High-throughput measurement of single-cell growth rates using serial microfluidic mass sensor arrays

Cell Size Regulation Induces Sustained Oscillations in the Population Growth Rate

We study the effect of correlations in generation times on the dynamics of population growth of microorganisms. We show that any nonzero correlation that is due to cell-size regulation, no matter how small, induces long-term oscillations in the population growth rate. The population only reaches its steady state when we include the often-neglected variability in the growth rates of individual cells. We discover that the relaxation timescale of the population to its steady state is determined by the distribution of single-cell growth rates and is surprisingly independent of details of the division process such as the noise in the timing of division and the mechanism of cell-size regulation. We validate the predictions of our model using existing experimental data and propose an experimental method to measure single-cell growth variability by observing how long it takes for the population to reach its steady state or balanced growth.

Successive Paradigm Shifts in the Bacterial Cell Cycle and Related Subjects

A paradigm shift in one field can trigger paradigm shifts in other fields. This is illustrated by the paradigm shifts that have occurred in bacterial physiology following the discoveries that bacteria are not unstructured, that the bacterial cell cycle is not controlled by the dynamics of peptidoglycan, and that the growth rates of bacteria in the same steady-state population are not at all the same. These paradigm shifts are having an effect on longstanding hypotheses about the regulation of the bacterial cell cycle, which appear increasingly to be inadequate. I argue that, just as one earthquake can trigger others, an imminent paradigm shift in the regulation of the bacterial cell cycle will have repercussions or "paradigm quakes" on hypotheses about the origins of life and about the regulation of the eukaryotic cell cycle.

Excessive Cell Growth Causes Cytoplasm Dilution And Contributes to Senescence

Cell size varies greatly between cell types, yet within a specific cell type and growth condition, cell size is narrowly distributed. Why maintenance of a cell-type specific cell size is important remains poorly understood. Here we show that growing budding yeast and primary mammalian cells beyond a certain size impairs gene induction, cell-cycle progression, and cell signaling. These defects are due to the inability of large cells to scale nucleic acid and protein biosynthesis in accordance with cell volume increase, which effectively leads to cytoplasm dilution. We further show that loss of scaling beyond a certain critical size is due to DNA becoming limiting. Based on the observation that senescent cells are large and exhibit many of the phenotypes of large cells, we propose that the range of DNA:cytoplasm ratio that supports optimal cell function is limited and that ratios outside these bounds contribute to aging.

Optimal Segregation of Proteins: Phase Transitions and Symmetry Breaking

Asymmetric segregation of key proteins at cell division-be it a beneficial or deleterious protein-is ubiquitous in unicellular organisms and often considered as an evolved trait to increase fitness in a stressed environment. Here, we provide a general framework to describe the evolutionary origin of this asymmetric segregation. We compute the population fitness as a function of the protein segregation asymmetry a, and show that the value of a which optimizes the population growth manifests a phase transition between symmetric and asymmetric partitioning phases. Surprisingly, the nature of phase transition is different for the case of beneficial proteins as opposed to deleterious proteins: a smooth (second order) transition from purely symmetric to asymmetric segregation is found in the former, while a sharp transition occurs in the latter. Our study elucidates the optimization problem faced by evolution in the context of protein segregation, and motivates further investigation of asymmetric protein segregation in biological systems.

Modeling cell population dynamics

 Quantitative modeling is quickly becoming an integral part of biology, due to the ability of mathematical models and computer simulations to generate insights and predict the behavior of living systems. Single-cell models can be incapable or misleading for inferring population dynamics, as they do not consider the interactions between cells via metabolites or physical contact, nor do they consider competition for limited resources such as nutrients or space. Here we examine methods that are commonly used to model and simulate cell populations. First, we cover simple models where analytic solutions are available, and then move on to more complex scenarios where computational methods are required. Overall, we present a summary of mathematical models used to describe cell population dynamics, which may aid future model development and highlights the importance of population modeling in biology.

Label-free density-based detection of adipocytes of bone marrow origin using magnetic levitation

The first report on application of magnetic levitation technology for detection of adipogenic cells based on single cell density measurement.

Modeling cell population dynamics

 Quantitative modeling is quickly becoming an integral part of biology, due to the ability of mathematical models and computer simulations to generate insights and predict the behavior of living systems. Single-cell models can be incapable or misleading for inferring population dynamics, as they do not consider the interactions between cells via metabolites or physical contact, nor do they consider competition for limited resources such as nutrients or space. Here we examine methods that are commonly used to model and simulate cell populations. First, we cover simple models where analytic solutions are available, and then move on to more complex scenarios where computational methods are required. Overall, we present a summary of mathematical models used to describe cell population dynamics, which may aid future model development and highlights the importance of population modeling in biology.

Linking single-cell measurements of mass, growth rate, and gene expression

Mass and growth rate are highly integrative measures of cell physiology not discernable via genomic measurements. Here, we introduce a microfluidic platform enabling direct measurement of single-cell mass and growth rate upstream of highly multiplexed single-cell profiling such as single-cell RNA sequencing. We resolve transcriptional signatures associated with single-cell mass and growth rate in L1210 and FL5.12 cell lines and activated CD8+ T cells. Further, we demonstrate a framework using these linked measurements to characterize biophysical heterogeneity in a patient-derived glioblastoma cell line with and without drug treatment. Our results highlight the value of coupled phenotypic metrics in guiding single-cell genomics.

Recent Advances in the Race to Design a Rapid Diagnostic Test for Antimicrobial Resistance

Even with advances in antibiotic therapies, bacterial infections persistently plague society and have amounted to one of the most prevalent issues in healthcare today. Moreover, the improper and excessive administration of antibiotics has led to resistance of many pathogens to prescribed therapies, rendering such antibiotics ineffective against infections. While the identification and detection of bacteria in a patient's sample is critical for point-of-care diagnostics and in a clinical setting, the consequent determination of the correct antibiotic for a patient-tailored therapy is equally crucial. As a result, many recent research efforts have been focused on the development of sensors and systems that correctly guide a physician to the best antibiotic to prescribe for an infection, which can in turn, significantly reduce the instances of antibiotic resistance and the evolution of bacteria "superbugs." This review details the advantages and shortcomings of the recent advances (focusing from 2016 and onward) made in the developments of antimicrobial susceptibility testing (AST) measurements. Detection of antibiotic resistance by genomic AST techniques relies on the prediction of antibiotic resistance via extracted bacterial DNA content, while phenotypic determinations typically track physiological changes in cells and/or populations exposed to antibiotics. Regardless of the method used for AST, factors such as cost, scalability, and assay time need to be weighed into their design. With all of the expansive innovation in the field, which technology and sensing systems demonstrate the potential to detect antimicrobial resistance in a clinical setting?

Microfluidic active loading of single cells enables analysis of complex clinical specimens

A fundamental trade-off between flow rate and measurement precision limits performance of many single-cell detection strategies, especially for applications that require biophysical measurements from living cells within complex and low-input samples. To address this, we introduce ‘active loading’, an automated, optically-triggered fluidic system that improves measurement throughput and robustness by controlling entry of individual cells into a measurement channel. We apply active loading to samples over a range of concentrations (1–1000 particles μL⁻¹), demonstrate that measurement time can be decreased by up to 20-fold, and show theoretically that performance of some types of existing single-cell microfluidic devices can be improved by implementing active loading. Finally, we demonstrate how active loading improves clinical feasibility for acute, single-cell drug sensitivity measurements by deploying it to a preclinical setting where we assess patient samples from normal brain, primary and metastatic brain cancers containing a complex, difficult-to-measure mixture of confounding biological debris.

Single-cell detection methods are limited by the trade-off between flow rate and measurement precision. Here the authors introduce active loading, an optically triggered microfluidic system to concentrate diluted cell samples, which reduces clogging and decreases processing time in single-cell assays.

Homeostasis of protein and mRNA concentrations in growing cells

Many experiments show that the numbers of mRNA and protein are proportional to the cell volume in growing cells. However, models of stochastic gene expression often assume constant transcription rate per gene and constant translation rate per mRNA, which are incompatible with these experiments. Here, we construct a minimal gene expression model to fill this gap. Assuming ribosomes and RNA polymerases are limiting in gene expression, we show that the numbers of proteins and mRNAs both grow exponentially during the cell cycle and that the concentrations of all mRNAs and proteins achieve cellular homeostasis; the competition between genes for the RNA polymerases makes the transcription rate independent of the genome number. Furthermore, by extending the model to situations in which DNA (mRNA) can be saturated by RNA polymerases (ribosomes) and becomes limiting, we predict a transition from exponential to linear growth of cell volume as the protein-to-DNA ratio increases.

Advances in online methods for monitoring microbial growth

Understanding the characteristics of microbial growth is of great significance to many fields including in scientific research, the food industry, health care, and agriculture. Many methods have been established to characterize the process of microbial growth. Online and automated methods, in which sample transfer is avoided, are popular because they can facilitate the development of simple, safe, and effective growth monitoring. This review focuses on advances in online monitoring methods over the last decade (2008-2018). We specifically focus on optic- and electrochemistry-based techniques, either through contact measurements or contactless measurement. Strengths and weaknesses of each set of methods are described and we also speculate on forthcoming trends in the field.

Current Trends of Microfluidic Single-Cell Technologies

The investigation of human disease mechanisms is difficult due to the heterogeneity in gene expression and the physiological state of cells in a given population. In comparison to bulk cell measurements, single-cell measurement technologies can provide a better understanding of the interactions among molecules, organelles, cells, and the microenvironment, which can aid in the development of therapeutics and diagnostic tools. In recent years, single-cell technologies have become increasingly robust and accessible, although limitations exist. In this review, we describe the recent advances in single-cell technologies and their applications in single-cell manipulation, diagnosis, and therapeutics development.

Cell size homeostasis: Metabolic control of growth and cell division

Joint regulation of growth rate and cell division rate determines cell size. Here we discuss how animal cells achieve cell size homeostasis potentially involving multiple signaling pathways converging at metabolic regulation of growth rate and cell cycle progression. While several models have been developed to explain cell size control, comparison of the two predominant models shows that size homeostasis is dependent on the ability to adjust cellular growth rate based on cell size. Consequently, maintenance of size homeostasis requires that larger cells can grow slower than small cells in relative terms. We review recent experimental evidence showing that such size adjustment occurs primarily at or immediately before the G1/S transition of the cell cycle. We further propose that bidirectional feedback between growth rate and size results in cell size sensing and discuss potential mechanisms how this may be accomplished.

Quantitative phase imaging unravels new insight into dynamics of mesenchymal and amoeboid cancer cell invasion

Observation and analysis of cancer cell behaviour in 3D environment is essential for full understanding of the mechanisms of cancer cell invasion. However, label-free imaging of live cells in 3D conditions is optically more challenging than in 2D. Quantitative phase imaging provided by coherence controlled holographic microscopy produces images with enhanced information compared to ordinary light microscopy and, due to inherent coherence gate effect, enables observation of live cancer cells' activity even in scattering milieu such as the 3D collagen matrix. Exploiting the dynamic phase differences method, we for the first time describe dynamics of differences in cell mass distribution in 3D migrating mesenchymal and amoeboid cancer cells, and also demonstrate that certain features are shared by both invasion modes. We found that amoeboid fibrosarcoma cells' membrane blebbing is enhanced upon constriction and is also occasionally present in mesenchymally invading cells around constricted nuclei. Further, we demonstrate that both leading protrusions and leading pseudopods of invading fibrosarcoma cells are defined by higher cell mass density. In addition, we directly document bundling of collagen fibres by protrusions of mesenchymal fibrosarcoma cells. Thus, such a non-invasive microscopy offers a novel insight into cellular events during 3D invasion.

Smart Cell Culture Systems: Integration of Sensors and Actuators into Microphysiological Systems

Technological advances in microfabrication techniques in combination with organotypic cell and tissue models have enabled the realization of microphysiological systems capable of recapitulating aspects of human physiology in vitro with great fidelity. Concurrently, a number of analysis techniques has been developed to probe and characterize these model systems. However, many assays are still performed off-line, which severely compromises the possibility of obtaining real-time information from the samples under examination, and which also limits the use of these platforms in high-throughput analysis. In this review, we focus on sensing and actuation schemes that have already been established or offer great potential to provide in situ detection or manipulation of relevant cell or tissue samples in microphysiological platforms. We will first describe methods that can be integrated in a straightforward way and that offer potential multiplexing and/or parallelization of sensing and actuation functions. These methods include electrical impedance spectroscopy, electrochemical biosensors, and the use of surface acoustic waves for manipulation and analysis of cells, tissue, and multicellular organisms. In the second part, we will describe two sensor approaches based on surface-plasmon resonance and mechanical resonators that have recently provided new characterization features for biological samples, although technological limitations for use in high-throughput applications still exist.

Opto-thermally excited multimode parametric resonance in graphene membranes

In the field of nanomechanics, parametric excitations are of interest since they can greatly enhance sensing capabilities and eliminate cross-talk. Above a certain threshold of the parametric pump, the mechanical resonator can be brought into parametric resonance. Here we demonstrate parametric resonance of suspended single-layer graphene membranes by an efficient opto-thermal drive that modulates the intrinsic spring constant. With a large amplitude of the optical drive, a record number of 14 mechanical modes can be brought into parametric resonance by modulating a single parameter: the pre-tension. A detailed analysis of the parametric resonance allows us to study nonlinear dynamics and the loss tangent of graphene resonators. It is found that nonlinear damping, of the van der Pol type, is essential to describe the high amplitude parametric resonance response in atomically thin membranes.

Opportunities and Challenges in Implementation of Multiparameter Single Cell Analysis Platforms for Clinical Translation

The high-content interrogation of single cells with platforms optimized for the multiparameter characterization of cells in liquid and solid biopsy samples can enable characterization of heterogeneous populations of cells ex vivo. Doing so will advance the diagnosis, prognosis, and treatment of cancer and other diseases. However, it is important to understand the unique issues in resolving heterogeneity and variability at the single cell level before navigating the validation and regulatory requirements in order for these technologies to impact patient care. Since 2013, leading experts representing industry, academia, and government have been brought together as part of the Foundation for the National Institutes of Health (FNIH) Biomarkers Consortium to foster the potential of high-content data integration for clinical translation.

Targeting minimal residual disease: a path to cure?

Therapeutics that block kinases, transcriptional modifiers, immune checkpoints and other biological vulnerabilities are transforming cancer treatment. As a result, many patients achieve dramatic responses, including complete radiographical or pathological remission, yet retain minimal residual disease (MRD), which results in relapse. New functional approaches can characterize clonal heterogeneity and predict therapeutic sensitivity of MRD at a single-cell level. Preliminary evidence suggests that iterative detection, profiling and targeting of MRD would meaningfully improve outcomes and may even lead to cure.

A Platform for High-Throughput Assessments of Environmental Multistressors

A platform compatible with microtiter plates to parallelize environmental treatments to test the complex impacts of multiple stressors, including parameters relevant to climate change and point source pollutants is developed. This platform leverages (1) the high rate of purely diffusive gas transport in aerogels to produce well-defined centimeter-scale gas concentration gradients, (2) spatial light control, and (3) established automated liquid handling. The parallel gaseous, aqueous, and light control provided by the platform is compatible with multiparameter experiments across the life sciences. The platform is applied to measure biological effects in over 700 treatments in a five-parameter full factorial study with the microalgae Chlamydomonas reinhardtii. Further, the CO2 response of multicellular organisms, Lemna gibba and Artemia salina under surfactant and nanomaterial stress are tested with the platform.

Stable Regulation of Cell Cycle Events in Mycobacteria: Insights From Inherently Heterogeneous Bacterial Populations

Model bacteria, such as E. coli and B. subtilis, tightly regulate cell cycle progression to achieve consistent cell size distributions and replication dynamics. Many of the hallmark features of these model bacteria, including lateral cell wall elongation and symmetric growth and division, do not occur in mycobacteria. Instead, mycobacterial growth is characterized by asymmetric polar growth and division. This innate asymmetry creates unequal birth sizes and growth rates for daughter cells with each division, generating a phenotypically heterogeneous population. Although the asymmetric growth patterns of mycobacteria lead to a larger variation in birth size than typically seen in model bacterial populations, the cell size distribution is stable over time. Here, we review the cellular mechanisms of growth, division, and cell cycle progression in mycobacteria in the face of asymmetry and inherent heterogeneity. These processes coalesce to control cell size. Although Mycobacterium smegmatis and Mycobacterium bovis Bacillus Calmette-Guérin (BCG) utilize a novel model of cell size control, they are similar to previously studied bacteria in that initiation of DNA replication is a key checkpoint for cell division. We compare the regulation of DNA replication initiation and strategies used for cell size homeostasis in mycobacteria and model bacteria. Finally, we review the importance of cellular organization and chromosome segregation relating to the physiology of mycobacteria and consider how new frameworks could be applied across the wide spectrum of bacterial diversity.

Learning from Noise: How Observing Stochasticity May Aid Microbiology

For many decades, the wedding of quantitative data with mathematical modeling has been fruitful, leading to important biological insights. Here, we review some of the ongoing efforts to gain insights into problems in microbiology - and, in particular, cell-cycle progression and its regulation - through observation and quantitative analysis of the natural fluctuations in the system. We first illustrate this idea by reviewing a classic example in microbiology - the Luria-Delbrück experiment - and discussing how, in that case, useful information was obtained by looking beyond the mean outcome of the experiment, but instead paying attention to the variability between replicates of the experiment. We then switch gears to the contemporary problem of cell cycle progression and discuss in more detail how insights into cell size regulation and, when relevant, coupling between the cell cycle and the circadian clock, can be gained by studying the natural fluctuations in the system and their statistical properties. We end with a more general discussion of how (in this context) the correct level of phenomenological model should be chosen, as well as some of the pitfalls associated with this type of analysis. Throughout this review the emphasis is not on providing details of the experimental setups or technical details of the models used, but rather, in fleshing out the conceptual structure of this particular approach to the problem. For this reason, we choose to illustrate the framework on a rather broad range of problems, and on organisms from all domains of life, to emphasize the commonality of the ideas and analysis used (as well as their differences).

Vibration of Mechanically-Assembled 3D Microstructures Formed by Compressive Buckling

Micro-electromechanical systems (MEMS) that rely on structural vibrations have many important applications, ranging from oscillators and actuators, to energy harvesters and vehicles for measurement of mechanical properties. Conventional MEMS, however, mostly utilize two-dimensional (2D) vibrational modes, thereby imposing certain limitations that are not present in 3D designs (e.g., multi-directional energy harvesting). 3D vibrational microplatforms assembled through the techniques of controlled compressive buckling are promising because of their complex 3D architectures and the ability to tune their vibrational behaviour (e.g., natural frequencies and modes) by reversibly changing their dimensions by deforming their soft, elastomeric substrates. A clear understanding of such strain-dependent vibration behaviour is essential for their practical applications. Here, we present a study on the linear and nonlinear vibration of such 3D mesostructures through analytical modeling, finite element analysis (FEA) and experiment. An analytical solution is obtained for the vibration mode and linear natural frequency of a buckled ribbon, indicating a mode change as the static deflection amplitude increases. The model also yields a scaling law for linear natural frequency that can be extended to general, complex 3D geometries, as validated by FEA and experiment. In the regime of nonlinear vibration, FEA suggests that an increase of amplitude of external loading represents an effective means to enhance the bandwidth. The results also uncover a reduced nonlinearity of vibration as the static deflection amplitude of the 3D structures increases. The developed analytical model can be used in the development of new 3D vibrational microplatforms, for example, to enable simultaneous measurement of diverse mechanical properties (density, modulus, viscosity etc.) of thin films and biomaterials.

Inertial picobalance reveals fast mass fluctuations in mammalian cells

The regulation of size, volume and mass in living cells is physiologically important, and dysregulation of these parameters gives rise to many diseases. Cell mass is largely determined by the amount of water, proteins, lipids, carbohydrates and nucleic acids present in a cell, and is tightly linked to metabolism, proliferation and gene expression. Technologies have emerged in recent years that make it possible to track the masses of single suspended cells and adherent cells. However, it has not been possible to track individual adherent cells in physiological conditions at the mass and time resolutions required to observe fast cellular dynamics. Here we introduce a cell balance (a 'picobalance'), based on an optically excited microresonator, that measures the total mass of single or multiple adherent cells in culture conditions over days with millisecond time resolution and picogram mass sensitivity. Using our technique, we observe that the mass of living mammalian cells fluctuates intrinsically by around one to four per cent over timescales of seconds throughout the cell cycle. Perturbation experiments link these mass fluctuations to the basic cellular processes of ATP synthesis and water transport. Furthermore, we show that growth and cell cycle progression are arrested in cells infected with vaccinia virus, but mass fluctuations continue until cell death. Our measurements suggest that all living cells show fast and subtle mass fluctuations throughout the cell cycle. As our cell balance is easy to handle and compatible with fluorescence microscopy, we anticipate that our approach will contribute to the understanding of cell mass regulation in various cell states and across timescales, which is important in areas including physiology, cancer research, stem-cell differentiation and drug discovery.

A drift-diffusion checkpoint model predicts a highly variable and growth-factor-sensitive portion of the cell cycle G1 phase

Even among isogenic cells, the time to progress through the cell cycle, or the intermitotic time (IMT), is highly variable. This variability has been a topic of research for several decades and numerous mathematical models have been proposed to explain it. Previously, we developed a top-down, stochastic drift-diffusion+threshold (DDT) model of a cell cycle checkpoint and showed that it can accurately describe experimentally-derived IMT distributions [Leander R, Allen EJ, Garbett SP, Tyson DR, Quaranta V. Derivation and experimental comparison of cell-division probability densities. J. Theor. Biol. 2014;358:129-135]. Here, we use the DDT modeling approach for both descriptive and predictive data analysis. We develop a custom numerical method for the reliable maximum likelihood estimation of model parameters in the absence of a priori knowledge about the number of detectable checkpoints. We employ this method to fit different variants of the DDT model (with one, two, and three checkpoints) to IMT data from multiple cell lines under different growth conditions and drug treatments. We find that a two-checkpoint model best describes the data, consistent with the notion that the cell cycle can be broadly separated into two steps: the commitment to divide and the process of cell division. The model predicts one part of the cell cycle to be highly variable and growth factor sensitive while the other is less variable and relatively refractory to growth factor signaling. Using experimental data that separates IMT into G1 vs. S, G2, and M phases, we show that the model-predicted growth-factor-sensitive part of the cell cycle corresponds to a portion of G1, consistent with previous studies suggesting that the commitment step is the primary source of IMT variability. These results demonstrate that a simple stochastic model, with just a handful of parameters, can provide fundamental insights into the biological underpinnings of cell cycle progression.

In vitro antimicrobial susceptibility testing methods: agar dilution to 3D tissue-engineered models

In the field of orthopaedic surgery, bacterial invasion of implants and the resulting periprosthetic infections are a common and unresolved problem. Antimicrobial susceptibility testing methods help to define the optimal treatment and identify antimicrobial resistance. This review discusses proven gold-standard techniques and recently developed models for antimicrobial susceptibility testing, while also providing a future outlook. Conventional, gold-standard methods, such as broth microdilution, are still widely applied in clinical settings. Although recently developed methods based on microfluidics and microdroplets have shown advantages over conventional methods in terms of testing speed, safety and the potential to provide a deeper insight into resistance mechanisms, extensive validation is required to translate this research to clinical practice. Recent optical and mechanical methods are complex and expensive and, therefore, not immediately clinically applicable. Novel osteoblast infection and tissue models best resemble infections in vivo. However, the integration of biomaterials into these models remains challenging and they require a long tissue culture, making their rapid clinical implementation unlikely. A method applicable for both clinical and research environments is difficult to realise. With a continuous increase in antimicrobial resistance, there is an urgent need for methods that analyse recurrent infections to identify the optimal treatment approaches. Graphical abstract Timeline of published and partly applied antimicrobial susceptibility testing methods, listed according to their underlying mechanism, complexity and application in research or clinics.

Towards microwave imaging of cells

Integrated detection techniques that can characterize the morphological properties of cells are needed for the widespread use of lab-on-a-chip technology. Herein, we establish a theoretical and experimental framework to use resonant microwave sensors in their higher order modes so that the morphological properties of analytes inside a microfluidic channel can be obtained electronically. We built a phase-locked loop system that can track the first two modes of a microstrip line resonator to detect the size and location of microdroplets and cells passing through embedded microfluidic channels. The attained resolution, expressed in terms of Allan deviation at the response time, is as small as 2 × 10^-8 for both modes. Additionally, simulations were performed to show that sensing with higher order modes can yield the geometrical volume, effective permittivity, two-dimensional extent, and the orientation of analytes. The framework presented here makes it possible to develop a novel type of microscope that operates at the microwave band, i.e., a radar for cells.

Precise mass determination of single cell with cantilever-based microbiosensor system

Having determined the mass of a single cell of brewer yeast Saccharomyces cerevisiae by means of a microcantilever-based biosensor Cantisens CSR-801 (Concentris, Basel, Switzerland), it was found that its dry mass is 47,65 ± 1,05 pg. Found to be crucial in this mass determination was the cell position along the length of the cantilever. Moreover, calculations including cells positions on the cantilever provide a threefold better degree of accuracy than those which assume uniform mass distribution. We have also examined the influence of storage time on the single cell mass. Our results show that after 6 months there is an increase in the average mass of a single yeast cell.

Determining therapeutic susceptibility in multiple myeloma by single-cell mass accumulation

Multiple myeloma (MM) has benefited from significant advancements in treatment that have improved outcomes and reduced morbidity. However, the disease remains incurable and is characterized by high rates of drug resistance and relapse. Consequently, methods to select the most efficacious therapy are of great interest. Here we utilize a functional assay to assess the ex vivo drug sensitivity of single multiple myeloma cells based on measuring their mass accumulation rate (MAR). We show that MAR accurately and rapidly defines therapeutic susceptibility across human multiple myeloma cell lines to a gamut of standard-of-care therapies. Finally, we demonstrate that our MAR assay, without the need for extended culture ex vivo, correctly defines the response of nine patients to standard-of-care drugs according to their clinical diagnoses. This data highlights the MAR assay in both research and clinical applications as a promising tool for predicting therapeutic response using clinical samples.

Multiple myeloma is characterized by high rates of drug resistance and relapse. Here the authors utilize a functional assay to assess the ex vivo drug sensitivity of single multiple myeloma cells based on measuring the mass accumulation rate of individual cells.

Details Matter: Noise and Model Structure Set the Relationship between Cell Size and Cell Cycle Timing

Organisms across all domains of life regulate the size of their cells. However, the means by which this is done is poorly understood. We study two abstracted “molecular” models for size regulation: inhibitor dilution and initiator accumulation. We apply the models to two settings: bacteria like Escherichia coli, that grow fully before they set a division plane and divide into two equally sized cells, and cells that form a bud early in the cell division cycle, confine new growth to that bud, and divide at the connection between that bud and the mother cell, like the budding yeast Saccharomyces cerevisiae. In budding cells, delaying cell division until buds reach the same size as their mother leads to very weak size control, with average cell size and standard deviation of cell size increasing over time and saturating up to 100-fold higher than those values for cells that divide when the bud is still substantially smaller than its mother. In budding yeast, both inhibitor dilution or initiator accumulation models are consistent with the observation that the daughters of diploid cells add a constant volume before they divide. This “adder” behavior has also been observed in bacteria. We find that in bacteria an inhibitor dilution model produces adder correlations that are not robust to noise in the timing of DNA replication initiation or in the timing from initiation of DNA replication to cell division (the C+D period). In contrast, in bacteria an initiator accumulation model yields robust adder correlations in the regime where noise in the timing of DNA replication initiation is much greater than noise in the C + D period, as reported previously (Ho and Amir, 2015). In bacteria, division into two equally sized cells does not broaden the size distribution.

The Effects of Stochasticity at the Single-Cell Level and Cell Size Control on the Population Growth

Establishing a quantitative connection between the population growth rate and the generation times of single cells is a prerequisite for understanding evolutionary dynamics of microbes. However, existing theories fail to account for the experimentally observed correlations between mother-daughter generation times that are unavoidable when cell size is controlled for, which is essentially always the case. Here, we study population-level growth in the presence of cell size control and corroborate our theory using experimental measurements of single-cell growth rates. We derive a closed formula for the population growth rate and demonstrate that it only depends on the single-cell growth rate variability, not other sources of stochasticity. Our work provides an evolutionary rationale for the narrow growth rate distributions often observed in nature: when single-cell growth rates are less variable but have a fixed mean, the population will exhibit an enhanced population growth rate as long as the correlations between the mother and daughter cells' growth rates are not too strong.

Functional precision cancer medicine-moving beyond pure genomics

The essential job of precision medicine is to match the right drugs to the right patients. In cancer, precision medicine has been nearly synonymous with genomics. However, sobering recent studies have generally shown that most patients with cancer who receive genomic testing do not benefit from a genomic precision medicine strategy. Although some call the entire project of precision cancer medicine into question, I suggest instead that the tools employed must be broadened. Instead of relying exclusively on big data measurements of initial conditions, we should also acquire highly actionable functional information by perturbing-for example, with cancer therapies-viable primary tumor cells from patients with cancer.

Single-Cell Growth Rates in Photoautotrophic Populations Measured by Stable Isotope Probing and Resonance Raman Microspectrometry

A new method to measure growth rates of individual photoautotrophic cells by combining stable isotope probing (SIP) and single-cell resonance Raman microspectrometry is introduced. This report explores optimal experimental design and the theoretical underpinnings for quantitative responses of Raman spectra to cellular isotopic composition. Resonance Raman spectra of isogenic cultures of the cyanobacterium, Synechococcus sp., grown in ¹³C-bicarbonate revealed linear covariance between wavenumber (cm⁻¹) shifts in dominant carotenoid Raman peaks and a broad range of cellular ¹³C fractional isotopic abundance. Single-cell growth rates were calculated from spectra-derived isotopic content and empirical relationships. Growth rates among any 25 cells in a sample varied considerably; mean coefficient of variation, CV, was 29 ± 3% (σ/x¯), of which only ~2% was propagated analytical error. Instantaneous population growth rates measured independently by in vivo fluorescence also varied daily (CV ≈ 53%) and were statistically indistinguishable from single-cell growth rates at all but the lowest levels of cell labeling. SCRR censuses of mixtures prepared from Synechococcus sp. and T. pseudonana (a diatom) populations with varying ¹³C-content and growth rates closely approximated predicted spectral responses and fractional labeling of cells added to the sample. This approach enables direct microspectrometric interrogation of isotopically- and phylogenetically-labeled cells and detects as little as 3% changes in cellular fractional labeling. This is the first description of a non-destructive technique to measure single-cell photoautotrophic growth rates based on Raman spectroscopy and well-constrained assumptions, while requiring few ancillary measurements.

Unraveling Antimicrobial Susceptibility of Bacterial Networks on Micropillar Architectures Using Intrinsic Phase-Shift Spectroscopy

With global antimicrobial resistance becoming increasingly detrimental to society, improving current clinical antimicrobial susceptibility testing (AST) is crucial to allow physicians to initiate appropriate antibiotic treatment as early as possible, reducing not only mortality rates but also the emergence of resistant pathogens. In this work, we tackle the main bottlenecks in clinical AST by designing biofunctionalized silicon micropillar arrays to provide both a preferable solid-liquid interface for bacteria networking and a simultaneous transducing element that monitors the response of bacteria when exposed to chosen antibiotics in real time. We harness the intrinsic ability of the micropillar architectures to relay optical phase-shift reflectometric interference spectroscopic measurements (referred to as PRISM) and employ it as a platform for culture-free, label-free phenotypic AST. The responses of E. coli to various concentrations of five clinically relevant antibiotics are optically tracked by PRISM, allowing for the minimum inhibitory concentration (MIC) values to be determined and compared to both standard broth microdilution testing and clinic-based automated AST system readouts. Capture of bacteria within these microtopologies, followed by incubation of the cells with the appropriate antibiotic solution, yields rapid determinations of antibiotic susceptibility. This platform not only provides accurate MIC determinations in a rapid manner (total assay time of 2-3 h versus 8 h with automated AST systems) but can also be employed as an advantageous method to differentiate bacteriostatic and bactericidal antibiotics.

Scaling by shrinking: empowering single-cell 'omics' with microfluidic devices

Recent advances in cellular profiling have demonstrated substantial heterogeneity in the behaviour of cells once deemed 'identical', challenging fundamental notions of cell 'type' and 'state'. Not surprisingly, these findings have elicited substantial interest in deeply characterizing the diversity, interrelationships and plasticity among cellular phenotypes. To explore these questions, experimental platforms are needed that can extensively and controllably profile many individual cells. Here, microfluidic structures - whether valve-, droplet- or nanowell-based - have an important role because they can facilitate easy capture and processing of single cells and their components, reducing labour and costs relative to conventional plate-based methods while also improving consistency. In this article, we review the current state-of-the-art methodologies with respect to microfluidics for mammalian single-cell 'omics' and discuss challenges and future opportunities.

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Microfluidic processing of biological samples typically involves differential manipulations of suspended particles under various force fields in order to spatially fractionate the sample based on a biological property of interest. For the resultant spatial distribution to be used as the assay readout, microfluidic devices are often subjected to microscopic analysis requiring complex instrumentation with higher cost and reduced portability. To address this limitation, we have developed an integrated electronic sensing technology for multiplexed detection of particles at different locations on a microfluidic chip. Our technology, called Microfluidic CODES, combines Resistive Pulse Sensing with Code Division Multiple Access to compress 2D spatial information into a 1D electrical signal. In this paper, we present a practical demonstration of the Microfluidic CODES technology to detect and size cultured cancer cells distributed over multiple microfluidic channels. As validated by the high-speed microscopy, our technology can accurately analyze dense cell populations all electronically without the need for an external instrument. As such, the Microfluidic CODES can potentially enable low-cost integrated lab-on-a-chip devices that are well suited for the point-of-care testing of biological samples.

Drug sensitivity of single cancer cells is predicted by changes in mass accumulation rate

Assays that can determine the response of tumor cells to cancer therapeutics could greatly aid the selection of drug regimens for individual patients. However, the utility of current functional assays is limited, and predictive genetic biomarkers are available for only a small fraction of cancer therapies. We found that the single-cell mass accumulation rate (MAR), profiled over many hours with a suspended microchannel resonator, accurately defined the drug sensitivity or resistance of glioblastoma and B-cell acute lymphocytic leukemia cells. MAR revealed heterogeneity in drug sensitivity not only between different tumors, but also within individual tumors and tumor-derived cell lines. MAR measurement predicted drug response using samples as small as 25 μl of peripheral blood while maintaining cell viability and compatibility with downstream characterization. MAR measurement is a promising approach for directly assaying single-cell therapeutic responses and for identifying cellular subpopulations with phenotypic resistance in heterogeneous tumors.