Virus Infection Variability by Single-Cell Profiling

Cell-to-cell variability of infection has long been known, yet it has remained one of the least understood phenomena in infection research. It impacts on disease onset and development, yet only recently underlying mechanisms have been studied in clonal cell cultures by single-virion immunofluorescence microscopy and flow cytometry. In this review, we showcase how single-cell RNA sequencing (scRNA-seq), single-molecule RNA-fluorescence in situ hybridization (FISH), and copper(I)-catalyzed azide-alkyne cycloaddition (click) with alkynyl-tagged viral genomes dissect infection variability in human and mouse cells. We show how the combined use of scRNA-FISH and click-chemistry reveals highly variable onsets of adenoviral gene expression, and how single live cell plaques reveal lytic and nonlytic adenovirus transmissions. The review highlights how scRNA-seq profiling and scRNA-FISH of coxsackie, influenza, dengue, zika, and herpes simplex virus infections uncover transcriptional variability, and how the host interferon response tunes influenza and sendai virus infections. We introduce the concept of "cell state" in infection variability, and conclude with advances by single-cell simultaneous measurements of chromatin accessibility and mRNA counts at high-throughput. Such technology will further dissect the sequence of events in virus infection and pathology, and better characterize the genetic and genomic stability of viruses, cell autonomous innate immune responses, and mechanisms of tissue injury.

A long-term epigenetic memory switch controls bacterial virulence bimodality

When pathogens enter the host, sensing of environmental cues activates the expression of virulence genes. Opposite transition of pathogens from activating to non-activating conditions is poorly understood. Interestingly, variability in the expression of virulence genes upon infection enhances colonization. In order to systematically detect the role of phenotypic variability in enteropathogenic E. coli (EPEC), an important human pathogen, both in virulence activating and non-activating conditions, we employed the ScanLag methodology. The analysis revealed a bimodal growth rate. Mathematical modeling combined with experimental analysis showed that this bimodality is mediated by a hysteretic memory-switch that results in the stable co-existence of non-virulent and hyper-virulent subpopulations, even after many generations of growth in non-activating conditions. We identified the per operon as the key component of the hysteretic switch. This unique hysteretic memory switch may result in persistent infection and enhanced host-to-host spreading.

DOI:http://dx.doi.org/10.7554/eLife.19599.001

Bacteria typically cope with harsh and changing environments by activating specific genes or accumulating those mutations that change genes in a beneficial way. Recently, it was also shown that the levels of gene activity can vary between otherwise identical bacteria in a single population. This provides an alternative strategy to deal with stressful conditions because it generates sub-groups of bacteria that potentially already adapted to different environments. Bacteria that enter the human body face many challenges, and this kind of pre-adaptation could help them to invade humans and overcome the immune system. However, this hypothesis had not previously been tested in a bacterium called enteropathogenic E.coli, which infects the intestines and is responsible for the deaths of many infants worldwide.

Ronin et al. show that cells in enteropathogenic E.coli colonies spontaneously form into two groups when exposed to conditions that mimic the environment inside the human body. Once triggered, one of these groups is particularly dangerous and this “hypervirulent” state is remembered for an extremely long time meaning that the bacteria remain hypervirulent for many generations. In addition, Ronin et al. identified the specific genes that control the switch to the hypervirulent state.

These findings have uncovered the existence of groups of enteropathogenic E.coli that are pre-adapted to invading human hosts. Finding out more about how the switching mechanism works and its relevance in other bacteria may help researchers to develop new therapies that can help fight bacterial infections.

DOI:http://dx.doi.org/10.7554/eLife.19599.002

Intra-tumour heterogeneity - going beyond genetics

Cancer patients die primarily due to disease recurrence after transient treatment responses. The emergence of therapy-resistant escape variants is fuelled by intra-tumour heterogeneity, underpinned by interference and Darwinian evolution among continuously developing sub-clones in the mutating tumour. Novel cancer cell variants build upon the pre-existing genetic landscape and tumour heterogeneity is often ascribed largely to genetic variability. While mutations are required for cancer development and studies of genetic evolution of tumours have improved our understanding of cancer biology, genetics only represents one dimension of the fitness of each cancer cell. Beyond the mutations, several non-genetic factors also add significant variability, resulting in a complex and highly dynamic tumour cell population that can drive disease under almost any condition. This viewpoint article summarizes the genetic basis of intra-tumour heterogeneity, before dissecting four major interdependent non-genetic factors we think critically contribute to the overall variability of tumour cells in all types of cancer: epigenetic regulation, cellular differentiation hierarchies, gene expression stochasticity and tumour microenvironment. We finally present the relevant technological approaches to address the combined contribution of both genetic and non-genetic factors to intra-tumour heterogeneity, focusing on genomic profiling, cellular lineage tracing and single-cell RNA sequencing technologies. This strategy will ultimately allow dissection of the full range and depth of intra-tumour heterogeneity. We thus believe that understanding how cancer genetics synergize with the emerging non-genetic factors will be key for development of therapies able to tackle tumour escape and thereby improve cancer patient survival.

Mitochondria and the non-genetic origins of cell-to-cell variability: More is different

Gene expression activity is heterogeneous in a population of isogenic cells. Identifying the molecular basis of this variability will improve our understanding of phenomena like tumor resistance to drugs, virus infection, or cell fate choice. The complexity of the molecular steps and machines involved in transcription and translation could introduce sources of randomness at many levels, but a common constraint to most of these processes is its energy dependence. In eukaryotic cells, most of this energy is provided by mitochondria. A clonal population of cells may show a large variability in the number and functionality of mitochondria. Here, we discuss how differences in the mitochondrial content of each cell contribute to heterogeneity in gene products. Changes in the amount of mitochondria can also entail drastic alterations of a cell's gene expression program, which ultimately leads to phenotypic diversity. Also watch the Video Abstract.

Stochastic exit from mitosis in budding yeast: model predictions and experimental observations

Unlike many mutants that are completely viable or inviable, the CLB2-dbΔ clb5Δ mutant of Saccharomyces cerevisiae is inviable in glucose but partially viable on slower growth media such as raffinose. On raffinose, the mutant cells can bud and divide but in each cycle there is a chance that a cell will fail to divide (telophase arrest), causing it to exit the cell cycle. This effect gives rise to a stochastic phenotype that cannot be explained by a deterministic model. We measure the inter-bud times of wild type and mutant cells growing on raffinose and compute statistics and distributions to characterize the mutant's behavior. We convert a detailed deterministic model of the budding yeast cell cycle to a stochastic model and determine the extent to which it captures the stochastic phenotype of the mutant strain. Predictions of the mathematical model are in reasonable agreement with our experimental data and suggest directions for improving the model. Ultimately, the ability to accurately model stochastic phenotypes may prove critical to understanding disease and therapeutic interventions in higher eukaryotes.