Label-free single-cell protein quantification using a drop-based mix-and-read system.
Ontology highlight
ABSTRACT:
SUBMITTER: Abbaspourrad A
PROVIDER: S-EPMC4522677 | biostudies-literature | 2015 Aug
REPOSITORIES: biostudies-literature
Publications
Label-free single-cell protein quantification using a drop-based mix-and-read system.
Abbaspourrad Alireza A Zhang Huidan H Tao Ye Y Cui Naiwen N Asahara Haruichi H Zhou Ying Y Yue Dongxian D Koehler Stephan A SA Ung Lloyd W LW Heyman John J Ren Yukun Y Ziblat Roy R Chong Shaorong S Weitz David A DA
Scientific reports 20150803
Quantitative protein analysis of single cells is rarely achieved due to technical difficulties of detecting minute amounts of proteins present in one cell. We develop a mix-and-read assay for drop-based label-free protein analysis of single cells. This high-throughput method quantifies absolute, rather than relative, amounts of proteins and does not involve antibody labeling or mass spectrometry. ...[more]
Similar Datasets
Project description:Background:Current approaches for quantification of major energy-storage forms in microalgae, including starch, protein and lipids, generally require cell cultivation to collect biomass followed by tedious and time-consuming analytical procedures. Thus, label-free, non-destructive and simultaneous quantification of such macromolecules at single-cell resolution is highly desirable in microalgal feedstock development and bioprocess control. Results:Here, we established a method based on single-cell Raman spectra (SCRS) that simultaneously quantifies the contents of starch, protein, triacylglycerol (TAG) and lipid unsaturation degree in individual Chlamydomonas reinhardtii cells. Measurement accuracy for the contents based on full SCRS spectrum each reached 96.86-99.24%, all significantly higher than single peak-based models. However, accuracy and reliability of measurement are dependent on the number of cells sampled, thus a formal mathematical framework was proposed and validated to rationally define "minimal sampling depth" for a given state of cellular population. Furthermore, a barcode consisting of 13 marker Raman peaks was proposed to characterize the temporal dynamics of these energy-storage products, which revealed that the average contents of starch and TAG increased, while their heterogeneity indices decreased, with those of protein being exactly the opposite. Finally, our method is widely applicable, as measurements among cells from liquid suspension culture, wet paste and frozen dried powder all exhibited excellent consistency. Conclusions:When sampled at proper depth, SCRS can serve as a quantitative and generally applicable tool for characterization and screening of strains and bioprocesses based on the profile of energy-storage macromolecules and their among-cell heterogeneity.
| S-EPMC5693592 | biostudies-literature
Project description:Mitochondrial dynamics play an important role within several pathological conditions, including cancer and neurological diseases. For the purpose of identifying therapies that target aberrant regulation of the mitochondrial dynamics machinery and characterizing the regulating signaling pathways, there is a need for label-free means to detect the dynamic alterations in mitochondrial morphology. We present the use of dielectrophoresis for label-free quantification of intracellular mitochondrial modifications that alter cytoplasmic conductivity, and these changes are benchmarked against label-based image analysis of the mitochondrial network. This is validated by quantifying the mitochondrial alterations that are carried out by entirely independent means on two different cell lines: human embryonic kidney cells and mouse embryonic fibroblasts. In both cell lines, the inhibition of mitochondrial fission that leads to a mitochondrial structure of higher connectivity is shown to substantially enhance conductivity of the cell interior, as apparent from the significantly higher positive dielectrophoresis levels in the 0.5-15 MHz range. Using single-cell velocity tracking, we show ∼10-fold higher positive dielectrophoresis levels at 0.5 MHz for cells with a highly connected versus those with a highly fragmented mitochondrial structure, suggesting the feasibility for frequency-selective dielectrophoretic isolation of cells to aid the discovery process for development of therapeutics targeting the mitochondrial machinery.
| S-EPMC5463269 | biostudies-literature
Project description:Despite data-independent acquisition (DIA) has been increasingly used for relative protein quantification, DIA-based label-free absolute quantification method has not been fully established. Here we present a novel DIA method using the TPA algorithm (DIA-TPA) for the absolute quantification of protein expressions in human liver microsomal and S9 samples. To validate this method, both data-dependent acquisition (DDA) and DIA experiments were conducted on 36 individual human liver microsome and S9 samples. The MS2-based DIA-TPA was able to quantify approximately twice as many proteins as the MS1-based DDA-TPA method, whereas protein concentrations determined by the two approaches were comparable. To evaluate the accuracy of the DIA-TPA method, we absolutely quantified carboxylesterase 1 concentrations in human liver S9 fractions using an established SILAC internal standard-based proteomic assay; the SILAC results were consistent with those obtained from DIA-TPA analysis. Finally, we employed a unique algorithm in DIA-TPA to distribute the MS signals from shared peptides to individual proteins or isoforms and successfully applied the method to the absolute quantification of several drug-metabolizing enzymes in human liver microsomes. In sum, the DIA-TPA method not only can absolutely quantify entire proteomes and specific proteins, but also has the capability quantifying proteins with shared peptides. SIGNIFICANCE: Data independent acquisition (DIA) has emerged as a powerful approach for relative protein quantification at the whole proteome level. However, DIA-based label-free absolute protein quantification (APQ) method has not been fully established. In the present study, we present a novel DIA-based label-free APQ approach, named DIA-TPA, with the capability absolutely quantifying proteins with shared peptides. The method was validated by comparing the quantification results of DIA-TPA with that obtained from stable isotope-labeled internal standard-based proteomic assays.
| S-EPMC6533198 | biostudies-literature
Project description:Drop-based microfluidics have recently become a novel tool by providing a stable linkage between phenotype and genotype for high throughput screening. However, use of drop-based microfluidics for screening high-affinity peptide binders has not been demonstrated due to the lack of a sensitive functional assay that can detect single DNA molecules in drops. To address this sensitivity issue, we introduced in vitro two-hybrid system (IVT2H) into microfluidic drops and developed a streamlined mix-and-read drop-IVT2H method to screen a random DNA library. Drop-IVT2H was based on the correlation between the binding affinity of two interacting protein domains and transcriptional activation of a fluorescent reporter. A DNA library encoding potential peptide binders was encapsulated with IVT2H such that single DNA molecules were distributed in individual drops. We validated drop-IVT2H by screening a three-random-residue library derived from a high-affinity MDM2 inhibitor PMI. The current drop-IVT2H platform is ideally suited for affinity screening of small-to-medium-sized libraries (10(3)-10(6)). It can obtain hits within a single day while consuming minimal amounts of reagents. Drop-IVT2H simplifies and accelerates the drop-based microfluidics workflow for screening random DNA libraries, and represents a novel alternative method for protein engineering and in vitro directed protein evolution.
| S-EPMC4778045 | biostudies-literature
Project description:Although membrane proteins are ubiquitous within all living organisms and represent the majority of drug targets, a general method for direct, label-free measurement of ligand binding to native membranes has not been reported. Here we show that backscattering interferometry (BSI) can accurately quantify ligand-receptor binding affinities in a variety of membrane environments. By detecting minute changes in the refractive index of a solution, BSI allows binding interactions of proteins with their ligands to be measured at picomolar concentrations. Equilibrium binding constants in the micromolar to picomolar range were obtained for small- and large-molecule interactions in both synthetic and cell-derived membranes without the use of labels or supporting substrates. The simple and low-cost hardware, high sensitivity and label-free nature of BSI should make it readily applicable to the study of many membrane-associated proteins of biochemical and pharmacological interest.
| S-EPMC3246389 | biostudies-literature
Project description:The majority of large-scale proteomics quantification methods yield long lists of quantified proteins that are often difficult to interpret and poorly reproduced. Computational approaches are required to analyze such intricate quantitative proteomics data sets. We propose a statistical approach to computationally identify protein sets (e.g., Gene Ontology (GO) terms) that are significantly enriched with abundant proteins with reproducible quantification measurements across a set of replicates. To this end, we developed PSEA-Quant, a protein set enrichment analysis algorithm for label-free and label-based protein quantification data sets. It offers an alternative approach to classic GO analyses, models protein annotation biases, and allows the analysis of samples originating from a single condition, unlike analogous approaches such as GSEA and PSEA. We demonstrate that PSEA-Quant produces results complementary to GO analyses. We also show that PSEA-Quant provides valuable information about the biological processes involved in cystic fibrosis using label-free protein quantification of a cell line expressing a CFTR mutant. Finally, PSEA-Quant highlights the differences in the mechanisms taking place in the human, rat, and mouse brain frontal cortices based on tandem mass tag quantification. Our approach, which is available online, will thus improve the analysis of proteomics quantification data sets by providing meaningful biological insights.
| S-EPMC4258137 | biostudies-literature
Project description:The incidence of cancer is increasing worldwide and metastatic disease, through the spread of circulating tumor cells (CTCs), is responsible for the majority of the cancer deaths. Accurate monitoring of CTC levels in blood provides clinical information supporting therapeutic decision making, and improved methods for CTC enumeration are asked for. Microfluidics has been extensively used for this purpose but most methods require several post-separation processing steps including concentration of the sample before analysis. This induces a high risk of sample loss of the collected rare cells. Here, an integrated system is presented that efficiently eliminates this risk by integrating label-free separation with single cell arraying of the target cell population, enabling direct on-chip tumor cell identification and enumeration. Prostate cancer cells (DU145) spiked into a sample with whole blood concentration of the peripheral blood mononuclear cell (PBMC) fraction were efficiently separated and trapped at a recovery of 76.2 ± 5.9% of the cancer cells and a minute contamination of 0.12 ± 0.04% PBMCs while simultaneously enabling a 20x volumetric concentration. This constitutes a first step towards a fully integrated system for rapid label-free separation and on-chip phenotypic characterization of circulating tumor cells from peripheral venous blood in clinical practice.
| S-EPMC5397835 | biostudies-literature
Project description:Selected reaction monitoring (SRM) is a mass spectrometry method with documented ability to quantify proteins accurately and reproducibly using labeled reference peptides. However, the use of labeled reference peptides becomes impractical if large numbers of peptides are targeted and when high flexibility is desired when selecting peptides. We have developed a label-free quantitative SRM workflow that relies on a new automated algorithm, Anubis, for accurate peak detection. Anubis efficiently removes interfering signals from contaminating peptides to estimate the true signal of the targeted peptides. We evaluated the algorithm on a published multisite data set and achieved results in line with manual data analysis. In complex peptide mixtures from whole proteome digests of Streptococcus pyogenes we achieved a technical variability across the entire proteome abundance range of 6.5-19.2%, which was considerably below the total variation across biological samples. Our results show that the label-free SRM workflow with automated data analysis is feasible for large-scale biological studies, opening up new possibilities for quantitative proteomics and systems biology.
| S-EPMC3426189 | biostudies-literature
Project description:In-depth understanding of the intricate interactions between biomolecules and nanoparticles is hampered by a lack of analytical methods providing quantitative information about binding kinetics. Herein, we demonstrate how label-free evanescent light-scattering microscopy can be used to temporally resolve specific protein binding to individual surface-bound (∼100 nm) lipid vesicles. A theoretical model is proposed that translates protein-induced changes in light-scattering intensity into bound mass. Since the analysis is centered on individual lipid vesicles, the signal from nonspecific protein binding to the surrounding surface is completely avoided, offering a key advantage over conventional surface-based techniques. Further, by averaging the intensities from less than 2000 lipid vesicles, the sensitivity is shown to increase by orders of magnitude. Taken together, these features provide a new avenue in studies of protein-nanoparticle interaction, in general, and specifically in the context of nanoparticles in medical diagnostics and drug delivery.
| S-EPMC8289281 | biostudies-literature
Project description:Successful quantitative mass spectrometry (MS) requires strategies to link the mass spectrometer response to the analyte abundance, with the response being dependent on more factors than just analyte abundance. Label-dependent strategies rely on the incorporation of an isotopically labeled internal standard into the sample. Current label-free strategies (performed without internal standards) are useful for analyzing samples that are unsuitable for isotopic labeling but are less accurate. Here we describe a label-free technique applicable to analysis of products from related genes (isotypes). This approach enables the invariant tryptic peptide sequences within the family to serve as "built-in" internal standards and the isotype-specific peptide sequences to report the amount of the various isotypes. A process of elimination segregates reliably trypsin-released standard and reporter peptides from unreliably released peptides. The specific MS response factors for these reporter and standard peptides can be determined using synthetic peptides. Analysis of HeLa tubulin digests revealed peptides from betaI-, betaII-, betaIII-, betaIVb-, and betaV-tubulin, eight of which were suitable; along with five standard peptides for quantification of the beta-tubulin isotypes. To show the utility of this method, we determined that betaI-tubulin represented 77% and betaIII-tubulin represented 3.2% of the total HeLa beta-tubulin.
| S-EPMC3711517 | biostudies-literature