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- Herausgeber: Wiley-VCH
- Kategorie: Fachliteratur
- Serie: Advanced Biotechnology
- Sprache: Englisch
Emphasizing their emerging capabilities, this volume provides a strong foundation for an understanding of how micro- and nanotechnologies used in biomedical research have evolved from concepts to working platforms.
Volume editor Christopher Love has assembled here a highly interdisciplinary group of authors with backgrounds ranging from chemical engineering right up to materials science to reflect how the intersection of ideas from biology with engineering disciplines has spurred on innovations. In fact, a number of the basic technologies described are reaching the market to advance the discovery and development of biopharmaceuticals.
The first part of the book focuses on microsystems for single-cell analysis, examining tools and techniques used to isolate cells from a range of biological samples, while the second part is dedicated to tiny technologies for modulating biological systems at the scale of individual cells, tissues or whole organisms. New tools are described which have a great potential for (pre)clinical development of interventions in a range of illnesses, such as cancer and neurological diseases.
Besides describing the promising applications, the authors also highlight the ongoing challenges and opportunities in the field.
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Veröffentlichungsjahr: 2016
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Table of Contents
Cover
Related Titles
Title Page
Copyright
List of Contributors
About the Series Editors
Preface
part I: Microsystems for Single-Cell Analysis
Chapter 1: Types of Clinical Samples and Cellular Enrichment Strategies
1.1 Introduction
1.2 Types of Clinical Samples
1.3 Sample Processing and Conventional Methods of Cell Enrichment
1.4 Microscale/Nanoscale Devices for Cellular Enrichment
1.5 Conclusion
References
Chapter 2: Genome-Wide Analysis of Single Cells and the Role of Microfluidics
2.1 Motivation for Single-Cell Analysis of Genomes and Transcriptomes
2.2 Single-Cell Genomics
2.3 Single-Cell Transcriptomics
2.4 The Future of Genome-Wide Single-Cell Analysis with Microfluidics
References
Chapter 3: Cellular Immunophenotyping: Industrial Technologies and Emerging Tools
3.1 Cellular Immune Status and Immunophenotyping
3.2 Surface Marker Phenotyping
3.3 Functional Phenotyping
3.4 Conclusion
References
Chapter 4: Microsystem Assays for Studying the Interactions between Single Cells
4.1 Introduction
4.2 Advantages of Single-Cell Analysis over Conventional Assay Systems
4.3 Analysis of Cell–Cell Communication between Pairs of Single Cells
4.4 Conclusions
Acknowledgments
References
Chapter 5: Modeling Microvascular Disease
5.1 Introduction
5.2 Microvascular Disease
5.3 Macromodeling
5.4 Micromodeling
5.5 Summary
References
Part II: Tiny Technologies for Modulating Biological Systems
Chapter 6: Nanotechnologies for the Bioelectronic Interface
6.1 Introduction
6.2 Modeling the Bioelectronic Interface
6.3 Experimental Approaches for Extra-Cellular Coupling
6.4 State-of-the-Art Extra-Cellular Nanoscale Interfaces
6.5 Experimental Approaches for Intra-Cellular Coupling
6.6 State-of-the-Art Intra-Cellular Nanoscale Interfaces
6.7 Experimental Approaches for In-Cell Coupling
6.8 Outlook
References
Chapter 7: Intracellular Delivery of Biomolecules by Mechanical Deformation
7.1 Introduction
7.2 Delivery Concept
7.3 Cytosolic Delivery by Diffusion
7.4 Applicability across Cell Types and Delivery Materials
7.5 Summary
7.6 Appendix
Acknowledgments
Conflict of Interest
References
Chapter 8: Microfluidics for Studying Pharmacodynamics of Antibiotics
8.1 Background on Antibiotic Resistance
8.2 Methods for Antibiotic Susceptibility Testing (AST)
8.3 Applying Pharmacokinetics/Pharmacodynamics to AST
8.4 Application of Microfluidic-Based Approach for PK/PD Modeling
8.5 Summary and Future Outlook
Acknowledgments
References
Chapter 9: Microsystems Models of Pathophysiology
9.1 Vascular and Hematologic Pathologies
9.2 Organ-Specific Pathologies
9.3 Cancer
9.4 Summary
References
Chapter 10: Microfluidic Systems for Whole-Animal Screening with C. elegans
10.1 Importance
10.2 Introduction
10.3 A Versatile Animal Model:
Caenorhabditis elegans
(
C. elegans
)
10.4 Microfluidics
10.5 Microfluidics for
C. elegans
Biology
10.6 Conclusions and Future Directions
Author Contributions
References
Index
End User License Agreement
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Guide
Cover
Table of Contents
Preface
part I: Microsystems for Single-Cell Analysis
Begin Reading
List of Illustrations
Chapter 1: Types of Clinical Samples and Cellular Enrichment Strategies
Figure 1.1 Schematic representation of stratified epithelium and underlying connective tissue (a) and simple intestinal epithelial crypt (b). Typical cells found in the epithelium and connective tissue include epithelial cells (E), dendritic cells (DC), lymphocytes (L), fibroblasts (F), and smooth muscle cells (SM). Stem cells (SC) can be found within the crypts of intestinal epithelium or within specialized compartments such as the bulge (B) in stratified epithelium. Additional structures such as blood vessels (BV) or sebaceous glands (SG) can be observed as well.
Figure 1.2 Schematic of conventional methods of blood processing. (Reproduced from Ref. [75] with permission of John Wiley and Sons.) Bulk separation of blood components by density gradient centrifugation (a); fluorescence-activated cell sorting (b); and magnetic activated cell sorting (c).
Figure 1.3 Schematic of different applications of cell isolation based on cell size. (a) Filtration methods typically include micropillars that act as sieves to trap cells. (b) Deterministic lateral displacement involves pillars placed in the channel that serves to deflect larger cells to the side of the channel while concentrating smaller cells in the center. (c) Inertial focusing is dependent on wall effect and shear gradient lifts that act to move larger cells to the center and smaller cells to the wall.
Figure 1.4 (a) Separation of bacterial cells from other cells using dielectrophoresis. The nonuniform electric field induces the bacterial cells to follow a different trajectory, thus enabling separation from other cells. (Reproduced from Ref. [108] with permission of The Royal Society of Chemistry.) (b) Separating particles of different sizes using surface acoustic waves. As particles such as cells flowing in the channel enters the path of the acoustic waves, the resultant force generated causes displacement of the particles. (Reproduced from Ref. [109] with permission of The Royal Society of Chemistry.) (c) Optical tweezers/traps: force fields are introduced by a single focused laser beam (left) or by two opposing laser beams on a dielectric object such as a cell. Color code: laser (red), dielectric object (blue), force field distribution (grid). (Reproduced from Ref. [110] with permission of The Royal Society of Chemistry.)
Chapter 2: Genome-Wide Analysis of Single Cells and the Role of Microfluidics
Figure 2.1 Schematic of techniques for single-cell isolation. (a) Fluorescence-activated cell sorting (FACS) instruments can be used to selectively sort and deposit individual cells into the wells of a multiwell plate based on fluorescence and scattering properties. (b) Laser capture microdissection (LCM) uses a microscope and laser to cut a thin membrane on which a tissue is deposited with single-cell resolution. A second laser then jettisons the cell into the cap of a microcentrifuge tube. (c) Mouth-pipetting can be used to suck an individual cell from a solution into a pipette under a microscope. (d) Limiting dilution can be used to load individual tubes or wells with either zero or one cells according to the Poisson distribution. Various microfluidic implementations of this also exist including microfabricated chambers and droplets. (e) There are now a variety of microfluidic devices that can capture cells introduced by fluid flow. A series of on-chip, microfluidic valves can be actuated to create individual, microscale reaction chambers for downstream processing of individual cells. (f) An infrared laser can be tightly focused with a microscope objective, allowing optical manipulation of individual cells by radiation pressure. So-called optical tweezers can be used to capture an individual cell from a group of cells in solution and transfer that cell to a different location or chamber in a microfluidic device.
Figure 2.2 Schematic of techniques for whole-genome amplification (WGA). (a) Primer extension preamplification (PEP) uses PCR with random 15-base primers to preamplify whole genomes from individual cells. (b) Multiple displacement amplification (MDA) uses random hexamer primers and isothermal, strand-displacement amplification with
φ29
DNA polymerase for WGA. MDA has been used for single-cell whole-genome sequencing from a variety of organisms. (c) Multiple annealing and looping based amplification cycles (MALBAC) uses adapter-linked random octamers and combines strand-displacement amplification by
Bst
DNA polymerase with conventional thermocycling to achieve uniform WGA. Adapters not only serve to facilitate downstream PCR amplification, but also to cause full-length amplicons to form loops, preventing the formation of chimeric amplicons in subsequent amplification cycles.
Figure 2.3 Schematic of techniques used for single-cell transcriptome analysis. (a) Reverse transcriptase quantitative PCR (RT-qPCR): mRNA is reversed transcribed using either an oligo(dT) primer, random hexamers, or both to synthesize cDNA. qPCR uses a TaqMan PCR mix, containing Taq DNA polymerase, gene-specific forward and reverse primers, and another oligonucleotide probe tagged with both a fluorophore and a quencher. The fluorophore is switched off when bound to the probe, due to quenching by the adjacent quencher, but fluoresces when cleaved off by DNA polymerase (which has 5′ to 3′ exonuclease activity). The increase in fluorescence is measured as the progress of the PCR reaction and quantifies the number of strand synthesized, which provides a quantitative measure of starting cDNA concentration. (b) Single-cell whole-transcriptome analysis by RNA-Seq as described by Tang
et al.
[37]: mRNA is reverse transcribed into cDNA using oligo(dT) anchored to a PCR primer (UP1). Following 3′-polyadenylation of first-strand cDNA, the second strand is synthesized with another PCR primer (UP2) with poly(dT) tail. cDNA then is amplified by PCR, fragmented, ligated with adapters, and the library is again amplified before sequencing. (c) STRT technique for high multiplex RNA-Seq [59]: mRNA is annealed to an oligo(dT) primer, followed by reverse transcription using a barcoded helper oligo with a GGG tail. Moloney murine leukemia virus-reverse transcriptase's (MMLV-RT) terminal transferase activity incorporates a CCC motif that hybridizes to the GGG tail of the helper oligo, and thus switches template and introduces a barcode and primer sequence into the cDNA. At this point, the cDNA includes adapters at both ends, allowing cDNA preamplification by PCR. Preamplified cDNA is then captured on a bead by the 5′-end, fragmented, ligated to an adapter, and amplified by PCR for sequencing. (d) Linear amplification approach by
in vitro
transcription (IVT) for RNA-Seq: mRNA is reverse transcribed using a cell-specific barcoded oligo(dT) primer, attached to part of the Illumina sequencing adapter and T7 promoter. After the RT step, samples from multiple individual cells are pooled and a second strand is synthesized for IVT reaction, where T7 RNA polymerase generates aRNA by linear amplification. aRNA is then fragmented, ligated with an adapter, reverse transcribed, and the resulting library is amplified and sequenced by paired-end sequencing. (e) Single-molecule RNA-FISH: After fixation and permeabilization of the cells, gene-specific oligonucleotide probes that are fluorescently labeled with a single fluorophore are hybridized to target RNA molecules inside the cell. Under a fluorescence microscope, each RNA molecule attached with fluorescent probes appears as a diffraction-limited spot, allowing direct quantification of RNA abundance.
Chapter 3: Cellular Immunophenotyping: Industrial Technologies and Emerging Tools
Figure 3.1 Differentiation of CD4
+
T cells and the change of immune function status. Typical samples for immunophenotyping analysis involve cells at varying levels of differentiation, maturity, as well as functional roles as indicated by the secretion of effector proteins. A T-cell sample sorted with surface markers (e.g., CD3/CD4/CCR7) still comprises a mixture of T cells at varying stages of the differentiation and with different combination of effector functions, highlighting the importance of functional phenotyping in addition to surface marker phenotyping in any particular sample of interest. The quality of a CD4
+
T-cell in immune protection or antiviral response is often correlated with the ability of this T cell to coproduce multiple effector proteins (polyfunctional cells). The terminally differentiated cells with a much narrower secretion spectrum are of lower quality and undergo apoptosis soon.
Figure 3.2 Typical process flow for flow cytometric cellular phenotyping. Blood or solid tissue samples are processed to yield single-cell suspension. The cells are stained with fluorescently labeled antibodies for surface marker immunophenotyping. T lymphocytes can be stained with fluorophore-tagged p/MHC (major histocompatability complex) tetramer to measure antigen specificity. The ability of activated immune cells to produce cytokines, chemokines, or growth factors can be assessed by blocking the secretion of these effector molecules followed by fixing, permeabilization, and intracellular staining. The obtained immunostained cell sample and an isotype control sample are examined by a flow cytometer, which allows cells pass through a microtube in single file and uses one or multiple lasers to probe the traveling cells one at a time. Forward scattering, side scattering, and fluorescence emission are detected per single cell and the results, often shown as pair-wise scatter plots, allow for visualization of different cell types and/or phenotypes. Forward and side scattering signals are correlated with the size and the shape irregularity, respectively, that give rise to phenotypic differentiation based upon cell morphology. Fluorescence signals from each cell permit accurate molecular phenotyping including surface markers, intracellular cytokines, and antigen specificity.
Figure 3.3 Example of problems associated with the standardization of flow cytometry. This particular example (data from Angelique Biancotto, National Heart, Lung, and Blood Institute, USA) shows the importance of commercial antibody choice in completing a flow cytometric analysis. For cell surface marker phenotyping, a CD83-specific antibody–APC conjugate was used to phenotype peripheral blood mononuclear cell (PBMC) cells. The two clones of the antibodies produced very different cytometric patterns as shown, despite the fact that the clones came from the same vendor. (Reproduced from Ref. [15] with permission from the Nature Publishing Group © 2012.)
Figure 3.4 Emerging microchip technologies for single-cell immune effector function phenotyping. (A) Three microchip technologies were reported recently and are now under commercialization development. (a) Microengraving permits comeasurement of ∼4 effector proteins secreted from single cells entrapped in nanowells [42]. (b) Single-cell barcode chip integrates microfluidic cell compartmentalization and a DNA-encoded antibody barcode array for co-detection of ∼12 immune effector proteins at the single-cell level [33]. (c) Combined spatial and spectral encoding allowed for ∼42-plex measurement of immune effector proteins secreted from single cells entrapped in an array of microchambers. (B) As the degree of multiplexing increases, it becomes challenging to analyze single-cell data and identify phenotypic diversity of a cell population based upon single-cell, high-dimensional proteomics data. As of now, several advanced data analysis tools have been deployed or developed to analyze complex phenotypic heterogeneity. Polyfunctional analysis pie charts simply displayed the frequency of cells coproducing one or multiple effector proteins at the same time. principal component analysis (PCA), Spanning Tree Progression of Density Normalized Events (SPADE), and viSNE incorporate the information from all dimensions and generate visualization in two-dimensional space to show phenotypic similarity or difference of the cells.
Chapter 4: Microsystem Assays for Studying the Interactions between Single Cells
Figure 4.1 Microwell arrays for monitoring immune-cell functions. (a) High-throughput single-cell analysis of CAR
+
T-cell cytolytic function using TIMING. Representative micrographs of single CAR
+
T cells killing and undergoing apoptosis when cocultured with NALM-6 tumor cells in nanowells. Scale bar, 50 µm. (b) Multikiller CD8
+
CAR
+
T cells (CAR8 cells) have faster killing kinetics in comparison to CD4
+
CAR
+
T cells (CAR4 cells). (c) CAR8 cells express higher levels of the cytotoxic protease Granzyme B. Flow-cytometric comparison of relative Granzyme B expression in CAR4 and CAR8 cells. Box and whisker plots, extremities indicate 99% confidence intervals. (d) Multikiller CAR
+
T cells undergo apoptosis at a lower frequency in comparison to single-killer CAR
+
T cells. (e) NK cells scan the target cell surface to promote killing. Time-lapse imaging showing the trajectory (white) of an IL-2-activated NK cell (black arrowhead) around the target cell before detachment. Scale bar: 10 µm. (f) IL-2 potentiates NK cell dynamicity and conjugation with tumor cells. Upper: Resting NK cell (red) approaching three tumor cells (green). Lower: Same as mentioned earlier but with IL-2-activated NK cells. Time points are given at the upper and lower sides of the Figure for upper and lower panels, respectively. IL-2-activated NK cell approaches the target cell sooner (
t
= 28 min), conjugates earlier (
t
= 38 min), and remains attached for a longer period (detach time
t
= 1 h 48 min).
Figure 4.2 Microwell arrays for detecting secreted biomolecules. (a) SCBC platform for pairwise cellular interactions. Left: SCBC device of 8700 chambers made of PDMS and glass layers. Right: Fluorescence image of pair of cells within an SCBC microchamber for center to center separation measurement. Three antibody array images showing central green alignment marker and peripheral red protein assay spots. (b) Schematic showing the operation of SCBC. Cells incubated on collagen-coated microchamber surface secrete proteins that get captured by specific antibodies on the barcode antibody array. Lysis buffer is introduced, relieving the SCBC, and the barcoded glass slide is developed using fluorophore-labeled secondary antibodies.
Figure 4.3 Dielectrophoresis (DEP)-based devices for promoting contact between homotypic or heterotypic cells. (a) DEP microfluidic device and cell-patterning process. A PDMS-based chip has network of channels and ITO electrodes (with a sharp electric field converging at electrode tips) that serve as points of cell capture points. Depicted is a patterned pair of HUVEC–HUVEC, HUVEC–A549, and a single HUVEC cell in the device after 4 h of culture. The A549 cell has been manually outlined. (b) Inhibition of VEGF signaling leads to synchronization of movement of cells in HUVEC–HUVEC pairs. Time-lapse images of HUVEC–HUVEC cell pairs showing oscillatory change in the distance between the cells. Scale bar: 50 µm. (c) Schematic depicting a DEP cage-based lab-on-chip platform for high-throughput manipulation and immunophenotyping of individual cells based on differential rosetting with microspheres functionalized with monoclonal antibodies (against an inhibitory NK-cell ligand HLA-G). (d) DEP array for functional NK-cell characterization. Two representative NK:target cell clusters are shown, each containing a single calcein-labeled 221 and 221. G1 cell, and four or five Natural killer-like (NKL) cells (first and second columns). Time course imaging showed that NK-sensitive 221 cell lyse 2 min after contact with NKL cells (calcein release assay), whereas NK-resistant 221.G1 cells were protected (calcein retention) for 20 min.
Figure 4.4 Hydrodynamic arrays for cell–cell pairing or fusion. (a) Three-dimensional schematic depicting the layout of a microfluidic cell-trapping device showing channel geometry, trapped cells, and cell flow. (b) Phase-contrast and fluorescent images showing intracellular dye diffuses between NIH3T3 fibroblasts. Only the depicted upper cell in both cell pairs was labeled with calcein AM dye. When the cells were not in direct membrane contact (left), no dye transfer occurred between the two trapped cells. When the cells were in direct membrane contact, however, transfer of the dye from the stained cells to the lower unstained cell happened within 16 h. (c) Three-step cell-loading protocol in a microfluidic device. In step 1, the cells were loaded “up” such that they get trapped at the back side of each capture cup (i). In the second step, when the direction of the flow was reversed, the cells from the back of capture cups were transferred to the larger front side of corresponding capture cups two rows below (ii). In a final step, a different type of cell was loaded onto the larger front side of the capture cups from the top such that they get trapped in front of the first cell type (iii). Scale bar: 50 µm. Overlay image of red and green fluorescent-labeled (Cell-Tracker stained) mouse 3T3 cells loaded into a 2 mm × 2 mm device (iv). Scale bar: 200 µm. (d) Time-scale of electrofusion of DsRed- and eGFP-expressing mouse 3T3s cells. Fluorescence exchange was seen between the cells immediately after the fusion pulse. Membrane reorganization began at time
t
= 10 min. Hybrid cells having contents of both cells were observed at time
t
= 20 min.
Figure 4.5 Optical methods to promote intercellular interaction. (a) Cell fusion between rat mesenchymal stem cells (rMSCs) and cardiomyocytes. Complete cell fusion was observed for DiO-labeled cardiomyocyte (green, upper, left) and DiI-labeled rMSC (red, upper, right) forming a double-nuclei, mixed fluorescent-labeled structure (middle, left) with membrane reorganization (middle, right, bright field). Partial cell fusion was observed by rMSC mitochondrial transfer (lower, left, and right). Labeled mitochondria (red) migrate across the membrane at contact area and accumulate near the cardiomyocyte nucleus. (b) Long-distance connections between rMSCs and cardiomyocytes in contact-preventive microwells. Two types of communication were observed, rMSC-origin tunneling nanotubes (determined by MSC surface marker, CD105) (upper, left, and right) and cardiomyocyte-origin filopodium-like structures (determined by cardiac marker, α-actinin) (middle, left, and right). Mitochondrial transfer from rMSC to its contacting cardiomyocyte can happen either through the nanotube (lower, left) or through the filopodia and accumulate around the cardiomyocyte's nucleus (lower, right) (white arrows point to the nanotubes/filopodia). (c) Optoelectronic tweezers (OET): Schematic of an OET device. Virtual electrodes are created when light pattern is focused on photoconductive (amorphous silicon, a-Si:H) layer. (d) Frames (i–iii): Single T cells (orange) and dendritic cells (green) trapped, fluorescently identified, and manipulated by a λ
max
of 450 nm micro-LED array (projected pixel size is 25 µm). Frames (iv–vi) show a T cell and a dendritic cell coming into contact and interruption of cell contact. Applied voltage 20 Vp-p, 30 kHz. Scale bar is 25 µm. (Reproduced with permission from Refs. [38] (a, b) and [89] (c,d).)
Figure 4.6 Magnetic methods for studying cell–cell interaction. (a) Invasion of
Toxoplasma gondii
(
T. gondii
) on human foreskin fibroblast (HFF) cells inclined on magnetic microflaps. Schematic image of microflaps for inclining HFF cells infected by
T. gondii
– vertical view (upper). Lower view is observed by inclining the microflaps using a magnetic field. (b) Time lapse images of
T. gondii
into HFF cells.
Figure 4.7 Acoustic methods to promote intercellular interaction. (a) Simulation of acoustic potential distribution (i–iv): acoustic wells are highly tunable in size and shape using different acoustic magnitudes. HeLa cell assemblies can be linear (v–viii) using linear-shaped acoustic wells or single layer (ix–xi) and spherical (xii) using spherically shaped acoustic wells. Scale: 50 µm. (b) Surface-acoustic wave (SAW) device-based experimental setup (b). Surface-acoustic wave (SAW) device-based experimental setup (i). Dye transfer between HEK 293 T linear cell assemblies over time (ii–v). Scale: 50 µm.
Chapter 5: Modeling Microvascular Disease
Figure 5.1 Vasculature. Schematic diagram of the vasculature illustrating microvascular capillary connections between an artery and vein.
Figure 5.2 Flow chamber. Schematic diagram of a parallel plate-flow chamber [20]. One side of the chamber is a glass slide covered with an artificial bilayer of cells, the other side is a machined polycarbonate base. A silastic rubber gasket separates the two surfaces creating a channel when vacuum is applied.
Figure 5.3 Schematic representation of photolithography. (a) SU-8 is spin-coated and prebaked on a bare wafer. (b) With a transparency photomask (black), UV light is exposed on the SU-8. (c) Exposed SU-8 is then baked after exposure and developed to define channel patterns. (d) PDMS mixed solution is poured on the wafer and cured. (e) Cured PDMS is then peeled from the wafer. (f) The device is trimmed, punched, and autoclaved ready for assembly [32].
Figure 5.4 Hamster cremaster microvascular model. (a) Fluorescent images of an intact hamster cremaster, (b) GIS-digitized map, (c) a PDMS synthetic microfluidic constructed from digitized map, and (d) final assembled device [44]. With permission from Springer © 2009, Springer Science+Business Media, LLC.
Figure 5.5 VEGF gradient microfluidic. (A) Schematic and (B) photograph of the device. Cells were seeded in the middle channel and the side channels used for generation of VEGF gradient. (C) Top–down and cross-sectional confocal images of endothelialized devices before (a,b) and after (c,d) exposure to VEGF gradient (CD31: green, actin cytoskeleton: red, nuclei: blue) [45].
Figure 5.6 Microvascular microfluidic model. (a) An assembled PDMS microdevice. (b) Software-generated image for the photolithography mask. (c) Bright-field images. (d) confocal microscopy of confluent HUVECs (human umbilical vein endothelial cells) in the microdevice (scale bars: 30 µm, cell membrane: red, nuclear: blue) [47].
Figure 5.7 Microfluidic characterization. (a) Bright-field and confocal microscopy of endothelialized microfluidic device with viable cells producing NO (green: DAF-2DA). (b) Confocal microscopy of VE-cadherin staining at endothelial cell junctions. (c) Computational fluid dynamic modeling of centerline flow velocities and viscosities [47].
Figure 5.8 BioFlux device. A microfluidic device coupled to an SBS-standard 48-well plate (viewed from the bottom). Each fluidic channel has a unique input (top wells) and output well (bottom wells) [66].
Figure 5.9 Braille display microfluidic. (a) Two PDMS layers, the top one containing channels and the bottom one separating the device from the Braille display. (b) Diagram of flow generated by the valves and pumps. (c) Schematic of the assembled experimental device [67].
Chapter 6: Nanotechnologies for the Bioelectronic Interface
Figure 6.1 Equivalent circuit model of the bioelectronic interface. The measured potential (
V
out
) primarily depends upon the access resistance (
R
a
), the seal resistance (
R
s
), and the resistance and capacitance of the patch of membrane and the electrode junction (
R
j
and
C
j
, respectively). The membrane resistance (
R
m
) and capacitance (
C
m
) determine the membrane time constant. In the absence of an applied current the membrane potential (
V
m
) is equal to the resting potential (
V
rest
). Using this equivalent circuit model we can calculate the electronic coupling strength (Δ
V
out
/Δ
V
m
) and plot this value as a function of
R
s
and
R
a
as shown in Figure 6.2.
Figure 6.2 Electronic coupling regimes. Calculating the voltage during a simulated action potential using the equivalent circuit model in Figure 6.1 allows us to plot the coupling strength (Δ
V
out
/Δ
V
m
) as a function of
R
a
and
R
s
. This plot reveals extra-, intra-, and in-cell regimes divided roughly by the dashed lines in the upper Figure The input action potential waveform is shown on the bottom left (green) while typical In-cell and Intra-cell recorded waveforms are shown at the bottom right (* and **, respectively). These voltage waveforms correspond to * and ** labeled regions in the upper plot.
Figure 6.3 Nanofabricated extra-cellular electrodes. (a) A silicon shank containing dozens of electrodes over a 1.3 mm length can record from many depths when inserted into neural tissue [20]. © J. Du, with permission. (b) Flexible electrodes and multiplexers imbedded in PDMS can conform to the surface of the brain for high-density ECoG [21]. © Nature Publishing Group (2011), with permission. (c) Free-floating electrodes may be addressable within neural tissue using ultrasound as a route for tether-less “neural dust.” Adapted from Ref. [22], © Seo. Scale bars: (a) 50 µm and (b) 1 mm.
Figure 6.4 Nanofabricated intra-cellular electrodes. (a) A kinked NW FET can be inserted into an HL-1 cell to record V
m
. Adapted from Ref. [54]. (b) Arrays of vertical NW electrodes can penetrate the cell membrane of primary neurons to both record and stimulate action potentials in cells grown directly on the silicon substrate [10]. © J. Robinson. (c) Arrays for vertical NW electrodes can also record action potentials from inside cardiac cells grown on the NW substrate. Adapted from Ref. [57], NCBI. Scale bars: (a) 5 µm, (b) 1 µm, and (c) 5 µm.
Figure 6.5 In-cell electrodes. (a) Schematic of the in-cell recording configuration. Adapted from Ref. [64], with permission from the Nature Publishing Group 2010. (b) Scanning electron micrograph of rat neurons cultured on top of a gold mushroom electrode array. Adapted from Ref. [65], NCBI. (c) Scanning electron micrograph of the profile of a gold mushroom electrode. (d) Transmission electron micrograph of interface between an
Aplysia
neuron and the gold mushroom electrode. Panels (c) and (d) Adapted from Ref. [9], NCBI. Scale bars: (b) 12 µm, (c) 0.5 µm, and (d) 0.5 µm.
Chapter 7: Intracellular Delivery of Biomolecules by Mechanical Deformation
Figure 7.1 Delivery mechanism and system design [37]. (a) Illustration of delivery hypothesis whereby the rapid deformation of a cell, as it passes through a microfluidic constriction, generates transient membrane holes. An electron micrograph of current parallel channel design with blue cells as an illustration. (b) Image of a finished device consisting of Pyrex bound to silicon for sealing. Scale bar at 2 mm. (c) Illustration of the delivery procedure where cells and delivery material are mixed in the inlet reservoir, run through the chip, and collected in the outlet reservoir. The mounting system consists of stainless steel and aluminum parts interfaced to the chip by inert O-rings. Scale bar is 20 mm. With permission from National Academy of Sciences, © 2013.
Figure 7.2 Schematic of the pressure system used to interface with the devices.
Figure 7.3 Delivery performance depends on cell speed and constriction design [37]. Constriction dimensions are denoted by numbers (e.g., 10-6 µm × 5) such that the first number corresponds to constriction length, the second to constriction width, and the third (if present) to the number of constrictions in series per channel. (a) Delivery efficiency and (b) cell viability 18 h posttreatment as a function of cell speed for three device designs. Delivery efficiencies and viabilities were measured by flow cytometry after propidium iodide staining. All data points were run in triplicate, and error bars represent two standard deviations.
Figure 7.4 Multiple delivery cycles. Delivery efficiency (3 kDa dextran) and viability of HeLa cells in response to multiple treatment cycles (within ∼1 min of each other) through a 10-6 µm device. Note that results from multiple delivery cycles are not analogous to treatment by a single chip containing the equivalent number of constrictions in series (Figure 7.3).
Figure 7.5 Diffusive delivery mechanism [37]. (a) Scans of different horizontal planes of a HeLa cell after the delivery of Cascade Blue–conjugated 3 kDa dextran, as measured by confocal microscopy. Note that 3 kDa dextran is small enough to enter the nuclear envelope [41]. Scans read from top to bottom, then left to right where the top left is at
z
= 6.98 µm and bottom right is at
z
= −6.7 µm. Scale bar represents 6 µm. (b) Live cell delivery efficiency of four different device designs. The time axis indicates the amount of time elapsed from initial treatment of cells before they were exposed to the target delivery solution. All results were measured by flow cytometry 18 h posttreatment. (c) Average intensity of the delivered cell population normalized by untreated cells to control for autofluorescence. Fluorescein-conjugated 70 kDa dextran and Cascade Blue–conjugated 3 kDa dextran are delivered to the cell (cycles 1 and 3) and removed from the cell (cycle 2) in consecutive treatment cycles. The control represents cells that were only exposed to the delivery solution and not treated by the device. (d) Gene knockdown, as a function of device type and cell speed, in live destabilized GFP (Green fluorescent protein) expressing HeLa cells 18 h after the delivery of anti-eGFP (Enhanced green fluorescent protein) siRNA at a delivery concentration of 5 μM. Lipofectamine 2000 was used as a positive control and scrambled controls were run at 500 mm s
−1
on a 10-6 µm × 5. All data points were run in triplicate and error bars represent two standard deviations. With permission from National Academy of Sciences, © 2013.
Figure 7.6 Additional validation of mechanism [37]. (a) Effect of device width on delivery efficiency. Flow cytometry data of the delivery efficiency of 10 kDa fluorescein-labeled dextran, delivered at 150 mm s
−1
, using 10-6 µm, 30-6 µm, or 30-8 µm devices. (b) Delivery performance at 20 °C versus 4 °C. Cascade Blue–labeled 3 kDa dextran and fluorescein-labeled 70 kDa dextran were delivered to HeLa cells at 4 °C or at room temperature (20 °C). For the 4 °C condition, cells and the device were kept on a cold plate (set at 4 °C) for 5 min before delivery, the delivery procedure was conducted on the plate, and the collected cells were subsequently incubated for 5 min on ice before being seeded onto a cell culture plate and incubated at 37 °C. The room temperature samples were kept at room temperature for all steps of the procedure before being seeded onto a cell culture plate and incubated at 37 °C. (c) Knockdown specificity. Gene knockdown due to anti-eGFP siRNA and scrambled controls delivered to HeLa cells expressing destabilized GFP. The 10-6 µm × 5 device was operated at a cell speed of 500 mm s
−1
, Lipofectamine 2000 was used as a positive control, and the results were measured at 18 h posttreatment. Control cells were only exposed to the delivery solution but not treated by the device. The ∼30% knock-up in gene expression in the Lipofectamine 2000 scrambled control is an artifact that we have observed in studies involving destabilized-GFP expressing HeLa cells. Rounded cell morphology due to treatment by Lipofectamine 2000 would indicate that treatment with these particles is causing significant amounts of cell stress, which could contribute to the observed GFP upregulation. Although this problem could potentially be removed with further optimization of Lipofectamine treatments, it is likely a consequence of using a toxic agent [43] in the sensitive destabilized-GFP assay. Experiments with eGFP expressing HeLa cells showed consistent knockdown after treatment by the device and had less pronounced artifacts (∼10% knock-up) in Lipofectamine 2000 controls. With permission from National Academy of Sciences, © 2013.
Figure 7.7 Dosage response. Increasing buffer concentrations of 70 kDa dextran yield higher delivery without causing a change in the baseline endocytosis rate (0 psi). Note that one of the data points at 0 psi corresponds to the untreated control.
Figure 7.8 Simulation of diffusive delivery. (a) Simulation results indicating the percentage of material delivered/lost from the cell as a function of membrane diffusivity when the material of interest is in the buffer (□) or in the cell (○) at the time of poration. (b) Graphical representation of the simulated system and the concentration gradient that forms across the membrane if material is delivered from the buffer (red area) to the cell (blue area).
Figure 7.9 SEM images of fixed cells at different time points posttreatment. HeLa cells were fixed with a glutaraldehyde solution after treatment by the device in accordance with previously reported methods [46]. The fixation involved addition of 100 µl of an aqueous solution of glutaraldehyde (initial concentration: 25% v/v) to 500 µl of the cell suspension 5 and 10 s after initiation of the ultrasound exposure (which lasted a total of 10 s). The cells were washed with PBS, resuspended in 2 ml of glutaraldehyde solution (5% v/v), and kept for 10 min at room temperature in a microcentrifuge tube. The cells were then washed successively in ethanol solutions containing, respectively, 30%, 50%, 70%, 95%, 100%, and 100% v/v of alcohol in water. Cells were kept in each solution for about 20 min, and the ethanol solution was removed by mild centrifugation for 5 s. Finally, 15 µl of the cell suspension was mounted on metal grids and left at room temperature overnight before gold coating by the imaging core staff. The contrast in morphology between treated and untreated cells was observed consistently across at least two independent experiments.
Figure 7.10 TEM images of cells fixed <1 s after treatment with gold nanoparticles. Left: Arrows indicate possible membrane disruptions ∼50 nm in diameter. Right: Brackets indicate possible membrane disruption ∼500 nm in diameter. These images were obtained as part of the experiments involving gold nanoparticle delivery (Figure 7.14). Images (a) and (b) are from one experimental set and (c) and (d) from a second independent one. The scale bars are at 100 nm (c–d) and 500 nm (a–b). To prepare cells for imaging, cells were fixed in 2.5% (w/v) glutaraldehyde, 3% (w/v) paraformaldehyde, and 5.0% (w/v) sucrose in 0.1 M sodium cacodylate buffer (pH 7.4). After an overnight fixation, the cells were postfixed in 1% (w/v) OsO
4
in veronal-acetate buffer for 1 h. They were then stained en bloc overnight with 0.5% uranyl acetate in veronal-acetate buffer (pH 6.0), dehydrated, and embedded in Spurr's resin. Sections were cut on a Reichert Ultracut E (Leica) at a thickness of 70 nm with a diamond knife. Sections were examined with an EM410 electron microscope (Phillips).
Figure 7.11 Applicability across cell types [37]. (a) Delivery efficiency and viability of NuFF cells treated with a 30-6μm device to deliver 3 and 70 kDa dextran. (b) Delivery efficiency and viability of spleen isolated, murine dendritic cells treated with a 10-4 µm device to deliver 3 and 70 kDa dextran. (c) Delivery efficiency and viability of murine embryonic stem cells treated with a 10-6 µm device to deliver 3 and 70 kDa dextran. (d) Delivery efficiency of 3 kDa and (e) 70 kDa dextran to B cells (CD19
+
), T cells (TCR-β
+
), and macrophages (CD11b
+
) isolated from whole mouse blood by centrifugation and treated by 30-5 µm and 30-5μm × 5 devices at 1000 mm s
−1
. Three kilodalton and 70 kDa dextran were labeled with Cascade Blue and fluorescein, respectively. All data points were run in triplicate, and error bars represent two standard deviations. With permission from National Academy of Sciences, © 2013.
Figure 7.12 Morphology and gene expression of mESCs following treatment by rapid mechanical deformation. (a) Images of control cells that did not flow though the device. (b) Cells that were treated by a 30-6 µm device once, (c) twice, or (d) three times in succession. (e) PCR (polymerase chain reaction) gene expression profile of POU5 and ALP (Alkaline phosphatase) for cells treated by rapid mechanical deformation. Typically, these genes would be downregulated significantly (usually >5×) in differentiating/differentiated cells [48]. Data are normalized by the expression levels of untreated cells. Single mESCs were able to form colonies within 24 h after treatment and exhibited normal undifferentiated mESC colony structure throughout a 2-week follow-up. Scale bar at 200 µm. With permission from Elsevier ©2006.
Figure 7.13 Fluorescence intensity histograms for immune cells treated by a 30-5 µm × 5 or 30-5 µm device to deliver Cascade Blue–labeled 3 kDa dextran and fluorescein-labeled 70 kDa dextran. These histograms correspond to the data presented in Figure 7.11.
Figure 7.14 Nanomaterial and antibody delivery [37]. (a,b) TEM images of gold nanoparticles (some indicated by arrows) in cells fixed ∼1 s after treatment by a 10-6 µm × 5 device. Scale bars at 500 nm. (c) Delivery efficiency and viability of HeLa cells treated with a 10-6 µm × 5 device to deliver Cascade Blue–labeled 3 kDa dextran and Cy5 labeled, DNA wrapped, carbon nanotubes. (d) Bright-field cell images overlaid with Raman scattering in the G-band (red) to indicate delivery of carbon nanotubes in treated cells (left) versus endocytosis (right). Scale bars at 2 µm. (e) Fluorescent micrograph of a HeLa cell 18 h after delivery of Cascade Blue–labeled 3 kDa dextran (middle panel) and antibodies to tubulin with an Alexa Fluor 488 tag (right panel). Scale bars at 3 µm. (f) Delivery efficiency and viability of HeLa cells treated with a 10-6 µm × 5 device, at 500 mm s
−1
, to deliver Alexa Fluor 488-labeled anti-tubulin antibodies. Delivery efficiency at different antibody concentrations is compared with an endocytosis control at 100 µg ml
−1
and untreated cells. With permission from Elsevier ©2006.
Figure 7.15 Reprogramming efficiency and colony pluripotency. (a) The average number of colonies present in a sample plate after the delivery cycles have been completed. Averages and standard deviations were calculated based on data from two independent experiments, each run in duplicate. (b) Alkaline phosphatase (AP) staining of a colony transformed by the device [48]. (c) Induction of differentiation was achieved by growing the iPS-like cells in suspension as previously described [52]. Briefly, iPS-like colonies were enzymatically dissociated form feeder layers and transferred to low adhesion Petri dishes. Subsequently, the resulting embryoid bodies (EBs) were incubated in EB media (KO-DMEM (Knockout Dulbecco Modified Eagle Medium) (Gibco) with 20% knockout serum (Gibco), 1 mM l-glutamine (Gibco), 0.1 mM β-mercaptoethanol, and 1% NEAA (Non-Essential Amino Acid) (Gibco)) at 37 °C. After 8 days, EBs were seeded on gelatin-covered chamber slides (Lab-Tek) and incubated for additional 10 days in EB media. IPS-like generation and cell differentiation were assessed by fluorescent immunohistochemical staining according to suggested protocols for cell reprogramming (Stemgent). All cells were fixed with 4% paraformaldehyde, permeabilized with 0.1% Triton-X, and visualized with a Zeiss Axiovert 200 fluorescent microscope after staining. IPS-like EBs were incubated with primary antibodies recognizing early human mesoderm marker brachyury (1 : 50; Santa Cruz Biotechnology), definitive endodermal marker Sox17 (1 : 50; R&D Systems), and Pax6 (1 : 100; Abcam) as markers for neuroectodermal cells. Nuclei were stained with 4′,6-diamidino-2-phenylindole (DAPI). Bound primary antibodies were detected by FITC (Fluorescein isothiocyanate)-labeled secondary antibody SC-2010 IgG-FITC (1 : 200; Santa Cruz Biotechnology). With permission from Elsevier © 2006.
Figure 7.16 Live cell confocal microscopy images demonstrating cytoplasmic staining and chemical accessibility of QD (Quantum dots) surface [38]. Images of treated cells (a) and control cells (b). Quantum dots that are successfully delivered to the cytosol undergo a reduction reaction that changes their fluorescent properties. Quantum dots that are endocytosed cannot undergo the aforementioned reaction as the endosome environment is oxidizing. This cytosol-specific reaction design enables one to confirm cytosolic delivery of quantum dots. Scale bar is 10 µm. With permission from the American Chemical Society © 2012.
Figure 7.17 Single-molecule detection. Individual quantum dots are detected in the cell cytoplasm of HeLa cells treated in the presence of (a) 10 nM or (b) 100 nM quantum dot concentrations. The presence of individual dots (e.g., particles A, B, and C) is confirmed by temporal variation in their emission signals. Scale bar is 10 µm. With permission from the American Chemical Society © 2012.
Figure 7.18 Defining delivery. (a) Definition of constriction dimensions illustrated in an SEM image. (b–c) Definition of delivery efficiency. (b) Sample fluorescence intensity histogram (from flow cytometry) of a control/endocytosis HeLa cell population that is exposed to Cascade Blue–conjugated 3 kDa dextran and (c) one that is treated by a 30-6 µm device. (d) Treatment of HeLa cells by the device alone (i.e., in the absence of dyes) did not lead to a significant change in fluorescence relative to the endocytosis case; hence the endocytosis control was selected for gating purposes. We chose the 10-6 µm × 5 devices since these generally yielded the highest delivery efficiencies in HeLa cells.
Figure 7.19 Autofluorescence in immune cells. Autofluorescence histograms for cells treated by a 30-5 µm × 5 device in the absence of Pacific Blue or fluorescein dyes. These data demonstrate that there is no significant change in autofluorescence in these channels due to device treatment. Note that the macrophages were stained with an FITC-tagged antibody to CD11b.
Chapter 8: Microfluidics for Studying Pharmacodynamics of Antibiotics
Figure 8.1 Examples of devices with enhanced detection sensitivity of bacteria based on (a) surface plasmon resonance or SPR [45], © Mohan, (b) electrochemical sandwich immunoassay [52], © IEEE, (c) magnetic bead rotation [47], with permission © 2012 WILEY-VCH, and (d) pH changes induced by antibiotic–bacteria interaction [53], with permission © 2013, American Chemical Society.
Figure 8.2 Examples of microfluidic devices for performing AST. (a) Plug-based microfluidic device [57], with permission © 2008, Royal Society of Chemistry. (b) Droplet-based microfluidic platform [58], with permission © 2012, Royal Society of Chemistry. (c) Microwell array [59], with permission © 2011, Elsevier. (d) Agarose microchannel network [60], with permission © 2013, Royal Society of Chemistry. (e) Integrated microfluidic platform [8], with permission © 2013 Elsevier. (f) Point-of-care microfluidic platform. [61], © 2012 Creative Commons Attribution.
Figure 8.3 Examples of time–kill curves for different antibiotic–bacteria combinations obtained using microfluidic approaches. (a) [47], with permission © 2012 WILEY-VCH.(b) [60], with permission © 2013, Royal Society of Chemistry.(c) [67], with permission © 2014, Royal Society of Chemistry.(d) [8], with permission © 2013 Elsevier.
Figure 8.4 Illustration of the various steps to predict cell numbers
in vivo
with a prescribed antibiotic dosing regimen: (a) Pretesting setup, including fabrication of microfluidic platforms and sample preparation; (b) antibiotic susceptibility testing, entailing derivation of time–kill curves; (c) pharmacodynamic modeling to determine parameters
in vitro
; and (d) pharmacokinetic modeling to predict
in vivo
action of the antibiotics on bacterial cell numbers over time.
Figure 8.5 (a) Ln(Cell number) or ln(
B
) versus time (
t
) to determine
G
from Eq. (8.6) for
E. coli
in the absence of antibiotics. (b) Ln(Cell number) or ln(
B
) versus time (
t
) (Eq. (8.7)) for
E. coli
at different concentrations of amikacin.
Figure 8.6 (a) Determination of Hill coefficient (
γ
) and MIC using Eq. (8.5) [85]. (b) Net growth rate of
E. coli
as a function of amikacin concentration, that is, the Hill curve.
Figure 8.7 Ln(Cell number) or ln(
B
) versus time (
t
) to determine
G
from Eq. (8.6) for the growth curves of
E. coli
. and
P. aeruginosa
in cocultures.
Figure 8.8 Ln(cell number) or ln(
B
) versus time (
t
) or time–kill curves (Eq. (8.7)) for
E. coli
and
P. aeruginosa
in cocultures at different antibiotic concentrations of amikacin.
Figure 8.9 Determination of Hill coefficient (
γ
) and MIC using Eq. (8.5) [85] for amikacin against
E. coli
and
P. aeruginosa
polymicrobial cultures.
Figure 8.10 Net growth rate of
E. coli
and
P. aeruginosa
as a function of amikacin concentration, that is, the Hill curves.
Figure 8.11 (a) Pharmacokinetic modeling for monomicrobial cultures of
P. aeruginosa
to predict the antibiotic concentrations of amikacin and tobramycin in the blood stream over the course of 24 h with antibiotic doses administered every 8 h. The
y
-axis represents antibiotic concentration values in terms of MIC, where 1 implies
C
t
= MIC. (b) Pharmacokinetic modeling to predict the net growth rate of
P. aeruginosa
in the blood stream over the course of 24 h with antibiotic doses administered every 8 h.
Figure 8.12 Predicted
P. aeruginosa
cell numbers
in vivo
over the course of 24 h with antibiotic doses (amikacin and tobramycin) administered every 8 h.
Chapter 9: Microsystems Models of Pathophysiology
Figure 9.1 Microfluidic models of thrombosis. (a) Multiple parallel microfluidic flow channels overlay a stripe of collagen produced by micropatterning. Platelets from whole blood flowing over the collagen will aggregate generating a focal thrombosis.
Figure 9.2 Microfluidic models of sickle cell vaso-occlusion. (a) Sickle cell pathology is due to vaso-occlusions, which occur when HbS polymerizes under deoxygenation. The HbS polymer stiffens the RBCs, causing them to occlude the microcirculation. (b) A microfluidic device to reproduce vaso-occlusion in whole blood
ex vivo
. The devices comprise a branching microfluidic channel that resembles the vasculature
in vivo
and a gas channel that diffusively changes the oxygen concentration in the blood. (c) Phase space of vaso-occlusion based on oxygen concentration, pressure, and vessel diameter shown as a 3D surface plot a several cross-sectional plots. ((a–c) Reproduced from Ref. [18] with permission from the National Academy of Science.) (d) A microfluidic device with only a single small capillary for blood flow diffusively coupled to a gas reservoir for controlling blood oxygen. Vaso-occlusions in this device are observed under constant pressure drop to mimic conditions
in vivo
. (e) Rate of conductance change after deoxygenation measured in device from (d) for two groups of patients. The “benign” group received no treatment or hospitalization within 12 months before measurement or 4 months after measurement. Measurement separates two groups with
p
< 0.01. (f) Receiver operating characteristic (solid line) for measurement (e) with area under the curve (AUC) = 0.85 compared with random assignment (dashed line). ((d–f) Adapted from Ref. [19], Copyright 2012, with permission from the American Association for the Advancement of Science.)
Figure 9.6 Schematic diagram of a standard Campenot chamber and image of neurons in a PDMS microfluidic-based Campenot chamber. (a) Top-down view of chamber with cell bodies in the center and axons spreading to the outer chambers via scratches in the bottom surface or through vacuum grease. (b) Cross-sectional view of Campenot chamber with neurons seeded within the device as described in (a). (c) Alternative cell positioning, here axons are seeded within the middle of the chamber so that they may be exposed treatment separate from cell bodies. ((a–c) Reproduced from Ref. [65], Copyright 2005, with permission from Nature Publishing Group.) (d) Fluorescent image of a compartmentalized microfluidic device in which two chambers are connected with microgrooves of 7.5 µm × 3 µm × 900 µm. Neurons, from rat hippocampus, on the left produce green fluorescent protein (GFP) and neurons on the right red fluorescent protein (RFP). This system allows for the investigation and manipulation of synapses between neurons.
Figure 9.3 Atherosclerosis. (a) Disturbed flow is generated in arteries with large curvature or points of bifurcation. In these regions, endothelium experience aberrantly low shear stresses, leading to pathological phenotypes including development of atherosclerotic plaques. (Reproduced from Ref. [40], Copyright 2009, with permission from the Nature Publishing Group.) (b) Endothelial shape measured in a microfluidic device that recapitulates disturbed flow conditions seen
in vivo
. (Reproduced from Ref. [41], Copyright 2011, with permission from the American Institute of Physics.)
Figure 9.4 Microfluidic model of vasculogenesis and angiogenesis. (a) Image of the device. (b) Schematic of the device. The outside chambers (green) are solid gel matrices seeded with lung fibroblasts. The inner chambers (purple) are perfused with liquid. The central chamber (blue) contains a fibrin gel matrix. (c) Endothelial cells are seeded in the fibrin gel, and interstitial flow through the gel induces vasculogenesis (d). Alternatively, endothelial cells can be seeded along the periphery of the fibrin gel (e), generating angiogenic sprouting (f). In both (d) and (f) complete perfusable vessels can be formed, allowing media, cells, and particles to flow between the perfusion chambers.
Figure 9.5 Lung-on-a-chip. (a) Device design. Device design supports epithelial culture and endothelial culture on opposite sides of an ECM-coated porous membrane. This membrane is sandwiched between two halves of a microfluidic channel. Adjacent to this channel are side channels to which a vacuum is applied to mimic the mechanical stretching associated with breathing. (b) Mechanical stretching in the lungs. Mechanical stretching of the capillary–alveolar barrier is due to a reduction in intrapleural pressure (
P
ip
)
induce
by diaphragm stretching. (c) Schematic of device fabrication. Each half of a PDMS device composed of thee adjacent microchannels is bonded to a 10-µm-thick porous PDMS membrane containing an array of 10 µm pores. Scale bar, 200 µm. (d) Schematic of device fabrication. Side channels of device are etched to remove the PDMS membrane; resulting in two large side chambers used as vacuum channels for mechanical stretching. Scale bar, 200 µm. (e) Top-down images of finished lung-on-chip microfluidic device.
Figure 9.7 Kidney-on-a-chip. Schematic of a nephron-on-a-chip in which a nephron is recapitulated by sandwiching membranes within multiple layers of the device. The device contains a glomerulus (G), tubule (T), loop of Henle (L), and connecting channels. In each component of the chip, the appropriate cell type was cultured and the connector (C) allows G, T, L as well as blood and waste streams to interact with each other. These features allow the nephron-on-a-chip to replicate many functions of the nephron including filtration and reabsorption of physiologically relevant solutes such as albumin, urea, and salts.
Figure 9.8 Microscale tumor models. (a) A microfluidic platform to generate microscale tumor spheroids using a microfluidic flow focusing junction. Tumor cells can be coencapsulated in a hydrogel matrix with stromal cells such as cancer-associated fibroblasts and ECM. The microscale spheroids can be analyzed and sorted using a large particle flow sorter. They can also be cultured with growth factors and small molecules and then analyzed at multiple time points. (b) Tumor spheroids produced using the device in (a) imaged using phase and epifluorescence. Cells are GFP-labeled from mouse non-small cell lung adenocarcinoma. Spheroids have been sorted according to numbers of encapsulated cells. Scale bars are 50 µm. (c) Growth of tumor cells in spheroids that incorporate specific ECM molecules at 20 ng ml
−1
. Fibronectin promotes growth relative to blank while collagen I inhibits growth (
p
< 0.01).
Figure 9.9 Metastasis. Some tumor cells may leave the primary tumor and invade the local matrix, eventually crossing the endothelial barrier and entering the vasculature (intravasation). Some of these cells have acquired a program to survive in circulation, and they travel around the body until they eventually arrest in a distant site. At the new site, these cells may migrate through the endothelial barrier into the underlying tissue (extravasation). If these cells survive and proliferate, they can colonize the new tissue, which can be fatal in critical organs such as the bone, liver, lung, and brain.
Figure 9.10 Microfluidic models of metastasis. (a) Schematic demonstrating the application of the capillary burst valve to create a device with multiple perfusion and gel matrix chambers side-by-side. Each individual gel is introduced as liquid and polymerized before the next gel is loaded. Finally, the outer chambers can be perfused with liquid. Reproduced from Ref. [96], Copyright 2009, with permission from the Royal Society of Chemistry. (b) A device for studying tumor cell intravasation. Device uses trapezoidal posts to separate gel and perfusion chambers. Microscope image shows endothelial cells (green) separating perfusion channel and gel matrix with tumor cells (red). Scale bar in drawing is 2 mm; scale bar in image is 300 µm. Reproduced from Ref. [97] with permission from the National Academy of Science. (c) A device for studying tumor cell extravasation within self-organized vascular networks. Vasculogenesis is induced in endothelial cells within a fibrin network and supported by signals from lung fibroblasts (NHLF). Fluorescent image shows breast cancer cells (green MDA-MB-231) inside of a vascular network (red) within the device. Scale bar is 30 µm. The percentage of tumor cells that have exited the vasculature is shown for multiple time points. Adapted from Ref. [98], Copyright 2013, with permission from the Royal Society of Chemistry.
Chapter 10: Microfluidic Systems for Whole-Animal Screening with C. elegans
Figure 10.1
C. elegans
as a model for studying neurodegenerative disease. (a)
C. elegans
has a nearly fully characterized genome and anatomy, and it is optically transparent. (b) Fluorescently tagged proteins aggregate under specific genetic conditions in a Huntington disease model.
Figure 10.2 COPAS Biosort system as the current state of the art for automated
C. elegans
–screening systems. (a) The individual
C. elegans
worms (orange) are directed into the flow cell where optical density, body length, and overall multichannel fluorescence can be characterized to sort the animals in real time. (Adapted from Ref. [51], copyright Springer Publishing Company, with permission.) (b) Line-scanning optical data of integrated fluorescence along the anterior–posterior body axis can be obtained. (c) A condensed view of line scanning data of a given population sorted by life stage.
Figure 10.3 Microfluidic circuit model. A basic fluidic circuit model of a microfluidic system with flow driven by a constant pressure source. The fluidic resistances of the tubing before and after the chip are represented by
R
tubing
and the overall fluidic resistance of the microfluidic chip is represented by
R
chip
. A fluid source under constant gauge pressure (known) is fed to the chip from tubing and can then exit the chip through additional tubing to a lower pressure point (usually to atmospheric pressure).
Figure 10.4 Microfluidic cell culture platform with an interface to multiwell plates. (a) The system includes a pneumatic regulation system (black box) coupled to a gasket that sits on top of the well plate format microfluidic chip interfacing with a commercially available 96-well plate (blue plate). (b) Schematic of the microchannels that interface with the well plate reservoirs. (c) The actual device with microchannels loaded with food coloring dye.
Figure 10.5 A binary microfluidic valve multiplexing. Here “
n
” samples are regulated by 2 × log
2
(
n
) control valves. (a) The orange sample is delivered to the common outlet by opening and closing a precise set of control valves. (b) The yellow sample can be subsequently delivered by switching on/off positions between just two valves.
Figure 10.6 Examples of single trap microfluidic devices for serially processing
C. elegans.
(a) A single-layer device with a tapered channel for characterizing neuronal responses to different chemical stimuli. (Adapted from Ref. [74], copyright the Nature Publishing Group, with permission.) (b) A double-layer device for imaging-based phenotypic screens (picture on the left). On the right, fluorescence images of wild-type and mutant synapse phenotypes that the system used as its sorting criteria (scale bar ∼10 µm).
Figure 10.7 The laser axotomy chip for imaging, laser nanoaxotomy, and housing of
C. elegans.
(a) Conceptual three-dimensional section renderings of the bilayer trap channels without and with a worm (green) immobilized by a membrane. (b) View of the trapping system: Valves 1–4 (yellow rectangles) respectively control inlet regulation (1), fine positioning of the worm (2 and 3), and gating to the recovery chambers (4) (scale bar ∼1 mm). (c) A fluorescence image of the mechanosensory neuron axon immediately after laser axotomy (1 min) and after reconnecting across the cut site (70 min).
Figure 10.8 Screening chemical modulators of axonal regrowth in a microfluidic chip. (a) A multiwell plate is seated on an angled stand to condense the worm populations in a corner of the well from which they can be delivered to the device. (b) The microfluidic device with key components of the immobilization area filled with different food-coloring dyes. (c) A few worms are delivered to the device, where a single animal is trapped and immobilized for axotomy and imaging after cleaning steps.
Figure 10.9 A fully automated serial laser nanoaxotomy platform. (a) Optical image of a dye-filled microfluidic device with black arrows indicating the direction of fluid flow. Orange dye fills the control layer and the blue dye identifies the flow channels. The loading chamber holds preloaded worms before their serial transportation into the staging and T-shaped immobilization area (A–A′). (b) Schematic cross-section referring to the sectioning arrows A–A′ in (a) that shows the flow direction through the sieves before membrane deflection, the location of the worm in the trapping area during delivery and after membrane deflection, and the relative heights of the microfluidic sieve and channel within the immobilization zone.
Figure 10.10 Multitrap microfluidic devices for parallel or serial processing of
C. elegans
. (a) A single-layer device with channels that taper to a minimal width that are arranged in parallel to trap several worms for various imaging studies. (Adapted from Ref. [77], copyright the Royal Society of Chemistry 2007, with permission.) (b) A double-layer device with membrane valves that flank worms loaded into thin channels arranged in parallel.
Figure 10.11
Population
delivery chip.
(a) A schematic of the device indicating the flow layer (blue) and control valve layer (red). There are 16 on-chip wells arranged in a 96-well plate format for initial loading of different worm populations. Columns and wells of the array are numbered according to order of delivery. Valves V1–V8 are multiplexer control valves and V9–V12 control flow in the main channel. (b) An image of the device with its microfluidic channels loaded with food-coloring dye, showing the flow layer (green) and control valve layer (orange) (scale bar ∼1 mm). (c) A macroscale view of the device with the 16-well array indicated by the yellow dashed lines and a schematic of worms loaded into one of the conical wells. (d) A macroscale view of the entire chip/gasket system with pressurized input lines in the experimental setup.
List of Tables
Chapter 1: Types of Clinical Samples and Cellular Enrichment Strategies
Table 1.1 Size and abundance of cells or platelets found in blood
Chapter 4: Microsystem Assays for Studying the Interactions between Single Cells
Table 4.1 Examples of techniques for studying intercellular interaction at the single-cell level
Chapter 7: Intracellular Delivery of Biomolecules by Mechanical Deformation
Table 7.1 Typical cell types targeted for intracellular delivery applications
Table 7.2 Target materials most commonly used for intracellular delivery applications
Table 7.3 A list of delivery methods commonly used for
in vitro
and
in vivo
applications
Table 7.4 A list of primary cell types and cell lines that have been successfully treated using our system
Table 7.5 A list of current device designs that have been tested in various applications
Chapter 8: Microfluidics for Studying Pharmacodynamics of Antibiotics
Table 8.1 Determination of precise MIC and Hill coefficient using pharmacodynamics modeling in monomicrobial cultures
Table 8.2 Determination of precise MIC and Hill coefficient using pharmacodynamics modeling in polymicrobial cultures
Table 8.3 Parameters used in pharmacokinetic modeling
