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The monitoring of mechanics in a single cell throughout the cell cycle has been hampered by the invasiveness of mechanical measurements. Here we quantify mechanical properties via acoustic scattering of waves from a cell inside a fluid-filled vibrating cantilever with a temporal resolution of < 1 min. Through simulations, experiments with hydrogels and the use of chemically perturbed cells, we show that our readout, the size-normalized acoustic scattering (SNACS), measures stiffness. To demonstrate the noninvasiveness of SNACS over successive cell cycles, we used measurements that resulted in deformations of < 15 nm. The cells maintained constant SNACS throughout interphase but showed dynamic changes during mitosis. Our work provides a basis for understanding how growing cells maintain mechanical integrity, and demonstrates that acoustic scattering can be used to noninvasively probe subtle and transient dynamics.Acoustic scattering in a suspended microchannel resonator can be used to measure mechanical properties of single cells in a noninvasive manner. The approach is applied to follow stiffness changes of individual cells throughout the cell cycle.

Simultaneously measuring multiple eigenmode frequencies of nanomechanical resonators can determine the position and mass of surface-adsorbed proteins, and could ultimately reveal the mass tomography of nanoscale analytes. However, existing measurement techniques are slow (<1 Hz bandwidth), limiting throughput and preventing use with resonators generating fast transient signals. Here we develop a general platform for independently and simultaneously oscillating multiple modes of mechanical resonators, enabling frequency measurements that can precisely track fast transient signals within a user-defined bandwidth that exceeds 500 Hz. We use this enhanced bandwidth to resolve signals from multiple nanoparticles flowing simultaneously through a suspended nanochannel resonator and show that four resonant modes are sufficient for determining their individual position and mass with an accuracy near 150 nm and 40 attograms throughout their 150-ms transit. We envision that our method can be readily extended to other systems to increase bandwidth, number of modes, or number of resonators. Nanomechanical resonators are sensitive to tiny changes in their mass. Here, the authors demonstrate a method for quickly measuring many resonator modes and use it to analyse the mass and position of multiple nanoparticles flowing in a fluid channel with a precision of 40 attograms and 150 nm, respectively.

Heterogeneity in growth phenotypes and drug susceptibility in bacterial and mammalian cells are assayed at the single-cell level using multiplexed resonant mass sensors. Methods to rapidly assess cell growth would be useful for many applications, including drug susceptibility testing, but current technologies have limited sensitivity or throughput. Here we present an approach to precisely and rapidly measure growth rates of many individual cells simultaneously. We flow cells in suspension through a microfluidic channel with 10–12 resonant mass sensors distributed along its length, weighing each cell repeatedly over the 4–20 min it spends in the channel. Because multiple cells traverse the channel at the same time, we obtain growth rates for >60 cells/h with a resolution of 0.2 pg/h for mammalian cells and 0.02 pg/h for bacteria. We measure the growth of single lymphocytic cells, mouse and human T cells, primary human leukemia cells, yeast, Escherichia coli and Enterococcus faecalis . Our system reveals subpopulations of cells with divergent growth kinetics and enables assessment of cellular responses to antibiotics and antimicrobial peptides within minutes.

Cell size is believed to influence cell growth and metabolism. Consistently, several studies have revealed that large cells have lower mass accumulation rates per unit mass (i.e., growth efficiency) than intermediate-sized cells in the same population. Sizedependent growth is commonly attributed to transport limitations, such as increased diffusion timescales and decreased surface-to-volume ratio. However, separating cell size- and cell cycle-dependent growth is challenging. To address this, we monitored growth efficiency of pseudodiploid mouse lymphocytic leukemia cells during normal proliferation and polyploidization. This was enabled by the development of large-channel suspended microchannel resonators that allow us to monitor buoyant mass of single cells ranging from 40 pg (small pseudodiploid cell) to over 4,000 pg, with a resolution ranging from ∼1% to ∼0.05%. We find that cell growth efficiency increases, plateaus, and then decreases as cell cycle proceeds. This growth behavior repeats with every endomitotic cycle as cells grow into polyploidy. Overall, growth efficiency changes 33% throughout the cell cycle. In contrast, increasing cell mass by over 100-fold during polyploidization did not change growth efficiency, indicating exponential growth. Consistently, growth efficiency remained constant when cell cycle was arrested in G₂. Thus, cell cycle is a primary determinant of growth efficiency. As growth remains exponential over large size scales, our work finds no evidence for transport limitations that would decrease growth efficiency.

Physical characterization of nanoparticles is required for a wide range of applications. Nanomechanical resonators can quantify the mass of individual particles with detection limits down to a single atom in vacuum. However, applications are limited because performance is severely degraded in solution. Suspended micro- and nanochannel resonators have opened up the possibility of achieving vacuum-level precision for samples in the aqueous environment and a noise equivalent mass resolution of 27 attograms in 1-kHz bandwidth was previously achieved by Lee et al. [(2010) Nano Lett 10(7):2537–2542]. Here, we report on a series of advancements that have improved the resolution by more than 30-fold, to 0.85 attograms in the same bandwidth, approaching the thermomechanical noise limit and enabling precise quantification of particles down to 10 nm with a throughput of more than 18,000 particles per hour. We demonstrate the potential of this capability by comparing the mass distributions of exosomes produced by different cell types and by characterizing the yield of self-assembled DNA nanoparticle structures.

The efficacy of cancer drugs is profiled by measuring changes in the mass of single tumor cells. 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.

We present a method for direct non-optical quantification of dry mass, dry density and water mass of single living cells in suspension. Dry mass and dry density are obtained simultaneously by measuring a cell's buoyant mass sequentially in an H2O-based fluid and a D2O-based fluid. Rapid exchange of intracellular H2O for D2O renders the cell's water content neutrally buoyant in both measurements, and thus the paired measurements yield the mass and density of the cell's dry material alone. Utilizing this same property of rapid water exchange, we also demonstrate the quantification of intracellular water mass. In a population of E. coli, we paired these measurements to estimate the percent dry weight by mass and volume. We then focused on cellular dry density - the average density of all cellular biomolecules, weighted by their relative abundances. Given that densities vary across biomolecule types (RNA, DNA, protein), we investigated whether we could detect changes in biomolecular composition in bacteria, fungi, and mammalian cells. In E. coli, and S. cerevisiae, dry density increases from stationary to exponential phase, consistent with previously known increases in the RNA/protein ratio from up-regulated ribosome production. For mammalian cells, changes in growth conditions cause substantial shifts in dry density, suggesting concurrent changes in the protein, nucleic acid and lipid content of the cell.

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.

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.

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.