Biomedical Engineering Reference
In-Depth Information
Test
chambers
SHM
(a)
(e)
Pheromone
Plain medium
1
2
CCA
I
SHM
(f )
CCA
3
4
BUD
Cells in
Cells out
II
RFS
5
Flow-
through
channels
RFS
(g)
1 mm
Outlet
Flow-
through
channels
(b)
BUD
III
Test
chambers
200 µm
(h)
(c)
(i)
180
368
267
127
96
92
55
82
1.0
0.8
0.6
(d)
0.4
I
II
III
0.2
0
0
10 20 30
Pheromone (nM)
40 50 60 70 80
FIGURE 6.77
Adaptive.gradient.sensing.in.yeast.in.a.microluidic.device..(From.Saurabh.Paliwal,.
Pablo. A.. Iglesias,. Kyle. Campbell,. Zoe. Hilioti,. Alex. Groisman,. and. Andre. Levchenko,. “MAPK-
mediated.bimodal.gene.expression.and.adaptive.gradient.sensing.in.yeast,”.
Nature
.446,.46-51,.
2007..Reprinted.with.permission.from.Nature.Publishing.Group.)
In 2008, a collaborative team led by Tau-Mu Yi and Noo Li Jeon from the University of
California (Irvine) used a very simple Y-mixer gradient generator to expose
S. cerevisiae
to
several gradient slopes of the mating pheromone α-factor (
Figure 6.78
). he gradient became
shallower downstream as the α-factor and the tracking dye (Dextran) had more time to difuse
(
Figure 6.78c
). Flow rates two times slower and ive times faster than the low rate used for this
study (1 μL/min) did not produce a change in cell behavior. he observed cell morphologies, and
speciically the robustness of projection formation, depended on the amount of α-factor the cells
were exposed to (
Figure 6.78d
). he authors found that mutations that impair the downregula-
tion of heterotrimeric G protein activation exhibited gradient-sensing defects that correlated
with the supersensitivity of the mutants cells to α-factor.
