Highlights d Numbers of constitutive and inducible mRNAs scale with cell size d Coordination of RNAPII initiation rates with cell size underpins scaling d Amounts of DNA-bound RNAPII increase with cell size and are limiting d Transcription of constitutive and periodic mRNAs is a nonbursty Poisson process

Understanding the demographic history of populations and species is a central issue in evolutionary biology and molecular ecology. In this work, we develop a maximum-likelihood method for the inference of past changes in population size from microsatellite allelic data. Our method is based on importance sampling of gene genealogies, extended for new mutation models, notably the generalized stepwise mutation model (GSM). Using simulations, we test its performance to detect and characterize past reductions in population size. First, we test the estimation precision and confidence intervals coverage properties under ideal conditions, then we compare the accuracy of the estimation with another available method (MSVAR) and we finally test its robustness to misspecification of the mutational model and population structure. We show that our method is very competitive compared with alternative ones. Moreover, our implementation of a GSM allows more accurate analysis of microsatellite data, as we show that the violations of a single step mutation assumption induce very high bias toward false contraction detection rates. However, our simulation tests also showed some limits, which most importantly are large computation times for strong disequilibrium scenarios and a strong influence of some form of unaccounted population structure. This inference method is available in the latest implementation of the MIGRAINE software package.

Motivation Normalization of single-cell RNA-sequencing (scRNA-seq) data is a prerequisite to their interpretation. The marked technical variability, high amounts of missing observations and batch effect typical of scRNA-seq datasets make this task particularly challenging. There is a need for an efficient and unified approach for normalization, imputation and batch effect correction. Results Here, we introduce bayNorm, a novel Bayesian approach for scaling and inference of scRNA-seq counts. The method’s likelihood function follows a binomial model of mRNA capture, while priors are estimated from expression values across cells using an empirical Bayes approach. We first validate our assumptions by showing this model can reproduce different statistics observed in real scRNA-seq data. We demonstrate using publicly available scRNA-seq datasets and simulated expression data that bayNorm allows robust imputation of missing values generating realistic transcript distributions that match single molecule fluorescence in situ hybridization measurements. Moreover, by using priors informed by dataset structures, bayNorm improves accuracy and sensitivity of differential expression analysis and reduces batch effect compared with other existing methods. Altogether, bayNorm provides an efficient, integrated solution for global scaling normalization, imputation and true count recovery of gene expression measurements from scRNA-seq data. Availability and implementation The R package ‘bayNorm’ is publishd on bioconductor at https://bioconductor.org/packages/release/bioc/html/bayNorm.html. The code for analyzing data in this article is available at https://github.com/WT215/bayNorm_papercode. Supplementary information Supplementary data are available at Bioinformatics online.

20 21Phenotypic cell-to-cell variability is a fundamental determinant of microbial fitness that 22 contributes to stress adaptation and drug resistance. Gene expression heterogeneity underpins 23 this variability, but is challenging to study genome-wide. Here we examine the transcriptomes 24 of >2000 single fission yeast cells in various environmental conditions by combining imaging, 25 single-cell RNA sequencing (scRNA-seq), and Bayesian true count recovery. We identify sets of 26 highly variable genes during rapid proliferation in constant conditions. By integrating scRNA-27 seq and cell-size data, we provide unique insights into genes regulated during cell growth and 28 division, including genes whose expression does not scale with cell size. We further analyse 29 the heterogeneity of gene expression during adaptive and acute responses to changing 30 environments. Entry into stationary phase is preceded by a gradual, synchronised adaptation 31 in gene regulation, followed by highly variable gene expression when growth decreases. 32Conversely, a sudden and acute heat-shock leads to a stronger, coordinated response and 33 adaptation across cells. This analysis reveals that the magnitude of global gene expression 34 heterogeneity is regulated in response to different physiological conditions within populations 35 of a unicellular eukaryote. 37Gene expression is tightly regulated at multiple levels, including chromatin structure, transcription, 38 mRNA degradation and translation. This multi-layered process underpins robust and timely expression 39 of single proteins as well as coordinated regulation of entire genetic programmes including dozens of 40 genes. Yet, even in constant environments, expression of specific genes varies between genetically 41 identical cells, leading to cell-to-cell heterogeneity in mRNA numbers and concentrations 1-3 . Cell-to-cell 42 variability in gene expression results from different phenomena. First of all, the random timing of 43 biological reactions makes transcription intrinsically stochastic. This form of variability, also called 44 intrinsic noise, is gene specific and depends on promoter sequence and chromatin states 4,5 . 45 Heterogeneity in quantitative traits such as cell size, growth rate, or concentration of transcription factors 46 also shapes gene expression variability in complex, non-trivial ways. This form of variability is not 47 entirely stochastic and depends on other single-cell attributes that affect biomolecule numbers 6,7 . 48 Furthermore, cells can enter dynamic cellular states characterised by specific gene expression 49 programmes. Examples are progression through the cell cycle or the adoption of distinct metabolic 50 states 8 . Different states co-exist in cell populations or tissues leading to dynamic, yet deterministic, cell-51 to-cell variability in gene expression. Finally, cells in metazoan tissues belong to different cell types that 52 are important for organ architecture and function. Although reversible and plastic, this form of 53 individuality i...

Isogenic cells sensing identical external signals can take markedly different decisions. Such decisions often correlate with pre-existing cell-to-cell differences in protein levels. When not neglected in signal transduction models, these differences are accounted for in a static manner, by assuming randomly distributed initial protein levels. However, this approach ignores the a priori non-trivial interplay between signal transduction and the source of this cell-to-cell variability: temporal fluctuations of protein levels in individual cells, driven by noisy synthesis and degradation. Thus, modeling protein fluctuations, rather than their consequences on the initial population heterogeneity, would set the quantitative analysis of signal transduction on firmer grounds. Adopting this dynamical view on cell-to-cell differences amounts to recast extrinsic variability into intrinsic noise. Here, we propose a generic approach to merge, in a systematic and principled manner, signal transduction models with stochastic protein turnover models. When applied to an established kinetic model of TRAIL-induced apoptosis, our approach markedly increased model prediction capabilities. One obtains a mechanistic explanation of yet-unexplained observations on fractional killing and non-trivial robust predictions of the temporal evolution of cell resistance to TRAIL in HeLa cells. Our results provide an alternative explanation to survival via induction of survival pathways since no TRAIL-induced regulations are needed and suggest that short-lived anti-apoptotic protein Mcl1 exhibit large and rare fluctuations. More generally, our results highlight the importance of accounting for stochastic protein turnover to quantitatively understand signal transduction over extended durations, and imply that fluctuations of short-lived proteins deserve particular attention.

Universal observations in Biology are sometimes described as "laws". In E. coli, experimental studies performed over the past six decades have revealed major growth laws relating ribosomal mass fraction and cell size to the growth rate. Because they formalize complex emerging principles in biology, growth laws have been instrumental in shaping our understanding of bacterial physiology. Here, we discovered a novel size law that connects cell size to the inverse of the metabolic proteome mass fraction and the active fraction of ribosomes. We used a simple whole-cell coarse-grained model of cell physiology that combines the proteome allocation theory and the structural model of cell division. This integrated model captures all available experimental data connecting the cell proteome composition, ribosome activity, division size and growth rate in response to nutrient quality, antibiotic treatment and increased protein burden. Finally, a stochastic extension of the model explains non-trivial correlations observed in single cell experiments including the adder principle. This work provides a simple and robust theoretical framework for studying the fundamental principles of cell size determination in unicellular organisms.

New small-scale, low-cost bioreactor designs provide researchers with exquisite control of environmental parameters of microbial cultures over long durations, allowing them to perform sophisticated, high-quality experiments that are particularly useful in systems biology, synthetic biology and bioengineering. However, existing setups are limited in their automated measurement capabilities, primarily because sensitive and specific measurements require bulky, expensive, stand-alone instruments (for example, most single-cell resolved measurements require a cytometer or a microscope). We present here ReacSight, a generic and flexible strategy to enhance multi-bioreactor platforms for automated measurements and reactive experiment control. We use ReacSight to assemble a platform for single-cell resolved characterization and reactive optogenetic control of parallel yeast continuous cultures. We demonstrate its usefulness by achieving parallel real-time control of gene expression with light in different bioreactors and by exploring the relationship between fitness, nutrient scarcity and cellular stress density using highly-controlled and informative competition assays.

Cellular resources are limited and their relative allocation to gene expression programmes determines physiological states and global properties such as the growth rate. Here, we determined the importance of the growth rate in explaining relative changes in protein and mRNA levels in the simple eukaryote Schizosaccharomyces pombe grown on non-limiting nitrogen sources. Although expression of half of fission yeast genes was significantly correlated with the growth rate, this came alongside wide-spread nutrient-specific regulation. Proteome and transcriptome often showed coordinated regulation but with notable exceptions, such as metabolic enzymes. Genes positively correlated with growth rate participated in every level of protein production apart from RNA polymerase II–dependent transcription. Negatively correlated genes belonged mainly to the environmental stress response programme. Critically, metabolic enzymes, which represent ∼55–70% of the proteome by mass, showed mostly condition-specific regulation. In summary, we provide a rich account of resource allocation to gene expression in a simple eukaryote, advancing our basic understanding of the interplay between growth-rate-dependent and nutrient-specific gene expression.