On the second day, researchers in the
lab will decide whether further selective
nutrient media and genotyping of the
colonies are needed. It may take a third
day before pure colonies can be identified and sensitivity tests conducted.
However, identification may still not be
possible, particularly if the organism is
not a well-known pathogen with primers
already included in the laboratory’s PCR
protocols or fingerprinting tests. In the
meantime, the sick person is most likely
receiving a general treatment that may or
may not be effective in alleviating symptoms or inhibiting the pathogen.
In a more troubling scenario, the
pathogen might be missed entirely.
As shown in the image to the right, a
laboratory technician selects a single
colony as being representative of a
particular type of pathogen of interest
(such as a colored colony on selective
agar), and he or she then sub-cultures
it and subjects it to sensitivity tests.
What happens if the technician chooses
the wrong representative colony? ELS
techniques eliminate this potential for
human error. Every colony is automatically selected, analyzed and classified
without human intervention.
ELS technology has the unique
potential to rapidly identify and distinguish between the electronic signatures
or fingerprints of high-priority agents like
anthrax, botulism and plague versus those
that cause more common threats such as
water and food contamination, ricin toxin
and viral encephalitis. It allows us to
quickly assess the potential risk and initiate the appropriate response even before
the precise identity of that organism is
known. In other words, if an unknown
ELS pattern arises, it can be defined
without any other information through
a comparison to all defined ELS patterns
in the networked database. This works
because ELS produces unique mathematical patterns for bacterial colonies.
Select 1 or 2
representative colonies
All colonies on the plate are
evaluated with ELS—none are missed
Selection
Analysis
If ELS is used, all colonies derived from the initial specimen culture are processed,
reducing the possibility of missing organisms.
pathogens across the community brings
an added dimension to food pathogen
monitoring. A large networked database
of bacterial signatures based on biophysical rather than genetic properties, paired
with a technology utilizing machine-learning tools for emerging pathogen
detection, creates a unique opportunity
to improve monitoring in food and
water production, clinical settings and
bioterrorist events.
The technology used to feed informa-
tion into this database would be based
on ELS patterns, from which unique
identifying patterns for most known
bacteria would be created and distrib-
uted around the world. We anticipate
that, within several years, this method
may become one of the most important
technologies we have in the microbio-
logical world. Who would have thought
that these beautiful optical patterns
would originate from bacterial colonies
that have been evaluated the same way
for over 100 years? Louis Pasteur would
be impressed! t
J. Paul Robinson ( jpr@flowcyt.cyto.purdue.edu)
is director of the Purdue University Cytometry
Laboratories in West Lafayette, Ind., U.S.A.
B.P. Rajwa, E. Bae, V. Patsekin, A.M. Roumani,
A.K. Bhunia, J.E. Dietz and V.J. Davisson
are with Purdue University. M.M. Dundar is
with Indiana University–Purdue University
in Indianapolis, Ind. J. Thomas is with West
Virginia University in Morgantown, W.Va. E.
Daniel Hirleman is with the University
of California, Merced, Calif. Member
In summary
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