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Abstract Here, we introduce intracellular genomic amplification (INgen), a method that harnesses the cell membrane as a natural reaction chamber for DNA amplification, enabling downstream sequencing and cell sorting. Unlike traditional single-cell techniques, INgen utilizes a strand-displacing, isothermal polymerase to amplify DNAwithinfixed, permeabilized cells while maintaining the cell’s structural integrity. This approach overcomes challenges associated with both typical single-cell DNA sequencing and hindrances encountered when previously attempting to sequence genetic material from fixed microbial cells, allowing amplification of genomic regions up to 100 kb and sequencing of whole genomes from diverse cell types, includingS. cerevisiae, B. subtilis, andE. coli. Additionally, INgen can be adapted for targeted DNA enrichment using biotinylated primers and for fluorescence-based cell sorting. 

Abstract Although multigenic traits are often assumed to be under some form of stabilizing selection, numerous aspects of the population-genetic environment can cause mean phenotypes to deviate from presumed optima, often in ways that effectively transform the fitness landscape to one of directional selection. Focusing on an asexual population, we consider the ways in which such deviations scale with the relative power of selection and genetic drift, the number of linked genomic sites, the magnitude of mutation bias, and the location of optima with respect to possible genotypic space. Even in the absence of mutation bias, mutation will influence evolved mean phenotypes unless the optimum happens to coincide exactly with the mean expected under neutrality. In the case of directional mutation bias and large numbers of selected sites, effective population sizes (Ne) can be dramatically reduced by selective interference effects, leading to further mismatches between phenotypic means and optima. Situations in which the optimum is outside or near the limits of possible genotypic space (e.g. a half-Gaussian fitness function) can lead to particularly pronounced gradients of phenotypic means with respect to Ne, but such gradients can also occur when optima are well within the bounds of attainable phenotypes. These results help clarify the degree to which mean phenotypes can vary among populations experiencing identical mutation and selection pressures but differing in Ne, and yield insight into how the expected scaling relationships depend on the underlying features of the genetic system. 

Abstract Over recent decades, studies of cell-to-cell heterogeneity have shown that meaningful variation in stress responses is an evolved trait, exemplified by bet hedging. Here, we expand upon this work by using a high-throughput single-cell transcriptomics approach inS. cerevisiaeto study cell-to-cell variation in stress responses. We measured individual transcriptional profiles of over ten thousand cells subjected to two types of stress: protein misfolding (an intrinsic stress) and nutrient deprivation (an extrinsic stress). Doing so, we saw substantial heterogeneity in responses to stress across and within different conditions. A previously defined canonical stress response is elevated in all stressful conditions, making it useful as a barometer of stress. However, it does not include the majority of the genes that are the most upregulated in response to any of the stresses we study, suggesting stress responses vary across environments. On the other hand, the genes that were downregulated with stress show a much stronger overlap across conditions. Even more striking is the cell-to-cell heterogeneity we observed in stress responses where some stressed cells exhibit neither a canonical stress response nor a transcriptional response that matches most other cells in the same vessel. Further investigation of these cell-to-cell differences revealed a misconception of population-averaged data: While transposable elements (TEs) and chaperones both appear positively correlated with stress at the population level, single-cell analysis revealed they are actually anticorrelated. In other words, some stressed cells upregulate TEs, while others upregulate chaperones. One possible mechanism for this involves chaperones, such as Hsp90, which are thought to help to silence TEs but are unable to do so effectively when these chaperones become overwhelmed during stress. In sum, we have used a powerful single-cell transcriptomics approach to quantify stress-response heterogeneity and to shed light on the mechanisms underlying cell-to-cell variation in stress responses, both of which are crucial for understanding cell evolution and disease. 

ABSTRACT The body plan of the human parasite Toxoplasma gondii has a well-defined polarity. The minus ends of the 22 cortical microtubules are anchored to the apical polar ring, which is a putative microtubule-organizing center. The basal complex caps and constricts the parasite posterior end and is crucial for cytokinesis. How this apical–basal polarity is initiated is unknown. Here, we have examined the development of the apical polar ring and the basal complex using expansion microscopy. We found that substructures in the apical polar ring have different sensitivities to perturbations. In addition, apical–basal differentiation is already established upon nucleation of the cortical microtubule array: arc forms of the apical polar ring and basal complex associate with opposite ends of the microtubules. As the nascent daughter framework grows towards the centrioles, the apical and basal arcs co-develop ahead of the microtubule array. Finally, two apical polar ring components, APR2 and KinesinA, act synergistically. The removal of individual proteins has a modest impact on the lytic cycle. However, the loss of both proteins results in abnormalities in the microtubule array and in highly reduced plaquing and invasion efficiency. 

Abstract The mitochondria contain their own genome derived from an alphaproteobacterial endosymbiont. From thousands of protein-coding genes originally encoded by their ancestor, only between 1 and about 70 are encoded on extant mitochondrial genomes (mitogenomes). Thanks to a dramatically increasing number of sequenced and annotated mitogenomes a coherent picture of why some genes were lost, or relocated to the nucleus, is emerging. In this review, we describe the characteristics of mitochondria-to-nucleus gene transfer and the resulting varied content of mitogenomes across eukaryotes. We introduce a ‘burst-upon-drift’ model to best explain nuclear-mitochondrial population genetics with flares of transfer due to genetic drift. 

ABSTRACT Across species and environments, the ribosome content of cell populations correlates with population growth rate. The robustness and universality of this correlation have led to its classification as a “growth law.” This law has fueled theories about how evolution selects for microbial organisms that maximize their growth rate based on nutrient availability, and it has informed models about how individual cells regulate their growth rates and ribosomal content. However, due to methodological limitations, this growth law has rarely been studied at the level of individual cells. Whilepopulationsof fast-growing cells tend to have more ribosomes thanpopulationsof slow-growing cells, it is unclear whether individual cells tightly regulate their ribosome content to match their environment. Here, we employ recent groundbreaking single-cell RNA sequencing techniques to study this growth law at the single-cell level in two different microbes,S. cerevisiae(a single-celled yeast and eukaryote) andB. subtilis(a bacterium and prokaryote). In both species, we observe significant variation in the ribosomal content of single cells that is not predictive of growth rate. Fast-growing populations include cells exhibiting transcriptional signatures of slow growth and stress, as do cells with the highest ribosome content we survey. Broadening our focus to non-ribosomal transcripts reveals subpopulations of cells in unique transcriptional states suggestive that they have evolved to do things other than maximize their rate of growth. Overall, these results indicate that single-cell ribosome levels are not finely tuned to match population growth rates or nutrient availability and cannot be predicted by a Gaussian process model that assumes measurements are sampled from a normal distribution centered on the population average. This work encourages the expansion of growth law and other models that predict how growth rates are regulated or how they evolve to consider single-cell heterogeneity. To this end, we provide extensive data and analysis of ribosomal and transcriptomic variation across thousands of single cells from multiple conditions, replicates, and species. 

Abstract The article discusses the Crow-Kimura model in the context of random transitions between different fitness landscapes. The duration of epochs, during which the fitness landscape is constant over time, is modeled by an exponential distribution. To obtain an exact solution, a system of functional equations is required. However, to approximate the model, we consider the cases of slow or fast transitions and calculate the first-order corrections using either the transition rate or its inverse. Specifically, we focus on the case of slow transitions and find that the average fitness is equal to the average fitness for evolution on static fitness landscapes, but with the addition of a load term. We also investigate the model for a small number of genes and identify the exact transition points to the transient phase. 

Abstract Yeasts are naturally diverse, genetically tractable, and easy to grow such that researchers can investigate any number of genotypes, environments, or interactions thereof. However, studies of yeast transcriptomes have been limited by the processing capabilities of traditional RNA sequencing techniques. Here we optimize a powerful, high‐throughput single‐cell RNA sequencing (scRNAseq) platform, SPLiT‐seq (Split Pool Ligation‐based Transcriptome sequencing), for yeasts and apply it to 43,388 cells of multiple species and ploidies. This platform utilizes a combinatorial barcoding strategy to enable massively parallel RNA sequencing of hundreds of yeast genotypes or growth conditions at once. This method can be applied to most species or strains of yeast for a fraction of the cost of traditional scRNAseq approaches. Thus, our technology permits researchers to leverage “the awesome power of yeast” by allowing us to survey the transcriptome of hundreds of strains and environments in a short period of time and with no specialized equipment. The key to this method is that sequential barcodes are probabilistically appended to cDNA copies of RNA while the molecules remain trapped inside of each cell. Thus, the transcriptome of each cell is labeled with a unique combination of barcodes. Since SPLiT‐seq uses the cell membrane as a container for this reaction, many cells can be processed together without the need to physically isolate them from one another in separate wells or droplets. Further, the first barcode in the sequence can be chosen intentionally to identify samples from different environments or genetic backgrounds, enabling multiplexing of hundreds of unique perturbations in a single experiment. In addition to greater multiplexing capabilities, our method also facilitates a deeper investigation of biological heterogeneity, given its single‐cell nature. For example, in the data presented here, we detect transcriptionally distinct cell states related to cell cycle, ploidy, metabolic strategies, and so forth, all within clonal yeast populations grown in the same environment. Hence, our technology has two obvious and impactful applications for yeast research: the first is the general study of transcriptional phenotypes across many strains and environments, and the second is investigating cell‐to‐cell heterogeneity across the entire transcriptome. 

Abstract Mitochondrial cristae architecture is crucial for optimal respiratory function of the organelle. Cristae shape is maintained in part by the mitochondrial inner membrane-localized MICOS complex. While MICOS is required for normal cristae morphology, the precise mechanistic role of each of the seven human MICOS subunits, and how the complex coordinates with other cristae shaping factors, has not been fully determined. Here, we examine the MICOS complex inSchizosaccharomyces pombe, a minimal model whose genome only encodes for four core subunits. Using an unbiased proteomics approach, we identify a poorly characterized inner mitochondrial membrane protein that interacts with MICOS and is required to maintain cristae morphology, which we name Mmc1. We demonstrate that Mmc1 works in concert with MICOS complexes to promote normal mitochondrial morphology and respiratory function. Bioinformatic analyses reveal that Mmc1 is a distant relative of the Dynamin-Related Protein (DRP) family of GTPases, which are well established to shape and remodel membranes. We find that, like DRPs, Mmc1 self-associates and forms high molecular weight assemblies. Interestingly, however, Mmc1 is a pseudoenzyme that lacks key residues required for GTP binding and hydrolysis, suggesting it does not dynamically remodel membranes. These data are consistent with a model in which Mmc1 stabilizes cristae architecture by acting as a scaffold to support cristae ultrastructure on the matrix side of the inner membrane. Our study reveals a new class of proteins that evolved early in fungal phylogeny and is required for the maintenance of cristae architecture. This highlights the possibility that functionally analogous proteins work with MICOS to establish cristae morphology in metazoans. 

Abstract The phrase “survival of the fittest” has become an iconic descriptor of how natural selection works. And yet, precisely measuring fitness, even for single-celled microbial populations growing in controlled laboratory conditions, remains a challenge. While numerous methods exist to perform these measurements, including recently developed methods utilizing DNA barcodes, all methods are limited in their precision to differentiate strains with small fitness differences. In this study, we rule out some major sources of imprecision, but still find that fitness measurements vary substantially from replicate to replicate. Our data suggest that very subtle and difficult to avoid environmental differences between replicates create systematic variation across fitness measurements. We conclude by discussing how fitness measurements should be interpreted given their extreme environment dependence. This work was inspired by the scientific community who followed us and gave us tips as we live tweeted a high-replicate fitness measurement experiment at #1BigBatch.