research papers
1078 doi:10.1107/S0021889810027378 J. Appl. Cryst. (2010). 43, 1078–1083
Journal of
Applied
Crystallography
ISSN 0021-8898
Received 2 March 2010
Accepted 9 July 2010
Nanovolume optimization of protein crystal growth
using the microcapillary protein crystallization
system
Cory J. Gerdts,a,b,c Glenn L. Stahl,a Alberto Napuli,d,e Bart Staker,a,d Jan
Abendroth,a,d Thomas E. Edwards,a,d Peter Myler,d,f Wesley Van Voorhis,e Peter
Nollertb and Lance J. Stewarta,b,c,d*
aEmerald BioStructures Inc., 7869 NE Day Road West, Bainbridge Island, WA 98110, USA,
bEmerald BioSystems Inc., 7869 NE Day Road West, Bainbridge Island, WA 98110, USA,
cAccelerated Technologies Center for Gene to 3D Structure, USA, dSeattle Structural Genomics
Center for Infectious Disease, USA, eUniversity of Washington, Seattle, WA 98195, USA, and
fSeattle BioMed, 307 Westlake Avenue North, Suite 500, Seattle, WA 98109, USA. Correspondence
e-mail: lstewart@embios.com
The Microcapillary Protein Crystallization System (MPCS) is a microfluidic,
plug-based crystallization technology that generates X-ray diffraction-ready
protein crystals in nanolitre volumes. In this study, 28 out of 29 (93%) proteins
crystallized by traditional vapor diffusion experiments were successfully
crystallized by chemical gradient optimization experiments using the MPCS
technology. In total, 90 out of 120 (75%) protein/precipitant combinations
leading to initial crystal hits from vapor diffusion experiments were successfully
crystallized using MPCS technology. Many of the resulting crystals produced
high-quality X-ray diffraction data, and six novel protein structures that were
derived from crystals harvested from MPCS CrystalCards are reported.
1. Introduction
New technologies to improve protein crystallization success
rates are the focus of continuous research and technology
development (Fox et al., 2008; Ng, Clark et al., 2008; Ng,
Stevens & Kuhn, 2008; Li et al., 2009, 2010; Hansen et al., 2002;
Cherezov et al., 2008, 2009; Dhouib et al., 2009; Sauter et al.,
2007; Hansen & Quake, 2003). Protein crystals are often so
difficult to produce that crystallographers are willing to try a
new crystallization technology, even if it might provide only a
small chance at crystallization success. However, a new tech-
nology will only be widely accepted if it is able to demonstrate
clear value to crystallographers. In this field, value to crys-
tallographers is measured by the ease with which diffraction
quality crystals and crystal structures can be produced from a
limited amount of protein supply.
The Accelerated Technologies Center for Gene to 3D
Structures (ATCG3D) has developed the Microcapillary
Protein Crystallization System (MPCS), which is a plug-based,
microfluidic protein crystallization technology capable of
quickly and easily setting up hundreds of batch-under-oil-style
crystallization experiments (Gerdts et al., 2008). The MPCS is
unique because it is capable of generating hundreds of
nanovolume (10–20 nl) experiments, each containing a slightly
different chemical composition (Gerdts et al., 2006; Zheng et
al., 2003, 2005). The result is on-chip formulation of finely
controlled concentration gradients over a series of drops
(plugs) that are effective at optimizing protein crystals.
Further, the peel-apart CrystalCards used as a part of the
MPCS allow simple crystal extraction for diffraction studies.
Combining these benefits yields a technology that is able to
carefully optimize crystal hits, generating protein crystals that
are ready for subsequent diffraction experiments. In this
report we have examined the ability of the MPCS technology
to perform crystal optimizations of 29 different soluble
proteins provided by the Seattle Structural Genomics Center
for Infectious Disease (SSGCID).
2. Research study workflow
SSGCID is one of two centers funded by the National Institute
of Allergy and Infectious Diseases (NIAID) and is a consor-
tium of four Pacific Northwest institutions (Seattle BioMed,
Emerald, University of Washington and Battelle). SSGCID’s
primary mission is to determine 75–100 new protein structures
annually for targets from NIAID category A–C agents, as well
as emerging and re-emerging infectious disease organisms for
a period of five years. In this study, SSGCID proteins were
used to test the ability of the MPCS technology to rapidly
optimize protein crystallization conditions using crystal-
lization hits from traditional sitting-drop vapor-diffusion
crystallization trials. A chart describing the workflow of the
study is shown in Fig. 1. Purified SSGCID proteins were
screened – using sitting-drop vapor diffusion – against a series
of common crystallization screens (Wizard I, Wizard II,
Wizard III, JCSG+ and Precipitant Synergy from Emerald

BioSystems, and Crystal Screen HT and Index HT from
Hampton Research). Proteins that did not yield initial crystals
were retired – consistent with the workflow of the high-
throughput SSGCID structure determination pipeline. If the
initial screens yielded single crystals ready for analysis by
X-ray diffraction, they were first tested for diffraction quality
before undergoing optimization using the MPCS (this was
done to avoid optimizing crystals that were not in need of any
improvement). Proteins that led to initial microcrystals or
single crystals that did not produce high-quality X-ray
diffraction underwent optimization using the MPCS. Thus, the
proteins examined in this study were those that generated
initial crystal hits but were otherwise randomly selected. In
total, 29 proteins underwent MPCS optimizations.
MPCS optimization experiments generated highly granular
gradients containing up to 400 individual crystallization
experiments in 20 nl drops called plugs. Approximately 2 ml of
leftover protein and 2 ml of the precipitant solution (used in
the initial screen) were combined inside the microfluidic
circuitry of the MPCS CrystalCard (Fig. 2a). Each plug was
formed at a slightly different concentration than the plugs
before and after. This was accomplished by dynamically
controlling the flow rates of the solutions used to form the
plugs. Computer control of flow rates generated a wide variety
of potential gradients. For simplicity, only two types of
gradients were used in this study. The two types are shown
schematically in Figs. 2(b) and 2(c). The goal of the two MPCS
optimization types was to carefully interrogate a narrow
region of crystallization phase space surrounding the initial
hit. Type 1 optimizations maintained protein concentration in
all of the plugs while varying the precipitant concentration.
Type 2 optimizations varied the protein and precipitant
concentration against one another in order to interrogate the
effect of varied ratios of protein and precipitant. Completed
optimization experiments were incubated in the CrystalCard
at 100% humidity to allow for crystal growth. Crystals in plugs
stored in CrystalCards at 100% humidity have been shown to
be stable for more than six months. Additionally – although
not pursued in this study – plugs can be intentionally dehy-
drated while in the CrystalCard to initiate crystal growth by
controlling the humidity of the storage container. After crys-
tals grew, they were harvested for analysis by X-ray diffraction
by peeling back the thin plastic bonding layer (Fig. 2d) and
harvesting the protein crystal directly from the microcapillary
(Fig. 2e).
3. Materials and methods
Plastic CrystalCards were manufactured from cyclic olefin
copolymer. Each CrystalCard has two separate micro-
capillaries with approximately 10 ml of useful volume. One
optimization experiment may be performed in each micro-
capillary. Plug formation in the CrystalCard requires a low-
surface-energy (hydrophobic) surface. This ensures that the
carrier fluid (FC-40) preferentially wets the walls of the
microcapillary. To prepare the microcapillary surface for plug
formation, Cytonix PFC 502AFA solution is used to coat the
inside of the microcapillary. To apply the coating, the Crys-
talCard is filled from the outlet with Cytonix 502AFA solution
and incubated under ambient conditions for 0.5–1 h. The
502AFA solution is removed from the CrystalCard via
vacuum, followed by curing at 333–343 K for 1 h.
The CrystalCard has four inlet ports for introducing liquids,
one each for the carrier fluid (1), protein (2), precipitant (3)
and buffer (4). The buffer, protein and precipitant inlet
channels merge at the 3 + 1 mixer, where the aqueous solu-
tions are combined and segmented into individual plugs by the
inert and immiscible carrier fluid. Syringes and Teflon tubings
are back-filled with the carrier fluid, and the desired amounts
of the aqueous solutions are aspirated into the ends of the
Teflon tubings. Connection to the CrystalCard is achieved via
the Teflon tubing and a polypropylene connector that forms an
airtight seal to the port in the CrystalCard. The component
liquids of the experiment are placed in the Teflon tubing of the
syringe pumping system and delivered to the CrystalCard in a
manner described previously using the MicroPlugger pump-
control software (Gerdts et al., 2008). The positioning of the
fluid lines on the CrystalCard is noted in Fig. 2.
The flow rates of the aqueous solutions can be varied such
that a smooth gradient over a series of plugs is generated. In
research papers
J. Appl. Cryst. (2010). 43, 1078–1083 Cory J. Gerdts et al.  Nanovolume optimization of protein crystal growth 1079
Figure 1
A flow chart describing the sequence of events undertaken in this study.
Purified protein was received and initial screening via sitting-drop vapor-
diffusion experiments was set up. If initial crystals were single and
harvestable, they were analyzed via X-ray diffraction. If the initial protein
crystals produced high-quality X-ray diffraction data, the structure was
solved without MPCS optimization. However, if the initial X-ray
diffraction data were poor, or if the initial crystals were small or not
harvestable, the crystals were optimized using the MPCS.

this study, we primarily used two types of gradients (Table 1
and Fig. 3). In gradient Type 1, the protein is delivered at a
constant rate (2 ml min1) while a linear gradient is made from
precipitant and buffer solutions (with the sum of the precipi-
tant and buffer flow rates remaining constant at 2 ml min1).
For most Type 1 gradients, the flow rate of the precipitant was
programmed to start at 2 ml min1 and slowly decrease as the
flow rate of the buffer slowly increased at the same rate.
Therefore in all Type 1 experiments, the concentration of the
protein remains constant as the concentration of the precipi-
tant varies (Table 1). In gradient Type 2, a dynamic gradient
between the protein and precipitant is generated. In Type 2
gradients, the flow rate of the protein is slowly decreased as
the flow rate of the precipitant is slowly increased and the
buffer flow rate is held constant (Table 1). In general, a Type 1
gradient was generated for a protein/precipitant combination
first and if, no crystals were seen, a Type 2 gradient was often
generated as a follow-up.
Precipitants used for this study were available commercially
from either Emerald Biosystems (Wizard I, Wizard II, Wizard
III, JCSG+ and Precipitant Synergy) or Hampton Research
(Crystal Screen HT and Index HT). Customized versions of
Wizard I and Wizard II (also commercially available from
Emerald BioSystems on request) were also used. The custo-
mized screen consisted of Wizard I andWizard II with primary
precipitant concentration increased by 50–100%.
Crystals were extracted directly
from the CrystalCards for subsequent
analysis by X-ray diffraction (Fig. 2d).
The 100 mm-thick plastic bonding layer
was pealed off in order to expose the
desired crystals to be harvested. Typi-
cally, ca 1 ml of a previously prepared
cryo-protectant solution was pipetted
directly onto the desired crystal. The
crystal was then pulled out of the
microcapillary using a traditional nylon
cryo-loop (ca 0.2 mm diameter from
Hampton Research) and stored in
liquid nitrogen for transport to an
X-ray source for analysis. On rare
occasions, a crystal was found to
remain on the 100 mm bonding layer. In
this scenario, the crystal was still
covered with the cryo-protectant solu-
tion and harvested from atop the
plastic layer.
4. Results
MPCS gradients were shown to opti-
mize vapor diffusion crystal hits with a
high rate of success. Of the 29 proteins
that underwent MPCS optimizations,
28 (93%) were crystallized using the
MPCS. Many of the proteins used in
this study produced crystals in more
than one precipitant solution during
the initial screening. In total, 120
different protein/precipitant combina-
tions produced crystals in the vapor
diffusion experiments. Of the 120
combinations, 90 (75%) produced
crystals during the MPCS optimization
experiments – a high success rate given
research papers
1080 Cory J. Gerdts et al.  Nanovolume optimization of protein crystal growth J. Appl. Cryst. (2010). 43, 1078–1083
Table 1
Flow rate scheme (ml min1) used for the Type 1 and Type 2 MPCS
gradients in this study.
Protein Precipitant Buffer Carrier fluid
Type 1 Starting flow rate 2 2 0 5
Ending flow rate 2 0–1 1–2 5
Type 2 Starting flow rate 2 0 0.2 5
Ending flow rate 0 2 0.2 5
Figure 2
(a) Ca 2 ml of leftover protein solution and ca 2 ml of precipitant solution from the initial experiment
(left) were used to generate an optimization experiment in the MPCS CrystalCard (right). In the
CrystalCard, aqueous solutions (protein, precipitant and buffer) were combined and spontaneously
segmented into individual drops (plugs) by the inert, immiscible carrier fluid. The resulting plugs
filled the microcapillary and were incubated as individual crystallization experiments. Scale bar =
400 mm. (b), (c) Generic protein crystallization phase diagrams indicating how crystallization phase
space is interrogated in MPCS optimizations. In Type 1 MPCS optimizations (b) protein
concentration is held constant while a gradient of precipitant concentration is generated over a
series of plugs. In Type 2 MPCS optimizations (c), protein concentration begins high and slowly
decreases as precipitant concentration begins low and slowly increases to generate a dynamic protein
versus precipitant gradient over a series of plugs. (d) A picture of an MPCS CrystalCard being peeled
apart in order to expose the crystals. Scale bar = 1 inch ’ 2.54 cm. (e) A picture of a protein crystal
being harvested from a CrystalCard using a 0.2 mm cryo-loop. Scale bar = 200 mm.

that many of the initial crystals were not single crystals but tiny
microcrystals or precipitation that looked crystalline (see
examples in Fig. 1). In addition, MPCS experiments under-
went a significant (50–100-fold) decrease in experimental
volume and were translated from sitting-drop vapor-diffusion-
style crystallization to batch-under-oil-style crystallization.
Further, ten precipitant solutions that did not generate crystals
in vapor diffusion experiments were used to generate crystals
in MPCS optimization experiments. These particular precipi-
tant solutions were tested using the MPCS (despite not
yielding crystals from vapor diffusion experiments) because
they possessed similarities in chemical composition to other
precipitant solutions that did yield crystals in vapor diffusion
experiments. In total, 17 precipitant solutions were tested in
this manner and ten yielded crystals (59%). This indicates that
using the MPCS to perform optimizations of every precipitant
in the standard crystallization screens may generate many
unique crystal hits that are being missed with a single vapor-
diffusion experiment – consistent with previously reported
data comparing vapor-diffusion-style crystallization with
batch-under-oil-style crystallization (Baldock et al., 1996;
D’Arcy et al., 2003). More than 90% of the precipitant solu-
tions that were tested using vapor-diffusion experiments went
untested in the MPCS, indicating a strong potential for
discovering new crystal hits using the MPCS. Awide variety of
solutions were represented in the precipitants used in this
study, including various salt solutions, low- (4.5) to high-pH
(10.5) solutions, high-viscosity polyethylene glycol solutions,
and organics such as 2-propanol and 2-methyl-2,4-pentane-
diol.
5. Discussion
The goal of the MPCS optimizations was to salvage protein
structures from initially screened proteins by (i) generating
single crystals when sitting-drop experiments did not and/or
(ii) improving the diffraction quality of initial single crystals
generated in the sitting-drop experiments. Of the 29 protein
targets involved in the study, six novel
protein structures have been deter-
mined using crystals from MPCS opti-
mizations and deposited in the PDB
for a successful salvage rate of 21%
(Figs. 3a–3f; for crystal optimization
data, see Table 2). This salvage rate
compares favorably to published data
from reductive methylation (Kim et al.,
2008) and limited proteolysis (Dong et
al., 2007). In two cases, high-quality
diffraction was also generated from
crystals grown from subsequent sitting-
drop vapor-diffusion experiments (in
one case, X-ray diffraction from the
MPCS crystal was of slightly higher
resolution and in one case diffraction
from the MPCS crystal was of slightly
lower resolution). The development of
the MPCS by ATCG3D has continued
throughout this study, leading to the
commercialization of the MCPS Plug
Maker (Fig. 3g). As shown in this study,
the MPCS technology has been a
successful method of optimizing
protein crystals in order to yield high-
quality X-ray diffraction results. Future
directions for this technology are
emerging and include incorporation of
lipidic cubic phase into plugs for
membrane protein crystallization (Li et
al., 2009) and high-throughput initial
screening of protein samples with the
hybrid method (Li et al., 2006) (sparse
matrix + gradient screening) made
possible in an automated fashion by
the availability of the MPCS Plug
Maker.
research papers
J. Appl. Cryst. (2010). 43, 1078–1083 Cory J. Gerdts et al.  Nanovolume optimization of protein crystal growth 1081
Figure 3
Pictures of crystals in plugs generated from MPCS optimizations that led to high-quality data sets
(2.5 A˚ or better) and/or novel structures. Corresponding ribbon structures are included below the
pictures of the plugs (for data collection and refinement statistics, see Table 2). All scale bars =
200 mm. (a) Enoyl-CoA hydratase from Mycobacterium tuberculosis (1.8 A˚; PDB code 3h81); (b)
aldehyde dehydrogenase from Bartonella henselae (2.1 A˚; PDB code 3i44; deposited structure for
PDB code 3i44 came from a sitting-drop optimization at 2.0 A˚ resolution; the 2.1 A˚-resolution data
set was generated from a crystal optimized using the MPCS); (c) methionine-R-sulfoxide reductase
from Burkholderia pseudomallei (1.7 A˚; PDB code 3cxk); (d) methylisocitrate lyase from Brucella
melitensis (2.9 A˚; PDB code 3eoo); (e) dihydrofolate reductase/thymidylate synthase from Babesia
bovis (2.5 A˚; PDB code 3i3r); ( f ) tRNA guanine-n1-methyltransferase from Bartonella henselae
(2.5 A˚; PDB code 3ief); (g) A picture of the commercial version of the MPCS Plug Maker. Left: The
touch screen user interface and live image of the CrystalCard. Right: Instrument stage that holds the
CrystalCard and crystallization samples.

This work was supported by the PSI-2 Specialized Center
Grant U54 GM074961, co-sponsored by NIGMS–NCRR for
the Accelerated Technologies Center for Gene to 3D Struc-
ture. All protein targets were provided by the NIAID-
supported Seattle Structural Genomics Center for Infectious
Disease (contract No. HHSN266200700057C). We thank Craig
Ogata at the Advanced Photon Source (APS) for assistance
and for helpful discussions. Part of this work is based upon
research conducted at the Northeastern Collaborative Access
Team beamlines of the APS, supported by award RR-15301
from the National Center for Research Resources at the
National Institute of Health. Use of the APS is supported by
the US Department of Energy, Office of Basic Energy
Sciences, under contract No. W-31-109-ENG-38. Some results
shown in this report are derived from work performed at
Argonne National Laboratory, Structural Biology Center, at
the APS. Argonne is operated by the University of Chicago
Argonne, LLC, for the US Department of Energy, Office of
Biological and Environmental Research, under contract No.
DE-AC02-06CH11357.
References
Baldock, P., Mills, V. & Shaw Stewart, P. (1996). J. Cryst. Growth, 168,
170–174.
Cherezov, V., Hanson, M. A., Griffith, M. T., Hilgart, M. C., Sanishvili,
R., Nagarajan, V., Stepanov, S., Fischetti, R. F., Kuhn, P. & Stevens,
R. C. (2009). J. R. Soc. Interface, 6 Suppl. 5, S587–597.
Cherezov, V., Liu, J., Griffith, M., Hanson, M. A. & Stevens, R. C.
(2008). Cryst. Growth Des. 8, 4307–4315.
D’Arcy, A., Mac Sweeney, A., Stihle, M. & Haber, A. (2003). Acta
Cryst. D59, 396–399.
Dhouib, K., Khan Malek, C., Pfleging, W., Gauthier-Manuel, B.,
Duffait, R., Thuillier, G., Ferrigno, R., Jacquamet, L., Ohana, J.,
Ferrer, J. L., Teheobald-Dietrich, A., Giege, R., Lorber, B. &
Sauter, C. (2009). Lab-On-A-Chip, 9, 1412–1421.
Dong, A. et al. (2007). Nat. Methods, 4, 1019–1021.
research papers
1082 Cory J. Gerdts et al.  Nanovolume optimization of protein crystal growth J. Appl. Cryst. (2010). 43, 1078–1083
Table 2
Crystal optimization data.
Protein Organism
Internal
protein
code
Potential
crystal hits
from initial
screening
Precipitants
tested using
MPCS
Precipitants
yielding
crystals
from MPCS
Best resolu-
tion from
vapor diffu-
sion crystals
Best resolu-
tion from
MPCS
crystals (A˚)
Crystal
structure?
Nucleoside diphosphate kinase Giardia lamblia Gila 438 23 23 16 – 6 No
Adenylate kinase Giardia lamblia Gila 297 10 10 8 – 8 No
Deoxynucleoside kinase Giardia lamblia Gila 1017 4 4 3 – 4 No
Arsenical pump-driving ATPase Giardia lamblia Gila 988 6 6 3 – – No
Peptide methionine sulfoxide
reductase msrB
Giardia lamblia Gila 536 1 1 1 – – No
Rab GDI Giardia lamblia Gila 634 2 2 2 – – No
Uracil phosphoribosyltransferase Giardia lamblia Gila 1401 2 2 0 – – No
Enoyl-CoA hydratase Mycobacterium tuberculosis Mytu 386 3 3 1 3.5 6 No
Enoyl-CoA hydratase Mycobacterium tuberculosis Mytu 358 76 8 6 1.8 2.05 Yes
Aldehyde dehydrogenase Bartonella henselae Bahe 886 79 7 7 2.0 2.0 Yes
Dihydrofolate reductase/
thymidylate synthase
Babesia bovis Babo 1191 18 10 6 – 2.5 Yes
Thymidylate synthase 1/2 TS-1 Encephalitozoon cuniculi Encu 1191 2 2 2 3.1 None No
Bifunctional dihydrofolate
reductase-thymidylate synthase
Toxoplasma gondii Togo 1191 3 3 2 – 3.8 No
Thymidylate synthase Burkholderia pseudomallei Bups 1181 3 3 1 – – No
UDP-N-acetylmuramate-l-alanine
ligase
Burkholderia pseudomallei Bups 137 5 5 4 6–7 6.8 No
RNA polymerase,  chain,
bacterial and organelle
Brucella melitensis Brab 66 3 3 1 – none No
tRNA (guanine-n1)-methyltrans-
ferase
Bartonella henselae Bahe 1015 8 8 8 3 2.4 Yes
Endonuclease/exonuclease/phos-
phatase
Giardia lamblia Gila 1102 2 2 1 None None No
Acetylglutamate kinase Bartonella henselae Bahe 993 8 4 4 5 4.5 No
Ribokinase Giardia lamblia Gila 1141 2 2 2 3.5 2.9 No
Probable thiosulfate sulfurtrans-
ferase
Mycobacterium tuberculosis Mytu 1241 2 1 1 2.1 2.6 Yes†
Glycine cleavage system protein H Mycobacterium tuberculosis Mytu 1046 1 1 1 1.75 – Yes†
Aldose reductase Giardia lamblia Gila 1452 2 1 1 2.7 3.6 No
Methionine-R-sulfoxide reductase Burkholderia pseudomallei Bups 33 2 2 2 – 1.7 Yes
Ribose-phosphate pyrophospho-
kinase
Burkholderia pseudomallei Bups 35 3 3 3 2.3 – Yes†
-Aminolevulinic acid dehydratase Burkholderia pseudomallei Bups 75 1 1 1 – – No
Recombinase A Burkholderia pseudomallei Bups 69 1 1 1 – – No
Glutaryl-CoA dehydrogenase Burkholderia pseudomallei Bups 27 1 1 1 2.2 – Yes†
Methylisocitrate lyase Burkholderia pseudomallei Bups 14 1 1 1 – 2.9 Yes
† The structure was solved after the completion of this study through subsequent salvage efforts.

Fox, B. G., Goulding, C., Malkowski, M. G., Stewart, L. & Deacon, A.
(2008). Nat. Methods, 5, 129–132.
Gerdts, C. J., Elliott, M., Lovell, S., Mixon, M. B., Napuli, A. J., Staker,
B. L., Nollert, P. & Stewart, L. (2008). Acta Cryst. D64, 1116–
1122.
Gerdts, C. J., Tereshko, V., Yadav, M. K., Dementieva, I., Collart, F.,
Joachimiak, A., Stevens, R. C., Kuhn, P., Kossiakoff, A. &
Ismagilov, R. F. (2006). Angew. Chem. Int. Ed. 45, 8156–8160.
Hansen, C. L. & Quake, S. R. (2003). Curr. Opin. Struct. Biol. 13, 538–
544.
Hansen, C. L., Skordalakes, E., Berger, J. M. & Quake, S. R. (2002).
Proc. Natl Acad. Sci. USA, 99, 16531–16536.
Kim, Y. et al. (2008). Nat. Methods, 5, 853–854.
Li, L., Du, W. & Ismagilov, R. F. (2010). J. Am. Chem. Soc. 132, 112–
119.
Li, L., Fu, Q., Kors, C. A., Stewart, L., Nollert, P., Laible, P. D. &
Ismagilov, R. F. (2009). Microfluidics Nanofluidics, 8, 789–798.
Li, L., Mustafi, D., Fu, Q., Tereshko, V., Chen, D. L., Tice, J. D. &
Ismagilov, R. F. (2006). Proc. Natl Acad. Sci. USA, 103, 19243–
19248.
Ng, J. D., Clark, P. J., Stevens, R. C. & Kuhn, P. (2008). Acta Cryst.
D64, 189–197.
Ng, J. D., Stevens, R. C. & Kuhn, P. (2008). Methods Mol. Biol. 426,
363–376.
Sauter, C., Dhouib, K. & Lorber, B. (2007). Cryst. Growth Des. 7,
2247–2250.
Zheng, B., Gerdts, C. J. & Ismagilov, R. F. (2005). Curr. Opin. Struct.
Biol. 15, 548–555.
Zheng, B., Roach, L. S. & Ismagilov, R. F. (2003). J. Am. Chem. Soc.
125, 11170–11171.
research papers
J. Appl. Cryst. (2010). 43, 1078–1083 Cory J. Gerdts et al.  Nanovolume optimization of protein crystal growth 1083