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

Cermak, Nathan; Ölçüm, Selim; Delgado, Francisco Feijó; Wasserman, Steven C.; Payer, Kristofor Robert; Murakami, Mark A.; Knudsen, Scott M.; Kimmerling, Robert J.; Stevens, Mark M.; Kikuchi, Yuki; Sandikci, Arzu; Ogawa, Masaaki; Agache, Vincent; Baleras, F.; Weinstock, David M.; Manalis, Scott R.

doi:10.1038/nbt.3666

Cited by 211 publications

(217 citation statements)

References 42 publications

(48 reference statements)

Supporting

Mentioning

201

Contrasting

Order By: Relevance

“…This approach typically provided on the order of 10 3 total tumor cells for measurement following purification by flow sorting. In order to measure samples of low cell count and volume, we implemented a next-generation SMR array device that greatly simplifies fluidic handling, increasing throughput by 20-fold and enables the use of low-volume samples 26 .…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2016

Self Cite

View full text Add to dashboard Cite

Assays that can determine the response of tumor cells to cancer therapeutics could greatly aid the selection of drug regimens for individual patients. However, no functional assays are currently implemented clinically, and predictive genetic biomarkers are available for only a small fraction of cancer therapies. Here we demonstrate that the single-cell mass accumulation rate (MAR), profiled over many hours with a suspended microchannel resonator, accurately defines the drug sensitivity or resistance of glioblastoma multiforme (GBM) and B-cell acute lymphocytic leukemia (B-ALL) cells. MAR reveals heterogeneity in drug sensitivity not only between different tumors but also within individual tumors and tumor-derived cell lines. MAR measurement predicts 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 within heterogeneous tumors.

show abstract

Section: Resultsmentioning

confidence: 99%

“…MAR measurements also initially suffered from low throughput. However, single-cell mass and MAR can now be obtained with a throughput exceeding 60 cells/hr/device without sacrificing precision 26 .…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2016

Self Cite

View full text Add to dashboard Cite

show abstract

“…New strategies for improved multi-omics are probably on the horizon, as are approaches for collecting additional relevant cellular metadata, such as mass 132–135 . Additionally, high-throughput methods, such as droplets and nanowells, provide a powerful means of probing and selecting single cells from large ensembles.…”

Section: Emerging Applicationsmentioning

confidence: 99%

“…Although it is hard to fully envision what the future holds, we predict that future applications will include new single-cell measurements (such as chromatin three-dimensional organization through Hi-C chromosome conformation capture (probably using valves)) 73,144 , biophysical parameters such as cell mass 132,133,135,145 (which requires tracking but not isolation), lineage determination 131 (using hydrodynamic traps), integrated multi-‘omic’ profiling (probably most easily implemented using valve-based 15 or well-based 96 devices) and studies of how microenvironmental considerations (soluble factors and other cells) 146,147 affect cellular behaviour.…”

Section: Figurementioning

confidence: 99%

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

2017

View full text Add to dashboard Cite

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.

show abstract

“…Recent single-cell measurements show that the growth of cell volumes is often exponential. These include bacteria [7][8][9][10], budding yeast [10][11][12][13][14] and mammalian cells [10,15]. Moreover, the mRNA and protein numbers are on average proportional to the cell volume throughout the cell cycle [16][17][18][19][20].…”

mentioning

confidence: 99%

Homeostasis of protein and mRNA concentrations in growing cells

Lin

Amir

2018

Preprint

View full text Add to dashboard Cite

Many experiments show that the concentrations of protein and mRNA fluctuate but on average are constant in growing cells, independent of the genome copy number. However, models of stochastic gene expression often assume constant gene expression rates that are proportional to the gene copy numbers, which are therefore incompatible with experiments. Here, we construct a minimal gene expression model to fill this gap. We show that (1) because the ribosomes translate all proteins, the concentrations of proteins are regulated in an exponentially growing cell volume; (2) the competition between genes for the RNA polymerases makes the gene expression rate independent of the genome number; (3) the fluctuations in ribosome level and cell density can generate a global extrinsic noise in protein concentrations; (4) correlations between mRNA and protein levels can be quantified.Despite the noisy nature of gene expression [1][2][3][4][5][6], various aspects of single cell dynamics, such as volume growth, are effectively deterministic. Recent single-cell measurements show that the growth of cell volumes is often exponential. These include bacteria [7][8][9][10], budding yeast [10][11][12][13][14] and mammalian cells [10,15]. Moreover, the mRNA and protein numbers are on average proportional to the cell volume throughout the cell cycle [16][17][18][19][20]. Therefore, the homeostasis of mRNA concentration and protein concentration is maintained in an exponentially growing cell volume. The exponential growths of mRNA and protein number indicate dynamical transcription and translation rates proportional to the cell volume, and also independent of the genome copy number. However, current gene expression models often assume a fixed cell volume with constant transcription and translation rates [1,5,[21][22][23], which are proportional to the gene copy number. Therefore, fixed cell volume models fail to reveal how cells keep constant mRNA and protein concentrations as the cell volume grows and the genome is replicated.The homeostasis of protein and mRNA concentrations imply that there must be a regulatory mechanism in place to prevent the accumulation of noise over time and to maintain a bounded distribution of concentrations. The goal of this work is to identify such a mechanism by developing a genome-wide coarse-grained model taking into account explicitly cell volume growth. We will consider an idealized cell in which genes are constitutively expressed for simplicity. The ubiquity of homeostasis suggests that the global machineries of gene expression, RNA polymerases (RNAPs) and ribosomes, should play a central role within the model. Indeed, as we will show later, the exponential growth of cell volume, protein and mRNA number originates from the auto-catalytic nature of ribosomes, the limiting factor in the translational process [24][25][26]. The bounded distributions of concentrations are a result of the fact that ribosomes make all proteins. The independence of the mRNA concentration of the genome copy number is a natural resul...

show abstract

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

Cited by 211 publications

References 42 publications

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

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

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

Homeostasis of protein and mRNA concentrations in growing cells

Contact Info

Product

Resources

About