1 3Assisted Conception Service, Glasgow Royal Infirmary University NHS Trust, Glasgow, G31 2ER and 2 Institute of Biological Sciences, Cledwyn Building, University of Wales, Aberystwyth, Ceredigion, Wales, SY23 3DD, UK
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Abstract |
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Key words: artificial neural networks/follicle fluid/follicle size/fourier transform infrared spectroscopy/steroid analysis
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Introduction |
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As an ovarian follicle grows, follicular fluid, a mixture of follicular secretions and plasma exudate, accumulates between the granulosa cells. The importance of this fluid cannot be underestimated since it is closely associated with the oocyte and its composition may influence the latter part of oocyte development. Many follicular fluid factors will have been secreted by cells in close association with the oocyte and the fluid contains factors responsible for ensuring the correct maturational status of the oocyte. Constituents of the fluid include steroids, lipids, glycosaminoglycans and numerous proteins and peptides.
It has long been a goal to identify factors in follicles which may indicate improved chances of embryo development and conception. The aim of this study was to establish if a definitive difference exists between follicular fluid derived from different sized follicles. Paired samples were subjected to the analytical technique of Fourier transform infrared (FTIR) spectroscopy.
FTIR is a physico-chemical method which measures the vibrations of bonds within functional groups (Griffiths and de Haseth, 1986; Stuart, 1997
). In FTIR analysis a particular bond absorbs light electromagnetic (EM) radiation at a specific wavelength; for example, the infrared (IR) spectra of proteins exhibit strong amide I absorption bands at 1653 cm1 associated with the characteristic stretching of C=O and C-N and the bending of the N-H bonds (Stuart, 1997
). Therefore by interrogating a biological sample with EM radiation of many wavelengths in the mid-IR range (usually defined as 4000600 cm1) one can construct an IR `fingerprint' of the original biological sample under investigation. Since different bonds absorb different wavelengths of EM radiation, these `fingerprints' are made up of the vibrational features of all chemical components in the sample analysed. Thus this method gives quantitative information about the total biochemical composition of a follicular fluid, without its destruction, and produces `fingerprints' which are reproducible and distinct for different biological materials. Within medicine there are precedents for using IR measurement techniques for the analysis of human biofluids such as urine, blood and synovial fluid (Wang et al., 1996
; Jackson et al., 1997
; Diem et al., 1999
).
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Materials and methods |
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Local ethical committee approval was granted for this project by the Glasgow Royal Infirmary University NHS Trust.
Follicular fluid collection
Two follicular fluid samples were collected from each patient. One from a follicle with a diameter >17mm (large) and one from a follicle <15mm (small). Follicle size was determined by using the mean of two perpendicular measurements. In order to minimize contamination of fluids, samples were collected following a needle flush so that there would be no dilution in the catheter with fluids from follicles not included in the study. In practice the small follicle was the first to be aspirated at retrieval. The large follicle used in the study was the first approach to the contralateral ovary. Only mid-stream samples, free from blood contamination, were collected and aliquots were stored at 20°C. All the samples used in this study were paired.
Fourier transformed IR spectroscopy
Aliquots (20µl) of the 108 follicular fluids were evenly applied onto a sand-blasted aluminium plate. Prior to analysis the samples were oven-dried at 50°C for 30 min. Samples were run in triplicate. The FTIR instrument used was the Bruker IFS28 FTIR spectrometer (Bruker Spectrospin Ltd., Banner Lane, Coventry, UK) equipped with an MCT (mercury-cadmium-telluride) detector cooled with liquid N2. The aluminium plate was then loaded onto the motorized stage of an adapted reflectance thin-layer chromatography accessory (Timmins et al., 1998). The personal computer used to control the IFS28 was also programmed (using OPUS version 2.1 software running under IBM O/S2 Warp provided by the manufacturers) to collect spectra over the wavenumber range 4000 cm1 to 600 cm1. Spectra were acquired at a rate of 20 s1. The spectral resolution used was 4 cm1. To improve the signal-to-noise ratio, 256 spectra were co-added and averaged. Each sample was thus represented by a spectrum containing 882 points and spectra were displayed in terms of absorbance as calculated from the reflectance-absorbance spectra using the Opus software [which is based on the Kubelka-Munk theory (Griffiths and deHaseth, 1986)].
To minimize problems arising from baseline shifts the following procedure was implemented: (i) the spectra were first normalized so that the smallest absorbance was set to 0 and the highest to +1 for each spectrum, (ii) next the first derivatives of the original FTIR spectra were smoothed using the Savitzky-Golay algorithm (Savitzky and Golay, 1964) using 5-point smoothing. Typical spectra, pre- and post-processing, are shown in Figure 1
.
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Artificial neural network analysis
When the desired responses (targets) associated with each of the inputs (spectra) are known then the system may be `supervized'. The goal of supervized learning is to find a model that will correctly associate the inputs with the targets; this is usually achieved by minimizing the error between the target and the model's response (output) (Massart et al., 1997).
The artificial neural network (ANN) method of standard back-propagation multi-layer perceptrons (MLP) (Rumelhart et al., 1986; Bishop, 1995
) was used, and was carried out with a neural network simulation program, NeuFrame version 3,0,0,0 (Neural Computer Sciences, Southampton, Hants, UK), which runs under Microsoft Windows NT on an IBM-compatible PC. In-depth descriptions of the modus operandi of this type of ANN analysis are given elsewhere (Goodacre et al., 1998
). The architecture of the ANNs was 878 input nodes, 10 nodes in the hidden layer, and a single output node (this topology can be represented as 878101). The ANNs were trained with the first 27 samples from the fluids from both small and large follicles [therefore there were 162 spectra (27*3*2) in the training set]. These ANN were trained for 1.5x103 epochs (calculations), when the RMS (root mean squared) error between the observed and desired outputs was typically 0.046 ± 0.004; on a Pentium 133, with 128 MB RAM, this typically took ~90 min. The ANN was then interrogated with both the training and test sets and a correct identity for a fluid from a small antral follicle was taken as <0.5 and for a fluid from a large follicle as
0.5; note that after training, the interrogation of these ANN takes only a few milliseconds.
Steroid hormone assays
Oestradiol and progesterone were analysed using a solid-phase chemiluminescent enzyme immunoassay (DPC, Los Angeles, CA, USA) on an Immulite automated immunoassay system. Matching paired samples were assayed in two batches with intra-assay coefficients of variations of 4.5% (oestradiol) and 6% (progesterone).
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Results |
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DFA were used to observe the relationships between the follicular fluids as judged from their derived FTIR spectra, and DFA was performed as detailed above. The resulting DFA plot is shown in Figure 2 where it can be seen that the follicular fluids from large follicles formed a homogeneous, closely related cluster whilst those fluids from small follicles were very heterogeneous and distinct from the fluids from large antral follicles.
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Discussion |
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FTIR spectroscopy coupled with ANN analysis distinguished successfully follicular fluid from large and small antral ovarian follicles indicating that biochemical differences do indeed exist between the fluids in different size follicles.
The differences in distribution of steroid concentrations do not reflect those of the FTIR, suggesting that other aspects of biochemical analysis are not under the same control mechanism as the steroids. These differences between factors in the fluids from large and follicles, which may be quantitative or qualitative, may be related to differences in oocyte quality. These results highlight a difference in the biochemical nature of fluids from large and small follicles which we suggest reflect the developmental stage of the follicle. Further work will investigate this premise and relate the FTIR spectra of a follicular fluid to the developmental capacity of the oocyte that the follicle contained.
In conclusion, this is the first time that FTIR spectroscopy has been used to analyse follicular fluids and it has shown that gross biochemical differences do exist between the follicular fluids from large and small antral ovarian follicles.
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Acknowledgments |
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Notes |
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References |
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Submitted on November 5, 1999; accepted on April 25, 2000.