Manipulating biological agents and cells in micro-scale volumes for applications in medicine

Abstract

Recent technological advances provide new tools to manipulate cells and biological agents in micro/nano-liter volumes. With precise control over small volumes, the cell microenvironment and other biological agents can be bioengineered; interactions between cells and external stimuli can be monitored; and the fundamental mechanisms such as cancer metastasis and stem cell differentiation can be elucidated. Technological advances based on the principles of electrical, magnetic, chemical, optical, acoustic, and mechanical forces lead to novel applications in point-of-care diagnostics, regenerative medicine, in vitro drug testing, cryopreservation, and cell isolation/purification. In this review, we first focus on the underlying mechanisms of emerging examples for cell manipulation in small volumes targeting applications such as tissue engineering. Then, we illustrate how these mechanisms impact the aforementioned biomedical applications, discuss the associated challenges, and provide perspectives for further development.

Figures

Fig. 1

Magnetic manipulation and assembly of cell-encapsulating hydrogels. (a) Magnified image of the assembled single-layer spheroid construct. (b) Maximum spheroid assembly size versus MNP concentration. (c) and (d) Images of engineered arc and dome constructs by employing a flexible surface. (e) 3-D assembly of fluoresencently tagged gels. Layers were stained with rhodamine-B (f), FITC-dextran (g), and TPB (1,1,4,4-tetraphenyl-1,3-butadiene) (h), respectively from inner to outer shell. Reproduced with permission.

Fig. 2

Capillary driven manipulation and assembly of cell-encapsulating hydrogels. (A and B) Fluorescence images of cross-shaped and rod-shaped hydrogels stained with FITC-dextran and Nile red, respectively. (C) Phase-contrast image of lock-and-key assemblies with three rods per cross. (D) Fluorescence image of lock-and-key assemblies. (E and F) Fluorescence images of single rod and two rod assemblies (scale bars are 200 μm). (G) The schematic of two objects and the coordinate system for eqn (4) and (5). The objects have a height of t and a width of w = 5t, and their proximate surfaces are separated by d. (H) For a range of object height, t = 1 mm to 100 nm, the change in interfacial free energy (non-dimensionalized with thermal energy, kT) in bringing two surfaces from infinite separation to a finite separation, d, is plotted. (I) The strength of interaction is correlated with the height of capillaries. Reproduced with permission.,

Fig. 3

Bioprinting technologies. (A) Piezo inkjet printing. (B) Thermal printing. (C) Electrohydrodynamic jetting. (D) Valve-based printing. (E) Laser guided direct writing. (F) Laser induced forward transfer. (G) Acoustic printing.

Fig. 4

Schematic for acoustic nozzles droplet generation technology. (A) Multiple ejectors and ejected cell encapsulating droplets from different reservoirs. (B) Comparison of acoustic wavelength with the cell diameter. (B) The interdigitated circular micromachined device. Scale bar is 250 μm. (C) Computerized printing of single cell encapsulating droplets via xyz stage control. (D) Cylindrical acoustic focus at the fluid surface. (E and F) 28 μm droplets ejected upward from an open pool. Droplets ejected downward from a 100 μm wide microfluidic channel spacer opening. The ejector generated single droplets drop-on-demand without satellites. Reproduced with permission.,

Fig. 5

Statistical and computational modeling of cell encapsulation and the printing process. (A–C) P(Xt) (eqn (3.6) in ref. 86) are cell encapsulation probability functions for the heterogeneous cell mixture including several cell loading concentrations. (A) Heterogeneous mixture of target and non-target cells inside an ejector reservoir. (B) Four parameters were distributed onto a matrix: (Xd) the number of droplets that contain cells, (Xc) number of cells per droplet, (Xt) number of target cells, and (Xs) droplets encapsulating a single target cell. (C) Cell encapsulation probability, P(Xt), as a function of number of target cells per droplet for cell concentration = 1.5 × 105 cells per mL. (D–F) Inner droplet representing the cell was assumed to be a highly viscous fluid and non-wetting (not sticking to the surface) while encapsulating droplets partially wetted the substrate. A moving contact line model, was utilized to predict the dynamic contact angle. (D and E) Pressure contours and pressure distribution on the cell were plotted at the left half and the right half, respectively. Shear stresses peaked in the vicinity of the triple point during the initial phase of droplet–surface interaction. Triple point is the point where outer droplets, receiving substrate and ambient air, coincide. Maximum pressure was located near the contact line just before recoiling, and migrated to the distal end from the receiving surface where it stayed there until the recoil phase. Governing non-dimensional numbers are: We = 0.5, Re = 30, do/di = 2.85, σo/σi = 2541, μc/μd = 10. (F) Sequential impact images of cell encapsulating droplets. (A–C) are reproduced with permissionand (D–F) are reproduced with permission.

Fig. 6

Microfluidic CD4 cell count performed by minimally trained personnel at MUHAS. (a) Microfluidic chips. (b) Preparation of surface chemistry with antibody injection. (c) Injection of blood sample. (d) Lensless imaging detection of CD4 cell count on-chip. Reproduced with permission.

Fig. 7

Microchip ELISA integrated with cell phone application for detection of ovarian cancer from urine. (A) Loading of a small volume (100 μL) of urine sample into the microchannel. (B) Principle of direct ELISA for detection of HE4 on-chip. (C) The color development in microchannels was imaged using a cell phone built-in camera. (D) The concentration of HE4 in urine reported on the cell phone screen via a mobile application. Reproduced with permission.

Fig. 8

Sperm sorting. (A) Lensless imaging platform (LUCAS) integrated with a microchip for sperm tracking as highlighted by Nature Photonics. Shadows of the sperm generated by diffraction can be imaged using CCD in one second. (B) Loading sperm samples into microchannels of the space-constrained microfluidic sorting (SCMS) system from the inlets. The SCMS system with different channel lengths is assessed for effective sperm sorting. (C) The chip has three layers: PMMA, double-sided adhesive film (DSA), and glass coverslip. (D) Image of the channel inlet with a diameter of 0.65 mm under a 2×. (E) Image of sperm swimming inside a microchannel under a 10× objective. (F) The channel outlet with 2 mm diameter viewed using a 2× objective. (G–I) Sperm shadows on LUCAS. (J) A schematic of the trajectory of a sperm performing a Persistent Random Walk (PRW), where S is the velocity, P is the persistence time, Δt is the time step, and θ is the angle the trajectory makes with the x-axis. (K) Sperm tracks from image analysis. (L) Bull’s eye plot showing sperm motility vectors in the horizontal (left) and vertical (right) configurations. The distance between the adjacent concentric circles is 100 μm. (M) Comparison of Average Path Velocity (VAP) and Straight Line Velocity (VSL) of sperm for non-sorted conditions, and at the inlet and outlet of the 7 mm long microfluidic channel. The VAP and VSL were observed to be significantly greater for the sperm cells imaged at the outlet of the microfluidic channel compared to non-sorted sperm and the sperm at the inlet. Therefore the microfluidic sperm tracking system presented here shows potential to be also used as a sorting platform (n = 33–66, brackets indicate statistical significance with p < 0.01 between the groups). Reproduced with permission.

Fig. 9

Isolation, purification, and enrichment of cells using thermoresponsive microfluidic channels for on-demand releasing the selectively captured cells from heterogeneous mixtures. (A) The thermoresponsive microfluidic chip is composed of three channels. The middle channel was used as an indicator of the temperature. (B) The indicator channel was covered with liquid crystal dye. (C) Schematic representing the mechanism of label-free selective capture of cells and on-demand release of cells in thermoresponsive microchannels. (D) Sample at 37 °C (e.g., blood) is injected into the channel, and the target cells (e.g., CD4+ or CD34+ cells) are captured. (E) The non-target cells in channels, which are not captured, are then washed off. (F) The channels are then cooled down below 32 °C. Upon cooling, the released cells are rinsed off the channels and collected at the outlet. Reproduced with permission.

Fig. 10

Manipulation of cells in microchannels through local capture and on-demand release. (A) Local release of captured cells. (B) Local capture of specific cells in microchannels. (C–F) Mechanism of local manipulation of cells in microchannels in the absence and presence of thermoelectric heating elements. (G) Local control of temperature thermoresponsive microchannels and a photograph of a microchip with a local temperature control. (H) Temperature responsive dye works between 32 °C to 41 °C, and displays green color at 37 °C, the temperature at which cells were captured. The dye appears black below 32 °C, at which on-demand local cell release is achieved. (I) Baseline RGB values represent the colors displayed by the temperature indicator channel. Reproduced with permission.

Fig. 11

A schematic of the blood cryopreservation platform. (a) Ejection and deposition of RBC encapsulating droplets on receiving paper (top) and droplet pictures (bottom). Scale bar is 500 μm. (b) Average size of ejected droplets is plotted for a range of sheath gas flow rates (3.2, 4.0, 4.8 L m−1), blood flow rates (160, 180, 200, and 220 mL min−1), and droplet collecting distances (60, 75, and 90 mm). Reproduced with permission.