Hedging their bets: how bacterial pathogens diversify to survive infection.

Phenotypic heterogeneity describes the occurrence of "nonconformist" cells within an isogenic population. The nonconformists show an expression profile partially different from that of the remainder of the population. Phenotypic heterogeneity affects many aspects of the different bacterial lifestyles, and it is assumed that it increases bacterial fitness and the chances for survival of the whole population or smaller subpopulations in unfavorable environments. Well-known examples for phenotypic heterogeneity have been associated with antibiotic resistance and frequently occurring persister cells. Other examples include heterogeneous behavior within biofilms, DNA uptake and bacterial competence, motility (i.e., the synthesis of additional flagella), onset of spore formation, lysis of phages within a small subpopulation, and others. Interestingly, phenotypic heterogeneity was recently also observed with respect to quorum-sensing (QS)-dependent processes, and the expression of autoinducer (AI) synthase genes and other QS-dependent genes was found to be highly heterogeneous at a single-cell level. This phenomenon was observed in several Gram-negative bacteria affiliated with the genera Vibrio, Dinoroseobacter, Pseudomonas, Sinorhizobium, and Mesorhizobium. A similar observation was made for the Gram-positive bacterium Listeria monocytogenes. Since AI molecules have historically been thought to be the keys to homogeneous behavior within isogenic populations, the observation of heterogeneous expression is quite intriguing and adds a new level of complexity to the QS-dependent regulatory networks. All together, the many examples of phenotypic heterogeneity imply that we may have to partially revise the concept of homogeneous and coordinated gene expression in isogenic bacterial populations.

Phenotypic heterogeneity is a hallmark of aggressive cancer behaviour and a clinical challenge. Despite much characterisation of this heterogeneity at a multi-omics level in many cancers, we have a limited understanding of how this heterogeneity emerges spontaneously in an isogenic cell population. Some longitudinal observations of dynamics in epithelial-mesenchymal heterogeneity, a canonical example of phenotypic heterogeneity, have offered us opportunities to quantify the rates of phenotypic switching that may drive such heterogeneity. Here, we offer a mathematical modeling framework that explains the salient features of population dynamics noted in PMC42-LA cells: (a) predominance of EpCAMhigh subpopulation, (b) re-establishment of parental distributions from the EpCAMhigh and EpCAMlow subpopulations, and (c) enhanced heterogeneity in clonal populations established from individual cells. Our framework proposes that fluctuations or noise in content duplication and partitioning of SNAIL—an EMT-inducing transcription factor—during cell division can explain spontaneous phenotypic switching and consequent dynamic heterogeneity in PMC42-LA cells observed experimentally at both single-cell and bulk level analysis. Together, we propose that asymmetric cell division can be a potential mechanism for phenotypic heterogeneity.

A Maternal Factor Unique to Ascidians Silences the Germline via Binding to P-TEFb and RNAP II Regulation

Wnt signaling usually functions through a spatial gradient. Localized Wnt3a signaling can induce the asymmetric division of mouse embryonic stem cells, where proximal daughter cells maintain self-renewal and distal daughter cells acquire hallmarks of differentiation. Here, we develop an approach, same cell epigenome and transcriptome sequencing, to jointly profile the epigenome and transcriptome in the same single cell. Utilizing this method, we profiled H3K27me3 and H3K4me3 levels along with gene expression in mouse embryonic stem cells with localized Wnt3a signaling, revealing the cell type-specific maps of the epigenome and transcriptome in divided daughter cells. H3K27me3, but not H3K4me3, is correlated with gene expression changes during asymmetric cell division. Furthermore, cell clusters identified by H3K27me3 recapitulate the corresponding clusters defined by gene expression. Our study provides a convenient method to jointly profile the epigenome and transcriptome in the same cell and reveals mechanistic insights into the gene regulatory programs that maintain and reset stem cell fate during differentiation.

In the framework of the modern microbiological paradigm, colonies of genetically identical microorganisms are considered as biosocial systems consisting of several heterogeneous clonal cell clusters (bacterial phenotypes) that respond differently to changes in the environment. Phenotypic heterogeneity was found in recent decades in all isogenic populations of pathogenic bacteria. Such heterogeneity provides a selective advantage of cellular phenotypes with changes in the physicochemical parameters of the environment and competitive interaction with other microorganisms. Heterogeneity in bacterial communities is of great importance for the survival of pathogenic bacteria in the host organism, the progression and persistence of infections, as well as the decrease in the effectiveness of antibiotic therapy. The modern spectrum of analytical tools for studying cellular phenotyping is presented both by optical imaging methods and qualitative structural characteristics of single cells, and by omix technologies of quantitative analysis and monitoring of molecular intracellular processes. These diverse tools make it possible not only to identify and modulate phenotypic heterogeneity in isogenic bacterial populations, but also to evaluate the functional significance of cellular phenotypes in the development of the infectious process. The aim of the review is the integration of modern concepts of heterogeneity in isogenic bacterial populations, with an emphasis on the presentation of modern analytical technologies for assessing and monitoring phenotypic typing of single cells.

Article Figures and data Abstract eLife digest Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract How phenotypically distinct states in isogenic cell populations appear and stably co-exist remains unresolved. We find that within a mature, clonal yeast colony developing in low glucose, cells arrange into metabolically disparate cell groups. Using this system, we model and experimentally identify metabolic constraints sufficient to drive such self-assembly. Beginning in a uniformly gluconeogenic state, cells exhibiting a contrary, high pentose phosphate pathway activity state, spontaneously appear and proliferate, in a spatially constrained manner. Gluconeogenic cells in the colony produce and provide a resource, which we identify as trehalose. Above threshold concentrations of external trehalose, cells switch to the new metabolic state and proliferate. A self-organized system establishes, where cells in this new state are sustained by trehalose consumption, which thereby restrains other cells in the trehalose producing, gluconeogenic state. Our work suggests simple physico-chemical principles that determine how isogenic cells spontaneously self-organize into structured assemblies in complimentary, specialized states. eLife digest Under certain conditions, single-celled microbes such as yeast and bacteria form communities of many cells. In some cases, the cells in these communities specialize to perform specific roles. By specializing, these cells may help the whole community to survive in difficult environments. These co-dependent communities have some similarities to how cells specialize and work together in larger living things – like animals or plants – that in some cases can contain trillions of cells. Research has already identified the genes involved in creating communities from a population of identical cells. It is less clear how cells within these communities become specialized to different roles. The budding yeast Saccharomyces cerevisiae can help to reveal how genetic and environmental factors contribute to cell communities. By growing yeast in conditions with a low level of glucose, Varahan et al. were able to form cell communities. The communities contained some specialized cells with a high level of activity in a biochemical system called the pentose phosphate pathway (PPP). This is unusual in low-glucose conditions. Further examination showed that many cells in the community produce a sugar called trehalose and, in parts of the community where trehalose levels are high, cells switch to the high PPP state and gain energy from processing trehalose. These findings suggest that the availability of a specific nutrient (in this case, trehalose), which can be made by the cells themselves, is a sufficient signal to trigger specialization of cells. This shows how simple biochemistry can drive specialization and organization of cells. Certain infections are caused by cell communities called biofilms. These findings could also contribute to new approaches to preventing biofilms. This knowledge could in turn reveal how complex multi-cellular organisms evolved, and it may also be relevant to studies looking into the development of cancer. Introduction During the course of development, groups of isogenic cells often form spatially organized, interdependent communities. The emergence of such phenotypically heterogeneous, spatially constrained sub-populations of cells is considered a requisite first step towards multicellularity. Here, clonal cells proliferate and differentiate into phenotypically distinct cells that stably coexist, and organize spatially with distinct patterns and shapes (Newman, 2016; Niklas, 2014). Through such collective behavior, groups of cells can maintain orientation, stay together, and specialize in different tasks through the division of labor, while remaining organized with intricate spatial arrangements (Ackermann, 2015; Newman, 2016). In both eukaryotic and prokaryotic microbes, such organization into structured, isogenic but phenotypically heterogeneous communities, is widely prevalent, and also reversible (Ackermann, 2015). Such phenotypic heterogeneity within groups of clonal cells enables several microbes to persist in fluctuating environments, thereby providing an adaptive benefit for the cell community (Wolf et al., 2005; Thattai and van Oudenaarden, 2004). A well studied example of spatially organized, phenotypically heterogeneous groups of cells comes from the Dictyostelid social amoeba, which upon starvation transition from individual protists to collective cellular aggregates that go on to form slime-molds, or fruiting bodies (Bonner, 1949; Du et al., 2015; Kaiser, 1986). Indeed, most microbes show some such complex, heterogeneous cell behavior, for example in the extensive spatial organization within clonal bacterial biofilms and swarms (Kearns et al., 2004; Kolter, 2007), or in the individuality exhibited in Escherichia coli populations (Spudich and Koshland, 1976). Despite its popular perception as a unicellular microbe, natural isolates of the budding yeast, Saccharomyces cerevisiae, also form phenotypically heterogeneous, multicellular communities (Cáp et al., 2012; Koschwanez et al., 2011; Palková and Váchová, 2016; Ratcliff et al., 2012; Váchová and Palková, 2018; Veelders et al., 2010; Wloch-Salamon et al., 2017). However, despite striking descriptions on the nature and development of phenotypically heterogeneous states within groups of cells, the rules governing the emergence and maintenance of new phenotypic states within isogenic cell populations remain unclear. Current studies emphasize genetic and epigenetic changes that are required to maintain phenotypic heterogeneity within a cell population (Ackermann, 2015; Sneppen et al., 2015). In particular, many studies emphasize stochastic gene expression changes that can drive phenotypic heterogeneity (Süel et al., 2007; Ackermann, 2015; Balázsi et al., 2011; Blake et al., 2003). Further, groups of cells can produce adhesion molecules to bring themselves together (Halfmann et al., 2012; Halme et al., 2004; Octavio et al., 2009; Váchová and Palková, 2018), or support possible co-dependencies (such as commensal or mutual dependencies on shared resources) within the populations (Ackermann, 2015). Such studies now provide insight into why such heterogeneous cell groups might exist, and what the evolutionary benefits might be. However, an underlying biochemical logic to explain how distinct, specialized cell states can emerge and persist in the first place is largely absent. This is particularly so for isogenic (and therefore putatively identical) groups of cells in seemingly uniform environments. In essence, are there simple chemical or physical constraints, derived from existing biochemical rules and limitations, that explain the emergence and maintenance of heterogeneous phenotypic states of groups of clonal cells in space and over time? Contrastingly, a common theme occurs in nearly all described examples of phenotypically heterogeneous, isogenic groups of cells. This is a requirement of some ‘metabolic stress’ or nutrient limitation that is necessary for the emergence of phenotypic heterogeneity and spatial organization, typically in the form of metabolically inter-dependent cells (Ackermann, 2015; Campbell et al., 2016; Cáp et al., 2012; Johnson et al., 2012; Liu et al., 2015). This idea has been explored experimentally, where approaches that systems-engineer metabolic dependencies between non-isogenic cells can result in interdependent populations that constitute mixed communities (Campbell et al., 2016; Campbell et al., 2015; Embree et al., 2015; Wintermute and Silver, 2010). These findings suggest that biochemical constraints derived from metabolism may determine the nature of phenotypic heterogeneity, and the spatial organization of cells in distinct states within the population. Therefore, if we can understand what these biochemical constraints are, and discern how metabolic states can be altered through these constraints, this may address how genetically identical cells can self-organize into distinct states. In this study, using clonal yeast cells, we experimentally and theoretically show how metabolic constraints imposed on a population of isogenic cells can determine the production, accumulation, and utilization of a specific, shared resource. The selective utilization of this resource enables the spontaneous emergence and persistence of cells exhibiting a counter-intuitive metabolic state, with spatial organization. These metabolic constraints create inherent threshold effects, enabling some cells to switch to new metabolic states, while restraining other cells to the original state which produces the resource. This thereby drives the overall self-organization of cells into specialized, spatially ordered communities. Finally, this group of spatially organized, metabolically distinct cells confer a collective growth advantage to the community of cells, rationalizing why such spatial self-organization of cells into distinct metabolic states benefits the cell community. Results Cells within S. cerevisiae colonies exhibit ordered metabolic specialization Using a well-studied S. cerevisiae isolate as a model (Reynolds and Fink, 2001), we established a simple system to study the formation of a clonal colony with irregular morphology. On 2% agar plates containing a complex rich medium with low glucose concentrations, S. cerevisiae forms rugose colonies with distinct architecture, after ~5–6 days (Figure 1A). Such colonies do not form in the typical, high (1–2%) glucose medium used for yeast growth (Figure 1A). Thus, as previously well established (Granek and Magwene, 2010; Reynolds and Fink, 2001), glucose limitation (with other nutrients such as amino acids being non-limiting) drives this complex colony architecture formation. Currently, the description of such colonies is limited to this external rugose morphology, and does not describe the phenotypic states of cells and/or any spatial organization in the colony. With only such a description, as observed in Figure 1A, the mature colony surface has an internal circle and some radial streaks near the periphery. We carried out a more detailed observation of entire colonies under a microscopic bright-field (using a 4x lens). Here, we unexpectedly noticed what appeared to be distinct internal patterning, and apparent spatial organization of cells within the colony (Figure 1B). As categorized purely based on these observed differences in visual optical density (‘dark’ or ‘light’), regions between the colony center and periphery had optically dense (dark) networks spanning the circumference of the colony, interspersed with optically rare regions. In contrast, the periphery of the mature colony appeared entirely light (Figure 1B). Based simply on these optical traits alone, we categorized cells present in these regions of the colony as dark cells and light cells (Figure 1B). At this point, our description is visual and qualitative, and does not imply any other difference in the cells in either region. However, this visual description is both robust and simple, and hence we use this nomenclature for the remainder of this manuscript. Figure 1 with 2 supplements see all Download asset Open asset Cells within S.cerevisiae colonies exhibit ordered metabolic specialization. (A) Low glucose is required for rugose colonies to develop. The panel shows the morphology of mature yeast colonies in rich medium, with supplemented glucose as the sole variable. Scale bar: 2 mm. (B) Reconstructed bright-field images of a mature wild-type colony. Within the colony, a network of dark and bright regions is clearly visible, as classified based purely on optical density. We classify the cells in the dark region as dark cells, and in the peripheral light region as light cells. Scale bar: 2 mm. (C) Spatial distribution of mCherry fluorescence across a colony, indicating the activity of (i) a reporter for hexokinase (HXK1) activity, or (ii) a gluconeogenesis dependent reporter (PCK1), in two different colonies. The percentage of fluorescent cells (in isolated light and dark cells from the respective colonies) were also estimated by flow cytometry, and is shown as bar graphs. Scale bar: 2 mm. Also see Figure 1—figure supplement 1A–B and Figure 1—figure supplement 2A for more information. (D) Western blot based detection of proteins involved in gluconeogenesis (Fbp1p and Pck1p), or associated with increased gluconeogenic activity (Icl1p), in isolated dark or light cells. The blot is representative of three biological replicates (n = 3). Also see Figure 1—figure supplement 2B for more information. (E) Comparative steady-state amounts of trehalose and glycogen (as gluconeogenesis end point metabolites), in light and dark cells (n = 3). Statistical significance was calculated using unpaired t test (*** indicates p<0.001) and error bars represent standard deviation. Since these structured colonies form only in glucose-limited conditions, we hypothesized that dissecting the expected metabolic requirements during glucose limitation might reveal drivers of this internal organization within the colony. The expected metabolic requirements of cells growing in glucose limited conditions are as follows: first, all cells would be expected to have constitutively high expression of the high-affinity hexokinase (Hxk1p) (Lobo and Maitra, 1977; Rodríguez et al., 2001). Further, during glucose-limited growth, all cells are expected to carry out extensive gluconeogenesis, as the default metabolic state (Broach, 2012; Haarasilta and Oura, 1975; Yin et al., 2000). Indeed, we confirmed this second expectation by measuring the amounts of the gluconeogenic enzymes Pck1 (phosphoenolpyruvate carboxykinase) and Fbp1 (fructose-1,6-bisphosphatase), in short-term (4–5 hr) liquid cultures of log-phase cells growing in either high glucose medium, or in the same glucose-limited medium we used for colony growth. Expectedly, we observed very high amounts of these gluconeogenic enzymes in cells growing in glucose-limited medium (Figure 1—figure supplement 1A), reiterating that even in well-mixed glucose-limited, cells are in a strongly gluconeogenic state. In order to now examine the mature colony and dissecting expected metabolic requirements in these conditions, we first designed visual indicators for these metabolic hallmarks of yeast cell growth in low glucose. We engineered two different fluorescent reporters, one dependent on HXK1 expression (mCherry under the HXK1 gene promoter), and the second on PCK1 expression as an indicator of gluconeogenic activity (mCherry under the PCK1 gene promoter) (Figure 1—figure supplement 1B). Cells carrying these reporters were seeded to develop into colonies, and the expression levels of these reporters were monitored in the mature, rugose colony (5–6 days). Expectedly, the HXK1-promoter dependent reporter showed constitutive, high expression in all cells across the entire colony (Figure 1C). Contrastingly, only the dark cells exhibited high gluconeogenesis reporter activity (Figure 1C). Notably, the light cells entirely lacked detectable gluconeogenic reporter activity (Figure 1C). To better quantify this phenomenon, cells were dissected out from dark or light regions respectively (under the light microscope, using a fine needle), and the percentage of fluorescent cells in each region was measured using flow cytometry. Based on flow cytometric readouts,~80% of the isolated dark cells showed strong fluorescence for the gluconeogenic reporter, while ~97% of the light cells were non-fluorescent for gluconeogenic activity (Figure 1C, Figure 1—figure supplement 1C). This spatial distribution of gluconeogenic activity is shown as a quantitative heat-map histogram overlaid on the entire colony, in Figure 1—figure supplement 2A. Since this observation was based solely on reporter activity, in order to more directly examine this observation, we estimated native protein amounts of enzymes associated with gluconeogenesis (Pck1, Fbp1, and Icl1- Isocitrate lyase from the glyoxylate shunt) in isolated light cells and dark cells. Only the dark cells showed expression of the gluconeogenic enzymes (Figure 1D, Figure 1—figure supplement 2B). Finally, we measured steady-state amounts of trehalose and glycogen within dark and light cells, using these metabolites as unambiguous biochemical readouts of the end-point biochemical outputs of gluconeogenesis (François et al., 1991). We observed that the dark cells had substantially higher amounts of both trehalose and glycogen (Figure 1E), indicating greater gluconeogenic activity in these cells. Collectively, these results strikingly reveal that intracellular gluconeogenic activity is spatially restricted to specific regions, resulting in a distinct pattern of metabolically specialized zones within the colony. Cells organize into spatially restricted, contrary metabolic states within the colony In the given nutrient conditions of low glucose, gluconeogenesis is an expected, constitutive metabolic process, essential for cells. This can therefore be considered as a necessary, permitted metabolic state in this condition. Paradoxically, in these mature colonies, gluconeogenic activity was spatially restricted to only within the dark cell region, with no discernible gluconeogenic activity in the cells located in the light region. This absence of gluconeogenic activity in these light cells, while concomitant with a constitutively high level of hexokinase activity, therefore poses a biochemical paradox. What might the metabolic state of these light cells be? To quickly address this using a crude but useful readout, we compared the ability of freshly isolated light and dark cells to proliferate in both gluconeogenic (low glucose), and non-gluconeogenic (high glucose) growth conditions. For simplicity, isolated light cells and dark cells were inoculated either into a medium where gluconeogenesis is essential (2% ethanol +glycerol as a sole carbon source), or in high (2%) glucose medium where cells rely on high glycolytic and pentose phosphate pathway (PPP) activity, and initial cell proliferation was monitored. Here, cells that had been growing in high glucose were used as a control. Expectedly, the dark cells grew robustly and reached significantly higher cell numbers (0D600) compared to the light cells in the gluconeogenic condition (Figure 2A). Conversely, light cells grew robustly when transferred to the high glucose medium, as compared to the dark cells (Figure 2A). While this was an overly simple, and not definitive experiment, counter-intuitively, this result suggested that despite being in a low-glucose environment, the light cells were well suited for growth in high glucose, and therefore might be in a metabolic state suited for growth in glucose. We therefore decided to more systematically investigate this phenomenon. Figure 2 with 1 supplement see all Download asset Open asset Cells organize into spatially restricted, contrary metabolic states within the colony. (A) Comparative immediate growth of isolated light cells and dark cells, transferred to a ‘gluconeogenic medium’ (2% ethanol as carbon source), or a ‘glycolytic medium’ (2% glucose as carbon source), based on increased absorbance (OD600) in culture. Wild-type cells growing in liquid medium (2% glucose) in log phase (i.e. in a glycolytic state) were used as controls for growth comparison (n = 3). (B) A schematic showing metabolic flow in glycolysis and the pentose phosphate pathway (PPP), and also illustrating the synthesis of nucleotides (dependent upon pentose phosphate pathway). TKL1 controls an important step in the PPP, and is strongly induced during high PPP flux. (C) Spatial distribution of mCherry fluorescence across a colony, based on the activity of a PPP- dependent reporter. Scale bar: 2 mm. Also see Figure 1—figure supplement 1A and Figure 2—figure supplement 1A. (D) LC-MS/MS based metabolite analysis, using exogenously added 13C Glucose, to compare flux of 13C Glucose into the PPP metabolite ribulose-5-phosphate (R-5-P), in light and dark cells. The red circles represent 13C labeled carbon atoms (n = 3). Also see Figure 2—figure supplement 1B. (E) Comparative metabolic-flux based analysis comparing 15N incorporation into newly synthesized nucleotides, in dark and light cells. Also see S2, and Materials and methods. (F) Light cells and dark cells isolated from a 7 day old wild-type complex colony re-form indistinguishable mature colonies when re-seeded onto fresh agar plates, and allowed to develop for 7 days. Scale bar = 2 mm. Statistical significance was calculated using unpaired t test (*** indicates p<0.001, ** indicates p<0.01) and error bars represent standard deviation. In the presence of glucose, yeast cells typically show high glycolytic and PPP activities, as part of the Crabtree (analogous to the Warburg) effect (Crabtree, 1929; De Deken, 1966; Figure 2B). Therefore, if the light cells in the colony were indeed behaving as though present in more glucose-replete conditions, they should exhibit high PPP To test we first designed a fluorescent reporter (mCherry under the of the 1 et al., gene Figure 1—figure supplement and monitored reporter activity across the mature colony. Indeed, only the light cells exhibited high activity (Figure Figure 2—figure supplement 1A). This spatial of high PPP activity across the colony is also shown as an overlaid quantitative heat-map histogram in Figure 2—figure supplement 1A. we directly the of these light cells exhibiting high PPP For we a based metabolic flux to the flux towards PPP in light and dark cells. Light and dark cells isolated from colonies were with glucose metabolites and the incorporation of this carbon into the PPP ribulose-5-phosphate and was measured by liquid The amounts of these labeled PPP were compared between the two cell or Notably, light cells significantly higher levels of 13C labeled glucose into PPP metabolites compared to the dark cells (Figure and Figure 2—figure supplement and see 1 for showing that the light cells are in a high PPP activity state. Finally, we if other biochemical end-point outputs high PPP were also high in the light cells. synthesis is a of PPP activity and The carbon of newly synthesized nucleotides is derived from the PPP, while the comes from amino acids and Figure and see 1 for We metabolic to in light and dark cells, as an end-point collective of high PPP activity with amino Light and dark cells, isolated from colonies were with a and incorporation of this into nucleotides was measured by liquid Light cells had higher flux into compared to the dark cells (Figure and see 1 for together, we find that light cells exhibit metabolic hallmarks of cells growing in glucose-replete conditions, increased PPP activity, and increased Thus, in the spatially organized colony, the light cells and dark cells have contrary metabolic states. This is despite the expectation that the gluconeogenic state, exhibited by the dark cells, is the metabolic state in the given growth conditions. 1 used for LC-MS/MS 15N has all has all has all has all and sugar 13C has all of which are the phosphate the phosphate the phosphate the phosphate the phosphate the phosphate Notably, the light cells or dark cells, when isolated and as a new colony, both develop into complex colonies (Figure This that these phenotypic differences between the light and dark cells are and do not genetic Collectively, these data reveal that cells within the colony organize into spatially metabolically specialized regions. Within these regions, cells exhibit metabolic states. of these states, where cells have high PPP activity, is counter-intuitive and be sustained given the external nutrient A model suggests constraints for the emergence and organization of cells in metabolic states What the emergence and spatial organization of a group of cells, in these contrary metabolic what can explain the emergence and proliferation of the light cells, which exhibit this counter-intuitive metabolic state, while the colony a of cells in the dark To address we a This model simple derived from our to the formation of a colony of and cells. The model was its was only to find a of that is sufficient to produce the overall spatial and of cell states observed in the colonies. The the model was not to all possible that explain this phenomenon. The model should only for both the emergence of light cells, as well as spatial organization with dark cells. Such a model could therefore suggest constraints that determine the emergence of light cells, and the organization of the colony with the observed organization, which can be experimentally While this we a of that be based on our data (Figure This (i) the dark cells to a light state, (ii) the of some resource by dark cells, which may be by the cells, for this resource, of this resource, and of cell division are (Figure we a of for groups of cells within the colony (Figure Here, each is either or by a group of cells see Materials and methods for we the simplicity, in order to colony to colonies) by that the either of all light or all dark cells. This is a that was At each step of all the shown in Figure are across the spatial using the (Figure In such an (i) all cells all nutrients in while glucose concentrations are (ii) dark cells and in the given conditions, dark cells produce a as a of existing metabolic state, this resource the and is dark cells switch to the light state if sufficient resource is present and the resource when can the light state cells, which can if there is an in the the resource is not present in that the light cells switch to dark cells. for Finally, this of a shared resource is the emergence of light cells from dark can only if the nutrient enables a switch to the new metabolic state. Figure with 2 supplements see all Download asset Open asset A model suggests constraints for the emergence and organization of cells in metabolic states. (A) based on into developing a

Cells have evolved intricate mechanisms for dividing their contents in the most symmetric way during mitosis. However, a small proportion of cell divisions results in asymmetric segregation of cellular components, which leads to differences in the characteristics of daughter cells. Although the classical function of asymmetric cell division (ACD) in the regulation of pluripotency is the generation of one differentiated daughter cell and one self-renewing stem cell, recent evidence suggests that ACD plays a role in other physiological processes. In cancer, tumor heterogeneity can result from the asymmetric segregation of genetic material and other cellular components, resulting in cell-to-cell differences in fitness and response to therapy. Defining the contribution of ACD in generating differences in key features relevant to cancer biology is crucial to advancing our understanding of the causes of tumor heterogeneity and developing strategies to mitigate or counteract it. In this Review, we delve into the occurrence of asymmetric mitosis in cancer cells and consider how ACD contributes to the variability of several phenotypes. By synthesizing the current literature, we explore the molecular mechanisms underlying ACD, the implications of phenotypic heterogeneity in cancer, and the complex interplay between these two phenomena.

Key microbial processes in many bacterial species are heterogeneously expressed in single cells of bacterial populations. However, the paucity of adequate molecular tools for live, real-time monitoring of multiple-gene expression at the single-cell level has limited the understanding of phenotypic heterogeneity. To investigate phenotypic heterogeneity in the ubiquitous opportunistic pathogen Pseudomonas aeruginosa, a genetic tool that allows gauging multiple-gene expression at the single-cell level has been generated. This tool, named pRGC, consists of a promoter-probe vector for transcriptional fusions that carries three reporter genes coding for the fluorescent proteins mCherry, green fluorescent protein (GFP), and cyan fluorescent protein (CFP). The pRGC vector has been characterized and validated via single-cell gene expression analysis of both constitutive and iron-regulated promoters, showing clear discrimination of the three fluorescence signals in single cells of a P. aeruginosa population without the need for image processing for spectral cross talk correction. In addition, two pRGC variants have been generated for either (i) integration of the reporter gene cassette into a single neutral site of P. aeruginosa chromosome that is suitable for long-term experiments in the absence of antibiotic selection or (ii) replication in bacterial genera other than Pseudomonas The easy-to-use genetic tools generated in this study will allow rapid and cost-effective investigation of multiple-gene expression in populations of environmental and pathogenic bacteria, hopefully advancing the understanding of microbial phenotypic heterogeneity.IMPORTANCE Within a bacterial population, single cells can differently express some genes, even though they are genetically identical and experience the same chemical and physical stimuli. This phenomenon, known as phenotypic heterogeneity, is mainly driven by gene expression noise and results in the emergence of bacterial subpopulations with distinct phenotypes. The analysis of gene expression at the single-cell level has shown that phenotypic heterogeneity is associated with key bacterial processes, including competence, sporulation, and persistence. In this study, new genetic tools have been generated that allow easy cloning of up to three promoters upstream of distinct fluorescent genes, making it possible to gauge multiple-gene expression at the single-cell level by fluorescence microscopy without the need for advanced image-processing procedures. A proof of concept has been provided by investigating iron uptake and iron storage gene expression in response to iron availability in P. aeruginosa.

Intratumoral heterogeneity can exist along multiple axes: Cancer stem cells (CSCs)/non-CSCs, drug-sensitive/drug-tolerant states, and a spectrum of epithelial-hybrid-mesenchymal phenotypes. Further, these diverse cell-states can switch reversibly among one another, thereby posing a major challenge to therapeutic efficacy. Therefore, understanding the origins of phenotypic plasticity and heterogeneity remains an active area of investigation. While genomic components (mutations, chromosomal instability) driving heterogeneity have been well-studied, recent reports highlight the role of non-genetic mechanisms in enabling both phenotypic plasticity and heterogeneity. Here, we discuss various processes underlying phenotypic plasticity such as stochastic gene expression, chromatin reprogramming, asymmetric cell division and the presence of multiple stable gene expression patterns ('attractors'). These processes can facilitate a dynamically evolving cell population such that a subpopulation of (drug-tolerant) cells can survive lethal drug exposure and recapitulate population heterogeneity on drug withdrawal, leading to relapse. These drug-tolerant cells can be both pre-existing and also induced by the drug itself through cell-state reprogramming. The dynamics of cell-state transitions both in absence and presence of the drug can be quantified through mathematical models. Such a dynamical systems approach to elucidating patterns of intratumoral heterogeneity by integrating longitudinal experimental data with mathematical models can help design effective combinatorial and/or sequential therapies for better clinical outcomes.

Investigating phenotypic heterogeneity can help to better understand and manage microbial communities. However, characterizing phenotypic heterogeneity remains a challenge, as there is no standardized analysis framework. Several optical tools are available, such as flow cytometry and Raman spectroscopy, which describe optical properties of the individual cell. In this work, we compare Raman spectroscopy and flow cytometry to study phenotypic heterogeneity in bacterial populations. The growth stages of three replicate Escherichia coli populations were characterized using both technologies. Our findings show that flow cytometry detects and quantifies shifts in phenotypic heterogeneity at the population level due to its high-throughput nature. Raman spectroscopy, on the other hand, offers a much higher resolution at the single-cell level (i.e., more biochemical information is recorded). Therefore, it can identify distinct phenotypic populations when coupled with analyses tailored toward single-cell data. In addition, it provides information about biomolecules that are present, which can be linked to cell functionality. We propose a computational workflow to distinguish between bacterial phenotypic populations using Raman spectroscopy and validated this approach with an external data set. We recommend using flow cytometry to quantify phenotypic heterogeneity at the population level, and Raman spectroscopy to perform a more in-depth analysis of heterogeneity at the single-cell level. © 2019 International Society for Advancement of Cytometry.

(1) Background: Chronic myeloid leukemia (CML) is a myeloproliferative disorder driven by the BCR::ABL oncoprotein. During the chronic phase, Philadelphia chromosome-positive hematopoietic stem cells generate proliferative myeloid cells with various stages of maturation. Despite this expansion, leukemic stem cells (LSCs) retain self-renewal capacity via asymmetric cell divisions, sustaining the stem cell pool. Quiescent LSCs are known to be resistant to tyrosine kinase inhibitors (TKIs), potentially through BCR::ABL-independent signaling pathways. We hypothesize that dysregulation of genes governing asymmetric division in LSCs contributes to disease progression, and that their expression pattern may serve as a prognostic marker during the chronic phase of CML. (2) Methods: Genes related to asymmetric cell division in the context of hematopoietic stem cells were extracted from the PubMed database with the keyword “asymmetric hematopoietic stem cell”. The collected relative gene set was tested on two independent bulk transcriptome cohorts and the results were confirmed by single-cell RNA sequencing. (3) Results: The expression of genes involved in asymmetric hematopoietic stem cell division was found to discriminate disease phases during CML progression in the two independent transcriptome cohorts. Concordance between cohorts was observed on asymmetric molecules downregulated during blast crisis (BC) as compared to the chronic phase (CP). This downregulation during the BC phase was confirmed at single-cell level for SELL, CD63, NUMB, HK2, and LAMP2 genes. Single-cell analysis during the CP found that CD63 is associated with a poor prognosis phenotype, with the opposite prediction revealed by HK2 and NUMB expression. The single-cell trajectory reconstitution analysis in CP samples showed CD63 regulation highlighting a trajectory cluster implicating HSPB1, PIM2, ANXA5, LAMTOR1, CFL1, CD52, RAD52, MEIS1, and PDIA3, known to be implicated in hematopoietic malignancies. (4) Conclusion: Regulation of CD63, a tetraspanin involved in the asymmetric division of hematopoietic stem cells, was found to be associated with poor prognosis during CML progression and could be a potential new therapeutic target.

During development, one mechanism for generating different cell types is asymmetric cell division, by which a cell divides and contributes different factors to each of its daughter cells. Asymmetric cell division occurs throughout the eukaryotic kingdom, from yeast to humans. Many asymmetric cell divisions occur in a defined orientation. This implies a cellular mechanism for sensing direction, which must ultimately lead to differences in gene expression between two daughter cells. In this review, we describe two classes of molecules: regulatory factors that are differentially expressed upon asymmetric cell division, and components of a signal transduction pathway that may define cell polarity. The lin-11 and mec-3 genes of C. elegans, the Isl-1 gene of mammals and the HO gene of yeast, encode regulatory factors that determine cell type of one daughter after asymmetric cell division. The CDC24 and CDC42 genes of yeast affect both bud positioning and orientation of mating projections, and thus may define a general cellular polarity. We speculate that molecules such as Cdc24 and Cdc42 may regulate expression of genes such as lin-11, mec-3, Isl-1 and HO upon asymmetric cell division.

Cell polarity is essential for many functions of cells and tissues including the initial establishment and subsequent maintenance of epithelial tissues, asymmetric cell division, and morphogenetic movements. Cell polarity along the apical-basal axis is controlled by three protein complexes that interact with and co-regulate each other: The Par-, Crumbs-, and Scrib-complexes. The localization and activity of the components of these complexes is predominantly controlled by protein-protein interactions and protein phosphorylation status. Increasing evidence accumulates that, besides the regulation at the protein level, the precise expression control of polarity determinants contributes substantially to cell polarity regulation. Here we review how gene expression regulation influences processes that depend on the induction, maintenance, or abolishment of cell polarity with a special focus on epithelial to mesenchymal transition and asymmetric stem cell division. We conclude that gene expression control is an important and often neglected mechanism in the control of cell polarity.

The asymmetric cell division determines cell diversity and distinct sibling cell fates by mechanisms linked to mitosis. Many adult stem cells divide asymmetrically to balance self‐renewal and differentiation. The process of asymmetric cell division involves an axis of polarity and, second, the localization of cell fate determinants at the cell poles. Asymmetric division of stem cells is achieved by intrinsic and extrinsic fate determinants such as signaling molecules, epigenetics factors, molecules regulating gene expression, and polarized organelles. At least some stem cells perform asymmetric and symmetric cell divisions during development. Asymmetric division ensures that the number of stem cells remains constant throughout life. The asymmetric division of stem cells plays an important role in biological events such as embryogenesis, tissue regeneration and carcinogenesis. This review summarizes recent advances in the regulation of asymmetric stem cell division in model organisms.

Stochastic Receptor Expression Allows Sensitive Bacteria to Evade Phage Attack. Part II: Theoretical Analyses