More than 95% of the clinical biochemistry laboratory workload is based on small molecule identification, which can be analyzed through metabolomics-based techniques. In this infographic, we explore how metabolomics has reshaped the clinical diagnostics landscape.
Download this infographic to discover:
- What metabolomics is
- Methods and technologies used in metabolomics-based diagnostics
- How metabolomics is being applied in clinical diagnostics
Why is metabolomics useful in clinical diagnostics?
How do we obtain
metabolites for
diagnostic analysis?
What technologies do we use in
metabolomics-based
clinical diagnostics?
The field of metabolomics has witnessed major advancements over recent decades, and subsequently it has become a critical component of modern medicine, particularly in the field of
diagnostics. Now, more than 95% of the clinical biochemistry laboratory workload is based on
small molecule identification, which can be analyzed through metabolomics-based techniques.1
In
this infographic, we explore how metabolomics has reshaped the clinical diagnostics landscape.
Blood and urine are commonly used biological specimens for metabolomics-based diagnostic tests. This is
because they are minimally invasive to obtain. However, other biological specimens have been studied for
diagnostic purposes, including:³
Advances in metabolomics research have been enabled by the continual
development and improvement of analytical techniques, namely:³
The type of biological sample is an important factor for determining the design
and complexity of a metabolomics analysis in diagnostics.³
Nuclear magnetic resonance (NMR)
spectroscopy and Mass spectrometry (MS)
How is metabolomics
being applied in clinical diagnostics?
Screening for inborn errors
of metabolism (IEM)⁶
Several other disorders
can benefit from a
metabolomics-based
diagnostics approach:
What challenges need to be overcome before
metabolomics can be fully
translated into the clinic?⁹
Where is clinical
metabolomics headed?
There are over 750 known IEMs, or inherited metabolic disorders.
• Most are caused by mutations in single genes coding for enzymes.
• This results in the accumulation of toxic/unwanted substances or a lack of essential
compounds.
• Each IEM is extremely rare.
• Collectively they are a key cause of morbidity and mortality.
• Early diagnosis and treatment is essential to prevent physical and mental disabilities.
Several IEMs are now routinely screened for in babies in the UK by the newborn blood spot or “heel prick” test.
The number of conditions screened for varies by country, but newborn screening has proven a valuable tool
worldwide to rapidly identify babies with IEMs and reduce the risk of them suering irreversible eects of the
disorders.
Many IEMs are not currently detectable, or tested for at birth. Continued advances in metabolomics research
could change this in the future.
Before the full potential of metabolomics can be translated into clinical benefits, improvements are needed in
the following areas:
Metabolomics could have a profound impact on the future of diagnostics. Here are some of the innovative developments on the
horizon, and key areas they are expected to benefit.
Phenylketonuria
Maple Syrup Urine Disease
Isovaleric Acidaemia
PKU
MCADD
MSUD
IVA
Homocystinuria (pyridoxine
unresponsive) HCU
Vitamin D disorders Cardiovascular disease Cancers
Diabetes
Making metabolomics
more visible Improvements in
sample collection
Data Analysis
Compared to the other omics,
metabolomics is less well known
by funding bodies, the media, and
the public. Increased awareness
of, and interest in the field is
essential to fuelling future clinical
translation.
Technology
A need for multi-platform
approaches and various types of
instrumentation can be costly and
beyond the reach of smaller
laboratories. Improved stability is
also important, to ensure
comparability between machines
and laboratories.
Uncovering the “Dark
Metabolome”
Up to 80-90% of metabolites in
an untargeted analysis are
unidentifiable. Reducing the
amount of these unknown
metabolites will be key in fully
realizing the potential of
metabolomics.
Collecting samples in the clinic
presents several di culties
compared to in a controlled
laboratory environment.
Interindividual variation can also
contribute to confounding results.
Collecting enough samples to ensure
statistically robust results can create
a data deluge. Access for all to fast
and e cient methods for
interpreting large, complex datasets
is needed to address this issue.
What is metabolomics?
The metabolome is defined as the sum of all small biochemicals (such as reactants, pathway intermediates
and product, in addition to stable structural molecules) at a given moment in time in a biological sample.2
Metabolomics sits under the "umbrella" OMICS field that also encompasses genomics, transcriptomics
and proteomics. Together, these disciplines provide deep insight into the molecular composition and
functionality of cells, tissues and whole organisms.
How? Well, let's take a look at the human body, for example.
In biology, the genome
provides a set of
instructions that lay out the
core fundamentals of how a
human body should be
built, and how it should
(technically speaking)
function.
The function is served
by the proteins
encoded by the
genome, the
"workhorses" that are
fundamental drivers of
the many
physiological pathways
in the human body.
Of course, in clinical diagnostics,
this is very useful. It allows us to
identify specific metabolites that
are associated with healthy and
diseased-states and are therefore
biomarkers. Metabolite
concentrations are often
amplified when we compare them
with gene or protein expression,
meaning the detection of
metabolites is a more sensitive
measure of biological status.³
The metabolites produced in
such pathways provide a
chemical readout of the
current phenotype of either a
particular sub-population of
cells or tissues in the body.
They also enable us to gather
insights into the interactions
of the human body with it’s
environment.
Cerebrospinal
fluid Bile
Intact tissue,
e.g a biopsy
Seminal
fluid
Saliva Amniotic
fluid
Gut
aspirate
Synovial
fluid
Medium-chain Acyl-CoA
Dehydrogenase Deficiency
GA1 Glutaric Aciduria Type 1
Personalized medicine and
pharmacometabolomics¹⁰
Treatments could be tailored to patients based
on their metabolic phenotype, reducing the
occurrence of adverse drug reactions and
improving response to therapy.
Prognostic biomarkers could also help flag the
onset of disease before symptoms manifest.
Increased newborn screening
Public health authorities around the world are
considering expanding newborn screening to
include other inherited metabolic disorders.
Improvements in technology and testing design
could make it simple to test for a much larger
repertoire of these disorders, enabling earlier
diagnosis and commencement of treatment.
Technological advances
Point-of-care devices, such as the
iKnife11, could become more common
in hospitals. Miniaturization and the
development of more portable, simple,
rapid, and low-cost devices will enable
wider distribution and use of
metabolomics-based diagnostics.
What is possible (25,000 genes)
Genomics
What appears to be happening
Transcriptomics
What makes it happen
(100 000 00 proteins)
What makes it happen
(3000-5000 metabolites)
Proteomics
Metabolomics
Phenotype
Blood
Metabolomic profiling of serum
and plasma provides a global view
of the metabolic status, as it
perfuses all living cells in the
human body. The NMR spectrum
of serum/plasma includes narrow
signals from small molecule
metabolites and broader signals
from lipids and proteins. An array
of spectral analysis methods are
available to detect small/ large
molecule samples. MS analysis of
serum is typically established
using extracts.³
In comparison to other fluid
specimens, urine contains a relatively
low concentration of proteins and
high concentrations of low molecular
weight compounds, minimizing
sample preparation and narrow line
widths in the spectral peaks in the
NMR spectra. Therefore, the process
of biomarker identification by NMR
for diagnostic purposes is enhanced.⁵
Urine Intact tissue
Advances in NMR approaches
mean that even a few mgs of intact
tissue sample are sucient to
achieve a high-quality spectra with
a resolution that is parallel to
solution-state spectra. As tissue
contains many metabolites, it's
profiling is useful for guiding the
detection of biomarkers in other
easily accessible biofluids.³
References:
1. Jacob et al. (2018). A targeted metabolomics approach for clinical diagnosis of inborn errors of metabolism. Analytica
Chimica Acta. DOI: 10.1016/j.aca.2018.03.058.
2. Kennedy et al. (2018). Metabolomics in the clinic: A review of the shared and unique features of untargeted metabolomics
for clinical research and clinical testing. Journal of Mass Spectrometry. DOI: https://doi.org/10.1002/jms.4292.
3. Nagana Gowda et al. (2014). Metabolomics-Based Methods for Early Disease Diagnostics: A Review. Expert Review of
Molecular Diagnostics. DOI: 10.1586/14737159.8.5.617.
4. Bujak, Struck-Lewicka, MarkuszewskiR and Kaliszan. (2015). Metabolomics for laboratory diagnostics. Journal of Pharma
ceutical and Biomedical Analysis. DOI: https://doi.org/10.1016/j.jpba.2014.12.017.
5. Zhang, Sun , Wu and Wang. (2012). Urine metabolomics. International Journal of Clinical Chemistry. DOI: 10.1016/j.
cca.2012.08.016.
6. Mussap, M., Zaffanello, M., & Fanos, V. (2018). Metabolomics: a challenge for detecting and monitoring inborn errors of
metabolism. Annals of Translational Medicine, 6(17), 338–338. https://doi.org/10.21037/atm.2018.09.18
7. https://www.nhs.uk/conditions/pregnancy-and-baby/newborn-blood-spot-test/
8. Cappuccio, G., Pinelli, M., Alagia, M., Donti, T., Day-Salvatore, D.-L., Veggiotti, P., … Elsea, S. H. (2017). Biochemical phenotyp
ing unravels novel metabolic abnormalities and potential biomarkers associated with treatment of GLUT1 deficiency with
ketogenic iet. PLOS ONE, 12(9), e0184022. https://doi.org/10.1371/journal.pone.0184022
9. Pinu, F. R., Goldansaz, S. A., & Jaine, J. (2019). Translational Metabolomics: Current Challenges and Future Opportunities.
Metabolites, 9(6), 108. https://doi.org/10.3390/metabo9060108
10. Johnson, C. H., & Gonzalez, F. J. (2012). Challenges and opportunities of metabolomics. Journal of Cellular Physiology,
227(8), 2975–2981. https://doi.org/10.1002/jcp.24002
11. Abu Rabie, P., Sheelan, D., Laures, A., Spaull, J., & Dowell, S. (2019). Increasing the discrimination power of rapid evapora
tive ionisation mass spectrometry (REIMS) in analytical control tissue quality screening and cell line sample identification.
Rapid Communications in Mass Spectrometry. https://doi.org/10.1002/rcm.8525
