CM10
目录号 : GC39215
A pan-ALDH1A inhibitor
Cas No.:692269-09-3
Sample solution is provided at 25 µL, 10mM.
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Quality Control & SDS
- View current batch:
- Purity: >99.50%
- COA (Certificate Of Analysis)
- SDS (Safety Data Sheet)
- Datasheet
CM10 is a pan-aldehyde dehydrogenase 1 (ALDH1A) inhibitor (IC50s = 1,700, 740, and 640 nM for human ALDH1A1, ALDH1A2, and ALDH1A3, respectively).1 It is selective for ALDH1A isoforms over ALDH2, -3A1, -4A1, -5A1, -1B1, and -1L1 at 20 ?M. CM10 induces necroptosis in ovarian cancer stem cells.
1.Chefetz, I., Grimley, E., Yang, K., et al.A Pan-ALDH1A inhibitor induces necroptosis in ovarian cancer stem-like cellsCell Rep.26(11)3061-3075(2019)
| Cas No. | 692269-09-3 | SDF | |
| Canonical SMILES | OC1=C(CNC2=NC3=CC=CC=C3N2CCC)C=CC=C1CC=C | ||
| 分子式 | C20H23N3O | 分子量 | 321.42 |
| 溶解度 | DMSO: 125 mg/mL (388.90 mM) | 储存条件 | Store at -20°C |
| General tips | 请根据产品在不同溶剂中的溶解度选择合适的溶剂配制储备液；一旦配成溶液，请分装保存，避免反复冻融造成的产品失效。 储备液的保存方式和期限：-80°C 储存时，请在 6 个月内使用，-20°C 储存时，请在 1 个月内使用。 为了提高溶解度，请将管子加热至37℃，然后在超声波浴中震荡一段时间。 |
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| Shipping Condition | 评估样品解决方案：配备蓝冰进行发货。所有其他可用尺寸：配备RT，或根据请求配备蓝冰。 | ||
| 制备储备液 | |||
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1 mg | 5 mg | 10 mg |
| 1 mM | 3.1112 mL | 15.556 mL | 31.1119 mL |
| 5 mM | 0.6222 mL | 3.1112 mL | 6.2224 mL |
| 10 mM | 0.3111 mL | 1.5556 mL | 3.1112 mL |
| 第一步：请输入基本实验信息（考虑到实验过程中的损耗，建议多配一只动物的药量） | ||||||||||
| 给药剂量 | mg/kg |  |
动物平均体重 | g | |
每只动物给药体积 | ul | |
动物数量 | 只 |
| 第二步：请输入动物体内配方组成（配方适用于不溶于水的药物；不同批次药物配方比例不同，请联系GLPBIO为您提供正确的澄清溶液配方） | ||||||||||
| % DMSO % % Tween 80 % saline | ||||||||||
| 计算重置 | ||||||||||
计算结果:
工作液浓度： mg/ml；
DMSO母液配制方法： mg 药物溶于 μL DMSO溶液（母液浓度 mg/mL，
体内配方配制方法：取 μL DMSO母液，加入 μL PEG300，混匀澄清后加入μL Tween 80，混匀澄清后加入 μL saline，混匀澄清。
1. 首先保证母液是澄清的；
2.
一定要按照顺序依次将溶剂加入，进行下一步操作之前必须保证上一步操作得到的是澄清的溶液，可采用涡旋、超声或水浴加热等物理方法助溶。
3. 以上所有助溶剂都可在 GlpBio 网站选购。
Phenotypic variation in Pseudomonas sp. CM10 determines microcolony formation and survival under protozoan grazing
FEMS Microbiol Ecol 2002 Jan 1;39(1):57-65.PMID:19709184DOI:10.1111/j.1574-6941.2002.tb00906.x.
Abstract We investigated the survival mechanism of the bacterium Pseudomonas sp. CM10 in the presence of a flagellate predator. The bacterium had been isolated from a continuous culture containing bacterivorous nanoflagellates. On agar plates, we found intraclonal dimorphism of Pseudomonas sp. CM10 colonies at high frequencies: The primary mucoid colony type generated a secondary non-mucoid form. Unlike the repeated generation of non-mucoid colonies from mucoid clones, we did not observe the occurrence of mucoid forms in non-mucoid populations. In semicontinuous and batch cultures, we investigated the ability of the two morphs to survive predation by the bacterivorous flagellate Ochromonas sp. under conditions of growth and starvation. In predator-free cultures, populations of both variants were unicellular but differed in some phenotypic characteristics such as cell motility and hydrophobicity. Grazing treatments revealed that the non-mucoid morph was reduced severely whereas the primary mucoid type survived due to the formation of inert suspended microcolonies stabilized by an extracellular matrix. Effectiveness and competitive trade-offs of microcolony formation were revealed by a competition experiment with the bacterium Pseudomonas putida MM1: Pseudomonas sp. CM10 was displaced in predator-free cultures but outgrew the defenseless and monomorphic competitor under flagellate grazing pressure. We conclude that intraclonal polymorphism may regulate the ability of Pseudomonas sp. CM10 to survive in situations of severe protistan grazing. The formation of inert microcolonies, however, is suggested to be detrimental to rapid growth and dispersal.
Preoperatively molecular staging with CM10 ProteinChip and SELDI-TOF-MS for colorectal cancer patients
J Zhejiang Univ Sci B 2006 Mar;7(3):235-40.PMID:16502512DOI:10.1631/jzus.2006.B0235.
Objectives: To detect the serum proteomic patterns by using SELDI-TOF-MS (surface enhanced laser desorption/ ionization-time of flight-mass spectrometry) technology and CM10 ProteinChip in colorectal cancer (CRC) patients, and to evaluate the significance of the proteomic patterns in the tumour staging of colorectal cancer. Methods: SELDI-TOF-MS and CM10 ProteinChip were used to detect the serum proteomic patterns of 76 patients with colorectal cancer, among them, 10 Stage I, 19 Stage II, 16 Stage III and 31 Stage IV samples. Different stage models were developed and validated by support vector machines, discriminant analysis and time-sequence analysis. Results: The Model I formed by 6 protein peaks (m/z: 2759.58, 2964.66, 2048.01, 4795.90, 4139.77 and 37761.60) could be used to distinguish local CRC patients (Stage I and Stage II) from regional CRC patients (Stage III) with an accuracy of 86.67% (39/45). The Model II formed by 3 protein peaks (m/z: 6885.30, 2058.32 and 8567.75) could be used to distinguish locoregional CRC patients (Stage I, Stage II and Stage III) from systematic CRC patients (Stage IV) with an accuracy of 75.00% (57/76). The Model III could distinguish Stage I from Stage II with an accuracy of 86.21% (25/29). The Model IV could distinguish Stage I from Stage III with accuracy of 84.62% (22/26). The Model V could distinguish Stage II from Stage III with accuracy of 85.71% (30/35). The Model VI could distinguish Stage II from Stage IV with accuracy of 80.00% (40/50). The Model VII could distinguish Stage III from Stage IV with accuracy of 78.72% (37/47). Different stage groups could be distinguished by the two-dimensional scattered spots figure obviously. Conclusion: This method showed great success in preoperatively determining the colorectal cancer stage of patients.
[Preoperative molecular staging of colorectal cancers by CM10 ProteinChip and SELDI-TOF-MS analysis]
Zhonghua Zhong Liu Za Zhi 2006 Oct;28(10):753-7.PMID:17366787doi
Objective: To detect the serum proteomic patterns by using SELDI-TOF-MS and CM10 ProteinChip techniques in colorectal cancer (CRC) patients, and to evaluate the significance of the proteomic patterns in colorectal cancer staging. Methods: A total of 76 serum samples were obtained from CRC patients at different clinical stages, including Dukes A (n = 10), Dukes B (n = 19), Dukes C (n = 16) and Dukes D (n = 31). Different stage models were developed and validated by bioinformatics methods of support vector machines, discriminant analysis and time-sequence analysis. Results: The model I formed by six proteins of peaks at m/z 2759.6, 2964.7, 2048.0, 4795.9, 4139.8 and 37 761.6 could do the best as potential biomarkers to distinguish local CRC patients (Dukes A and Dukes B) from regional CRC patients (Dukes C ) with an accuracy of 86.7%. The model II formed by 3 proteins of peaks at m/z 6885.3, 2058.3 and 8567.8 could do the best to distinguish locoregional CRC patients (Dukes A, B and C) from systematic CRC patients (Dukes D) with an accuracy of 75.0%. The mode III could distinguish Dukes A from Dukes B with an accuracy of 86.2% (25/29). The model IV could distinguish Dukes A from Dukes C with an accuracy of 84.6% (22/26). The model V could distinguish Dukes B from Dukes C with an accuracy of 85.7% (30/35). The model VI could distinguish Dukes B from Dukes D with an accuracy of 80.0% (40/50). The model VII could distinguish Dukes C from Dukes D with an accuracy of 78.7% (37/47). Different stage groups could be distinguished by the two-dimensional scattered spots figure obviously. Conclusion: Our findings indicate that this method can well be used in preoperative staging of colorectal cancers and the screened tumor markers may serve for guidance of integrating treatment of colorectal cancers.
Seven-transmembrane receptor signaling through beta-arrestin
Sci STKE 2005 Nov 1;2005(308):CM10.PMID:16267056DOI:10.1126/stke.2005/308/CM10.
Cell surface receptors are important communicators of external stimuli to the cell interior where they lead to initiation of various signaling pathways and cellular responses. The largest receptor family is the seven-transmembrane receptor (7TMR) family, with approximately 1000 coding genes in the human genome. When 7TMRs are stimulated with agonists, they activate heterotrimeric guanine nucleotide-binding proteins (G proteins), leading to the production of signaling second messengers, such as adenosine 3',5'-monophosphate, inositol phosphates, and others. Activated receptors are rapidly phosphorylated on serine and threonine residues by specialized enzymes called G protein-coupled receptor kinases. Phosphorylated receptors bind the multifunctional adaptor proteins beta-arrestin1 and beta-arrestin2 with high affinity. Beta-arrestin binding blocks further G protein coupling, leading to "desensitization" of G protein-dependent signaling pathways. For several years, this was considered the sole function of beta-arrestins. However, novel functions of beta-arrestins have been discovered. Beta-arrestins are now designated as important adaptors that link receptors to the clathrin-dependent pathway of internalization. Beta-arrestins bind and direct the activity of several nonreceptor tyrosine kinases in response to 7TMR stimulation. Beta-arrestins also bind and scaffold members of such signaling cascades as the mitogen-activated protein kinases (MAPKs). Beta-arrestins are crucial components in 7TMR signaling leading to cellular responses that include cell survival and chemotaxis. Beta-arrestins act as endocytic adaptors and signal mediators not only for the 7TMRs, but also for several receptor tyrosine kinases.
Proteomics in autoimmune thyroid eye disease
Horm Metab Res 2009 Jun;41(6):465-70.PMID:19373747DOI:10.1055/s-0029-1214413.
Ocular and systemic autoimmune diseases impair the proteome patterns of tear fluid. To learn more about the complex pathological processes in autoimmune thyroid eye disease (TED) it is essential to get detailed information on these proteins. Therefore, the purpose of this prospective and controlled study was to detect and evaluate possible changes in the proteomic patterns in tear fluid of patients with TED. Tear samples from TED patients with various disease severity and activity, and healthy controls were analyzed with the SELDI-TOF-MS-technology using arrays with different chromatographic surfaces (CM10 cation exchange, H50 reversed-phase). Data were analyzed by multivariate statistical techniques and artificial neural networks. The discriminate analysis revealed significant changes (p<0.05) in the protein profiles of TED patients compared to controls. We obtained a set of protein biomarkers that allowed us to clearly discriminate between patients and controls with a very high sensitivity and specificity (ROC curve, r=0.99). All possible biomarkers found in this study had a molecular weight between 3000 and 20,000 Da. The majority of the proteins was downregulated in the patient group, with only few proteins overexpressed in comparison to healthy controls. The SELDI-TOF-MS is an accurate method for proteome analysis in tear fluid of TED patients. These proteins may serve as biomarkers for diagnosis and follow-up during treatment.