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Quality control and standardization of embryo morphology scoring and viability markers

Reproductive BioMedicine Online, Volume 31, Issue 4, October 2015, Pages 459 - 471


A so-called ‘good-quality embryo’ may be defined as an embryo that has the potential to implant into the uterine endometrium and give rise to the birth of a healthy child. A standardized and objective scoring of embryo ‘quality’ is therefore crucial in the classification and selection of embryos. However, embryo scoring is still being performed mainly via ocular evaluation, which often results in different interpretations of embryo quality. The addition of viability markers, such as measuring gene expression or the uptake/release of metabolites, proteins or RNA/DNA molecules in the culture media, would increase the possibility of standardized measurements. However, no single biomarker has yet been introduced into standard clinical practice, mainly due to the complexity of the techniques and the influence of biological variations and differences in culture conditions. In this paper different methods for the scoring of embryos and the possibility of standardizing and implementing quality control systems are discussed.

Keywords: embryo, human, IVF, quality control, validation.


Although the efficiency of an IVF laboratory can to a large extent be controlled by rules and regulations, such as the International Organization for Standardization (ISO) norms, a majority of the work is still dependent on subjective evaluations and decisions. In particular, morphological assessment and scoring of embryos is dependent upon a number of variables, being more or less subjective.

The subjective nature of morphological scoring by an embryologist, and in addition the existence of confounding (laboratory) factors such as differences in culture media and culture environment, as well as different handling of oocytes and embryos in the laboratory, makes it difficult to compare embryo scores – and thereby also success – rates between clinics.

The aim of this paper is to discuss the possibility of standardizing and implementing quality control systems for the scoring of embryos and to evaluate alternative methods being currently implemented into standard clinical practice.

Morphological variables predictive of implantation and live birth

Numerous embryo morphology scoring systems have been developed throughout the years, and there are several classification schemes being used. Some list the embryo features individually, often ranked according to their supposed importance for embryo viability, while others use an algorithm to calculate a cumulative, single score based on a number of (weighted) features. Main morphological features scored include the number of cells, grade of fragmentation, cell size and multinucleation/number of mononucleated cells; and for blastocyst stage embryos, expansion grade and the status of the inner cell mass (ICM) and the trophectoderm.

Ideally, embryo scoring should be included in quality assurance schemes, which should be fast and simple and based on variables with proven predictive power for live birth. Van Loendersloot et al. (2014) constructed a ranking model for day 3 transfers, using one data set for constructing the model and another data set for validation of the method ( Van Loendersloot et al., 2014 ). They found that blastomere numbers on day 2 and day 3, morphological score on day 3 and morula on day 3 (yes/no) were correlated with implantation. These results were very similar to those shown by Van Royen et al. (2001) , who in short demonstrated that the presence of four cells on day 2 and eight cells on day 3, together with a low fragmentation grade on day 3, results in the best implantation rates ( Van Royen et al., 2001 ). It is important to note, however, that in the study by Loendersloot et al., the time range for the scoring could be up to 5 h, a source of variation that will be discussed later in this review.

In two other papers, regression analyses of large data sets have been used to standardize embryo scoring and build predictive models for day 2 ( Holte et al., 2007 ) and for day 2/day 3 ( Racowsky et al., 2009 ). In both these models, it was found that blastomere number was the most powerful predictor for implantation, while in Racowsky's model, the importance of fragmentation for day 3 increased compared with day 2.

In a prospective study of 6252 single embryo transfer cycles where the model for day 2 embryos by Holte et al. (2007) was used, Rhenman et al. (2015) demonstrated that the variables blastomere number, proportion of mononucleate blastomeres and degree of fragmentation were all predictive for live birth ( Rhenman et al., 2015 ). The previously integrated variable, equal sized blastomeres, was correlated with live birth in the univariate analysis, but did not come out as predictive in the regression analysis.

For blastocysts, grading three morphological features, degree of blastocoele expansion and hatching stage, ICM, and trophectoderm, have all been shown to be correlated with pregnancy and live birth. Furthermore, scoring systems taking into account the appearance of all three features have been proven to significantly improve selection of viable blastocysts and prediction of clinical outcome compared with scoring of a single feature (Balaban et al, 2006, Dokras et al, 1991, Dokras et al, 1993, Gardner, Schoolcraft, 1999, Richter et al, 2001, and Shapiro et al, 2008b). The most prominent grading system described by Gardner and Schoolcraft (1999) has been validated by several studies to show that the transfer of two top-scoring blastocysts with high grades for all three features achieves the highest implantation rates (Balaban et al, 2000, Balaban et al, 2006, Gardner et al, 2000, and Gardner et al, 2004). However, retrospective studies attempting to determine the independent predictive strength of each feature and even rank their importance are so far inconclusive. In a multicentre trial by Van Den Abbeel et al. (2013) , expansion and hatching stage was determined as the only significant predictor of live birth (P = 0.002), while Ahlstrom et al. (2011a) and Hill et al. (2013) found trophectoderm grade to be the only statistically significant independent predictor of live birth (Ahlstrom et al, 2011a, Hill et al, 2013, and Van Den Abbeel et al, 2013).

Standardization and proficiency

When discussing and implementing embryo scoring and embryo quality, it is important to consider the potential variations in embryo scoring within each individual (intra-observer), between individuals (interindividual) and between centres (intercentre). These – often considerable – variations will influence the interpretation of embryo quality, and thereby the data being used for analysis of correlations with implantation and live birth. This is a problem, both during interpretation of published studies regarding impact on success rates and during clinical application of criteria used to select embryos.

Intra- and interobserver analyses of embryo scoring may have different goals. Firstly, to analyse the agreement when scoring individual features of the embryo such as number of cells, grade of fragmentation, etc. Secondly, to determine the agreement in classifying the embryos on a scale (‘top’, ‘good’, ‘moderate’, ‘poor’) and thirdly, to select the embryo(s) most suitable for transfer and cryopreservation. Thus, the ability to transform the individual scoring to an overall classification (top, good, fair, poor) and thereafter to rank it for a clinical decision within each cohort of embryos is just as important as being able to correctly count the number of cells and the percentage of fragments.

In a paper by Baxter Bendus et al. (2006) , inter- and intra-observer variations for 26 embryologists who graded day 3 embryos in video sessions were compared ( Baxter Bendus et al., 2006 ). Poor interobserver agreement (median Kappa value 0.24, range 0.03–0.49) was found, while the intra-observer agreement (scoring the same embryo several times) was good (median Kappa value 0.69, range 0.44–1.00).

In another paper by Arce et al. (2006) , more than 15,000 embryos were scored (with the help of an embryo atlas) by a total of 37 local IVF laboratories ( Arce et al., 2006 ). The same embryos were then scored using 2D electronic images, by three ‘central’ embryologists (interobserver comparison). These embryologists had been practising embryo scoring together in joint training sessions in order to improve the interobserver agreement. It was shown that for the individual variables cell number, fragmentation and blastomere size the interobserver agreement between the three central embryologists was good to high (kappa values: 0.61–0.94), while the agreement between the local laboratories and the collective scores of the central embryologists was lower (kappa values: 0.43–0.88). However, the overall agreement of what would be classified as a top-quality, a good-quality and a ‘transferable’ embryo, respectively, was good to high between all observers (0.64–0.90).

Paternot et al. (2011a) performed an intra- and interobserver analysis based on multi-level images of days 1, 2 and 3, distributed via a website ( Paternot et al., 2011b ). Five embryologists from four IVF centres participated. For the interobserver agreement, a good kappa value was seen for number of blastomeres on day 2 and 3 (0.73 and 0.63, respectively) while a moderate agreement (between 0.40 and 0.60) was found for fragmentation and poor agreement for size of blastomeres. For the overall decision of embryo destiny (transfer, cryopreservation, discard) a good agreement (0.71) was found. The intra-observer agreement scored similar to the interobserver agreement, but with slightly higher kappa values.

The subjective grading of blastocyst features and the level of variability that may exist in these assessments have yet to be evaluated in published studies.

It is obvious that a move towards more standardized and less subjective methods of embryo scoring is needed. One way of trying to do this has been to introduce strict timings for scoring ( Alpha Scientists in Reproductive Medicine and ESHRE Special Interest Group of Embryology, 2011 ). However, due to practical reasons these time intervals are usually ±1 h, and as it was recently shown by Montag et al. (2011) , considerable changes may occur in embryo development during these two hours, towards both higher embryo grades and lower grades ( Montag et al., 2011 ). The authors showed that 49.1% of embryos changed morphology and/or cell numbers between 38 and 42 h, while 32.6% changed between 40 and 42 h. Half of them changed from a low score at 38–40 h to a top score 2–4 h later. Similarly, observations by time-lapse imaging show that small fragments may be permanent or transient and small fragments created during the cleavage process can later fuse with the blastomere (Hardarson et al, 2002 and Van Blerkom, 2007). In another time-lapse study by Ergin et al. (2014) , it was shown that only 27% of multinucleated embryos were identified if traditional scoring times were used ( Ergin et al., 2014 ). Static observations will therefore always be subjected to the risk of being performed in a non-ideal time. In addition, development versus timing will depend on culture conditions.

Paternot et al. (2011b) suggested that multi-level images and a computer-assisted scoring system (CASS) could be a way towards a less subjective scoring ( Paternot et al., 2011a ). However, in this semi-automatic system the operator still has to decide what is considered a cell or a fragment, thereby maintaining a subjective perspective.

Alternatively, several authors have made attempts to replace subjective assessments with more quantitative measurements, referred to as morphometry, and to describe optimal measurements related to implantation (Richter et al, 2001 and Shapiro et al, 2008a). For example, Richter et al. (2001) described a method of measuring ICM dimensions and found that blastocysts with slightly oval ICM shape and size greater than 4500 µm2 had highest implantation rates. Shapiro et al. 2008a demonstrated that a large blastocyst diameter, >190 µm on day 5 or >205 µm on day 6, was the most significant predictor of clinical pregnancy in a multivariate model (P < 0.0001). However, results from these studies have not been widely adapted; the implementation of time-lapse and ease of performing multiple morphometric measurements may re-ignite investigations.

Validation of scoring schemes and quality control

The ability of embryologists to score embryo morphology ‘correctly’ with minimum subjectivity and with high intra- and interobserver agreement is dependent upon competence (i.e. skills and training), accuracy and consistency. The only way to achieve this is through constant education, training and validation of operator competency, which should therefore be a priority during continuous training of embryologists.

In addition, it is important that there is a consensus as to what and how embryologists should score and that they constantly validate what is done and benchmark with others how they perform.

In 2010, the Society for Assisted Reproductive Technologies (SART), decided to implement mandatory reporting of a standardized embryo morphology scoring system, and add these data into their database (SART clinic outcomes reporting system, SART CORS). The system is a three-point score (good, fair, poor) based on scoring of cell number, fragmentation and symmetry for early stage embryos, compaction and fragmentation for morulas, and expansion, ICM and trophectoderm for blastocysts.

These national embryo morphology data have now been evaluated and validated regarding day 3 embryo morphology and live birth outcome (Luke et al, 2014, Racowsky et al, 2009, and Vernon et al, 2011). Vernon et al. found that in 16,550 double-embryo transfers (DET) on day 3 with equally graded embryos, both live birth and multiple birth rate were positively correlated with the score (good, fair, poor) of the embryo. Racowsky et al. (2011) performed a more detailed analysis of the SART CORS data, analysing both single embryo and double embryo day 3 transfers. They found that live birth was positively correlated with increasing cell number up to eight cells, and negatively with increasing fragmentation and blastomere asymmetry (size and/or shape). These data are consistent with those recently presented by Luke et al. (2014) , who performed an analysis of 121,500 cycles registered in the SART CORS database with single or double embryo transfers day 3 or day 5. They found that the grade, stage, fragmentation and symmetry were significant predictors of live birth for the day 3 model, while grade, stage and trophectoderm were significant predictors in the day 5 model. ICM was significant when DET was performed. It is important to note that in this study not only were transfers with similar embryos analysed, but the highest graded embryo for transfer was used in the analysis.

The external quality control programme (EQCP) implemented by the Spanish Association for the Study of Reproductive Biology (ASEBIR) has been used for an analysis of how to best assign base values for quality control of embryo scoring ( Ruiz De Assin et al., 2009 ). A total of 120 videos of embryos in cohorts of five from the EQCP data set was evaluated by 40 laboratories as well as by five senior embryology ‘experts’. They were asked to classify the embryos as optimal, moderate or poor, and to make a clinical decision for each embryo within each cohort (transfer, cryopreserve, reject). The authors found that the five experts had an interobserver agreement of 98.3% for classification and 93.3% for clinical decisions, while the respective intercentre values for the laboratories were 44.2% and 42.5%. The recommendation was that the values to be used in a quality control programme should be based on consensus values obtained from experts.

The scoring scheme proposed by the UK Association of Clinical Embryologists (ACE) has also been evaluated, although not on a national basis ( Stylianou et al., 2012 ). This scheme includes cell number (defined as cell doublings per day = growth rate), fragmentation grade and evenness of blastomeres. The three-year, single-centre study found the reported embryo growth rate and fragmentation to be significant predictors of pregnancy (P < 0.001 and P = 0.004, respectively) but not cell evenness. The authors propose a ranking ‘algorithm’ and conclude that this scoring scheme can be considered a feasible and robust scheme to be used in daily practice and with the possibility of leading to a better standardization. However, further validation in a multicentre setting is called for.

Thus, validations should be performed both within individuals, within centres (between individuals) and between centres, and participation in EQCP is strongly recommended. It was demonstrated by Ruiz De Assin et al. (2011) that after having participated in training sessions of embryo evaluation, junior embryologists had a significantly increased agreement in embryo scoring compared with a panel of senior embryologists ( Ruiz De Assin et al., 2011 ). The panel of senior embryologists defined the embryo grades through a series of consensus meetings at which they first scored the embryos, then discussed their interobserver variations and thereafter scored again. It was found that their agreement increased from 36% at the pre-consensus test to 84% at the post-consensus test.

Castilla et al. (2010) analysed results from the Spanish EQC programme during a five-year period ( Castilla et al., 2010 ). Videos were sent out to the participating laboratories asking them to classify embryos as optimal, moderate or poor, and to make a clinical decision within each cohort of whether to transfer, cryopreserve or discard. It was found that the proportion of embryos on which a high degree of agreement of the classification was achieved increased during the 5 years from 35% to 55%, while no improvement was seen for the clinical decisions. The authors speculate that the laboratories improved due to a higher standardization of their scoring criteria just by being part of the programme, demonstrating the value of participating in training schemes and benchmarking.

A basic requirement of embryo scoring is to correctly, and in a standardized manner, document each embryo variable. This documentation not only ensures traceability, but also allows future studies to evaluate accumulated data and determine the predictive value of individual and/or combined embryo variables.

Genetic embryo screening

It has been shown that the ploidy status of embryos on day 2 and 3 has a certain correlation with embryo morphology; i.e. an embryo with the optimal number of cells, correct stage-specific cell size and low grade of fragmentation will have a higher chance of being euploid than an embryo with lower grade quality (Magli et al, 2001, Magli et al, 2007, Munne et al, 2007, and Ziebe et al, 2003). However, these studies also showed that this correlation is far from reliable, and that a high rate of aneuploidy is also frequent in top-quality embryos, supporting the need for genetic preimplantation screening (PGS).

At later stages of embryo development, Hardarson et al. (2003) found that blastocysts derived from embryos of poor quality on day 2/3 had a significantly higher degree of chromosomal aberrations compared with blastocysts derived from good-quality embryos.

In addition, a large observational study by Capalbo et al. (2014) found that blastocyst morphology was linearly predictive of the euploidy rate, as assessed by comprehensive chromosomal screening, with an euploidy rate ranging from 56.4 to 25.5% for the excellent, good, average and poor morphology blastocysts, respectively ( Capalbo et al., 2014 ). Poor morphology also correlated with the number of multiple chromosomal errors.

Therefore, although the validity of PGS is still being debated, in general, quantitative genetic methods such as comparative genomic hybridization (CGH), microarray and sequencing may be seen as easier to standardize and benchmark ensuring quality control, than use of traditional morphological scoring.

Time-lapse imaging and morphokinetic variables

The introduction of time-lapse imaging allows continuous monitoring of embryo development during intervals that have been previously omitted from observation. Acquiring more frequent images enables the observer to better define the timing and duration of morphological events as they occur, transforming previously static observations to quantitative dynamic measurements of development, often referred to as morphokinetic variables. This technology is proposed to improve the strength and efficiency of scoring systems for prediction of embryo viability and pregnancy (Aparicio et al, 2013, Herrero et al, 2013, and Wong et al, 2013). Improvements may arise from the incorporation of new predictive morphokinetic variables and/or improved quality of scoring, as images can be viewed an unlimited number of times to consolidate the scoring being made (Hardarson et al, 2002, Lemmen et al, 2008, and Montag et al, 2011). These improvements can lead to a higher standardization and superior decision-making/embryo-selection process ( Conaghan et al., 2013 ).

A number of retrospective studies have shown significant correlations between morphokinetic variables and embryo viability, measured by pregnancy outcome ( Table 1 ) or by the surrogate end-points blastocyst development and aneuploidy (Aguilar et al, 2014, Azzarello et al, 2012, Chamayou et al, 2013, Dal Canto et al, 2012, Hlinka et al, 2012, Kirkegaard et al, 2013, Rubio et al, 2014, and Vermilyea et al, 2014 and reviewed by Kaser and Racowsky, 2014 ). Not surprisingly, conclusions about the most predictive parameters and optimal timings for these events vary considerably between studies. It has been shown that patient- and treatment-related factors such as maternal age, infertility indication, sperm quality, stimulation regimes and day of transfer as well as fertilization method, culture media, oxygen concentrations and other variations between laboratories, influence embryo kinetics and viability (Basile et al, 2013, Campbell et al, 2013a, Campbell et al, 2013b, Chavez et al, 2012, Ciray et al, 2012, Cruz et al, 2013, Freour et al, 2013, Hampl, Stepan, 2013, Knez et al, 2013, Lemmen et al, 2008, Munoz et al, 2013, and Wissing et al, 2014). In addition, analytical variations resulting from differences in study design, study size, clinical end-points, frequency and/or duration of image acquisition, and the definition and number of morphokinetic variables may account for the lack of agreement (reviewed by Chen et al, 2013 and Kaser, Racowsky, 2014). These multiple sources of variation may account for the overlapping ranges reported across studies for viable and non-viable outcomes and explain why proposed morphokinetic-based models of embryo selection have not been transferable to other clinics (Campbell et al, 2013a, Conaghan et al, 2013, Kirkegaard et al, 2014, and Meseguer et al, 2011). It would seem that generating universally applicable reference ranges is more complicated than first anticipated, and that establishing a universal algorithm for selection of the best embryos may not be feasible.

Table 1 Studies evaluating predicitive time-lapse monitoring parameters of pregnancy outcome.

Author Study design Embryo source/number of embryos and patients Culture condiitions TLM system Start time/image acquistiion interval Description of duration of acquistion Day of transfer Number of embryos with known outcome studied Predictive TLM parameters
Lemmen et al. (2008) Retrospective cohort 19 IVF/ICSI patients

102 embryos
Cook cleavage media, 5% CO2, atmospheric O2 Nikon Diaphot microscope with integrated camera Time of insemination or ICSI/

5 min
From fertilization check to day 2 (20–24 h) Day 2 6 implanted

13 non-implanted
Synchrony in appearance of nuclei after first cleavage
Meseguer et al. (2011) Retrospective cohort Standard patients and oocyte donors

285 ICSI patients

522 embryos
Quinn's advantage cleavage media, 5% CO2, atmospheric O2 Embryoscope Time of ICSI/15 min From day 0 to day 3 (72 h) Day 3 61 implanted

186 non-implanted
Duration of 2-cell stage (<11.9 h)

Duration of 3-cell stage (<0.76 h)

Time to 5-cell stage (48.8–56.6 h)
Azzarello et al. (2012) Prospective cohort 130 ICSI patients

159 embryos
Cook cleavage media, 5.5% CO2, 5% O2 Embryoscope Time of ICSI/

20 min
From fertilization to day 2 Day 2 ET 46 live birth

113 no live birth
PN breakdown cut-off 20 h 45 min
Dal Canto et al. (2012) Retrospective cohort 22 IVF and 49 ICSI cycles

459 embryos
ISM1 media and then BlastAssist media, 6% CO2, 5% O2 Embryoscope Time of insemination or ICSI/20 min From fertilization check (16–18 h post-insemination) to day of transfer Day 3 (20 cycles) or day 5 (51 cycles) ET 19 implanted

115 non-implanted
Time to 8-cell stage
Hlinka et al. (2012) Prospective cohort ICSI patients, number not reported

180 embryos
Culture media type not reported

5% CO2, atmospheric O2
Primo vision Time of ICSI/10 min From 2PN check to day 5 Day 5 SET 28 implanted

86 non-implanted
Time intervals for cleavage cycles (timely/untimely)
Kirkegaard et al. (2013) Prospective cohort 92 ICSI patients 571 embryos Sydney IVF cleavage and then Blast media, 6% CO2, 5% O2 Embryoscope Time of ICSI /

20 min
Direct after ICSI to day 6 Day 6 SET 26 implanted

58 non-implanted
Chamayou et al. (2013) Retrospective cohort 78 ICSI patients

244 embryos
HTF cleavage Quinn's media and then Quinn's blastocyst media, 6% CO2, 5% O2 Embryoscope Time of ICSI/

20 min
From day 0 to day 5 Day 5 ET 72 implanted

106 non-implanted
Duration of third cleavage cycle, cc3 (from 3- to 5-cell, between 9.7 h to 21 h)
Aguilar et al. (2014) Retrospective cohort Donor oocytes, 842 ICSI patients

1448 embryos
Life Global culture media, 6% CO2, atmospheric O2 Embryoscope Time of ICSI/20 min From day 0 to day 3 (72 h) Day 3 ET, Meseguer et al. (2011) , TLM-based model used for embryo selection 212 implanted

687 non implanted
Time of second polar body extrusion, time from PN appearance to PN fading and time of PN fading
Rubio et al. (2014) Prospective randomized controlled trial 408 donor and 435 autologous patients Cook cleavage media and then Vitrolife CCM media, 5.5% CO2 and atmospheric O2 Embryoscope Time of ICSI/15–20 min From after ICSI to day 5 Day 3 and day 5 ET, Meseguer et al. (2011) , TLM-based model used for embryo selection, majority DET 51.5% ongoing pregnancy rate using selection model versus 41.7% in control group Duration of 2-cell stage (<11.9 h)

Duration of 3-cell stage (<0.76 h)

Time to 5-cell stage (48.8–56.6 h)
Vermilyea et al., 2014 Prospective, blinded, non selection 205 IVF and ICSI patients

331 embryos
Various culture conditions at 6 clinics Eeva Scope Automated cell tracking, from first cleavage/5 min From 2PN check (day 1) to day 3 Not reported 91 implanted

240 non implanted
Duration of 2-cell and 3-cell to categorize into Low, medium and high potential

DET = double-embryo transfer; ET = embryo transfer; ICSI = intracytoplasmic sperm injection; PN, pronucleus; SET = single-embryo transfer; TLM = time-lapse monitoring.

Only one clinically applied model has been tested in a randomized controlled trial (RCT) ( Rubio et al., 2014 ). This hierarchical model uses both morphological observations and kinetic timings to rank embryos in 10 different previously established categories of descending implantation potential ( Meseguer et al., 2011 ). This large RCT (n = 856) reported an increase in ongoing pregnancy (odds ratio [OR] of 1.23, confidence interval [CI] 1.06–1.43) and implantation (OR of 1.43, CI 1.05–1.39) when embryos were cultured in a time-lapse incubator and selected for transfer according to the hierarchical selection model (TLM) compared with embryos cultured in conventional incubators and selected solely by static morphological grade. A subsequent randomized study also showed a significant difference in ongoing pregnancy rate for PGS patients when one or two euploid embryos were cultured in a time-lapse system and then selected using the morphokinetic criteria published by Meseguer and colleagues (68.9% versus 40.5%, respectively, P = 0.019), compared with standard incubator and morphological scoring criteria ( Yang et al., 2014 ). However, in a Cochrane review by Armstrong et al. insufficient evidence of differences in live birth, miscarriage, stillbirth or clinical pregnancy was found between the time-lapse system and conventional incubation ( Armstrong et al., 2015 ). The authors concluded that there are no studies comparing the time-lapse system with or without using an algorithm and that this should be required before recommendations for a change of routine practice can be justified.

In addition, selection by time-lapse imaging is still mainly subjective and non-standardized. Most time-lapse sequences are manually annotated to calculate the precise timings of morphokinetic variables. Furthermore, different timing variables and starting points are used (see also Table 1 ) and terminology and definitions of the same variables are inconsistent between studies. In particular, variation arises from the time points (frames) selected to define each event and calculate durations or synchronicities. For example, events and boundaries for time intervals can be defined by the initial, midway or last frame of observation, making it difficult to compare and interpret published data and even evaluate the wider application of the morphokinetic variables.

In order to standardize the nomenclature and methodology of performing manual annotations several authors have published best practice guidelines (Ciray et al, 2014, Kaser, Racowsky, 2014, and Kirkegaard et al, 2012). Current proposals to standardize nomenclature still differ and may need further work in order to arrive at a consensus. The documentation of these annotations may also be limited by the time-lapse software associated with each system.

Thus, consistency and accuracy of performing time-lapse annotations will greatly impact the ability to determine their predictive value. However, this has still only been investigated in one study, which concluded that intra- and interobserver agreement was generally high for all parameters investigated when performing these annotations at a single clinical site, with a small number of observers (n = 3) with varying levels of experience ( Sundvall et al., 2013 ). In this study, almost perfect agreement (intraclass correlation coefficient, between 0.8 and 1) was found for dynamic parameters including pronuclei breakdown, first nuclei disappearance, nuclei appearance after first division, timings for all cleavage stages and fully hatched blastocysts. The variability of scoring did, however, increase when scoring poorer quality embryos. Poor intra- and interobserver agreement (intraclass correlation coefficient 0.3) was also found for the appearance of pronuclei, and scoring of binary morphological-based parameters, multinucleation and evenness of blastomeres at the 2-cell stage, which would result in random error and weaken the correlation to pregnancy outcome and its usefulness in a predictive model. Unfortunately and probably due to the lack of an established consensus, no current data are available comparing the precision of scoring at multiple clinical sites and/or with a larger number of observers.

Alternatively, trying to eliminate the variability that may arise due to the observer, one commercial time-lapse system has incorporated cell-tracking software to automatically annotate the timing of the first two mitotic divisions (Conaghan et al, 2013 and Wong et al, 2013). More recently, Chavez et al. has developed this software to automatically detect fragmentation ( Chavez et al., 2012 ). Automation has in many other research areas shown to improve standardization of practices and precision of analysis ( Nezhat et al., 2010 ). Although no comparative studies have reported an improvement, further development of this software to annotate additional morphokinetic variables could save much time for analysis of embryos and potentially improve standardization of time-lapse annotations ( Cicconet et al., 2014 ).

Viability markers

Biomarkers in spent embryo culture media

It has long been postulated that embryo consumption (uptake) and production of components within the culture media differ between embryos of high and low reproductive potential, and that measuring these differences can be used to predict embryo viability. Clinical applications of such metric methods would be a huge step towards more objective and standardized embryo selection schemes. Candidate markers of viability include metabolites, proteins and more recently RNA and DNA molecules. The main challenges when measuring spent culture media are the chemical complexity and heterogeneity of metabolites, limitations of measuring techniques, the throughput of the measurements and lack of standardized protocols. Any technique which is going to be clinically successful will need to be robust enough to be able to withstand changes in culture conditions.

Investigations have targeted a number of key players in metabolic processes to estimate turnover rates and to relate these to pregnancy outcome. However, results are somewhat dependent on the culture conditions used. For example, some early studies showed clear correlations between high pyruvate and glucose uptake and embryo viability (Gardner et al, 2001, Hardy et al, 1989, Houghton et al, 2002, and Van Den Bergh et al, 2001), while others did not (Conaghan et al, 1993 and Jones et al, 2001). These contradictory results were argued to be related to the composition of the culture media used during these experiments ( Sakkas and Gardner, 2008 ). Similarly, measurements of amino acid turnover by high performance liquid chromatography (HPLC) showed that changes in the turnover rates were correlated with the ability of early cleavage embryos to develop to blastocysts and implant (Brison et al, 2004 and Houghton et al, 2002). Although the overall profiles were similar and suggested that viable human embryos can be selected by low amino acid turnover rates, the predictive power of particular amino acids differed between studies. Furthermore, for these studies it was necessary to use in-house culture media (human embryo culture medium, HCM) containing 18 amino acids at physiological concentrations, as variations in metabolite usage were significantly influenced by the concentrations of amino acids supplemented in the media ( Sturmey et al., 2008 ).

A number of proteins secreted into the culture media have also been studied as potential markers of embryo viability, but so far no single protein has yet been identified as a reliable marker of embryo competence (Cervero et al, 2005, Dominguez et al, 2008, Mains et al, 2011, and Roudebush et al, 2002). One controversial example is secretion of human leukocyte antigen-G (HLA-G), which is thought to play an important role in embryo implantation and maternal tolerance of fetal tissue (Kanai et al, 2001, Marchal-Bras-Goncalves et al, 2001, and Rajagopalan, Long, 1999). Several reports found a significant association between soluble HLA-G concentrations in embryo spent culture media and pregnancy rates (Noci et al, 2005 and Sher et al, 2005). However, subsequent studies found no association and final arguments suggested that differences in culture conditions, day of collection, number of embryos transferred and/or sensitivity of enzyme-linked immunosorbent assay (ELISA) methods used probably explained these variations (Menezo et al, 2006 and Sageshima et al, 2007). In a multicentre trial conducted to resolve these issues, 726 samples were collected from three clinics and analysed at a central laboratory using a standardized ELISA method with high sensitivity. A significant correlation between HLA-G concentration and implantation rate was found in only one of the three participating clinics (P = 0.0379). Moreover, it was found that the proportion of HLA-G positive samples (lowest 19% to highest 44%) and the concentrations of HLA-G (3.3 to 18 ng/ml) varied considerably between clinics, possibly due to the assisted reproductive technology procedures, culture conditions and patient populations ( Tabiasco et al., 2009 ). Thus, although standardized methods are clinically available for measuring HLA-G, the validity of this single biomarker for prediction of embryo viability remains questionable.

To analyse broader metabolic profiles a number of spectroscopic-based technologies, including Fourier transform infrared spectroscopy (FT-IR), Near-infrared spectroscopy (NIR) and Raman spectroscopy, have been used (Botros et al, 2008 and Goodacre et al, 2004). These technologies enable rapid, standardized and objective analysis of small (5–10 µl) volumes of sample with no prior preparation, but generate a torrent of spectral data that can only be interpreted by bioinformatics and algorithm-building methods (Goodacre, 2005 and Goodacre et al, 2004). The accuracy of a predictive algorithm is dependent upon the accuracy of the training data set, the size of the spectral differences, the algorithm optimization process and the reproducibility of spectral data ( Gributs and Burns, 2006 ). In addition, as no proper standards are available to ensure data are captured correctly and to calibrate machine drift, the quality of spectral data and analysis has not been possible to measure in clinical practice (Goodacre et al, 2004 and Shulaev, 2006).

Initial studies, using Raman and NIR spectroscopy to analyse spent embryo culture media showed that spectral profiles exhibited discrete differences between embryos with positive and negative pregnancy outcomes, and these differences were successfully quantified in a multivariate algorithm to generate viability scores that were related to implantation potential (Scott et al, 2008, Seli et al, 2007, and Vergouw et al, 2008). Furthermore, a blinded cross-validation showed that a predictive algorithm could maintain its predictive value from one clinic to another using different culture media ( Ahlstrom et al., 2011b ). However, when the application of this technology was tested in a prospective RCT for selection of embryos on day 2 and day 5 for transfer, its use in adjunct to morphology did not significantly improve the ongoing pregnancy rate in the study group (n = 164) when compared with use of morphology alone in the control group (n = 163) (34.8% versus 36.2%) ( Hardarson et al., 2012 ). These results were then supported by another RCT, which also failed to show improvement of live birth rate after day 3 transfer when using NIR spectroscopy ( Vergouw et al., 2012 ). Evaluations of this technical platform by some principal investigators suggested that the threshold of spectral signal used to predict viability was not strong enough and was highly sensitive to signal noise, impairing the robustness and reproducibility of the analysis and the performance of the predictive algorithms (Sakkas, 2014 and Vergouw et al, 2014).

Analysis of transcriptome

Analysis of transcriptome profiles of human oocytes and embryos so far suggest that stage-specific gene expression patterns are related to embryo competence and can be used to identify candidate biomarkers (Dobson et al, 2004, Jones et al, 2008, Parks et al, 2011, and Wells et al, 2005). Recent evidence even suggests that gene expression profiles of cumulus and granulosa cells are related to embryo competence (Adriaenssens et al, 2010, Cillo et al, 2007, Hamel et al, 2008, Van Montfoort et al, 2008, and Zhang et al, 2005) and pregnancy outcome (Assou et al, 2008 and Hamel et al, 2010) and may provide a non-invasive strategy to predict viability. Knowledge of such profiles could be used to establish biomarkers of oocyte quality and embryo competence that can be objectively measured and standardized. However, there are a number of problems with using gene expression technologies for early embryonic development, which may be one explanation why no panel of markers has been agreed upon and why there are no clinically applied methods using these techniques for embryo selection.

Particular challenges arise from patients exhibiting high intra- and intervariability (Feuerstein et al, 2012, Maston et al, 2006, and Sapey et al, 2008) and variations in gene expression, which have been linked to a number of patient and treatment characteristics (Bridges et al, 2011, Maston et al, 2006, and Wathlet et al, 2011). Consequently, some biomarkers indicated as strong predictors of developmental potential in specific patient populations may not be appropriate to select between embryos in other patient populations or even between embryos produced by the same individual. Another is the high degree of expression plasticity seen in early stage embryos, being reflected in the fact that the culture conditions can alter the gene expression pattern ( Lonergan et al., 2006 ). Furthermore, weaknesses in the commonly used methodological platforms can influence the validity of reported results. Quantitative reverse transcription PCR is considered to be a highly sensitive and reproducible method for studying a selected number of genes. However, conclusions from these studies are dependent on the validity of internal control (house-keeping) genes used to normalize gene-expression data. Evaluation of a number of commonly used control genes show that expression levels can vary considerably and suggest that accurate normalization of samples can only be achieved when multiple internal controls are used (Radonic et al, 2004 and Vandesompele et al, 2002). Results from studies lacking validated controls should be considered with caution.

Similarily, the reliability and reproducibility of microarray data have also been challenged by several publications (Chen et al, 2007 and Ioannidis et al, 2009). In one study, analyses of variance between five microarray platforms showed that approximately one-third of gene expression patterns were inconsistent across platforms. Moreover, intensities or fold-changes for some genes can vary from small to large depending on the platform used ( Chen et al., 2007 ). Evaluating the reproducibility of data analyses, another study re-analysed data published in 18 studies and showed an inability to reproduce the same results for 10 studies, while results from only two studies could be completely reproduced ( Ioannidis et al., 2009 ). These results illustrate that different analytical approaches can produce varying results, thereby challenging the standardization of these methods.


This review has focused on methods for embryo scoring and classification and the possible implementation of quality control and standardization.

It is obvious that scoring and selecting embryos by morphology and development rate is still the dominant method, although it is today expanded with more exact morphokinetics data from time-lapse measurements. It is also clear that regarding embryo morphology, despite numerous trials to find new variables, the only proven and validated independently predictive variables are still numbers of cells and fragmentation, followed by cell symmetry and nucleation. These variables are today subjectively scored and thereby difficult to standardize. The most important way forward at present is to implement operator competency and quality control by means of education and training, taking part in quality control schemes and benchmarking.

For timing aspects, consensus is not yet clear. Several studies have shown that current models for selection through morphokinetic variables are not transferable to other settings, suggesting dependency on biological and technical variations. Also, the timing aspects are difficult to standardize, since they are still manually annotated.

As for quality control and standardization in embryo morphology, it seems that embryologists are proficient in classifying embryos and ranking them in a cohort (good agreement), but less so in scoring individual variables in a homogenous way. However, training sessions and consensus meetings can increase proficiency.

Quantitative measurement of biomarkers in spent media, if successful, will improve objective scoring of viability, make it easier to standardize protocols, and reduce intra- and inter-clinic variation. However, many if not all of the protocols measuring non-invasive biomarkers in follicular fluid or spent embryo culture media are still experimental and not applicable clinically.

In comparison to univariate measurements, whereby reference standards can be used to ensure measurement precision, there are no simple standards for ‘omic’ measurements, which have hundreds or thousands of variables that can mask drift in accuracy. Without standardized protocols, differences in biomarker concentrations can be interpreted as biological effects while having little or no clinical impact, and although some tested target metabolites seem to have prognostic value, these techniques have not been possible to introduce into clinical routine, mainly due to the poor capacity for high sample throughput and concern for variation resulting from the effect of media composition and other differences in culture conditions. Standardization of these methods is therefore required before their clinical evaluation and possible use. In addition, the techniques need to be robust and withstand changes in culture conditions.


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Kersti Lundin obtained her PhD in Zoophysiology at the University of Gothenburg, Sweden in 1991. She thereafter started as an embryologist and researcher at the Unit of Reproductive Medicine, Sahlgrenska Hospital, Gothenburg, where she became Laboratory Director of Reproductive Medicine in 1997 and Associate Professor in 2004. Her main subjects of interest are basic and clinical embryology, including embryo development and cryopreservation.


Reproductive Medicine, Sahlgrenska University Hospital, Göteborg 413 45, Sweden

* Corresponding author.