scholarly journals The MTT Assay: Utility, Limitations, Pitfalls, and Interpretation in Bulk and Single-Cell Analysis

2021 ◽  
Vol 22 (23) ◽  
pp. 12827
Author(s):  
Mahshid Ghasemi ◽  
Tyron Turnbull ◽  
Sonia Sebastian ◽  
Ivan Kempson
Keyword(s):  
Cell Viability ◽  
Single Cell ◽  
Metabolic Activity ◽  
Mtt Assay ◽  
Single Cell Analysis ◽  
Culture Media ◽  
Cancer Cell Line ◽  
Prostate Cancer Cell Line ◽  
Serum Starvation ◽  
Treatment Toxicity

The MTT assay for cellular metabolic activity is almost ubiquitous to studies of cell toxicity; however, it is commonly applied and interpreted erroneously. We investigated the applicability and limitations of the MTT assay in representing treatment toxicity, cell viability, and metabolic activity. We evaluated the effect of potential confounding variables on the MTT assay measurements on a prostate cancer cell line (PC-3) including cell seeding number, MTT concentration, MTT incubation time, serum starvation, cell culture media composition, released intracellular contents (cell lysate and secretome), and extrusion of formazan to the extracellular space. We also assessed the confounding effect of polyethylene glycol (PEG)-coated gold nanoparticles (Au-NPs) as a tested treatment in PC-3 cells on the assay measurements. We additionally evaluated the applicability of microscopic image cytometry as a tool for measuring intracellular MTT reduction at the single-cell level. Our findings show that the assay measurements are a result of a complicated process dependant on many of the above-mentioned factors, and therefore, optimization of the assay and rational interpretation of the data is necessary to prevent misleading conclusions on variables such as cell viability, treatment toxicity, and/or cell metabolism. We conclude, with recommendations on how to apply the assay and a perspective on where the utility of the assay is a powerful tool, but likewise where it has limitations.

scholarly journals Optimized Data-Independent Acquisition Approach for Proteomic Analysis at Single-Cell Level

2021 ◽  
Author(s):  
Yuefan Wang ◽  
Tung-Shing Mamie Lih ◽  
Lijun Chen ◽  
Yuanwei Xu ◽  
Morgan D. Kuczler ◽  
...  
Keyword(s):  
Single Cell ◽  
Proteomic Analysis ◽  
Single Cell Analysis ◽  
Cancer Cell Line ◽  
Cellular Heterogeneity ◽  
Single Cell Level ◽  
Cell Level ◽  
Data Independent Acquisition ◽  
Peptide Precursors ◽  
Proteomic Study

Abstract Background: Single-cell proteomic analysis provides valuable insights into cellular heterogeneity allowing the characterization of the cellular microenvironment which is difficult to accomplish in bulk proteomic analysis. Currently, single-cell proteomic studies utilize data-dependent acquisition (DDA) mass spectrometry (MS) coupled with a TMT labelled carrier channel. Due to the extremely imbalanced MS signals among the carrier channel and other TMT reporter ions, the quantification is compromised. Thus, data-independent acquisition (DIA)-MS should be considered as an alternative approach towards single-cell proteomic study since it generates reproducible quantitative data. However, there are limited reports on the optimal workflow for DIA-MS-based single-cell analysis. Methods: We report an optimized DIA workflow for single-cell proteomics using Orbitrap Lumos Tribrid instrument. We utilized a breast cancer cell line (MDA-MB-231) and induced drug resistant polyaneuploid cancer cells (PACCs) to evaluate our established workflow. Results: We found that a short LC gradient was preferable for peptides extracted from single cell level with less than 2 ng sample amount. The total number of co-searching peptide precursors was also critical for protein and peptide identifications at nano- and sub-nano-gram levels. Post-translationally modified peptides could be identified from a nano-gram level of peptides. Using the optimized workflow, up to 1,500 protein groups were identified from a single PACC corresponding to 0.2 ng of peptides. Furthermore, about 200 peptides with phosphorylation, acetylation, and ubiquitination were identified from global DIA analysis of 100 cisplatin resistant PACCs (20 ng). Finally, we used this optimized DIA approach to compare the whole proteome of MDA-MB-231 parental cells and induced PACCs at a single-cell level. We found the single-cell level comparison could reflect real protein expression changes and identify the protein copy number. Conclusions: Our results demonstrate that the optimized DIA pipeline can serve as a reliable quantitative tool for single-cell as well as sub-nano-gram proteomic analysis.

scholarly journals Optimized Data-Independent Acquisition Approach for Proteomic Analysis at Single-Cell Level

2021 ◽  
Author(s):  
Yuefan Wang ◽  
Tung-Shing Mamie Lih ◽  
Lijun Chen ◽  
Yuanwei Xu ◽  
Morgan D Kuczler ◽  
...  
Keyword(s):  
Single Cell ◽  
Proteomic Analysis ◽  
Single Cell Analysis ◽  
Cancer Cell Line ◽  
Cellular Heterogeneity ◽  
Single Cell Level ◽  
Cell Level ◽  
Data Independent Acquisition ◽  
Peptide Precursors ◽  
Proteomic Study

Single-cell proteomic analysis provides valuable insights into cellular heterogeneity allowing the characterization of the cellular microenvironment which is difficult to accomplish in bulk proteomic analysis. Currently, single-cell proteomic studies utilize data-dependent acquisition (DDA) mass spectrometry (MS) coupled with a TMT labelled carrier channel. Due to the extremely imbalanced MS signals among the carrier channel and other TMT reporter ions, the quantification is compromised. Thus, data-independent acquisition (DIA)-MS should be considered as an alternative approach towards single-cell proteomic study since it generates reproducible quantitative data. However, there are limited reports on the optimal workflow for DIA-MS-based single-cell analysis. Herein, we report an optimized DIA workflow for single-cell proteomics using Orbitrap Lumos Tribrid instrument. We utilized a breast cancer cell line (MDA-MB-231) and induced drug resistant polyaneuploid cancer cells (PACCs) to evaluate our established workflow. We found that a short LC gradient was preferable for peptides extracted from single cell level with less than 2 ng sample amount. The total number of co-searching peptide precursors was also critical for protein and peptide identifications at nano- and sub-nano-gram levels. Post-translationally modified peptides could be identified from a nano-gram level of peptides. Using the optimized workflow, up to 1,500 protein groups were identified from a single PACC corresponding to 0.2 ng of peptides. Furthermore, about 200 peptides with phosphorylation, acetylation, and ubiquitination were identified from global DIA analysis of 100 cisplatin resistant PACCs (20 ng). Finally, we used this optimized DIA approach to compare the whole proteome of MDA-MB-231 parental cells and induced PACCs at a single-cell level. We found the single-cell level comparison could reflect real protein expression changes and identify the protein copy number. Our results demonstrates that the optimized DIA pipeline can serve as a reliable quantitative tool for single-cell as well as sub-nano-gram proteomic analysis.

scholarly journals Beyond Single-Cell Analysis of Metallodrugs by ICP-MS: Targeting Cellular Substructures

2021 ◽  
Vol 22 (17) ◽  
pp. 9468
Author(s):  
Audrey Galé ◽  
Lukas Hofmann ◽  
Nicola Lüdi ◽  
Martin Nils Hungerbühler ◽  
Christoph Kempf ◽  
...  
Keyword(s):  
Single Cell ◽  
Inductively Coupled Plasma ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Cancer Cell Line ◽  
Advanced Lung Cancer ◽  
Lower Amount ◽  
Platinum Resistance ◽  
Cell Organelles ◽  
Icp Ms

Platinum compounds such as cisplatin (cisPt) embody the backbone of combination chemotherapy protocols against advanced lung cancer. However, their efficacy is primarily limited by inherent or acquired platinum resistance, the origin of which has not been fully elucidated yet, although of paramount interest. Using single cell inductively coupled plasma mass spectrometry (SC-ICP-MS), this study quantifies cisPt in single cancer cells and for the first time in isolated nuclei. A comparison of cisPt uptake was performed between a wild type (wt) cancer cell line and related resistant sublines. In both, resistant cells, wt cells, and their nuclei, cisPt uptake was measured at different incubation times. A lower amount of cisPt was found in resistant cell lines and their nuclei compared to wt cells. Moreover, the abundance of internalized cisPt decreased with increasing resistance. Interestingly, concentrations of cisPt found within the nuclei were higher than compared to cellular concentrations. Here, we show, that SC-ICP-MS allows precise and accurate quantification of metallodrugs in both single cells and cell organelles such as nuclei. These findings pave the way for future applications investigating the potency and efficacy of novel metallodrugs developed for cancer treatment.

scholarly journals Optimized Data-Independent Acquisition Approach for Proteomic Analysis at Single-Cell Level

2021 ◽  
Author(s):  
Yuefan Wang ◽  
T. Mamie Lih ◽  
Lijun Chen ◽  
Yuanwei Xu ◽  
Morgan Kuczler ◽  
...  
Keyword(s):  
Single Cell ◽  
Proteomic Analysis ◽  
Single Cell Analysis ◽  
Cancer Cell Line ◽  
Cellular Heterogeneity ◽  
Single Cell Level ◽  
Cell Level ◽  
Data Independent Acquisition ◽  
Peptide Precursors ◽  
Proteomic Study

Abstract Single-cell proteomic analysis provides valuable insights into cellular heterogeneity allowing the characterization of the cellular microenvironment which is difficult to accomplish in bulk proteomic analysis. Currently, single-cell proteomic studies utilize data-dependent acquisition (DDA) mass spectrometry (MS) coupled with a TMT labelled carrier channel. Due to the extremely imbalanced MS signals among the carrier channel and other TMT reporter ions, the quantification is compromised. Thus, data-independent acquisition (DIA)-MS should be considered as an alternative approach towards single-cell proteomic study since it generates reproducible quantitative data. However, there are limited reports on the optimal workflow for DIA-MS-based single-cell analysis. Herein, we report an optimized DIA workflow for single-cell proteomics using Orbitrap Lumos Tribrid instrument. We utilized a breast cancer cell line (MDA-MB-231) and induced drug resistant polyaneuploid cancer cells (PACCs) to evaluate our established workflow. We found that a short LC gradient was preferable for peptides extracted from single cell level with less than 2 ng sample amount. The total number of co-searching peptide precursors was also critical for protein and peptide identifications at nano- and sub-nano-gram levels. Post-translationally modified peptides could be identified from a nano-gram level of peptides. Using the optimized workflow, up to 1,500 protein groups were identified from a single PACC corresponding to 0.2 ng of peptides. Furthermore, about 200 peptides with phosphorylation, acetylation, and ubiquitination were identified from global DIA analysis of 100 cisplatin resistant PACCs (20 ng). Finally, we used this optimized DIA approach to compare the whole proteome of MDA-MB-231 parental cells and induced PACCs at a single-cell level. We found the single-cell level comparison could reflect real protein expression changes and identify the protein copy number. Our results demonstrate that the optimized DIA pipeline can serve as a reliable quantitative tool for single-cell as well as sub-nano-gram proteomic analysis.

scholarly journals Anticarcinogenic effects from plasma elderly trained: the role of mitochondrial function

2021 ◽  
Author(s):  
Alessandra Peres ◽  
Gilson Pires Dorneles ◽  
Gisele Branchini ◽  
Fernanda Bordignon Nunes ◽  
Pedro RT Romão ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Growth Factor ◽  
Exercise Training ◽  
Cell Viability ◽  
Cell Line ◽  
Cancer Cell ◽  
Prostate Cancer Cell ◽  
Cancer Cell Line ◽  
Prostate Cancer Cell Line ◽  
Before And After

This study aimed to evaluate the effects of multimodal exercise training on systemic cytokine levels of the elderly, and the impact of post-exercise training plasma on prostate cancer cell viability and proliferation in vitro. Fasting blood samples were collected from eight institutionalized elderly before and after eight weeks of multimodal exercise training (twice a week). The levels of interleukin(IL)-1ra, IL-1β, IL-2, IL-6, IL-10, IL-17, interferon (IFN)-α, tumor necrosis factor (TNF)-α, fibroblast growth factor (FGF)-1, platelet-derived growth factor (PDGF) and transforming growth factor (TGF)-α were evaluated in the peripheral blood. PC3 prostate cancer cell line was incubated with 10% plasma acquired before and after exercise training from each participant. Multimodal exercise training increased the plasma levels of IL-2, IL-10, IFN-α, and FGF-1, and decreased TNF-α concentrations. PC3 cells presented decreased cell viability evaluated by MTT and lactate dehydrogenase activity as well as lower rates of cell proliferation after the incubation with post-training plasma samples. The incubation of PC-3 cells with post-training plasma decreased the mitochondrial membrane polarization and increased mitochondrial reactive oxygen species (ROS) production without changes in cytosolic ROS. Post-training plasma did not change apoptosis or necrosis rates in the PC-3 cell line. In conclusion, we showed that systemic adaptations in plasma mediators of institutionalized elderly might alter cell viability and proliferation by targeting mitochondrial ROS in a prostate cancer cell line.

scholarly journals In Vitro Anticancer Drug Sensitivity Sensing through Single-Cell Raman Spectroscopy

Biosensors ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 286
Author(s):  
Jingkai Wang ◽  
Kaicheng Lin ◽  
Huijie Hu ◽  
Xingwang Qie ◽  
Wei E. Huang ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Line ◽  
Anticancer Drugs ◽  
Cancer Cell ◽  
Anticancer Drug ◽  
Metabolic Activity ◽  
Drug Sensitivity ◽  
Cancer Cell Line ◽  
Sensitivity Testing

Traditional in vitro anticancer drug sensitivity testing at the population level suffers from lengthy procedures and high false positive rates. To overcome these defects, we built a confocal Raman microscopy sensing system and proposed a single-cell approach via Raman-deuterium isotope probing (Raman-DIP) as a rapid and reliable in vitro drug efficacy evaluation method. Raman-DIP detected the incorporation of deuterium into the cell, which correlated with the metabolic activity of the cell. The human non-small cell lung cancer cell line HCC827 and human breast cancer cell line MCF-7 were tested against eight different anticancer drugs. The metabolic activity of cancer cells could be detected as early as 12 h, independent of cell growth. Incubation of cells in 30% heavy water (D2O) did not show any negative effect on cell viability. Compared with traditional methods, Raman-DIP could accurately determine the drug effect, meanwhile, it could reduce the testing period from 72–144 h to 48 h. Moreover, the heterogeneity of cells responding to anticancer drugs was observed at the single-cell level. This proof-of-concept study demonstrated the potential of Raman-DIP to be a reliable tool for cancer drug discovery and drug susceptibility testing.

scholarly journals Pseudotime estimation: deconfounding single cell time series

2015 ◽  
Author(s):  
John E Reid ◽  
Lorenz Wernisch
Keyword(s):  
Time Series ◽  
Single Cell ◽  
Cancer Cell Line ◽  
Prostate Cancer Cell Line ◽  
Temporal Dimension ◽  
Cross Sectional ◽  
Data Set ◽  
Paracrine Signalling ◽  
Cell Progression ◽  
Cell Assays

Cross-sectional time series single cell data confound several sources of variation, with contributions from measurement noise, stochastic cell to cell variation and cell progression at different rates. Time series from single cell assays are particularly susceptible to confounding as the measurements are not averaged over populations of cells. When several genes are assayed in parallel these effects can be estimated and corrected for under certain smoothness assumptions on cell progression. We present a principled probabilistic model with a Bayesian inference scheme to analyse such data. We demonstrate our method's utility on public microarray, nCounter and RNA-seq data sets from three organisms. Our method almost perfectly recovers withheld capture times in an Arabidopsis data set, it accurately estimates cell cycle peak times in a human prostate cancer cell line and it correctly identifies two precocious cells in a study of paracrine signalling in mouse dendritic cells. Furthermore, our method compares favourably with Monocle, a state-of-the-art technique. We also show using held out data that uncertainty in the temporal dimension is a common confounder and should be accounted for in analyses of cross-sectional time series.

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