© 2017. Published by The Company of Biologists Ltd | Journal of Cell Science (2017) 130, 819-826 doi:10.1242/jcs.181024

COMMENTARY Organelles – understanding noise and heterogeneity in at an intermediate scale Amy Y. Chang and Wallace F. Marshall*

ABSTRACT this heterogeneity affects cellular behavior and, finally, the Many studies over the years have shown that non-genetic challenges posed by such heterogeneity in the treatment of mechanisms for producing cell-to-cell variation can lead to highly various diseases. ‘ ’ variable behaviors across genetically identical populations of cells. At a molecular level, the state ofacellwilldependonalarge Most work to date has focused on noise as the number of microscopic variables. One must consider not just primary source of phenotypic heterogeneity, yet other sources may expression levels of genes and proteins, but also the vast multitude also contribute. In this Commentary, we explore organelle-level of protein phosphorylation sites and other post-translational heterogeneity as a potential secondary source of cellular ‘noise’ that modifications, not to mention alternative splice isoforms, small contributes to phenotypic heterogeneity. We explore mechanisms for RNAs, second messengers and metabolites. Although tools exist to generating organelle heterogeneity and present evidence of study various state variables, there are currently no methods to functional links between organelle morphology and cellular comprehensively measure all molecular state variables at the same behavior. Given the many instances in which molecular-level time within the same cell. Moreover, most of the methods for heterogeneity has been linked to phenotypic heterogeneity, we measuring molecular state variables are destructive to cells, rendering posit that organelle heterogeneity may similarly contribute to overall it impossible to study temporal variation or correlate molecular states phenotypic heterogeneity and underline the importance of studying with downstream behavior. Is there a way to comprehend how organelle heterogeneity to develop a more comprehensive variation in these thousands or millions of molecular-state variables understanding of phenotypic heterogeneity. Finally, we conclude map onto observable variation in cell behavior? with a discussion of the medical challenges associated with To address this question, this Commentary will focus on phenotypic heterogeneity and outline how improved methods for heterogeneity at the level of subcellular structure, that is to say, at characterizing and controlling this heterogeneity may lead to the level of organelle size, shape and distribution (Rafelski and improved therapeutic strategies and outcomes for patients. Marshall, 2008; Marshall, 2011; Chan and Marshall, 2010), and attempt to characterize the sources and consequences of fluctuations Introduction at this scale. It is at such a scale that molecular processes become Living cells are complex machines, comparable perhaps in cellular processes, and it is therefore possible, at least in theory, that complexity to computers. The analogy of machines is often used organelle scale variation reflects molecular-level variations. Many to describe molecules and cells, with important elements of the organelles serve as reaction vessels for biochemical reactions, and central dogma being performed by ‘protein machines,’ yet several just as in the design of a chemical plant, a chemical engineer must features set biological systems apart from human-made machines. carefully tune the size and geometry of reaction vessels to achieve a One of the dominant features of living systems is the high level of desired yield and purity, so too can we imagine that the size and noise or variability. Biological molecular interactions are dominated geometry of organelles may be tied to their functional output. For by non-covalent bonds, the formation of which depend on example, the volume of an organelle will influence its capacity for inherently stochastic processes. Moreover, many important storing reaction intermediates, whereas its surface area will regulatory molecules are present only in low numbers, such that influence the flux of molecules between the cytoplasm and the small fluctuations in number over time, or between cells, can lead to organelle lumen. For cellular structures with more mechanical large changes in the activity of molecular pathways. Because of the functions (e.g. flagella, focal adhesions and stress fibers), the size low numbers, weak interactions and constant random motion of and shape of structures will directly influence their mechanical cellular components, cells are subject to continual variation across properties. It is therefore reasonable to hypothesize that variation in many parameters. Despite this variability, cells are able to perform organelle size and shape may lead to variation in a number of important physiological functions, respond to external stimuli and cellular behaviors and functions, such as motility, proliferation, develop into complex multicellular organisms. The question of the apoptosis and tumorigenicity, amongst others. These differences in mechanisms and consequences of intercellular heterogeneity is not behavior will be important on a cellular level but might also be purely academic given that the treatment of many diseases is important at the population level as a means to phenotypically complicated by heterogeneity that renders some cells more resistant diversify the population, and either better meet the physiological to treatment than others. In this Commentary, we review evidence needs of the organism (e.g. cells of the adaptive immune system that concerning the underlying sources of organelle heterogeneity, how express highly variable antigen receptors) or improve the overall fitness of a population (e.g. Escherichia coli persistence, where a Department of Biochemistry and Biophysics, Center for Cellular Construction, few cells in each population adopt a drug-tolerant state). University of California, San Francisco, CA 94158, USA. A second motivation for studying heterogeneity at the subcellular level is that it may lend fundamental insight into how the cellular *Author for correspondence ([email protected]) structure is formed. Often in physics, once one has a model that can

W.F.M., 0000-0002-8467-5763 account for the average behavior of a system, a second way to test Journal of Cell Science

819 COMMENTARY Journal of Cell Science (2017) 130, 819-826 doi:10.1242/jcs.181024 the validity of the model is to ask whether or not it can account for at the individual cell level such that each cell’s choice occurs the fluctuations as well as the average behavior of the system. We independently of its neighbors’. In this case, there is no global therefore believe that quantitative analysis of noise in a biological program dictating the precise spatial arrangement of photoreceptors, system will be a powerful way to probe the fundamental principles only an overall average dictated by the statistical properties of each that govern its behavior. Analyses of fluctuations and noise have, in choice. the past, been used to probe a wide range of molecular phenomena, In addition to the distinction between ‘directed’ and ‘non-directed’ such as ion channel conductance (Neher and Stevens, 1977), heterogeneities, another important distinction can be drawn between flagellar rotation (Samuel and Berg, 1995; Shaevitz et al., 2005) and irreversible differentiation and reversible fluctuations. Some cell-to- neurotransmitter exocytosis (Neher and Sakaba, 2001). We believe cell variation reflects permanent committed heterogeneities (e.g. that similar approaches may provide a way to probe the mechanisms lineage commitments, photoreceptor color choice, apoptosis), that regulate cellular architecture and behavior. whereas other heterogeneities fluctuate and are reversible, as is the In this Commentary, we begin with an overview of cellular case in Escherichia coli persistence (Balaban et al., 2004) and heterogeneity and illustrate the many contexts in which it is Bacillus subtilis competence (Maamar et al., 2007). observed. We continue with a detailed discussion of possible These observations underline the complexities inherent in sources of heterogeneity, which include temporal intercellular heterogeneities and highlight the important role they progression, noisy gene expression and fluctuations in organelle play in organismal development and behavior. Although important dynamics. We then address questions of physiological relevance – in normal healthy biology, these heterogeneities are also prevalent in other words, does heterogeneity at the level of cellular structure in many diseases and present significant therapeutic challenges, a and composition lead to differences in cellular behavior and topic we will explore in the second half of this Commentary. function? Finally, we motivate the study of cellular noise and heterogeneity with the observation that many clinically important Noise at different scales – molecular versus organelle-level diseases are characterized by extensive heterogeneity and thus an variation improved understanding of this heterogeneity will be integral in Cellular structure is a multi-scale phenomenon, with molecular- developing better therapeutic strategies. scale processes interacting with those at the organelle and cellular levels. We draw a distinction between molecular events, by which Types of intercellular phenotypic variability we mean events of the central dogma (e.g. , At the behavioral level, phenotypic heterogeneities can be broadly and protein turnover) and organelle dynamics – i.e. the synthesis, subdivided into two classes: (i) ‘directed’ heterogeneities that play a inheritance and degradation of organelles. Given the fact that role in normal developmental processes, and (ii) ‘non-directed’ heterogeneities, which do not exist as part of coordinated A B developmental pathways, but instead occur spontaneously owing ‘Directed’ ‘Non-directed’ to the inherent stochasticity of molecular processes. We note that the distinction between directed and non-directed is purely teleological, rather than mechanistic, and is presented primarily to inform the reader of different modes by which heterogeneities arise rather than to imply any mechanism. Directed heterogeneities are the result of cellular decision- making processes that are coordinated within a population of cells to produce variable behavior across cells in a spatially precise and Morphogen gradient Stochastic decisions replicable fashion (Fig. 1A). During the development of multicellular organisms, a single cell undergoes many cycles of division to form a collection of cells that, although genetically clonal, exhibits specialized functions within each cell or group of Head Thorax Abdomen cells. A common source of directed heterogeneity is asymmetrical cell division, in which a cell divides to produce two daughter cells of differing fates. A classic example of this type of deterministic heterogeneity is found in Drosophila melanogaster development, where ganglion mother cells consistently undergo asymmetric – division to give rise to two daughter cells a larger cell that will Body plan development Photoreceptor color choice become a motor neuron and a smaller sibling cell of unspecified developmental fate (Bhat et al., 2014). Fig. 1. ‘Directed’ vs ‘non-directed’ heterogeneity. (A) Schematic illustration ‘ ’ In contrast, non-directed heterogeneity arises through mechanisms of an example of directed heterogeneity in which a morphogen gradient produces a coordinated, replicable and spatially precise pattern of cellular whereby cells in a population make individual statistically differentiation (top). This type of process is responsible for the coordinated independent decisions. One example of this is the generation of process of body plan development in Drosophila melanogaster (bottom). color-specific photoreceptors in the compound eye of D. (B) Schematic illustration of an example of ‘non-directed’ heterogeneity shows melanogaster (Fig. 1B). During eye maturation, stochastic each cell making a stochastic cell fate decision that is statistically independent expression directs each photoreceptor cell to from those of its neighbors (top). This type of process is responsible for the express either a blue- or green-sensitive variant of rhodopsin (Wernet stochastic choice of which color-sensitive variant of rhodopsin will be et al., 2006; Mikeladze-Dvali et al., 2005). Once these variants are expressed in each photoreceptor cell of the Drosophila melanogaster compound eye (bottom). The image of the Drosophila compound eye is expressed, the cells become behaviorally heterogeneous, exhibiting courtesy of Justin P. Kumar (Indiana University, Bloomington, IN), and the preferential sensitivity to different wavelengths of light. It is image in the inset has been reproduced from Wernet et al. (2006) with important to note that photoreceptor choice is made stochastically permission (@Nature Publishing Group). Journal of Cell Science

820 COMMENTARY Journal of Cell Science (2017) 130, 819-826 doi:10.1242/jcs.181024 organelles can encapsulate entire biochemical pathways, relevant, this type of allometric size scaling will result in functional fluctuations in organelle abundance or size are expected to have heterogeneity among a population of cells at different stages of significant effects on biochemical output (Marshall, 2012). It is growth. There are thus two sources of size variation for any important to note that fluctuations at the organelle and cellular organelle – variation linked to cell size through a scaling scales do not necessarily follow directly from molecular-level relationship, and individual variation that may arise either fluctuations, underlining the importance of studying fluctuations at spontaneously at the organelle level or as a result of fluctuations many different scales. at the molecular level. These can in principle be distinguished by Perhaps the most extensively studied source of variation is first estimating the scaling relationship that best describes the molecular variation. All cellular processes are under genetic control, average behavior of many cells and then quantifying the degree to often mediated by complex signaling pathways. Because gene which individual organelle measurements deviate from the expression, signaling and epigenetic regulation all involve organelle size predicted by this scaling relationship. potentially small numbers of molecules and their weak Several studies have been undertaken to quantify the contribution intermolecular interactions, statistical fluctuations can have large of the cell cycle state to the overall population-level heterogeneity, effects on gene expression and signaling (Raser and O’Shea, 2004; with differing conclusions depending on the method of statistical Elowitz et al., 2002; Volfson et al., 2006; Eldar and Elowitz, 2010; analysis. Single-cell RNA sequencing (RNA-Seq) comparisons of Ladbury and Arold, 2012). Fluctuations arise not only from known cell cycle-related transcripts with transcripts of unknown variation in the production rates of molecules but also from importance to the cell cycle suggest that only 7% of intercellular variation in the partitioning of small numbers of molecules during transcriptional variability arises from differences in cell cycle state cell division (Huh and Paulsson, 2011). Because noise at the level of (Buettner et al., 2015; McDavid et al., 2016). However, these gene expression has been extensively reviewed elsewhere, in this studies focus on transcriptional variability at a fixed time point, Commentary, we focus instead on variation at the organelle level, which can give a deceptive view of the level of stochasticity at work with the understanding that heterogeneity at this level may in individual cells, suggesting that live-cell measurements will be contribute to intercellular heterogeneity in ways different from essential in developing a more comprehensive understanding of those engendered by molecular heterogeneity. intercellular heterogeneity.

Sources of organelle-level heterogeneity Temporal heterogeneity resulting from the circadian cycle Temporal heterogeneity resulting from the cell cycle The cell cycle is an important source of temporal variation but is by Random sampling from an asynchronous population of cells no means the only one. In many cells, the circadian cycle can drive undergoing deterministic temporal variation in the form of the variation in cell behavior and structure. This is most apparent in cell cycle will produce heterogeneity that is impossible to photosynthetic cells for which the time of day is of direct distinguish from noise. However, this type of variation is likely to physiological relevance. In plant and algal cells, the circadian clock be an important factor in intercellular heterogeneity because many regulates remodeling of chloroplast architecture (Nassoury et al., aspects of cellular structure and function undergo regulated changes 2005). In flagellated green algae, such as Chlamydomonas as a function of the cell cycle. reinhardtii, flagella typically begin forming just before the onset of In , microtubules undergo a dramatic reorganization daylight and then gradually grow throughout the day. During the dark during which interphase microtubules are disassembled to form phase of the cycle, when cells undergo division, the flagella resorb shorter mitotic spindles that facilitate chromosome condensation (Kater, 1929). When flagellar lengths are measured, the variance of and congression (Cassimeris, 1999). Similarly, the endoplasmic the length distribution is substantially greater in cultures that are ‘free reticulum (ER) has been shown to rearrange both its structure and running’–i.e. grown under continuous light, compared to cultures in subcellular localization during mitosis (Bergman et al., 2015), which the cells are synchronized and measured at specific time points presumably as an aid to segregating the organelle to both daughters. in the cycle (W.F.M., unpublished data). This phenomenon has been During the G1–S transition, mitochondria become highly fused. shown to extend to mammalian cells, where the circadian clock drives Maintenance of this hyperfused state through inhibition of the periodic changes in actin dynamics (Gerber et al., 2013) and Golgi mitochondrial fission protein Dnp1 permanently blocks entry into S organization (Muueller and Gerber, 1985). phase (Mitra et al., 2009), highlighting the link between Both sources of temporal variation described here (cell cycle and mitochondrial morphology and cell cycle progression. circadian cycle) are deterministic predictable processes, although it During G1, cells do not undergo drastic morphological changes is worth noting that if cells are not synchronized, the contribution of but instead undergo continuous growth, such that cells further along these processes to intercellular heterogeneity becomes difficult to in G1 are larger. This effect ramifies down to the organelle level, as unravel. For example, if cells with a circadian rhythm are grown in many organelles have been shown to scale with cell size, with the the absence of an entraining stimulus, at any point in time, different size of an organelle (as judged by its surface area or volume) often cells in the population will be at different phases of their circadian scaling as a power law with the volume of the cell (Wuehr et al., cycles, thus generating what looks like an apparently random source 2008; Chan and Marshall, 2010; Uchida et al., 2011; Rafelski et al., of heterogeneity at the population level, even though the underlying 2012; Chan and Marshall, 2014). The surface area and volume can, source is a deterministic process at the single cell level. and often do, show different scaling exponents (Chan and Marshall, 2014), and as a result, as a cell grows, not only does organelle size Heterogeneity resulting from fluctuations in organelle biogenesis increase but organelle shape can additionally change as a function rates of the surface-to-volume ratio of the organelle. Quantifying the As discussed earlier, the molecular processes of life are subject to relationship between organelle and cell size scaling for a given cell substantial fluctuations owing to the stochasticity of thermodynamic type is thus critical for dissecting the possible contribution of cell forces that drive molecular interactions. Consequently, all cellular growth to heterogeneity. If either the absolute size of an organelle, trafficking, biosynthetic and self-assembly processes are subject to or the size of an organelle relative to the cell size, is functionally noise and variation, and given that the structure of cells and organelles Journal of Cell Science

821 COMMENTARY Journal of Cell Science (2017) 130, 819-826 doi:10.1242/jcs.181024 is the result of many such processes, it is to be expected that overall both more and less unequal than that predicted by binominal statistics organelle and cellular structure may also exhibit heterogeneity. This (Fig. 2). For example, the budding yeast Saccharomyces cerevisiae hypothesis has been proven using live-cell imaging experiments, exhibits more-than-random asymmetrical partitioning, where where stochastic fluctuations in growth rates have been observed for mitochondria are evenly partitioned between the mother and bud neurite outgrowth (Odde and Buettner, 1998) as well as endocytotic cells during cell division, despite the bud cell being significantly activity (Dey et al., 2014). Similar fluctuations are seen when cellular smaller in volume (Boldogh et al., 2001). In contrast, studies of GFP- processes are reconstituted in vitro – for example, microtubules tagged Golgi complexes in HeLa cells show less-than-random display momentary fluctuations in stability that influence microtubule asymmetrical partitioning, a phenomenon attributed to the growth and shrinkage rates (Duellberg et al., 2016). Many organelles association of Golgi complexes into mitotic clusters that lower the also undergo fission and fusion, processes which themselves are overall number of units of distribution (Shima et al., 1997). potentially subject to stochastic variation. In such cases, although the These deviations from binomial partitioning highlight the presence total mass of an organelle can only change as a result of biogenesis or of cell-intrinsic mechanisms that result in asymmetrical partitioning, degradation, fission and fusion events can drive changes in the size of although these mechanisms are likely to differ depending on the individual organelles without altering their total mass within the cell. degree of asymmetry (e.g. some mechanisms may augment Interestingly, a recent model predicts that – assuming organelle- asymmetry, whereas others may diminish it). Importantly, the intrinsic rate constants that are independent of the number of existence of non-binomially distributed partitioning does not organelles – fission and fusion events, coupled with organelle eliminate noise as a potential contributor to asymmetry, it only tells synthesis and degradation, can produce substantial heterogeneity in us that noise in and of itself cannot fully explain the observed data. It organelle abundance (Fig. 3) (Mukherji and O’Shea, 2014). Such remains possible that observed distributions reflect a convolution of models have been found to be in good agreement with measurements cell-intrinsic mechanisms coupled with stochastic noise. of intercellular variability in the number of yeast vacuoles, A second factor that influences organelle heterogeneity is the peroxisomes and Golgi (Mukherji and O’Shea, 2014). Recent timescale on which organelle abundance in daughter cells recovers quantitative analysis of size in living cells indicates that the rate of organelle biogenesis is independent of vacuole size or cell size (Chan A et al., 2016), consistent with the assumptions of the Mukherji and O’Shea model. 1:1 Binomial Organelle biogenesis entails two processes, both of which are potentially subject to noise. The first is biosynthesis of the component molecules. As discussed previously, noise in gene More random than binomial Less random than binomial expression leads to variation in the quantity of precursor molecules produced (Fig. 3). The second facet of biogenesis is intracellular trafficking. In many cases, components of an organelle are synthesized in one cellular location before being trafficked to the final site of assembly, such that variation in trafficking pathways can directly lead to variations in organelle size (Fig. 3) (Chan and B 1 Marshall, 2014). These trafficking processes themselves have been 0.9 shown to be subject to large variation. For example, assembly of 0.8 cilia and flagella require kinesin-based intraflagellar transport (IFT) 0.7 of tubulin and other building blocks, a process mediated by IFT 0.6 protein complexes. These complexes organize into linear arrays 0.5 called IFT trains, where both the size of these trains and the timing 0.4 of their entry into the cilia are variable. Similar fluctuations in processes contributing to organelle biogenesis have been observed 0.3 0.2 in nuclear trafficking (Kubitscheck et al., 2005), cytoskeletal Probability density function transport (Wacker et al., 1997; Bouzat, 2016) and membrane vesicle 0.1 0 fusion during exocytosis (Bennett and Kearns, 2000). –3 –2 –1 0 1 2 3

Heterogeneity resulting from asymmetry in organelle partitioning Fig. 2. Variable models for organelle partitioning during cellular division. Partitioning of organelles between daughter cells during cell (A) Schematic illustration of four different modes of organelle partitioning. Strict division can be an important source of variability, but this 1:1 partitioning (green) partitions organelles evenly between two daughter depends on (a) how evenly the partitioning system distributes cells. Noise-driven partitioning produces partitioning of organelles with a organelles between the two daughter cells and (b) how rapidly, if at binomial distribution (blue). Cell-intrinsic mechanisms that actively contribute to asymmetrical partitioning may produce asymmetries that are either more all, organelle abundance in the daughter cells is restored to the (red) or less (yellow) random than those predicted by binomial statistics. (B) A average. From a statistical perspective, if organelles are randomly simulated probability density function showing the possible range of partitioned into two equally sized daughter cells, the number of distributions available in each partitioning model. In 1:1 partitioning, only one organelles inherited will follow a binomial distribution. Some possible partitioning distribution is available, represented by a single vertical partitioning events are clearly not random, as evidenced by the near line (green). Models following less-than-random partitioning statistics (yellow) 1:1 segregation of chromosomes and centrosomes during mitosis. are more tightly constrained than those following binomial partitioning statistics (blue), whereas models following more-than-random partitioning statistics However, other organelles, such as mitochondria and endosomes, are (red) are less tightly constrained. Depending on the consistency of cell-intrinsic known to exhibit asymmetrical partitioning (Boldogh et al., 2001; partitioning mechanisms, these distributions may change [e.g. in a 100% Catlett and Weisman, 2000; Knoblach and Rachubinski, 2015). consistent mechanism for producing more-than-random partitioning, the area

Interestingly, asymmetrical partitioning can produce distributions surrounding x=0 (symmetrical partitioning) would not be accessible]. Journal of Cell Science

822 COMMENTARY Journal of Cell Science (2017) 130, 819-826 doi:10.1242/jcs.181024 to the population mean. This reversion to the mean has been differential sensitivity to external inputs (e.g. drug response, cell- observed for centrioles; cells have a mechanism for adjusting copy extrinsic apoptosis and differentiation cues). Currently, intercellular number by dampening centriole synthesis when too many centrioles phenotypic variability has mostly been studied at the level of are present and conversely for triggering synthesis when no molecular variability in gene expression, with the contributions of centriole is present (Marshall, 2007). Similar observations have organelle variability remaining largely unexplored. In the second been made in algal cells, which modulate chloroplast synthesis to half of this Commentary, we present evidence linking organelle size dampen any variation in chloroplast number after their random and shape to cellular behavior to underline the potential role that partitioning in mitosis (Hennis and Birky, 1984). We note that the organelle heterogeneity plays in intercellular phenotypic variability. propensity for and rate of recovery is likely to vary from organelle to We conclude with an overview of the medical challenges posed by organelle, as well as between cell types. intercellular heterogeneity and discuss how organelle-level studies may contribute to a fuller understanding of this heterogeneity. Heterogeneity resulting from fluctuations in organelle degradation The hypothesis that organelle-level heterogeneity may lead to rates intercellular behavioral heterogeneity is premised on extensive links Degradation of organelles is a final contributor to the steady-state between organelle morphology and cellular behavior that have been quantity of any organelle (Fig. 3). The statistical nature of the observed through the years. As an example, apoptosis, or decision-making process that drives organelle degradation has not programmed cell death, is characterized by extensive yet been extensively studied. Under the simplest model, if organelle mitochondrial fragmentation (Karbowski et al., 2004). Inhibition degradation is triggered by a single random decision, organelle of the proteins responsible for fragmentation results in hyperfused lifetimes are expected to follow an exponential distribution. If, mitochondria and a consequent decrease in apoptosis (Thomenius however, organelle degradation is triggered by changes in their et al., 2011), suggesting that fragmentation may be integral to functional state or by accumulated damage, their lifetimes may be apoptosis rather than strictly a secondary consequence. At the more narrowly distributed. In mitochondria, accumulated oxidative clinical level, changes in mitochondrial morphology have been damage is associated with a slowdown in rates of degradation, linked to several neurodegenerative disorders such as Alzheimer’s producing enlarged mitochondria with lower rates of ATP (Fig. 4A) (Wang et al., 2008), optic atrophy (Frank, 2006) and production and increased rates of cell death (Moreira et al., 2010). Charcot–Marie–Tooth neuropathy (Frank, 2006). Similar findings These observations suggest the existence and importance of cell- have been observed for the ER, with ER morphology expanding in regulatory pathways that control degradation rates. response to cellular stress (Wikstrom et al., 2013), as well as in the pancreatic β-cells of type 2 diabetic patients compared to healthy From molecular and organelle heterogeneity to functional control patients (Fig. 4B). At the nuclear level, changes in heterogeneity morphology have long been diagnostic of cancer, with the extent We have discussed a number of sources of heterogeneity, both at the of nuclear aberration often trending with the severity of prognosis molecular and organelle levels, but for these heterogeneities to have (Fig. 4C) (Cibas and Ducatman, 2009). biological consequence, they must relate, in some way, to cellular Although these correlative observations do not denote a causal phenotype and behavior. These differences could include link between organelle morphology and cellular behavior, the differences in cell-intrinsic behaviors and outputs (e.g. cell ensemble of these observations leads to the compelling question of growth, cell-intrinsic apoptosis and signaling), as well as whether organelle morphology itself may play an integral role in determining cellular state and function, especially if we consider that organelle-level heterogeneity need not be a strict corollary of molecular heterogeneity, as previously discussed. An improved understanding of the link between organelle morphology and cellular behavior, whether it be correlative, causal, or a mix of the Fusion two, will prove critical in furthering our understanding of the principles that govern cellular architecture and behavior.

Degradation Trafficking Intercellular heterogeneity as a key challenge in medicine In this final section, we examine intercellular heterogeneity as it contributes to the development, progression, and treatment of two of Fragmentation the most pervasive and clinically challenging diseases in the world today – cancer and HIV/AIDS. We illustrate the challenges presented by intercellular heterogeneities and outline future Transcriptional noise avenues by which these heterogeneities may be controlled in the Biogenesis hopes of advancing therapeutic strategies and improving patient outcomes. Cancer is a highly heterogeneous disease characterized by a vast array of subtypes, each with its own unique set of genomic Fig. 3. Schematic illustration of several mechanisms that contribute to signatures and molecular markers. Compounding this complexity is organelle morphology. In this case, mitochondrial (pink) morphology is the observation of significant genomic heterogeneity within single influenced by transcriptional noise (blue) of molecular precursors, as well as tumors (Navin and Hicks, 2010; Torres et al., 2007; Zhang et al., variability in intracellular trafficking of these precursors to the site of assembly. 2014), such that chemotherapeutics targeting single pathways or Other mechanisms include organelle biogenesis, degradation (by the lysosome, green), fusion and fragmentation. Temporally driven processes, oncogenic drivers often fail at fully abolishing a tumor (Fisher et al., such as the cell and circadian cycles, also influence organelle morphology but 2013; Diaz Jr et al., 2012; Gore and Larkin, 2011; Szerlip et al., are not pictured here. 2012). This difficulty in treatment can translate to poorer patient Journal of Cell Science

823 COMMENTARY Journal of Cell Science (2017) 130, 819-826 doi:10.1242/jcs.181024

A B

C

Fig. 4. Organelle morphology changes observed in diseased states. (A) Fluorescence images taken of fibroblast cells taken from a healthy control patient (left) and a patient diagnosed with Alzheimer’s disease (right). Note the severe condensation and perinuclear localization of mitochondria (yellow-orange) in the Alzheimer’s patient as compared to in the healthy control. (B) Electron micrographs of a pancreatic β-cell taken from a healthy control patient (left) and a patient diagnosed with type II diabetes (right). ER, endoplasmic reticulum; IG, insulin granules; M, mitochondria; N, nucleus. Note the enlarged ribbons of ER in the diabetic patient as compared to in the healthy control. (C) Cervical cells derived from a healthy control patient (left) and a patient diagnosed with late-stage cervical cancer (right). Note the severely enlarged nuclei (blue) and diminished cell-to-nucleus ratio in the cancer patient as compared to the healthy control. Images in A reproduced courtesy of Wang et al. (2008), ©Elsevier; images in B reproduced courtesy of Marchetti et al. (2007), ©Springer; images in C reproduced courtesy of Cibas and Ducatman (2009), ©Elsevier. outcomes, as studies have shown that higher intratumoral genetic integrated in resting CD4+ T cells (Chun et al., 1995), with heterogeneity can correlate with a higher likelihood for patient spontaneous reactivation in the absence of cART quickly producing relapse (Zhang et al., 2014). viral loads similar to those observed before treatment (Dahabieh To complicate matters further, intratumoral heterogeneity extends et al., 2015). to the epigenetic and transcriptional levels as well. Recent studies One promising approach to developing a cure for HIV/AIDS have demonstrated that, within populations of clonally expanded would be to reactivate the latent pool of HIV-1 provirus within the cancer cells, individual cells can exhibit variable chemotherapeutic protective presence of cART; eventually, the latent pool would be responses as a function of either their transcriptional (Lee et al., cleared and cART could be discontinued. However, this approach 2014) or epigenetic (Sharma et al., 2010) states. Furthermore, these has proven challenging, as the mechanisms of HIV-1 reactivation chemotherapeutic sensitivities have been found to be transient and are multifaceted, and studies have shown that upon 99%+ reversible, with outgrowth of drug-resistant cells producing a mixed reactivation of T cells harboring latent HIV-1 provirus, less than population of drug-resistant and drug-sensitive cells. These 1% of cells show successful viral induction (Ho et al., 2013). observations highlight the importance of non-genetic drivers Although many cells had genetic mutations or deletions that of behavioral heterogeneity as important determinants of rendered the virus replication-incompetent, a significant fraction chemotherapeutic response. Non-genomic markers have (11.7%) of cells remained replication-competent from a genetic additionally proven clinically useful in diagnosing patients and in standpoint. Additionally, the authors found that, upon a second assessing their prognosis, as is the case in glioblastoma, where the round of T-cell activation, a fraction of previously non-induced cells promoter methylation state of a DNA repair enzyme has been began actively producing virus; this transient nature of HIV latency identified as the most clinically predictive biomarker (Parker et al., has been attributed to stochastic gene expression of Tat, the HIV 2016). trans-activator of transcription (Weinberger et al., 2005). The ensemble of these observations underlines the considerable In theory, one way to achieve more complete induction of latent challenges posed by intercellular heterogeneity in the treatment of HIV-1 provirus would be to increase the noisiness of HIV induction cancer. Although extensive, our understanding of this heterogeneity such that, over a given period of time, a higher proportion of cells is still limited, and given previous arguments for the existence of falls into an induction-competent state. This principle was illustrated organelle-level heterogeneity and the potential consequences for in a recent study that screened for compounds altering the cellular behavior, we believe that future studies of organelle-level expression noise of the HIV LTR promoter; the authors found heterogeneity may aid in developing a more comprehensive that, in combination with T-cell-activating compounds, noise- understanding of intratumoral heterogeneity and the consequences enhancing compounds could successfully reactivate HIV in up to posed therein for tumor behavior. 50% of resting T cells (Dar et al., 2014). The task of controlling and mitigating intercellular heterogeneity These findings highlight the potential promise of therapies is extremely challenging, but recent work in the field of HIV/AIDS targeting intercellular heterogeneity, and although much work presents promising evidence that this may be possible. HIV/AIDS remains to be done to develop and refine modalities for modulating treatment was revolutionized in 1997 with the development of this heterogeneity, these recent findings present a promising proof- combination antiretroviral therapies (cART) (Hammer et al., 1997; of-principle that addressing sources of intercellular heterogeneity Gulick et al., 1997). This advancement dramatically increased the can productively alter cellular behavior and disease pathology. life expectancy of HIV/AIDS patients by changing HIV/AIDS from an acute disease to a chronic one in which HIV loads can be Conclusions and future prospects managed at low levels over many decades. However, cART can Studies in recent years have highlighted the incredible complexity of never be discontinued, as a stable reservoir of latent HIV-1 remains biological organisms, with many layers of noise and variability built Journal of Cell Science

824 COMMENTARY Journal of Cell Science (2017) 130, 819-826 doi:10.1242/jcs.181024 on top of an otherwise deterministic genome. These layers Cassimeris, L. (1999). Accessory protein regulation of microtubule dynamics contribute to the phenomenon of intercellular phenotypic throughout the cell cycle. Curr. Opin. Cell Biol. 11, 134-141. Catlett, N. L. and Weisman, L. S. (2000). Divide and multiply: organelle partitioning variability, whereby genetically identical cells can vary widely in in yeast. Curr. Opin. Cell Biol. 12, 509-516. their behavior. Many studies to date have identified molecular-level Chan, Y.-H. M. and Marshall, W. F. (2010). Scaling properties of cell and organelle variability as an important source of phenotypic variability. size. Organogenesis 6, 88-96. Chan, Y.-H. M. and Marshall, W. F. (2014). Organelle size scaling of the budding However, given significant mechanisms for organelle-level yeast vacuole is tuned by membrane trafficking rates. Biophys. 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Challenges and opportunities for converting renal cell carcinoma into a chronic disease with targeted therapies. Br. J. Cancer Acknowledgements 104, 399-406. We would like to acknowledge past and present members of the Marshall lab for Gulick, R. M., Mellors, J. W., Havlir, D., Eron, J. J., Gonzalez, C., McMahon, D., guiding our thoughts about cellular heterogeneity. We also thank Adam Olshen for Richman, D. D., Valentine, F. T., Jonas, L., Meibohm, A. et al. (1997). helpful discussions about statistics. Treatment with indinavir, zidovudine, and lamivudine in adults with human immunodeficiency virus infection and prior antiretroviral therapy. N. Engl. J. Med. Competing interests 337, 734-739. The authors declare no competing or financial interests. Hammer, S. M., Squires, K. E., Hughes, M. D., Grimes, J. M., Demeter, L. M., Currier, J. S., Eron, Jr. J. J., Feinberg, J. E., Balfour, H. H., Deyton, L. R. et al. Funding (1997). 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