RESULTS: A total of 8,685 adult MT cases involving transfusion of 126,622 RBCs were analyzed with Australian and New Zealand data analyzed separately. Mean storage times for these cases were (by quartile in ascending order) as follows: Australia, 12.5 days (range, 3.1-15.5 days), 17.7 (15.5-19.9), 22.3 (19.9-24.9), and 29.8 (24.9-41.7); New Zealand, 11.3 days (3.6-13.7), 15.3 (13.7-16.8), 18.7 (16.8-20.7), and 24.5 (20.7-35.6). The odds ratios comparing in-hospital mortality for each quartile with that of the control first quartile (freshest blood), proportion of longer-stored (≥30 days) RBCs, and scalar age of blood index were not statistically significant across all bleeding contexts. CONCLUSION: We find no correlation between in-hospital mortality and storage time of transfused RBCs in a large cohort of adult MT patients representing all bleeding contexts. These results are consistent with those of recent large multicenter trials. LEVEL OF EVIDENCE: Epidemiologic, level III; Therapeutic, level IV.

Real-time tracking of cell cycle progression during CD8+ effector and memory T-cell differentiation.

The precise pathways of memory T-cell differentiation are incompletely understood. Here we exploit transgenic mice expressing fluorescent cell cycle indicators to longitudinally track the division dynamics of individual CD8(+) T cells. During influenza virus infection in vivo, naive T cells enter a CD62L(intermediate) state of fast proliferation, which continues for at least nine generations. At the peak of the anti-viral immune response, a subpopulation of these cells markedly reduces their cycling speed and acquires a CD62L(hi) central memory cell phenotype. Construction of T-cell family division trees in vitro reveals two patterns of proliferation dynamics. While cells initially divide rapidly with moderate stochastic variations of cycling times after each generation, a slow-cycling subpopulation displaying a CD62L(hi) memory phenotype appears after eight divisions. Phenotype and cell cycle duration are inherited by the progeny of slow cyclers. We propose that memory precursors cell-intrinsically modulate their proliferative activity to diversify differentiation pathways.

T cell signaling. Antigen affinity, costimulation, and cytokine inputs sum linearly to amplify T cell expansion.

T cell responses are initiated by antigen and promoted by a range of costimulatory signals. Understanding how T cells integrate alternative signal combinations and make decisions affecting immune response strength or tolerance poses a considerable theoretical challenge. Here, we report that T cell receptor (TCR) and costimulatory signals imprint an early, cell-intrinsic, division fate, whereby cells effectively count through generations before returning automatically to a quiescent state. This autonomous program can be extended by cytokines. Signals from the TCR, costimulatory receptors, and cytokines add together using a linear division calculus, allowing the strength of a T cell response to be predicted from the sum of the underlying signal components. These data resolve a long-standing costimulation paradox and provide a quantitative paradigm for therapeutically manipulating immune response strength.

Stretched cell cycle model for proliferating lymphocytes.

Stochastic variation in cell cycle time is a consistent feature of otherwise similar cells within a growing population. Classic studies concluded that the bulk of the variation occurs in the G1 phase, and many mathematical models assume a constant time for traversing the S/G2/M phases. By direct observation of transgenic fluorescent fusion proteins that report the onset of S phase, we establish that dividing B and T lymphocytes spend a near-fixed proportion of total division time in S/G2/M phases, and this proportion is correlated between sibling cells. This result is inconsistent with models that assume independent times for consecutive phases. Instead, we propose a stretching model for dividing lymphocytes where all parts of the cell cycle are proportional to total division time. Data fitting based on a stretched cell cycle model can significantly improve estimates of cell cycle parameters drawn from DNA labeling data used to monitor immune cell dynamics.

Activation-induced B cell fates are selected by intracellular stochastic competition.

In response to stimulation, B lymphocytes pursue a large number of distinct fates important for immune regulation. Whether each cell's fate is determined by external direction, internal stochastic processes, or directed asymmetric division is unknown. Measurement of times to isotype switch, to develop into a plasmablast, and to divide or to die for thousands of cells indicated that each fate is pursued autonomously and stochastically. As a consequence of competition between these processes, censorship of alternative outcomes predicts intricate correlations that are observed in the data. Stochastic competition can explain how the allocation of a proportion of B cells to each cell fate is achieved. The B cell may exemplify how other complex cell differentiation systems are controlled.

A minimum of two distinct heritable factors are required to explain correlation structures in proliferating lymphocytes.

During the adaptive immune response, lymphocyte populations undergo a characteristic three-phase process: expansion through a series of cell divisions; cessation of expansion; and, finally, most of the accumulated lymphocytes die by apoptosis. The data used, thus far, to inform understanding of these processes, both in vitro and in vivo, are taken from flow cytometry experiments. One significant drawback of flow cytometry is that individual cells cannot be tracked, so that it is not possible to investigate interdependencies in the fate of cells within a family tree. This deficit in experimental information has recently been overcome by Hawkins et al. (Hawkins et al. 2009 Proc. Natl Acad. Sci. USA 106, 13 457-13 462 (doi:10.1073/pnas.0905629106)), who reported on time-lapse microscopy experiments in which B-cells were stimulated through the TLR-9 receptor. Cells stimulated in this way do not aggregate, so that data regarding family trees can be recorded. In this article, we further investigate the Hawkins et al. data. Our conclusions are striking: in order to explain the familial correlation structure in division times, death times and propensity to divide, a minimum of two distinct heritable factors are necessary. As the data show that two distinct factors are necessary, we develop a stochastic model that has two heritable factors and demonstrate that it can reproduce the key features of the data. This model shows that two heritable factors are sufficient. These deductions have a clear impact upon biological understanding of the adaptive immune response. They also necessitate changes to the fundamental premises behind the tools developed by statisticians to draw deductions from flow cytometry data. Finally, they affect the mathematical modelling paradigms that are used to study these systems, as these are widely developed based on assumptions of cellular independence that are not accurate.

High precision quantum control of single donor spins in silicon.

The Stark shift of the hyperfine coupling constant is investigated for a P donor in Si far below the ionization regime in the presence of interfaces using tight-binding and band minima basis approaches and compared to the recent precision measurements. In contrast with previous effective mass-based results, the quadratic Stark coefficient obtained from both theories agrees closely with the experiments. It is also shown that there is a significant linear Stark effect for an impurity near the interface, whereas, far from the interface, the quadratic Stark effect dominates. This work represents the most sensitive and precise comparison between theory and experiment for single donor spin control. Such precise control of single donor spin states is required particularly in quantum computing applications of single donor electronics, which forms the driving motivation of this work.