Elder K. Human preimplantation embryo selection

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HUMAN PREIMPLANTATION EMBRYO SELECTION

DEFINITIONS

Metabolomics is an emerging science that represents

the continuation of the ever-expanding disciplines

we call the ‘omics’.

3–6

Metabolomics represents the

study of cellular processes that are downstream

events of those studied in the fields of genomics

and proteomics. As a new scientific discipline, the

nomenclature of metabolomics is rapidly evolving

in parallel with the science itself, and is only now

being defined. As a consequence, there is currently

no universal definition of metabolomics, or its com-

panion techniques; some of the more commonly

used definitions that are used to frame the concept

of metabolomics include: (1) the study of global

metabolite profiles in a system (cell, tissue, or

organism) under a given set of conditions; (2) the

‘systematic study of the unique chemical finger-

prints that specific cellular processes leave behind’,

specifically, the study of their small molecule

metabolite profiles; and (3) the systematic analysis

of the inventory of metabolites, as small molecule

biomarkers, that represent the functional pheno-

type at the cellular level (Figure 20.1.)

The metabolome

refers to the complete inventory

of small-molecule, non-proteinaceous compounds,

such as metabolic intermediates, ATP, fatty acids,

glucose, cholesterol, hormones, and other signaling

molecules, and secondary metabolites that are

found within a biological sample. These molecules

are the ultimate products of cellular metabolism.

The metabolome is dynamic, changing from second

to second, based upon the activation and interaction

of different metabolic pathways within the cell; thus,

it is of particular importance to the rapidly changing

development of the embryo in nature and its interac-

tion with the changing conditions in the laboratory.

Ultimately, the metabolome represents the global

population of biomarkers that can be assessed in the

study of metabolomics. Mining of the metabolome

provides information that best describes the pheno-

type, and this can also be used to infer gene function.

The slightly different term metabonomics is

commonly used in the context of drug toxicity

assessment. There is some discussion over the exact

differences between ‘metabolomics’ and ‘metabo-

nomics’, but in general, the term metabolomics is

more commonly used in the context of emphasis on

comprehensive metabolic profiling, while metabo-

nomics attempts to quantify the multiple metabolic

changes caused by a biological perturbation. In

practice, there is still a large degree of overlap in the

way the terms are used, and they are often used

interchangeably.

THE CASE FOR METABOLOMICS

The dogma of molecular biology suggests that DNA is

transcribed into RNA which is then translated into

proteins. Protein and post-translational modifica-

tions of proteins ultimately give rise to small mole-

cules within the cell which become the biomarkers

that can be studied by metabolomics. It is generally

agreed that there are more than 25 000 genes, 100 000–

200 000 transcripts, and up to 1 000 000 proteins,

whereas there may be as few as 2500–3000 small

molecule metabolites/biomarkers that make up the

human metabolome. By systematically measuring this

population of small molecule biomarkers or metabo-

lites, scientists can establish ‘profiles’ or ‘signatures’ of

healthy individuals versus those with specific illnesses.

The realization that knowing the DNA sequence

of the human genome, or that of other organisms,

does not in itself explain the fundamental nature of

many normal biological processes and disease states

has triggered a marked increase in seeking paradigms

that can relate gene expression to the phenotype.

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Metabolomics

Metabolomics can be thought of as the interface

between genomics and a functional phenotype:

Genomics → Transcriptomics → Proteomics

→ Metabolomics → Functional Phenotype

Definition: systematic analysis of the inventory of

metabolites – as small molecule biomarkers –

that represent the functional phenotype at the

cellular level.

Figure 20.1 Definitions of ‘metabolomics’. Metabolomics is a

natural offshoot of the fields of genomics and proteomics, and

represents the downstream events of those cellular processes.

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METABOLOMIC PROFILING OF OXIDATIVE STRESS BIOMARKERS

Even with the sequencing of the total human

genome, it is clear that our knowledge of gene func-

tion is limited; for example, as many as 35–40%

of open reading frames have no known function.

Consequently, there is a need to better understand

the downstream effects of gene expression, which are

translated via the transcriptome or proteome, and

amplified at the level of the metabolome. This allows

for a greater degree of sensitivity when mining the

biomarker population of the metabolome compared

with other levels of cellular regulation. More impor-

tantly, metabolic fluxes are not regulated by gene

expression alone (considering post-transcriptional

and post-translational events), so the metabolome is

considered to be the closest expression of pheno-

type.

Metabolomics can provide indications of a

metabolic problem or lesion with high throughput at

lower costs than genomics, transcriptomics, or pro-

teomics, and is therefore well suited for widespread

investigations in the life sciences.

In summary, metabolomics is an extension of the

growing understanding of the body’s many different

genetic and molecular players, including genes and

proteins. We know that small changes in the flux

through an intracellular pathway can lead to large

changes in the concentration of metabolites.

These

changes can be detected by metabolomic profiling of

key biomarkers, thus signaling when crucial distur-

bances have occurred or are about to occur. In con-

trast, research focusing on genomics or proteomics

alone only examines a small portion of the essential

events that define normal biological function. While

genes can tell us what we might become or the poten-

tial for a biological error, and proteins tell us which

genes have been expressed, metabolomics provides us

with a real-time ‘snapshot’ of the downstream events

that characterize gene expression. For these reasons,

metabolomics is expected to be a relevant tool in the

management of various medical conditions, and, thus

an important method for studying basic biological

function in concert with other methods.

HISTORY

It is generally assumed that metabolomics was con-

ceived in 1970 by Arthur Robinson of the Department

of Chemistry at the University of California at San

Diego. Although it was not called metabolomics at

the time, Robinson and Pauling published their first

paper in 1971 in the Proceedings of the National

Academy of Sciences.

Robinson’s core concept was

that information-rich data that reflect the func-

tional status of a complex biological system reside in

the quantitative and qualitative pattern of metabo-

lites in body fluids. It was his expectation that body

fluid analysis could be optimized to create a low

cost, informative, and medically relevant means of

measuring metabolic changes even when standard

clinical chemistry markers were in the ‘normal

range’.

Later, the term ‘metabolomics’ was coined in

1998.

Six years later, in 2004, the Metabolomics

Society (http://www.metabolomicssociety.org) was

formed to promote the study of metabolomics. The

Human Metabolome Project (HMP) was launched in

Canada in 2005 and has identified approximately 2000

metabolites that make up the human metabolome.

The goal of HMP is to make this information

available worldwide through its database (www.

hmdb.ca). Many of the bioanalytical methods used

for metabolomics research have been adapted from

existing biochemical techniques and enhanced by

the advent of newer methodologies, such as various

forms of spectral analysis.

Three characteristics common to metabolomic

research are: (1) metabolite or small molecule bio-

markers are profiled without bias towards a specific

analyte or group of metabolites; (2) the biomarkers

or metabolites are identified and quantified by vari-

ous forms of analytical, biochemical and spectral

analysis; and (3) relationships between the metabo-

lites are characterized, primarily by multivariate

mathematical methods often referred to collectively

as bioinformatics or computational biology.

BIOINFORMATICS AND

COMPUTATIONAL BIOLOGY

In 1952, the British neurophysiologists and Nobel

Prize winners Hodgkin and Huxley constructed

a mathematical model of the nerve cell. In 1960,

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Denis Noble developed the first computer model of

a beating heart. Since then, mathematical computa-

tions of biological systems have rapidly escalated to

include analysis, quantification, and descriptions of

complex molecular level events. These progressively

important developments quickly gave rise to the

field of bioinformatics and computational biology,

which is defined as the use of techniques including

applied mathematics, informatics, statistics, com-

puter science, artificial intelligence, chemistry, and

biochemistry to solve biological problems, usually

at the molecular level. Research in computational

biology often overlaps with systems biology. Major

research efforts in the field include sequence align-

ment, gene finding, genome assembly, protein

structure alignment, protein structure prediction,

prediction of gene expression, and protein–protein

interactions, and the modeling of evolution.

The terms bioinformatics and computational

biology are often used interchangeably. However,

bioinformatics more properly refers to the creation

and advancement of algorithms, transformations,

discriminant analysis, computational, and statistical

techniques and theory to solve formal and practical

problems posed by or inspired from the management

and analysis of biological data. Computational biol-

ogy, on the other hand, refers to hypothesis-driven

investigation of a specific biological problem using

computers, carried out with experimental and sim-

ulated data, with the primary goal of discovery and

the advancement of biological knowledge. A typical

metabolomics experiment, for example, will gener-

ate volumes of data that need to be converted into

useful information and knowledge.

SYSTEMS BIOLOGY

Systems biology studies the interactions between

the components of a biological system, and how these

interactions give rise to the function and behavior

of that system; for example, the interactions between

enzymes and metabolites in a specific metabolic

pathway.

Typically, a cellular network is modeled

mathematically. Due to the large number of param-

eters, variables, and constraints in cellular networks,

numerical and computational techniques are used.

Other aspects of computer science are also used

in systems biology, including text mining to find

parameter data from literature, online databases,

and repositories for sharing data and models.

The systems biology approach is characterized

by a cycle of theory, computational modeling and

experiment to quantatively describe cells or cell

processes.

Since the objective is to model all the

interactions in a system, the experimental techniques

that most suit systems biology are those that are sys-

tem-wide and attempt to be as complete as possible.

Therefore, metabolomics, proteomics, and high-

throughput techniques are used to collect quantitative

data for the construction and validation of models.

ANALYTICAL TECHNIQUES USED

IN METABOLOMICS

Metabolite or biomarker analysis typically addresses

two issues: (1) separation of the biomarkers, usually

by a form of chromatography or electrophoresis,

particularly capillary electrophoresis; and (2) detec-

tion and quantification of the biomarkers, depend-

ing on the methodologies used. A summary of the

more commonly used separation and detection

techniques is provided below.

SEPARATION METHODS

GAS CHROMATOGRAPHY

Gas chromatography (GC) is often used in concert

with mass spectrometry (MS). This combination

method, often referred to as GCMS, is one of the

most widely used and powerful methods. It offers

very high chromatographic resolution, but requires

chemical derivatization for many biomolecules:

only volatile chemicals can be analyzed without

derivatization. Many large and polar metabolites

cannot be analyzed by GC.

HIGH PERFORMANCE LIQUID CHROMATOGRAPHY

Compared with GC, high performance liquid chro-

matography (HPLC) has lower chromatographic

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resolution, but it does have the advantage that a much

wider range of analytes can potentially be measured.

CAPILLARY ELECTROPHORESIS

Capillary electrophoresis (CE) is the newest tool to be

added to the armamentarium of methods used for

metabolomic profiling. There are a number of advan-

tages of CE: it has a higher theoretical separation

efficiency of analytes than HPLC, and is suitable for

use with a wider range of metabolite classes than is

GC. As for all electrophoretic techniques, it is most

appropriate for charged analytes.

DETECTION METHODS

MASS SPECTROMETRY

Mass spectrometry is perhaps the most commonly

used technique to identify and, in some (but not all)

cases, to quantify metabolites after separation by

GC, HPLC, or CE. GCMS is the most ‘natural’ com-

bination of techniques, and was the first to be devel-

oped. Measuring time-of-flight of fragmentation

pattern constituents also provides useful informa-

tion regarding the makeup of the specimen being

analyzed by MS. In addition, mass spectral finger-

print libraries exist, or can be developed, allowing

identification of a metabolite according to its frag-

mentation pattern. MS is both sensitive and specific.

There are also a number of metabolomic studies

which use MS as a stand-alone technology: the sam-

ple is infused directly into the mass spectrometer

with no prior separation. In this scenario MS serves

to both separate and detect metabolites.A disadvan-

tage of mass spectrometry is that the specimen is

consumed in the analysis, and cannot be recovered.

MS instrumentation for metabolomic analysis is

generally very costly.

NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY

Nuclear magnetic resonance (NMR) has the added

advantage that the metabolites don’t always need to

be separated prior to analysis, and the sample can

thus be recovered for further analyses. Several small

molecule metabolites can be measured simultane-

ously. NMR is close to being a universal detector, as

it produces detailed structural information about

the compounds. However, it also possesses one major

disadvantage, which is that it is relatively insensitive

compared with mass spectrometry-based techniques.

NMR is not generally regarded as a reliable quanti-

tative method. It is also the most complex and

expensive instrumentation to acquire and maintain.

FOURIER TRANSFORM INFRARED SPECTROSCOPY

Fourier Transform Infrared spectroscopy (FTIR)

is a physicochemical method that measures vibra-

tions around chemical bonds within different

functional groups of molecules using light electro-

magnetic radiation at specific wavelengths. This

method gives quantitative information about the

total biochemical composition of a specimen without

consuming it. However, substantial sample prepara-

tion is required before analysis can be initiated by

FTIR, thus adding time and technical requirements

to the analysis.

RAMAN AND NEAR INFRARED SPECTROSCOPY

While MS and NMR are by far the two leading tech-

nologies used for metabolomics research, Raman

and near infrared (NIR) spectroscopy have also been

used successfully to identify and quantify biomark-

ers in metabolomics. These methods have similar

levels of analytical sensitivity to the previous meth-

ods but also have several added advantages: direct

sample measurement – no preparation is required;

little chemical bias; instrumentation costs are con-

siderably less; instrumentation size is much smaller

and the instruments are easier to run and maintain;

and rapid, simultaneous analysis of multiple bio-

markers at one time. These techniques are not always

suitable for all biomarker targets, but are becoming

more widely used for a number of metabolomic

applications. Our scientific team has successfully

utilized Raman and NIR spectroscopy as the plat-

form for its metabolomics technology and has coined

the phrase ‘biospectroscopy-based metabolomics’or

BSM for such applications.

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KEY APPLICATIONS

Metabolomics is an example of a true ‘disruptive

technology’ the availability of which is expected

to markedly impact discovery-driven as well as

hypothesis-driven research paradigms. As a result,

metabolomics is gaining interest across a wide

variety of disciplines, including functional genomics,

biomarker discovery, diagnostic and therapeutic

monitoring, drug discovery and development, phar-

macogenomics, and other life science applications.

As metabolic biomarkers are identified they can be

used to better understand specific disease processes

based on the pathways involved. This information

can give rise to therapeutic intervention through

new drug discovery. Metabolomic studies of cells in

culture has added immensely to the understanding

of biological diversity and complexity in both basic

science and medicine.

MOLECULAR DIAGNOSTICS

The sensitivity and specificity of metabolomic

profiling is expected to lead to the development

of a new class of molecular diagnostics that provides

rapid, cost-effective assessment of the functional phe-

notype in health and disease. Metabolomic analysis

can pinpoint changes in complex networks of meta-

bolic reactions, where outputs from one enzymatic

pathway are inputs to other chemical reactions,

thus identifying deviations in normal biology.

Metabolomics can be used to systematically distin-

guish between the often subtle differences that

separate normal physiology from the onset or pro-

gression of disease or an individual’s response to a

therapeutic compound, thus leading to (1) new

diagnostic and monitoring paradigms; (2) the for-

mation of new industry standards of specificity

and sensitivity; (3) the generation of reliable, new

measurements on which insurance providers can

establish more accurate and thus broader reimburse-

ment criteria; and (4) new designs for sensitive, high

throughput instruments that yield cost-effective,

user-friendly devices that produce rapid results

without the need for specimen preparation.

THERANOSTICS

Metabolomic technology will enable the healthcare

industry to rapidly move towards the practice of

personalized medicine, or ‘theranostics’, allowing the

pharmaceutical and biotechnology industries to shift

from products that only treat disease to new analytical

methods that can detect, monitor, and help stratify ill-

nesses. This trend in medical care looks at disease as a

process rather than a state, and allows physicians to

diagnose as well as track disease progression in order

to provide the most appropriate treatment interven-

tions on a patient-by-patient basis.

Pharmaceutical

and biotechnology companies are using metabolomics

to develop ‘companion diagnostics’ so that their new

therapeutic compounds can be used more effectively

and safely, following FDA approval.

DRUG DISCOVERY AND DEVELOPMENT:

PHARMACODIAGNOSTICS

Metabolomic profiling of biomarkers has the poten-

tial to revolutionize drug discovery and develop-

ment through novel diagnostic testing paradigms

referred to as pharmacodiagnostics. This approach

is expected to generate more efficacious clinical trial

designs that achieve the goals of cost-effective and

expeditious clinical research. Pharmacodiagnostic

strategies that are based on metabolomics and

pharmacokinetic monitoring have the potential

to reduce clinical trial failures and shorten drug

development timelines, as well as to create new

measurements of safety and efficacy that can be

used to predict and monitor adverse events. With

increased regulatory oversight, pharmacodiagnos-

tics are also expected to be used increasingly in the

design of phase IV or FDA-imposed postmarketing

studies.

BIOMARKER DISCOVERY

The identification of metabolites that could serve

as biomarkers of disease is a key application of

metabolomics. Over time, reliance on single ana-

lyte/biomarker efficiency has been replaced by the

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METABOLOMIC PROFILING OF OXIDATIVE STRESS BIOMARKERS

concept of multivariate biomarker profiles that are

more sensitive and predictive. An initial approach in

biomarker development is to create databases from

metabolomic profiling of control populations, and

then test populations having a specific disease or

condition of interest. This approach statistically

compares the metabolomic profile of the diseased

population with the profile of healthy controls. The

data are mined by bioinformatics analysis in order

to identify the distribution of metabolites/biomarkers

that can discriminate the disease state and which

could then be used as predictors of that condition.

The challenge in this process is to utilize rigorous

statistical methodologies followed by validation of

the database. The biomarkers can be further vali-

dated for their predictive or monitoring value for

clinical applications, or can be adopted as surrogate

markers for pharmacokinetic and pharmaco-

diagnostic applications. Finally, once the biological

function of individual biomarkers is established,

they may become viable molecular targets for drug

development processes.

TOXICITY ASSESSMENT/TOXICOLOGY

Metabolomic profiling of biological fluids can be

used to detect the physiological changes caused by

toxic insult of a chemical or a mixture of chemicals.

These applications can address environmental,

agricultural, or medical issues. In many cases, the

observed changes can be related to specific syndromes,

e.g. a specific lesion in the central nervous system

(CNS), liver, or kidney. This is of particular rele-

vance to pharmaceutical companies that need to test

the efficacy as well as toxicity of potential drug can-

didates. Eliminating a compound on the grounds of

poor efficacy or adverse toxicity before it reaches

clinical trials could save enormous expense and

time in otherwise faulty trials.

FUNCTIONAL GENOMICS

Metabolomics is an excellent tool for determining

the phenotype created in nature or by genetic

manipulation such as gene deletion or insertion.

For example, metabolomics can be used to detect

any phenotypic changes in a genetically modified

organism or in a plant species intended for human

or animal consumption. More exciting is the prospect

of predicting the function of unknown genes by

studying the metabolomic profiles of biomarkers

that result from the deletion or insertion of known

genes. Such advances are likely to come from models

based on microorganisms,

plants,

and experimen-

tal animals. Transgenic mouse models of Huntington’s

disease, for example, have already demonstrated the

power of metabolomics to decipher the phenotypic

human clinical picture in an animal carrying the

transfected gene.

Likewise atherosclerosis in labo-

ratory mice has been diagnosed more efficiently and

accurately using metabolomics than by genomics or

proteomics alone.

HOST–PATHOGEN INTERACTIONS

Metabolomic investigations are also expected to

prove valuable in host–pathogen interactions.

Using plant models, MS was used to identify discrim-

inatory lipid metabolites and certain phospholipids

that are know to be important in protecting plants

from infectious diseases.

Parallel applications in

human infectious disease are equally plausible given

the cascade of metabolic events that are mounted by

a host’s defense mechanisms in response to infectious

agents.

NUTRIGENOMICS

This is a generalized term which links genomics,

transcriptomics, proteomics, and metabolomics to

human nutrition. In general the metabolome of

a given body fluid is influenced by endogenous

factors such as age, sex, body composition, and

genetics as well as underlying pathologies. Diet and

drugs are the main exogenous factors, and diet can

then be further broken down to nutrients and non-

nutrients. Metabolomics can be used to determine

the biological endpoints that reflect the balance

of these variables in an individual’s nutritional

makeup.

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APPLICATIONS OF METABOLOMICS IN THE

ASSISTED REPRODUCTIVE TECHNOLOGIES

METABOLOMICS-BASED EMBRYO SELECTION

A technology that is capable of identifying viable

gametes and embryos in an IVF program is expected

to lead to a reduction in the number of embryos

transferred and, eventually, the adoption of single

embryo transfer (SET). The availability of rapid,

non-invasive metabolomic profiling of embryos

could potentially empower practitioners to overcome

the IVF dilemma and move away from the current

paradigm of multiple embryo transfer. The practical

benefit of rapid, non-invasive metabolomic profil-

ing in IVF is the improvement of embryo and

gamete selection procedures thus allowing a reduc-

tion in the number of embryos transferred without

compromising pregnancy rates. The longer term

benefit of this technology to patients, physicians,

and insurance providers is the potential to limit the

incidence of multiple births and associated health-

care risks and expense.

CURRENT APPLICATIONS

The advent of metabolomics in the field of assisted

reproduction is very recent. Only a small number of

abstracts and papers have been published prior to

the work of the Metabolomics Study Group for

ART. The publications in the field so far are summa-

rized below.

(1) FTIR spectroscopy. Thomas et al

reported

substantial differences in the biochemical fin-

gerprints of follicular fluid from large follicles

(⬎17 mm) versus small (⬍15 mm) antral folli-

cles in patients undergoing IVF. The differences

in the distribution of steroid concentrations

between large and small follicles were signifi-

cant but did not follow protein bonding charac-

teristics as observed by FTIR. While not labeled

specifically as a metabolomics study, the authors

concluded that the differences detected in

metabolite profiles by FTIR reflect the develop-

mental stage of the follicle, and possibly oocyte

quality. FTIR has also been used successfully

to examine biological fluids for other medical

applications.

23,24

(2) HPLC. Houghton et al

25,26

and Brison et al

investigated amino acid turnover of embryo

culture media to establish metabolomic profiles,

or signatures, of developing human embryos.

Using HPLC as their analytical tool, the authors

observed differential amino acid compositions

in the culture media of viable versus non-viable

embryos and correlated their findings with preg-

nancy outcomes. The studies concluded that

amino acid analysis may be used as a non-invasive

test to predict embryonic viability.

(3) FTIR spectroscopy. Hollywood (in preparation)

used FTIR spectroscopy to examine the meta-

bolomic profile of biomarkers in embryo cul-

ture media. Cluster analysis of media specimen

FTIR spectra from embryos that did not pro-

duce a pregnancy differed significantly from that

of media from embryos that resulted in positive

biochemical and clinical (ultrasound) pregnan-

cies. The authors concluded that metabolomic

fingerprinting of media can be used to assess

the status of individual human embryos and

their potential for producing a pregnancy.

(4) Respirometry (oxygen consumption). Lopes

et al

28,29

studied oxidative pathways of metabo-

lism in individual bovine embryos by direct

quantification of oxygen consumption. Using a

non-invasive micro-electrode, these investigators

postulated that embryonic respiration rates, in

association with other embryo selection meth-

ods, allowed an accurate assessment of embryo

quality. The authors discussed the potential

use of this technology as a diagnostic tool for

improving embryo selection in IVF.

(5) GCMS. In a related, but non-ART study, inves-

tigators used GCMS in metabolomic studies to

assess biomarkers related to pre-eclampsia.

The authors identified metabolomic profiles in

plasma of pregnant women to discriminate those

with normal pregnancies from those at risk for

developing pre-eclampsia. The metabolomic

data showed high sensitivity and specificity,

targeting only three biomarkers in the analysis.

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DATA FROM THE METABOLOMICS STUDY

GROUP FOR ART

The Metabolomics Study Group for ART has con-

ducted proof of principle studies of gamete and

embryo viability using a proprietary metabolomics

platform and bioinformatics developed by Molecular

Biometrics, LLC, Chester, NJ. Two retrospective

studies (Yale University, Stanford University and

Reproductive Biology Associates of Atlanta) and one

prospective blinded study (Reproductive Medicine

Associates of New Jersey) were performed.

31,32

STUDY AIMS

The Metabolomics Study Group for ART was organ-

ized to test the hypothesis that biomarkers of oxida-

tive stress (OS) could be targeted using metabolomics

technology based on various forms of biospec-

troscopy, coupled with specific bioinformatics, to

assess embryo and gamete viability in IVF; and that

the resulting metabolomic profiles could adequately

distinguish between viable and non-viable embryos

and gametes within a cohort group.

TECHNOLOGY PLATFORM

Free radicals such as reactive oxygen species (ROS)

exert their effect at the molecular level in all cell

types and play a role in both normal physiological

and pathological functions. Complex interactions

between the pro-oxidants and antioxidants are cru-

cial in the maintenance of intracellular homeostasis.

An imbalance in these reactions results in oxidative

stress, which has been implicated in a number of

medical conditions and diseases.

33,34

Moreover, bio-

markers of OS have been found in the male and

female reproductive tracts and are known to affect

the quality of gametes, early embryo development,

and implantation, which in turn affect pregnancy

outcomes. ROS have been shown to influence sperm,

oocytes and embryos and to modulate their interac-

tion in their respective microenvironments.

The

technology platform of Molecular Biometrics tar-

gets biomarkers of oxidative stress in culture media

of the developing embryo or in the respective

microenvironment of the gametes, i.e. in follicular

fluid (oocyte) and in seminal plasma (sperm).

Molecular Biometrics technology is based on the

confluence of two scientific disciplines: (1) biospec-

troscopy, the application of different forms of spectral

analysis in human biology that is used to identify,

quantify, and validate proteomic and small molecule

biomarkers; and (2) metabolomics, the science that

systematically examines and integrates the dynamic

interplay between multiple, small molecule biomark-

ers that are uniquely characteristic of complex biolog-

ical functions that define the normal phenotype. The

term ‘biospectroscopy-based metabolomics’ (BSM)

was coined to describe this technology platform.

These methodologies are used in concert to quantify a

sample’s molecular biomarker makeup, specifically,

biomarkers of oxidative stress, and then convert that

data into a novel ‘metabolomic profile’or ‘fingerprint’.

Each profile is analyzed using proprietary bioinfor-

matics and chemometrics to determine the probabil-

ity of normal, competent cellular function versus

the presence or predisposition to abnormal or even

pathological states of cellular activity. The technology

platform is illustrated in Figure 20.2.

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Technology Platform

A methodology reduced to practice

Hypothesis driven

Discovery driven

Biomarker targets

(oxidative stress, proteomic, small molecules)

Spectral analysis

(Metabolomic profile by NMR, Raman, NIR)

Chemimetrics and bioinformatics

(Bayesian statistics, Haar transform, Matlab, Genetic algorithm)

Phenotypic function

Figure 20.2 Technology platform. Diagrammatic representation

of the pivotal steps that constitute the technology platform for

assessing biomarkers of oxidative stress using metabolomic

profiling. The platform can be driven from either a hypothesis or

discovery approach and includes biomarker targets, methods of

spectral analysis, chemometric and bioinformatic analysis of data,

leading to an assessment of the phenotypic function of the cell or

organism. NMR, nuclear magnetic resonance; NIR, near infrared.

Courtesy of Molecular Biometrics, LLC, Chester, NJ, USA.

HPE_Chapter20.qxp 7/17/2007 3:38 PM Page 253

HUMAN PREIMPLANTATION EMBRYO SELECTION

Metabolomic profiling was performed using

a small, prototype instrument (6 inches ⫻ 6 inches ⫻

4 inches) that was programmed with proprietary

spectroscopic and bioinformatics capabilities

(Figure 20.3.) Initially, sample cells were filled with

30 microliters of discarded culture media obtained

at the time of embryo assessment and selection. The

original prototype has been replaced by a commer-

cially manufactured instrument having a sample

size of 5–10 microliters. This high-speed procedure

takes about 1 minute to complete.

STUDY DESIGN

Infertility patients enrolling in an IVF program

were treated according to the standard IVF protocol

of the participating program. Informed consent was



Interference filter

Microprocessor

Array

detector

Ball lenses

LED light source

Disposable sample cell

Sample

Light

2.1”

USB To

computer

LED

display

electronic

‘telephone card’

Figure 20.3 Instrument prototype design. The instrument design for obtaining spectral signatures of biomarkers utilizes a disposable

sample cell that contains 5–10 ␮l of specimen. The sample cell is inserted into a fixed position in the light path within the instrument.

The stable light source (left) passes light (energy) into the sample, which then travels through a wavelength filter to select the specified

wavelength for the analysis. The photons of energy which emerge from the filter are captured by a light-detecting diode and delivered

to the bioinformatics microprocessor chip (far right) where the biomarker profiles are calculated. The total time of analysis is

approximately 1 minute. The instrument can be interfaced with any computer system to download data to patient files. This

prototype design has now been incorporated into a commercial product for large scale production.

INSTRUMENTATION

The metabolomics platform described here has

been reduced to practice within the least expensive

and most compact instrumentation available. The

goal was to engineer a generation of affordable,

point-of-care instruments for direct metabolomics

applications that are accessible to all IVF laborato-

ries. Unlike other metabolomics technologies that

rely on very large and costly NMR, FTIR, and MS

instruments, the current metabolomic applications

are based on Raman and NIR spectroscopy. These

instruments are robust, easy to use, do not require

specimen preparation and do not destroy or con-

sume the specimen. The device can interface with

any computer system and can download data

directly into patients’ medical records.

HPE_Chapter20.qxp 7/17/2007 3:38 PM Page 254

METABOLOMIC PROFILING OF OXIDATIVE STRESS BIOMARKERS

obtained from each patient enrolled in the study

and treatment information for that patient, includ-

ing pregnancy outcome data, was noted in the

patient’s medical record. Samples of normally dis-

carded embryo culture media were collected at the

time of embryo selection/transfer (Figure 20.4). All

specimens were placed in Nunc vials and rapidly

frozen in liquid N

or dry ice and methanol and stored

at ⫺80°C or liquid N

. Appropriate media controls

were also prepared from media droplets that never

received an embryo but were handled in a parallel

manner with media droplets containing developing

embryos.

Pregnancy was confirmed via detection of fetal

heartbeat by ultrasound. The pregnancy outcome

groups were defined as follows: (1) no implantation or

0% pregnancy: regardless of the number of embryos

transferred, no pregnancies were recorded; 50%

pregnancy: four embryos were transferred and two

implanted producing twins while the two corre-

sponding embryos did not implant; and (2) 100%

pregnancy: regardless of the number of embryos

transferred, all embryos implanted and produced a



Sampling pipette

(5–10 µl volume)

Oil

Embryo

Embryo culture media

(35–65 µl microdrop)

Figure 20.4 Non-invasive study design. This illustration depicts

the non-invasive procedure for collecting culture media for

analysis of embryo viability. A small volume of media, usually

5–10 ␮l, is collected directly from the media microdrop contain-

ing the developing embryo. Care is taken to avoid aspiration of

oil. The media is inserted directly into the disposable sample cell

(as described in Figure 20.3) for analysis.

pregnancy (e.g. two embryos transferred, twins;

three embryos transferred, triplets, etc.). All speci-

mens were shipped to McGill University, Montreal

Quebec for metabolomic measurements and bio-

informatic analysis.

METABOLOMIC PROFILING OF OS BIOMARKERS

Specimens were analyzed using both Raman and

near infrared spectroscopy, which measures the

bonds within functional groups of molecules at spe-

cific wavelengths. The spectra obtained from each

instrument were separately analyzed by proprietary

bioinformatics using a wavelength selective genetic

algorithm (GA) to determine regions predictive

of pregnancy outcome as determined by a logistic

regression of light attenuation at specified wave-

lengths. To avoid random correlations, a leave-one

out cross-validation was used. Sensitivity and speci-

ficity were calculated for each assay.

RESULTS

Two different forms of spectroscopic analysis were

used to report metabolomic parameters that were

established on the basis of observed OS biomarker

targets. Spectral profiles describing differences in

-CH, -NH, -SH, C⫽C, and -OH concentrations

showed distinct differences between culture media

of embryos that resulted in pregnancy compared

with those that did not. The ratio of C⫽C to -SH

content in the media that is reflective of oxidative

stress was also different between the two groups

(Figure 20.5). Using GA with Raman spectroscopy,

four spectral regions associated with these molecu-

lar species were reproducibly identified as predictive

of pregnancy outcome in two pilot studies. Compiled

outcomes from the leave-one-out cross-validation

of the logistic regression using Raman measure-

ments resulted in a specificity of 90% and a sensitiv-

ity of 100%.

In a separate blinded study, analysis of media

specimens by NIR required two wavelength regions

identified by GA. The specificity and sensitivity of

these assays was 83% and 73%,respectively. Significant

differences were observed between 0 and 100%

HPE_Chapter20.qxp 7/17/2007 3:38 PM Page 255