Biomedical Engineering Reference
In-Depth Information
reciprocal interactions. Unlike single-cell arrays in which each node is independent contact with the
external environment (previous section), in network-level single-cell arrays, cells interface with each
other and produce interdependent responses that cumulate in network-level behavior. Thus the two
most important parameters are: (1) identity of the neighboring cells, as the different cells elicit different
interactions, and (2) distance between individual cells, as distance defines the mode of cellular com-
munication (direct contact vs. paracrine signaling). Two research areas that utilize single-cell arrays are
cancer invasion and neural networks.
Tumor invasion is a critical step in cancer progression to lethal metastatic disease, and understand-
ing the cancer-stroma interface will provide clues to new therapeutic targets. Stroma is connective and
supportive tissue. Fabricating artificial tissue interfaces at the single-cell level allows for the study of
high-resolution cell-cell interactions at the micrometer length scale. Microfluidic and micropatterned
devices have been fabricated to study both paracrine signaling (
Hong et al., 2012
) and direct contact
events (
Frimat et al., 2011
;
Zhang et al., 2014
;
Nikkhah et al., 2011
), but are limited to isolated pair-wise
interactions. Direct-write tools, such as laser-based bioprinters, are advantageous in depositing cells
over a homogeneous area to study network-level behavior. Printing cells onto homogeneous substrates
ensures that the development of the construct is influenced solely by guidance cues from neighboring
cells, rather than limited by physical chambers or adhesion islands. In addition, there has been a push
toward 3D tissue models to better study the
in vivo
cancer-stroma cross-talk. Several researchers have
demonstrated the patterning of single hydrogel microbeads containing cancer cells into small-scale pat-
terns (
Dolatshahi-Pirouz et al., 2014
;
Phamduy et al., 2012
). Larger patterns have also been generated
by coprinting 3D hydrogel biomaterials and cells (
Gruene et al., 2011
;
Kingsley et al., 2013
).
In monoculture neural networks, guiding spontaneous synaptic connectivity requires defined spacing
and pathways between individual cells. Several schemes have been developed toward this end.
Dinh
et al. (2013)
generated high-density interconnected circuits of compartmentalized neurons in separate
microfluidic chambers, with a biomaterials-guided outgrowth path between pairs of cells.
Sanjana and
Fuller (2004)
and
Macis et al. (2007)
printed adhesion islands onto nonadherent substrates to allow for
both cell attachment and directed outgrowth. In such examples, cell bodies are stationary and outgrowth
occurs due to physical guides.
Difato et al. (2011)
utilized a laser-based optical tweezers technique to
generate neural network patterns on homogeneous surfaces, which allows for cell migration in addition
to synaptic development due to soluble guidance cues. Extracellular recording of action potential stimu-
lation reveals that increasing internodal distance accelerates signal propagation (
Wu et al., 2012
), but de-
creases signal propagation efficiency (
James et al., 2004
). Single-neuron patterns with defined synaptic
networks open up new avenues for fundamental biology studies and neural sensor/actuator applications.
4.5.3
NEXT-GENERATION SINGLE-CELL ARRAYS: INTEGRATED,
COMPUTATION-DRIVEN ANALYSIS
As single-cell arrays are generally compared against a combinatorial library of soluble cues or first-
neighbor cells, computational tools could be used to predict or validate empirical data in next-generation
platforms. Standalone technology for high-throughput single-cell analysis currently exists. However,
integration of existing technologies with single-cell arrays, especially in the context of network arrays,
would produce a synergistic platform that is greater than the sum of its parts. Such improved plat-
forms would provide
in situ
, multiplexed analysis of cell behavior, including morphology, biochemical
state (
Burguera et al., 2010
), biomechanical stresses, genetic (
Vanneste et al., 2012
), metabolic, and
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