Use of Gas Chromatography to Quantify Short Chain Fatty Acids in the Serum, Colonic Luminal Content and Feces of Mice

引用 收藏 提问与回复 分享您的反馈 Cited by



Jan 2018



Short-Chain Fatty Acids (SCFAs) are a product of the fermentation of resistant starches and dietary fibers by the gut microbiota. The most important SCFA are acetate (C2), propionate (C3) and butyrate (C4). These metabolites are formed and absorbed in the colon and then transported through the hepatic vein to the liver. SCFAs are more concentrated in the intestinal lumen than in the serum. Butyrate is largely consumed in the gut epithelium, propionate in the liver and acetate in the periphery. SCFAs act on many cells including components of the immune system and epithelial cells by two main mechanisms: activation of G-protein coupled receptors (GPCRs) and inhibition of histone deacetylase. Considering the association between changes in SCFA concentrations and the development of diseases, methods to quantify these acids in different biological samples are important. In this study, we describe a protocol using gas chromatography to quantify SCFAs in the serum, feces and colonic luminal content. Separation of compounds was performed using a DB-23 column (60 m x 0.25 mm internal diameter [i.d.]) coated with a 0.15 µm thick layer of 80.2% 1-methylnaphatalene. This method has a good linear range (15-10,000 µg/ml). The precision (relative standard deviation [RSD]) is less than 15.0% and the accuracy (error relative [ER]) is within ± 15.0%. The extraction efficiency was higher than 97.0%. Therefore, this is cost effective and reproducible method for SCFA measurement in feces and serum.

Keywords: Short chain fatty acid (短链脂肪酸), Gas chromatography (气相色谱法), Gut microbiota (肠道微生物), Acetate (醋酸盐), Butyrate (丁酸盐), Propionate (丙酸盐)


The microbiota is a complex, heterogeneous and dynamic group of microorganisms (bacteria, viruses and fungi) that colonizes the tissues in direct contact with the external environment including the skin and the genitourinary, intestinal and respiratory tracts. Several studies have demonstrated recently that these microorganisms are important for maintaining host homeostasis. Indeed, changes in their composition can lead to dysbiosis, as observed in specific pathological conditions, and have been associated with their development, as shown for inflammatory bowel disease, asthma, obesity and other chronic inflammatory conditions (Ferreira et al., 2014; Nishida et al., 2018; Sokolowska et al., 2018).

Short chain fatty acids are bacterial metabolites produced by components of the microbiota. The most abundant and studied molecules of this class are acetic, propionic and butyric acids, which are normally found in their deprotonated forms (e.g., acetate, propionate and butyrate). SCFAs modify important processes including metabolism, immune system development and activation, and intestinal barrier function strategies (den Besten et al., 2013; Correa-Fiz et al., 2016; Koh et al., 2016) have been used for changing SCFAs bioavailability in the gut including administration of probiotics (Andrade-Oliveira et al., 2015; Mendes et al., 2017), antibiotics (Fellows et al., 2018), diets with different fiber contents (Vieira et al., 2017), SCFAs in the drinking water or the butyric acid pro-drug (tributyrin) (Vinolo et al., 2012; Vieira et al., 2017). Taken together, these strategies allow us to investigate the role of these metabolites in vivo in different disease models. In this context, it is essential to have a robust method for measuring the levels of these molecules in biological samples such as feces, luminal content or serum.

Gas chromatography (GC) is most commonly used for SCFA analysis due to compatibility with the chemical properties of SCFAs, such as volatility, and the suitability of the detectors that can be coupled to this equipment, such as flame ionization detector (FID). FID is the most widely used for analysis of SCFAs. Due to the large amount of compounds present in the matrix, the sample should be pretreated using extraction and derivatization procedures. Recently, several techniques of derivatization have been described to obtain more stable compounds and provide greater compatibility between the stationary phase and the analytes (Karlsson et al., 2010; Walton et al., 2012; Zhang et al., 2013). On the other hand, the use of derivation techniques has critical disadvantages, including the points that it is time-consuming, there can be losses of SCFAs, it is costly due to the use of large quantities of reagents, and the risk of occupational exposure to allergenic and toxic reagents. Some authors have described filtration and ultracentrifugation techniques to avoid the disadvantages of derivatization and obtain a fast sample preparation (Cuervo et al., 2013; Salazar et al., 2015). In spite of this, the run time is usually increased due to low purification of the samples, which increases the quantities and variety of compounds needed for separation in the column. We have chosen the ultracentrifugation technique in this protocol. Here, we describe a method that we have used to measure SCFAs in biological samples using gas chromatography equipped with FID and a liquid-liquid extraction technique. The column used in this method is composed of a high polarity stationary phase, which makes analysis of the SCFAs possible. The liquid-liquid extraction offers some advantages such as greater sample recuperation, higher sample purification, time optimization and greater separation and peak resolution in chromatograms.

Materials and Reagents

  1. 100-1,000 µl pipette tips (KASVI, catalog number: K8-1000B)
  2. Eppendorf Safe-Lock Tubes, 1.5 ml (Eppendorf, catalog number: 0030120086)
  3. Amber, write-on spot, certified, 2 ml, screw top vial packs (Agilent Technologies, catalog number: 5182-0554)
  4. Serum, colonic luminal content and feces of C57BL/6 mice
  5. Hydrochloric acid, 37% (v/v) (Sigma-Aldrich, catalog number: 320331)
  6. Anhydrous citric acid (Cinética Produtos Químicos, catalog number: 278)
  7. Sodium chloride (Cinética Produtos Químicos, catalog number: 415)
  8. Tetrahydrofuran (Merck, catalog number: 1081012500)
  9. Acetonitrile (Sigma-Aldrich, catalog number: 60004)
  10. N-butanol (Sigma-Aldrich, catalog number: 34867)
  11. Acetic acid, analytical standard, GC assay ≥ 9.8% (Sigma-Aldrich, catalog number: 71251)
  12. Propionic acid, analytical standard, GC assay ≥ 9.8% (Sigma-Aldrich, catalog number: 94425)
  13. Butyric acid, analytical standard, GC assay ≥ 9.8% (Sigma-Aldrich, catalog number: 19215)
  14. HCl
  15. Distilled water
  16. 1-methylnaphthalene
  17. N2
  18. H2
  19. 0.1 M HCl solution (see Recipes)
  20. 3,000 µg/ml acetic acid in serum stock solution (see Recipes)
  21. 3,000 µg/ml propionic acid stock solution (see Recipes)
  22. 3,000 µg/ml butyric acid stock solution (see Recipes)
  23. 1,000 µg/ml SCFAs standard mixture (see Recipes)


  1. J&W DB-23 GC column, 60 m, 0.32 mm, 0.25 µm, 7 inch cage (Agilent Technologies, catalog number: 123-2362)
  2. Forceps
  3. Freezer FE26/127V (Electrolux Appliances, model: FE26, catalog number: 04251FBA106/ 04251FBB206)
  4. Stainless steel double spatula, 180 mm length x 3 mm diameter (Metalic Acessórios para Laboratório, catalog number: 063-A3)
  5. PIPETMAN Classic P1000 pipette (Gilson, catalog number: 20170-170)
  6. Gas Chromatograph Agilent 6850 series (Agilent Technologies, discontinued by the manufacturer)
  7. DB 23 Agilent capillary column, 60 m x 0.25 mm internal diameter (i.d.)
  8. Oven
  9. Automatic liquid sampler 7683B (Agilent Technologies, catalog number: G2880A)
  10. Analytical balance (SHIMADZU, model: ATX224 series, catalog number not found)
  11. Vortex mixer (Phenix Luferco, model: AP56)
  12. Centrifuge 5810 R (Eppendorf, model: 5810 R, catalog number: 5810000424)
  13. Gas Chromatograph Shimadzu 2010 model, equipped with FID (SHIMADZU, catalog number: C184-E019)
  14. AOC-20i automatic liquid sampler (SHIMADZU, model: AOC-20i)


  1. EZChrom software, 3.3.1 version (Agilent Technologies)
  2. GC solution software, 3.2 version (SHIMADZU)


  1. Sample preparation
    1. Collect blood from cardiac puncture or axillary plexus. Maintain blood at room temperature (RT) for 30 min and centrifuge (3,000 x g, 8 min). Collect the serum and freeze at -80 °C in 1.5 ml microtubes.
    2. Collect fecal pellets directly from each mouse in microtubes. For this, raise the animal holding the tail, position the microtube in the proximity of the anus and collect the pellets that are excreted. Two or three pellets from each animal are adequate.
    3. After euthanasia, collect the colons and gently remove the luminal contents of the proximal part. To do this, use the forceps to collect directly into microtubes and freeze at -80 °C.
    4. Keep all biological samples frozen at -80 °C until the day of analysis.
    5. For feces and colonic luminal content, transfer 20 mg of these into 1.5 ml microtubes and add 200 µl of distilled water. Homogenize using a metal spatula. 
    6. In 1.5 ml microtubes, add the sample (200 µl serum or homogenate of feces with water, see Step A5), a 200 µl mixture of organic solvents composed of N-butanol, tetrahydrofuran and acetonitrile in a 50:30:20 ratio, 40 µl HCl 0.1 M, 20 mg citric acid and 40 mg sodium chloride. Shake the microtubes vigorously using the vortex stirrer for 1 min.
    7. Centrifuge the samples at 14,870 x g at room temperature for 10 min.
    8. Using an automatic pipette, transfer the supernatant to chromatographic vials equipped with 200 µl inserts and analyze by GC-FID (Figure 1).

      Figure 1. Sample preparation. A. Liquid-liquid extraction procedure of samples. Serum samples (200 µl) were extracted with the same volume of a mixture of solvents. B. Liquid-liquid extraction procedure for feces and colonic luminal content samples. Fecal samples (20 mg) were previously homogenized with distilled water and subjected to the extraction procedures described in Steps A5-A8 of Sample preparation section. 

  2. GC procedure
    1. Adjust the temperature of the injector to 250 °C.
    2. Inject contents (5 µl) in a split ratio of 25:1, using the DB 23 capillary column Agilent, 60 m x 0.25 mm of internal diameter (i.d.), and coated with a film of 0.15 µm composed of 80.2% 1-methylnaphthalene.
    3. The mobile phase is composed of N2 at an initial flow rate of 1 ml/min and maintaining this for 1 min, then changing to 0.8 ml/min for 1 min, changing to 0.6 ml/min for 1 min and then reverting to 1 ml/min for 9.2 min.
    4. Adjust and maintain the temperature of the FID detector at 260 °C.
    5. Adjust the flow of the H2 and the synthetic air to 30 ml/min and 350 ml/min, respectively.
    6. Adjust the temperature program of the oven to100 °C, maintain for 7 min and increase to 200 °C at a rate of 25 °C/min and maintain for 5 min.

Data analysis

  1. Chromatograms generated by the analysis of the serum (Figure 1A) and the feces (Figure 1B) are shown in Figure 2. SCFAs are identified by the times of retention in comparison with analytical standard solutions.

    Figure 2. Chromatogram of serum (A) and fecal (B) samples. The retention times of each SCFA are as follows: acetate, 5.2 min; propionate, 7.1 min; butyrate, 9.08 min. The analysis is performed using a Gas Chromatograph Shimadzu 2010 model equipped with 7683B automatic liquid sampler and FID.

  2. Use the EZChrom software to integrate the areas of each peak, corresponding to its respective SCFA.
  3. Prepare standard calibration curves in triplicate, with a concentration range of 15-1,000 µg/ml for SCFAs using the matrices in this study (serum and feces) as solvent to dilute the analytes. Construct the calibration curve in serum matrix, make a 1,000 µg/ml stock solution of SCFA using serum as solvent, and dilute serially in a range of 15-1,000 µg/ml (see Recipes).
  4. Construct the calibration curve in feces matrix, make a 1000 µl/ml SCFAs using distilled water as solvent in accordance with obtaining method of stock solution of SCFA in serum (see Recipes).
  5. Prepare samples according to the liquid-liquid extraction procedures described in the Procedure A, Steps 5-8 and analyze the samples by GC-FID as described in the Procedure B, GC procedure.
    The standard calibration curves have presented linear regression relationships for all acids (R2 = 0.998) (Figure 3).

    Figure 3. Linearity of standard calibration curves of (A) serum and (B) feces. Calibration curves obtained using serum (A) and feces (B). Linearity obtained using Gas Chromatograph Agilent 6850 series, equipped with 7683B automatic liquid sampler and FID following the parameters described in this article.

  6. The retention time is 7.2, 8.2, and 9.2 min for acetic, propionic and butyric acid, respectively and the total run time for each analysis is 16.5 min.
  7. The precision, expressed as relative standard deviation (RSD) was less than 15.0%, and the accuracy, expressed as Relative Error (RE) was within ± 15.0%. The extraction efficiency (EE) was greater than 97.0%. To calculate the RSD, RE and EE, use the formulas described below:

  8. For standard curves, plot areas of each SCFA standard sample on the y-axis against your respective theoretical concentration values (15-1,000 µg/ml) and obtain the equation of the function y = ax + b, where a is the angular coefficient, x is the area found for the sample and b is the linear coefficient.
  9. Use the equation showed below to calculate the concentrations of each SCFA in the samples. The concentration of each SCFA is expressed as mg/ml.

  10. Convert the concentrations for mM and mmol/kg for the serum and fecal/colonic luminal content samples, respectively using the formulas described below:


  1. Agilent equipment is not available in our laboratory. Thus, we have used the GC Shimadzu 2010 model to obtain the chromatograms to illustrate analytes separation (Figures 2 and 4). The chromatograms were obtained following the bioanalytical method conditions described in this article.

    Figure 4. Overlap of chromatograms obtained of colonic luminal content from antibiotic-treated mice and control animals (not treated with antibiotics). The chromatogram shows animals treated with antibiotics (blue line) and untreated animals (black line) (Fellows et al., 2018). The analyses are performed using a Gas Chromatograph Shimadzu 2010 model equipped with AOC-20i automatic liquid sampler and FID, in accordance with the parameters described in this article. The chromatograms were integrated using the GC solution software.

  2. The retention time and the detection range of the analytes may vary according to capillary column type and length and equipment brands. DB-23 capillary column Agilent can be used in GC Shimadzu 2010 model.
  3. This method was developed and validated in accordance with the RDC 27/2012 of ANVISA (National Sanitary Vigilance Agency).
  4. No alterations to the protocol are required if different mouse strains are used.
  5. Animals were housed in facility of the Institute of Biomedical Sciences, University of Sao Paulo and Institute of Biology, University of Campinas.
  6. Blank serum is the serum without SCFAs standards addition.


  1. 0.1 M HCl solution
    0.835 ml HCl 37% (v/v)
    Add 99.165 ml distilled water
  2. 3,000 µg/ml acetic acid in serum stock solution
    5.7 µl analytical standard acetic acid, GC assay ≥ 99.8%
    Add 2,000 µl blank serum
    Vortex for 1 min
  3. 3,000 µg/ml propionic acid stock solution
    6.06 µl analytical standard propionic acid, GC assay ≥ 99.8%
    Add 2,000 µl blank serum
    Vortex for 1 min
  4. 3,000 µg/ml butyric acid stock solution
    6.2 µl analytical standard butyric acid, GC assay 99.8%
    Add 2,000 µl blank serum
    Vortex for 1 min
  5. 1,000 µg/ml SCFAs standard mixture
    Mix 1 ml each SCFA stock solution to give 1,000 µg/ml each SCFA (final volume 3 ml)
    Standard SCFA solutions
    1. Perform a serial dilution of this mixed organic acid solution using serum as solvent to generate a range of 15-1,000 µg/ml as a standard curve of SCFAs in serum matrix
    2. Perform the same serial dilution using distilled water as matrix for the construction of the standard curve of SCFAs in feces matrix. To carry this out, prepare seven tubes containing 20 mg of feces and add 200 µl of their respectively solutions obtained in the serial dilution in each tube


We are grateful to Claudete Justina Valduga and Patrícia Sartorelli for providing equipment for this study. This work was supported by FAPESP 2012/50410-8 and CNPq (The National Council for Scientific and Technological Development), project number 486037/2012-6 to C.M.F, and 2017/10653-9 to M.A.V.

Competing interests

The authors have declared that no conflict of interest exists.


All experimental procedures were approved by the Ethics Committee on Animal Use of the Institute of Biology, University of Campinas (protocol number 3742-1), and Animal Care Committee of the University of Sao Paulo (protocol number. 181/114/02). Also, it was performed in accordance with the guidelines of the Brazilian Control and Experimentation Committee.


  1. Andrade-Oliveira, V., Amano, M. T., Correa-Costa, M., Castoldi, A., Felizardo, R. J., de Almeida, D. C., Bassi, E. J., Moraes-Vieira, P. M., Hiyane, M. I., Rodas, A. C., Peron, J. P., Aguiar, C. F., Reis, M. A., Ribeiro, W. R., Valduga, C. J., Curi, R., Vinolo, M. A., Ferreira, C. M. andCamara, N. O. (2015). Gut bacteria products prevent AKI induced by ischemia-reperfusion. J Am Soc Nephrol 26(8): 1877-1888.
  2. Correa-Fiz, F., Fraile, L. and Aragon, V. (2016). Piglet nasal microbiota at weaning may influence the development of Glasser's disease during the rearing period. BMC Genomics 17: 404.
  3. Cuervo, A., Salazar, N., Ruas-Madiedo, P., Gueimonde, M. and Gonzalez, S. (2013). Fiber from a regular diet is directly associated with fecal short-chain fatty acid concentrations in the elderly. Nutr Res 33(10): 811-816.
  4. den Besten, G., van Eunen, K., Groen, A. K., Venema, K., Reijngoud, D. J. and Bakker, B. M. (2013). The role of short-chain fatty acids in the interplay between diet, gut microbiota, and host energy metabolism. J Lipid Res 54(9): 2325-2340.
  5. Fellows, R., Denizot, J., Stellato, C., Cuomo, A., Jain, P., Stoyanova, E., Balazsi, S., Hajnady, Z., Liebert, A., Kazakevych, J., Blackburn, H., Correa, R. O., Fachi, J. L., Sato, F. T., Ribeiro, W. R., Ferreira, C. M., Peree, H., Spagnuolo, M., Mattiuz, R., Matolcsi, C., Guedes, J., Clark, J., Veldhoen, M., Bonaldi, T., Vinolo, M. A. R. and Varga-Weisz, P. (2018). Microbiota derived short chain fatty acids promote histone crotonylation in the colon through histone deacetylases. Nat Commun 9(1): 105.
  6. Ferreira, C. M., Vieira, A. T., Vinolo, M. A., Oliveira, F. A., Curi, R. and Martins Fdos, S. (2014). The central role of the gut microbiota in chronic inflammatory diseases. J Immunol Res 2014: 689492.
  7. Karlsson, C., Ahrne, S., Molin, G., Berggren, A., Palmquist, I., Fredrikson, G. N. and Jeppsson, B. (2010). Probiotic therapy to men with incipient arteriosclerosis initiates increased bacterial diversity in colon: a randomized controlled trial. Atherosclerosis 208(1): 228-233.
  8. Koh, A., De Vadder, F., Kovatcheva-Datchary, P. and Backhed, F. (2016). From dietary fiber to host physiology: Short-chain fatty acids as key bacterial metabolites. Cell 165(6): 1332-1345.
  9. Mendes, E., Acetturi, B. G., Thomas, A. M., Martins, F. D. S., Crisma, A. R., Murata, G., Braga, T. T., Camara, N. O. S., Franco, A., Setubal, J. C., Ribeiro, W. R., Valduga, C. J., Curi, R., Dias-Neto, E., Tavares-de-Lima, W. and Ferreira, C. M. (2017). Prophylactic supplementation of Bifidobacterium longum 51Aprotects mice from ovariectomy-induced exacerbated allergic airway inflammation and airway hyperresponsiveness. Front Microbiol 8: 1732.
  10. Nishida, A., Inoue, R., Inatomi, O., Bamba, S., Naito, Y. and Andoh, A. (2018). Gut microbiota in the pathogenesis of inflammatory bowel disease. Clin J Gastroenterol 11(1): 1-10.
  11. Salazar, N., Dewulf, E. M., Neyrinck, A. M., Bindels, L. B., Cani, P. D., Mahillon, J., de Vos, W. M., Thissen, J. P., Gueimonde, M., de Los Reyes-Gavilan, C. G. and Delzenne, N. M. (2015). Inulin-type fructans modulate intestinal Bifidobacterium species populations and decrease fecal short-chain fatty acids in obese women. Clin Nutr 34(3): 501-507.
  12. Sokolowska, M., Frei, R., Lunjani, N., Akdis, C. A. and O'Mahony, L. (2018). Microbiome and asthma. Asthma Res Pract 4: 1.
  13. Vieira, A. T., Castelo, P. M., Ribeiro, D. A. and Ferreira, C. M. (2017). Influence of oral and gut microbiota in the health of menopausal women. Front Microbiol 8: 1884.
  14. Vinolo, M. A., Rodrigues, H. G., Festuccia, W. T., Crisma, A. R., Alves, V. S., Martins, A. R., Amaral, C. L., Fiamoncini, J., Hirabara, S. M., Sato, F. T., Fock, R. A., Malheiros, G., dos Santos, M. F. and Curi, R. (2012). Tributyrin attenuates obesity-associated inflammation and insulin resistance in high-fat-fed mice. Am J Physiol Endocrinol Metab 303(2): E272-282.
  15. Walton, G. E., Lu, C., Trogh, I., Arnaut, F. and Gibson, G. R. (2012). A randomised, double-blind, placebo controlled cross-over study to determine the gastrointestinal effects of consumption of arabinoxylan-oligosaccharides enriched bread in healthy volunteers. Nutr J 11: 36.
  16. Zhang, H., Sun, J., Liu, X., Hong, C., Zhu, Y., Liu, A., Li, S., Guo, H. and Ren, F. (2013). Lactobacillus paracasei subsp. paracasei LC01 positively modulates intestinal microflora in healthy young adults. J Microbiol 51(6): 777-782.


短链脂肪酸(SCFAs)是通过肠道微生物群发酵抗性淀粉和膳食纤维的产物。最重要的SCFA是乙酸盐(C2),丙酸盐(C3)和丁酸盐(C4)。这些代谢物在结肠中形成并被吸收,然后通过肝静脉运输到肝脏。 SCFA在肠腔中比在血清中更集中。丁酸盐主要在肠上皮,肝脏中的丙酸盐和外周的醋酸盐中消耗。 SCFAs通过两种主要机制作用于许多细胞,包括免疫系统和上皮细胞的组分:G蛋白偶联受体(GPCR)的激活和组蛋白脱乙酰酶的抑制。考虑到SCFA浓度变化与疾病发展之间的关联,在不同生物样品中量化这些酸的方法是重要的。在本研究中,我们描述了一种使用气相色谱法定量血清,粪便和结肠腔内容物中SCFAs的方案。使用涂覆有0.15μm厚的80.2%1-甲基萘三烯的DB-23柱(60m×0.25mm内径[ i.d。])进行化合物的分离。该方法具有良好的线性范围(15-10,000μg/ ml)。精度(相对标准偏差[RSD])小于15.0%,精度(误差相对[ER])在±15.0%以内。提取效率高于97.0%。因此,这是用于粪便和血清中SCFA测量的成本有效且可重复的方法。

【背景】微生物群是一组复杂,异质和动态的微生物(细菌,病毒和真菌),它们与组织直接接触,包括皮肤和泌尿生殖系统,肠道和呼吸道。最近几项研究表明,这些微生物对维持宿主体内平衡很重要。实际上,如特定病理状况所观察到的,其组成的变化可导致生态失调,并且与其发展有关,如炎症性肠病,哮喘,肥胖和其他慢性炎症状况所示(Ferreira 等。< / em>,2014; Nishida et al。,2018; Sokolowska et al。,2018)。

短链脂肪酸是由微生物群的组分产生的细菌代谢物。该类中最丰富和研究的分子是乙酸,丙酸和丁酸,它们通常以去质子化形式存在(例如,乙酸盐,丙酸盐和丁酸盐)。 SCFAs修改重要的过程,包括代谢,免疫系统发育和激活,以及肠屏障功能策略(den Besten et al。,2013; Correa-Fiz et al。,2016; Koh et al。,2016)已用于改变肠道中SCFAs的生物利用度,包括益生菌的使用(Andrade-Oliveira et al。,2015; Mendes et al。,2017),抗生素(Fellows et al。,2018),不同纤维含量的饮食(Vieira et al。,2017),SCFAs in饮用水或丁酸前药(三丁酸甘油酯)(Vinolo et al。,2012; Vieira et al。,2017)。总之,这些策略使我们能够研究这些代谢物体内在不同疾病模型中的作用。在这种情况下,必须有一种稳健的方法来测量生物样品中这些分子的水平,如粪便,管腔内容物或血清。

气相色谱(GC)最常用于SCFA分析,因为它与SCFAs的化学性质相容,例如挥发性,以及可以耦合到该设备的检测器的适用性,例如火焰离子化检测器(FID)。 FID是最广泛用于SCFA分析的。由于基质中存在大量化合物,应使用萃取和衍生化程序对样品进行预处理。最近,已经描述了几种衍生化技术以获得更稳定的化合物并在固定相和分析物之间提供更大的相容性(Karlsson et al。,2010; Walton et al。,2012; Zhang et al。,2013)。另一方面,衍生技术的使用具有关键的缺点,包括耗时,可能存在SCFAs损失,由于使用大量试剂而成本高,以及职业暴露的风险过敏和有毒试剂。一些作者描述了过滤和超速离心技术,以避免衍生化的缺点并获得快速的样品制备(Cuervo et al。,2013; Salazar et al。,2015)。尽管如此,由于样品的纯化率低,通常会增加运行时间,这增加了柱中分离所需化合物的数量和种类。我们在这个协议中选择了超速离心技术。在这里,我们描述了一种方法,我们已经使用配备FID和液 - 液萃取技术的气相色谱法测量生物样品中的SCFAs。该方法中使用的色谱柱由高极性固定相组成,这使得SCFA的分析成为可能。液 - 液萃取具有一些优点,例如更大的样品回收,更高的样品纯化,时间优化以及色谱图中更大的分离和峰分辨率。

关键字:短链脂肪酸, 气相色谱法, 肠道微生物, 醋酸盐, 丁酸盐, 丙酸盐


  1. 100-1,000μl移液器吸头(KASVI,目录号:K8-1000B)
  2. Eppendorf Safe-Lock管,1.5 ml(Eppendorf,目录号:0030120086)
  3. 琥珀,写字,认证,2毫升,螺旋盖小瓶包装(安捷伦科技,目录号:5182-0554)
  4. C57BL / 6小鼠的血清,结肠腔内容物和粪便
  5. 盐酸,37%(v / v)(Sigma-Aldrich,目录号:320331)
  6. 无水柠檬酸(CinéticaProdutosQuímicos,目录号:278)
  7. 氯化钠(CinéticaProdutosQuímicos,目录号:415)
  8. 四氢呋喃(Merck,目录号:1081012500)
  9. 乙腈(Sigma-Aldrich,目录号:60004)
  10. 正丁醇(Sigma-Aldrich,目录号:34867)
  11. 乙酸,分析标准品,GC测定≥9.8%(Sigma-Aldrich,目录号:71251)
  12. 丙酸,分析标准品,GC测定≥9.8%(Sigma-Aldrich,目录号:94425)
  13. 丁酸,分析标准品,GC测定≥9.8%(Sigma-Aldrich,目录号:19215)
  14. 盐酸
  15. 蒸馏水
  16. 1-甲基萘
  17. Ñ<子> 2
  18. ħ<子> 2
  19. 0.1 M HCl溶液(见食谱)
  20. 血清原液中含有3,000μg/ ml乙酸(参见食谱)
  21. 3,000μg/ ml丙酸储备液(参见食谱)
  22. 3,000μg/ ml丁酸储备液(参见食谱)
  23. 1,000μg/ ml SCFA标准混合物(见食谱)


  1. J&amp; W DB-23 GC色谱柱,60 m,0.32 mm,0.25μm,7英寸笼(Agilent Technologies,目录号:123-2362)
  2. 钳子
  3. 冷冻柜FE26 / 127V(Electrolux Appliances,型号:FE26,目录号:04251FBA106 / 04251FBB206)
  4. 不锈钢双刮刀,长180 mm,直径3 mm(MetalicAcessóriosparaLabalratório,目录号:063-A3)
  5. PIPETMAN Classic P1000移液器(Gilson,产品目录号:20170-170)
  6. 气相色谱仪Agilent 6850系列(安捷伦科技,制造商停产)
  7. DB 23安捷伦毛细管柱,内径60 m x 0.25 mm( i.d。)
  8. 烤箱
  9. 自动液体采样器7683B(Agilent Technologies,目录号:G2880A)
  10. 分析天平(SHIMADZU,型号:ATX224系列,未找到产品目录号)
  11. 涡旋混合器(Phenix Luferco,型号:AP56)
  12. 离心机5810 R(Eppendorf,型号:5810 R,目录号:5810000424)
  13. 气相色谱仪Shimadzu 2010型号,配备FID(SHIMADZU,目录号:C184-E019)
  14. AOC-20i自动液体采样器(SHIMADZU,型号:AOC-20i)


  1. EZChrom软件,3.3.1版本(安捷伦科技)
  2. GC解决方案软件,3.2版(SHIMADZU)


  1. 样品制备
    1. 收集心脏穿刺或腋窝丛的血液。在室温(RT)下保持血液30分钟并离心(3,000 x g ,8分钟)。收集血清并在-80℃下在1.5ml微管中冷冻。
    2. 在微管中直接从每只小鼠收集粪便颗粒。为此,抬起抱着尾巴的动物,将微管置于肛门附近并收集排出的颗粒。每只动物的两个或三个颗粒就足够了。
    3. 安乐死后,收集结肠,轻轻取出近端部分的腔内容物。为此,使用镊子直接收集到微管中并在-80°C下冷冻。
    4. 将所有生物样品保持在-80°C冷冻直至分析当天。
    5. 对于粪便和结肠腔内容物,将20mg这些转移到1.5ml微管中并加入200μl蒸馏水。使用金属刮刀均质化。&nbsp;
    6. 在1.5 ml微量管中,加入样品(200μl血清或粪便匀浆,参见步骤A5),200μl有机溶剂混合物,由正丁醇,四氢呋喃和乙腈组成,比例为50:30:20,40 μlHCl0.1M,20mg柠檬酸和40mg氯化钠。使用涡旋搅拌器剧烈摇动微管1分钟。
    7. 在室温下将样品在14,870 x g 离心10分钟。
    8. 使用自动移液器,将上清液转移到配有200μl插入物的色谱小瓶中,并通过GC-FID进行分析(图1)。

      图1.样品制备。 A.样品的液 - 液萃取程序。用相同体积的溶剂混合物提取血清样品(200μl)。 B.粪便和结肠腔内容物样品的液 - 液萃取程序。将粪便样品(20mg)预先用蒸馏水均化,并进行样品制备部分的步骤A5-A8中所述的提取步骤。&nbsp;

  2. GC程序
    1. 将进样器的温度调节至250°C。
    2. 使用DB 23毛细管柱Agilent 60 mx 0.25 mm内径( id ),以25:1的分流比注入内容物(5μl),并涂上0.15μm的薄膜组成80.2%的1-甲基萘。
    3. 流动相由N 2组成,初始流速为1 ml / min,保持1 min,然后变为0.8 ml / min,持续1 min,更换为0.6 ml / min 1分钟,然后恢复到1毫升/分钟,持续9.2分钟。
    4. 调整并保持FID检测器的温度在260°C。
    5. 将H 2和合成空气的流量分别调节至30 ml / min和350 ml / min。
    6. 将烤箱的温度程序调整为100°C,保持7分钟,以25°C / min的速度升至200°C并保持5分钟。


  1. 通过分析血清(图1A)和粪便(图1B)产生的色谱图如图2所示。与分析标准溶液相比,SCFA通过保留时间进行鉴定。

    图2.血清(A)和粪便(B)样品的色谱图。每种SCFA的保留时间如下:乙酸盐,5.2分钟;丙酸,7.1分钟;丁酸盐,9.08分钟。使用配备有7683B自动液体取样器和FID的气相色谱仪Shimadzu 2010型进行分析。

  2. 使用EZChrom软件集成每个峰的区域,对应于其各自的SCFA。
  3. 使用本研究中的基质(血清和粪便)作为溶剂稀释分析物,制备标准校准曲线,一式三份,SCFAs浓度范围为15-1,000μg/ ml。构建血清基质中的校准曲线,使用血清作为溶剂制备1,000μg/ ml的SCFA储备溶液,并在15-1,000μg/ ml的范围内连续稀释(参见配方)。
  4. 在粪便基质中构建校准曲线,根据血清中SCFA储备液的获得方法,使用蒸馏水作为溶剂制备1000μl/ ml SCFA(参见配方)。
  5. 根据程序A,步骤5-8中描述的液 - 液萃取程序准备样品,并按照程序B,GC程序中所述通过GC-FID分析样品。
    标准校准曲线显示所有酸的线性回归关系(R 2 = 0.998)(图3)。

    图3.(A)血清和(B)粪便的标准校准曲线的线性。使用血清(A)和粪便(B)获得的校准曲线。使用气相色谱仪Agilent 6850系列获得线性,配备7683B自动液体进样器和FID,遵循本文所述的参数。

  6. 乙酸,丙酸和丁酸的保留时间分别为7.2,8.2和9.2分钟,每次分析的总运行时间为16.5分钟。
  7. 精度,表示为相对标准偏差(RSD)小于15.0%,精度,表示为相对误差(RE)在±15.0%之内。提取效率(EE)大于97.0%。要计算RSD,RE和EE,请使用下面描述的公式:

  8. 对于标准曲线,在y轴上绘制每个SCFA标准样品的面积与相应的理论浓度值(15-1,000μg/ ml),并获得函数y = ax + b的等式,其中 a 是角系数, x 是为样本找到的区域, b 是线性系数。
  9. 使用下面显示的等式计算样品中每种SCFA的浓度。每种SCFA的浓度表示为mg / ml。

  10. 使用下述公式分别转换血清和粪/肠腔内容物样品的浓度为mM和mmol / kg:


  1. 我们的实验室不提供安捷伦设备。因此,我们使用GC Shimadzu 2010模型获得色谱图以说明分析物分离(图2和4)。色谱图按照本文所述的生物分析方法条件获得。

    图4.从抗生素处理的小鼠和对照动物(未用抗生素处理)获得的结肠腔内容物的色谱图重叠。 色谱图显示用抗生素(蓝线)和未处理动物(黑线)处理的动物(Fellows et al。,2018)。根据本文所述的参数,使用配备有AOC-20i自动液体取样器和FID的气相色谱仪Shimadzu 2010型进行分析。使用GC解决方案软件整合色谱图。

  2. 分析物的保留时间和检测范围可能根据毛细管柱类型和长度以及设备品牌而有所不同。 DB-23毛细管柱安捷伦可用于GC Shimadzu 2010型号。
  3. 该方法是根据ANVISA(National Sanitary Vigilance Agency)的RDC 27/2012开发和验证的。
  4. 如果使用不同的小鼠品系,则不需要改变方案。
  5. 动物被安置在圣保罗大学生物医学科学研究所和坎皮纳斯大学生物学研究所。
  6. 空白血清是未添加SCFAs标准的血清。


  1. 0.1 M HCl溶液
    0.835毫升HCl 37%(v / v)
  2. 血清原液中含有3,000μg/ ml乙酸
  3. 3,000μg/ ml丙酸储备液
  4. 3,000μg/ ml丁酸储备液
  5. 1,000μg/ ml SCFA标准混合物
    将每种SCFA储备溶液混合1ml,得到每种SCFA1000μg/ ml(最终体积3ml)
    1. 使用血清作为溶剂对该混合有机酸溶液进行连续稀释,以产生15-1,000μg/ ml的范围作为血清基质中SCFAs的标准曲线。
    2. 使用蒸馏水作为基质进行相同的系列稀释,以构建粪便基质中SCFAs的标准曲线。为了实现这一点,准备7个含有20毫克粪便的试管,并在每个试管中加入200μl分别在连续稀释液中获得的溶液


我们感谢Claudete Justina Valduga和PatríciaSartorelli为本研究提供设备。这项工作得到了FAPESP 2012 / 50410-8和CNPq(国家科学和技术发展委员会)的支持,项目编号为486037 / 2012-6至C.M.F,2017 / 10653-9为M.A.V.






  1. Andrade-Oliveira,V.,Amano,MT,Correa-Costa,M.,Castoldi,A.,Felizardo,RJ,de Almeida,DC,Bassi,EJ,Moraes-Vieira,PM,Hiyane,MI,Rodas,AC, Peron,JP,Aguiar,CF,Reis,MA,Ribeiro,WR,Valduga,CJ,Curi,R.,Vinolo,MA,Ferreira,CM andCamara,NO(2015)。 肠道细菌产品可预防缺血再灌注引起的AKI。 J Am Soc Nephrol 26(8):1877-1888。
  2. Correa-Fiz,F.,Fraile,L。和Aragon,V。(2016)。 断奶时的仔猪鼻腔微生物群可能会影响饲养期间Glasser病的发展。 BMC Genomics 17:404。
  3. Cuervo,A.,Salazar,N.,Ruas-Madiedo,P.,Gueimonde,M。和Gonzalez,S。(2013)。 常规饮食中的纤维与老年人的粪便短链脂肪酸浓度直接相关。< / a> Nutr Res 33(10):811-816。
  4. den Besten,G.,van Eunen,K.,Groen,A.K.,Venema,K.,Reijngoud,D。J. and Bakker,B.M。(2013)。 短链脂肪酸在饮食,肠道微生物群和宿主能量代谢之间的相互作用中的作用。 J Lipid Res 54(9):2325-2340。
  5. 研究员,R.,Denizot,J.,Stellato,C.,Cuomo,A.,Jain,P.,Stoyanova,E.,Balazsi,S.,Hajnady,Z。,Liebert,A.,Kazakevych,J., Blackburn,H.,Correa,RO,Fachi,JL,Sato,FT,Ribeiro,WR,Ferreira,CM,Peree,H.,Spagnuolo,M.,Mattiuz,R.,Matolcsi,C.,Guedes,J., Clark,J.,Veldhoen,M.,Bonaldi,T.,Vinolo,MAR和Varga-Weisz,P。(2018)。 微生物衍生的短链脂肪酸通过组蛋白去乙酰化酶促进结肠中的组蛋白巴豆酰化。 Nat Commun 9(1):105。
  6. Ferreira,C.M.,Vieira,A.T.,Vinolo,M.A.,Oliveira,F.A.,Curi,R。和Martins Fdos,S。(2014)。 肠道微生物群在慢性炎症性疾病中的核心作用。 J Immunol Res 2014:689492。
  7. Karlsson,C.,Ahrne,S.,Molin,G.,Berggren,A.,Palmquist,I.,Fredrikson,G。N.和Jeppsson,B。(2010)。 对患有早期动脉硬化的男性进行益生菌治疗可以促进结肠细菌多样性增加:一项随机对照试验。 动脉粥样硬化 208(1):228-233。
  8. Koh,A.,De Vadder,F.,Kovatcheva-Datchary,P。和Backhed,F。(2016)。 从膳食纤维到宿主生理学:短链脂肪酸作为关键的细菌代谢物。 Cell 165(6):1332-1345。
  9. Mendes,E.,Acetturi,BG,Thomas,AM,Martins,FDS,Crisma,AR,Murata,G.,Braga,TT,Camara,NOS,Franco,A.,Setubal,JC,Ribeiro,WR,Valduga,CJ ,Curi,R.,Dias-Neto,E.,Tavares-de-Lima,W。和Ferreira,CM(2017)。 预防性补充长双歧杆菌5 1A保护小鼠免受卵巢切除术引起的加剧过敏性气道炎症和气道高反应性。 Front Microbiol 8:1732。
  10. Nishida,A.,Inoue,R.,Inatomi,O.,Bamba,S.,Naito,Y。和Andoh,A。(2018)。 肠炎微生物群在炎症性肠病的发病机制中的应用。 Clin J Gastroenterol 11(1) :1-10。
  11. Salazar,N.,Dewulf,EM,Neyrinck,AM,Bindels,LB,Cani,PD,Mahillon,J.,de Vos,WM,Thissen,JP,Gueimonde,M.,de Los Reyes-Gavilan,CG and Delzenne, NM(2015年)。 菊粉型果聚糖调节肠道双歧杆菌种群,减少肥胖妇女的粪便短链脂肪酸。 Clin Nutr 34(3):501-507。
  12. Sokolowska,M.,Frei,R.,Lunjani,N.,Akdis,C.A。和O'Mahony,L。(2018)。 微生物组和哮喘。 哮喘治疗 4:1 。
  13. Vieira,A。T.,Castelo,P。M.,Ribeiro,D。A.和Ferreira,C。M.(2017)。 口腔和肠道微生物群对更年期妇女健康的影响。 前微生物 8:1884。
  14. Vinolo,MA,Rodrigues,HG,Festuccia,WT,Crisma,AR,Alves,VS,Martins,AR,Amaral,CL,Fiamoncini,J.,Hirabara,SM,Sato,FT,Fock,RA,Malheiros,G., dos Santos,MF和Curi,R。(2012)。 Tributyrin可减轻高脂喂养小鼠的肥胖相关炎症和胰岛素抵抗。 Am J Physiol Endocrinol Metab 303(2):E272-282。
  15. Walton,G.E.,Lu,C.,Trogh,I.,Arnaut,F。和Gibson,G.R。(2012)。 一项随机,双盲,安慰剂对照的交叉研究,以确定消耗的胃肠道影响阿拉伯木聚糖 - 寡糖在健康志愿者中富含面包。 Nutr J 11:36。
  16. Zhang,H.,Sun,J.,Liu,X.,Hong,C.,Zhu,Y.,Liu,A.,Li,S.,Guo,H。和Ren,F。(2013)。 副干酪乳杆菌亚种。 paracasei LC01可以正常调节健康年轻人的肠道菌群。 J Microbiol 51(6):777-782。
  • English
  • 中文翻译
免责声明 × 为了向广大用户提供经翻译的内容, 采用人工翻译与计算机翻译结合的技术翻译了本文章。基于计算机的翻译质量再高,也不及 100% 的人工翻译的质量。为此,我们始终建议用户参考原始英文版本。 Bio-protocol., LLC对翻译版本的准确性不承担任何责任。
Copyright: © 2018 The Authors; exclusive licensee Bio-protocol LLC.
引用:Ribeiro, W. R., Vinolo, M. A., Calixto, L. A. and Ferreira, C. M. (2018). Use of Gas Chromatography to Quantify Short Chain Fatty Acids in the Serum, Colonic Luminal Content and Feces of Mice. Bio-protocol 8(22): e3089. DOI: 10.21769/BioProtoc.3089.