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Stem Cells

Stem cells are unspecialized cells with the ability to develop into different cell types in the body.
They can self-renew and divide to produce more stem cells or differentiate into specialized cells.
Stem cell research is vital for understanding human development, tissue regeneration, and the potential treatment of diseases.
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Most cited protocols related to «Stem Cells»

1
Integrative Analysis of ScRNA-Seq Datasets
We obtained a read count matrix for the SMART-Seq2 dataset (Nestorawa et al.)37 under the GEO accession GSE81682, and considered 765 annotated progenitors cells expressing at least 4,000 genes. The authors generously provided lineage annotations for each cell (corresponding to Figure 4 in the original publication, used in our Figure 3). We obtained a batch-corrected UMI count matrix for the MARS-Seq dataset38 (link) from the authors’ online resource (http://compgenomics.weizmann.ac.il/tanay/?page_id=649), where we also obtained the MARS-Seq cluster IDs for each cell. This dataset had been previously filtered to remove cells with less than 500 detected UMI for a total of 2,686 single cells.
Both datasets contain cycling progenitors, and heterogeneity between cell cycle stages for these cells has been previously been shown to confound developmental analyses. Therefore, independently for both datasets, we first assigned a cell cycle score to each cell using the PCA method57 (link) on a previously annotated list of cell cycle genes58 (link). We then used the ScaleData function in Seurat (using the cell cycle score as latent variable in a linear regression framework) to mitigate this source of variation in the dataset, prior to CCA.
Butler A., Hoffman P., Smibert P., Papalexi E, & Satija R. (2018). Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nature biotechnology, 36(5), 411-420.
Publication 2018
Cells Genes Genetic Heterogeneity Stem Cells
2
Reconstructing Developmental Trajectories from scRNA-seq
We check the scree plot to choose ten dimension as the intrinsic dimensions to reconstruct the developmental trajectory for the Paul dataset (cells used in Figure 1 of the original study9 (link)). Five branch points and six terminal lineages (monocytes, neutrophils or eosinophil, basophils, dendritic cells, megakaryocytes, and erythrocytes) are revealed. We ordered the cells using genes Paul et al. used to cluster their data rather than the genes from dpFeature, for the sake of consistency with their clusetering analysis. Similarly, we reconstruct Olsson datasets in four dimensions. The major bifurcation between the granulocyte and monocyte branch (GMP) as well as the intricate branch between GMP and megakaryocyte/erythrocyte (Ery/Meg) are revealed. Top 1, 000 genes from dpFeature based on WT cells are used in both of the WT and full datasets. The distribution (related to confusion matrix) of percentages of cells in each cluster from the original papers over each segment (state in Monocle 2) of the principal graph are calculated and visualized in the heatmap.
We applied BEAM analysis to identify genes significantly bifurcating between Ery/Meg and GMP branch on the Olsson wildtype dataset. We then calculate the instant log ratios (ILRs) of gene expression between Ery/Meg and GMP branch and find genes have mean ILR larger than 0.5. The ILRs are defined as:
ILRt=log(Y1tY2t)
So
ILRt is calculated as the log ratio of fitted value at interpolated pseudotime point
t for the Ery/Meg lineage and that for the GMP lineage. Those genes are used to calculate the lineage score (simply calculated as average expression of those genes in each cell, same as stemness score below) for both of the Olsson and the Paul dataset which is used to color the cells in a tree plot transformed from the high dimensional principal graph (see Supplementary Notes). The same genes are used to create the multi-way heatmap for both of the Paul and Olsson dataset (see plot multiple_branches_heatmap function). Critical functional genes from this procedure are identified. Car1, Car2 (important erythroid functional genes for reversible hydration of carbon dioxide) as well as Elane, Prtn3 (important proteases hydrolyze proteins within specialized neutrophil lysosomes as well as proteins of the extracellular matrix) are randomly chosen as example for creating multi-lineage kinetic curves in both of the Olsson and Paul dataset (see plot_multiple_branches_pseudotime function).
In addition, pseudotime dependent genes for the Ery/Meg and GMP branch are identified in the Olsson wildtype dataset. All genes that always have lower expression from both lineages than the average in the progenitor cells are selected. Those genes are used to calculate the stemness score for both of the Olsson and the Paul dataset which is used to color the cells in the tree plot.
Qiu X., Mao Q., Tang Y., Wang L., Chawla R., Pliner H.A, & Trapnell C. (2017). Reversed graph embedding resolves complex single-cell trajectories. Nature methods, 14(10), 979-982.
Publication 2017
Basophils Carbon dioxide Dendritic Cells Endopeptidases Eosinophil Erythrocytes Extracellular Matrix Proteins Gene Expression Genes Genetic Engineering Granulocyte Kinetics lysosomal proteins Megakaryocytes Monocytes Neutrophil Stem Cells Trees
3
Reconstructing Developmental Trajectories from scRNA-seq
We check the scree plot to choose ten dimension as the intrinsic dimensions to reconstruct the developmental trajectory for the Paul dataset (cells used in Figure 1 of the original study9 (link)). Five branch points and six terminal lineages (monocytes, neutrophils or eosinophil, basophils, dendritic cells, megakaryocytes, and erythrocytes) are revealed. We ordered the cells using genes Paul et al. used to cluster their data rather than the genes from dpFeature, for the sake of consistency with their clusetering analysis. Similarly, we reconstruct Olsson datasets in four dimensions. The major bifurcation between the granulocyte and monocyte branch (GMP) as well as the intricate branch between GMP and megakaryocyte/erythrocyte (Ery/Meg) are revealed. Top 1, 000 genes from dpFeature based on WT cells are used in both of the WT and full datasets. The distribution (related to confusion matrix) of percentages of cells in each cluster from the original papers over each segment (state in Monocle 2) of the principal graph are calculated and visualized in the heatmap.
We applied BEAM analysis to identify genes significantly bifurcating between Ery/Meg and GMP branch on the Olsson wildtype dataset. We then calculate the instant log ratios (ILRs) of gene expression between Ery/Meg and GMP branch and find genes have mean ILR larger than 0.5. The ILRs are defined as:
ILRt=log(Y1tY2t)
So
ILRt is calculated as the log ratio of fitted value at interpolated pseudotime point
t for the Ery/Meg lineage and that for the GMP lineage. Those genes are used to calculate the lineage score (simply calculated as average expression of those genes in each cell, same as stemness score below) for both of the Olsson and the Paul dataset which is used to color the cells in a tree plot transformed from the high dimensional principal graph (see Supplementary Notes). The same genes are used to create the multi-way heatmap for both of the Paul and Olsson dataset (see plot multiple_branches_heatmap function). Critical functional genes from this procedure are identified. Car1, Car2 (important erythroid functional genes for reversible hydration of carbon dioxide) as well as Elane, Prtn3 (important proteases hydrolyze proteins within specialized neutrophil lysosomes as well as proteins of the extracellular matrix) are randomly chosen as example for creating multi-lineage kinetic curves in both of the Olsson and Paul dataset (see plot_multiple_branches_pseudotime function).
In addition, pseudotime dependent genes for the Ery/Meg and GMP branch are identified in the Olsson wildtype dataset. All genes that always have lower expression from both lineages than the average in the progenitor cells are selected. Those genes are used to calculate the stemness score for both of the Olsson and the Paul dataset which is used to color the cells in the tree plot.
Qiu X., Mao Q., Tang Y., Wang L., Chawla R., Pliner H.A, & Trapnell C. (2017). Reversed graph embedding resolves complex single-cell trajectories. Nature methods, 14(10), 979-982.
Publication 2017
Basophils Carbon dioxide Dendritic Cells Endopeptidases Eosinophil Erythrocytes Extracellular Matrix Proteins Gene Expression Genes Genetic Engineering Granulocyte Kinetics lysosomal proteins Megakaryocytes Monocytes Neutrophil Stem Cells Trees
4
TCGA Tumor Expression Profiling Across Platforms
TCGA level 3 gene expression levels were obtained from the TCGA Data Portal45 in March 2013. In this study, we used 10 tumour types from four platforms: Affymetrix HT-HG-U133A (one-colour type—that is, one RNA sample is labelled with a fluorophore and hybridized to a microarray), Agilent G4502A (two-colour type—that is, one sample and one reference are labelled with different fluorophores and hybridized together on a same microarray), RNAseq (quantified as Reads Per Kilobase per Million mapped reads)46 (link) and RNAseqV2 (quantified through RNA-seq by Expectation Maximization)47 (link) (Table 1). The tumour types selected for our study were among the first tumour types analysed through TCGA and were selected as cancer types studied in TCGA’s Pan-Cancer project. In addition, we used 31 data sets of microarray expression or SNP array copy numbers from Gene Expression Omnibus48 (link) and ArrayExpress49 (link), glioblastoma expression data set from the Repository of Molecular Brain Neoplasia Data50 , cancer cell line expression data set from Cancer Cell Line Encyclopedia (CCLE)51 (link) and a glioma stem-like cell expression data set from researchers at MD Anderson Cancer Center (Supplementary Table S1).
Yoshihara K., Shahmoradgoli M., Martínez E., Vegesna R., Kim H., Torres-Garcia W., Treviño V., Shen H., Laird P.W., Levine D.A., Carter S.L., Getz G., Stemke-Hale K., Mills G.B, & Verhaak R.G. (2013). Inferring tumour purity and stromal and immune cell admixture from expression data. Nature Communications, 4, 2612.
Publication 2013
Brain Neoplasms Cell Lines Gene Expression Glioblastoma Glioma Malignant Neoplasms Microarray Analysis Neoplasms RNA-Seq Stem Cells
5
Monocle 2 Branch-Dependent Gene Expression
Monocle assigns each cell a pseudotime value and a “State” encoding the segment of the trajectory it resides upon based on the PQ-tree algorithm (see the supplemental material for Trapnell and Cacchiarelli et al for further information18 ). Transcript counts values were variance-stabilized49 via the technique described by Anders and Huber prior to tree construction.
In Monocle 2, we extended the capability to test for branch-dependent gene expression by formulating the problem as a contrast between two negative binomial GLMs.
The null model
NB(Census counts)∼sm.ns(Pseudotime) for the test assumes the gene being tested is not a branch specific gene, whereas the alternative model:
NB(Census counts)∼sm.ns(Pseudotime)+Branch+sm.ns(Pseudotime):Branch assumes that the gene is a branch specific gene where : represents an interaction term between branch and transformed pseudotime, NB means negative binomial distribution. Each model includes a natural spline (here with three degrees of freedom) describing smooth changes in mean expression as a function of pseudotime. The null model fits only a single curve, whereas the alternative will fit a distinct curve for each branch. Our current implementation of Monocle 2 relies on VGAM’s “smart” spline fitting functionality, hence the use of the sm.ns() function instead of the more widely used ns() function from the splines package in R50 (link). Likelihood ratio testing was performed with the VGAM lrtest() function, similar to Monocle’s other differential expression tests50 (link). A significant branch-dependent genes means that the gene has distinct expression dynamics along each branch, with smoothed curves that have different shapes.
To fit the full model, each cell must be assigned to the appropriate branch, which is coded through the factor “Branch” in the above model formula. Monocle’s function for testing branch dependence accepts an argument specifying which branches are to be compared. These arguments are specified using the ‘State’ attribute assigned by Monocle during trajectory reconstructions. For example, in our analysis of the Truetlein et al data 25 (link), Monocle reconstructed a trajectory with two branches (LAT1, LAT2 for AT1 and AT2 lineages, respectively), and three states (SBP, SAT1, SAT2 for progenitor, AT1, or AT2 cells). The user specifies that he or she wants to compare LAT1 and LAT2 by providing SAT1 and SAT2 as arguments to the function. Monocle then assigns all the cells with state SAT1 to branch LAT1 and similarly for the AT2 cells. However, the cells with SBP must be members of both branches, because they are on the path from each branch back to the root of the tree. In order to ensure the independence of data points required for the LRT as well as the robustness and stability of our algorithm, we implemented a strategy to partition the progenitor cells into two groups, with each branch receiving a group. The groups are computed by simply ranking the progenitor cells by pseudotime and assigning the odd-numbered cells to one group and the even numbered cells to the other. We assign the first progenitor to both branches to ensure they start at the same time which is required for downstream spline fitting and clustering. The branch plots in Figure 3d visualize the branch specific spline curves fit by this method.
Qiu X., Hill A., Packer J., Lin D., Ma Y.A, & Trapnell C. (2017). Single-cell mRNA quantification and differential analysis with Census. Nature methods, 14(3), 309-315.
Publication 2017
Gene Expression Genes Glioma of Brain, Familial Reconstructive Surgical Procedures Root, Dorsal Stem Cells Trees

Most recents protocols related to «Stem Cells»

1
Cytotoxicity of Plant Stem Cell Extracts
Not available on PMC !

Example 7

The MTT Cell Proliferation assay determines cell survival following apple stem cell extract treatment. The purpose was to evaluate the potential anti-tumor activity of apple stem cell extracts as well as to evaluate the dose-dependent cell cytotoxicity.

Principle: Treated cells are exposed to 3-(4,5-dimethythiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT). MTT enters living cells and passes into the mitochondria where it is reduced by mitochondrial succinate dehydrogenase to an insoluble, colored (dark purple) formazan product. The cells are then solubilized with DMSO and the released, solubilized formazan is measured spectrophotometrically. The MTT assay measures cell viability based on the generation of reducing equivalents. Reduction of MTT only occurs in metabolically active cells, so the level of activity is a measure of the viability of the cells. The percentage cell viability is calculated against untreated cells.

Method: A549 and NCI-H520 lung cancer cell lines and L132 lung epithelial cell line were used to determine the plant stem cell treatment tumor-specific cytotoxicity. The cell lines were maintained in Minimal Essential Media supplemented with 10% FBS, penicillin (100 U/ml) and streptomycin (100 μg/ml) in a 5% CO2 at 37 Celsius. Cells were seeded at 5×103 cells/well in 96-well plates and incubated for 48 hours. Triplicates of eight concentrations of the apple stem cell extract were added to the media and cells were incubated for 24 hours. This was followed by removal of media and subsequent washing with the phosphate saline solution. Cell proliferation was measured using the MTT Cell Proliferation Kit I (Boehringer Mannheim, Indianapolis, IN) New medium containing 50 μl of MTT solution (5 mg/ml) was added to each well and cultures were incubated a further 4 hours. Following this incubation, DMSO was added and the cell viability was determined by the absorbance at 570 nm by a microplate reader.

In order to determine the effectiveness of apple stem cell extracts as an anti-tumor biological agent, an MTT assay was carried out and IC50 values were calculated. IC50 is the half maximal inhibitory function concentration of a drug or compound required to inhibit a biological process. The measured process is cell death.

Results: ASC-Treated Human Lung Adenocarcinoma Cell Line A549.

TABLE 7
Results of cytotoxicity of apple stem cell extract on lung cancer cell
line A549 as measured by MTT assay (performed in triplicate).
Values of replicates are % of cell death.
Concentration*replicatereplicatereplicateMean of% Live
(μg/ml)123replicatesSDSEMCells
25093.1890.8690.3491.461.510.878.54
10086.8885.1885.6985.920.870.5014.08
5080.5879.4981.0480.370.800.4619.63
2574.2873.8176.3974.831.380.7925.17
12.567.9868.1371.7569.282.131.2330.72
6.2561.6762.4567.1063.742.931.6936.26
3.12555.3756.7762.4558.203.752.1641.80
1.56249.0751.0857.8052.654.572.6447.35
0.78142.7745.4053.1547.115.403.1252.89

Results: ASC-Treated Human Squamous Carcinoma Cell Line NCI-H520.

TABLE 8
Results of cytotoxicity of apple stem cell extract on lung cancer
cell line NCI-H520 measured by MTT assay (performed in triplicate).
Values of replicates are % of cell death.
Concen-%
tration*replicatereplicatereplicateMean ofLive
(μg/ml)123replicatesSDSEMcell
25088.2889.2987.7388.430.790.4611.57
10078.1379.1978.1378.480.610.3521.52
5067.9869.0968.5468.540.560.3231.46
2557.8358.9958.9458.590.660.3841.41
12.547.6848.8949.3448.640.860.5051.36
6.2537.5338.7939.7538.691.110.6461.31
3.12527.3728.6930.1528.741.390.8071.26
1.56217.2218.5920.5618.791.680.9781.21
0.781 7.07 8.4810.96 8.841.971.1491.16

Results: ASC-treated Lung Epithelial Cell Line L132.

TABLE 9
Results of cytotoxicity of apple stem cell extract on
lung epithelial cell line L132 as measured by MTT assay
(performed in triplicate). Values of replicates are % of cell death.
Concen-rep-rep-rep-Mean%
tration*licatelicatelicateofLive
(μg/ml)123replicatesSDSEMcell
25039.5142.5244.0342.022.301.3357.98
10032.9334.4433.6933.690.750.4466.31
5030.6028.9430.5230.020.940.5469.98
2527.9627.8127.1327.630.440.2572.37
12.525.6225.5525.4025.520.120.0774.48
6.2523.1320.8718.6120.872.261.3179.13
3.12513.3411.0811.8312.081.150.6687.92
1.562 6.56 7.31 9.57 7.811.570.9192.19
0.781 8.06 4.30 3.54 5.302.421.4094.70

Summary Results: Cytotoxicity of Apple Stem Cell Extracts.

TABLE 10
IC50 values of the apple stem cell extracts on the on the target
cell lines as determined by MTT assay.
Target Cell
LineIC50
A54912.58
NCI-H52010.21
L132127.46

Apple stem cell extracts killed lung cancer cells lines A549 and NCI-H520 at relatively low doses: IC50s were 12.58 and 10.21 μg/ml respectively as compared to 127.46 μg/ml for the lung epithelial cell line L132. Near complete anti-tumor activity was seen at a dose of 250 μg/ml in both the lung cancer cell lines. This same dose spared more than one half of the L132 cells. See Tables 7-10. The data revealed that apple stem cell extract is cytotoxic to lung cancer cells while sparing lung epithelial cells. FIG. 6 shows a graphical representation of cytotoxicity activity of apple stem cell extracts on lung tumor cell lines A549, NCIH520 and on L132 lung epithelial cell line (marked “Normal”). The γ-axis is the mean % of cells killed by the indicated treatment compared to unexposed cells. The difference in cytotoxicity levels was statistically significant at p≤05.

Example 9

The experiment of Example 7 was repeated substituting other plant materials for ASC. Plant stem cell materials included Dandelion Root Extract (DRE), Aloe Vera Juice (AVJ), Apple Fiber Powder (AFP), Ginkgo Leaf Extract (GLE), Lingonberry Stem Cells (LSC), Orchid Stem Cells (OSC) as described in Examples 1 and 2. The concentrations of plant materials used were nominally 250, 100, 50, 25, 6.25, 3.125, 1.562, and 0.781 μg/mL. These materials were tested only for cells the human lung epithelial cell line L132 (as a proxy for normal epithelial cells) and for cells of the human lung adenocarcinoma cell line A549 (as a proxy for lung cancer cells).

A549 cells lung cancer cell line cytotoxicity results for each of the treatment materials.

DRE-Treated Lung Cancer Cell Line A549 Cells.

TABLE 11
Triplicate results of cell death of DRE-treated
A549 cells measured by MTT assay.
Percentage of live cells calculated as 100% − Mean of triplicates.
Concentration%
(μg/mL)-DRE-Live
treated A549% of cell deathMeanSDSEMcell
25080.4376.4074.8477.232.891.6722.77
10067.6075.2663.7768.885.853.3831.12
5065.3262.9459.9462.732.701.5637.27
2556.8357.9748.1454.315.383.1145.69
6.2555.5949.6949.1751.483.572.0648.52
3.12551.7648.4545.3448.523.211.8551.48
1.56243.6944.0036.0241.244.522.6158.76
0.78137.4726.1919.5727.749.055.2372.26

AVJ-Treated Lung Cancer Cell line A549 Cells.

TABLE 12
Triplicate results of cell death of AVJ-treated
A549 cells measured by MTT assay.
Percentage of live cells calculated as 100% − Mean of triplicates.
Concentration%
(μg/mL)-AVJ-treatedLive
A549% of cell deathMeanSDSEMcell
25076.8178.1675.8876.951.140.6623.05
10076.4075.2673.7175.121.350.7824.88
5065.3266.1559.9463.803.371.9536.20
2550.1048.4556.6351.734.322.5048.27
6.2547.5246.3846.1746.690.720.4253.31
3.12539.8638.6143.7940.752.701.5659.25
1.56232.4019.7730.5427.576.823.9472.43
0.78120.5015.6332.1922.778.514.9277.23

AFP-Treated Lung Cancer Cell line A549 Cells.

TABLE 13
Triplicate results of cell death of AFP-treated
A549 cells measured by MTT assay.
Percentage of live cells calculated as 100% − Mean of triplicates.
Concentration%
(μg/mL)-AFP-treatedLive
A549% of cell deathMeanSDSEMcell
25086.1387.9986.6586.920.960.5613.08
10079.5081.0682.0980.881.300.7519.12
5073.6072.4671.3372.461.140.6627.54
2568.0167.7066.9867.560.530.3132.44
6.2560.8762.1160.7761.250.750.4338.75
3.12549.4851.7650.7250.661.140.6649.34
1.56240.0641.7247.0042.933.622.0957.07
0.78139.2337.7836.8537.961.200.6962.04

GLE-treated Lung Cancer Cell line A549 Cells.

TABLE 14
Triplicate results of cell death of GLE-treated
A549 cells measured by MTT assay.
Percentage of live cells calculated as 100% − Mean of triplicates.
Concentration%
(μg/mL)-GLE-treatedLive
A549% of cell deathMeanSDSEMcell
25088.4291.4990.4490.121.560.909.88
10084.3983.7783.1683.770.610.3516.23
5079.4781.5876.7579.272.421.4020.73
2573.6072.5471.4072.511.100.6327.49
6.2562.8963.6859.9162.161.991.1537.84
3.12550.1854.4751.8452.162.171.2547.84
1.56246.9344.3043.3344.851.861.0755.15
0.78139.5639.3940.9639.970.870.5060.03

LSC-treated lung cancer cell lines A549 cells.

TABLE 15
Triplicate results of cell death of LSC-treated
A549 cells measured by MTT assay.
Percentage of live cells calculated as 100% − Mean of triplicates.
Concentration
(μg/mL)% Live
LSC treated A549% of cell deathMeanSDSEMcell
25077.5478.8578.2078.200.650.3821.80
10077.1476.0476.5976.590.550.3223.41
5066.4268.5266.8267.251.120.6532.75
2559.8067.2264.1663.733.732.1536.27
6.2550.5348.8248.0749.141.260.7350.86
3.12541.1443.6042.7242.491.240.7257.51
1.56239.4739.7440.6139.940.600.3460.06
0.78138.5531.8336.7935.723.482.0164.28

OSC-treated Lung Cancer Cell line A549 Cells.

TABLE 16
Triplicate results of cell death of OSC-treated
A549 cells measured by MTT assay.
Percentage of live cells calculated as 100% − Mean of triplicates.
Concentration
(μg/mL)% Live
OSC-treated A549% of cell deathMeanSDSEMcell
25070.8465.5771.4969.303.251.8730.70
10048.8150.9157.2852.334.412.5547.67
5046.5949.6053.3349.843.381.9550.16
2538.7740.8136.5838.722.111.2261.28
6.2535.7440.7941.0539.193.001.7360.81
3.12534.5533.6837.0235.081.731.0064.92
1.56233.8633.4427.6331.643.482.0168.36
0.78121.3220.0034.8225.388.214.7474.62

L132 cells (“normal” lung epithelial cell line) cytotoxicity results for each of the treatment materials.

DRE-Treated Lung Epithelial Cell Line L132 cells.

TABLE 17
Triplicate results of cell death of DRE-treated
L132 cells measured by MTT assay.
Percentage of live cells calculated as 100% − Mean of triplicates.
Concentration% of %
(μg/mL)cellLive
DRE-treated L132deathMeanSDSEMcell
25086.6686.6186.6686.640.030.0213.36
10076.2977.3976.8476.840.550.3223.16
5065.9268.1767.0167.031.130.6532.97
2555.5458.9557.1957.231.700.9842.77
6.2545.1749.7347.3747.422.281.3252.58
3.12534.8040.5037.5437.612.851.6562.39
1.56224.4231.2827.7227.813.431.9872.19
0.78114.0522.0617.8918.004.012.3182.00

AVJ-Treated Lung Epithelial Cell Line L132 cells.

TABLE 18
Triplicate results of cell death of AVJ-treated
L132 cells measured by MTT assay.
Percentage of live cells calculated as 100% − Mean of triplicates
AFP-treated lung epithelial cell line L132 cells.
Concentration % of %
(μg/mL)cellLive
AVJ-treated L132deathMeanSDSEMcell
25057.0355.9353.6255.531.741.0044.47
10050.9949.7847.0449.272.031.1750.73
5044.9543.6340.4543.012.311.3456.99
2538.9137.4933.8636.752.601.5063.25
6.2532.8831.3427.2830.502.891.6769.50
3.12526.8425.1920.6924.243.181.8475.76
1.56220.8019.0514.1117.983.472.0082.02
0.78114.7612.90 7.5211.733.762.1788.27

AFP-Treated Lung Epithelial Cell Line L132 cells.

TABLE 19
Triplicate results of cell death of AFP-treated
L132 cells measured by MTT assay.
Percentage of live cells calculated as 100% − Mean of triplicates
AFP-treated lung epithelial cell line L132 cells.
Concentration
(μg/mL)% Live
AFP-treated L132% of cell deathMeanSDSEMcell
25056.1555.4357.1956.260.880.5143.74
10049.9548.2447.6448.611.200.6951.39
5043.7441.0538.0940.962.831.6359.04
2537.5433.8628.5433.324.532.6166.68
6.2531.3426.6718.9925.676.243.6074.33
3.12525.1419.489.4418.027.954.5981.98
1.56218.9412.2910.8714.034.312.4985.97
0.78112.73 5.10 6.81 8.214.002.3191.79

GLE-Treated Lung Epithelial Cell Line L132 cells.

TABLE 20
Triplicate results of cell death of GLE-treated
L132 cells measured by MTT assay.
Percentage of live cells calculated as 100% − Mean of triplicates
AFP-treated lung epithelial cell line L132 cells.
Concentration
(μg/mL)% Live
GLE-treated L132% of cell deathMeanSDSEMcell
25084.4283.2083.0883.570.740.4316.43
10080.0579.2978.5979.310.730.4220.69
5072.7571.5974.1072.811.260.7227.19
2580.0581.8679.9980.631.060.6119.37
6.2568.2670.1368.2668.881.080.6231.12
3.12560.6263.0760.6261.441.410.8238.56
1.56248.0748.7748.8348.560.420.2451.44
0.78146.2745.5746.6746.170.560.3253.83

LSC-Treated Lung Epithelial Cell Line L132 cells.

TABLE 21
Triplicate results of cell death of LSC-treated
L132 cells measured by MTT assay.
Percentage of live cells calculated as 100% − Mean of triplicates
AFP-treated lung epithelial cell line L132 cells.
Concentration
(μg/mL)% Live
LSC-treated L132% of cell deathMeanSDSEMcell
25086.4185.8285.7686.000.350.2014.00
10081.2181.2779.9980.820.720.4219.18
5075.9674.7473.5174.741.230.7125.26
2574.7472.7571.4772.991.650.9527.01
6.2570.1368.3268.2668.901.060.6131.10
3.12554.0358.0553.4455.172.511.4544.83
1.56253.9751.9851.9852.641.150.6647.36
0.78146.7945.6244.9245.78 0.940.54 54.22

OSC-Treated Lung Epithelial Cell Line L132 cells.

TABLE 22
Triplicate results of cell death of OSC-treated
L132 cells measured by MTT assay.
Percentage of live cells calculated as 100% − Mean of triplicates
AFP-treated lung epithelial cell line L132 cells.
Concentration %
(μg/mL)Live
OSC-treated L132% of cell deathMeanSDSEMcell
25061.8462.3760.4461.551.000.5738.45
10054.1453.4452.1053.231.040.6046.77
5042.9442.3040.3241.851.370.7958.15
2535.9434.4833.3134.581.320.7665.42
6.2533.9632.6732.0332.890.980.5767.11
3.12527.4826.2026.7226.800.650.3773.20
1.562 9.80 7.29 7.35 8.151.430.8391.85
0.781 7.29 8.98 8.05 8.110.850.4991.89

Calculated values.

TABLE 23
Calculated IC50 doses (ug/mL) and therapeutic ratios
(IC50 for L132 cells/IC50 for A549 cells) for each
treatment material. Values greater than one indicate
that a material would be more selective in killing cancer
cells than normal cells. ASC results imported from
Example 8. These studies indicate that at least
some of the materials may be effective anti-cancer agents.
ASC has outstanding selectivity compared to other materials.
ASCDREAVJAFPGLELSCOSC
A549 12.589.82211.4811.9811.1 13.733.9 
IC50
L132 127.4656.88 62.6682.6577.6369.26715.38
IC50
Ther.10.15.8 5.56.97.0 0.70.5
Ratio

US11872258B2. Oral cavity treatments (2024-01-16). None [US]. Inventors: Maryam Rahimi [US].
Full text: Click here
Patent 2024
14-3-3 Proteins 43-63 61-26 A549 Cells Action Potentials Adenocarcinoma of Lung Aloe Aloe vera Antineoplastic Agents Biological Assay Biological Factors Biological Processes Bromides Cardiac Arrest Cell Death Cell Extracts Cell Lines Cell Proliferation Cells Cell Survival Cytotoxin diphenyl DNA Replication Epistropheus Epithelial Cells Fibrosis Formazans Genetic Selection Ginkgo biloba Ginkgo biloba extract Homo sapiens Lingonberry Lung Lung Cancer Lung Neoplasms Malignant Neoplasms Mitochondria Mitochondrial Inheritance Neoplasms Neoplastic Stem Cells Oral Cavity PEG SD-01 Penicillins Pharmaceutical Preparations Phosphates Plant Cells Plant Leaves Plant Roots Plants Powder Psychological Inhibition Saline Solution SD 31 SD 62 SEM-76 Squamous Cell Carcinoma Stem, Plant Stem Cells Streptomycin Succinate Dehydrogenase Sulfoxide, Dimethyl Taraxacum Tetrazolium Salts
2
Quantifying IL-2 Effects on T Cell Activation
Not available on PMC !

Example 7

Impact of IL-2 signalling on Teff responses is characterised in a T cell activation assay, in which intracellular granzyme B (GrB) upregulation and proliferation are examined. Previously frozen primary human Pan T cells (Stemcell Technologies) are labelled with eFluor450 cell proliferation dye (Invitrogen) according to manufacturer's recommendation, and added to 96-U-bottom well plates at 1×105 cells/well in RPMI 1640 (Life Technologies) containing 10% FBS (Sigma), 2 mM L-Glutamine (Life Technologies) and 10,000 U/ml Pen-Strep (Sigma). The cells are then treated with 10 μg/ml anti-CD25 antibodies or control antibodies followed by Human T-Activator CD3/CD28 (20:1 cell to bead ratio; Gibco) and incubated for 72 hrs in a 37° C., 5% CO2 humidified incubator. To assess T cell activation, cells are stained with the eBioscience Fixable Viability Dye efluor780 (Invitrogen), followed by fluorochrome labelled antibodies for surface T cell markers (CD3-PerCP-Cy5.5 clone UCHT1 Biolegend, CD4-BV510 clone SK3 BD Bioscience, CD8-Alexa Fluor 700 clone RPA-T8 Invitrogen, CD45RA-PE-Cy7 clone HI100 Invitrogen, CD25-BUV737 clone 2A3 BD Bioscience) and then fixed and permeabilized with the eBioscience™ Foxp3/Transcription Factor Staining Buffer Set (Invitrogen) before staining for intracellular GrB and intranuclear FoxP3 (Granzyme B-PE clone GB11 BD Bioscience, FoxP3-APC clone 236A/E7). Samples are acquired on the Fortessa LSR X20 Flow Cytometer (BD Bioscience) and analysed using the BD FACSDIVA software. Doublets are excluded using FCS-H versus FCS-A, and lymphocytes defined using SSC-A versus FCS-A parameters. CD4+ and CD8+ T cell subsets gated from the live CD3+ lymphocytes are assessed using a GrB-PE-A versus proliferation eFluor450-A plot. Results are presented as percentage of proliferating GrB positive cells from the whole CD4+ T cell population. Graphs and statistical analysis is performed using GraphPad Prism v7. (results not shown)

US11873341B2. Anti-CD25 for tumour specific cell depletion (2024-01-16). Tusk Therapeutics Ltd. [GB], Cancer Research Technology Limited [GB]. Inventors: Anne Goubier [GB], Beatriz Goyenechea Corzo [GB], Josephine Salimu [GB], Kevin Moulder [GB], Pascal Merchiers [GB], Sergio Quezada [GB].
Full text: Click here
Patent 2024
Anti-Antibodies Antibodies Biological Assay Buffers CD4 Positive T Lymphocytes Cell Proliferation Cells Clone Cells CY5.5 cyanine dye Eragrostis Fluorescent Dyes Freezing Glutamine GZMB protein, human Homo sapiens IL2RA protein, human Lymphocyte prisma Protoplasm Stem Cells Streptococcal Infections T-Lymphocyte T-Lymphocyte Subsets Transcriptional Activation Transcription Factor
3
Bioceramic Composition Enhances Cell Adhesion
Not available on PMC !

Example 8

Cell adhesion was also evaluated by means of in vitro scratch wound-healing assay. HDPSCs cells were analyzed by difference in staining with phalloidin (cell nucleus) and DAPI to visualize actin cytoskeleton.

Cell adhesion results showed excellent interaction and adhesion between neighboring cells in the presence of bioceramic composition. The Bioceramic composition sealer (CB5) and Bioceramic composition repair (CB6), showed a gradual increase in growth over time, an extended morphology and a high content of F-Actin (cell microfilamen), reaching confluence after 72 hours of culture.

The analysis of cell proliferation (via cell viability study), apoptosis, cell adhesion and morphology (via cell adhesion study) and migration (via cell migration study) showed very positive results, indicating that the proposed bioceramic composition induces the odonto/osteogenic mineralization and differentiation process in the presence of tooth-specific human stem cells (hDPSCs pulp). While a market resin sealer was also used in the comparative studies, however, all results were not satisfactory for this product.

US11857558B2. Dental and medical compositions having a multiple source of metallic ions (2024-01-02). ANGELUS INDÚSTRIA DE PRODUTOS ODONTOLÓGICOS S/A [BR]. Inventors: César Eduardo Bellinati [BR], Cristiane Vila [BR], William Pereira Dos Santos [BR], Maíra Bendlin Calzavara [BR].
Full text: Click here
Patent 2024
Apoptosis Biological Assay Cell Adhesion Cell Nucleus Cell Proliferation Cell Survival DAPI Dental Pulp Differentiations, Cell F-Actin Homo sapiens Microfilaments Migration, Cell Osteogenesis Phalloidine Physiologic Calcification Resins, Plant Stem, Plant Stem Cells Tooth
4
Ventral Midbrain Dopaminergic Progenitor Cell Verification
Not available on PMC !

Example 3

Verification of CD117 as a Marker for Ventral Midbrain Dopaminergic Progenitor Cells Derived from Different Pluripotent Stem Cell Sources

To verify that the correlation of CD117 with the highly important intracellular marker FoxA2, thus highlighting the ventral midbrain dopaminergic progenitor cells, is independent from the sources auf pluripotent stem cells. We could show this correlation for one iPS line (F5) and one ES line (1(15).

US11866729B2. Method for the generation of a cell composition ventral midbrain patterned dopaminergic progenitor cells (2024-01-09). Miltenyi Biotec B.V. & Co. KG [DE]. Inventors: Andreas Bosio [DE], Andrej Smiyakin [DE].
Full text: Click here
Patent 2024
Cells Dopaminergic Neurons Hydrochloride, Dopamine LINE-1 Elements Mesencephalon Pluripotent Stem Cells Protoplasm Stem Cells
5
Characterization of Neutrophil CKA
Not available on PMC !

Example 16

Demonstrating CKA of CD34+ Stem Cell Derived Neutrophils (SCDNs)

Results were obtained via the xCELLigence assay with further populations of CD34+ Stem Cell Derived Neutrophils (FIG. 6), and were consistent with results obtained via the MTT assay as described above. SCDNs (generated ex vivo) were again shown to have differential CKA, with culture 5 representing low CKA neutrophils and culture 1 representing high CKA neutrophils.

US11878033B2. Cancer-killing cells (2024-01-23). LIFT BIOSCIENCES LTD [GB]. Inventors: Alex Blyth [GB], Nico Bruyniks [GB].
Full text: Click here
Patent 2024
Biological Assay Cells Malignant Neoplasms Neutrophil Population Group Stem Cells

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More about "Stem Cells"

Stem cells are undifferentiated biological cells that have the remarkable ability to develop into diverse specialized cell types in the body.
These versatile cells can self-renew, dividing and replenishing their own population, as well as differentiate into specific cell lineages, such as neurons, cardiomyocytes, or hepatocytes.
Stem cell research is a vital field of study that provides crucial insights into human development, tissue regeneration, and potential treatments for a wide range of diseases.
PubCompare.ai is a cutting-edge platform that enhances the reproducibility and accuracy of stem cell research by empowering researchers to quickly locate the best protocols from literature, preprints, and patents.
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Researchers can leverage PubCompare.ai's advanced technology to access a wealth of information related to stem cell culture and differentiation.
This includes protocols and techniques for using essential materials like fetal bovine serum (FBS), penicillin/streptomycin, Lymphoprep (a density gradient medium for isolating mononuclear cells), Matrigel (an extracellular matrix substrate), MTeSR1 medium (a defined, feeder-free medium for human embryonic stem cell culture), the ALDEFLUOR kit (for identifying and isolating stem and progenitor cells), L-glutamine (a crucial amino acid supplement), Accutase (an enzyme solution for dissociating cells), GlutaMAX (a stable L-glutamine alternative), and DMEM/F12 (a common culture medium for stem cells).
By leveraging PubCompare.ai's innovative technology, researchers can streamline their stem cell studies, enhance reproducibility, and accelerate the pace of discoveries in this vital field of science.