soup,” Kim says. This was the case in
Xie’s single-enzyme studies: Thousands
of partner molecules floated past a solitary enzyme, and each one had an equal
chance of reacting with it. But in real cells,
each enzyme usually meets only with the
partners that happen to live with it.
And different cells may make different
numbers of any particular enzyme, even
when the cells are genetically identical.
A 2008 study in Science showed that this
difference can literally mean life or death
for a cell: Tumor cells that survived treatment with chemotherapy were shown
to make more molecules of a particular
enzyme than cells that succumbed to the
drug, hinting that the enzyme might play
a role in drug resistance.
This variation could also mean that
even if one cell follows the Michaelis-Menten equation, large groups of cells
taken all together might not, Kim says.
And drugs designed using equations
that ignore the differences between cells
could therefore be less effective.
“Even in an ideal situation where the
Michaelis-Menten equation might be
working well inside a single cell, it is still
unavoidable to witness its breakdown at
a population of cells,” Kim says.
Kim and Price showed mathematically that using the Michaelis-Menten
equation to calculate how fast a large
group of cells will perform a reaction
gives a different answer than averaging
the reaction speeds of each individual
cell. By comparing the old equation with
new data on single cells, the researchers
found that the standard predictions for
how fast enzymes work can be off by
about 25 percent.
“ When we first started this we thought,
oh, this looks interesting, but maybe it’s
negligible. Turns out they have pretty
huge effects,” Price says. “For any
scenario where we know protein copy
number varies between cells, which looks
to be common, you’d be off.”
Cell-to-cell variation in enzyme level and survival
Source: cohen et al./science 2008
14 17 20 23 26 29 32 35 38 41 44 47
Time after drug addition (hours)
Life or death Differences in enzyme levels among individual cells were tracked using a red
fluorescent marker (right). human tumor cells (at left, each line represents one cell) that withstood a dose of the anticancer drug camptothecin made more of the DDx5 enzyme (as measured
by fluorescence level), while enzyme levels dropped in the cells eventually killed by the treatment.
Blue lines mark cells that survived treatment. other colors show cells that died, with the darker
colors corresponding to earlier cell death.
Fluorescence (A.U.) × 105
predictions and then test them, all at one
go. This would be the killer.”
Such an experiment may be around
the corner. At the moment, there are
limited techniques for getting quantita-
tive data on individual molecules inside
a cell without killing the cell in the pro-
cess. But several groups—including
Xie’s— are developing more. A recent
review paper in Trends in Biotechnol-
ogy heralds single-cell analyses as a new
frontier that will transform differences
between cells “from a source of noise to
a source of new discoveries.”
Even if future observations of the
location and concentration of enzymes
vindicate Michaelis and Menten,
though, many scientists think that the
emerging street-level details of the cel-
lular city will continue to challenge
“Any reaction occurring inside the
cell will be impacted by these conditions, but we don’t know exactly how,”
Grima says. “We’re probably sitting on
top of the iceberg.” s
Lisa Grossman is a writer in Seattle.
Understanding how the differences
within and among cells change reaction
rates can also eventually let scientists
engineer better enzymes. Most of Price’s
research focuses on building computa-
tional models of metabolic networks in
cells, which means that he is concerned
with what the cell eats and excretes. Ulti-
mately, he says, better models will mean
s Daojing Wang and Steven Bodovitz.
“Single cell analysis: the new frontier
in ‘omics.’” trends in Biotechnology,