Recent Advances in Microfluidic Techniques for Systems Biology

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Recent advances in microfluidic techniques for systems biology Gongchen Sun, and Hang Lu Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.8b04757 • Publication Date (Web): 08 Nov 2018 Downloaded from http://pubs.acs.org on November 9, 2018

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Analytical Chemistry

Recent advances in microfluidic techniques for systems biology

Gongchen Sun and Hang Lu*

School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA

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1. Introduction Systems biology seeks to understand complex biological systems using a holistic approach instead of the reductionist approach, which is to study each part of the system individually.1 By examining the differences, interaction, and dynamics between cellular or molecular components, systems biology researchers aim to decipher the complexity of biological systems and to understand the functional networks that form living organisms. Such an understanding has implications in practical applications in many fields, including cancer biology, immunology, microbiology, neuroscience, precision medicine, therapeutic management, and many more.2-3 Thanks to the advances in analytical techniques and high-throughput experimentations, the field of system biology has undergone rapid growth in recent years. With enhanced sensitivity and specificity, a wide spectrum of single-cell and single-molecule analysis techniques, including optical, electrochemical, spectrometric, and sequencing-based methods, enable us to understand the structures and functions of individual biological building blocks.4-5 In order to generate sufficient amount of data to analyze the complexity of biological systems, one needs to integrate analysis techniques with rapid sample handling and perform high-throughput experimentations to collect data, while preserving individual heterogeneities. For the study of certain living biological systems, such as living cells, tissues, or whole organisms, it is often useful to characterize at multiple time-points to probe dynamics of the system in response to perturbations, all in a controlled culturing environment.2, 6 In order to meet these needs, microfluidic techniques have often been used extensively to handle and process samples, and to integrate cutting-edge analytical tools. Recent studies showed that microfluidic techniques are mainly beneficial in three aspects: 1) because of its comparable size with microscale bio-entities like cells and micro-organisms, microfabricated devices provide various strategies to isolate single cells or microorganisms from each other and to process them with much-improved speed as compared to convention approaches; 2) the 2 ACS Paragon Plus Environment

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unique physical phenomena under the microfluidic regime, such as laminar flow, diffusiondominated mass transport, and favorably scaled electrical effects, allow for the precise control of the micro-environment and perturbation of the biological samples, and facilitate the investigation of system dynamics;7 3) integration of analytical methods by microfluidics enables simultaneous acquisition of multi-dimensional data, which results in a more comprehensive picture to understand biological systems. The fundamental mechanism has been well studied to use microfluidics for high-throughput instrumentation in the past decades. Many of these methods start to see practical applications and become popular in recent three to five years. In this review, we will introduce recent progress of system biology studies that benefit from novel microfluidic techniques. In particular, we will focus on how these microfluidic strategies facilitate the experimentation to deepen our understanding of complex biological systems from the three aforementioned aspects. In the last section, we will also provide a prospective outlook on potential future directions.

2. Microfluidic sample handling for high-throughput and parallel experimentation One fundamental challenge for systems-level biological experimentation is high-throughput and parallel sample manipulation. Conventional life science tools used to handle biological samples are often labor-intensive, time-consuming and prone to mistakes. In comparison, microfabricated devices, whose scale better matches that of molecular reactions, single cells, and single micro-organisms, can be more efficient and accurate in handling samples; further, microfluidic systems are broadly used for sample preparation to automate and parallelize experiments. Not only can microfluidics facilitate high-throughput data acquisition, they can also provide the capability of isolating individual biological specimens such as single cells.



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Various microfluidic techniques have been developed to physically separate individual biological entities in order to preserve the intrinsic heterogeneity of a biological population. As reviewed by Prakadan, et al.,8 there are three major microfluidic compartmentalization strategies: valve-based integrated fluidic circuits (IFCs), droplet-based microfluidics, and microwell-based on-chip array. The IFCs can actively flow and stop the fluid through microchannels by on-chip pressure-controlled valves, and hence confine single cells or micro-organisms in a sealed channel. IFCs can also precisely control reagent exchange necessary for on-chip reactions and enable complex workflows. A parallel strategy to IFCs is the use of emulsion droplets. The isolation of single cells or low-number molecular components is achieved by encapsulating aqueous suspensions or solutions as surfactant-stabilized droplets separated by an inert carrier oil. In this scheme, up to thousands of droplets can be generated per second, which is ideal for high-throughput applications. In a third strategy, microwell-based on-chip array separates biological specimens spatially at different locations. This approach generally needs fewer periphery equipment and with less complicated operations. It also allows for multiple different measurements on the same samples since they are associated with fixed spatial locations. The only difference from the previous two techniques is that molecular diffusion could still occur between samples. For general details of these strategies, we refer readers to several recent reviews.7-9 In this section, we will illustrate the advantages of different microfluidic sample processing techniques with examples taken from two different aspects of single-cell analysis, namely single-cell “omics” and single-cell biophysics in living cells. 2.1. Single-cell “omics” analysis Single-cell “omics” analysis quantifies the abundance and distribution of molecular contents of cells. Thanks to the development of advanced molecular profiling tools, such as nextgeneration sequencing and highly sensitive protein assays, now single-cell genomes, epigenomes, transcriptomes, and proteomes can all be measured. Such in-depth characterization 4 ACS Paragon Plus Environment

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can greatly help us with the classification of cell types and understanding of the intrinsic heterogeneities in complex cell populations. The analysis based on “omics” measurement is highly informative in understanding fundamental mechanisms, such as how organisms develop, and how cells and organisms sense and respond to environmental perturbations; it is equally useful in biomedical applications, such as cancer, immunological responses, and drug responses. In single-cell “omics” studies, different microfluidic techniques have been extensively used with distinctive advantages. The first type, as mentioned above, is the valve-based integrated fluidic circuits (IFCs). A variety of IFCs has been commercialized. Due to the complexity in device design and fabrication, the throughput of IFCs is not very high (on the order of a hundred). However, IFCs is ideal for precise control of complex fluid exchanges. In applications where it is important to perform localized on-chip reactions, this technique is particularly useful. Recent studies showed novel applications of IFCs for simultaneous single-cell “multi-omics” studies. This type of studies can give insight into the interrelationships between different cellular components.8 One example by Genshaft, et al. used a commercialized Fluidigm C1 system and achieved multiplexed profiling of single-cell proteomes and transcriptomes.10 In a single reaction, the authors mapped the abundance of both the targeted proteins and RNAs to DNA-based reporters by proximity extension assays (PEA) with oligonucleotide-tagged antibodies and reverse transcription. The DNA reporters were further quantified by qPCR. Using this method, the authors analyzed the transcript and proteomic response of a human breast adenocarcinoma cell line to a chemical perturbation. The power of IFCs was again shown in the work by Cheow et al., in which they used Fluidigm C1 AutoPrep IFC to investigate single-cell transcriptome and DNA methylome in primary lung adenocarcinomas and human fibroblasts undergoing reprogramming.11 Coupled with customized molecular probes and specific workflows, IFCs shows great versatility to cover many aspects of single-cell “omics” analysis, from genomics, epigenomics, transcriptomics, to proteomics.



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The second strategy to compartmentalize single cells for sequencing by using emulsion droplets have been gaining steam in the last few years. Due to the scalability and the high-speed of droplet generation, droplet microfluidics is the most prominent technique at present to achieve an ultra large-scale single-cell “omics” analysis. The first method using droplet to realize microcompartment of single cells for RNA sequencing was presented by Macosko et al..12 The method, called Drop-seq, encapsulated an individual cell, together with a distinct molecularly barcoded acrylic microbead in each droplet. The microbead captured mRNAs from the co-isolated cell. Hence, the identity of the cell and its transcript were labeled by the unique barcode on the bead. Reverse-transcription, amplification, and sequencing can be performed in one reaction after all the beads were pooled (Figure 1A). The authors used Drop-seq to profile the transcriptome of 44,808 cells from a complex neural tissue, mouse retina, and they identified 39 distinct subpopulations. Similar to Genshaft, et al., Stoeckius et al. combined Drop-seq with custom-designed oligonucleotide-labeled antibodies and achieved simultaneous epitope and transcriptome profiling in single cells.13 These demonstrated the power of Drop-seq to resolve the biological heterogeneity of complex tissues and cell populations. While powerful, Drop-seq is not perfectly efficient in that only a small fraction of the cells was profiled in each run as both cells and microbeads had to be prepared at low concentrations to avoid multiple loading into one droplet.8 Klein et al. reported an improved technique call InDrop that addresses this efficiency issue.14 Instead of using polymetric microbead for barcoding, the authors developed a deformable hydrogel capture microsphere that can be closely packed during loading. As a result, nearly 100% of the droplets were occupied with barcoded hydrogel microsphere, which maximized the cell labeling and enhanced the efficiency. With this technique, Klein and colleagues could achieve a cell capturing rate of 4,000–12,000 per hour, or 2,000-3,000 cells barcoded for every 100µL of emulsion. They further used this technique to study population


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structure and cellular heterogeneity in 11,149 mouse embryonic stem cells during their differentiation before and after leukemia inhibitory factor (LIF) withdrawal. This droplet microfluidics-barcoded labeling scheme was soon adapted and modified for many different applications. Habib et al. modified the Drop-seq method to achieve single-nucleus RNA sequencing from tissues that are preserved or hard to be dissociated.15 Briggs et al. adapted the InDrop method to probe the gene expression of a whole developing vertebrate embryo, Xenopus tropicalis, over ten time-points, demonstrating the capability of the microfluidic method in dissecting developmental dynamics at the scale of entire organisms.16 Others coupled the droplet microfluidic scheme with CRISPR-mediated perturbation to realize profiling combinatorial CRISPR perturbations in a pooled format for large-scale genetic screens.17-18 Unlike most dropletbased methods aim to isolate single cells, a work from Fu et al. stood out by using droplet emulsion to improve sequencing quality of whole-genome amplification (WGA).19 Instead of encapsulating a whole cell into a drop, Fu and colleagues divided single-cell genome fragments into a large number (105) of droplets and performed multiple-displacement amplification (MDA) in all drops. By saturating amplification in each drop, they were able to minimize the amplification bias and hence to improve the uniformity. The maturation of these droplet-based methods was very fast, as some of them were soon commercialized to better serve the scientific community.20 Similarly, the third microfluidic isolation strategy, the microwell-based on-chip array can also be married with the barcoded-labeling strategy to profile single-cell “omics” so as to achieve a relatively high throughput, with typically a much simpler device operation scheme. Recent developments focused on improving cell sampling efficiency. For example, Fan et al. developed a method named CytoSeq which partitioned single cells and barcoded microbeads into a scalable open microwell array for single-cell RNA sequencing.21 Cells and barcoded microbeads were loaded into wells simply by gravity (Figure 1B). By employing a recursive Poisson strategy and optimizing bead and well size, an efficient bead loading allowed for about a few thousand cells to 7 ACS Paragon Plus Environment


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be profiled simultaneously. To demonstrate the versatility of this technique, the authors applied it to multiparameter genetic classification of the hematopoietic system, to cellular heterogeneity characterization in immune response, and to rare cell identification in a population. Later, Han et al. scaled up the CytoSeq platform and successfully profiled the gene expression of more than 400,000 single cells covering all of the major mouse organs; in this way they constructed a mouse cell atlas, the first transcriptome-based single-cell atlas for complex mammalian systems.22 In a recent work, Gierahn et al. improved the microwell-based method by using a semipermeable membrane to seal the well array.23 The selective membrane enabled rapid solution exchange for efficient cell lysis but trapped biological macromolecules, resulting in improved transcript capture efficiency and reduced cross-contamination. Other similar efforts were also reported to reduce cross-contamination and to precisely delivery lysis buffer and reverse transcription reagents by integrating the microwell array into microchannels.24-25 With no need of complex periphery equipment, the major advantage of microwell-based array method is its simplicity, low-cost and the ease to implement. Microwell-based array method is also particularly appealing for single-cell proteomic studies. Because of the ultra-small volume in each well (typically picoliter to nanoliter), the sensitivity of on-chip immunoassay can be enhanced due to minimized sample dilution and higher analyte concentrations. Consequently, microfluidic techniques have achieved simultaneous and multiplexed single-cell protein assays, which are difficult using conventional methods due to significant sample loss and low sensitivity. By using microwell-based array technology, samples are distributed to known locations, and the assay time for protein detection is greatly reduced in the micro-scale reactor confined by each well. Lu et al. combined a multiplexed antibody barcode array slide (for protein quantification) with a micro-chamber array (for single-cell isolation) to profile 42 immune effector proteins secreted from single immune cells (Figure 1C).26-27 This technique was applied to study heterogeneity in immune response to pathogenic ligands or


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signaling proteins from other immune cells.28 To profile protein isoforms that cannot be discerned by antibodies, Hughes and colleagues integrated single-cell western blotting in a microwell array.29-30 They fabricated the array out of photoactive polyacrylamide. The proteins were photocaptured in a gel matrix and probed by antibodies, after single cells were captured in the microwell, lysed and performed rapid protein electrophoresis. The electrophoresis step separated proteins of different sizes, which provided the specificity in the multiplexed measurement (Figure 1D). The abundance of up to 11 different proteins per cell was examined. This multiplexed singlecell protein profiling technique has made many possible applications, such as evaluating heterogeneity in circulating tumor cells, single-cell response to cancer chemotherapy, invasive potential of tumor cells.31-33 2.2. Single-cell biophysics in living cells Besides its molecular components, a cell’s physical properties, such as the cell mass, size, and mechanical properties, can also be informative to distinguish cell types and give clues to certain physiological outcome, e.g. cell fate. These measurements of single cell biophysical properties provide complementary information from single-cell “omics” data. Microfabricated devices, due to their unique ability to handle and interrogate with individual cells, offer the opportunity to integrate functional assays; in addition, with high-throughput and automated cell handing for system-wide characterization of single-cell biophysics. A continuous-flow based technique to measure cell mass has been reported by the Manalis lab.34-35 The unique feature of suspended microchannel resonator (SMR) is a cantilever resonator on which a sealed microfluidic channel is integrated. When a suspended cell flows through the interior of the cantilever, the cell’s buoyant mass can be inferred by the transient change in cantilever’s resonant frequency. SMRs can measure single mammalian cells with a resolution of 0.05pg (0.1% of a cell’s buoyant mass) or better. Because of the high sensitivity of SMRs and the ease to allow continuous flow, Cermak et al. designed a high-throughput single-cell growth rate 9 ACS Paragon Plus Environment


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assay by connecting an SMR array with serial microfluidic channels (Figure 2A).35 The mass of each single cell was measured repeatedly when flowed through the series of SMRs. The growth rate was then calculated. The platform could obtain growth rate for >60 cells per hour with a resolution of 0.2 pg per hour for mammalian cells and 0.02 pg per hour for bacteria. To demonstrate the versatility of the method, Cermak and colleagues measured the growth of a wide range of cells, including single lymphocytic cells, mouse and human T cells, primary human leukemia cells, yeast, Escherichia coli and Enterococcus faecalis. The technique was soon modified and applied to other fields and applications, such as evaluating therapeutic susceptibility of human multiple myeloma cell lines, and uncovering heterogeneity in drug sensitivity of glioblastoma and B-cell acute lymphocytic leukemia cells.36-37 In a separated work, researchers were also able to quantify single-cell volume and deformability simultaneously with mass through this method, by changing the buffer in which cells were suspended and tuning the size of microchannel on the cantilever.38-40 Continuous-flow based microfluidic techniques are also designed to characterize cell mechanical properties, such as cell volume and deformability, and are useful for high-throughput cell sorting, all in a label-free manner. The appeals of these techniques here are their simplicity in designs and versatility in applications. Otto et al. reported a very simple single-cell deformability cytometry.41 Flowed through a straight microfluidic channel, cells were deformed by shear stresses and pressure gradients in their device. The deformation of the cell was imaged, and the cell size and deformability could be calculated based on a hydrodynamic model in real-time. Cytoskeletal alterations and different cell-cycle phases were shown distinguishable by this method. The strength was that this method could analyze cells on-the-fly with a rate greater than 100 cells per second. Therefore, the techniques could be potentially integrated with downstream cell sorting and the total number of cells analyzed was, in principle, unlimited. Using a similar hydrodynamic stretching idea, another high-throughput assay for cell mechanics is the inertia


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microfluidic-based vortex-mediated deformability cytometry developed by the Di Carlo lab.42 Che et al. showed that they could isolate and purify cells on-chip based on sizes in local vortices induced by channel geometry and flow.43 After releasing the purified cells, they characterized the deformability of single cells by hydrodynamic stretching (Figure 2B).43-46 This technique could process 10 mL blood sample within 1 hour and characterize 2000 cells per second when coupled with a high-speed camera. Researchers could use this method to identify subpopulations of circulating tumor cells (CTCs) from patient samples, or to classify physical phenotypes of pluripotent cells and their derivatives.43-44 In comparison with hydrodynamic-based techniques, Kim et al. presented a high-throughput microfluidic platform called mechano-node-pore sensing to characterize cell mechanical phenotypes using electrical impedance-based measurements.47 The platform was a single-channel microfluidic device in which the microchannel was composed of pores, connecting nodes of different shapes and a contraction channel. Four on-chip electrodes connected to the terminals of the microchannel to measure the ionic current through the channel when a single cell was flowed pass the pores and the contraction channel. The unique feature of this platform was that, aside from the cell size and deformability, it could measure the recovery dynamics of single cells from the deformation via the measured call translocation current and time. With this technique, Kim and colleagues accomplished a profiling throughput of 350 cells per minute. They were able to study the cellular migration process during cancer metastasis and to distinguish immortal epithelial cells from malignant from non-malignant tumors. In addition, in the mechano-node-pore sensing scheme, cells are squeezed flowing through nodes and are in contact with the channel walls. Functionalized microchannel walls with different antibodies were also used to profile surface markers of single cells using the same measurement scheme.48 Other than high-throughput characterizations, the continuous-flow based strategy has also been used for mechanical property-based cell sorting in various medical applications, such as capturing circulating tumor cell clusters, examining heterogenic cancer cell chemotherapy responses, and screening drug compounds by cell viability.49-52 11 ACS Paragon Plus Environment


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Another high-throughput microfluidic format, parallel array-type device, provides different strategies for intact single cell biophysics analysis. Pushkarsky et al. developed an array-format single-cell force cytometry by taking advantage of the interaction between single cells and soft surface of a pattern elastomeric material (Figure 2C).53 The shape of the polymer micropattern would change when contracted by a single cell, and hence indicated the single-cell force. This technique could profile 1,000 - 30,000 cells simultaneously, which was a 100-fold improvement in throughput compared with conventional techniques for measuring cell force like traction force microscopy. Thanks to the open-surface format, drugs or reagents could be added easily into the device to examine single-cell force change in response to external stimuli. Furthermore, array type microfluidic devices can be easily used with general analytical tools such as optical microscopy to perform non-invasive analysis of chemical components in intact cells.54-55 For instance, Lee et al. presented a high-density microfluidic single-cell trapping array (1,600 addressable traps).54 It realized size-based separation to isolate leukemia cells and white blood cells (WBCs) from whole blood. To identify leukemia cells, the authors characterized the changes between free/bound nicotinamide adenine dinucleotide (NADH) in living cells by fluorescence lifetime imaging microscopy. No only could they distinguish leukemia cells from WBCs, they were also able to quantitatively classify different leukemia cell lines. This technique is potentially useful for both early leukemia detection and tumor heterogeneity interrogation. Similarly, we envision other newly developed analytical tools, such as molecular force microscopy or DNA-origami tension probes, can be integrated with array type microfluidic to allow for systemwide characterization of molecular forces for living single cells.56-57 In summary, for the past decade, the microfluidic technique for sample handling has grown rapidly. Some methods, such as IFCs and droplet microfluidics, are mature enough to allow us to gain a system-wide understanding of a complex organ or even a living organism with single-cell resolution. Further technical developments are now towards combining the merits of different 12 ACS Paragon Plus Environment

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microfluidic compartmentalization methods. For example, Cole et al. printed picoliter drops generated by droplet microfluidics into an intricate droplet array.58 The droplet generation controlled the on-demand cell and reagent encapsulation, while the array enabled time course studies and integration of multiple measurement modalities.58-59 In other works, inertia microfluidics and droplet microfluidics are combined to allow for complex sample purification and pretreatment before encapsulating single cells into droplets for characterization.60-61

3. Microfluidic control of microenvironment to probe system dynamics To understand how complex biological systems function, one needs high-throughput highcontent measurements at many scales (as reviewed in the previous section); further, it is also important to be able to probe the dynamics of these complex systems and understand how molecular networks, cells, and organisms deal with environmental changes. This latter type of studies is important in microbiology, immunology, developmental biology, cancer biology, and many other fields. Conventional techniques usually call for batch culture of the biological systems. Manipulating fluid media in the macro-scale is almost impossible to be precise at the cellular resolution; controlling sample interaction in these situations are also cumbersome. Microfluidics, in contrast, is an excellent alternative. It is well known that with the small Reynold’s number in microscale, fluid flows in microfluidic devices are mostly laminar flows with defined boundaries. Therefore, not only can microfluidic techniques process biological samples with high speed, they can also manipulate fluid flow with high precision, resulting in well-controlled microenvironments in the microdevice. Integrated with live-imaging tools, microfluidic techniques can enable time course studies and deliver external stimuli to understand the dynamics of a large-scale system. With an emphasis on high-throughput experimentation, we will review recent technical developments which advance dynamic studies for both cells and micro-organisms in this section.



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3.1. Spatiotemporal culturing and environment control for cells and cell aggregates Culturing cells and tissue models with microfabricated devices have long been studied. Because multi-timepoint characterization of cells typically requires long-term on-chip culturing, techniques based on microfluidic arrays are favored to achieve a high throughput. In addition, microfluidic arrays can be integrated with various transport mechanisms to deliver perturbations to the cultured cells, enabling studies of transient or delayed cellular response. Cell signaling and cell growth can be better studied by perturbing cells with dynamic environmental cues and examining the cellular activities in response. Due to the intrinsic stochasticity of biological systems, one important question is to understand the heterogeneity in single-cell response to dynamic external soluble biochemical cues. Based on a high-density deterministic single-cell trap array,62 two studies showed an integrated and programmable microfluidic platform to deliver dynamic chemical stimuli via on-chip valves or perforated membranes (Figure 3A).63-65 The array could hold ~1,000 cells. The strength of this platforms was that it could deliver complex stimuli patterns with varying temporal frequency and chemical concentrations. This platform was used to probe non-adherent cells such as Jurkat T Cell, and systematically studied single-cell response to stimulations like extracellular Ca2+ or H2O2. Another work done by Ramji et al. directly fabricated a single-cell trapping array in a microchannel.66 Hundreds of suspended cells could be captured by passive flow within minutes and held over 12 hours for imaging. Ramji and colleagues used this device to characterize the noisy dynamic of latent HIV activation in single T cells. A separated effort was presented by Cao et al..55 They engineered a scalable microfluidic array of parallel single cell culture chamber and combined it with stimulated Raman scattering microscopy. Over 3-day culture, they characterized the response of lipid droplet (LD) formation in response to free-fatty-acid stimuli with single-LD resolution for over a thousand live cells in 10 different culturing environments. This platform provided an opportunity to study single-cell lipid metabolism and dynamic behavior through a non14 ACS Paragon Plus Environment

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invasive manner. The single-cell resolution and the precise chemical cue control could be challenging, if not impossible, to achieve by conventional techniques. Besides the response dynamics to environmental changes, cell signaling events due to cellcell interactions are also of importance in many fields including immunology. Such studies require precise cell pairing in a controlled microenvironment. A versatile cell-pairing array was invented by Dura et al..67-69 The microfluidic device sequentially trapped and deterministically paired cells in a one-to-one fashion into each on-chip “cage” by passive hydrodynamics and flow-induced deformation (Figure 3B). The device supported both homotypic and heterotypic cell pairing, with efficiencies up to 80%. Since its invention, the device has enabled functional and longitudinal investigation of lymphocyte interactions in controlled microenvironment at the single-cell level, and up to hundreds of cells simultaneously.68-69 Another example from Chen et al. reported a patterned cell co-culture array device which captured and paired cell through a DNA-programmed adhesion.70 This platform allowed up to four distinct cell types seeded in each spot and controlled cell-contact time during long-term culture. The dynamics of single adult neural stem cell fate decision was studied by this array device in response to competing juxtacrine signals. The key that made these studies possible was the ability of high-throughput and on-demand cell pairing by specific microfluidic designs. Beyond cell pairing, in vitro culture of cell populations or cell aggregates provide more physiologically relevant models to study cell communication, differentiation, and interaction with different extracellular matrices. Both 2D and 3D cell culture methods incorporating tissue-specific matrices have been developed. The challenge, however, is to recapitulate the in vivo cellular environment while still maintaining the accessibility to cultured cells for manipulation and characterization. Here we will discuss the development of several novel microfluidic techniques to address this challenge. One emerging method is the open-surface microfluidic array. The first unique advantage offered by open-surface microfluidic arrays is that cells can be easily and 15 ACS Paragon Plus Environment


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precisely accessed during culture. Benefiting from this feature, Ozkumur et al. presented a highthroughput functional cell-based screening assay via patterned 2D cell culture array.71 After coating a microscope slide with a monolayer of target cells, they used a high-density PDMS nanowell array to press the cell monolayer and selectively removed cells for desired patterns. This process was termed as nanowell-assisted cell patterning (NWAP). During the patterning process, biochemical stimuli preloaded in the nanowell could be directly released to target cells. The delayed signal development in patterned cell array was characterized by cell motility, proliferation, and death. This technique was later applied to screen neutralizing antibodies against HIV. The second property of open-surface microfluidic arrays is that fluid can be driven by surface tension, which eliminates the need of external pumps and simplifies the device operation. Li et al. reported a flat-surface cell patterning device with a heterogeneous surface wettability.72 Doubleexclusive liquid repellency (double-ELR), a surface tension-driven phenomenon on such devices, allowed them to capture cells on patterned spots for culture by simply sweeping cell-containing liquid across the surface, resulting in high-efficient cell distribution. Importantly, researchers could use double-ELR to construct “modular” higher-order architectures to imitate physiologically relevant microenvironments for different cell-culture based assays, such as 3D multi-cell-type culture, cultures with selective mechanical cues of extracellular matrix, and spheroid cultures. In a surface tension-driven microfluidic device, fluid can be exchanged simply by pipetting with minimized shear-stress applied on culture samples. Therefore, various surface tension-driven devices have been shown in applications of both 2D microbial culture73 and 3D spheroid culture74. One particular 3D cell culture method benefits from the open-surface format is the hanging drop technique. A microfluidic-actuated hanging drop array was recently reported, combining the merits of hanging drops and microfluidic networks.75 In this system, cells and tissues could be easily loaded and harvested through the open drops, while the nutrients could be precisely 16 ACS Paragon Plus Environment

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delivered to each drop via interconnected microchannels in designed orders (Figure 3C). In a follow-up study, an on-chip pneumatic valve was integrated with the microfluidic-connected hanging drop networks.76 Using the pump to control local surface tension, this platform enabled closed-loop medium circulation within the hanging drop network with little shear stresses. Researchers could thus perform multiple microtissue formations on the same chip and study interorgan metabolic communication. Optical analytical tools, such as time-lapse imaging77 and fluorescence-activated cell sorting78 were also integrated with this platform to enhance the characterization power. Other closed microfluidic strategies for 3D cell culture were also developed in parallel, aiming for a tighter environment control and a higher throughput. Jackson-Holmes et al. developed a microfluidic array device to isolate and culture single stem cell aggregates.79 This device had a serial microchannel for hydrodynamic perfusion to regulate the microenvironment in each cell traps. They demonstrated that multi-day culture of aggregate in device reduced the phenotypic heterogeneity of cell aggregates. The platform was also compatible with longitudinal imaging of individual cell aggregates and on-chip immunostaining as end-point assays for multiparameter analysis. Another ultra-high throughput alternative to control 3D microenvironment is using droplet microfluidics.80-81 Zhang et al. showed that they could use a one-step microfluidic device to generate monodispersed microcompartment hydrogel droplets with a high speed. In each microgel particle, cells could be precisely assembled, and 3D gel matrix could be finely tuned. They further demonstrated this technique by creating a complex stem cell niche microenvironment and studied the environmental effect on stem cell fate.81 The control of the cell and matrix encapsulation by droplet microfluidics was very robust. Since this approach could be easily scaled up, it could in theory generate an unlimited number of 3D cell culturing particles under the same condition for large-scale investigation. Overall, the advantage of microfluidic culturing techniques



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is that the precise tissue-specific environment control to recapitulate in vivo functions can be realized, which also provides accessibility to individual samples for various measurements. 3.2. Culturing control, stimuli delivery, and screening for micro-organisms Like cell-based studies, microfluidic techniques facilitate high-throughput and long-term experimentation for studies of model micro-organisms as well, from microbes to multi-cellular animals. Two classes of microfluidic techniques are largely used to study micro-organisms: 1) high-throughput and long-term culturing platform for phenotypical screening; 2) stimuli delivery devices for functional and behavior screening. We will discuss notable microfluidic methods by using examples mainly from recent studies of the fruit fly Drosophila melanogaster and the soil nematode Caenorhabditis elegans, to illustrate how these techniques help advance our knowledge in neuroscience, developmental biology, and disease modeling. In general, the first class of microfluidic technique - long-term culturing platforms - tend to take microchamber array format, with media perfused through chambers via connecting microchannels. Single organisms are isolated into individual chambers and cultured under controlled microenvironments. The phenotype of each animal can be characterized over time, typically by optical microscopy, to understand a particular biological question. Work by Chung et al. showed a microfluidic trapping array to parallelly trap and orient Drosophila melanogaster embryos, allowing quantitative analysis of morphogenesis in the dorsoventral patterning system.82 Levario et al. used a similar device and studied anoxia-induced developmental arrest in Drosophila embryos by controlling the chemical stimuli perfused with the media.83 Using the similar device concept, Cornaglia et al. designed a platform to collect newly laid C. elegans embryos from on-chip cultured adult worms and imaged the entire embryogenesis of these eggs.84 With this platform, they elucidated the role of mitochondrial unfolded protein response in C. elegans embryogenesis.


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High-throughput culturing and screening C. elegans require modifications of the microfluidic chamber array devices described above because worms have a slender shape and are highly motile. Kopito et al. presented an array of straight micro-traps defined by micropillars to capture individual worms and immobilize them by the physical confinement.85 The micropillar boundary design allowed for media exchange and minimized the stress applied to worms. They successfully immobilized individual worms for more than 24 hours. By multi-timepoint high-resolution imaging of worms cultured in a restricted food level, they could study the stress response of worms and the corresponding physiological process (Figure 4A). As an example, Lee et al. further used the WormSpa device to perform long-term time-lapse imaging of worm’s pharyngeal pumping, a feeding behavior.86 They analyzed the pumping when worms were fed in different food conditions and found that pumping could have different modes, in response to environmental food concentration. Similar experiments with different strains with mutations suggested that serotonin, a neurotransmitter, played an important role in the induction of one mode - feeding burst. Using this idea of immobilizing C. elegans by microfluidic trap confinement, Mondal et al. developed a large-scale microfluidic chip in 96-well format with 40 parallel straight traps per well. 96 different populations of C. elegans could be immobilized within minutes for screening.87

They then

screened ~100,000 animals to probe polyglutamine (PolyQ) aggregation in C. elegans, a model relevant to Huntington’s disease, and tested the efficacy of ~1,000 drugs in improving the aggregation phenotype. With the same microfluidic design for immobilization, this group could also perform parallel laser axotomy, and carry out longitudinal studies of nerve regeneration in C. elegans.88 The second class of microfluidic technique, stimuli delivery devices, provide unique assays for neuroscience study with model organism C. elegans. Recent developments show that two kinds of stimuli, mechanical cues, and chemical cues, can be precisely applied to the model organism only by microfluidic techniques. Shown by Cho et al., Mechanical stimuli could be



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delivered by pneumatically actuated on-chip valves which deflected the PDMS side wall to touch the worm in the adjacent microchannel.89-90 The on-chip valve precisely stimulated the desired section of the worm body with tunable magnitude (Figure 4B). Cho et al. quantified the neuronal response to the stimuli by simultaneously imaging a strain whose mechanosensory neurons expressing the GCaMP fluorescent calcium reporter. Worms were loaded into the microchannel for functional imaging sequentially to achieve a high-throughput. They then used this device to perform a drug screening for small molecules that act on the mechanosensory pathway. This microfluidic device could also be scaled down for larval C. elegans to study the development of mechanosensation.90 Complementary to neuron functional imaging, McClanahan et al. designed a device to characterize the behavioral response to mechanical stimuli.91 Their device contained 64 sinusoidal channels through which worms could crawl. Multiple hydraulic valves delivered tunable and spatially localized mechanical stimuli to crawling worms by deflecting parts of the top wall of the sinusoidal channels (Figure 4C). Responses in locomotion of several dozen animals could be measured simultaneously. They used this method to study the reception field of gentle and

harsh

touch

neurons

history-dependent

behavior

responses.

In

addition

to

mechanosensation studies, Rahman et al. recently reported a micropillar-based microfluidic arena device, named “NemaFlex”, in which they could measure muscle strength of freely moving C. elegans by the deflection of micropillars.92 This quantitative platform could be used in highthroughput genetic and drug screening related to neuromuscular function and degenerative processes in aging and diseases. For chemical stimuli, Rouse et al. engineered a microfluidic circuit which could deliver a random sequence of multiple chemical reagents to C. elegans at a sub-second frequency.93 They imaged the activity of chemosensory neuron and observed different neuronal activity patterns in response to complex chemical stimulations. Another approach presented by Aubry et al. encapsulated C. elegans into droplets.94 By merging the droplet containing a worm with the droplet


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containing chemical reagents, the worm was stimulated and its behavior response could be recorded. Aubry et al. arranged these droplets into an array to increase the throughput. They expected this platform to be useful for combinatorial drug screening. A final example is presented by Larsch et al..95 They designed a micropillar arena which could host a population of worms. A microfluidic gradient generator was connected to the micropillar arena to deliver spatiotemporally controlled chemical stimuli. Impressively, with a customized microscopy system, they were able to simultaneously record neuronal activity and behavior of freely moving C. elegans in response to complex chemical stimuli (Figure 4D). This technique was later used to understand how C. elegans olfactory circuity mediates gradient climbing. In summary, built on existing high-throughput methods, microfluidic techniques can apply great control of on-chip microenvironment and thus create new assays for system-wide dynamic studies. Many new methods are developed in the recent decade. They have advanced our knowledge in systems biology in various fields, including immunology, stem cell biology, developmental biology, and neuroscience. Since many techniques are new, they need to be validated with existing tools before they can be widely applied. Especially for 3D culture for complex biological samples such as organoid and tissue models. Standard protocols must be established to make sure the culture samples are physiologically relevant to in vivo models.

4. Integrated microfluidics for multidimensional biological characterization We have discussed the power of microfluidic techniques to manipulate biological samples and surrounding microenvironments. In some experiments, it is important to gather data (of single type) from a large number of system components and from different time-points. In other studies, it is equally critical to perform different characterizations of a system, i.e. generations of different data types from the same systems. It seems that the ultimate goal of systems biology would



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require us to investigate a system both in the expanded amount and in the complexity of the information gathered. In this section, we will discuss how microfluidic techniques integrate different assays for multidimensional data acquisition at high throughput, and collaborate with advanced data analysis tools for deep phenotyping. 4.1. Multidimensional data acquisition enabled by microfluidics Unlike multiplexing measurement of one assay (such as multi-target immunoassay), multidimensional experimentation seeks to perform different assays on the same samples. For example, extracting genetic and phenotypic information simultaneously from single cells can better classify the identity of the cells and link the genotype to the function of the cells. Current high-throughput microfluidic techniques facilitate the multidimensional characterization in two ways: 1) combining multiple assays on-chip, and 2) labeling samples with unique identities to transfer between different assays. Because samples can be moved deterministically in microfluidic devices, the most straightforward way is to integrate different assays at different location on-chip and examine the samples sequentially. For example, Apichitsopa et al. developed a multiparameter cell-tracking intrinsic cytometry to characterize five intrinsic markers of single cells, such as cell size, deformability, and polarizability.96 Different modules were integrated on the same chip (Figure 5A). As single cells flowed through different modules, the cell deformability was characterized by constriction-based deformability measurement, and the cell polarizability was tested by multifrequency dielectrophoresis (DEP) spring.96-98 They used an optical cell tracking method to record the translocation of cells through the chip, and to track the single-cell identity in different modules. All parameters were proven important to classify cells by a machine learning method. Using a similar idea, Rane et al. designed a sheathless, microfluidic imaging flow cytometry equipped with stroboscopic illumination.99 The chip-based cytometer was able to rapidly capture multi-color, fluorescence, bright-field and dark field images while extracting sizes of individual cells to map 22 ACS Paragon Plus Environment

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intracellular heterogeneities. Because of the implement of stroboscopic illumination, they could achieve an ultra-high throughput (>50,000 cells/s) and still accurately enumerate apoptotic cells and discriminate cell-cycle phase. Other than flow cytometry type devices, integrated fluid circuits (IFC) can also introduce multiparameter assays on a single chip. Junkin et al. reported such a chip that combined nanoliter immunoassays, microfluidic input generation, and time-lapse microscopy, enabling the study of single-cell transcription factor dynamics and cytokine secretion in the same cell under complex inputs.100 They employed this platform to analyze macrophage signaling under pathogen assault, and found that surprisingly the dynamics of TNF secretion were uncorrelated with the dynamics of the transcription factor, NF-κB. Another strategy to carry out multidimensional measurement is to combine endpoint assays with on-chip culture. As an example, Lin et al. combined a motility assay with single-cell electrophoretic protein profiling to link invasive motility to protein expression in single tumor cells.33 The microfluidic device was made of polyacrylamide to mimic tissue-like stiffness and to present chemokine gradients along the microchannel. After 10 hours culture of the human glioblastoma tumor-initiating cells (TICs) in the channel, their locations were measured to characterize single-cell motility. Subsequently with the polyacrylamide side wall as a gel matrix, the cells were lysed and subjected to single-cell western blotting (Figure 5B). These two orthogonal assays suggested correlations between motility and Nestin and EphA2 expression, which could not be done by population-based assays. In other experiments, when one of the assays requires off-chip instrumentation, such as nextgeneration sequencing, microfluidic techniques are still useful for keeping track of the sample identity and linking measurements from different assays. Castellarnau et al. came up with a stochastic particle barcoding approach to track cell identity.101 They isolated a large number of single cells in a microwell array, and encapsulated single cells along with a random collection of fluorescent beads within an enzymatically-degradable hydrogel block. They then transferred the 23 ACS Paragon Plus Environment


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bead barcoded cell-hydrogel blocks between bioanalytical platforms without losing the cell identity. The application of this approach was demonstrated by single-cell protein secretion assay followed by subsequent PCR-based assays of cell lysates. Another way to label sample identity is by online sorting in the microfluidic device. Kimmerling et al. studied the single-cell mass growth rate and linked it to the corresponding single-cell gene expression pattern.102 They used a serial suspended microchannel resonator platform to measure the cell mass. Once they detected a signal, they would sort this cell and partition it off-chip for single-cell RNA sequencing. The same strategy was also applied to map single-cell biophysical measurement (mass and deformability) with membrane protein expression of circulating tumor cells, and to study the relationship between single-cell lineage and transcriptome.103-104 4.2. Deep phenotyping by microfluidics and advanced data analysis Through the help of microfluidic techniques, the volumes of high-content and multilayer data generated in systems biology experiments are soon beyond human ability to process. Computational approaches must be developed to efficiently process and to understand the inner structure of the complex dataset. Dimensional reduction techniques, such as principal component analysis (PCA) and clustering, are first used for analysis like gene cluster identification. A more recent trend is to adapt machine learning methods, including support vector machine (SVM) and logistic regression models. Customized algorithms can greatly facilitate the feature classification of high-dimensional data generated by microfluidic-assisted high-throughput experiments. As mentioned in Section 2.2, researchers from Di Carlo lab developed a multiparameter deformability

cytometry

which

carried

out

high-dimensional

single-cell

biophysical

characterization. To demonstrate the advantage of having a multiparameter measurement, they trained SVM models to classify different cell types and found they could identify induced pluripotent stem cells, mesenchymal stem cells, neural stem cells and their derivatives solely by characterization from the biophysical phenospace.44 In multicellular organism space, using 24 ACS Paragon Plus Environment

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morphometric measurements on fluorescent reporters, San-Miguel et al. discussed a deep phenotyping approach to screen genetic mutations that result in only subtle differences in the synaptic morphology of C. elegans.105 They designed an imaging pipeline to extract heuristic and statistical features from high-resolution fluorescent images of a certain synaptic structure of C. elegans, the synaptic domain of a motor neuron. Based on these features, they then developed a stepwise logistic regression classifier to identify differences in the synaptic morphology. Combining this classifier with a flow cytometry type microfluidic sorting platform, they could rapidly image, process and screen weak mutants whose altered synaptic morphology could hardly be discerned by human eyes. Saberi-Bosari et al. further integrated this data analysis method, with a microfluidic-based C. elegans culturing platform to quantitatively analyze aging-induced synaptic changes.106 All these examples demonstrate that the power of high-throughput microfluidic techniques can only be fully released when coupled with advanced data analysis tools.

5. Future outlook In this review, we have briefly introduced some recent microfluidic techniques for system biology experimentation. We emphasized three unique functions enabled by microfluidic methods:

1)

high-throughput

and

parallel

experimentation;

2)

dynamic

control

of

microenvironments; 3) multidimensional analysis by integrated microfluidics. After a decade of development, high-throughput microfluidics has matured significantly. Commercialized products from companies such as Fluidigm and 10X Genomics are available for single cell analysis. Components of a complex biological system, from the molecular length scale to the organism length scale, can be profiled separately with an unprecedented speed. Dynamics of a living system can be probed under a wide range of environmental conditions. New theoretical models



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can be build based on the large-scale dataset which is only collected from microfluidic-assisted platforms to understand fundamental biology. For future technical development, there seem to be three fertile grounds. First, many current microfluidic systems, such as single-cell analysis platform, still require sample pretreatment steps like tissue dissociation. This step is likely to cause loss of cells and the spatial information within the tissue. If an integrated platform that can dissect the tissue on-chip and automate the downstream single-cell analysis without losing the spatial information of each individual cells, much more valuable information can be gathered. In addition, several biochemical techniques have been reported to realize single-cell transcriptomic or proteomic profiling in 3D intact tissues.107-108 However, these techniques are still slow and labor intensive. Second, low-cost microfluidic devices and integrated periphery instrumentation are still needed for many applications. For example, a droplet-based platform has been built by 3D printing and low-cost electronics as a stand-alone equipment for single-cell RNA sequencing.109 It was tested and validated by biologists or physicians without much engineering expertise. This could greatly broaden the impact and usability of the technique. In parallel, even though commercialized products exist, many laboratories would like to customize their platforms. Low-cost and simple fabrication process and materials may be designed for easy prototyping. For instance, a tapebased microfluidic device fabricated by a razor printer was reported for cell co-culture.110-111 It was also a pipet accessible device which required minimum expertise to operate. Another example was a paper-based array device patterned with synthetic RNA probes.112-113 This device could be dried and stored in room temperature and used upon the need for large-scale RNA profiling. The usage of paper and synthetic biomaterials made this device low-cost and easy to fabricate. Methods like these are much easier to transfer to other laboratories and benefit the entire systems biology society. Third, although microfluidic techniques have been studied for nearly two decades, in our opinion, the development of methods for multidimensional characterization and advanced


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data analysis tools has just begun. Future research of microfluidic techniques needs to consider new analytical tools, such as light-sheet microscopy, as well as data analytics tools. These integrated methods may eventually become a cornerstone for future systems biology experimentation.

Author Information Corresponding Author: *E-mail: [email protected]. Phone: 1-404-804-8473. Fax: 1-404-894-4200 Author Contribution: Both authors contributed to the writing of the manuscript and have approved the final version. Notes: The authors declare no competing financial interest. Biographies: Gongchen Sun is a postdoctoral fellow in Chemical and Biomolecular Engineering at Georgia Institute of Technology. He graduated from Peking University in 2012 with a B.S. in Microelectronics and received his doctoral degree from University of Notre Dame in 2017 in Chemical and Biomolecular Engineering. He worked on electrokinetically driven micro/nanofluidic and ionic circuits during his doctoral studies. His current research interests include the development

and

applications

of

microfluidic-enabled

technologies

for

neurobiology,

developmental biology, and point-of-care theranostics.



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Hang Lu is the Love Family Professor of Chemical and Biomolecular Engineering, the Director of the Interdisciplinary Bioengineering Program at Georgia Tech, and the associate director of the Southeast Center for Mathematics and Biology (SCMB) supported by NSF and Simons Foundation. Her current research interests are microfluidics, automation, quantitative analyses, and their applications in neurobiology, cell biology, cancer, and biotechnology. Her award and honors include the ACS Analytical Chemistry Young Innovator Award, an NSF CAREER award, an Alfred P. Sloan Foundation Research Fellowship, a DuPont Young Professor Award, a DARPA Young Faculty Award, and Council of Systems Biology in Boston (CSB2) Prize in Systems Biology; she was also named an MIT Technology Review TR35 top innovator, and invited to give the RPI Van Ness Award Lectures in 2011, and the Saville Lecture at Princeton in 2013. She is an elected fellow of AAAS and AIMBE.

Acknowledgment The authors acknowledge the U.S. National Science Foundation (Grant 1707401 to H.L.) and the U.S. National Institutes of Health (Grants to R01AG056436, R21DC015652, R01NS096581, R01GM088333, R21 EB021676, R21EB020424, and R01GM108962 to H.L.) for funding support. The authors would also like to thank Dr. D. S. Patal and Dr. E. Jackson-Holmes for helpful suggestions.


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Figures

Figure 1. Microfluidic techniques for single-cell “omics” studies. (A) Schematic of single-cell mRNA-seq library preparation with Drop-seq. Single cells are isolated with unique barcoded 29 ACS Paragon Plus Environment


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microbeads in separated drops to achieve cell-specific labeling. Reprinted from Cell, 161(5), Macosko, E. Z.; Basu, A.; Satija, R.; Nemesh, J.; Shekhar, K.; Goldman, M.; Tirosh, I.; Bialas, A. R.; Kamitaki, N.; Martersteck, E. M.; Trombetta, J. J.; Weitz, D. A.; Sanes, J. R.; Shalek, A. K.; Regev, A.; McCarroll, S. A., Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets, 1202-1214 (ref 12). Copyright (2015), with permission from Elsevier. (B) Experimental procedure for CytoSeq. Single cells and barcoded microbeads are compartmentalized in microwells by gravity loading. From Fan, H. C.; Fu, G. K.; Fodor, S. P., Expression profiling. Combinatorial labeling of single cells for gene expression cytometry. Science 2015, 347 (6222), 1258367 (ref 21). Reprinted with permission from AAAS. (C) Workflow of profiling of 42 single-cell secreted immune effector proteins. Single cells are isolated in microchambers and immune effectors are quantified by a multiplexed antibody barcode array. Reproduced with permission from Proceedings of the National Academy of Sciences USA Lu, Y.; Xue, Q.; Eisele, M. R.; Sulistijo, E. S.; Brower, K.; Han, L.; Amir el, A. D.; Pe'er, D.; Miller-Jensen, K.; Fan, R., Highly multiplexed profiling of single-cell effector functions reveals deep functional heterogeneity in response to pathogenic ligands. Proc Natl Acad Sci U S A 2015, 112 (7), E60715 (ref 27). (D) Single-cell Western blotting. Single cells are separated in polyacrylamide microwells. Different proteins are separated by parallel gel electrophoresis. Reprinted by permission from Macmillan Publishers Ltd: Nature Methods, Hughes, A. J.; Spelke, D. P.; Xu, Z.; Kang, C. C.; Schaffer, D. V.; Herr, A. E., Single-cell western blotting. Nat Methods 2014, 11 (7), 749-55 (ref 29). Copyright 2014.


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Figure 2. Microfluidic techniques for single-cell biophysics analysis in living cells. (A) The serial suspended microchannel resonator (SMR) array to measure cell mass growth rate. (i) The workflow of the serial SMR array. Single-cell mass is measured repeatedly when flowed through the series of SMR connected by delay channels. (ii) Design of the serial SMR array. Adapted with permission from Macmillan Publishers Ltd: Nature Biotechnology, Cermak, N.; Olcum, S.; Delgado, F. F.; Wasserman, S. C.; Payer, K. R.; M, A. M.; Knudsen, S. M.; Kimmerling, R. J.; Stevens, M. M.; Kikuchi, Y.; Sandikci, A.; Ogawa, M.; Agache, V.; Baleras, F.; Weinstock, D. M.; Manalis, S. R., High-throughput measurement of single-cell growth rates using serial microfluidic mass sensor arrays. Nat Biotechnol 2016, 34 (10), 1052-1059 (ref 35). Copyright 2016. (B) Microfluidic deformability cytometry to measure cell deformability by hydrodynamic stretching. (i) The microfluidic device. (ii) Schematics of a single cell entering an extensional flow zone and deforming under stretching. (iii) Bright-field images of the process in (ii). Adapted with permission from Lin, J.; Kim, D.; Henry, T. T.; Tseng, P.; Peng, L.; Dhar, M.; Karumbayaram, S.; Di Carlo, D., High-throughput physical phenotyping of cell differentiation. Microsystems & Nanoengineering 31 ACS Paragon Plus Environment


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2017, 3, 17013 (ref 44). under the terms of the Creative Commons Attribution 4.0 International License, https://creativecommons.org/licenses/by/4.0. (C) Schematics of the array format singlecell force cytometry. Scale bar: 25μm. Adapted with permission from Macmillan Publishers Ltd: Nature Biomedical Engineering, Pushkarsky, I.; Tseng, P.; Black, D.; France, B.; Warfe, L.; KoziolWhite, C. J.; Jester, W. F.; Trinh, R. K.; Lin, J.; Scumpia, P. O.; Morrison, S. L.; Panettieri, R. A.; Damoiseaux, R.; Di Carlo, D., Elastomeric sensor surfaces for high-throughput single-cell force cytometry. Nature Biomedical Engineering 2018, 2 (2), 124-137 (ref 53). Copyright 2018.


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Figure 3. Microfluidic techniques for spatiotemporal culturing control of cell and cell aggregates. (A) Programmable microfluidic cell trap array to study single cell response to external soluble cues. (i) Image of the pneumatic valve controlled microfluidic device. (ii) Enlarged image of the on-chip pneumatic valves. (iii) False color image of Jurkat cells (green) trapped in the cell chamber (red dotted line). (iv-v) Fluorescent images the programmable stimuli delivery. Adapted from He, L.; Kniss, A.; San-Miguel, A.; Rouse, T.; Kemp, M. L.; Lu, H., An automated programmable platform enabling multiplex dynamic stimuli delivery and cellular response monitoring for highthroughput suspension single-cell signaling studies. Lab Chip 2015, 15 (6), 1497-507 (ref 63). with permission of The Royal Society of Chemistry. (B) Microfluidic device for large-scale immune 33 ACS Paragon Plus Environment


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cell pairing. (i) Image of the device. Scale bar: 5mm. (ii) Scanning electron micrograph image of the cell pairing trap. Scale bars: 100μm, 20μm (inset). (iii) The cell loading and pairing protocol. Scale bar: 20μm. (iv) Overlaid phase contrast and fluorescence images showing the large-scale cell pairing array. Adapted with permission from Macmillan Publishers Ltd: Nature Communications, Dura, B.; Dougan, S. K.; Barisa, M.; Hoehl, M. M.; Lo, C. T.; Ploegh, H. L.; Voldman, J., Profiling lymphocyte interactions at the single-cell level by microfluidic cell pairing. Nat Commun 2015, 6, 5940 (ref 68). Copyright 2015. (C) Microfluidic-actuated hanging drop array. (i) The layout of the hanging drop array and upstream microfluidic network to deliver media with various compositions. (ii) Images of the hanging drop array with four different concentration condition to show the working principle. Adapted with permission from Macmillan Publishers Ltd: Nature Communications, Frey, O.; Misun, P. M.; Fluri, D. A.; Hengstler, J. G.; Hierlemann, A., Reconfigurable microfluidic hanging drop network for multi-tissue interaction and analysis. Nat Commun 2014, 5, 4250 (ref 75). Copyright 2015.


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Figure 4. Microfluidic techniques for studies of micro-organisms. (A) The WormSpa device for long-term and repeated high-resolution imaging of C. elegans without significant stress in physiology. (i) The long-term experimental set-up and the image of the WormSpa array. Scale bar: 100μm. (ii) Micrograph of a worm immobilized in a straight worm trap defined by micropillars. Scale bar: 100μm. Adapted from Kopito, R. B.; Levine, E., Durable spatiotemporal surveillance of Caenorhabditis elegans response to environmental cues. Lab Chip 2014, 14 (4), 764-70 (ref 85). with permission of The Royal Society of Chemistry. (B) Microfluidic device to study the neuronal response of C elegans to mechanical stimulation. (i) Schematics of the mechanical stimulation channel geometry. (ii) Example images of worms stimulated by different applied pressures. The worm outline express fluorescence. Scale bar: 25μm. Adapted with permission from Cho, Y.; Oakland, D. N.; Lee, S. A.; Schafer, W. R.; Lu, H., On-chip functional neuroimaging with mechanical stimulation in Caenorhabditis elegans larvae for studying development and neural circuits. Lab Chip 2018, 18 (4), 601-609 (ref 90) – Published by The Royal Society of Chemistry. under the terms of the Creative Commons Attribution 3.0 Unported License, https://creativecommons.org/licenses/by/3.0/. (C) Microfluidic device to study the behavioral response of C elegans to mechanical stimulation. (i) Schematics of the working principle to deliver mechanical touch stimuli. (ii) Darkfield images of C elegans crawling in the worm channels overlaid with touch channels. (iii) Schematics of the device containing 64 worm channels and 15 touch channels. Adapted from McClanahan, P. D.; Xu, J. H.; Fang-Yen, C., Comparing Caenorhabditis elegans gentle and harsh touch response behavior using a multiplexed hydraulic microfluidic device. Integr Biol (Camb) 2017, 9 (10), 800-809 (ref 91). with permission of the 35 ACS Paragon Plus Environment


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Oxford University Press. (D) Microfluidic arena device to study the neuronal and behavioral response of C elegans to chemical stimulation. (i) The experimental set-up of the chemical stimuli delivery module, the microfluidic arena device, and the customized imaging system. (ii) Brightfield image of the microfluidic arena device containing three animals. Scale bar: 1mm. (iii-iv) Representative behavior image and neuronal signal image of GCaMP-labeled AWA neuron. Adapted with permission from Proceedings of the National Academy of Sciences USA Larsch, J.; Ventimiglia, D.; Bargmann, C. I.; Albrecht, D. R., High-throughput imaging of neuronal activity in Caenorhabditis elegans. Proc Natl Acad Sci U S A 2013, 110 (45), E4266-73 (ref 95).


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Figure 5. Microfluidic techniques for Multidimensional data acquisition. (A) Schematics of the multiparameter cell-tracking intrinsic cytometry. (i) Overview of the platform that combines different microfluidic modules on one substrate with cell tracking module. (ii-iv) Different modules to measure cell size, polarizability and deformability as cells flow through modules. Adapted from Apichitsopa, N.; Jaffe, A.; Voldman, J., Multiparameter cell-tracking intrinsic cytometry for singlecell characterization. Lab Chip 2018, 18 (10), 1430-1439 (ref 96). with permission of The Royal Society of Chemistry. (B) Microfluidic integration of single-cell motility assay and single-cell Western blotting to link tumor cell motility with selected protein expression with single-cell resolution. Adapted from Lin, J. G.; Kang, C. C.; Zhou, Y.; Huang, H.; Herr, A. E.; Kumar, S., Linking invasive motility to protein expression in single tumor cells. Lab Chip 2018, 18 (2), 371384 (ref 33). with permission of The Royal Society of Chemistry.



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References 1.

Lazebnik, Y., Can a biologist fix a radio?—Or, what I learned while studying apoptosis. Cancer Cell 2002, 2 (3), 179-182.

2.

Breslauer, D. N.; Lee, P. J.; Lee, L. P., Microfluidics-based systems biology. Mol Biosyst 2006, 2 (2), 97-112.

3.

Kitano, H., Systems biology: a brief overview. Science 2002, 295 (5560), 1662-4.

4.

Armbrecht, L.; Dittrich, P. S., Recent Advances in the Analysis of Single Cells. Anal Chem 2017, 89 (1), 2-21.

5.

Sibbitts, J.; Sellens, K. A.; Jia, S.; Klasner, S. A.; Culbertson, C. T., Cellular Analysis Using Microfluidics. Anal Chem 2018, 90 (1), 65-85.

6.

Zhan, M.; Chingozha, L.; Lu, H., Enabling systems biology approaches through microfabricated systems. Anal Chem 2013, 85 (19), 8882-94.

7.

Duncombe, T. A.; Tentori, A. M.; Herr, A. E., Microfluidics: reframing biological enquiry. Nat Rev Mol Cell Biol 2015, 16 (9), 554-67.

8.

Prakadan, S. M.; Shalek, A. K.; Weitz, D. A., Scaling by shrinking: empowering single-cell 'omics' with microfluidic devices. Nat Rev Genet 2017, 18 (6), 345-361.

9.

Gawad, C.; Koh, W.; Quake, S. R., Single-cell genome sequencing: current state of the science. Nat Rev Genet 2016, 17 (3), 175-88.

10.

Genshaft, A. S.; Li, S.; Gallant, C. J.; Darmanis, S.; Prakadan, S. M.; Ziegler, C. G.; Lundberg, M.; Fredriksson, S.; Hong, J.; Regev, A.; Livak, K. J.; Landegren, U.; Shalek, A. K., Multiplexed, targeted profiling of single-cell proteomes and transcriptomes in a single reaction. Genome Biol 2016, 17 (1), 188.

11.

Cheow, L. F.; Courtois, E. T.; Tan, Y.; Viswanathan, R.; Xing, Q.; Tan, R. Z.; Tan, D. S.; Robson, P.; Loh, Y. H.; Quake, S. R.; Burkholder, W. F., Single-cell multimodal profiling reveals cellular epigenetic heterogeneity. Nat Methods 2016, 13 (10), 833-6.

12.

Macosko, E. Z.; Basu, A.; Satija, R.; Nemesh, J.; Shekhar, K.; Goldman, M.; Tirosh, I.; Bialas, A. R.; Kamitaki, N.; Martersteck, E. M.; Trombetta, J. J.; Weitz, D. A.; Sanes, J. R.; Shalek, A. K.; Regev, A.; McCarroll, S. A., Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets. Cell 2015, 161 (5), 1202-1214.

13.

Stoeckius, M.; Hafemeister, C.; Stephenson, W.; Houck-Loomis, B.; Chattopadhyay, P. K.; Swerdlow, H.; Satija, R.; Smibert, P., Simultaneous epitope and transcriptome measurement in single cells. Nat Methods 2017, 14 (9), 865-868.


Page 39 of 50 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


14.

Klein, A. M.; Mazutis, L.; Akartuna, I.; Tallapragada, N.; Veres, A.; Li, V.; Peshkin, L.; Weitz, D. A.; Kirschner, M. W., Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 2015, 161 (5), 1187-1201.

15.

Habib, N.; Avraham-Davidi, I.; Basu, A.; Burks, T.; Shekhar, K.; Hofree, M.; Choudhury, S. R.; Aguet, F.; Gelfand, E.; Ardlie, K.; Weitz, D. A.; Rozenblatt-Rosen, O.; Zhang, F.; Regev, A., Massively parallel single-nucleus RNA-seq with DroNc-seq. Nat Methods 2017, 14 (10), 955-958.

16.

Briggs, J. A.; Weinreb, C.; Wagner, D. E.; Megason, S.; Peshkin, L.; Kirschner, M. W.; Klein, A. M., The dynamics of gene expression in vertebrate embryogenesis at single-cell resolution. Science 2018, 360 (6392).

17.

Dixit, A.; Parnas, O.; Li, B.; Chen, J.; Fulco, C. P.; Jerby-Arnon, L.; Marjanovic, N. D.; Dionne, D.; Burks, T.; Raychowdhury, R.; Adamson, B.; Norman, T. M.; Lander, E. S.; Weissman, J. S.; Friedman, N.; Regev, A., Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens. Cell 2016, 167 (7), 18531866 e17.

18.

Adamson, B.; Norman, T. M.; Jost, M.; Cho, M. Y.; Nunez, J. K.; Chen, Y.; Villalta, J. E.; Gilbert, L. A.; Horlbeck, M. A.; Hein, M. Y.; Pak, R. A.; Gray, A. N.; Gross, C. A.; Dixit, A.; Parnas, O.; Regev, A.; Weissman, J. S., A Multiplexed Single-Cell CRISPR Screening Platform Enables Systematic Dissection of the Unfolded Protein Response. Cell 2016, 167 (7), 1867-1882 e21.

19.

Fu, Y.; Li, C.; Lu, S.; Zhou, W.; Tang, F.; Xie, X. S.; Huang, Y., Uniform and accurate single-cell sequencing based on emulsion whole-genome amplification. Proc Natl Acad Sci U S A 2015, 112 (38), 11923-8.

20.

Zheng, G. X.; Terry, J. M.; Belgrader, P.; Ryvkin, P.; Bent, Z. W.; Wilson, R.; Ziraldo, S. B.; Wheeler, T. D.; McDermott, G. P.; Zhu, J.; Gregory, M. T.; Shuga, J.; Montesclaros, L.; Underwood, J. G.; Masquelier, D. A.; Nishimura, S. Y.; Schnall-Levin, M.; Wyatt, P. W.; Hindson, C. M.; Bharadwaj, R.; Wong, A.; Ness, K. D.; Beppu, L. W.; Deeg, H. J.; McFarland, C.; Loeb, K. R.; Valente, W. J.; Ericson, N. G.; Stevens, E. A.; Radich, J. P.; Mikkelsen, T. S.; Hindson, B. J.; Bielas, J. H., Massively parallel digital transcriptional profiling of single cells. Nat Commun 2017, 8, 14049.

21.

Fan, H. C.; Fu, G. K.; Fodor, S. P., Expression profiling. Combinatorial labeling of single cells for gene expression cytometry. Science 2015, 347 (6222), 1258367.

22.

Han, X.; Wang, R.; Zhou, Y.; Fei, L.; Sun, H.; Lai, S.; Saadatpour, A.; Zhou, Z.; Chen, H.; Ye, F.; Huang, D.; Xu, Y.; Huang, W.; Jiang, M.; Jiang, X.; Mao, J.; Chen, Y.; Lu, C.; Xie, 39 ACS Paragon Plus Environment


Page 40 of 50

J.; Fang, Q.; Wang, Y.; Yue, R.; Li, T.; Huang, H.; Orkin, S. H.; Yuan, G. C.; Chen, M.; Guo, G., Mapping the Mouse Cell Atlas by Microwell-Seq. Cell 2018, 172 (5), 1091-1107 e17. 23.

Gierahn, T. M.; Wadsworth, M. H., 2nd; Hughes, T. K.; Bryson, B. D.; Butler, A.; Satija, R.; Fortune, S.; Love, J. C.; Shalek, A. K., Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput. Nat Methods 2017, 14 (4), 395-398.

24.

Bose, S.; Wan, Z.; Carr, A.; Rizvi, A. H.; Vieira, G.; Pe'er, D.; Sims, P. A., Scalable microfluidics for single-cell RNA printing and sequencing. Genome Biol 2015, 16, 120.

25.

Yuan, J.; Sims, P. A., An Automated Microwell Platform for Large-Scale Single Cell RNASeq. Sci Rep 2016, 6, 33883.

26.

Lu, Y.; Chen, J. J.; Mu, L.; Xue, Q.; Wu, Y.; Wu, P. H.; Li, J.; Vortmeyer, A. O.; MillerJensen, K.; Wirtz, D.; Fan, R., High-throughput secretomic analysis of single cells to assess functional cellular heterogeneity. Anal Chem 2013, 85 (4), 2548-56.

27.

Lu, Y.; Xue, Q.; Eisele, M. R.; Sulistijo, E. S.; Brower, K.; Han, L.; Amir el, A. D.; Pe'er, D.; Miller-Jensen, K.; Fan, R., Highly multiplexed profiling of single-cell effector functions reveals deep functional heterogeneity in response to pathogenic ligands. Proc Natl Acad Sci U S A 2015, 112 (7), E607-15.

28.

Xue, Q.; Lu, Y.; Eisele, M. R.; Sulistijo, E. S.; Khan, N.; Fan, R.; Miller-Jensen, K., Analysis of single-cell cytokine secretion reveals a role for paracrine signaling in coordinating macrophage responses to TLR4 stimulation. Sci Signal 2015, 8 (381), ra59.

29.

Hughes, A. J.; Spelke, D. P.; Xu, Z.; Kang, C. C.; Schaffer, D. V.; Herr, A. E., Single-cell western blotting. Nat Methods 2014, 11 (7), 749-55.

30.

Kang, C. C.; Yamauchi, K. A.; Vlassakis, J.; Sinkala, E.; Duncombe, T. A.; Herr, A. E., Single cell-resolution western blotting. Nat Protoc 2016, 11 (8), 1508-30.

31.

Sinkala, E.; Sollier-Christen, E.; Renier, C.; Rosas-Canyelles, E.; Che, J.; Heirich, K.; Duncombe, T. A.; Vlassakis, J.; Yamauchi, K. A.; Huang, H.; Jeffrey, S. S.; Herr, A. E., Profiling protein expression in circulating tumour cells using microfluidic western blotting. Nat Commun 2017, 8, 14622.

32.

Kang, C. C.; Lin, J. M.; Xu, Z.; Kumar, S.; Herr, A. E., Single-cell Western blotting after whole-cell imaging to assess cancer chemotherapeutic response. Anal Chem 2014, 86 (20), 10429-36.

33.

Lin, J. G.; Kang, C. C.; Zhou, Y.; Huang, H.; Herr, A. E.; Kumar, S., Linking invasive motility to protein expression in single tumor cells. Lab Chip 2018, 18 (2), 371-384.


Page 41 of 50 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


34.

Son, S.; Tzur, A.; Weng, Y.; Jorgensen, P.; Kim, J.; Kirschner, M. W.; Manalis, S. R., Direct observation of mammalian cell growth and size regulation. Nat Methods 2012, 9 (9), 9102.

35.

Cermak, N.; Olcum, S.; Delgado, F. F.; Wasserman, S. C.; Payer, K. R.; M, A. M.; Knudsen, S. M.; Kimmerling, R. J.; Stevens, M. M.; Kikuchi, Y.; Sandikci, A.; Ogawa, M.; Agache, V.; Baleras, F.; Weinstock, D. M.; Manalis, S. R., High-throughput measurement of single-cell growth rates using serial microfluidic mass sensor arrays. Nat Biotechnol 2016, 34 (10), 1052-1059.

36.

Cetin, A. E.; Stevens, M. M.; Calistri, N. L.; Fulciniti, M.; Olcum, S.; Kimmerling, R. J.; Munshi, N. C.; Manalis, S. R., Determining therapeutic susceptibility in multiple myeloma by single-cell mass accumulation. Nat Commun 2017, 8 (1), 1613.

37.

Stevens, M. M.; Maire, C. L.; Chou, N.; Murakami, M. A.; Knoff, D. S.; Kikuchi, Y.; Kimmerling, R. J.; Liu, H.; Haidar, S.; Calistri, N. L.; Cermak, N.; Olcum, S.; Cordero, N. A.; Idbaih, A.; Wen, P. Y.; Weinstock, D. M.; Ligon, K. L.; Manalis, S. R., Drug sensitivity of single cancer cells is predicted by changes in mass accumulation rate. Nat Biotechnol 2016, 34 (11), 1161-1167.

38.

Byun, S.; Son, S.; Amodei, D.; Cermak, N.; Shaw, J.; Kang, J. H.; Hecht, V. C.; Winslow, M. M.; Jacks, T.; Mallick, P.; Manalis, S. R., Characterizing deformability and surface friction of cancer cells. Proc Natl Acad Sci U S A 2013, 110 (19), 7580-5.

39.

Byun, S.; Hecht, V. C.; Manalis, S. R., Characterizing Cellular Biophysical Responses to Stress by Relating Density, Deformability, and Size. Biophys J 2015, 109 (8), 1565-73.

40.

Shaw Bagnall, J.; Byun, S.; Begum, S.; Miyamoto, D. T.; Hecht, V. C.; Maheswaran, S.; Stott, S. L.; Toner, M.; Hynes, R. O.; Manalis, S. R., Deformability of Tumor Cells versus Blood Cells. Sci Rep 2015, 5, 18542.

41.

Otto, O.; Rosendahl, P.; Mietke, A.; Golfier, S.; Herold, C.; Klaue, D.; Girardo, S.; Pagliara, S.; Ekpenyong, A.; Jacobi, A.; Wobus, M.; Topfner, N.; Keyser, U. F.; Mansfeld, J.; Fischer-Friedrich, E.; Guck, J., Real-time deformability cytometry: on-the-fly cell mechanical phenotyping. Nat Methods 2015, 12 (3), 199-202, 4 p following 202.

42.

Dhar, M.; Wong, J.; Karimi, A.; Che, J.; Renier, C.; Matsumoto, M.; Triboulet, M.; Garon, E. B.; Goldman, J. W.; Rettig, M. B.; Jeffrey, S. S.; Kulkarni, R. P.; Sollier, E.; Di Carlo, D., High efficiency vortex trapping of circulating tumor cells. Biomicrofluidics 2015, 9 (6), 064116.

43.

Che, J.; Yu, V.; Garon, E. B.; Goldman, J. W.; Di Carlo, D., Biophysical isolation and identification of circulating tumor cells. Lab Chip 2017, 17 (8), 1452-1461. 41 ACS Paragon Plus Environment


44.

Page 42 of 50

Lin, J.; Kim, D.; Henry, T. T.; Tseng, P.; Peng, L.; Dhar, M.; Karumbayaram, S.; Di Carlo, D., High-throughput physical phenotyping of cell differentiation. Microsystems & Nanoengineering 2017, 3, 17013.

45.

Gossett, D. R.; Tse, H. T.; Lee, S. A.; Ying, Y.; Lindgren, A. G.; Yang, O. O.; Rao, J.; Clark, A. T.; Di Carlo, D., Hydrodynamic stretching of single cells for large population mechanical phenotyping. Proc Natl Acad Sci U S A 2012, 109 (20), 7630-5.

46.

Dudani, J. S.; Gossett, D. R.; Tse, H. T.; Di Carlo, D., Pinched-flow hydrodynamic stretching of single-cells. Lab Chip 2013, 13 (18), 3728-34.

47.

Kim, J.; Han, S.; Lei, A.; Miyano, M.; Bloom, J.; Srivastava, V.; Stampfer, M. R.; Gartner, Z. J.; LaBarge, M. A.; Sohn, L. L., Characterizing cellular mechanical phenotypes with mechano-node-pore sensing. Microsystems & nanoengineering 2018, 4, 17091.

48.

Balakrishnan, K. R.; Whang, J. C.; Hwang, R.; Hack, J. H.; Godley, L. A.; Sohn, L. L., Node-pore sensing enables label-free surface-marker profiling of single cells. Anal Chem 2015, 87 (5), 2988-95.

49.

Sarioglu, A. F.; Aceto, N.; Kojic, N.; Donaldson, M. C.; Zeinali, M.; Hamza, B.; Engstrom, A.; Zhu, H.; Sundaresan, T. K.; Miyamoto, D. T.; Luo, X.; Bardia, A.; Wittner, B. S.; Ramaswamy, S.; Shioda, T.; Ting, D. T.; Stott, S. L.; Kapur, R.; Maheswaran, S.; Haber, D. A.; Toner, M., A microfluidic device for label-free, physical capture of circulating tumor cell clusters. Nat Methods 2015, 12 (7), 685-91.

50.

Tasadduq, B.; McFarland, B.; Islam, M.; Alexeev, A.; Sarioglu, A. F.; Sulchek, T., Continuous Sorting of Cells Based on Differential P Selectin Glycoprotein Ligand Expression Using Molecular Adhesion. Anal Chem 2017, 89 (21), 11545-11551.

51.

Islam, M.; Mezencev, R.; McFarland, B.; Brink, H.; Campbell, B.; Tasadduq, B.; Waller, E. K.; Lam, W.; Alexeev, A.; Sulchek, T., Microfluidic cell sorting by stiffness to examine heterogenic responses of cancer cells to chemotherapy. Cell Death Dis 2018, 9 (2), 239.

52.

Islam, M.; Brink, H.; Blanche, S.; DiPrete, C.; Bongiorno, T.; Stone, N.; Liu, A.; Philip, A.; Wang, G.; Lam, W.; Alexeev, A.; Waller, E. K.; Sulchek, T., Microfluidic Sorting of Cells by Viability Based on Differences in Cell Stiffness. Sci Rep 2017, 7 (1), 1997.

53.

Pushkarsky, I.; Tseng, P.; Black, D.; France, B.; Warfe, L.; Koziol-White, C. J.; Jester, W. F.; Trinh, R. K.; Lin, J.; Scumpia, P. O.; Morrison, S. L.; Panettieri, R. A.; Damoiseaux, R.; Di Carlo, D., Elastomeric sensor surfaces for high-throughput single-cell force cytometry. Nature Biomedical Engineering 2018, 2 (2), 124-137.


Page 43 of 50 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


54.

Lee, D. H.; Li, X.; Ma, N.; Digman, M. A.; Lee, A. P., Rapid and label-free identification of single leukemia cells from blood in a high-density microfluidic trapping array by fluorescence lifetime imaging microscopy. Lab Chip 2018, 18 (9), 1349-1358.

55.

Cao, C.; Zhou, D.; Chen, T.; Streets, A. M.; Huang, Y., Label-Free Digital Quantification of Lipid Droplets in Single Cells by Stimulated Raman Microscopy on a Microfluidic Platform. Anal Chem 2016, 88 (9), 4931-9.

56.

Brockman, J. M.; Blanchard, A. T.; Pui-Yan, V. M.; Derricotte, W. D.; Zhang, Y.; Fay, M. E.; Lam, W. A.; Evangelista, F. A.; Mattheyses, A. L.; Salaita, K., Mapping the 3D orientation of piconewton integrin traction forces. Nat Methods 2018, 15 (2), 115-118.

57.

Dutta, P. K.; Zhang, Y.; Blanchard, A. T.; Ge, C.; Rushdi, M.; Weiss, K.; Zhu, C.; Ke, Y.; Salaita, K., Programmable Multivalent DNA-Origami Tension Probes for Reporting Cellular Traction Forces. Nano Lett 2018, 18 (8), 4803-4811.

58.

Cole, R. H.; Tang, S. Y.; Siltanen, C. A.; Shahi, P.; Zhang, J. Q.; Poust, S.; Gartner, Z. J.; Abate, A. R., Printed droplet microfluidics for on demand dispensing of picoliter droplets and cells. Proc Natl Acad Sci U S A 2017, 114 (33), 8728-8733.

59.

Siltanen, C. A.; Cole, R. H.; Poust, S.; Chao, L.; Tyerman, J.; Kaufmann-Malaga, B.; Ubersax, J.; Gartner, Z. J.; Abate, A. R., An Oil-Free Picodrop Bioassay Platform for Synthetic Biology. Sci Rep 2018, 8 (1), 7913.

60.

Kamalakshakurup, G.; Lee, A. P., High-efficiency single cell encapsulation and size selective capture of cells in picoliter droplets based on hydrodynamic micro-vortices. Lab Chip 2017, 17 (24), 4324-4333.

61.

Dhar, M.; Lam, J. N.; Walser, T.; Dubinett, S. M.; Rettig, M. B.; Di Carlo, D., Functional profiling of circulating tumor cells with an integrated vortex capture and single-cell protease activity assay. Proc Natl Acad Sci U S A 2018, 115 (40), 9986-9991.

62.

Chung, K.; Rivet, C. A.; Kemp, M. L.; Lu, H., Imaging single-cell signaling dynamics with a deterministic high-density single-cell trap array. Anal Chem 2011, 83 (18), 7044-52.

63.

He, L.; Kniss, A.; San-Miguel, A.; Rouse, T.; Kemp, M. L.; Lu, H., An automated programmable platform enabling multiplex dynamic stimuli delivery and cellular response monitoring for high-throughput suspension single-cell signaling studies. Lab Chip 2015, 15 (6), 1497-507.

64.

Chingozha, L.; Zhan, M.; Zhu, C.; Lu, H., A generalizable, tunable microfluidic platform for delivering fast temporally varying chemical signals to probe single-cell response dynamics. Anal Chem 2014, 86 (20), 10138-47.



65.

Page 44 of 50

Kniss-James, A. S.; Rivet, C. A.; Chingozha, L.; Lu, H.; Kemp, M. L., Single-cell resolution of intracellular T cell Ca(2+) dynamics in response to frequency-based H2O2 stimulation. Integr Biol (Camb) 2017, 9 (3), 238-247.

66.

Ramji, R.; Wong, V. C.; Chavali, A. K.; Gearhart, L. M.; Miller-Jensen, K., A passive-flow microfluidic device for imaging latent HIV activation dynamics in single T cells. Integr Biol (Camb) 2015, 7 (9), 998-1010.

67.

Dura, B.; Liu, Y.; Voldman, J., Deformability-based microfluidic cell pairing and fusion. Lab Chip 2014, 14 (15), 2783-90.

68.

Dura, B.; Dougan, S. K.; Barisa, M.; Hoehl, M. M.; Lo, C. T.; Ploegh, H. L.; Voldman, J., Profiling lymphocyte interactions at the single-cell level by microfluidic cell pairing. Nat Commun 2015, 6, 5940.

69.

Dura, B.; Servos, M. M.; Barry, R. M.; Ploegh, H. L.; Dougan, S. K.; Voldman, J., Longitudinal multiparameter assay of lymphocyte interactions from onset by microfluidic cell pairing and culture. Proc Natl Acad Sci U S A 2016, 113 (26), E3599-608.

70.

Chen, S.; Bremer, A. W.; Scheideler, O. J.; Na, Y. S.; Todhunter, M. E.; Hsiao, S.; Bomdica, P. R.; Maharbiz, M. M.; Gartner, Z. J.; Schaffer, D. V., Interrogating cellular fate decisions with high-throughput arrays of multiplexed cellular communities. Nat Commun 2016, 7, 10309.

71.

Ozkumur, A. Y.; Goods, B. A.; Love, J. C., Development of a High-Throughput Functional Screen Using Nanowell-Assisted Cell Patterning. Small 2015, 11 (36), 4643-50.

72.

Li, C.; Yu, J.; Paine, P.; Juang, D. S.; Berry, S. M.; Beebe, D. J., Double-exclusive liquid repellency (double-ELR): an enabling technology for rare phenotype analysis. Lab Chip 2018, 18 (18), 2710-2719.

73.

Barkal, L. J.; Theberge, A. B.; Guo, C. J.; Spraker, J.; Rappert, L.; Berthier, J.; Brakke, K. A.; Wang, C. C.; Beebe, D. J.; Keller, N. P.; Berthier, E., Microbial metabolomics in open microscale platforms. Nat Commun 2016, 7, 10610.

74.

de Groot, T. E.; Veserat, K. S.; Berthier, E.; Beebe, D. J.; Theberge, A. B., Surface-tension driven open microfluidic platform for hanging droplet culture. Lab Chip 2016, 16 (2), 33444.

75.

Frey, O.; Misun, P. M.; Fluri, D. A.; Hengstler, J. G.; Hierlemann, A., Reconfigurable microfluidic hanging drop network for multi-tissue interaction and analysis. Nat Commun 2014, 5, 4250.


Page 45 of 50 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


76.

Rismani Yazdi, S.; Shadmani, A.; Burgel, S. C.; Misun, P. M.; Hierlemann, A.; Frey, O., Adding the 'heart' to hanging drop networks for microphysiological multi-tissue experiments. Lab Chip 2015, 15 (21), 4138-47.

77.

Frey, O.; Rudolf, F.; Schmidt, G. W.; Hierlemann, A., Versatile, simple-to-use microfluidic cell-culturing chip for long-term, high-resolution, time-lapse imaging. Anal Chem 2015, 87 (8), 4144-51.

78.

Birchler, A.; Berger, M.; Jaggin, V.; Lopes, T.; Etzrodt, M.; Misun, P. M.; Pena-Francesch, M.; Schroeder, T.; Hierlemann, A.; Frey, O., Seamless Combination of FluorescenceActivated Cell Sorting and Hanging-Drop Networks for Individual Handling and Culturing of Stem Cells and Microtissue Spheroids. Anal Chem 2016, 88 (2), 1222-9.

79.

Jackson-Holmes, E. L.; McDevitt, T. C.; Lu, H., A microfluidic trap array for longitudinal monitoring and multi-modal phenotypic analysis of individual stem cell aggregates. Lab Chip 2017, 17 (21), 3634-3642.

80.

Utech, S.; Prodanovic, R.; Mao, A. S.; Ostafe, R.; Mooney, D. J.; Weitz, D. A., Microfluidic Generation of Monodisperse, Structurally Homogeneous Alginate Microgels for Cell Encapsulation and 3D Cell Culture. Adv Healthc Mater 2015, 4 (11), 1628-33.

81.

Zhang, L.; Chen, K.; Zhang, H.; Pang, B.; Choi, C. H.; Mao, A. S.; Liao, H.; Utech, S.; Mooney, D. J.; Wang, H.; Weitz, D. A., Microfluidic Templated Multicompartment Microgels for 3D Encapsulation and Pairing of Single Cells. Small 2018, 14 (9), 1702955.

82.

Chung, K.; Kim, Y.; Kanodia, J. S.; Gong, E.; Shvartsman, S. Y.; Lu, H., A microfluidic array for large-scale ordering and orientation of embryos. Nat Methods 2011, 8 (2), 171176.

83.

Levario, T. J.; Zhao, C.; Rouse, T.; Shvartsman, S. Y.; Lu, H., An integrated platform for large-scale data collection and precise perturbation of live Drosophila embryos. Sci Rep 2016, 6, 21366.

84.

Cornaglia, M.; Mouchiroud, L.; Marette, A.; Narasimhan, S.; Lehnert, T.; Jovaisaite, V.; Auwerx, J.; Gijs, M. A., An automated microfluidic platform for C. elegans embryo arraying, phenotyping, and long-term live imaging. Sci Rep 2015, 5, 10192.

85.

Kopito, R. B.; Levine, E., Durable spatiotemporal surveillance of Caenorhabditis elegans response to environmental cues. Lab Chip 2014, 14 (4), 764-70.

86.

Lee, K. S.; Iwanir, S.; Kopito, R. B.; Scholz, M.; Calarco, J. A.; Biron, D.; Levine, E., Serotonin-dependent kinetics of feeding bursts underlie a graded response to food availability in C. elegans. Nat Commun 2017, 8, 14221.



87.

Page 46 of 50

Mondal, S.; Hegarty, E.; Martin, C.; Gokce, S. K.; Ghorashian, N.; Ben-Yakar, A., Largescale microfluidics providing

high-resolution and high-throughput screening of

Caenorhabditis elegans poly-glutamine aggregation model. Nat Commun 2016, 7, 13023. 88.

Gokce, S. K.; Hegarty, E. M.; Mondal, S.; Zhao, P.; Ghorashian, N.; Hilliard, M. A.; BenYakar, A., A multi-trap microfluidic chip enabling longitudinal studies of nerve regeneration in Caenorhabditis elegans. Sci Rep 2017, 7 (1), 9837.

89.

Cho, Y.; Porto, D. A.; Hwang, H.; Grundy, L. J.; Schafer, W. R.; Lu, H., Automated and controlled mechanical stimulation and functional imaging in vivo in C. elegans. Lab Chip 2017, 17 (15), 2609-2618.

90.

Cho, Y.; Oakland, D. N.; Lee, S. A.; Schafer, W. R.; Lu, H., On-chip functional neuroimaging with mechanical stimulation in Caenorhabditis elegans larvae for studying development and neural circuits. Lab Chip 2018, 18 (4), 601-609.

91.

McClanahan, P. D.; Xu, J. H.; Fang-Yen, C., Comparing Caenorhabditis elegans gentle and harsh touch response behavior using a multiplexed hydraulic microfluidic device. Integr Biol (Camb) 2017, 9 (10), 800-809.

92.

Rahman, M.; Hewitt, J. E.; Van-Bussel, F.; Edwards, H.; Blawzdziewicz, J.; Szewczyk, N. J.; Driscoll, M.; Vanapalli, S. A., NemaFlex: a microfluidics-based technology for standardized measurement of muscular strength of C. elegans. Lab Chip 2018, 18 (15), 2187-2201.

93.

Rouse, T.; Aubry, G.; Cho, Y.; Zimmer, M.; Lu, H., A programmable platform for subsecond multichemical dynamic stimulation and neuronal functional imaging in C. elegans. Lab Chip 2018, 18 (3), 505-513.

94.

Aubry, G.; Lu, H., Droplet array for screening acute behaviour response to chemicals in Caenorhabditis elegans. Lab Chip 2017, 17 (24), 4303-4311.

95.

Larsch, J.; Ventimiglia, D.; Bargmann, C. I.; Albrecht, D. R., High-throughput imaging of neuronal activity in Caenorhabditis elegans. Proc Natl Acad Sci U S A 2013, 110 (45), E4266-73.

96.

Apichitsopa, N.; Jaffe, A.; Voldman, J., Multiparameter cell-tracking intrinsic cytometry for single-cell characterization. Lab Chip 2018, 18 (10), 1430-1439.

97.

Rosenbluth, M. J.; Lam, W. A.; Fletcher, D. A., Analyzing cell mechanics in hematologic diseases with microfluidic biophysical flow cytometry. Lab Chip 2008, 8 (7), 1062-70.

98.

Su, H. W.; Prieto, J. L.; Voldman, J., Rapid dielectrophoretic characterization of single cells using the dielectrophoretic spring. Lab Chip 2013, 13 (20), 4109-17.


Page 47 of 50 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


99.

Rane, A. S.; Rutkauskaite, J.; deMello, A.; Stavrakis, S., High-Throughput Multiparametric Imaging Flow Cytometry. Chem 2017, 3 (4), 588-602.

100.

Junkin, M.; Kaestli, A. J.; Cheng, Z.; Jordi, C.; Albayrak, C.; Hoffmann, A.; Tay, S., HighContent Quantification of Single-Cell Immune Dynamics. Cell Rep 2016, 15 (2), 411-22.

101.

Castellarnau, M.; Szeto, G. L.; Su, H. W.; Tokatlian, T.; Love, J. C.; Irvine, D. J.; Voldman, J., Stochastic particle barcoding for single-cell tracking and multiparametric analysis. Small 2015, 11 (4), 489-98.

102.

Kimmerling, R. J.; Prakadan, S. M.; Gupta, A. J.; Calistri, N. L.; Stevens, M. M.; Olcum, S.; Cermak, N.; Drake, R. S.; Shalek, A. K.; Manalis, S. R., Linking single-cell measurements of mass, growth rate, and gene expression. bioRxiv 2018, DOI: 10.1101/331686.

103.

Shaw Bagnall, J.; Byun, S.; Miyamoto, D. T.; Kang, J. H.; Maheswaran, S.; Stott, S. L.; Toner, M.; Manalis, S. R., Deformability-based cell selection with downstream immunofluorescence analysis. Integr Biol (Camb) 2016, 8 (5), 654-64.

104.

Kimmerling, R. J.; Lee Szeto, G.; Li, J. W.; Genshaft, A. S.; Kazer, S. W.; Payer, K. R.; de Riba Borrajo, J.; Blainey, P. C.; Irvine, D. J.; Shalek, A. K.; Manalis, S. R., A microfluidic platform enabling single-cell RNA-seq of multigenerational lineages. Nat Commun 2016, 7, 10220.

105.

San-Miguel, A.; Kurshan, P. T.; Crane, M. M.; Zhao, Y.; McGrath, P. T.; Shen, K.; Lu, H., Deep phenotyping unveils hidden traits and genetic relations in subtle mutants. Nat Commun 2016, 7, 12990.

106.

Saberi-Bosari, S.; Huayta, J.; San-Miguel, A., A microfluidic platform for lifelong highresolution and high throughput imaging of subtle aging phenotypes in C. elegans. Lab Chip 2018, 18 (20), 3090-3100.

107.

Wang, X.; Allen, W. E.; Wright, M. A.; Sylwestrak, E. L.; Samusik, N.; Vesuna, S.; Evans, K.; Liu, C.; Ramakrishnan, C.; Liu, J.; Nolan, G. P.; Bava, F. A.; Deisseroth, K., Threedimensional intact-tissue sequencing of single-cell transcriptional states. Science 2018, 361 (6400), eaat5691.

108.

Gut, G.; Herrmann, M. D.; Pelkmans, L., Multiplexed protein maps link subcellular organization to cellular states. Science 2018, 361 (6401), eaar7042.

109.

Stephenson, W.; Donlin, L. T.; Butler, A.; Rozo, C.; Bracken, B.; Rashidfarrokhi, A.; Goodman, S. M.; Ivashkiv, L. B.; Bykerk, V. P.; Orange, D. E.; Darnell, R. B.; Swerdlow, H. P.; Satija, R., Single-cell RNA-seq of rheumatoid arthritis synovial tissue using low-cost microfluidic instrumentation. Nat Commun 2018, 9 (1), 791. 47 ACS Paragon Plus Environment


110.

Page 48 of 50

Stallcop, L. E.; Alvarez-Garcia, Y. R.; Reyes-Ramos, A. M.; Ramos-Cruz, K. P.; Morgan, M. M.; Shi, Y.; Li, L.; Beebe, D. J.; Domenech, M.; Warrick, J. W., Razor-printed sticker microdevices for cell-based applications. Lab Chip 2018, 18 (3), 451-462.

111.

Alvarez-Garcia, Y. R.; Ramos-Cruz, K. P.; Agostini-Infanzon, R. J.; Stallcop, L. E.; Beebe, D. J.; Warrick, J. W.; Domenech, M., Open multi-culture platform for simple and flexible study of multi-cell type interactions. Lab Chip 2018, 18 (20), 3184-3195.

112.

Pardee, K.; Green, A. A.; Ferrante, T.; Cameron, D. E.; DaleyKeyser, A.; Yin, P.; Collins, J. J., Paper-based synthetic gene networks. Cell 2014, 159 (4), 940-54.

113.

Takahashi, M. K.; Tan, X.; Dy, A. J.; Braff, D.; Akana, R. T.; Furuta, Y.; Donghia, N.; Ananthakrishnan, A.; Collins, J. J., A low-cost paper-based synthetic biology platform for analyzing gut microbiota and host biomarkers. Nat Commun 2018, 9 (1), 3347.


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ToC Figure (For Table of Contents Only)



ToC figure

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Recent Advances in Microfluidic Techniques for Systems Biology

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