Robotic platform for microinjection into single cells in brain tissue

Article30 August 2019Open Access Source DataTransparent process Robotic platform for microinjection into single cells in brain tissue Gabriella Shull Gabriella Shull Department of Biomedical Engineering, University of Minnesota, Twin Cities, MN, USA Department of Biomedical Engineering, Duke University, Durham, NC, USA Search for more papers by this author Christiane Haffner Christiane Haffner Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany Search for more papers by this author Wieland B Huttner Wieland B Huttner orcid.org/0000-0003-4143-7201 Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany Search for more papers by this author Suhasa B Kodandaramaiah Corresponding Author Suhasa B Kodandaramaiah [email protected] orcid.org/0000-0002-7767-2644 Department of Biomedical Engineering, University of Minnesota, Twin Cities, MN, USA Department of Mechanical Engineering, University of Minnesota, Twin Cities, MN, USA Search for more papers by this author Elena Taverna Corresponding Author Elena Taverna [email protected] orcid.org/0000-0002-2430-4725 Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany Search for more papers by this author Gabriella Shull Gabriella Shull Department of Biomedical Engineering, University of Minnesota, Twin Cities, MN, USA Department of Biomedical Engineering, Duke University, Durham, NC, USA Search for more papers by this author Christiane Haffner Christiane Haffner Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany Search for more papers by this author Wieland B Huttner Wieland B Huttner orcid.org/0000-0003-4143-7201 Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany Search for more papers by this author Suhasa B Kodandaramaiah Corresponding Author Suhasa B Kodandaramaiah [email protected] orcid.org/0000-0002-7767-2644 Department of Biomedical Engineering, University of Minnesota, Twin Cities, MN, USA Department of Mechanical Engineering, University of Minnesota, Twin Cities, MN, USA Search for more papers by this author Elena Taverna Corresponding Author Elena Taverna [email protected] orcid.org/0000-0002-2430-4725 Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany Search for more papers by this author Author Information Gabriella Shull1,2,‡, Christiane Haffner3,‡, Wieland B Huttner3, Suhasa B Kodandaramaiah *,1,4 and Elena Taverna *,3,5 1Department of Biomedical Engineering, University of Minnesota, Twin Cities, MN, USA 2Department of Biomedical Engineering, Duke University, Durham, NC, USA 3Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany 4Department of Mechanical Engineering, University of Minnesota, Twin Cities, MN, USA 5Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany ‡These authors contributed equally to this work *Corresponding author. Tel: +1 612 301 1636; E-mail: [email protected] *Corresponding author. Tel: +49 341 3550; E-mail: [email protected] EMBO Reports (2019)20:e47880https://doi.org/10.15252/embr.201947880 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Microinjection into single cells in brain tissue is a powerful technique to study and manipulate neural stem cells. However, such microinjection requires expertise and is a low-throughput process. We developed the "Autoinjector", a robot that utilizes images from a microscope to guide a microinjection needle into tissue to deliver femtoliter volumes of liquids into single cells. The Autoinjector enables microinjection of hundreds of cells within a single organotypic slice, resulting in an overall yield that is an order of magnitude greater than manual microinjection. The Autoinjector successfully targets both apical progenitors (APs) and newborn neurons in the embryonic mouse and human fetal telencephalon. We used the Autoinjector to systematically study gap-junctional communication between neural progenitors in the embryonic mouse telencephalon and found that apical contact is a characteristic feature of the cells that are part of a gap junction-coupled cluster. The throughput and versatility of the Autoinjector will render microinjection an accessible high-performance single-cell manipulation technique and will provide a powerful new platform for performing single-cell analyses in tissue for bioengineering and biophysics applications. Synopsis This study reports the Autoinjector, a robotic tool that enables microinjection of hundreds of cells within a single organotypic slice. The robot is validated in brain tissues and renders microinjection a broadly accessible high-performance cell manipulation technique. We have developed a robot to inject chemicals into single neural stem cells and neurons in brain tissue. The robot allows to follow and change the behavior of single cells in tissues, to study how they contribute to tissue morphogenesis. The robot is a useful tool to study developmental and evolutionary neuroscience at the cellular level. Introduction Microinjection into cells, in which a glass micropipette is briefly inserted into the cytoplasm or nucleus of a cell to introduce femtoliters of reagents, notably membrane-impermeant ones, is an important tool to manipulate and track single cells and, if applicable, their progeny 1-4. Recently, microinjection has been adapted to target single neural stem cells in organotypic slices from the developing brain tissue, where it offers several unique advantages 5, 6. First, thanks to its excellent single-cell resolution, microinjection of fluorescent dyes allows correlating single-cell behavior as observed upon live imaging with tissue morphogenesis. Second, microinjection provides flexibility with regard to the chemical composition and complexity of the solution delivered into the cells. For example, pools of RNAs, or even an entire transcriptome, can be delivered into a cell, which allows for the combinatorial analysis of genes affecting brain development 6-9. Moreover, unlike electroporation, microinjection enables the delivery of both charged and non-charged molecules. Finally, recent work has shown that microinjection can target neural stem cells from multiple species 5 and can be used for CRISPR/Cas9-mediated disruption of gene expression 7. Despite the advantages it offers, microinjection suffers of several limitations: It is a low-throughput and low-yield process, and it requires a high level of skill and significant practice to master. Ideally, the stereotyped operation of precisely steering the microinjection pipette to cells while visualizing the pipette and tissue under microscope guidance can be implemented by a robotic system, so that sources of variability such as the depth of microinjection, the spacing between injections, and the volume of solution delivered to the cells can be precisely controlled. Such a robot would greatly increase throughput and yield of microinjection in tissue, opening this technology to a greater user base within neuroscience, developmental biology, cell biology, and biophysics. Here, we report the development of an image-guided microinjection robot, the "Autoinjector", that utilizes images acquired from a microscope to guide the microinjection needle into single cells in tissue at controlled pressure with micrometer-scale precision. This process can be repeated to target hundreds of cells, thereby increasing the rate and success of microinjection by an order of magnitude as compared with manual operation. The Autoinjector allowed us to target neural stem cells and follow their lineage progression in culture over time. The Autoinjector was also used to study the cell-to-cell communication in the developing mouse telencephalon. We focused our attention on gap-junctional coupling in neural stem and progenitor cells and found that coupled clusters contain both apical and basal progenitors, the two main classes of stem and progenitor cells in the mouse developing brain. Finally, we made use of the micrometer-scale precision and the flexibility of the robot by targeting single newborn neurons in organotypic slices from the mouse and human developing brain, a result never achieved before. The Autoinjector can be implemented on any standard microscope setup and will be a valuable resource for developmental neurobiologists to study brain development. Not limited to the developmental biology field, the Autoinjector will enable the quantitative analysis of single-cell behavior in brain tissue in developmental biology, cell biology, and biophysics. Results The Autoinjector—a robot for image-guided microinjection into single cells in brain tissue A typical microinjection experiment involves a user guiding the injection micropipette to the surface of the tissue, inserting the pipette tip into the tissue while the micropipette is held under positive pressure to perform the injection, and withdrawing the micropipette back within a brief period. The depth of tissue penetration and time the micropipette stays inserted inside the cell affect the efficacy of microinjection. Both these parameters are highly dependent on individual experimenter's skill and experience and are thus prone to inconsistencies, thereby leading to low yield 5, 6. Lastly, the procedure is extremely tedious to perform. All these hurdles prevent users from injecting a large number of cells, making microinjection into cells in tissue challenging to be used as a robust tool to track and manipulate cells. We built a robot, the "Autoinjector", which can precisely control the position of the injection micropipette using microscope image guidance (Fig 1). The Autoinjector requires relatively simple modifications to a conventional microinjection station. The injection micropipette is attached to a micropipette holder with a pressure inlet which is mounted on a manipulator for programmatic three-axis position control (Fig 1A). The manipulator is integrated into an inverted microscope (Fig 1A and C), and the pressure inlet is connected to a custom pressure regulator for precise pressure control (Fig 1B and C). Images acquired from the microscope camera are used by an algorithm to guide the injection micropipette to precise locations in the microscope field of view (FOV; Fig 1C, and 2A and B; see also User Manual). Figure 1. Autoinjector—an image-guided microinjection platform Photograph of the manipulator end of the Autoinjector. Scale bar is 40 mm. Photograph showing the custom pressure controller used for programmatic pressure control during automated microinjection. Scale bar is 8 mm. Overall hardware schematic. A computer interfaces with all the components of the platform, including the pressure controller, manipulator, and microscope camera. Images from the microscope camera are used to control the position of the micropipette using the manipulator. The pressure controller is used to precisely deliver injection pressure to the micropipette during microinjection. House pressure is coarsely downregulated by a mechanical regulator, followed by fine downregulation using an electronic regulator controlled by a microcontroller. Delivery of pressure to the micropipette is digitally controlled using a solenoid valve. The trajectory of the micropipette is controlled by the manipulator and is guided using images acquired using the microscope. Black arrows indicate digital interface routes, and blue arrows indicate pneumatic route. Download figure Download PowerPoint Figure 2. Image-guided microinjection protocol and model system Schematic of targeted telencephalon: Left, E14.5 brain; middle, coronal section of the brain; right, inset illustrating the organization and major cell types of the cortical wall. APs attached to the ventricle are targeted by automated microinjection. Microscope images of the tissue and microinjection pipette before (top) and after (bottom) annotation of the tissue surface and microinjection micropipette tip by the experimenter. Scale bars: top 100 μm, bottom 10 μm. Ap indicates the apical surface, and BL indicates the basal lamina. A cartoon schematic of the microinjection protocol. (i) The user brings the micropipette into the microscope FOV close to the tissue. (ii) The manipulator is calibrated to allow for image-guided positioning of the micropipette using a calibration algorithm (see Appendix Note S1, and Appendix Fig S2 for additional details). (iii) The user annotates image with the desired trajectory of microinjection by dragging a cursor over the edge of the tissue and clicks a point indicating the tip of the micropipette. (iv) Before injection begins, the user brings the tip of the micropipette close to the tissue surface and specifies the starting point. (v) The following steps are fully automated. The Autoinjector positions the micropipette at the surface of the tissue and advances the micropipette to a specified depth under pressure into the tissue resulting in microinjection. The micropipette is then retracted out of the tissue and repositioned at the next microinjection site. This is repeated until the micropipette reaches the end of the microinjection trajectory. The user can independently specify the depth of microinjection, retraction, and spacing between microinjections (see Appendix Note S2, and Appendix Fig S2 for additional information). Ap indicates the apical surface, and BL indicates the basal lamina. Download figure Download PowerPoint A Movie illustrating the operation of the Autoinjector using organotypic slice cultures of embryonic day 16.5 (E16.5) mouse telencephalon is shown in Movie EV1. First, the magnification of the microscope objective is set by the user in the graphic user interface (GUI, Appendix Fig S1). The injection micropipette is brought into the microscope FOV and imaged along with the tissue (Fig 2A and B top, 2C.i). This is followed by a calibration step, where displacement of the injection micropipette in three dimensions is projected onto the corresponding displacement in the two-dimensional microscope image (Appendix Fig S2A, Appendix Note S1). Once the calibration step is completed, the Autoinjector can guide the injection micropipette to specific locations in the FOV using the micromanipulator, similar to previous algorithms 10, 11. The user then draws a line along the desired path of microinjection on the microscope image using the graphical user interface (GUI; Fig 2B bottom, 2C.iii; see also User Manual). This is followed by specifying the starting point of the injection micropipette by clicking the tip of the microinjection pipette in the GUI (Fig 2B, bottom, 2C.iv). The algorithm then computes a trajectory (Appendix Note S2) based on the depth into tissue the injection micropipette penetrates for each microinjection attempt, the distance the micropipette is pulled out of the tissue after an injection attempt, and the spacing between subsequent microinjection attempts. Each of these parameters can be independently specified by the user in the GUI (Appendix Figs S1 and S2, Appendix Note S2). Next, the Autoinjector positions the tip of the injection micropipette at the surface of the tissue, inserts the injection micropipette into the tissue to perform a microinjection, retracts the injection micropipette, and positions the injection micropipette at the next location along the path (Fig 2C.v, Movie EV1). This process is repeated until microinjections are completed along the entire path annotated by the user. A constant user-defined pressure is applied to the injection micropipette using the pressure controller throughout this process (see also User Manual). Prior to microinjection experiments, we investigated the Autoinjector's ability to target locations specified by the user in the microscope FOV. The experimenter annotates the tip of the microinjection pipette at various steps during calibration. Differences in individual perception of the micropipette tip may lead to systematic errors in positioning the pipette after calibration. To test for these effects, three experimenters with no prior experience using the Autoinjector performed calibration (Appendix Note S3). The angle between the camera FOV reference axes and the micropipette reference axes, a key parameter used in performing the transformation between the two coordinate systems (Appendix Note S1), was not significantly different when calibration was performed by either experimenter (P = 0.400, P = 0.700, P = 0.800 for experienced user vs. inexperienced user 1, 2, and 3, respectively). To quantify the spatial error of the Autoinjector, pipettes were calibrated by the expert experimenter and commanded to eight locations spread across 75% of the FOV which represents the area where the targeted tissue is located. The error in image-guided positioning of the pipette was 0.211 ± 0.182 μm along the x axis and 0.345 ± 0.415 μm along the y axis of the manipulator (n = 8 locations, five measurements per location). This error in spatial positioning is much smaller than dimensions of the trajectories (10's to 100's of μm) used and was thus sufficient for image-guided targeting of single cells across the samples. Optimizing automated microinjection Having established that the injection micropipette could be guided to locations in the microscope FOV with micrometer-scale precision, we attempted injection of fluorescent dye into apical progenitors (APs) in organotypic slices of the E14.5 mouse telencephalon and optimized the parameters for automated microinjection (Fig 3 and Movie EV2). APs are important cells for the generation of mature neurons in the neocortex and are key to understanding how higher level cognitive functions evolved in mammals 12-15. Injecting APs with dye and tracking their progeny provides a good model for studying stem cell biology and cell fate specification in tissue 5-7. The apical plasma membrane of APs faces the ventricle (Fig 2A), and their nuclei are found at distances ranging from 5 to 150 μm from the apical plasma membrane depending on the phase of cell cycle 14-16. During microinjection, we approached APs from the ventricular surface. An ideal microinjection attempt targets the apical process of the APs injecting roughly 10% of the cytosolic volume 6. Experimenters modulate the internal pressure of the injection micropipette between 75 and 125 mbar pressure during microinjection 5, 6. This internal pressure is a key determinant of the injection volume. We used this range as a heuristic starting point and investigated how the internal pressure of the injection micropipette affected microinjection yield (Fig 3A and B). Microinjections were performed at a depth of 10 μm from the ventricular surface. Microinjections performed at 75 mbar resulted in 35% of cells successfully injected (n = 314 attempts total, two slices). When performed at 100 mbar, 35% of cells were successfully injected (n = 185 attempts total, two slices), whereas when microinjections performed at 125 mbar resulted in 8% of cells successfully injected (n = 287 attempts total, two slices). Given that there was no significant difference between the microinjection efficiency obtained when applying an internal pressure set at 75 mbar vs. 100 mbar, we decided to use a pressure of 75 mbar pressure to minimize the stress applied to the cells and to the tissue during microinjection. Figure 3. Autoinjector performance (Left) Confocal image of a section of mouse telencephalon fixed and stained immediately after microinjection. Dashed lines indicate locations in the slice where microinjections were attempted at different pressures. Microinjections were attempted at pressure of 75 mbar (left area), 100 mbar (center area), and 125 mbar (right area). (Right) Percentage of successful injections achieved by the Autoinjector at 75 mbar (35%, n = 314 total, 110 successful), 100 mbar (35%, n = 185 total, 65 successful), and 125 mbar (8%, n = 287 total, 23 successful). (Left) Confocal images of a section of mouse telencephalon fixed and stained immediately after microinjection. Dashed lines indicate locations in the slice where microinjections were attempted at different depths keeping the pressure constant at 75 mbar. Microinjections were attempted at depths of 10 μm (left area), 15 μm (center area), and 25 μm (right area) into the apical surface. (Right) Percentage of successful injections achieved by the Autoinjector at depths of 10 μm (68%, n = 163 total, 111 successful), 15 μm (22%, n = 170 total, 37 successful), and 25 μm (11%, n = 169 total, 19 successful) into the apical surface. Successful injections represented as a percentage of total for a novice user on the manual microinjection system, an experienced user on the manual microinjection system, and the Autoinjector. Total injection attempts per minute for a novice user on the manual microinjection system, an experienced user on the manual microinjection system, and the Autoinjector. Successful injections per minute for a novice user on the manual microinjection system, an experienced user on the manual microinjection system, and the Autoinjector. Cells targeted at a constant spacing of 30 μm, scale bar is 10 μm. Data information: In (A, B), scale bars are 20 μm and v indicates a vessel. For (C–E), n = 190 attempts, five slices for novice user, n = 181 attempts, four slices for an experienced user, and n = 864 attempts, four slices for a novice user on the Autoinjector. Significance was tested using a Mann–Whitney U-test, error bars indicate standard deviation, and **P < 0.001. Source data are available online for this figure. Source Data for Figure 3 [embr201947880-sup-0010-SDataFig3.xlsx] Download figure Download PowerPoint We next assessed how the depth to which the pipette penetrates tissue affects microinjection yield. The Autoinjector was programmed to axially penetrate to depths of 10, 15, and 25 μm from the assigned ventricular surface for microinjection (Fig 3B) with the internal pressure of the micropipette set to 75 mbar. With a 45° approach angle relative to the normal to the tissue surface, this translated to 5, 11, and 18 μm depths from the assigned ventricular surface. This range of depths accounted for variation in cell locations and potential deformations of the ventricular surface during microinjection. Microinjections performed at depths of 10 μm resulted in 68% of cells successfully injected (n = 163 microinjection attempts, two slices). Microinjections performed at depths of 15 μm resulted in 22% of cells successfully injected (n = 170 microinjection attempts, two slices). Microinjections performed at depths of 25 μm resulted in 11% of cells successfully injected (n = 169 microinjection attempts, two slices). For the subsequent experiments, we used an injection depth of 10 μm for injecting APs unless otherwise stated. Robotic control of pressure and position thus allowed us to systematically explore parameters affecting microinjection yield. Comparison of manual and automated microinjection We next compared the performance of the Autoinjector to manual microinjections performed by a novice experimenter (no prior microinjection experience) and an experienced experimenter (5 years of microinjection experience, Fig 3C–E). The Autoinjector was operated by a user with no prior microinjection experience. The optimized depth and pressure parameters derived above were used in these experiments (pressure = 75 mbar, depth = 10 μm). A successful microinjection was indicated by co-localization of the microinjected dye, Dx3-Alexa488, and DAPI, in slices fixed immediately after the experiment (Appendix Fig S3 and Movie EV3). We found that a novice experimenter performing manual microinjection had a success of 7.46 ± 4.26% with an injection rate of 14.15 ± 3.13 attempts/min (n = 190 attempts, five slices). This corresponds to a successful microinjection rate of 1.09 ± 0.67 injections/min. An experienced experimenter performing manual microinjection had a success of 20.41 ± 2.91% with an injection rate of 24.14 ± 3.38 attempts/min (n = 181 attempts, four slices). This corresponds to a successful microinjection rate of 4.95 ± 1.05 injections/min. A novice user performing microinjection using the Autoinjector had a success of 43.73 ± 9.11% (Figs 3C and EV1) with an injection rate of 35.92 ± 2.12 attempts/min (n = 864 attempts, four slices; Fig 3D). This corresponds to a successful microinjection rate of 15.52 ± 2.48 injections/min (Fig 3E). We found that the experienced user had a significantly higher injection success, and rate compared to a novice user using the manual microinjection (see Materials and Methods, P = 0.004), the novice user using the Autoinjector had a significantly higher injection success, and rate compared with the novice user on the manual system (see Materials and Methods, P = 0.0079), and the novice user using the Autoinjector had a significantly higher injection success, and rate compared with the experienced user on the manual system (see Materials and Methods, P = 0.0079). The novice experimenter using the Autoinjector achieved a 15-fold increase in successful injection rate relative to a novice experimenter performing manual microinjections, and a 3-fold increase in successful injection rate relative to an experienced experimenter performing manual microinjections. The increase in injection rate enabled by the Autoinjector represents a significant improvement in yield (Fig 3F; see also Fig EV1). Click here to expand this figure. Figure EV1. Autoinjector performance using different chemicals Top: schematic of telencephalon microinjected with different classes of chemicals. Bottom: list of chemicals that were tested (microinjection solution), divided by category. Successful injections represented as a percentage of total all chemicals. The results are divided by experiment and by slice to show the intrinsic variability of the system. Source data are available online for this figure. Download figure Download PowerPoint We next assessed if robotic microinjection affected cell viability by quantifying cell death (Appendix Fig S4) and the progression through cell cycle (Appendix Fig S5). Cell death was assessed by counting the number of picnotic nuclei in the VZ and SVZ and by performing cumulative EdU labeling. We first assessed if the cell viability was affected by the slice culture procedure by comparing the tissue in vivo (n = 5 sections) with slices that did not undergo microinjection (non-injected slices, n = 5 slices). We found that the slice culture procedure increases the number of picnotic nuclei as compared to tissue in vivo (P = 0.004 using Wilcoxon rank-sum test see Materials and Methods and Appendix Fig S4). This is to be expected because the tissue slicing can be a traumatic process compromising cell viability. We next assessed if the cell viability was affected by the microinjection process. We compared the number of picnotic nuclei in non-injected slices (n = 5 slices) and in slices that underwent manual (n = 7 slices) or automated microinjection (n = 6 slices). We did not observe any significant difference in the number of picnotic nuclei when comparing non-injected with manually injected slices (P = 0.6806, using Wilcoxon rank-sum test see Materials and Methods and Appendix Fig S4), nor when comparing non-injected with slices injected with the Autoinjector (P = 0.6688, using Wilcoxon rank-sum test see Materials and Methods and Appendix Fig S4). Cell viability and progression through cell cycle were also assessed using 24 h EdU cumulative labeling (Appendix Fig S5). We saw no difference between the EdU incorporation in non-injected slices (n = 4 slices) and non-injected cells in a manually microinjected slice (n = 1 slice, P = 0.6000, using Wilcoxon rank-sum test see Materials and Methods and Appendix Fig S5) and non-injected cells in an automated microinjected slice using the dye alone (n = 3 slices, P = 0.5714, using Wilcoxon rank-sum test see Materials and Methods and Appendix Fig S5). We saw no difference between the EdU incorporation in non-injected slices (n = 4 slices) and injected cells in a manually microinjected slice (n = 4 slices, P = 0.5571, using Wilcoxon rank-sum test see Materials and Methods and Appendix Fig S5) and injected cells in an automated microinjected slice using the dye alone (n = 3 slices, P = 0.5714, using Wilcoxon rank-sum test see Materials and Methods and Appendix Fig S5), or the dye and RFP mRNA (n = 1 slice, P = 0.4000, using Wilcoxon rank-sum test see Materials and Methods and Appendix Fig S5). Based on these observations, we can conclude that the Autoinjector does not compromise tissue and cell viability. Autoinjector allows tracing cell fate transition and lineage progression of neural stem and progenitor cells in tissue Microinjection is a useful tool to track neural stem and progenitor cells, and their progeny in organotypic slice culture as individual cells can be labeled in a sparse and spatially defined fashion 6. We used the Autoinjector to inject APs along the ventricular surface and tracked the location, morphology, and cell identity of the injected cells and their progeny after 0, 24, and 48 h in culture (Figs 4 and EV2). The injected cells and their progeny were scored based on the distance of the