Phenotypic cell-to-cell variability within clonal populations may be a manifestation of 'gene expression noise', or it may reflect stable phenotypic variants. Such 'non-genetic cell individuality' can arise from the slow fluctuations of protein levels in mammalian cells. These fluctuations produce persistent cell individuality, thereby rendering a clonal population heterogeneous. However, it remains unknown whether this heterogeneity may account for the stochasticity of cell fate decisions in stem cells. Here we show that in clonal populations of mouse haematopoietic progenitor cells, spontaneous 'outlier' cells with either extremely high or low expression levels of the stem cell marker Sca-1 (also known as Ly6a; ref. 9) reconstitute the parental distribution of Sca-1 but do so only after more than one week. This slow relaxation is described by a gaussian mixture model that incorporates noise-driven transitions between discrete subpopulations, suggesting hidden multi-stability within one cell type. Despite clonality, the Sca-1 outliers had distinct transcriptomes. Although their unique gene expression profiles eventually reverted to that of the median cells, revealing an attractor state, they lasted long enough to confer a greatly different proclivity for choosing either the erythroid or the myeloid lineage. Preference in lineage choice was associated with increased expression of lineage-specific transcription factors, such as a >200-fold increase in Gata1 (ref. 10) among the erythroid-prone cells, or a >15-fold increased PU.1 (Sfpi1) (ref. 11) expression among myeloid-prone cells. Thus, clonal heterogeneity of gene expression level is not due to independent noise in the expression of individual genes, but reflects metastable states of a slowly fluctuating transcriptome that is distinct in individual cells and may govern the reversible, stochastic priming of multipotent progenitor cells in cell fate decision.

Clonal populations of mammalian cells are inherently heterogeneous. They contain cells that display non-genetic variability resulting from gene expression noise and the fact that gene networks have multiple stable states. These stable, heritable variants within one cell type can exhibit different levels of responsiveness to environmental conditions. Hence, they could in principle serve as a temporary substrate for natural selection in the absence of mutations. We suggest that such ubiquitous but non-genetic variability can contribute to the somatic evolution of cancer cells, hence accelerating tumour progression independently of genetic mutations.

Summary Background Each year,1.1 million babies die from prematurity, andmany survivors are disabled. Worldwide, 15 million babies are preterm(<37 weeks’ gestation),withtwo decades of increasing ratesinalmost all countries with reliable data. Improved care of babies has reduced mortality in high-income countries, although effective interventions have yet to be scaled-up in most low-income countries. A 50% reduction goal for preterm-specific mortality by 2025 has been set in the “Born Too Soon” report. However, for preterm birth prevention,understanding of drivers and potential impact of preventive interventions is limited. We examine trends and estimate the potential reduction in preterm birthsforvery high human development index (VHHDI) countries if current evidence-based interventions were widely implemented. This analysis is to inform a “Born Too Soon” rate reduction target. Methods Countries were assessed for inclusion based on availability and quality ofpreterm prevalence data (2000-2010), and trend analyses with projections undertaken. We analysed drivers of rate increases in the USA, 1998-2004. For 39 VHHDI countrieswith >10,000 births, country-by-country analyses were performed based on target population, incremental coverage increase,and intervention efficacy. Cost savings were estimated based on reported costs for preterm care in the USAadjusted usingWorld Bank purchasing power parity. Findings From 2010, even if all VHHDI countries achieved annual preterm birth rate reductions of the best performers, (Sweden and Netherlands), 2000-2010 or 2005-2010(Lithuania, Estonia)), rates would experience a relative reduction of<5% by 2015 on average across the 39 countries.Our analysis of preterm birth rise 1998-2004 in USA suggests half the change is unexplained, but important drivers includeinductions/cesareandelivery and ART.For all 39 VHHDI countries, five interventionsmodeling at high coveragepredicted 5%preterm birth rate relative reduction from 9.59 to 9.07% of live births:smoking cessation (0.01 rate reduction), decreasing multiple embryo transfers during assisted reproductive technologies (0.06), cervical cerclage (0.15), progesterone supplementation (0.01), and reduction of non-medically indicated labour induction or caesarean delivery (0.29).These translate to 58,000 preterm births averted and total annual economic cost savings of ~US$ 3 billion. Interpretation Even with optimal coverage of current interventions, many being complex to implement, the estimated potential reduction in preterm birth is tiny. Hence we recommenda conservative target of 5% preterm birth rate relative reductionby 2015. Our findings highlight the urgent need for discovery research into underlying mechanisms of preterm birth, and developmentof innovative interventions. Furthermore, the highest preterm birth rates occur in low-income settings where the causes of prematurity may differand have simpler solutions, such as birth spacing and treatment of infections in pregnancy. Urgent focus on these settings also is critical t...

Cell fate choice and commitment of multipotent progenitor cells to a differentiated lineage requires broad changes of their gene expression profile. But how progenitor cells overcome the stability of their gene expression configuration (attractor) to exit the attractor in one direction remains elusive. Here we show that commitment of blood progenitor cells to the erythroid or myeloid lineage is preceded by the destabilization of their high-dimensional attractor state, such that differentiating cells undergo a critical state transition. Single-cell resolution analysis of gene expression in populations of differentiating cells affords a new quantitative index for predicting critical transitions in a high-dimensional state space based on decrease of correlation between cells and concomitant increase of correlation between genes as cells approach a tipping point. The detection of “rebellious cells” that enter the fate opposite to the one intended corroborates the model of preceding destabilization of a progenitor attractor. Thus, early warning signals associated with critical transitions can be detected in statistical ensembles of high-dimensional systems, offering a formal theory-based approach for analyzing single-cell molecular profiles that goes beyond current computational pattern recognition, does not require knowledge of specific pathways, and could be used to predict impending major shifts in development and disease.

To study the effects of malaria-control interventions on parasite population genomics, we examined a set of 1,007 samples of the malaria parasite Plasmodium falciparum collected in Thiès, Senegal between 2006 and 2013. The parasite samples were genotyped using a molecular barcode of 24 SNPs. About 35% of the samples grouped into subsets with identical barcodes, varying in size by year and sometimes persisting across years. The barcodes also formed networks of related groups. Analysis of 164 completely sequenced parasites revealed extensive sharing of genomic regions. In at least two cases we found first-generation recombinant offspring of parents whose genomes are similar or identical to genomes also present in the sample. An epidemiological model that tracks parasite genotypes can reproduce the observed pattern of barcode subsets. Quantification of likelihoods in the model strongly suggests a reduction of transmission from 2006-2010 with a significant rebound in 2012-2013. The reduced transmission and rebound were confirmed directly by incidence data from Thiès. These findings imply that intensive intervention to control malaria results in rapid and dramatic changes in parasite population genomics. The results also suggest that genomics combined with epidemiological modeling may afford prompt, continuous, and cost-effective tracking of progress toward malaria elimination. malaria | genomics | epidemiology I ntensive intervention to reduce the burden of malaria has proven successful in a number of countries in Africa (1). In certain regions of Senegal, implementation of a redesigned National Malaria Control Program (NMCP) in 2006 that included rapid diagnostic tests, artemisinin combination therapies, enhanced insecticide-treated bed nets, and indoor residual spraying resulted in a more than 95% decrease in the number of confirmed cases by 2009 (2). We had been collecting parasite samples in one of these regions annually since 2006. These samples afford a unique opportunity to determine the extent to which intensive intervention is manifested in genetic changes in the parasite population. Genetic changes would be expected to include bottlenecks in the parasite population size, increased random genetic drift, reduced genetic variation, greater self-fertilization during transmission, and increased allele sharing and identity by descent.A key question for tracking malaria elimination is whether such genomic changes would be large enough to be detected in a cost-effective manner in samples of reasonable size. If changes in parasite population genomics took place rapidly enough after intervention, and if they were large enough to be detected, then parasite genomics could play an important role in malaria elimination. Given sufficiently rapid onset and detectability of changes in parasite genomics, an epidemiological model that incorporates parasite genotypes could in principle be used to estimate the epidemiological parameters that most closely match the genomic observations. Estimates of epidemiological parameters s...

The ongoing novel coronavirus disease (COVID-19) pandemic has already infected millions worldwide and, with no vaccine available, interventions to mitigate transmission are urgently needed. While there is broad agreement that travel restrictions and social distancing are beneficial in limiting spread, recommendations around face mask use are inconsistent. Here, we use mathematical modeling to examine the epidemiological impact of face masks, considering resource limitations and a range of supply and demand dynamics. Even with a limited protective effect, face masks can reduce total infections and deaths, and can delay the peak time of the epidemic. However, random distribution of masks is generally suboptimal; prioritized coverage of the elderly improves outcomes, while retaining resources for detected cases provides further mitigation under a range of scenarios. Face mask use, particularly for a pathogen with relatively common asymptomatic carriage, is an effective intervention strategy, while optimized distribution is important when resources are limited.

We describe a new method (STAMP) for characterization of pathogen population dynamics during infection. STAMP analyzes the frequency changes of genetically “barcoded” organisms to quantify population bottlenecks and infer the founding population size. Analyses of intra-intestinal Vibrio cholerae revealed infection-stage and region-specific host barriers to infection, and unexpectedly showed V. cholerae migration counter to intestinal flow. STAMP provides a robust, widely applicable analytical framework for high confidence characterization of in vivo microbial dissemination.

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