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Cell Lines

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Most cited protocols related to «Cell Lines»

A total of 947 independent cancer cell lines were profiled at the genomic level (data available at www.broadinstitute.org/ccle and Gene Expression Omnibus (GEO) using accession numbers GSE36139) and compound sensitivity data was obtained for 479 lines (Supplementary Table 11). Mutation information was obtained both by using massively parallel sequencing of >1,600 genes (Supplementary Table 12) and by mass spectrometric genotyping (OncoMap), which interrogated 492 mutations in 33 known oncogenes and tumor suppressors. Genotyping/copy number analysis was performed using Affymetrix Genome-Wide Human SNP Array 6.0 and expression analysis using the GeneChip Human Genome U133 Plus 2.0 Array. 8-point dose response curves were generated for 24 anticancer drugs using an automated compound-screening platform. Compound sensitivity data were used for two types of predictive models that utilized the naive Bayes classifier or the elastic net regression algorithm. The effects of AHR expression silencing on cell viability were assessed by stable expression of shRNA lentiviral vectors targeting either this gene or luciferase as control. The effect of compound treatment on AHR target gene expression was assessed by quantitative RT-PCR. A full description of the Methods is included in the Supplementary Information.
Publication 2012
Cell Lines Cell Survival Cloning Vectors Gene Chips Gene Expression Genes Genome, Human Hypersensitivity Luciferases Malignant Neoplasms Mass Spectrometry Mutation Oncogenes Pharmaceutical Preparations Reverse Transcriptase Polymerase Chain Reaction Short Hairpin RNA Tumor Suppressor Genes
We performed two in silico experiments to assess the detection limits of different deconvolution algorithms. In the first experiment (Supplementary Fig. 6), we used the same cell line GEPs described above to compare CIBERSORT and RLR with five other GEP deconvolution methods4 (link)–8 (link). We evaluated detection limit using Jurkat cells (spike-in concentrations of 0.5%, 1%, 2.5%, 5%, 7.5%, and 10%), whose reference GEP (median of three replicates in GSE11103) was added into randomly created background mixtures of the other three blood cell lines. Five mixtures were created for each spike-in concentration. Predicted Jurkat fractions were assessed in the presence of differential tumor content, which we simulated by adding HCT116 (described above) in ten even increments, from 0% to 90%. Of note, we also used the same marker or signature genes described for simulated tumors (above). In a second experiment (Supplementary Fig. 7a), we compared CIBERSORT with QP5 (link), LLSR4 (link), PERT6 (link), and RLR. We spiked naïve B cell GEPs from the leukocyte signature matrix into four random background mixtures of the remaining 21 leukocyte subsets in the signature matrix. The same background mixtures were used for each spike-in. We also tested the addition of unknown content by adding defined proportions (0 to 90%) of randomly permuted expression values from a naïve B cell reference transcriptome (median expression profile from samples used to build LM22, Supplementary Table 1). We then repeated this analysis for each of the remaining leukocyte subsets in LM22 (Supplementary Fig. 7b).
Publication 2015
B-Lymphocytes BLOOD Cell Lines Cytosol Genes Jurkat Cells Leukocyte Count Leukocytes Neoplasms Transcriptome
Large-scale, arrayed format RNAi screens to identify genes essential for proliferation/viability were performed as described3 (link),14 (link). The effect of introducing each of the 5002 shRNAs (targeting 957 genes) was determined in 19 cell lines, and normalized using the B-score metric4 (link). Feature selection of shRNA B-score data was performed using the Comparative Marker Application Suite in GenePattern5 (link) and was independently analyzed using RIGER analysis6 (link) to compute NES for each gene. Secondary screen viability data was normalized using a percent of control statistic, given the biased nature of the candidate shRNA plate. Expression profiling was used to generate a signature that correlates with KRAS activation and implicated NF-κB signaling in cell lines and tumors dependent on KRAS. Regulation of NF-κB by TBK1 was shown using biochemical and cell biological approaches. Details of the analytical methods are provided in the Full Methods.
Publication 2009
Biopharmaceuticals Cell Lines Cells Genes K-ras Genes Neoplasms RELA protein, human RNA Interference Short Hairpin RNA TBK1 protein, human
Large-scale, arrayed format RNAi screens to identify genes essential for proliferation/viability were performed as described3 (link),14 (link). The effect of introducing each of the 5002 shRNAs (targeting 957 genes) was determined in 19 cell lines, and normalized using the B-score metric4 (link). Feature selection of shRNA B-score data was performed using the Comparative Marker Application Suite in GenePattern5 (link) and was independently analyzed using RIGER analysis6 (link) to compute NES for each gene. Secondary screen viability data was normalized using a percent of control statistic, given the biased nature of the candidate shRNA plate. Expression profiling was used to generate a signature that correlates with KRAS activation and implicated NF-κB signaling in cell lines and tumors dependent on KRAS. Regulation of NF-κB by TBK1 was shown using biochemical and cell biological approaches. Details of the analytical methods are provided in the Full Methods.
Publication 2009
Biopharmaceuticals Cell Lines Cells Genes K-ras Genes Neoplasms RELA protein, human RNA Interference Short Hairpin RNA TBK1 protein, human

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Publication 2014
Cell Lines Cell Nucleus Cells Formaldehyde Ligation Microtubule-Associated Proteins Nucleotides Streptavidin Technique, Dilution

Most recents protocols related to «Cell Lines»

Example 49

The functional activity of compounds was determined in a cell line where p70S6K is constitutively activated. Test article was dissolved in DMSO to make a 10 μM stock. PathScan® Phospho-S6 Ribosomal Protein (Ser235/236) Sandwich ELISA Kit was purchased from Cell Signaling Technology. A549 lung cancer cell line, was purchased from American Type Culture Collection. A549 cells were grown in F-12K Medium supplemented with 10% FBS. 100 μg/mL penicillin and 100 μg/mL streptomycin were added to the culture media. Cultures were maintained at 37° C. in a humidified atmosphere of 5% CO2 and 95% air. 2.0×105 cells were seeded in each well of 12-well tissue culture plates for overnight. Cells were treated with DMSO or test article (starting at 100 μM, 10-dose with 3 fold dilution) for 3 hours. The cells were washed once with ice cold PBS and lysed with 1× cell lysis buffer. Cell lysates were collected and samples were added to the appropriate wells of the ELISA plate. Plate was incubated for overnight at 4° C. 100 μL of reconstituted Phospho-S6 Ribosomal Protein (Ser235/236) Detection Antibody was added to each well and the plate was incubated at 37° C. for 1 hour. Wells were washed and 100 μl of reconstituted HRP-Linked secondary antibody was added to each well. The plate was incubated for 30 minutes at 37° C. Wash procedure was repeated and 100 μL of TMB Substrate was added to each well. The plate was incubated for 10 minutes at 37° C. 100 μL of STOP Solution was added to each well and the absorbance was read at 460 nm using Envision 2104 Multilabel Reader (PerkinElmer, Santa Clara, CA). IC50 curves were plotted and IC50 values were calculated using the GraphPad Prism 4 program based on a sigmoidal dose-response equation.

TABLE 2
In vitro biological data for representative compounds of Formula
I-IX Unless otherwise noted, compounds that were tested had an IC50
of less than 50 μM in the S6K binding assay.
Example NumberS6K Binding Activity
1A
2B
3B
4A
5A
6A
7A
8A
9B
10B
11B
12C
13C
14C
15A
16A
17B
18A
19A
20A
21A
22C
23B
24A
25A
26C
27A
28C
29C
30C
31A
32A
33C
34C
35C
36C
37C
38A
39A
40A
41A

Unless otherwise noted, compounds that were tested had an IC50 of less than 50 μM in the S6K Binding assay. A=less than 0.05 μM; B=greater than 0.05 μM and less than 0.5 μM; C=greater than 0.5 μM and less than 10 μM;

Patent 2024
A549 Cells Atmosphere Biological Assay Biopharmaceuticals Buffers Cell Lines Cells Cold Temperature Culture Media Enzyme-Linked Immunosorbent Assay Immunoglobulins Lung Cancer Penicillins prisma Psychological Inhibition Ribosomal Proteins Ribosomal Protein S6 Ribosomal Protein S6 Kinases, 70-kDa Streptomycin Sulfoxide, Dimethyl Technique, Dilution Tissues

Example 17

To further validate the activity of the DMPK siRNAs, many of the sequences that showed the best activity in the initial screen were selected for a follow-up evaluation in dose response format. Once again, two human cell lines were used to assess the in vitro activity of the DMPK siRNAs: first, SJCRH30 human rhabdomyosarcoma cell line; and second, Myotonic Dystrophy Type 1 (DM1) patient-derived immortalized human skeletal myoblasts. The selected siRNAs were transfected in a 10-fold dose response at 100, 10, 1, 0.1, 0.01, 0,001, and 0.0001 nM final concentrations or in a 9-fold dose response at 50, 5.55556, 0.617284, 0.068587, 0.007621, 0.000847, and 0.000094 nM final concentrations. The siRNAs were formulated with transfection reagent Lipofectamine RNAiMAX (Life Technologies) according to the manufacturer's “forward transfection” instructions. Cells were plated 24 h prior to transfection in triplicate on 96-well tissue culture plates, with 8500 cells per well for SJCRH30 and 4000 cells per well for DM1 myoblasts. At 48 h (SJCRH30) or 72 h (DM1 myoblasts) post-transfection cells were washed with PBS and harvested with TRIzol® reagent (Life Technologies). RNA was isolated using the Direct-zol-96 RNA Kit (Zymo Research) according to the manufacturer's instructions. 10 μl of RNA was reverse transcribed to cDNA using the High Capacity cDNA Reverse Transcription Kit (Applied Biosystems) according to the manufacturer's instructions. cDNA samples were evaluated by qPCR with DMPK-specific and PPIB-specific TaqMan human gene expression probes (Thermo Fisher) using TaqMan® Fast Advanced Master Mix (Applied Biosystems). DMPK values were normalized within each sample to PPIB gene expression. The quantification of DMPK downregulation was performed using the standard 2−ΔΔCt a method. All experiments were performed in triplicate, with Tables 16A-B, 17A-B, and 18A-B presenting the mean values of the triplicates as well as the calculated IC50 values determined from fitting curves to the dose-response data by non-linear regression.

TABLE 16A
sense strandSEQantisense strandSEQ
sequence (5′-3′)IDsequence (5′-3′)ID
ID #1Passenger Strand (PS)NO:Guide Strand (GS)NO:
535GGGCGAGGUGUCGUGCUUA9349UAAGCACGACACCUCGCCC12053
584GACCGGCGGUGGAUCACGA9398UCGUGAUCCACCGCCGGUC12102
716AUGGCGCGCUUCUACCUGA9530UCAGGUAGAAGCGCGCCAU12234
1028CAGACGCCCUUCUACGCGA9842UCGCGUAGAAGGGCGUCUG12546
1276UUUCGAAGGUGCCACCGAA10090UUCGGUGGCACCUUCGAAA12794
1825UGCUCCUGUUCGCCGUUGA10639UCAACGGCGAACAGGAGCA13343
1945CCCUAGAACUGUCUUCGAA10759UUCGAAGACAGUUCUAGGG13463
2529CUUCGGCGGUUUGGAUAUA11343UAUAUCCAAACCGCCGAAG14047
2558GUCCUCCGACUCGCUGACA11372UGUCAGCGAGUCGGAGGAC14076
2628CCGACAUUCCUCGGUAUUA11442UAAUACCGAGGAAUGUCGG14146
2636CCUCGGUAUUUAUUGUCUA11450UAGACAAUAAAUACCGAGG14154
119mer position in NM_001288766.1

TABLE 16B
IC50
ID #1qPCR2qPCR3qPCR4qPCR5qPCR6qPCR7qPCR8(nM)
535111.9105.4106.382.436.729.535.70.165
58490.590.284.767.838.025.828.30.190
71688.985.281.962.032.619.320.30.181
102888.581.883.061.332.727.331.50.127
127687.085.084.066.140.534.036.40.150
182585.185.983.769.136.225.225.00.259
194585.081.774.444.922.917.717.20.070
252983.381.875.350.624.617.517.70.103
255884.381.174.345.423.413.311.80.088
262885.384.079.559.830.323.525.10.140
263686.386.974.344.019.812.413.00.070
2SJCRH30; 0.0001 nM; % DMPK mRNA
3SJCRH30; 0.001 nM; % DMPK mRNA
4SJCRH30; 0.01 nM; % DMPK mRNA
5SJCRH30; 0.1 nM; % DMPK mRNA
6SJCRH30; 1 nM; % DMPK mRNA
7SJCRH30; 10 nM; % DMPK mRNA
8SJCRH30; 100 nM; % DMPK mRNA

TABLE 17A
sense strandSEQantisense strandSEQ
sequence (5′-3′)IDsequence (5′-3′)ID
ID #1Passenger Strand (PS)NO:Guide Strand (GS)NO:
2600CAAUCCACGUUUUGGAUGA11414UCAUCCAAAACGUGGAUUG14118
2636CCUCGGUAUUUAUUGUCUA11450UAGACAAUAAAUACCGAGG14154
2675CCCCGACCCUCGCGAAUAA11489UUAUUCGCGAGGGUCGGGG14193
2676CCCGACCCUCGCGAAUAAA11490UUUAUUCGCGAGGGUCGGG14194
2679GACCCUCGCGAAUAAAAGA11493UCUUUUAUUCGCGAGGGUC14197
2680ACCCUCGCGAAUAAAAGGA11494UCCUUUUAUUCGCGAGGGU14198
2681CCCUCGCGAAUAAAAGGCA11495UGCCUUUUAUUCGCGAGGG14199
2682CCUCGCGAAUAAAAGGCCA11496UGGCCUUUUAUUCGCGAGG14200
119mer position in NM_001288766.1

TABLE 17B
IC50
ID #1qPCR2qPCR3qPCR4qPCR5qPCR6qPCR7(nM)
2600107.5107.6108.1106.3103.172.731.31
263681.181.174.047.225.711.50.073
267588.188.384.364.638.120.70.151
267688.978.984.472.744.935.60.204
267984.087.382.753.331.413.50.091
268087.485.385.168.544.539.60.110
268187.085.477.649.626.516.00.061
268282.483.977.150.827.331.10.047
2SJCRH30; 0.000094 nM; % DMPK mRNA
3SJCRH30; 0.000847 nM; % DMPK mRNA
4SJCRH30; 0.007621 nM; % DMPK mRNA
5SJCRH30; 0.068587 nM; % DMPK mRNA
6SJCRH30; 0.617284 nM; % DMPK mRNA
7SJCRH30; 5.55556 nM; % DMPK mRNA

TABLE 18A
sense strandSEQantisense strandSEQ
sequence (5′-3′)IDsequence (5′-3′)ID
ID #1Passenger Strand (PS)NO:Guide Strand (GS)NO:
584GACCGGCGGUGGAUCACGA9398UCGUGAUCCACCGCCGGUC12102
716AUGGCGCGCUUCUACCUGA9530UCAGGUAGAAGCGCGCCAU12234
1265UUUACACCGGAUUUCGAAA10079UUUCGAAAUCCGGUGUAAA12783
1297AUGCAACUUCGACUUGGUA10111UACCAAGUCGAAGUUGCAU12815
1945CCCUAGAACUGUCUUCGAA10759UUCGAAGACAGUUCUAGGG13463
1960CGACUCCGGGGCCCCGUUA10774UAACGGGGCCCCGGAGUCG13478
2529CUUCGGCGGUUUGGAUAUA11343UAUAUCCAAACCGCCGAAG14047
2530UUCGGCGGUUUGGAUAUUA11344UAAUAUCCAAACCGCCGAA14048
2531UCGGCGGUUUGGAUAUUUA11345UAAAUAUCCAAACCGCCGA14049
2554CCUCGUCCUCCGACUCGCA11368UGCGAGUCGGAGGACGAGG14072
2628CCGACAUUCCUCGGUAUUA11442UAAUACCGAGGAAUGUCGG14146
2629CGACAUUCCUCGGUAUUUA11443UAAAUACCGAGGAAUGUCG14147
2681CCCUCGCGAAUAAAAGGCA11495UGCCUUUUAUUCGCGAGGG14199
119mer position in NM_001288766.1

TABLE 18B
IC50
ID #1qPCR2qPCR3qPCR4qPCR5qPCR6qPCR7(nM)
58490.877.097.771.945.029.70.228
71696.582.577.064.643.333.90.080
126568.580.968.057.137.525.70.146
129771.467.269.453.540.525.40.171
194571.862.341.729.822.415.30.006
196063.065.462.145.831.128.30.068
252963.558.749.231.122.921.90.017
253069.366.753.143.238.824.50.016
253169.972.457.340.235.425.60.018
255468.270.151.243.032.117.30.043
262869.767.962.538.431.617.10.042
262972.165.669.042.134.413.70.078
268182.491.587.655.529.319.60.084
2DM1 myoblasts; 0.000094 nM; % DMPK mRNA
3DM1 myoblasts; 0.000847 nM; % DMPK mRNA
4DM1 myoblasts; 0.007621 nM; % DMPK mRNA
5DM1 myoblasts; 0.068587 nM; % DMPK mRNA
6DM1 myoblasts; 0.617284 nM; % DMPK mRNA
7DM1 myoblasts; 5.55556 nM; % DMPK mRNA

Patent 2024
Cell Lines Cells DNA, Complementary Down-Regulation Gene Expression Homo sapiens Lipofectamine Myoblasts Myoblasts, Skeletal Myotonic Dystrophy NM-107 Patients PPIB protein, human Reverse Transcription Rhabdomyosarcoma RNA, Messenger RNA, Small Interfering Tissues Transfection trizol
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

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

Example 9

An analysis of gene ontology (GO) categories associated with ADAR1 dependent cells revealed that NCI-H1650 and HCC366 (“HCC-366”), two ADAR1 dependent cell lines, both have elevated basal expression of interferon inducible genes (FIG. 35). The expression levels of interferon-inducible genes were also elevated in NCI-H196 cells (FIG. 36).

In light of the correlation between ADAR1 dependency and the expression of interferon-inducible genes, additional cancer cell lines from the Molecular Signatures Database (MSigDB) (Liberzon et al. (2015) Cell Systems 1:417-425) was examined. Cancer Cell Line Encyclopedia (CCLE) clustering was performed based on the Type I/Interferon-a gene set, which contained 97 genes including PKR. The resulting cluster included HCC366, NCI-H1650 and 9 additional lung cell lines. Among these cell lines, HCC1438 and NCI-H596 were sensitive to knockout of ADAR1 by lentiviral CRISPR-Cas9 (FIG. 37).

All the above-identified ADAR1 dependent cancer cell lines showed elevated interferon signaling markers, e.g., phosphorylation of STAT1 and expression of interferon-stimulated gene (ISGs) (FIG. 38). Elevated interferon signaling in the ADAR1 dependent cancer cell lines did not necessarily lead to PD-L1 overexpression (FIG. 38). Cell lines in the high interferon signaling cluster (LN215_CENTRAL_NERVOUS_SYSTEM, NCIH596_LUNG, HCC1438_LUNG, T3M10_LUNG, NCIH1869_LUNG, SW900_LUNG, HCC366_LUNG, SKLU1_LUNG, NCIH1650_LUNG, HCC4006_LUNG, and NCIH1648_LUNG) displayed high IFN-β, but not IFN-α (FIG. 39). As such, cancer cell lines sensitive to ADAR1 or ISG15 knockdown displayed elevated interferon secretion and downstream signaling. To further investigate the relationship between ADAR1 and IFN-β secretion, it was found that ADAR1 knockout led to amplified IFN-β secretion in cell lines primed with high basal interferon activation (FIG. 40). It was also found that ADAR1 dependent cell lines do not show enhanced sensitivity to IFN-α or IFN-β alone (FIG. 41).

Patent 2024
CD274 protein, human Cell Lines Cells Central Nervous System Clustered Regularly Interspaced Short Palindromic Repeats Gene Expression Genes Hypersensitivity Interferon-alpha Interferons Interferon Type I Light Lung Malignant Neoplasms Phosphorylation secretion STAT1 protein, human
Not available on PMC !

Example 1

1) Tucaresol

Tucaresol (0-1200 μM) is exposed for 72 hours to a panel of human liquid, hematological, and solid tumors such as multiple myeloma, leukemia, colorectal, non-small cell lung cancer (squamous and adenocarcinoma), hepatocellular, renal, pancreatic and breast cancer cell lines, and human non-tumor such as HUVEC, PBMC, skin fibroblast cells lines. Tucaresol is studied either alone or in combination with standard-of-care agents (1-100 μM). All cell lines are grown in standard serum-containing media with an exposure time of 24-144 hours. Cell viability is measured using, for example, the Cell TiterGlo® Viability Assay. The potency (IC50) and efficacy (% cell kill) are determined from the percent cell growth of the vehicle control.

2) Tucaresol Plus PD-1 Antibody

Tucaresol (0-1200 μM) in the presence of a PD-1 antibody is exposed for 72 hours to a panel of human liquid, hematological, and solid tumor such as multiple myeloma, leukemia, colorectal, non-small cell lung cancer (squamous and adenocarcinoma), hepatocellular, renal, pancreatic and breast cancer cell lines, and human non-tumor such as HUVEC, PBMC, skin fibroblast cells lines, and the viability of the cell lines are measured as described above. The viability of the cell lines in the presence of tucaresol plus PD-1 antibody is compared to the viability of the cell lines in the presence of a CTLA-4 antibody plus the PD-1 antibody or PD-1 antibody alone.

3) CTLA-4 Antibody Plus PD-1 Antibody

CTLA-4 antibody in the presence of a PD-1 antibody is exposed for 72 hours to a panel of human liquid, hematological, and solid tumor such as multiple myeloma, leukemia, colorectal, non-small cell lung cancer (squamous and adenocarcinoma), hepatocellular, renal, pancreatic and breast cancer cell lines, and human non-tumor such as HUVEC, PBMC, skin fibroblast cells lines, and the viability of the cell lines are measured as described above.

4) Tucaresol Plus Plinabulin

Tucaresol (0-1200 μM) in the presence of Plinabulin is exposed for 72 hours to a panel of human liquid, hematological, and solid tumor such as multiple myeloma, leukemia, colorectal, non-small cell lung cancer (squamous and adenocarcinoma), hepatocellular, renal, pancreatic and breast cancer cell lines, and human non-tumor such as HUVEC, PBMC, skin fibroblast cells lines, and the viability of the cell lines are measured as described above.

The viability of the cell lines in the presence of tucaresol, tucaresol plus PD-1 antibody, CTLA-4 antibody plus the PD-1 antibody, and tucaresol plus plinabulin are compared.

Patent 2024
Adenocarcinoma Biological Assay Cell Lines Cells Cell Survival Cytotoxic T-Lymphocyte Antigen 4 Fibroblasts Homo sapiens Immunoglobulins Kidney Leukemia MCF-7 Cells Multiple Myeloma Neoplasms Non-Small Cell Lung Carcinoma Pancreas plinabulin Serum Skin tucaresol

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Fetal Bovine Serum (FBS) is a cell culture supplement derived from the blood of bovine fetuses. FBS provides a source of proteins, growth factors, and other components that support the growth and maintenance of various cell types in in vitro cell culture applications.
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Penicillin is a type of antibiotic used in laboratory settings. It is a broad-spectrum antimicrobial agent effective against a variety of bacteria. Penicillin functions by disrupting the bacterial cell wall, leading to cell death.
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Streptomycin is a broad-spectrum antibiotic used in laboratory settings. It functions as a protein synthesis inhibitor, targeting the 30S subunit of bacterial ribosomes, which plays a crucial role in the translation of genetic information into proteins. Streptomycin is commonly used in microbiological research and applications that require selective inhibition of bacterial growth.
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More about "Cell Lines"

Cell cultures, immortalized cell lines, and cultured cells are widely used in biomedical research to study cellular processes, test drug candidates, and develop new therapies.
These renewable and homogeneous resources offer advantages over primary cells, allowing researchers to conduct consistent and controlled experiments.
Cell lines are derived from animal or human tissues and can be maintained and propagated in vitro.
Popular cell culture media like DMEM, RPMI 1640, and supplements such as FBS, L-glutamine, penicillin, and streptomycin, provide the necessary nutrients and growth factors to support cell growth and proliferation.
Leveraging the power of AI-driven platforms like PubCompare.ai, researchers can streamline their cell line studies by identifying the optimal protocols and products from scientific literature, preprints, and patents.
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