Robotic Cell Manipulation

By Dong Sun

Robotic Cell Manipulation introduces up-to-date research to realize this new theme of medical robotics. The book is organized in three levels: operation tools (e.g., optical tweezers, microneedles, dielectrophoresis, electromagnetic devices, and microfluidic chips), manipulation types (e.g., microinjection, transportation, rotation fusion, adhesion, separation, etc.), and potential medical applications (e.g., micro-surgery, biopsy, gene editing, cancer treatment, cell-cell interactions, etc.). The technology involves different fields such as robotics, automation, imaging, microfluidics, mechanics, materials, biology and medical sciences. The book provides systematic knowledge on the subject, covering a wide range of basic concepts, theories, methodology, experiments, case studies and potential medical applications.

It will enable readers to promptly conduct a systematic review of research and become an essential reference for many new and experienced researchers entering this unique field.

• Introduces the applications of robot-assisted manipulation tools in various cell manipulation tasks
• Defines many essential concepts in association with the robotic cell manipulation field, including manipulation strategy and manipulation types
• Introduces basic concepts and knowledge on various manipulation devices and tasks
• Describes some cutting-edge cell manipulation technologies and case studies

• Language: English
• Publisher: Academic Press
• Release date: May 31, 2022
• ISBN: 9780323852609

Dong Sun

Dong Sun is currently a Chair Professor and Head of the Department of Biomedical Engineering, and also Director of the Centre of Robotics and Automation, City University of Hong Kong. He is among the leading contributors worldwide in pioneering work in robotic manipulation of biological cells. His research has breakthrough in the use of combined robotics and various micro-engineering tools including optical tweezers, micro-needles and electromagnetic devices to achieve cell manipulation, diagnosis and micro-surgery at the single cell level. He led the invention of the magnetically driven microrobots that deliver cells to precise locations in the body. Over the past 20 years, he has co-authored 17 books and book chapters, 420 journal and conference papers with h-Index of over 50, and holds 13 international patents. He has received a lot of awards including best paper awards, Natural Science Award from China, and Hong Kong Awards for Industry. He is a fellow of Canadian Academy of Engineering and a fellow of IEEE.

Book preview

Robotic Cell Manipulation

Dong Sun

Department of Biomedical Engineering, Centre of Robotics and Automation, City University of Hong Kong, Hong Kong, China

Table of Contents

Cover image

Title page

Copyright

Dedication

Preface

Acknowledgments

1. Introduction

1.1. Overview of robot-facilitated cell manipulation

1.2. Outline of the book

1.3. Conclusions

2. Cell manipulation tools

2.1. Introduction

2.2. Microneedle

2.3. Optical tweezers

2.4. Electrokinetics

2.5. Magnetic manipulator

2.6. Atomic force microscopy

2.7. Imaging for intracellular manipulation

2.8. Conclusions

3. Robotic cell injection

3.1. Introduction

3.2. Robot-assisted cell microinjection system with microneedles

3.3. Hybrid position and force control for automated batch injection of cells

3.4. Universal piezo-driven ultrasonic cell microinjection

3.5. Automated microinjection for small human cells

3.6. Automated high-productivity microinjection for adherent cells

3.7. Single-cell transfection through precise microinjection with quantitatively controlled injection volumes

3.8. Characterization of mechanical properties of cells through microinjection

3.9. Conclusions

4. Cell stretching and compression

4.1. Introduction

4.2. Cell stretching with optical tweezers

4.3. Probing cell biophysical behavior based on actin cytoskeleton modeling of cells and stretching manipulation with optical tweezers

4.4. Cell stretching with dielectrophoresis technology

4.5. Cell compression under mechanical confinement

4.6. Magnet-based cell deformation for intracellular delivery

4.7. Conclusions

5. Cell transport with optical tweezers

5.1. Introduction

5.2. Basic theory and methods

5.3. Motion and path planning for automatic cell transportation

5.4. Unified motion control design

5.5. Multiple cell transportation for cell pairing

5.6. Cell transportation for multiprocessing automation tasks

5.7. Conclusions

6. Cell rotation

6.1. Introduction

6.2. Automated in-plane rotation of cells using a robot tweezers manipulation system

6.3. Automated out-of-plane rotation of cells using a robot tweezers manipulation system

6.4. Conclusions

7. Three-dimensional image reconstruction and intracellular surgery

7.1. Introduction

7.2. 3D image reconstruction

7.3. Robot-assisted intracellular delivery with 3D image reconstruction information

7.4. Conclusions

8. Cell sorting and separation

8.1. Introduction

8.2. Cell sorting using combined optical tweezers and microfluidic chip technology

8.3. Cell isolation and deposition

8.4. A simplified sheathless cell separation approach

8.5. Conclusions

9. Cell stimulation and migration control

9.1. Introduction

9.2. Dynamic model of chemoattractant-induced cell migration

9.3. Cell migration control using a stimulus-induced robotic manipulation system

9.4. Electrical stimulation based on calcium spike patterns of MSCs to improve osteogenic differentiation

9.5. Conclusions

10. Cell patterning

10.1. Introduction

10.2. Cell patterning with robotically controlled optical tweezers

10.3. Cell patterning using a dielectrophoresis-based multilayer scaffold structure

10.4. Cell patterning using a gravitational sedimentation-based microfluidic approach

10.5. Conclusions

11. Cell adhesion

11.1. Introduction

11.2. Manipulating cell adhesion with optical tweezers

11.3. Adhesion-mediated cell–cell interaction

11.4. A case study of cell adhesion characterization

11.5. Conclusions

12. Cell fusion

12.1. Introduction

12.2. Laser-induced fusion with optical tweezers

12.3. Cell fusion with combined optical tweezers and microwell array technology

12.4. A case study of transforming liver cancer cells into tumor initiating-like cells by cell fusion

12.5. Conclusions

13. Cell navigation and delivery in vivo

13.1. Introduction

13.2. In vivo navigation of single cells with an optical tweezers-based manipulator

13.3. Magnetic microrobot for carrying and delivering cells in vivo

13.4. Precise delivery of stem cells for cancer therapy using magnet-driven and image-guided degradable microrobots

13.5. Conclusions

14. Organelle biopsy and gene editing of single cells

14.1. Introduction

14.2. Automated organelle biopsy of single cells using microneedles

14.3. Gene-delivery approaches for MSC function improvement

14.4. Automated optical tweezers manipulation for mitochondrial transfer

14.5. Conclusions

15. Summary

15.1. Operation tools for cell manipulations

15.2. Cell manipulation types

15.3. Potential medical applications

Index

Copyright

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Notices

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Dedication

To my wife—Sheryl Jiang.

Preface

Cell manipulation is a technique that uses special technical methods to shape and manipulate cells, including changing cell functions, which can help treat and fight diseases and also aid cells to regain their vitality. Traditional cell manipulation methods have limitations in accuracy, success rate, proficiency, efficiency, and consistency. Robotic cell manipulation is an emerging technology that combines robotics and micromanipulation tools to automatically and accurately complete various manipulation tasks on individual cells. The use of robotics to promote cell manipulation will provide solutions to surmount the challenges faced by traditional cell manipulation methods and promote the practical application of cell therapy.

This book introduces the combination of robotics and various micromanipulation tools, such as microneedles, optical tweezers, dielectrophoresis, electromagnetic devices, and microfluidic chips, to achieve efficient and accurate automatic cell manipulation. Using these robot-assisted operating tools, many cell manipulation tasks can be successfully completed, such as cell microinjection, cell stretching, cell transportation, cell orientation and 3D imaging, cell sorting and separation, cell stimulation and migration, cell patterning, cell adhesion, cell fusion, in vivo cell navigation and delivery, single-cell biopsy, and gene editing. These emerging technologies will allow many new and unforeseen clinical applications that were previously considered impossible.

This book is organized over three levels: operation tools from microneedles to optical tweezers, manipulation types such as microinjection and transportation, and potential medical applications across microsurgery, gene editing, and cancer treatment. The following chapters are included in the book:

Chapter 1 Introduction

Chapter 2 Cell Manipulation Tools

Chapter 3 Robotic Cell Injection

Chapter 4 Cell Stretching and Compression

Chapter 5 Cell Transport with Optical Tweezers

Chapter 6 Cell Rotation

Chapter 7 3D Image Reconstruction for Intracellular Surgery

Chapter 8 Cell Sorting and Separation

Chapter 9 Cell Stimulation and Migration Control

Chapter 10 Cell Patterning

Chapter 11 Cell Adhesion

Chapter 12 Cell Fusion

Chapter 13 Cell Navigation and Delivery in Vivo

Chapter 14 Organelle Biopsy and Gene Editing of Single Cells

Chapter 15 Summary

The key objectives of this book are as follows:

1. Introduces the application of robot-assisted manipulation tools in various cell manipulation tasks, which can potentially diagnose and treat diseases at the single-cell level.

2. Defines many essential concepts in association with the robotic cell manipulation field, including manipulation strategy and manipulation types.

3. Introduces the basic concept and knowledge of various manipulation devices and tasks.

4. Describes some cutting-edge cell manipulation technologies and case studies.

This book systemically introduces robotic cell manipulation technologies and discusses their application prospects in the early diagnosis and treatment of diseases. It covers a wide range of basic concepts, theories, methodologies, experiments, case studies, and potential medical applications. It will enable readers to conduct a systematic review of research and is an essential reference for both new and experienced researchers in this unique field.

Acknowledgments

First, I would like to thank my students listed below. Over the past few years, they have studied for a Ph.D. degree at the City University of Hong Kong under my guidance and have participated in many research developments reported in this book. Without their outstanding contributions and strong support, it would have been impossible for me to complete this book.

Haibo Huang (graduated in 2008)

Haoyao Chen (graduated in 2009)

Yu Xie (graduated in 2010)

Youhua Tan (graduated in 2010)

Songyu Hu (graduated in 2011)

Xiangpeng Li (graduated in 2011)

Yanhua Wu (graduated in 2011)

Xiaolin Wang (graduated in 2012)

Tao Ju (graduated in 2012)

Xiao Yan (graduated in 2013)

Kaiqun Wang (graduated in 2013)

Shuxun Chen (graduated in 2014)

Xue Gou (graduated in 2014)

Hao Yang (graduated in 2015)

Mingyang Xie (graduated in 2016)

Ran Wang (graduated in 2016)

Bill Yu-ting Chow (graduated in 2016)

Weicheng Ma (graduated in 2017)

Zhijie Huan (graduated in 2017)

Xiaojian Li (graduated in 2017)

Fuzhou Niu (graduated in 2017)

Junyang Li (graduated in 2018)

Jundi Hou (graduated in 2018)

Tao Luo (graduated in 2018)

Ke Meng (graduated in 2018)

Dongce Ma (graduated in 2018)

Adnan Shakoor (graduated in 2019)

Pan Liao (graduated in 2019)

Dongfang Li (graduated in 2019)

Wendi Gao (graduated in 2020)

Tanyong Wei (graduated in 2020)

Lei Fan (graduated in 2021)

Fei Pan (graduated in 2021)

Zhangyan Guan (graduated in 2021)

Liuxi Xing (graduated in 2022)

I would also like to thank my research collaborators for their long-term support of my research, especially Professor James K. Mills from the University of Toronto, Canada, Professor Nancy Kwan Man from the University of Hong Kong, Professor Gang Li from the Chinese University of Hong Kong, and Professor Shuk Han Cheng and Professor Raymond Lam from City University of Hong Kong. I would also like to thank Dr. Henry Chu, who was my postdoctoral researcher from 2013 to 2015.

Finally, I am most sincerely grateful to my wife, Sheryl Jiang, for her love, care, and unconditional support.

1: Introduction

Abstract

This chapter presents a broad overview of robotic cell manipulation along with basic technologies and applications to ensure an understanding of the topic. First, the reader will have the opportunity to learn about the background of cell manipulations and how robotics technology can be integrated with the cell manipulation process to improve accuracy and efficiency. Some general manipulation tools, such as microneedles, optical tweezers, electrical and magnetic devices, and microfluidic chips are introduced. Second, an outline of the book is presented. After a general introduction to cell manipulation tools, various cell manipulations and their specific applications are introduced in later chapters, which include cell injection, cell stretching and deformation, cell transportation and rotation, 3D image reconstruction and intracellular surgery, cell sorting and isolation, cell stimulation and migration, cell patterning, cell adhesion, cell fusion, cell navigation and delivery in vivo, and organelle biopsy and gene editing of cells. Finally, a short summary is provided.

Keywords

Cell manipulation; Cell transport; Microfluidics; Microinjection; Robotics

1.1. Overview of robot-facilitated cell manipulation

Cells are the smallest functional units of all living organisms. Discovered more than 300 years ago, cells remain a very challenging yet interesting subject for medical and biological researchers. To date, the process of understanding the mechanisms that allow cells to function are still being studied. The functions of cells and organisms are essentially controlled by individual biomolecules and their interactions, whereas cell studies have been limited by the availability of high-resolution technologies.

Cell manipulation refers to a process that can shape and manipulate cells of any type of organism through special technical means, including changing cell functions, which is beneficial to treating and resisting diseases and in aiding cells to rejuvenate. Clinical trials based on cell therapies, such as restoring the vision of a blind patient via stem cell therapy (Science Alert, 2016) and curing an infant's leukemia via gene editing (BBC News, 2015), have been reported. Traditionally, cell manipulation is achieved by chemical methods, such as chemical transfection, to improve the quality of cells. Under certain conditions, physical manipulations, such as optical, mechanical, and electrical techniques, have also been used more frequently in recent years. Recent decades have witnessed transformative developments in the use of various manipulation tools including microneedles, optical tweezers, and electromagnetic devices to achieve single-cell manipulations, such as cell fusion (Chen et al., 2013), optical transfection (Antkowiak et al., 2013), extraction of subcellular organelles (Ando et al., 2008), and cell enucleation (Feng et al., 2013).

Over the past few years, robotics technologies have been demonstrated as proven tools in control, vision, and system intelligence for macro-object manipulations, and they have been further extended to microscale objects successfully by integrating microelectromechanical system (MEMS) technology. The ability to manipulate cells and various biological molecules with high precision and high throughput is essential in many real applications. Combining robotics technology with traditional physical operations has attracted increasing attention because it can achieve high-quality, high-volume, and high-precision automated manipulations at the single-cell level. Currently, robot-assisted cell manipulations rely on the use of microneedles, optical tweezers, electrical and magnetic devices, and microfluidic chip technologies.

1.1.1. Robotic manipulation of biological objects

Robotic manipulation of objects for industrial manufacturing has been sufficiently studied since the 1990s (Sun et al., 1999; Sun and Mills, 2002). In recent decades, there has been a tremendous increase in micro- and nanoscale biological tasks related to human health and well-being. Examples include injecting pronuclei DNA into cellular embryos (oocytes) to produce transgenic organisms, injecting microRNA into cells and embryos, cell transfection, gene editing, and cell therapy. In these biological processes, the demand for high-precision and high-throughput automated manipulations continues to increase, providing new micro/nanoscale research opportunities in the field of robotics and automation. Although some biomedical processes have been automated, many are conducted manually by highly skilled workers. Given the significant challenge of controlling the position and force applied to biological cells and the variability in human manual control, the success rate of manual operation is relatively low. Therefore, there is a clear need to use robotic devices to automate many micro/nanoscale biological processes.

Benefitting from the great progress of robots in visual servoing, motion and force control, and image processing, robot manipulation of biological objects has made tremendous progress in the past few decades (Sun and Nelson, 2002; Arai et al., 2006; Huang et al., 2009; Chen and Sun, 2012). The manipulation of biological objects requires the ability to observe, locate, and physically transform objects using external forces. For this reason, force sensing and control are often required to enhance these operations to achieve improved manipulation results. In some applications, such as diagnosis based on individual cells, obtaining force information is the main goal (Sun and Nelson, 2002). MEMS-based force-sensing devices have been developed and used to measure microforces encountered in micromanipulation tasks (Mencaissi et al., 2003; Xie et al., 2010). Force control technology has been applied to many micromanipulations, such as PI force feedback (Mencaissi et al., 2004), force-guided assembly of micromirrors (Shen et al., 2003), and dynamic force feedback for automated batch microinjection (Lu et al., 2007).

Visual feedback has been used extensively to perform micro/nano tasks. In the early stages of development, operators used visual feedback through a microscope to manually perform operations. With the improvement of desktop computer capabilities, intense research activities have been carried out using visual feedback to control microscale tasks, such as pick-and-place operations (Kasaya et al., 1999). During microassembly operations, force measurement is important to prevent damage to microparts (Anis et al., 2006). Vision-based feedback has been used to control micromanipulation processes in engineering and biomedical applications (Nelson et al., 1998). Imaging processing is indispensable for successful intracellular microscopy. The identification, positioning, tracking, reconstruction, motion, and orientation control of target specimens and manipulating tools largely depend on the quality of the sampled image.

Computed tomography (CT) and magnetic resonance imaging (MRI) have been widely used to visualize bones, tissues, and organs, but they are not suitable for imaging small specimens such as cells and organelles. Gao et al. (2020) developed an automated intracellular delivery method using a robot-assisted microscope system with 3D reconstruction information. This method uses the movement of the objective lens to optically sample the target cells, and employs wide-field fluorescence microscopy (WFFM) to optically slice the sampled image and store it in the computer. Wei et al. (2020) used photoacoustic imaging technology to visualize the real-time transportation of microrobots in living animals for cell delivery.

1.1.2. Cell microinjection with microneedles

Cell microinjection is a process in which a sharp needle penetrates the cell membrane to transfer biological materials to a specific area of living cells. This method is advantageous because the concentration of the required material can be controlled and injected directly into the cells. Moreover, the required material can be injected into the required area of the cell; for example, DNA can be injected into the cell nucleus. This technique has been widely used in pronuclear DNA injection (Liu et al., 2013), in vitro fertilization (Lassalle et al., 1987), intracytoplasmic sperm injection (van Steirteghem et al., 1993), and drug development (Chamorro et al., 2009).

Cell microinjection techniques can be divided into three categories, namely, manual, semiautomatic, and fully automatic microinjection. Manual microinjection requires the use of a manually controlled micromanipulator to hold the target cells and the use of an injection pipette to inject biological materials while applying positive pressure to the tip. The entire experiment is performed by human hands under a microscope. Operators of this technology should be fully familiar with the operating procedures. Even when operated by a well-trained technician, the productivity of manual operation is usually very low. Semiautomatic microinjection consists of a system with a pipette and a holding pipette, which is mounted on a motorized robotic arm or workbench and controlled by a computer (Sun and Nelson, 2002; Huang et al., 2009). The operator needs to control the robotic arm to manually perform cell injection. However, in this case, the operator does not have to manually control the movement of the pipette, because an electric stage can be used. For the fully automated microinjection system (Pan et al., 2020), the operator only needs to set up the robotic experimental device and locate the target cell. The computer completes the remaining work, that is, aligning the injection pipette with the cell and performing cell injection. The role of the operator is to monitor the process, not to manipulate the stage and pipette. In common computer-assisted cell injection systems, a charge-coupled device (CCD) camera is used to capture real-time video from the microscope and send it to the computer for further processing.

Microinjection has been studied for many years and used in biomedical applications, such as clustered regularly interspaced short palindromic repeat (CRISPR) genome editing using CRISPR-associated protein 9 (Cas9), high-throughput transfection of deoxyribonucleic acid (DNA), and measurement of cell-to-cell communication in gap junctions. Robotic microinjection can accurately deliver a variety of foreign materials to almost all types of cells at the single-cell level and significantly avoid the immunogenicity, chemical toxicity, and high mortality of electroporation caused by virus or chemical-mediated cell transfection.

1.1.3. Cell manipulation with optical tweezers

As one of the most promising tools for manipulating micro/nano-sized objects, optical tweezers have played an increasingly important role in biology since their invention in 1986 (Ashkin et al., 1986). Using low-power laser beams to trap and manipulate particles ranging in size from tens of nanometers to tens of microns, optical tweezers provide a revolutionary solution for the manipulation and characterization of biological materials, especially single biological cells. Several types of cells derived from the body, namely, somatic cells, red blood cells, and nerve cells, as well as gametes (such as human sperm), have been manipulated by optical tweezers. Optical tweezers with multiple laser beam trap capabilities have significant advantages over predecessor optical tweezers with a single laser trap (Rodrigo et al., 2004; Arai et al., 2006).

The increasing demand for high precision and high throughput for cell manipulation has highlighted the need for automated processes by integrating robotics technology into optical tweezers manipulation. Given that optical tweezers can apply force and deformation to micro-objects in the order of micronewtons (pN, 10 −¹² N) and nanometers (nm, 10 −⁹ m) in a noncontact manner (Pine and Chow, 2009), the synergy of micromanipulation technology with optical tweezers will produce new and exciting solutions to achieve advanced biological cell manipulation. Such advancements will enable many biomedical applications that have not been possible thus far. In the past few years, optical tweezers have been widely used in single-molecule and cell research due to their flexible and precise control capabilities. Some studies include cell membrane or DNA stretching (Wang et al., 2013), cell sorting (Wang et al., 2011), cell transport (Chen and Sun, 2012), cell fusion (Chen et al., 2013), cell adhesion (Hu et al., 2013), and cell migration (Gou et al., 2014). Various cell types have been manipulated by optical tweezers, including red blood cells, nerve cells, and stem cells. Using holographic technology, multiple optical tweezers can generate multiple traps on the focal plane at the same time, which shows more advantages than a single optical trap.

1.1.4. Dielectrophoresis-based cell manipulation

The dielectrophoresis (DEP) method (Cui et al., 2009) utilizes a nonuniform electric field to polarize and deform cells through a pair of electrodes, and the hardware setup is relatively simple. The majority of DEP-based cell manipulations have been performed using microfluidic chips (Pethig, 2010) as a platform for sorting (Hu et al., 2005), trapping (Rosenthal and Voldman, 2005), and transporting (Hawkin et al., 2009) different biological cells. In cell sorting, the DEP method can be used to separate cells of similar size based on differences in dielectric properties, such as human mesenchymal stem cells (MSCs) and their differentiated progeny (osteoblasts) (Song et al., 2015). Most reported DEP separators require sheath flow or well-designed electrodes to actively prefocus the input cell mixture to the side/center of the microfluidic channel before the input cell mixture enters the DEP separation zone. Luo et al. (2018) proposed a method of combining gravity-based precipitation-based sheathless prefocusing and DEP separation in a simple microfluidic device for continuous cell separation based on size and dielectric properties. In cell stretching, the DEP method can be combined with microfluidic chip technology to provide greater stretching force than optical stretching. To characterize the elastic modulus of diseased cells through the DEP-based deformability test, Zhang et al. (2015) developed a DEP-based microfluidic device to quickly characterize the biomechanical properties of drug-treated cells. Bai et al. (2017) developed a DEP-based method that further combines actin skeleton modeling to characterize the mechanical properties of leukemia NB4 cells. In cell patterning, DEP technology can be used to polarize cells to build 3D cell patterns on multilayer structures with high cell density (Chu et al., 2015). A 3D porous structure called an engineered scaffold is usually used to provide a template for cells to adhere to and gradually proliferate into functional tissues. Scaffolds fabricated by conventional microfabrication techniques such as particle leaching, gas foaming, and electrospinning can achieve high levels of porosity, but the pore size, pore distribution, and interconnection between pores are not easy to control (Levenberg et al., 2005). Advanced 3D manufacturing technologies, such as rapid prototyping, laser cutting, and micromolding technologies, can provide a high degree of controllability (Moutos et al., 2007), but these stents usually have low dimensional resolution. The density of cells seeded on these scaffolds should be increased. The DEP method can be successfully used to polarize cells to construct 3D cell patterns on multilayer structures with high cell density (Chu et al., 2015).

1.1.5. Cell transport with magnetic microrobots

Clinical cell therapy has experienced 20 years of development, and is currently facing a major challenge of how to accurately and quantitatively deliver therapeutic cells to the target site in vivo. Existing blood circulation methods for cell delivery can only ensure that a small percentage of cells reaches their target sites. Therefore, a large number of cells must be used, which may lead to overproduction of cytokines and excessive immune responses. This problem can be solved by developing degradable microrobots that can accurately carry and transport cells into the body. To reduce invasiveness, the design size of the microrobot is smaller than millimeters, thereby solving the problem that traditional medical equipment cannot enter the unprecedented narrow spaces of the body. By using magnetic, acoustic, optical, chemical, and other biological hybrid drive technologies, various actuation methods have been developed to drive the microrobots (Li et al., 2017). Among these methods, the magnetic drive method is the most commonly used method, because this method has a series of unique advantages, including precise controllability, strong drive capability, and good operational safety in the in vivo environment (Sitti et al., 2015; Chen et al., 2017; Alapan et al., 2020). The magneto-dynamic microrobot provides a miniature 3D scaffold through which cells can attach, migrate, proliferate, differentiate, and form 3D living microtissues. These microrobots can be transported magnetically in vitro, and cells can be attached to microrobot structures with good biocompatibility (Steager et al., 2013; Hollister et al., 2005).

To design a microrobot that can fully meet the needs of clinical operations in a minimally invasive manner, manufacturing and material issues are among the most important considerations. In 2012, a 3D printing technology called direct laser writing (NanoScript) was applied to manufacture helical microrobots from polymer materials (Tottori et al., 2012). NanoScript is a 3D direct laser writing tool that provides great flexibility in the process of microrobot design and structural optimization. This tool can achieve a printing resolution of ∼200nm and manufacture microrobots from 5 to 1000μm. In addition, a variety of structural designs can be easily implemented, so that direct laser writing can be used to manufacture spherical microrobots and many other microstructures, such as brackets, fixtures, and microlenses. In recent years, hydrogel has become an increasingly important smart material in the field of biomedicine, and it has been used as a delivery vehicle for therapeutic proteins and genes. Combined with the hydrogel delivery system, mobile microrobots show great potential by providing high capacity/surface for drug loading and tissue-specific ligand binding, protecting cargo, and releasing low off-target therapeutic agents spatiotemporally (Erkoc et al., 2019). Microrobots made of biodegradable hydrogel composite materials show unique advantages in accurately transporting biological samples in the body (Wei et al., 2020). If magnetic materials (such as Fe3O4) and other functional materials are further coated on the surface of the microrobot, the microrobot can be driven and controlled by an external magnetic field to reach inaccessible locations in the body. Therefore, microrobots can be used to achieve targeted cell delivery and other types of micromanipulations, promoting precision and regenerative medicine.

1.1.6. Microfluidic chips used for cell manipulation

Microfluidics is a technology that can accurately manipulate fluids at the picoliter (pL) to microliter (μL) scale (Cole et al., 2017). It is an interdisciplinary technology based on analytical chemistry, microelectronics, micromechanics, bioengineering, and nanotechnology. The basic principle is to integrate operations related to the fields of chemistry and biology, such as sample preparation, reaction, separation, and detection, as well as cell culture, sorting, and lysis, on a chip of a few square centimeters or less. These on-chip functions can be connected through a microchannel network, in which fluid flow can be accurately controlled. Microfluidics can replace various functional units in traditional chemical or biological laboratories; therefore, it is also called the micro total analysis system (microTAS) or lab-on-chip (Svendsen, 2015). The development of soft lithography technology and improvement of the on-chip integration level have enabled the microfluidic technology to be gradually more functional and diversified. Microfluidics technology is used in various fields, including precision medicine, chemical synthesis and analysis, biology, information, and optics. In particular, the cell-based microfluidics technology used in research into biological cell systems has become a research hotspot.

Currently, cell manipulation methods based on microfluidics are divided into two types: active and passive. Active methods refer to the use of external forces to actively control fluids and cells. They include valve-based (Unger et al., 2000), droplet-based (Mazutis et al., 2013), electricity (Thomas et al., 2009; Chow et al., 2018), magnetism (Adams et al., 2008), optical (Wang et al., 2011; Chiu et al., 2018), and acoustic manipulation (Ding et al., 2012; Li et al., 2014). At the same time, the passive method uses the interaction between channel structure and fluid, cell and fluid, and cell and structure, that is, the principle of fluid dynamics, to precisely control the movement of fluid and cells in the microchannel (Carlo, 2009; Choi et al., 2011; Yan et al., 2015; Luo et al., 2018). The passive method is simple and has high throughput.

1.2. Outline of the book

This book introduces a number of robotic systems and technologies to achieve cell manipulation for various biomedical applications. The outline of the book is as follows.

Chapter 2 introduces the technical tools for cell manipulation. The analysis starts with a microneedle or micropipette used to control cell aspiration or achieve cell microinjection. A robotic manipulation system based on optical tweezers is introduced to trap, assemble, transport, and sort cells, which is followed by the introduction of electrokinetic manipulation tools based on dielectrophoresis (DEP) to achieve automatic cell trapping, cell separation, and cell patterning. Next, a magnetic actuation system is introduced, which uses a magnetic field gradient to drive cell-carrying microrobots to achieve targeted delivery in an in vivo environment. Finally, a variety of imaging techniques for intracellular manipulation are introduced, including the state-of-the-art microscopes, such as confocal fluorescence microscopy (CFM), two-photon fluorescence microscopy (TPFM), and light sheet fluorescence microscopy (LSFM), as well as other imaging techniques such as light photoacoustic microscopy (PAM), traditional wide-field fluorescence microscopy (WFFM), advanced structured illumination microscopy (SIM), and stimulated emission loss microscopy (STEDM). The successful development of these tools combined with robotics can greatly support cell manipulation.

Chapter 3 introduces the robotic microinjection of suspension cells and adherent cells. The size of the injected cells ranges from a relatively large size of 500–1000μm (such as zebrafish embryos) to a small size of 20μm (such as human cells). The use of robotics technology can realize efficient and accurate automatic injection. The setup and operational strategy of the cell microinjection system are introduced first. The hybrid position and force control for automatic injection of batches of cells are introduced, followed by universal piezoelectric-driven ultrasound cell microinjection, aimed at improving the survival rate of injected cells. Various methods to achieve high performance, high throughput, and high productivity in cell injection are further discussed. Finally, two case studies using robotic microinjection methods for single-cell transfection and cell mechanical characterization are introduced. This chapter illustrates how robotics can help achieve efficient, automated, and precise single-cell injection. Single-cell transfection by robotic microinjection is particularly useful for applications where cell transfection is challenging and requires genetic modification of selected cells.

Chapter 4 discusses cell stretching and deformation using optical tweezers. Various robotic micromanipulation tools are introduced, which can deform cells to characterize cell functions and regulate intracellular transmission. This chapter introduces five methods. First, robotically controlled optical tweezers are used to stretch the cells to explore the mechanobiological characteristics of the cells. Two case studies report on stretching hematopoietic cells to understand the property difference between normal and leukemia conditions, as well as stretching human embryonic stem cells to explore mechanobiological properties in cardiac differentiation. Second, a model of the actin cytoskeleton under optical stretching is established to provide insights into the relationship between cell mechanical behavior and cell function changes. Third, the DEP stretching of individual cells is introduced. Compared with optical stretching, DEP force can provide greater stretching force. Fourth, the method of compressing cells by mechanical restraint is introduced. Compared with optical and DEP stretching methods, mechanical confinement can cause large cell deformations, thereby identifying cell biomechanical properties at different levels. Finally, a unique method is introduced that uses millimeter-sized iron rods or spheres driven by magnetic force to selectively deform cells. This method can be used to achieve in situ intracellular delivery. This chapter demonstrates that robotic cell manipulation techniques combined with different manipulation tools can be successfully used to stretch and compress individual cells, which can play an important role in regulating and characterizing cell functions in biological research and medical treatment.

Chapter 5 discusses automatic cell transfer using the optical tweezers manipulator based on dynamic analysis, motion planning, and controller design. First, the basic theory of using optical tweezers to transport cells is introduced. A controller is developed that can control single and multiple cells in automatic transport without requiring the precise values of the trapping stiffness and drag coefficient. Second, the motion and path planning of automatic cell transportation are introduced. The motion parameters are optimized so that the cells can move effectively while maintaining their position in the laser trap. Several path-planning methods, such as improved A∗ algorithm, RRT-based path planner, and dynamic path planner, are reported, which can solve the interference problem in the dynamic solution. Third, a unified controller is proposed to simultaneously control cell positioning, cell trapping, and obstacle avoidance, without the need for an additional trajectory planner. The controller is designed based on a geometric coalition model, which aims to confine the cells in the light trap while considering obstacle avoidance. Fourth, a potential field-based controller is introduced, which can drive the two groups of particles into a pair of common arrays while controlling the distance between the pair of particles. Finally, a multistep processing control method is introduced to sequentially transfer a group of cells to a series of task regions, while keeping the cells in an effective trapping area and avoiding conflicts. The controller can simultaneously control cell positions, cell trapping, and collision avoidance in a coordinated manner, and continuously transfer grouping cells to different task areas without interruption. This chapter demonstrates that robotic optical tweezers technology can be successfully used to transfer single and multiple cells, and has the potential to be used in many biomedical applications, such as drug discovery, cell–cell interaction, and tissue engineering.

Chapter 6 introduces the control of cell rotation by the optical tweezers manipulator. After establishing the general dynamic equation of the suspension cell movement, a controller is developed to realize the one-degree-of-freedom in-plane rotation control of the suspension cell, while considering the position control in the plane translation. The automatic control of out-of-plane reorientation is then introduced. On the basis of the general dynamic equation of the trapped cell rotation and the image-processing algorithm to estimate the orientation of the cell, a vision-based adaptive controller is proposed to realize the closed-loop rotation control of the cell out of the image plane. Experiments are performed to illustrate the performance of the in-plane and out-of-plane cell rotation control methods. Successful cell rotation will facilitate single-cell manipulation and enable many cell therapy applications based on cell orientation.

Chapter 7 introduces a 3D image reconstruction method for intracellular surgery using a robot-assisted microscope system. First, a series of optical sections along the vertical direction is obtained by moving the microscope lens. After several process of deconvolution, segmentation, and reconstruction, a 3D reconstruction model of the specimen is established. The experimental results illustrate the effectiveness of the proposed method by using simulated spheres with various noise levels and measuring fluorescent microspheres of specific sizes. Second, a precise intracellular delivery method with 3D image reconstruction information is introduced. The optimal delivery position can be derived from the volume-based model in the geometric analysis. This position guides a robust controller to drive the moving stage and micropipette tip to perform delivery tasks without being affected by external interference. Reconstruction accuracy experiments are performed on the nucleus, and the results are compared with a confocal fluorescence microscope. Intracellular nucleus delivery experiments are further carried out. In summary, the 3D image reconstruction method for intracellular surgery can largely meet the needs of various biomedical applications that require precise delivery.

In Chapter 8, cell sorting and separation are discussed. Two strategies that combine robotic cell manipulation with microfluidic chip technology are introduced. The first strategy is to use a robot-assisted optical tweezers manipulation method to achieve high-precision cell sorting and separation of a small number of cell samples. Using the first strategy, a universal single-cell manipulation tool integrating optical tweezers, microfluidic chips, and imaging-processing technology is developed. Multiple optical traps can be generated at the same time for parallel sorting of multiple cells. This method is ideally suited to the separation and enrichment of rare cells. The second strategy is to combine the sheathless method with the DEP method to separate cells of different sizes or dielectric properties. Sheathless cell focusing can be achieved via gravity sedimentation of the cells in a tubing inserted into the inlet of the microfluidic chip. Efficient separation of cells of interest from cell mixtures is the key to many biomedical applications.

Chapter 9 discusses cell stimulation and migration. This chapter introduces the chemoattractant-induced cell migration model and control based on the stimulus-induced robotic manipulation system. First, a cell migration model that simulates in vivo migration by using optically manipulated chemoattractant-loaded microsources is presented. Using this model, the relationship among protrusion force, cell motility, and chemoattractant gradient can be quantitatively characterized. Experiments on migrating cancer cells show that the ideal migration ability can be achieved by appropriately selecting the chemoattractant gradient and concentration at the front edge of the cell. Second, a potential field function-based controller is developed to enable the optical tweezers to manipulate the microsource beads, thereby stimulating cell migration to the desired area. Here, the chemoattractant-loaded microbeads trapped by the optical tweezers are used to release the stimulus, thereby inducing the target cells to migrate to the desired area. A geometric model is established to confine the microsource beads to the effective trapping area of the optical tweezers and the high-motility area of the cells. Third, the application of electrical stimulation (ES) to stimulate cells is presented, which aims to improve the osteogenic differentiation of MSCs. ES parameters can be optimized depending on the calcium spike pattern of MSCs, which can be used to improve the osteogenic differentiation of MSCs. This method provides a new opportunity for optimizing osteogenic differentiation and can be useful in clinical treatments, such as bone fractures.

Chapter 10 introduces cell patterning. Cell patterning refers to arranging cells in a desired shape at the desired position. The first method used to control a group of cells to form the desired cell pattern is to use robotically controlled optical tweezers based on a multilevel topology design. A potential function-based controller with pattern adjustment control can drive cells to form the required pattern without collision. The second method is to use DEP to manipulate and pattern cells on a multilayer 3D engineering scaffold, which is further applied to automatically seed cells into a multilayer honeycomb pattern for bone tissue engineering applications. Studies have shown that cell pattern technology based on integrated DEP can enhance cell seeding to develop high-quality artificial tissues. The third method is to use the gravitational sedimentation-based approach to realize cell patterning coculture on a microfluidic platform, in which multiple cell types can be patterned simultaneously to form a well-organized cell coculture without complicated hardware settings or sophisticated chip design and fabrication. This method does not require an external field to help the cell arrangement in the microchannel, so it can pattern cells without introducing any stimuli that may affect normal cell behavior.

Chapter 11 introduces the method for manipulating cell adhesion. Cell adhesion is the process by which cells come into contact with each other or with their substratum through specialized protein complexes. This process can be performed using optical tweezers and is helpful for the study of cell–cell interactions. The adhesion of leukemia cancer cells to bone marrow stromal cells is especially studied, and the fluorescence intensity is used as a marker to study the Wnt signaling pathway of leukemia cells. On the basis of the adhesion characteristics of leukemia cells to stromal cells, different adhesion types of tight adhesion, loose adhesion, and free suspension can be clarified. Cell adhesion manipulations will provide a new opportunity to study otherwise inaccessible mechanisms of cell–cell interactions.

Chapter 12 discusses another interesting topic of cell fusion. Cell fusion is the cellular process of combining two or more cells to form a single entity. This chapter introduces cell engineering fusion using robotic manipulation with optical tweezers. The basic working principle is introduced based on the fusion of human embryonic stem cells (hESCs), in which the cells are manipulated by optical tweezers and fused under pulsed ultraviolet (UV) laser irradiation. By using a microwell array-based microfluidic chip, fusion with high selectivity and controllability can be achieved. Finally, a case study of artificial fusion of stem cells and liver cancer cells to isolate fused cell lines is reported. The results of genetic analysis show that the fusion of cancer cells and stem cells results in cancer cells having stronger tumorigenicity and higher chemoresistance. The results also show that the fusion cells are highly similar to tumor-initiating cells in biological processes or cell compartments. The successful development of this laser-induced single-cell fusion technology will provide a unique method for many important studies that facilitate cell differentiation, maturation, and reprogramming.

Chapter 13 discusses the in vivo transport of cells using microrobot technology. This process is particularly useful for precision medicine, such as targeted cancer therapy. First, the manipulation technology based on robotic optical tweezers is used to transport single cells in blood vessels. An enhanced disturbance compensation controller is introduced to minimize the influence of fluid on cells. Simulation and experimental results show that the proposed controller can effectively and automatically transfer a single cell to the desired position. This research is potentially useful for many precision medicine applications, such as metastasis study and drug delivery. Second, a microrobot designed with a burr-like porous spherical structure is proposed to carry and deliver cells in vivo under the driving mechanism of a magnetic gradient field. The microrobot is fabricated using 3D laser lithography technology, which provides sufficient flexibility for the microrobot structure design. Experiments have proved that the proposed microrobot design can enhance the magnetic driving ability, improve the cell carrying capacity, and benefit cell viability. Carrying cells can be spontaneously released from the microrobot to the surrounding tissues. The autonavigation of cell culture microrobots is demonstrated in zebrafish embryos. These results support the feasibility of using magnet-driven microrobots to accurately deliver targeted cells in vivo. Third, the magnetic image-guided degradable microrobot is further designed to precisely deliver engineered stem cells for orthotopic liver tumor treatment. The microrobot is made of synthetic composite materials, which can meet the requirements of degradability, mechanical strength, and magnetic actuation capability simultaneously. The microrobot is guided by a unique photoacoustic imaging technology. Preclinical tests have been performed on nude mice to prove that the microrobot transports these cells to the target site and releases loaded cells, and the cells significantly inhibit tumor growth. Preclinical trials have revealed the feasibility of using degradable microrobots to accurately deliver therapeutic cells in vascular tissues, and have proven their efficacy in tumor treatment.

Chapter 14 introduces intracellular-level manipulation technology to achieve organelle biopsy and single-cell gene editing for precision medicine. The chapter has three perspectives. First, a surgical system based on robotic microneedles is introduced to realize automated single-cell biopsy of small cells (diameter <20μm). The enabling technologies of microfluidic chip device, sliding mode nonlinear controller, and imaging processing are integrated to achieve biopsy of mitochondria and nuclei of human cells. Second, a method for gene delivery to MSCs using high-throughput robotic microinjection is reported. Compared with traditional methods such as polyethyleneimine (PEI), cationic liposomes (cLipo), and calcium phosphate nanoparticles (CaP), robotic microinjection has higher transfection efficiency, lower cell damage, and stronger ability to retain MSC pluripotency and therapeutic gene function. Third, an automatic optical tweezers manipulation method for single-cell mitochondrial transfer is introduced. This method uses a microfluidic cell positioning device to pattern cells and mitochondria and then employs optical tweezers to collect a predetermined number of healthy isolated mitochondria and automatically transport them to the top of the cell. These mitochondria are taken up by the host cells through endocytosis. Compared with the traditional coculture passive transfer method, this method can effectively control the quantity and quality of mitochondria before transfer. A case study on the transfer of mitochondria from fetal MSCs to adult MSCs to improve antiaging and metabolic gene expression demonstrates the potential value of this method in precision medicine and cell therapy for mtDNA-related diseases.

1.3. Conclusions

This chapter aims to provide readers with an overview of robot-assisted cell manipulation. Various types of cell operations and their target applications are introduced. Traditional cell manipulation methods have limitations in accuracy, success rate, proficiency, efficiency, and consistency. Robotics can overcome these challenging problems and promote the practical application of cell manipulation. An outline of the book is presented.

The following is a list of the main contents related to this chapter:

• Cell manipulation concept and its biomedical applications;

• Automating cell manipulation with high efficiency and accuracy using robotics technology;

• Summary of the key manipulation tools including microneedle, optical tweezers, electrical and magnetic device, and microfluidic chip-in-cell manipulations;

• Review of robotic cell manipulations in achieving cell injection, cell stretching, cell transport and rotation, 3D image reconstruction of cells, cell sorting and separation, cell stimulation and migration, cell patterning, cell adhesion, cell fusion, in vivo cell navigation and delivery, single-cell biopsy, and gene editing.

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2: Cell manipulation tools

Abstract

This chapter reviews the technical tools for cell manipulation. First, microneedles or micropipettes for controlling cell aspiration or achieving microinjection of cells are introduced. By incorporating robotics technology into microinjection, multiple operations such as cell transfection and biopsy can be efficiently and automatically realized. Second, a robotic cell manipulation system based on optical tweezers is introduced to trap, assemble, transport, and sort cells. By using holographic technology, multiple optical traps can be generated simultaneously to manipulate multiple cells. Third, electrokinetic manipulation tools based on electrophoresis or dielectrophoresis (DEP) are introduced. DEP-based manipulation has become the main technology for automatic cell trapping, cell separation, and cell patterning. Fourth, a magnetic manipulator is introduced, which uses magnetic field gradients to apply and measure forces on magnetic particles. Magnetic manipulators show significant advantages in driving microrobots to deliver cells or drugs in the in vivo environment. Atomic force microscopy is then introduced, which has been widely used to study cell biomechanics. Finally, the imaging technology of intracellular manipulation is introduced. Some of the state-of-the-art microscopes on the market include confocal fluorescence microscopy, two-photon fluorescence microscopy, and light sheet fluorescence microscopy. Other imaging technologies that are introduced include photoacoustic microscopy, traditional wide-field fluorescence microscopy, advanced structured illumination microscopy, and stimulated emission loss microscopy. The successful development of these tools, combined with robotics technology, has greatly supported the rapid development of cell manipulation in the past few decades.

Keywords

Atomic force microscope; Cell manipulation tools; Dielectrophoresis; Imaging for intracellular manipulation; Magnetic manipulator; Microneedle; Optical tweezers

2.1. Introduction

The micromanipulation of biological cells is playing an increasingly important role in biomedical