BackgroundThe difficulty of directly measuring cellular dose is a significant obstacle to application of target tissue dosimetry for nanoparticle and microparticle toxicity assessment, particularly for in vitro systems. As a consequence, the target tissue paradigm for dosimetry and hazard assessment of nanoparticles has largely been ignored in favor of using metrics of exposure (e.g. μg particle/mL culture medium, particle surface area/mL, particle number/mL). We have developed a computational model of solution particokinetics (sedimentation, diffusion) and dosimetry for non-interacting spherical particles and their agglomerates in monolayer cell culture systems. Particle transport to cells is calculated by simultaneous solution of Stokes Law (sedimentation) and the Stokes-Einstein equation (diffusion).ResultsThe In vitro Sedimentation, Diffusion and Dosimetry model (ISDD) was tested against measured transport rates or cellular doses for multiple sizes of polystyrene spheres (20-1100 nm), 35 nm amorphous silica, and large agglomerates of 30 nm iron oxide particles. Overall, without adjusting any parameters, model predicted cellular doses were in close agreement with the experimental data, differing from as little as 5% to as much as three-fold, but in most cases approximately two-fold, within the limits of the accuracy of the measurement systems. Applying the model, we generalize the effects of particle size, particle density, agglomeration state and agglomerate characteristics on target cell dosimetry in vitro.ConclusionsOur results confirm our hypothesis that for liquid-based in vitro systems, the dose-rates and target cell doses for all particles are not equal; they can vary significantly, in direct contrast to the assumption of dose-equivalency implicit in the use of mass-based media concentrations as metrics of exposure for dose-response assessment. The difference between equivalent nominal media concentration exposures on a μg/mL basis and target cell doses on a particle surface area or number basis can be as high as three to six orders of magnitude. As a consequence, in vitro hazard assessments utilizing mass-based exposure metrics have inherently high errors where particle number or surface areas target cells doses are believed to drive response. The gold standard for particle dosimetry for in vitro nanotoxicology studies should be direct experimental measurement of the cellular content of the studied particle. However, where such measurements are impractical, unfeasible, and before such measurements become common, particle dosimetry models such as ISDD provide a valuable, immediately useful alternative, and eventually, an adjunct to such measurements.

Although the ERK pathway has a central role in the response of cells to growth factors, its regulatory structure and dynamics are incompletely understood. To investigate ERK activation in real time, we expressed an ERK-GFP fusion protein in human mammary epithelial cells. On EGF stimulation, we observed sustained oscillations of the ERK-GFP fusion protein between the nucleus and cytoplasm with a periodicity of B15 min. The oscillations were persistent (445 cycles), independent of cell cycle phase, and were highly dependent on cell density, essentially disappearing at confluency. Oscillations occurred even at ligand doses that elicited very low levels of ERK phosphorylation, and could be detected biochemically in both transfected and nontransfected cells. Mathematical modeling revealed that negative feedback from phosphorylated ERK to the cascade input was necessary to match the robustness of the oscillation characteristics observed over a broad range of ligand concentrations. Our characterization of single-cell ERK dynamics provides a quantitative foundation for understanding the regulatory structure of this signaling cascade.

Environmental transitions often result in resource mixtures that overcome limitations to microbial metabolism, resulting in biogeochemical hotspots and moments. Riverine systems, where groundwater mixes with surface water (the hyporheic zone), are spatially complex and temporally dynamic, making development of predictive models challenging. Spatial and temporal variations in hyporheic zone microbial communities are a key, but understudied, component of riverine biogeochemical function. Here, to investigate the coupling among groundwater–surface water mixing, microbial communities and biogeochemistry, we apply ecological theory, aqueous biogeochemistry, DNA sequencing and ultra-high-resolution organic carbon profiling to field samples collected across times and locations representing a broad range of mixing conditions. Our results indicate that groundwater–surface water mixing in the hyporheic zone stimulates heterotrophic respiration, alters organic carbon composition, causes ecological processes to shift from stochastic to deterministic and is associated with elevated abundances of microbial taxa that may degrade a broad suite of organic compounds.

We report on the quantitative proteomic analysis of single mammalian cells. Fluorescence-activated cell sorting was employed to deposit cells into a newly developed nanodroplet sample processing chip, after which samples were analyzed by ultrasensitive nanoLC-MS. An average of circa 670 protein groups were confidently identified from single HeLa cells, which is a far greater level of proteome coverage for single cells than has been previously reported. We demonstrate that the single-cell proteomics platform can be used to differentiate cell types from enzyme-dissociated human lung primary cells and identify specific protein markers for epithelial and mesenchymal cells.

Effective extension of mass spectrometry-based proteomics to single cells remains challenging. Herein we combined microfluidic nanodroplet technology with tandem mass tag (TMT) isobaric labeling to significantly improve analysis throughput and proteome coverage for single mammalian cells. Isobaric labeling facilitated multiplex analysis of single cell-sized protein quantities to a depth of ~1,600 proteins with median CV of 10.9% and correlation coefficient of 0.98. To demonstrate in-depth high throughput single cell analysis, the platform was applied to measure protein expression in 72 single cells from three murine cell populations (epithelial, immune, and endothelial cells) in <2 days instrument time with over 2,300 proteins identified. Principal component analysis grouped the single cells into three distinct populations based on protein expression with each population characterized by well-known cell-type specific markers. Our platform enables high throughput and unbiased characterization of single cell heterogeneity at the proteome level.

Mass spectrometry (MS)-based proteomics has great potential for overcoming the limitations of antibody-based immunoassays for antibody-independent, comprehensive, and quantitative proteomic analysis of single cells. Indeed, recent advances in nanoscale sample preparation have enabled effective processing of single cells. In particular, the concept of using boosting/carrier channels in isobaric labeling to increase the sensitivity in MS detection has also been increasingly used for quantitative proteomic analysis of small-sized samples including single cells. However, the full potential of such boosting/carrier approaches has not been significantly explored, nor has the resulting quantitation quality been carefully evaluated. Herein, we have further evaluated and optimized our recent boosting to amplify signal with isobaric labeling (BASIL) approach, originally developed for quantifying phosphorylation in small number of cells, for highly effective analysis of proteins in single cells. This improved BASIL (iBASIL) approach enables reliable quantitative single-cell proteomics analysis with greater proteome coverage by carefully controlling the boosting-to-sample ratio (e.g. in general <100×) and optimizing MS automatic gain control (AGC) and ion injection time settings in MS/MS analysis (e.g. 5E5 and 300 ms, respectively, which is significantly higher than that used in typical bulk analysis). By coupling with a nanodroplet-based single cell preparation (nanoPOTS) platform, iBASIL enabled identification of ∼2500 proteins and precise quantification of ∼1500 proteins in the analysis of 104 FACS-isolated single cells, with the resulting protein profiles robustly clustering the cells from three different acute myeloid leukemia cell lines. This study highlights the importance of carefully evaluating and optimizing the boosting ratios and MS data acquisition conditions for achieving robust, comprehensive proteomic analysis of single cells.

We describe the combined use of 15N-metabolic labeling and a cysteine-reactive biotin affinity tag to isolate and quantitate cysteine-containing polypeptides (Cys-polypeptides) from Deinococcus radiodurans as well as from mouse B16 melanoma cells. D. radiodurans were cultured in both natural isotopic abundance and 15N-enriched media. Equal numbers of cells from both cultures were combined and the soluble proteins extracted. This mixture of isotopically distinct proteins was derivatized using a commercially available cysteine-reactive reagent that contains a biotin group. Following trypsin digestion, the resulting modified peptides were isolated using immobilized avidin. The mixture was analyzed by capillary reversed-phase liquid chromatography (LC) online with ion trap mass spectrometry (MS) as well as Fourier transform ion cyclotron resonance (FTICR) MS. The resulting spectra contain numerous pairs of Cyspolypeptides whose mass difference corresponds to the number of nitrogen atoms present in each of the peptides. Designation of Cys-polypeptide pairs is also facilitated by the distinctive isotopic distribution of the 15N-labeled peptides versus their 14N-labeled counterparts. Studies with mouse B16 cells maintained in culture allowed the observation of hundreds of isotopically distinct pairs of peptides by LC-FTICR analysis. The ratios of the areas of the pairs of isotopically distinct peptides showed the expected 1:1 labeling of the 14N and 15N versions of each peptide. An additional benefit from the present strategy is that the 15N-labeled peptides do not display significant isotope-dependent chromatographic shifts from their 14N-labeled counterparts, therefore improving the precision for quantitating peptide abundances. The methodology presented offers an alternate, cost-effective strategy for conducting global, quantitative proteomic measurements.

Single-cell proteomics can provide critical biological insight into the cellular heterogeneity that is masked by bulk-scale analysis. We have developed a nanoPOTS (nanodroplet processing in one pot for trace samples) platform and demonstrated its broad applicability for single-cell proteomics. However, because of nanoliter-scale sample volumes, the nanoPOTS platform is not compatible with automated LC-MS systems, which significantly limits sample throughput and robustness. To address this challenge, we have developed a nanoPOTS autosampler allowing fully automated sample injection from nanowells to LC-MS systems. We also developed a sample drying, extraction, and loading workflow to enable reproducible and reliable sample injection. The sequential analysis of 20 samples containing 10 ng tryptic peptides demonstrated high reproducibility with correlation coefficients of >0.995 between any two samples. The nanoPOTS autosampler can provide analysis throughput of 9.6, 16, and 24 single cells per day using 120, 60, and 30 min LC gradients, respectively. As a demonstration for single-cell proteomics, the autosampler was first applied to profiling protein expression in single MCF10A cells using a label-free approach. At a throughput of 24 single cells per day, an average of 256 proteins was identified from each cell and the number was increased to 731 when the Match Between Runs algorithm of MaxQuant was used. Using a multiplexed isobaric labeling approach (TMT-11plex), ∼77 single cells could be analyzed per day. We analyzed 152 cells from three acute myeloid leukemia cell lines, resulting in a total of 2558 identified proteins with 1465 proteins quantifiable (70% valid values) across the 152 cells. These data showed quantitative single-cell proteomics can cluster cells to distinct groups and reveal functionally distinct differences.