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. Author manuscript; available in PMC: 2025 Nov 26.
Published in final edited form as: Exp Eye Res. 2020 Aug 6;199:108166. doi: 10.1016/j.exer.2020.108166

Label-free microfluidic enrichment of photoreceptor cells

Nicholas E Stone 1, Andrew P Voigt 2, Jessica A Cooke 2, Joseph C Giacalone 2, Srinivas Hanasoge 1, Robert F Mullins 2, Budd A Tucker 2, Todd Sulchek 1,*
PMCID: PMC12646284  NIHMSID: NIHMS1623661  PMID: 32771499

Abstract

Inherited retinal degenerative disorders such as retinitis pigmentosa and Usher syndrome are characterized by progressive death of photoreceptor cells. To restore vision to patients blinded by these diseases, a stem cell-based photoreceptor cell replacement strategy will likely be required. Although retinal stem cell differentiation protocols suitable for generating photoreceptor cells exist, they often yield a rather heterogenous mixture of cell types. To enrich the donor cell population for one or a few cell types, scientists have traditionally relied upon the use of antibody-based selection approaches. However, these strategies are quite labor intensive and require animal derived reagents and equipment that are not well suited to current good manufacturing practices (cGMP). The purpose of this study was to develop and evaluate a microfluidic cell sorting device capable of exploiting the physical and mechanical differences between retinal cell types to enrich specific donor cell populations such as RPE cells and photoreceptor cells. Using this device, we were able to separate a mixture of RPE and iPSC-derived photoreceptor precursor cell lines into two substantially enriched fractions. The enrichment factor of the RPE fraction was 2 and that of the photoreceptor precursor cell fraction was 2.7. Similarly, when human retina, obtained from 3 independent donors, was dissociated and passed through the sorting device, the heterogeneous mixture could be reliably sorted into RPE and photoreceptor cell rich fractions. In summary, microfluidic cell sorting is a promising approach for antibody free enrichment of retinal cell populations.

Keywords: Atomic force microscopy (AFM), photoreceptor cell, retinal pigment epithelial (RPE) cell, microfluidic cell sorting

Introduction

Inherited retinal degenerative diseases such as retinitis pigmentosa, Leber congenital amaurosis and Usher syndrome are collectively a major cause of incurable blindness in the developed world. A unifying feature of this group of disorders is progressive death of the light sensing photoreceptor cells of the outer neural retina. These diseases are genetically very heterogeneous and as a result many specific treatments will likely be required to treat patients affected with them. For example, of the more than 40 different genes that have been reported to cause retinitis pigmentosa, only a few cause more than 1% of the disease in the total population (Bohrer et al., 2019).

For patients who receive a molecular diagnosis early in the course of their disease and still have a large number of photoreceptor cells remaining, it may be possible to restore gene function and prevent disease progression with some form of viral or nanoparticle-mediated gene replacement. To evaluate the efficacy of such gene-based therapeutics, scientists have traditionally relied upon the use of animal models. Unfortunately, for many retinal degenerative disorders, animal models that faithfully recapitulate critical aspects of the disease phenotype do not exist. For instance, structural differences between human and non-primate photoreceptor cells have resulted in rodent models of Usher syndrome that fail to develop retinal degeneration (Sahly et al., 2012). To overcome this problem some investigators are beginning to employ patient-derived induced pluripotent stem cell (iPSC) culture systems. By using patient-derived iPSCs to generate retinal neurons, either as 3D laminated organoids (Capowski et al., 2019; Meyer et al., 2009; Ueda et al., 2018; Wiley et al., 2016a) or as a 2D monolayer of cells (Lamba et al., 2006; Tucker et al., 2013), one can often identify disease specific phenotypes that can be used to evaluate treatment efficacy (Bohrer et al., 2019; Dalvi et al., 2019; Huang et al., 2019; Meyer et al., 2009; Wiley et al., 2016b). A limitation of this approach is that stem cell differentiation protocols often yield a rather heterogenous mixture of cell types. Thus, to be able to evaluate therapeutic efficacy in a single cell population such as human photoreceptor cells, some type of cell isolation or enrichment method is often needed.

For patients with advanced retinal degenerative disease, who have lost the majority of their photoreceptor cells, restorative stem cell-based photoreceptor cell replacement will likely be required. For this approach, the need for cellular enrichment is even greater. Specifically, as many patients with inherited retinal degeneration retain both RPE and inner retinal neurons for years after complete loss of their photoreceptor cells (Mullins et al., 2012), photoreceptor cell enrichment methods that reduce the number of unneeded RPE and inner retinal neurons in the transplanted cell population would be useful. To date, scientists have primarily used antibodies to cell surface antigens coupled with magnetic bead pull down or fluorescence activated cell sorting (FACS) to enrich specific cell types in a heterogeneous mixture (Eberle et al., 2014; Eberle et al., 2011; Gagliardi et al., 2018; Lakowski et al., 2011). Although potentially useful, these approaches require the use of expensive reagents and equipment that are not ideally suited for current good manufacturing practices (cGMP). In addition, for applications such as isolating photoreceptors from a mixed population of retinal cells, reliable extracellular markers may not exist.

In this study, we demonstrate the use of a novel microfluidic cell sorting device to perform antibody-free sorting of a mixed population of retinal cells into biologically meaningful fractions. The microfluidic device itself is biologically inert and sorts cells based on their mechanical and physical properties and thus does not require the use of antibodies or similar non-cGMP-compliant reagents (Wang et al., 2013; Wang et al., 2015b). In our hands the use of similar devices have little impact on cell viability (Liu et al., 2020). The device itself is quite small, making it readily usable within standard biological safety cabinets in existing cGMP spaces. In addition to being useful for disease modelling purposes, this approach could also be used for cGMP-compliant purification of patient-derived retinal cells in regenerative medicine applications.

Methods

Human iPSC derived photoreceptor precursor cell line generation and culture:

Retinal progenitor cells were generated as previously described (Wiley et al., 2017; Wiley et al., 2016a). In brief, as per Figure 1A, iPSCs, derived from a normal non-diseased individual, were maintained on recombinant human laminin 521 coated tissue culture plates and fed with Essential 8 medium supplemented with rhFGF2. For 3-D differentiation, hiPSCs were passaged with TrypLE, centrifuged, and resuspended in 3-D differentiation medium supplemented with Y-27632 ROCK inhibitor and IWR1e. From this suspension, 1 × 104 cells were added per well to a 96 well ultra-low adhesion tissue culture plate. On days 2–10, the 3-D differentiation media was supplemented with 1% ECM. On day 12, spheres were transferred to 100mm ultralow attachment culture dishes. On days 14 to 17, the 3-D differentiation medium was supplemented with 1% ECM mixture, 40 nM CHIR99021, and 100 nM SAG. On day 18, the media was switched to neural retina medium. At day 45, 30–50 spheres were dissociated using Accutase. Cell suspensions were then counted and plated on Matrigel coated wells. For immortalization, twenty-four hours after plating, cells were washed with fresh neural retina media and transduced with a lentiviral cocktail containing vectors driving CRX (Figure 1B) and NRL (Figure 1C) to induce photoreceptor cell fate commitment, and GRK1 promoter driving hTERT (Figure 1D) for immortalization. Five days following transduction, immortalized photoreceptor precursor cells were pharmacologically selected via blasticidin selection (2ug/ml for 14 days). Immortalization was achieved via CRX/NRL induced activation of the GRK1 promoter, which in turn drives hTERT expression.

Figure 1. Method for generating iPSC-derived photoreceptor precursor cell line.

Figure 1.

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A-D: Schematic diagrams depicting the methods used for iPSC-PPC line generation (A) and the lentiviral vectors used for forced expression of CRX (B), NRL (C) and cell line immortalization (D).

Lentiviral vector generation

Lentiviral vectors were generated in a stepwise fashion. The EF1 alpha promoter and GRK1 promoter were subcloned in the pENTR™ 5’-TOPO™ vector. HTERT, CRX, and NRL were subcloned into the pENTR™/D-TOPO™ vector. Then using LR clonase, three lenti viral vectors were generated: 1) GRK1p-hTERT, 2) EF1alpha-NRL, and 3) EF1alpha-CRX. Of note, both EF1alpha-NRL and EF1alpha-CRX vectors contained the MPGK promoter driving blasticidin resistance cassette which was used to select for transduced cells.

Immunocytochemical analysis:

ARPE-19 and human iPSC-derived photoreceptor precursor cells (hiPS-PPCs) were fixed in 4% PFA and stained using our previously published protocols (Wiley et al., 2016a). ARPE-19 cells were stained using mouse anti-MITF (Exalpha Biologicals), mouse anti-RPE65 and mouse anti-ZO1 (Thermo Fisher) antibodies. HiPS-PPCs were stained using goat biotinylated-anti-OTX2 (R&D Systems), goat biotinylated-anti-NRL (R&D Systems), mouse anti-rhodopsin (EMD Millipore), sheep anti-CRX (R&D Systems), rabbit anti-rabbit NR2E3 (EMD Millipore), and rabbit anti-recoverin (EMD Millipore) antibodies. Primary antibodies were detected using the species appropriate, fluorescently conjugated Alexa Fluor secondary antibodies [Life Technologies/Thermo Fisher Scientific; goat anti-mouse 488, goat anti-rabbit 568, Streptavidin 647 and donkey anti-sheep 647. Cell nuclei were counterstained using DAPI. Cells were imaged using a Leica DM 2500 SPE confocal microscope (Leica Microsystems).

AFM and force curve analysis:

To obtain global stiffness measurements of each cell line, 7.32 μm spherical polystyrene particles were attached to tipless silica nitride cantilevers (Bruker Probes) using a two-part epoxy, which was dried overnight prior to use. To characterize the mechanical properties of each cell, we used force spectroscopy to obtain force-indentation curves with an atomic force microscope (AFM) (Asylum Research) with an integrated optical microscope (Nikon) on a vibration isolation table using our previously published protocols (Bongiorno et al., 2016; Xu et al., 2012). Before each day of measurements, the AFM was calibrated by taking a single force curve on a clean FluoroDish (World Precision Instruments) to determine the deflection inverse optical lever sensitivity (i.e., the voltage read in the photodetector for a given amount of cantilever deflection) for each cantilever. Next, the Sader calibration method was used to obtain cantilever spring constants (k is approximately 5 – 20 pN/nm) based on the thermal vibration of the cantilever. For cell measurements, the cantilever probe was visually aligned with the cell center and moved with a velocity of 1 μm/s to indent the cell with increasing compressive force until a force trigger of 10 nN was reached. The cantilever was held in position for 10 seconds to allow viscous relaxation of the cell before reversing the direction of the cantilever’s velocity.

We calculated the cellular reduced Young’s modulus (Pelling, 2015) based upon the Hertzian model of non-adhesive elastic contact between two bodies. The contact point was estimated by the intersection of the flat, undeformed region of the force curve with a line fit to the region of the force curve corresponding to the cantilever’s contact with the cell. Next, we identified the true contact point by iteratively testing the points around the estimated contact point with the minimal residual difference between the measured force curve and a non-linear fit described by the governing Hertz contact mechanics equation between an elastic sphere and an elastic half space. We additionally fit the dwell region of the force curve to a biexponential decay curve to identify fast and slow viscous time constants (Moreno-Flores et al., 2010).

Human donor retina dissociation:

Human donor eyes were acquired through the Iowa Lion’s Eye Bank, with consent from the donors’ next of kin and in full accordance with the Declaration of Helsinki. For each donor, 8-mm trephine punch biopsies were acquired and retina, RPE, and choroid was dissected away from the underlying sclera. Retinal, RPE and choroidal tissue were pooled and incubated in 20 units/mL of papain with 0.005% DNase (Worthington Biochemical Corporation, Lakewood NJ) for 1.25 hours at 37°C with gentle agitation. Dissociated cells were filtered through a 70um filter to remove aggregates before resuspension in PBS [+,+] containing 0.1% BSA, 30% Percoll, 0.006% Tween-20 and 100U/mL DNAse I. Cells were resuspended to a concentration of 5–10 million cells/mL using the Countess II FL Automated Cell Counter (ThermoFisher Scientific, Waltham MA) before cell sorting.

Quantitative RT-PCR:

Cell type specific transcript expression was assessed using TaqMan probes targeted against BEST1, RPE65, recoverin, rhodopsin, PKCα and POU4F2 (Life Technologies/Thermo Fisher Scientific). Total RNA was isolated using Trizol (Life Technologies/Thermo Fisher Scientific) according to manufacturer’s instructions. One microgram of RNA was reverse transcribed using the Superscript VILO cDNA Synthesis Kit (Life Technologies/Thermo Fisher Scientific; Cat #: 11754050). Quantitative RT-PCR was performed using a QuantStudio 6 Flex Real-time PCR system (Life Technologies/Thermo Fisher Scientific). A probe set targeting beta-actin was used as a loading control.

Microfluidic device fabrication and cell sorting:

Devices were manufactured using standard soft lithography techniques to cast PDMS on molds consisting of SU-8 photoresist patterned on silicon wafers, as described previously (Islam et al., 2017; Islam et al., 2018). The size of the gap between each ridge and the surface of the device (see Figure 2) was selected based on measured cell size. For the cell line sorting experiments, a device with 9um gaps was fabricated. For the human retina sorting experiments, a device with 2um gaps was fabricated. The dimensions of the mold ridge heights were measured with profilometry (Dektak 150 profiler) and optical microscopy. Five outlet devices were tested to evaluate the accuracy of fractionation of the heterogeneous cells to isolate target retinal cell types. The mold pattern was translated to polydimethylsiloxane (PDMS), inlet and outlet holes were punched with biopsy punch, and the chip bonded to glass. Cells were transferred to a syringe and infused into the microfluidic device through Teflon tubing using a syringe pump (PHD 2000, Harvard Apparatus, and BS-300, Braintree Scientific) at specified flow rates (15–45ul/min). In experiments in which cell trajectories were recorded, an inverted bright-field microscope (Eclipse Ti, Nikon) was used equipped with a high-speed camera (Phantom v7.3, Vision Research) set at a frame rate of 2000 frames per second (Jeong et al., 2018; Tasadduq et al., 2017; Wang et al., 2015a). In experiments of fluorescently labeled cell lines, cells were labeled with CellTracker™ deep red (ARPE-19) and green (hiPS-PPCs) (Molecular Probes Inc.) according to manufacturer protocols. After loading the cells with the dye, the accuracy of sorting could be quantified using flow cytometry (BD Biosciences, LSR II).

Figure 2. Microfluidic cell sorting device designed for sorting of retinal cells.

Figure 2.

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A: Schematic diagram depicting the design of the microfluidic cell sorting device. B: Phase micrograph of the cell sorting device flight path – area containing diagonal ridges designed to deflect cells to the left or right side of the device based on each cell’s size, modulus, and viscosity. Upper panel scale bar = 1mm. Lower panel scale bar = 400um. C: Photograph of a cell sorting device demonstrating its size and design.

Results

Design and optimization of a microfluidic retinal cell sorting device.

The purpose of this study was to develop a microfluidic device, capable of exploiting differences in mechanical and physical properties of retinal cells, to enrich for specific cell types. As depicted in Figure 2, this device was designed with 3 input ports, one for loading a mixed cell population and the remaining two for injection of sheathing fluid to generate flow through the center of the device. The input ports are connected to a flight path chamber with a number of diagonal fins separated from the base by small gaps that serve to channel cells toward one of 5 different output ports where the enriched cell populations are collected.

Our first goal was to determine if RPE cells could be separated from iPSC-derived photoreceptor precursor cells. In order to determine the degree of enrichment that results from passage through the device, it is necessary to know the exact proportion of each cell type in the starting population. Human iPSC derived retinal organoids and samples of primary retinal tissue both vary in the percentage of each cell type present and are therefore less suitable for this experiment than homogeneous stable cell lines. Although several excellent immortalized human RPE cell lines exist (e.g., ARPE-19 and hTERT RPE-1), similar photoreceptor precursor cell lines are not available. As such, for these experiments a photoreceptor precursor cell line (hiPS-PPC) was generated as per the methods section. As expected, the transgenes CRX and NRL are robustly expressed following lentiviral transduction (Figure 3A). As shown in Figure 3b, expression of the retinal progenitor cell marker SOX2, and the photoreceptor cell markers S Opsin, M/L Opsin, RP1, recoverin and RPGR ORF15 was detectable via rt-PCR in cultures of immortalized human iPSC-derived photoreceptor precursor cells for at least 20 passages. Likewise, the photoreceptor precursor cell markers OTX2 (Figure 3C) and recoverin (Figure 3D), and the rod photoreceptor cell marker rhodopsin (Figure 3E) could be detected via immunocytochemical staining.

Figure 3. Generation of a stable human iPSC-derived photoreceptor precursor cell line.

Figure 3.

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A: rt-PCR analysis performed using RNA isolated from human iPSC-PPCs following lentiviral transduction. B: rt-PCR analysis performed using RNA isolated from human donor retina (hRetina) as a control and iPSC-PPCs. POLR2A was included as a loading control. B-D: Immunocytochemical analysis of immortalized hiPS-PPCs using antibodies targeted against OTX2, recoverin and rhodopsin.

To determine whether this newly generated iPSC-derived photoreceptor precursor cell line and the commonly used RPE cell line, ARPE-19, differed enough in their mechanical properties to be sorted using our device, we first measured the stiffness and viscosity of each line using atomic force microscopy (AFM) (Figure 4A-B). As shown in Figure 4, ARPE-19 cells are smaller (C), slightly stiffer (D) and more viscous (E & F) than human iPSC derived photoreceptor precursor cells (hiPS-PPCs), suggesting that these populations could be different enough to be sortable using this microfluidic approach.

Figure 4. Mechanical characterization of retinal cell lines.

Figure 4.

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A: Representative phase micrograph depicting the AFM probe as it approaches cells for mechanical analysis. B: force curves obtained following mechanical analysis of ARPE-19 (blue lines) and hiPS-PPCs (red lines). C: Area of ARPE-19 and hiPS-PPCs as they approach and pass beneath each ridge within the device. D: Modulus (Pa) of ARPE-19 and hiPS-PPCs. E-F: Fast and slow time constant (i.e. measure of viscosity) for ARPE-19 and hiPS-PPCs. Collectively these data show that human ARPE-19 cells are smaller, stiffer and less viscous than human hiPS-PPCs (see supplemental information for force curve data files).

To determine the optimal conditions for sorting and to evaluate the degree of enrichment, ARPE-19 and human iPSC-derived photoreceptor precursor cells were first injected individually into the sorting device via a syringe pumped at a constant rate of either 15 ul/min, 30 ul/min or 45 ul/min. Cellular trajectories were tracked for each cell line using high-speed microscopy and the characteristic deflections that occurred as the cells passed beneath each ridge of the device are shown in Figure 5. The slowest injection rate, 15ul/min, resulted in the greatest variability in per ridge deflection (Figure 5A). This variability was much less when cells were injected at a constant rate of 45ul/min, but at this rate, the difference in mean cumulative deflection between the two cell lines was the smallest of the 3 injection rates tested (Figure 5C). The greatest difference in characteristic deflections between the two cell lines and the smallest variability in per ridge deflection was seen at a flow rate of 30 ul/min (Figure 5B). Thus, this injection rate was chosen for the subsequent cell-line enrichment experiments.

Figure 5. Optimization of cell enrichment parameters.

Figure 5.

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A-C: Ridge induced deflections as a function of injection speed (A= 15uL/min, B=30uL/min, C= 45uL/min). The largest cumulative deflection difference with least amount of overlap between ARPE-19 and iPS-PPCs was detected at a constant injection rate of 30ul/min (B). Note: the box marks the limits of the 1st and 3rd quartile, the line within the box represents the median and the whiskers represent the maximum and minimum values detected.

Separation of RPE and iPSC-derived photoreceptor precursor cell lines via microfluidic cell sorting.

To test whether the device could be used to enrich for specific retinal cell populations, ARPE-19 and hiPS-PPCs were fluorescently labeled and then mixed at a 1:1.7 ratio and injected through the device via a syringe pump at a constant rate of 30 uL/min. As indicated above, knowing the precise makeup of the starting population (i.e., 37% ARPE-19 and 63% iPS-PPCs) is critical in order to determine the percent enrichment for each cell type at each of the device’s output ports.

As shown in Figure 6, at these conditions we were able to achieve enrichment values of 2 for ARPE-19 and 2.7 for hiPS-PPCs. Specifically, outlet 1, which we predicted would contain larger cells with the greatest deflection rates, was heavily enriched for hiPS-PPCs. Outlet 4, which we predicted would contain smaller cells that had lower deflection rates was highly enriched for ARPE-19 cells. Outlet 5 had very few of either cell type.

Figure 6. Sorting of ARPE-19 and hiPS-PPCs using a microfluidic cell sorting device.

Figure 6.

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Following microfluidic cell sorting, outlet 1 contained the greatest number of hiPS-PPCs and the lowest number of ARPE-19 cells (hiPS-PPCs enrichment factor = 2.75). Conversely outlet 4 contained the greatest number of ARPE-19 cells and the lowest number of hiPS-PPCs (ARPE-19 enrichment factor = 1.97). As outlet 5 did not contain a significant number of either cell type the enrichment factor was not plotted.

Microfluidic sorting of mature human donor derived retinal cells.

To determine if a similar microfluidic cell sorting strategy could be used to sort mature retinal cells, new devices were fabricated that contained the same number of ridges but a ridge gap size of 2um, which was selected based upon the average size of all cell types that would be present in the input cell population. For this experiment, human donor eyes were dissected, the neural retinal and RPE cell layers were harvested, and the tissues were mixed and dissociated into a single cell suspension. As the contribution of each of the different retinal cell types in the starting population was unknown, it was not possible to calculate an enrichment factor in this experiment. Instead, quantitative RT-PCR using primers targeted against transcripts specific to several of the dominant retinal cell populations was performed on the fractions collected from each of the 5 output ports. As in the cell line sorting experiment described above, the dissociated heterogeneous input cell population was injected via a syringe pump at a rate of 30ul/min. As shown in Figure 7, expression of RPE, photoreceptor and inner retinal cell markers varied based on outlet, indicating separation of the heterogeneous input into biologically relevant subpopulations. Specifically, cells expressing the RPE cell markers BEST1 (Figure 7A) and RPE65 (Figure 7B) were most abundant in outlet 1. The abundance of these markers decreased dramatically in outlet 2, and little to no RPE marker expression was detected in outlets 3 through 5. The photoreceptor cell markers recoverin (expressed in both rods and cones) and rhodopsin (expressed exclusively in rods), were virtually absent in outlet 1 (Figure 7C & D). While recoverin was expressed at equal levels in outlets 2 through 4 (Figure 7C), rhodopsin expression was increased in outlet 2 and was higher again in outlets 3 and 4 (Figure 7D). Interestingly, both recoverin and rhodopsin were expressed at low levels in outlet 5, similar to what was observed in outlet 1 (Figure 7C & D), indicating that most photoreceptor cells were captured in outlets 2, 3 and 4. In contrast to cultured cells, in this experiment the RPE cells are the largest and least stiff cells present in the input population. As such the finding that the larger softer cells were deflected to the left toward outlets 1–3 and the smaller stiffer cells were deflected toward the right into outlets 3–5 held true. Expression of PKCα, a bipolar cell marker, was detected at low levels in outlet 1, increased to a similar level in outlets 2 and 3 and was detected at the highest levels in outlet 4 (Figure 7E). The ganglion cell marker POU4F2 was detected at low levels across all 5 outlets, with a slight skew toward outlets 1–3 (Figure 7E). Collectively, these data demonstrate that a microfluidic cell sorting device can be used to successfully enrich for specific retinal cell populations in a label-free manner.

Figure 7. Microfluidic sorting of retinal cells obtained from human donor eyes.

Figure 7.

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A-F: Quantitative RT-PCR analysis of human retina using primers targeted BEST1 (A), RPE65 (B), recoverin (C), rhodopsin (D), PKCα (E) and POU2F4 (F) following microfluidic cell sorting. N=3 independent donors. Error bars = Standard deviation. DDCt was compared against BActin as a method to normalize for differences in cell number at each outlet.

Discussion

Currently, autologous cell therapies are under development for a wide variety of conditions, ranging from cancer to neurodegenerative blindness. As stem cell-based cell replacement strategies enter the clinic, sophisticated cell sorting technologies designed to enrich the donor cell population for the preferred cell types will be desirable (Bongiorno et al., 2018; Du et al., 2019; Hur et al., 2012). This is especially true considering that the majority of current stem cell differentiation protocols are designed to recapitulate embryonic development and therefore give rise to a variety of organ specific cell types that may not be required for the target application. For instance, retinal differentiation protocols are very effective in generating photoreceptor cells but also produce contaminating retinal cell types including retinal pigment epithelial cells and bipolar neurons. For the treatment of patients with photoreceptor cell specific diseases, such as retinitis pigmentosa, who often retain all other retinal cell types until very late in disease, transplantation of non-photoreceptor cells will likely be unnecessary (Mullins et al., 2012) and possibly detrimental.

The majority of the preclinical photoreceptor cell enrichment strategies published to date have been focused on identifying cell surface antigens and antibodies that can be used to capture specific cell populations using traditional ‘gold standard’ cell-sorting approaches, such as FACS and/or MACS. For instance, in 2012 Eberle and colleagues demonstrated that by using a primary antibody targeted against the cell surface antigen CD73, and a secondary antibody conjugated to MACS micro-beads, they could magnetically isolate with high efficiency rod photoreceptor precursor cells from dissociated neonatal mouse retinas (Eberle et al., 2014). Lakowski and colleagues later expanded on this work by identifying several other cell surface antigens, namely CD24, CD133 and CD47, that could be used in conjunction with CD73 to further enrich for transplantable photoreceptor precursor cells via FACS or MACS (Lakowski et al., 2015).

As efficient as FACS- and MACS-based enrichment approaches are, when cells are destined to be transplanted into patients, where all reagents and procedures must be guaranteed to be safe and reproducible in order to gain FDA approval, avoiding the use of antibodies and stains that can alter cell function and viability is desirable. Likewise, from a clinical production perspective, large specialized pieces of equipment such as FACS units that are difficult to adapt to cGMP conditions should be avoided when possible. Of interest, one approach that has been used extensively by us and others for enrichment of specific cell populations post-differentiation, is drug based positive selection following lentiviral incorporation of an antibiotic resistance cassettes. For instance, in a recent study we demonstrated how puromycin antibiotic resistance delivered in a homology directed repair construct could be used to select patient derived iPSCs following CRISPR correction and differentiation (Burnight et al., 2017). This approach could readily be adapted to cGMP and modified to select for PPCs as described in this study. That said, antibiotic selection strategies are most appropriate when the specific target is well defined, as was the case both here for forced PPC production and in the above cited CRISPR report. The ideal retinal graft is unlikely to contain only one cell type. Rather a mixture of rods, cones (red, green and blue) and even support cells such as muller glia, may be beneficial. In such a case enrichment of late stage retinal progenitor cells that are capable of giving rise to all photoreceptor cell populations and glial cells would be idea. With that in mind, the purpose of this study was to develop a method for enriching retinal cell populations based on the cells physical and mechanical characteristics (i.e., size, stiffness and viscosity) (Du et al., 2019; Hur et al., 2012; Tasadduq et al., 2017; Wang et al., 2015a; Wang et al., 2013) rather than surface protein expression. The platform described in this study was designed such that cells are exposed to repeated compressions by a series of narrow gaps oriented diagonally to the direction of fluid flow. As cells interact with these gaps, which are chosen to be smaller than the cell’s diameter, they deflect laterally to a degree that is related to their size and stiffness. The design of the gaps allows cell debris and aggregates to move along the diagonal fins into a gutter and to exit the device without causing clogs.

As designed, the device selectively deflects larger softer cells to the left toward outlets 1 and the smaller stiffer cells to the right toward outlet 5. The results of our two cell line mixture experiment (Figure 6) led us to hypothesize that RPE cells would be deflected toward outlets on the left of the device (i.e., larger cells would be deflected into outlets 1–3) and photoreceptor cells would be sorted into outlets on the right of the device (i.e., smaller cells would be deflected into outlets 3–5). As shown in Figure 7, the majority of human primary RPE cells were indeed sorted into outlet 1. A sharp decline in RPE cell message was detected in the cells in outlet 2 and almost no message was detected in the cells from outlets 3–5. Likewise, almost no photoreceptor cells, as determined by a lack of recoverin and rhodopsin expression, were sorted into outlet 1. Rather there was an equal distribution of recoverin positive cells (i.e. rods and cones) in outlets 2–4 and a sharp decline in recoverin expressing cells in outlet 5. Interestingly, the number of cells expressing rhodopsin (rod specific marker) steadily increased beyond outlet 2, with peak expression detectable in outlet 4. As with recoverin expression, rhodopsin expression sharply dropped in outlet 5. These findings suggest that rod photoreceptor cells are smaller or softer than cone photoreceptor cells as one would predict based on histopathology (Curcio et al., 1993; Curcio et al., 1987). It would also suggest that by selecting only cells in outlet 2 one would have a population that is enriched for cone photoreceptor cells, whereas outlet 4 would be more heavily skewed toward rod photoreceptor cells. Interestingly, inner retinal neurons expressing PKCα (i.e. bipolar cells) were skewed toward outlet 4, while POU4F2 expressing cells (i.e. ganglion cells, which vary widely in size in the human retina) were distributed across all 5 outlets with a slight skew toward outlets 1–3 with lower numbers in outlet 4 and 5. Compared to RPE and photoreceptor cells, the amount of message attributed to these inner retinal neurons across all outlets was relatively low. We suspect that this is due to the fact that there are far fewer bipolar neurons and retinal ganglion cells in the human retina than photoreceptor cells, and that more aggressive cellular dissociation techniques are required to liberate these cell types, which unlike the RPE are embedded within the inner plexiform and nerve fiber layers respectively. Regardless, from a cell replacement perspective, the fact that retinal ganglion cells, which during stem cell differentiation develop on the inner most surface of retinal organoids and often die before mature photoreceptor precursor cells are made (Wiley et al., 2016a), suggest that removal of this cell type will be less of a concern. Likewise, the numbers of bipolar cells generated and sustained during retinal differentiation is often low (Wiley et al., 2016a) and inclusion of a small number of bipolar interneurons in a photoreceptor cell replacement based-approach may actually be beneficial.

In addition to the application discussed above, this platform also has applicability to other tissue types, including isolation of limbal stem cells from the limbal region of the eye (Bongiorno et al., 2016), undifferentiated stem cells from differentiated stem cells (Bongiorno et al., 2018), and cancer cells from liquid patient samples (Xu et al., 2012). We also believe that the device described in this paper could be enhanced to improve throughput by parallelizing individual flow channels and that sorting sensitivity/specificity could be further improved through additional optimization.

In summary, we believe that antibody-free sorting will be a favorable choice for clinical applications due to its low cost, high throughput and cGMP compatibility. In this work, we present a high-throughput, antibody-free platform capable of sorting pooled primary retinal cells into biologically relevant subpopulations. We have demonstrated that the device is sensitive to the differences between cell types common in the human retina, and thus should be capable of sorting cells desired for transplant from other, contaminating cell types. This device represents a key technological advancement for the development of future cell therapies, including iPSC derived treatments for inherited retinal degeneration.

Supplementary Material

1

Highlights.

  • Biomechanical analysis of ARPE-19 and human iPSC derived photoreceptor precursor cells.

  • Microfluidic enrichment of human iPSC derived photoreceptor precursor cells.

  • Label-free microfluidic fractionation of human retinal cells to enrich primary photoreceptor cells.

Funding Sources:

NIH, NEI-RO1EY024605

Footnotes

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References

  1. Bohrer LR, Wiley LA, Burnight ER, Cooke JA, Giacalone JC, Anfinson KR, Andorf JL, Mullins RF, Stone EM, Tucker BA, 2019. Correction of NR2E3 Associated Enhanced S-cone Syndrome Patient-specific iPSCs using CRISPR-Cas9. Genes (Basel) 10. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Bongiorno T, Chojnowski JL, Lauderdale JD, Sulchek T, 2016. Cellular Stiffness as a Novel Stemness Marker in the Corneal Limbus. Biophys J 111, 1761–1772. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Bongiorno T, Gura J, Talwar P, Chambers D, Young KM, Arafat D, Wang G, Jackson-Holmes EL, Qiu P, McDevitt TC, Sulchek T, 2018. Biophysical subsets of embryonic stem cells display distinct phenotypic and morphological signatures. PLoS One 13, e0192631. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Burnight ER, Gupta M, Wiley LA, Anfinson KR, Tran A, Triboulet R, Hoffmann JM, Klaahsen DL, Andorf JL, Jiao C, Sohn EH, Adur MK, Ross JW, Mullins RF, Daley GQ, Schlaeger TM, Stone EM, Tucker BA, 2017. Using CRISPR-Cas9 to Generate Gene-Corrected Autologous iPSCs for the Treatment of Inherited Retinal Degeneration. Mol Ther 25, 1999–2013. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Capowski EE, Samimi K, Mayerl SJ, Phillips MJ, Pinilla I, Howden SE, Saha J, Jansen AD, Edwards KL, Jager LD, Barlow K, Valiauga R, Erlichman Z, Hagstrom A, Sinha D, Sluch VM, Chamling X, Zack DJ, Skala MC, Gamm DM, 2019. Reproducibility and staging of 3D human retinal organoids across multiple pluripotent stem cell lines. Development 146. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Curcio CA, Millican CL, Allen KA, Kalina RE, 1993. Aging of the human photoreceptor mosaic: evidence for selective vulnerability of rods in central retina. Invest Ophthalmol Vis Sci 34, 3278–3296. [PubMed] [Google Scholar]
  7. Curcio CA, Sloan KR Jr., Packer O, Hendrickson AE, Kalina RE, 1987. Distribution of cones in human and monkey retina: individual variability and radial asymmetry. Science 236, 579–582. [DOI] [PubMed] [Google Scholar]
  8. Dalvi S, Galloway CA, Singh R, 2019. Pluripotent Stem Cells to Model Degenerative Retinal Diseases: The RPE Perspective. Adv Exp Med Biol 1186, 1–31. [DOI] [PubMed] [Google Scholar]
  9. Du M, Kavanagh D, Zhang Z, Kalia N, 2019. Designing Microfluidic Devices to Sort Haematopoietic Stem Cells Based on Their Mechanical Properties. Stem Cells Int 2019, 8540706. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Eberle D, Santos-Ferreira T, Grahl S, Ader M, 2014. Subretinal transplantation of MACS purified photoreceptor precursor cells into the adult mouse retina. J Vis Exp, e50932. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Eberle D, Schubert S, Postel K, Corbeil D, Ader M, 2011. Increased integration of transplanted CD73-positive photoreceptor precursors into adult mouse retina. Invest Ophthalmol Vis Sci 52, 6462–6471. [DOI] [PubMed] [Google Scholar]
  12. Gagliardi G, Ben M’Barek K, Chaffiol A, Slembrouck-Brec A, Conart JB, Nanteau C, Rabesandratana O, Sahel JA, Duebel J, Orieux G, Reichman S, Goureau O, 2018. Characterization and Transplantation of CD73-Positive Photoreceptors Isolated from Human iPSC-Derived Retinal Organoids. Stem Cell Reports 11, 665–680. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Huang KC, Wang ML, Chen SJ, Kuo JC, Wang WJ, Nhi Nguyen PN, Wahlin KJ, Lu JF, Tran AA, Shi M, Chien Y, Yarmishyn AA, Tsai PH, Yang TC, Jane WN, Chang CC, Peng CH, Schlaeger TM, Chiou SH, 2019. Morphological and Molecular Defects in Human Three-Dimensional Retinal Organoid Model of X-Linked Juvenile Retinoschisis. Stem Cell Reports 13, 906–923. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Hur SC, Brinckerhoff TZ, Walthers CM, Dunn JC, Di Carlo D, 2012. Label-free enrichment of adrenal cortical progenitor cells using inertial microfluidics. PLoS One 7, e46550. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Islam M, Brink H, Blanche S, DiPrete C, Bongiorno T, Stone N, Liu A, Philip A, Wang G, Lam W, Alexeev A, Waller EK, Sulchek T, 2017. Microfluidic Sorting of Cells by Viability Based on Differences in Cell Stiffness. Sci Rep 7, 1997. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Islam M, Mezencev R, McFarland B, Brink H, Campbell B, Tasadduq B, Waller EK, Lam W, Alexeev A, Sulchek T, 2018. Microfluidic cell sorting by stiffness to examine heterogenic responses of cancer cells to chemotherapy. Cell Death Dis 9, 239. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Jeong J, Frohberg NJ, Zhou E, Sulchek T, Qiu P, 2018. Accurately tracking single-cell movement trajectories in microfluidic cell sorting devices. PLoS One 13, e0192463. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Lakowski J, Gonzalez-Cordero A, West EL, Han YT, Welby E, Naeem A, Blackford SJ, Bainbridge JW, Pearson RA, Ali RR, Sowden JC, 2015. Transplantation of Photoreceptor Precursors Isolated via a Cell Surface Biomarker Panel From Embryonic Stem Cell-Derived Self-Forming Retina. Stem Cells 33, 2469–2482. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Lakowski J, Han YT, Pearson RA, Gonzalez-Cordero A, West EL, Gualdoni S, Barber AC, Hubank M, Ali RR, Sowden JC, 2011. Effective transplantation of photoreceptor precursor cells selected via cell surface antigen expression. Stem Cells 29, 1391–1404. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Lamba DA, Karl MO, Ware CB, Reh TA, 2006. Efficient generation of retinal progenitor cells from human embryonic stem cells. Proc Natl Acad Sci U S A 103, 12769–12774. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Liu A, Yu T, Young K, Stone N, Hanasoge S, Kirby TJ, Varadarajan V, Colonna N, Liu J, Raj A, Lammerding J, Alexeev A, Sulchek T, 2020. Cell Mechanical and Physiological Behavior in the Regime of Rapid Mechanical Compressions that Lead to Cell Volume Change. Small 16, e1903857. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Meyer JS, Shearer RL, Capowski EE, Wright LS, Wallace KA, McMillan EL, Zhang SC, Gamm DM, 2009. Modeling early retinal development with human embryonic and induced pluripotent stem cells. Proc Natl Acad Sci U S A 106, 16698–16703. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Moreno-Flores S, Benitez R, Vivanco M, Toca-Herrera JL, 2010. Stress relaxation and creep on living cells with the atomic force microscope: a means to calculate elastic moduli and viscosities of cell components. Nanotechnology 21, 445101. [DOI] [PubMed] [Google Scholar]
  24. Mullins RF, Kuehn MH, Radu RA, Enriquez GS, East JS, Schindler EI, Travis GH, Stone EM, 2012. Autosomal recessive retinitis pigmentosa due to ABCA4 mutations: clinical, pathologic, and molecular characterization. Invest Ophthalmol Vis Sci 53, 1883–1894. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Pelling K.H.a.A.E., 2015. Investigating cell mechanics with atomic force microscopy. J. R. Soc. Interface vol. 12, no. 104, p. 20140970. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Sahly I, Dufour E, Schietroma C, Michel V, Bahloul A, Perfettini I, Pepermans E, Estivalet A, Carette D, Aghaie A, Ebermann I, Lelli A, Iribarne M, Hardelin JP, Weil D, Sahel JA, El-Amraoui A, Petit C, 2012. Localization of Usher 1 proteins to the photoreceptor calyceal processes, which are absent from mice. J Cell Biol 199, 381–399. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Tasadduq B, Lam W, Alexeev A, Sarioglu AF, Sulchek T, 2017. Enhancing size based size separation through vertical focus microfluidics using secondary flow in a ridged microchannel. Sci Rep 7, 17375. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Tucker BA, Mullins RF, Streb LM, Anfinson K, Eyestone ME, Kaalberg E, Riker MJ, Drack AV, Braun TA, Stone EM, 2013. Patient-specific iPSC-derived photoreceptor precursor cells as a means to investigate retinitis pigmentosa. Elife 2, e00824. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Ueda K, Onishi A, Ito SI, Nakamura M, Takahashi M, 2018. Generation of three-dimensional retinal organoids expressing rhodopsin and S- and M-cone opsins from mouse stem cells. Biochem Biophys Res Commun 495, 2595–2601. [DOI] [PubMed] [Google Scholar]
  30. Wang G, Crawford K, Turbyfield C, Lam W, Alexeev A, Sulchek T, 2015a. Microfluidic cellular enrichment and separation through differences in viscoelastic deformation. Lab Chip 15, 532–540. [DOI] [PubMed] [Google Scholar]
  31. Wang G, Mao W, Byler R, Patel K, Henegar C, Alexeev A, Sulchek T, 2013. Stiffness dependent separation of cells in a microfluidic device. PLoS One 8, e75901. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Wang G, Turbyfield C, Crawford K, Alexeev A, Sulchek T, 2015b. Cellular enrichment through microfluidic fractionation based on cell biomechanical properties. Microfluid Nanofluidics 19, 987–993. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Wiley LA, Anfinson KR, Cranston CM, Kaalberg EE, Collins MM, Mullins RF, Stone EM, Tucker BA, 2017. Generation of Xeno-Free, cGMP-Compliant Patient-Specific iPSCs from Skin Biopsy. Curr Protoc Stem Cell Biol 42, 4A 12 11–14A 12 14. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Wiley LA, Burnight ER, DeLuca AP, Anfinson KR, Cranston CM, Kaalberg EE, Penticoff JA, Affatigato LM, Mullins RF, Stone EM, Tucker BA, 2016a. cGMP production of patient-specific iPSCs and photoreceptor precursor cells to treat retinal degenerative blindness. Sci Rep 6, 30742. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Wiley LA, Burnight ER, Drack AV, Banach BB, Ochoa D, Cranston CM, Madumba RA, East JS, Mullins RF, Stone EM, Tucker BA, 2016b. Using Patient-Specific Induced Pluripotent Stem Cells and Wild-Type Mice to Develop a Gene Augmentation-Based Strategy to Treat CLN3-Associated Retinal Degeneration. Hum Gene Ther 27, 835–846. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Xu W, Mezencev R, Kim B, Wang L, McDonald J, Sulchek T, 2012. Cell stiffness is a biomarker of the metastatic potential of ovarian cancer cells. PLoS One 7, e46609. [DOI] [PMC free article] [PubMed] [Google Scholar]

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