Biology subjects

Brettner, L. M.

Publications and source records attributed to Brettner, L. M..

4 recordsLinked to original sources

bioRxiv2024

Single-cell heterogeneity and the growth laws

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. While populations of fast-growing cells tend to have more ribosomes than populations of 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) and B. 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.

cell biology↗

bioRxiv2022

An ultra high-throughput, massively multiplexable, single-cell RNA-seq platform in yeasts

Yeasts are naturally diverse, genetically tractable, and easy to grow in a myriad of experimental conditions such that researchers have the ability to investigate any number of genotypes, strains, environments, or the interaction thereof. However, studies of variation in the yeast transcriptome have been limited by the processing capabilities of available RNA sequencing techniques. Here we optimize a powerful, high-throughput single-cell RNA sequencing (scRNAseq) platform for yeasts. 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 we use 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 samples 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 report transcriptionally distinct cell states related to cell cycle, growth rate, metabolic strategies, stress responses, etc. 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.

molecular biology↗

bioRxiv2019

Microbial single-cell RNA sequencing by split-pool barcoding

Single-cell RNA-sequencing (scRNA-seq) has become an essential tool for characterizing multi-celled eukaryotic systems but current methods are not compatible with bacteria. Here, we introduce microSPLiT, a low cost and high-throughput scRNA-seq method that works for gram-negative and gram-positive bacteria and can resolve transcriptional states that remain hidden at a population level. We applied microSPLiT to >25,000 Bacillus subtilis cells sampled from different growth stages, creating a detailed atlas of changes in metabolism and lifestyle. We not only retrieve detailed gene expression profiles associated with known but rare states such as competence and PBSX prophage induction, but also identify novel and unexpected gene expression states including heterogeneous activation of a niche metabolic pathway in a subpopulation of cells. microSPLiT empowers high-throughput analysis of gene expression in complex bacterial communities.

bioengineering↗

bioRxiv2016

Self-destructive altruism in a synthetic developmental program enables complex feedstock utilization

Stochastic differentiation and programmed cell death are common developmental processes in microbes, driving diverse altruistic behaviors that promote cooperation. Utilizing cell death in developmental programs requires control over the rate of differentiation to balance cell proliferation against the utility of sacrifice. However, the regulatory networks that control these behaviors are often complex and have yet to be successfully harnessed as biotechnology. Here, we engineered a synthetic developmental gene network that couples stochastic differentiation with programmed cell death to implement a two-member division of labor. Progenitor cellobiose consumer cells were engineered to grow on cellobiose and differentiate at a controlled rate into self-destructive altruists that release an otherwise sequestered cellulase enzyme payload through autolysis to form a developmental Escherichia coli consortium that utilizes cellulose for growth. We used an experimentally parameterized model of task switching, payload delivery and cellulose conversion to nutrients to set key parameters to achieve overall population growth supported by cellulase release, liberating 14-23% of the available carbon. An inevitable consequence of engineering altruistic developmental behaviors is the emergence of cheaters that undermine cooperation. We observed cheater phenotypes for consumers and altruists, identified mutational hotspots and constructed a predictive model of circuit longeivity based on mutation rate estimates for each mode of evolutionary escape. This work introduces the altruistic developmental program as a new tool for synthetic biology, demonstrates the utility of population dynamics models to engineer complex phenotypes and provides a testbed for probing the evolutionary biology of self-destructive altruism.

synthetic biology↗