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Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URLShare a linkShare onEmailFacebookTwitterLinked InRedditWechat Abstract Droplet microfluidics has revolutionized the study of single cells. The ability to compartmentalize cells within picoliter droplets in microfluidic devices has opened up a wide range of strategies to extract information at the genomic, transcriptomic, proteomic, or metabolomic level from large numbers of individual cells. Studying the different molecular landscapes at single-cell resolution has provided the authors with a detailed picture of intracellular heterogeneity and the resulting changes in cellular phenotypes. In addition, these technologies have aided in the discovery of rare cells in tumors or in the immune system, and left the authors with a deeper understanding of the fundamental biological processes that determine cell fate. This review aims to provide a detailed overview of the various droplet microfluidic strategies reported in the literature, taking into account the sometimes subtle differences in workflow or reagents that enable or improve certain protocols. Specifically, approaches to targeted- and whole-genome analysis, as well as whole-transcriptome profiling techniques, are reviewed. In addition, an up-to-date overview of new methods to characterize and quantify single-cell protein levels, and of developments to screen secreted molecules such as antibodies, cytokines, or metabolites at the single-cell level, is provided. 1 Introduction Most of what we know about the fundamental rules underpinning biological systems has been gathered by studying populations: populations of organisms, or populations of cells within organisms. However, the past decade has seen the development of a plethora of techniques that allow the study of large numbers of individual cells, revealing cellular function in intricate detail, but also showing a high level of cellular heterogeneity.1, 2 This heterogeneity only becomes apparent at the single-cell level and is a result of cellular programming networks, the inherent noise in gene expression, as well as signaling dynamics.3, 4 Quantifying and understanding cellular heterogeneity, and linking it to cellular function, is of great importance for many applications, for example, the discovery of rare cells (for example in the immune system),5, 6 gaining a better understanding of the development of embryos,7, 8 studying the fundamental properties of genetic and signaling networks,9 and analyzing the composition of solid tumors.10, 11 Isolation and characterization of single cells at the genome, transcriptome, proteome, or metabolome level, can be achieved in a variety of different ways, ranging from very high throughput (but limited number of data points per cell) flow cytometry experiments, to early studies showing whole-genome or whole-transcriptome sequencing of a limited number of individual cells placed in microliter wells.12 We refer the interested reader to a number of recent reviews that focus on single-cell biology1, 2, 6, 11 or give an overview of the different technologies used.13, 14 Despite an exponential increase in the number of single-cell studies reported, these experiments are by no means trivial, with many technical challenges remaining, including the isolation of single cells (from solid samples), the handling of low quantities of biological materials, and laborious workflows. In recent years, microfluidics, and in particular droplet microfluidics has emerged as a technology of choice for an easily implementable, high throughput, and relatively cheap approach for a broad range of single-cell analysis studies (Figure 1). The purpose of this review is to compare and contrast the available droplet microfluidics technologies for single-cell characterization in order to allow potential users to rapidly implement the best tools. We have structured this review as follows: first, we will briefly introduce the field of droplet microfluidics and discuss the key characteristics that make it an ideal technology for single-cell analysis. Second, we will introduce different methods for single cells sample preparation. After this, we will provide a state-of-the-art overview of key breakthroughs in single-cell genetic, epigenetic, transcriptomic, proteomic, and metabolic studies. Figure 2 provides a graphical overview of the topics discussed in this review. Combined, the review will be useful for both novice researchers in the field, but also for experienced scientists who wish to explore new combinations of single-cell studies and hope to adapt existing technologies to broaden their capabilities. Microfluidics is the technology that controls the flow of liquids through micron-size channels. Forcing two immiscible liquids through these channels leads to the formation of plugs of one fluid within a carrier fluid. This type of two-phase microfluidics is typically known as droplet microfluidics and has become enormously popular in the last decade and a half. The pico- to nanoliter droplets that are formed at frequencies up to several MHz essentially function as individual reaction flasks. Hence, each droplet is equivalent to a well on a microliter plate but with a reaction volume a million times smaller, and cells encapsulated in droplets are physically and (bio)chemically isolated from each other. Briefly, water-in-oil based droplet microfluidic technologies use a water-based solution as the dispersed phase and oil with a surfactant as the continuous phase. The flows of these two immiscible phases are precisely controlled by microfluidic pumps and by specially designed chips with molded or engraved microchannels. Droplet formation can be achieved in a highly repeatable manner using device geometries such as T-junctions, flow focusing, and co-flow (Figure 3). The most common geometry for droplet formation is flow-focusing15 where the injected dispersed phase (water phase) is sheared by the continuous phase (oil with surfactant) pumped from two side channels positioned perpendicularly to the aqueous stream. As soon as fluids meet, water-in-oil droplets are formed and stabilized by the surfactant dissolved in the oil carrier phase. Droplets can be loaded with single cells by using a dilute cell suspension as the aqueous phase. The distribution of cells in droplets follows a Poisson distribution,16, 17 minimizing the risk of encapsulation of multiple cells in one droplet, but yielding up to ≈90% of droplets containing no cells. Once formed, droplets with encapsulated cells can be manipulated in various ways: they can be merged, split, re-loaded in a second microfluidic device, incubated within the microfluidic chip, detected, sorted, etc. (Figure 3). Technologies for droplet formation and manipulation have been reviewed extensively and will not be further addressed here.18-21 Figure 3Open in figure viewerPowerPoint Microfluidic drop maker geometries and manipulation modules applied in single-cell workflows. Droplets can be generated using microfluidic devices using three different geometries: T-junction, flow focusing, and co-flow. After droplet generation, microfluidic modules are available for droplet manipulation: merging of droplets with different contents, splitting of bigger droplets into droplets with smaller volume, re-loading of the emulsion into a new device, static on-chip incubation of the emulsion at different temperatures, detection of (fluorescence) signal in droplets, and sorting droplets of interest (using e.g., electric field). Although throughput is not as high as in flow cytometry, droplet generation and sorting are typically performed at speeds of a few to tens of kHz. It is worth mentioning that droplets can be produced in large amounts, circumventing limitations in the number of cells that can be studied when using well-based technologies. In the next paragraphs, we will outline how droplet microfluidic technology can be used to extract valuable information from large numbers of single cells. Single-cell compartmentalization into pL to nL droplets requires cells to be in suspension. Any sample preparation method must aim to obtain a sufficient number of cells, without debris, contamination, or unwanted cells. Two main methods are used for cell isolation from solid tissues: standard digestion or laser-capture microdissection (LCM). Standard digestion protocols can be applied to isolate cells from a tissue sample taking into consideration different parameters (e.g., type of tissue, species of origin) to maximize the yield of viable cells.22 The alternative to digestion treatment is LCM.23, 24 However, this technique is low-throughput, the quality of LCM-harvested cells is relatively poor (especially in the case of RNA), and often requires prior preservation of the material and more technical skills. In the case of primary material (e.g., blood), cells of interest (e.g., white blood cells, circulating tumor cells) can be isolated by density gradient centrifugation (Percoll, Ficoll),25, 26 negative/positive depletion using antibodies conjugated with magnetic beads followed by separation on a magnetic column,27 by size-based microfluidic separation after prior red blood cells (RBCs) selective lysis by osmotic shock and centrifugation, or by apheresis.28, 29 A wide range of microfluidic tools that exploit physical (size, deformability, density, electric charge) or biological (expression of certain proteins) properties can be applied to isolate cells of interest.27, 30, 31 Fluorescence-activated cell sorting and magnetic-assisted cell sorting, based on the selection of cells expressing certain markers, are widely used both as isolation and purification techniques.32, 33 Bacteria and other types of cells from soil can be dislodged by sonication, and when particles settle, aliquots of supernatant can be taken for further processing. It should be stressed that the type of analysis influences if whole cells or only certain organelles (e.g., only single nuclei are encapsulated in single-cell chromatin accessibility analysis) are used further in the protocol. As a point of a technical challenge, cells also tend to cluster and/or sediment in the syringe and in the inner surface of the tubing during loading onto the chip. To circumvent, or minimize this problem researchers have either used density matching agents such as Iodixanol,34 physically stirred the cell suspension with a magnet in the syringe,35, 36 or introduced conically tapered chip inlet regions.37 Alternatively, flow rates and channel geometries can be adjusted to achieve “super-Poisson” encapsulation of cells, leading to much higher fractions of droplets containing single cells.38, 39 After cell encapsulation, workflows differ significantly depending on the type of analysis that is performed. Therefore, each technology, that is, profiling the (targeted- and whole-) genome, epigenome, (targeted- and whole-) transcriptome, proteome (secreted proteome or intracellular and membrane proteome), or metabolome, will be discussed separately. DNA-sequencing of individual cells offers unique opportunities to investigate de novo assembly of the genome of rare or uncultivable microbes,40, 41 heterogeneity of cells in a tumor (DNA mutations)42 or acquisition of resistance during treatment.43, 44 Extensive comparison of the genomic heterogeneity between the primary tumor and the metastatic tumor sites has led to a deeper understanding of metastasis formation,43 and an understanding of the genetic changes in the tumor environment leading to drug resistance.44 Technologies for single-cell genomic analysis which do not use droplet microfluidics, but wells: single-cell retrotransposon capture sequencing (scRC-seq),45 single nucleus exome sequencing (nuc-seq/SNES)46 or valves: direct deterministic phasing (DDP),47 will not be reviewed here. A critical step in single-cell genome analysis is the amplification of pico- or nanogram amounts of DNA into microgram amounts. Two strategies can be distinguished to amplify single-cell derived genomic material: i) targeted-genome amplification (only targeted fragments are amplified), or ii) whole-genome amplification (WGA). Targeted-genome amplification utilizes a standard PCR reaction. Designed primers bind only to the specific sequence flanking the sequence of interest in the genome that is amplified in the next step. In contrast, primers used for WGA bind to many places in the genome to start replication, thus the whole DNA strand is amplified. In this section, droplet microfluidic technologies for single-cell genome analysis are introduced and compared. Three droplet microfluidic technologies allow single-cell targeted genome analysis: single copy genetic amplification (SCGA), agarose-based PCR, and PCR-Activated Cell Sorting (PACS). Single copy genetic amplification (SCGA)48 is based on the generation of uniform nanoliter droplets to perform bead PCR. Figure 4a shows the general workflow of SCGA. Briefly, single cells are co-encapsulated in water-in-oil droplets with: an agarose bead covalently labeled with reverse primers, and PCR mixture containing dye-labeled forward primers and enzymes. Subsequently, the droplets are collected off-chip, cells are lysed, and the emulsion undergoes a series of PCR thermocycles which generate dye-labeled double-stranded product on the bead surface. Following droplet PCR, de-emulsification is induced and the beads coated with the fluorescent amplicons are recovered. Detection of the target DNA is performed by measuring single-bead fluorescence intensity by flow cytometry. DNA fragments up to 1139 bp were obtained using SCGA with 40% PCR efficiency, meaning that bead-attached products can serve as a template for bulk sequencing. In addition, SCGA constitutes a fast and cheap detection method, with only three, high throughput steps: emulsification, PCR, and the detection of the fluorescence intensity of the beads using flow cytometry. A disadvantage of SCGA is the low droplet generation frequency (below 6 Hz). To address this issue, Zeng et al. created a microfabricated emulsion generator array (MEGA) device for high-throughput generation of droplets.49 The 96-channel MEGA device reaches a droplet generation rate of 940 Hz. A second disadvantage of SCGA is that droplet PCR (dPCR) has lower efficiency compared to bulk PCR: 40% for dPCR compared to bulk PCR but with equivalent template and bead concentrations. Finally, SCGA encapsulates both beads and cells into droplets from very dilute solutions following Poisson statistics, leading to low efficiency in co-encapsulation: 1 in 100 droplets containing both a single cell and a single bead. Yangs group developed agarose-based emulsion PCR (ePCR).50 Figure 4b shows the general workflow. An agarose solution is emulsified at 540 Hz with cells (2, 1, or 0.5 cells per droplet) and PCR mix. Similarly to SCGA, the PCR mix contains fluorescently labeled forward primers and enzymes. However, in this case, the reverse primers are covalently attached to the agarose instead of being attached to a bead. After emulsification, the droplets are collected for further PCR to amplify the target DNA of interest. Subsequently, the agarose droplets are gelated into solid beads with the fluorescently labeled PCR amplicons covalently attached to the agarose matrix. Finally, the beads are washed to remove the excess of fluorescently labeled forward primers which have not been used during PCR reaction and are then analyzed using fluorescence microscopy or flow cytometry. Agarose droplet microfluidic ePCR offers multiple advantages compared to SCGA. First, it shows one order of magnitude increase in the efficiency of cells and primers co-encapsulation since primers are part of the agarose liquid phase, and are therefore present in all droplets, while in SCGA the primers are carried on solid beads (in 10% of the formed droplets). Increase in co-encapsulation efficiency enables characterization of whole-cell populations, and decreases the duration and costs of the experiment. A further simplification is related to the usage of agarose droplets that overcome the need of using beads labeled with primers. PCR-Activated Cell Sorting (PACS) allows sorting of bacteria based on the interrogation of small genomic regions (hundreds of bases).51 The general workflow of PACS is shown in Figure 5a. Bacteria and PCR reagents are co-encapsulated in droplets. Cells are lysed and if their DNA contains the sequence of interest, the TaqMan probe (containing a reporter dye and a quencher in close proximity) anneals to it. Hydrolysis of the probe by the exonuclease activity of the Taq polymerase allows the release of the reporter dye from the probe and thus fluorescence is increased due to the elimination of the quenching effect. Upon PCR amplification, the intensity of the fluorescence signal increases in the droplet at every cycle. Subsequently, the fluorescent droplets are sorted for downstream whole-genome analysis. Mission Bio developed a platform for single-cell targeted-genome analysis and commercialized it as Tapestri technology.52 Figure 5b presents the workflow. First, single cells are encapsulated in droplets with proteases. After cell lysis in the droplets, the temperature is increased to inactivate the proteases. Subsequently, the droplets containing the genome of single cells are coupled by electrocoalescence with a second droplet containing PCR reagents and barcoding hydrogel beads (at 1:1 ratio). The barcoding hydrogel beads are labeled with oligonucleotides containing a cell-specific barcode and a gene-specific primer sequence. After co-encapsulation of the hydrogel beads with the genome-containing droplets, the primers labeling the hydrogel bead are photo-released by UV exposure and the target DNA sequences are cell-barcoded by PCR amplification. Finally, the emulsion is broken and libraries are prepared in bulk, followed by sequencing. Pellegrino et al.52 showed a proof-of-concept experiment using 62 DNA targets to analyze the genetic heterogeneity of individual acute myeloid leukemia cells. Whole-genome analysis at the single-cell level is challenging due to: i) difficulties surrounding the isolation of DNA from individual cells, ii) efficiency and reliability of whole-genome amplification (WGA), iii) verification of sequences that can be used for identification of variants, and iv) data analysis and interpretation.53 In this section, we first introduce different whole-genome amplification techniques which have been one of the main technical challenges in whole-genome analysis in the past decades. Then we describe two full pipelines for single-cell whole-genome analysis using droplet microfluidics: SiC-seq and CNV (10X Genomics). The concept of degenerate oligonucleotide-primed polymerase chain reaction or pure PCR-based amplification (DOP-PCR) was introduced in 1992 by Telenius et al.54 DOP-PCR is based on the use of a single primer containing a random sequence (degenerate sequence). DOP-PCR begins with a few cycles of pre-amplification at a low initial annealing temperature in order to allow the random primers to anneal. Subsequently, DNA amplicons are further amplified by PCR. DOP-PCR has two main advantages: it amplifies DNA at multiple loci and it is species-independent. However, DOP-PCR provides low genome coverage so any differences in the amplification are exponentially enlarged, resulting in overamplified and underamplified regions along the genome. Multiple displacement amplification (MDA), developed by Dean and co-workers in 2001,55 is based on isothermal amplification. Random hexamer primers are hybridized to the template, followed by strand displacement DNA synthesis by the high fidelity ϕ29 polymerase. DNA amplification using MDA results in much higher genome coverage compared to DOP-PCR.56 However, MDA, similarly to DOP-PCR, is based on an exponential amplification that leads to sequence-dependent overamplification or underamplification along the whole genome that is not reproducible from cell to cell. Different variations of MDA were developed to overcome the amplification bias and improve the throughput, that is, MIDAS,57 IMS-MIDAS,58 SNES,46 ddMDA.59 MDA was successfully implemented in droplet microfluidics to increase its efficiency.60-62 Multiple displacement amplification in droplets (sd-MDA) was also demonstrated on genomes derived from single cells.63 Hybrid methods intend to overcome some of the shortcomings of the two aforementioned methods by combining some of their strengths. Multiple Annealing and Looping Based Amplification Cycles (MALBAC) and displacement DOP-PCR (PicoPLEX) are examples of hybrid methods that combine isothermal amplification followed by PCR amplification of generated amplicons. MALBAC is based on quasi-linear amplification that reduces the sequence-dependent bias, and the key improvement is not to generate copies of copies but oppositely, amplify only the original genome sequence by protecting the already amplified products. This is achieved by utilization of random primers that possess a sequence (anchor) to promote looping of amplicons and prevent further amplification before the second PCR. In contrast, PicoPLEX uses degenerate primers in the first reaction to add an anchor sequence, followed by priming to the added sequence for the final PCR amplification. Yu et al. demonstrated an integrated chamber microfluidic device designed for single-cell MALBAC reactions to identify copy number variations.64 Comparison of the different amplification methods have been reviewed elsewhere and will not be further addressed in this review.65, 66 Single-cell genome sequencing at ultrahigh-throughput (SiC-seq) developed by Lan et al.67 was the first platform for single-cell genome analysis where most of the reactions are performed in droplets. SiC-seq labels all the DNA material from an individual cell with a barcode unique to this cell. The workflow comprises the following steps (Figure 6a): 1) generation of barcoding droplets, 2) cell encapsulation in agarose droplets, lysis, purification of genomic DNA, and gelation of the cell beads, 3) re-encapsulation of the purified cell beads with tagmentation reagents, 4) merging of droplets bearing tagmented genome with droplet containing PCR reagents and barcoding droplets, 5) sequencing and data analysis. Figure 6Open in figure viewerPowerPoint Schematic overview of droplet microfluidic workflows used in whole-genome analysis. a) Single-cell genome sequencing at ultrahigh-throughput (SiC-seq). b) Single Cell Copy Number Variation (CNV). In the first step, SiC-seq requires preparation of a barcoding droplet library. 15-base random oligonucleotides flanked by constant sequences are co-encapsulated with: PCR reagents and primers (complementary to constant regions of the barcodes and containing the Illumina P7 flow cell adapter). Subsequently, the content of droplets with all reagents is amplified via droplet digital PCR (≈10 million barcoding droplets are generated in a few hours). After the barcoding library is prepared, single cells must be isolated. The cell suspension is merged with a molten agarose stream using a co-flow droplet maker. The agarose droplets are solidified by cooling and transferred from oil to aqueous carrier. The agarose beads are permeable to enzymes, detergents, and small molecules, but sterically trap larger structures as genomic DNA, making them the ideal substrate to allow washing steps to be performed on encapsulated cells. Subsequently, the cell wall is digested, and a series of enzymatic and detergent treatments (solubilization of lipids and digestion of proteins) are performed. Purified microbeads with genomic DNA are re-encapsulated in droplets containing tagmentation reagents in order to fragment the genome and attach universal sequences to act as PCR handles. After tagmentation, these droplets are in turn merged sequentially with two other droplets: one droplet containing the PCR reagents and one barcoding droplet. The obtained droplets are thermo-cycled to allow amplification of the product and ligation of the P5 and P7 adapter required for Illumina sequencing. After library preparation, the droplets are pooled together for sequencing. Sequencing data are filtered for quality and grouped by barcode, providing a genomic sequence for all cells. SiC-seq has several advantages compared to the targeted-genome single-cell analysis tools described above. First, SiC-seq allows an unbiased analysis of the single-cell genome. In addition, all steps can be performed using microfluidics, limiting manual handling steps and increasing reproducibility. However, the microfluidic manipulation is more complex and time-consuming compared to the targeted-genome analysis methods. Single Cell Copy Number Variation (CNV) was introduced to the market by 10X Genomics to study single-cell genomic heterogeneity and clonal evolution in a high throughput manner (hundreds to thousands of cells),68 the workflow of CNV is shown in Figure 6b. First, single cells are encapsulated at limiting dilution in a gel matrix with paramagnetic particles within a single-use microfluidic chip. Once droplets gelate, cells are trapped inside the beads and are subjected to lysis followed by the removal of all nuclear proteins, while the genomic DNA remains trapped in the gel matrix. Subsequently, purified genomic DNA inside the cell beads is co-encapsulated in a second microfluidic device with barcoding beads (10X Barcoded Gel Beads) and enzymes. Importantly, both the cell beads and the barcoding beads are closely packed, allowing high-efficiency co-encapsulation of one cell bead and one barcoding bead in each droplet (≈80% of droplets contain one cell bead and one barcoding bead). DNA inside the droplet is amplified to generate single-cell barcoded libraries ready for sequencing and analysis. Importantly, all generated DNA from individual cells share a common 10X barcode. It is worth mentioning that due to a recent loss of a patent lawsuit brought by Bio-Rad regarding the use of non-fluorinated channels, 10X Genomics had to change their device design. The new “Next GEM chip” appears to use step emulsification instead of flow focusing for droplet generation, although we cannot verify this at present. Single-cell epigenetic analysis enables the investigation of heritable changes in phenotype that do not involve changes in the DNA sequence. As epigenetic information comes in various forms, for example, covalent modifications on DNA (methylation), chromatin accessibility and compaction, and higher-order organization of chromosome domains, as well as post-translational modifications of histones, it is clear that different biochemical approaches are required to extract each layer of molecular information.69 DNA methylation is the most commonly investigated epigenetic DNA modification. Methylation represses gene expression, regulates various cellular processes, and plays a role in the development of cancer.70 In the mammalian genome, methylation only takes place at cytosine bases that are directly followed by a guanine base (CpG dinucleotide). CpG dinucleotides are underrepresented in the genome but can be found in CpG islands (regions with a high frequency of CpG sites) which are normally hypomethylated. In this context, methylation of the mammalian genome is mostly studied in CpG islands and hypermethylation of CpG islands has been described in almost every type of tumor.71, 72 DNA methylation can be studied at a single-cell level using different tools: single-cell hydroxymethylation sequencing (scAba-seq),73 chemical-labeling-enabled C-to-T conversion sequencing (CLEVER-seq),74 single-nucleus methylcytosine sequencing (snmC-seq),75 single-cell combinatorial indexing for methylation analysis (sci-MET),76 improved version of single-nucleus methylcytosine sequencing (snmC-seq2),77 methylated DNA immunoprecipitation followed by next-generation sequencing (MeDIP-seq).78 Single-cell bisulfite sequencing (scBS-seq) is considered a gold standard for studying DNA methylation. This method is based on treating DNA with sodium bisulfite which deaminates cytosine residues and converts them to uracil, while 5-mC residues are left unaffected.79, 80 After bisulfite-conversion, the DNA is PCR-amplified and sequenced. Information about the methylation state can be retrieved from the sequencing results as unmethylated cytosines are converted into thymines, whereas methylated cytosines remain cytosines. Bisulfide sequencing was combined with droplet microfluidics (microdroplet PCR) to increase the efficiency of the technique.81 Presently, there are no droplet microfluidic routes for high throughput single-cell methylation analysis, even though groups are working on single-cell bisulfite sequencing: Drop-BS.82 Chromatin organization controls access to genetic information and regulatory elements and is therefore responsible for part of the diversity of cells and tissues. In eukaryotes, chromatin is formed by nucleosomes that are composed of DNA wrapped around histone octamers. Histones can carry a range of covalent modifications, but acetylation and methylation are the most commonly studied since they play a crucial role in the regulation of accessibility of DNA and gene transcription.83 Traditional methods for detection of histone modifications include mass spectrometry, immunoblotting, and Coomassie staining, or chromatin immunoprecipitation (ChIP). Three variants of ChIP have been reported: i) ChIP combined with microarrays (ChIP-on-chip or ChIP-chip) to provide extensive maps of histone modifications and their associated DNA, ii) ChIP followed by qPCR if the target loci is known a priori (ChIP-qCPR), and iii) ChIP combined with whole DNA sequencing (ChIP-seq). ChIP-seq, and many variations thereof, is now the primary technology used to examine histone modifications and DNA-protein interactions.84, 85 These methods allow mapping of histone modifications at a population level but are insensitive to cell-to-cell variation. Single-cell analysis of histone modification has proven to be challenging in particular due to the high level of experimental noise during the immunoprecipitation step when using small amounts of material. Droplet-based single-cell chromatin immune-precipitation sequencing (Drop-ChIp), developed in 2015, is a microfluidic variation of the well-established ChIP-seq used to map, at a single-cell level, posttranslational modifications of histones that can be associated with chromatin activity states.86 Drop-ChIp overcomes the challenges associated with low input starting material from single cells by labeling chromatin from single cells before immunoprecipitation and combining it for immunoprecipitation. An overview of the full workflow is presented in Figure 7. First, a library of droplets with DNA barcodes is prepared by emulsification of DNA from multi-well plates. Each barcoding droplet contains multiple copies of the same double-stranded DNA sequence. The DNA sequence is symmetrically designed allowing ligation on either side of the target DNA and comprises: a unique barcode sequence, an adapter, and a restriction site for selecting the desired product (PaCI site). Subsequently, single cells are encapsulated in aqueous droplets with lysis buffer containing weak detergent and micrococcal nuclease (MNase). MNase preferentially cuts accessible linker DNA and digests the chromatin in the droplets. Indexing of cells is performed by merging chromatin-containing droplets with a barcoding droplet and a droplet containing labeling buffer (with DNA ligase) within a third microfluidic chip. Barcode sequences are then ligated to both ends of the single-cell derived DNA fragments, the emulsion is broken, and the combined chromatin from many single cells is immunoprecipitated in the presence of “carrier” chromatin (chromatin extracted from other species). PaCI sites on the barcoded fragments are digested and the resulting products are PCR amplified. Finally, after sequencing the obtained reads are clustered by their barcode sequences to extract single-cell chromatin profiles. Although Drop-ChIp allows the profiling of chromatin structure at a single-cell level, it still presents a number of limitations. First, only ≈800 peaks are detected per single-cell (≈5% overall sensitivity for peak detection). The single-cell profiles lack the sensitivity that is required in de novo peak calling. However, the detection of ≈800 true peaks with high specificity might be sufficient to classify or group individual cells with related chromatin profiles. In addition, the pool of 1152 different barcodes can only barcode a maximum of 100 cells to avoid repetition of a barcode sequence (4% multi-barcoding error). This limitation is overcome by adding a sample index sequence during the PCR amplification and pooling together more of these barcoded libraries before immunoprecipitation. Another method to overcome this limit would be to prepare a more complex barcoding library in a similar way to SiC-seq.67 Importantly, some of these limitations were solved by the introduction of scChIp-seq which allowed the detection of 10.000 unique loci per cell and the increase in throughput to 5000 cells analyzable per run.87 Chromatin accessibility can be defined as the degree to which macromolecules are able to physically contact packed DNA. This highly dynamic property is determined by the topological arrangement of nucleosomes and other chromatin-binding factors (non-histone macromolecules) that modify access to DNA in order to elicit transcription, differentiation, cell division, and DNA repair.88, 89 Methods that provide quantitative information on chromatin accessibility are primarily focused on measuring the susceptibility of chromatin to either enzymatic methylation or DNA cleavage. A number of methods have been developed to profile chromatin accessibility at the single-cell level: i) assay for transposase-accessible chromatin using sequencing (scATAC-seq),90 and variations including combinatorial cellular indexing applied in assay for transposase-accessible chromatin sequencing (sci-ATAC-seq),90 single-cell transposome hypersensitive site sequencing (scTHS-seq);91 ii) method to detect genome-wide DNase I hypersensitive sites (scDNase-seq);92 iii) single-cell micrococcal nuclease sequencing (scMNase-seq);93 and iv) nucleosome occupancy and methylome-sequencing (NOMe-seq).94 In 2019, the Buenrostro lab developed a high throughput droplet-based single-cell assay for transposase-accessible chromatin using sequencing (dscATAC-seq) to unravel the single-cell chromatin profile by studying chromatin compaction and DNA-binding proteins regulating gene expression.95 Figure 8 depicts the general workflow of dscATAC-seq. First, single nuclei are isolated and treated in bulk by addition of transposase enzyme (Tn5) in order to preferentially fragment the DNA in open regions of the chromatin and to insert adapter sequences to the ends of the DNA fragments. Then, single transposed nuclei are encapsulated in nanoliter droplets together with barcoding beads and PCR reagents. Barcoding beads are labeled with DNA primers containing a cell barcode sequence and a PCR handle. Nuclei are heavily diluted prior to encapsulation in order to have one nucleus per droplet following Poisson statistics, while beads are loaded at higher concentrations. Droplets with multiple beads encapsulated can be identified during analysis thanks to a computational approach developed by the group. After emulsification, the droplets are thermocycled allowing single-nucleus barcoding of the accessible DNA fragments, PCR amplification of the barcoded product and preparation of ready to sequence libraries. Figure 8Open in figure viewerPowerPoint Overview of the workflow of droplet-based single-cell assay for transposase-accessible chromatin using sequencing (dscATAC-seq). Single Cell ATAC has the same workflow apart from the barcoding beads encapsulation. In dscATAC-seq beads are super-Poisson loaded in the droplets and the presence of doublets beads is corrected computationally, while in Single Cell ATAC there are almost no bead doublets encapsulated in droplets thanks to the high encapsulation efficiency reached using deformable hydrogel beads. The throughput of dscATAC-seq was further improved by integrating it with combinatorial indexing (dsciATAC-seq) to beat Poisson loading of cells in droplets (limited dilution of cells in order to have one cell per droplet). Briefly, in dsciATACseq, the Tn5 transposase is loaded with barcoded DNA adapter to add a well-specific barcode to open chromatin. After well-barcoding, the nuclei are transposed, barcoded, and libraries are prepared in the same way as in dscATAC-seq. The difference is that in dsciATAC-seq individual cells are recognized by a unique combination of the droplet-specific bead barcode and the well-specific Tn5 barcode, consequently allowing the possibility of overloading the cells in droplets. Single Cell ATAC (10X Genomics) constitutes another version of dscATAC-seq with the main differences in the use of hydrogel beads for barcoding, which dissolve in the droplets releasing the barcoding primers. Crucially, high cell barcoding efficiency is reached as a result of closely packed beads in the channel.96, 97 Single-cell transcriptomic sequencing was first reported by Tang et al. in 2009.98 In recent years many technologies for single-cell transcriptomic sequencing emerged which do not use droplet microfluidics, but: i) wells: massively parallel single-cell RNA-sequencing (MARS-Seq),99 Seq-Well,100 gene expression cytometry (CytoSeq),101 cell expression by linear amplification and sequencing (CEL-Seq),102, 103 Quartz-Seq,104 Smart-seq,105 single-cell tagged reverse transcription (STRT),106 split-pool ligation-based transcriptome sequencing (SPLiT-seq),107 single-cell mRNA 3-prime end sequencing (SC3-seq),108 single cell RNA barcoding and sequencing (SCRB-seq),109 and ii) valve microfluidics: Hydro-Seq.110 Although some of these technologies show great promise, they are beyond the scope of this review since they do not make use of droplet microfluidics. Targeted single-cell mRNA analysis in hydrogel beads was developed in 2016 by Rakszewska et al.111 using droplet microfluidics. The number of transcripts per cells was assessed by counting the number of fluorescent dots inside each gel bead. However, this technology had several limitations: time-consuming imaging and quantification of the fluorescent dots, possibility to characterize only one target mRNA (which could possibly be extended to a few), and the limited range of mRNA strands that could be quantified before reaching overlap of fluorescent spots. These limitations were solved by workflows using sequencing as a detection method, allowing for whole-genome transcriptome analysis. The field of single-cell transcriptomic profiling gained enormous popularity after the development of the high-throughput droplet microfluidic technologies inDrop112, 113 and Drop-seq114 in 2015. Even though the fundamental working principles of both technologies are the same, there are some considerable differences as also discussed below.115, 116 inDrop (indexing droplets) uses droplet microfluidics to co-encapsulate a highly diluted cell solution with barcoding hydrogel beads (Figure 9a). The hydrogel beads are produced prior to the experiment, using a flow-focusing microfluidic device with as dispersed phase a solution of acrylamide:bis-acrylamide supplemented with acrydite-modified DNA primers. After the emulsion is gelated by thermal incubation, the hydrogel beads undergo two split-pool synthesis rounds to elongate all the primers labeling the beads with a unique cell barcode. 147 456 different barcodes are available allowing the barcoding of ≈3000 cells with 99% unique labeling. An important improvement for the technologies using detection by sequencing such as inDrop and Drop-seq is the implementation of a unique molecular identifier (UMI)117, 118 to allow quantitative determination of mRNA transcripts avoiding amplification bias. Briefly, UMIs are random sequences of 4–12 bp which can be added to barcode the analyte of interest. After library preparation and sequencing, the amplification bias can be removed by enumerating the distinct number of UMIs aligned to each position. Each hydrogel bead used for inDrop contains ≈109 covalently bound barcoding primers, each of them containing a photocleavable spacer, a T7 promoter region, PCR handle, cell barcode, UMI and poly(dT) tail (Figure 11). Cell encapsulation follows Poisson encapsulation, while hydrogel beads are loaded in a super Poisson fashion, yielding 60–90% of droplets with exactly one barcoding bead encapsulated and consequently 60–90% cell barcoding efficiency. The deformability of hydrogel beads allows them to closely pack in the channel and order into a stream with regular spacing, providing a regular flow of beads, effectively beating Poisson encapsulation statistics.119 After co-encapsulation of single cells with barcoding beads, reverse transcription (RT) and lysis mix in droplets, the cells are lysed and the primers labeling the hydrogel beads are photo-released by UV exposure. Upon thermal incubation, the poly(A) tail of the mRNA material hybridizes to the poly(dT) tail of the barcoding beads and mRNA is reversely transcribed into cDNA which is single-cell barcoded. After RT, the single-cell barcoded cDNA material is combined by breaking the emulsion and sequencing libraries are prepared following the CEL-seq protocol.102, 103 CEL-seq uses in vitro transcription (IVT) linear amplification followed by RT-PCR to amplify the barcoded cDNA product. After sequencing, the number of transcripts per cell can be quantified thanks to the presence of the cell barcode and of the UMI sequence. Figure 10 shows how the concept of UMIs and cell barcodes can be used to retrieve quantitative information (without amplification bias) about the mRNA content of the cell from the mRNA libraries. Detection of ≈1*103 different genes per cell and 3*103 transcripts per cell has been reported using inDrop technology.116 Figure 10Open in figure viewerPowerPoint General concept of using cell barcodes and UMIs to remove amplification bias and allow quantitative determination of mRNA transcripts. Reads having the same cell barcode sequence are derived from the same single cell. Reads having the same cell barcode, the same transcript read, but different UMIs, originate from different unique molecules. Reads with the same cell barcode, the same transcript read and the same UMI, result from PCR amplification and originate from one single molecule. Drop-seq114 shares many features with inDrop, including beads to barcode the transcriptomic material in nanoliter droplets. Figure 9b shows the general Drop-seq workflow. Drop-seq beads are labeled with ≈108 DNA primers comprising the PCR handle, the cell barcode, the UMI sequence, and a poly(dT) tail. The key difference compared to inDrop is that Drop-seq uses Poisson statistics for the encapsulation of barcoding beads made out of a hard resin. This allows easier emulsification but reduces considerably the cell capture efficiency to 2–4% cells barcoded compared to the 60–90% of inDrop. Second, while Klein et al. encapsulated the reverse transcription enzymes for RT in nanoliter droplets (after photo-cleavage of the primers), Macosko et al. barcoded the mRNA material by RT in bulk after de-emulsification. Importantly, the primers in Drop-seq remain attached to the solid bead during the RT reaction in bulk. Performance of RT in bulk constitutes a potential advantage of Drop-seq compared to inDrop because it eliminates the risk of RT inhibition in picoliter volumes and reduces the time in which the enzymes need to stay at room temperature before being used for RT. Finally, the library preparation is different. Both protocols use Moloney murine leukemia virus (MMLV) reverse transcriptase which intrinsically adds a few C nucleotides at the 3 end of the cDNA only when the reverse transcriptase reaches the end of the mRNA template but not when the reverse transcription is prematurely terminated. Drop-seq does make use of the introduction of C nucleotides at the 3 end of the cDNA to add a PCR handle by using “SMART” technology.105, 120, 121 Briefly, after RT of the mRNA in cDNA, a template switch oligo (TSO) primer with a PCR handle and a poly(dG) tail is used to hybridize to the untemplated C nucleotides on the cDNA 3′ end, yielding a single-cell barcoded full-length cDNA with a PCR handle at the 3′. After reverse transcription with template switching, the barcoded cDNA material is amplified only by PCR. Importantly, the use of “SMART” technology considerably increases the percentage of full-length clones compared to standard barcoding protocols, since only the full-length clones are PCR-amplified. Removal of the IVT step in Drop-seq has the advantage to reduce the overall time required for library preparation, although linear amplification has the potential advantage of minimizing later PCR amplification bias. Detection of ≈2*103 different genes per cell and 7*103 transcripts per cell has been reported using Drop-seq technology.116 Interestingly, Drop-seq technology has also been adapted to analyze single nuclei gene expression (single nuclei droplet-based sequencing: snDrop-seq).91 The only modification is related to the sample preparation since in snDrop-seq single-nuclei instead of single cells are encapsulated in droplets with barcoding beads, allowing for the capture of nuclear-polyadenylated mRNA transcripts as well as pre-mRNAs. In addition, Drop-seq has also been modified to allow simultaneous measurement of the transcriptome and a few targeted RNA amplicons (droplet-assisted RNA targeting by single-cell sequencing: DART-seq).122 In this case, custom primers are enzymatically attached to a subset of poly(dT) tailed primers on the Drop-seq barcoding beads and subsequently, the Drop-seq protocol can be used unaltered. High-Throughput Single-Cell Labeling (Hi-SCL) was developed by Weitz lab in 2015 contemporarily to inDrop and Drop-seq.123 Hi-SCL encapsulates single-cells in droplets with lysis buffer. In parallel, barcoding water-in-oil droplets are prepared by emulsifying ≈109 barcodes per droplet, similarly to Drop-ChIp (Section 5.2.1). After cell lysis, the droplets containing the single-cell content are fused by electrocoalescence together with two additional droplets: one containing the barcoding DNA primers and one containing the RT mix. Upon RT, single-cell barcoded cDNA is produced by exploiting the annealing of the poly(dT) tail of the barcoding primers with the poly(A) tail of the mRNA. The droplets are then de-emulsified, the cDNA is purified and the library is prepared by PCR amplification, followed by sequencing and data analysis. The major drawback of Hi-SCL is that the barcoding primers do not have the UMI sequence, preventing quantification of the transcripts. In addition, the number of available barcode sequences in Hi-SCL is considerably lower (≈103) compared to the other technologies presented above (≈105 inDrop, ≈7*105 Single Cell Gene Expression System, ≈107 Drop-seq) limiting the number of cells that can processed per run in order to avoid repetition of the same barcode for two different cells. The widely used 10X Genomics Chromium platform124 combines characteristics of inDrop and Drop-seq technologies with some modifications for 3′ gene expression analysis. Figure 9c shows the workflow of Single Cell Gene expression 3′. Co-encapsulation of barcoding bead and single cells using droplet microfluidics follows the inDrop protocol, while the library preparation consists only of reverse transcription with template switching and PCR amplification, similarly to Drop-seq. The key difference between Single Cell Gene expression 3′ and inDrop or Drop-seq is the use of primer-carrying beads that dissolve upon co-encapsulation of cells. It might be that dissolution of the beads is a crucial factor in the higher number of genes and transcripts identified per cell that have been reported: ≈3*103 different genes per cell and ≈17*103 transcripts per cell.116 All the above-described methods use cDNA construction methods which allow for 3′ coverage of the mRNA since RT starts from the poly(A) tail, but lead to an underrepresentation of the 5′ end of the mRNA. The Chromium platform allows for Single Cell Gene expression 5′ by including a TSO sequence at the end of the barcoding primers instead of a poly(dT) tail and primers with a poly(dT) tail in the RT.125-127 Briefly, in the 5′assay, the poly(dT) primers bind to the poly(A) tail of the mRNA and the mRNA is reverse transcribed by MMLV which also adds a few C nucleotides at the 3′ end of the cDNA only when the reverse transcriptase reaches the end of the mRNA template and not when the reverse transcription is prematurely terminated. Subsequently, the poly(dG) sequence (on the barcoding primers) hybridizes to the untemplated C nucleotides on the cDNA 3′ end switching oligo template and leading to the transcript extension, that yields a single-cell barcoded full-length cDNA. All technologies described above have advantages and disadvantages due to the implementation of slightly different protocols. The interested reader may refer to recent papers comparing single-cell transcriptomic methods.116, 128 In Table 1 an overview of the detailed specifications of the aforementioned droplet microfluidic technologies for transcriptome analysis is provided. Worth stressing is that the number of genes and UMIFM per cell are highly cell type dependent and they are provided here for comparison using the same cell type in inDrop, Drop-seq, and Single Cell Gene Expression. Table 1. Comparison of specifications of droplet microfluidic technologies for single-cell whole-transcriptome profiling 20 to have 1% multi-barcoding error, 100 used in123 leads to 4% multi-barcoding error Throughput: the maximum number of cells that can be barcoded per sequencing run in each technology is calculated based on two main considerations: i) the number of different barcodes possibilities (1.47*105 for inDrop, 1.6*107 for Drop-seq, 7.34*105 for Single Cell Gene Expression, and 1.15*103 for Hi-SC) from which we calculated the maximum number of cells that can be barcoded setting the multi-barcoding error to 1% and using the so-called “birthday problem” as explained in the supplemental experimental procedures of Klein et al.112; ii) the time and reagents constraints which are particularly pronounced for techniques with low cell barcoding efficiency. Approximate duration of an experiment: the cell barcoding step comprises the system setup, the microfluidic cell encapsulation and the reverse transcription when it is done before de-emulsification. Genes per cells and UMIFM per cell (UMIFM stands for UMI filtered mapped) for inDrop, Drop-seq, and Single Cell Gene Expression were taken from the comparative analysis of Zhang et al.116 which used the GM1289 cell line for all three technologies, while for Hi-SCL data were taken directly from the original paper.123 It should be stressed that cell hashing antibodies introduced by Stoeckius et al. in 2018129 present a solution that can be incorporated in all technologies to enable sample multiplexing (processing of multiple samples in parallel). Briefly, cells from different samples are uniquely labeled by staining them with oligo-tagged antibodies against ubiquitously expressed surface proteins. After staining, the cells derived from different samples can be pooled together and encapsulated using the microfluidic system of choice. By sequencing the cell hashing tags alongside the cellular transcriptome, each cell can be assigned to its original sample, considerably reducing batch effects. Cell hashing not only helps to avoid technology-dependent batch effects but also makes it possible to load cells in droplets at higher concentrations compared to what is usually allowed by Poisson distribution. It is worth mentioning that one of the key differences between the various single-cell transcriptomic technologies is the barcoding beads that are used. Their physical properties will dictate the encapsulation efficiency (e.g., hydrogel beads which can deform and closely pack; dissolvable vs. undissolvable beads), and the DNA sequence labeling will influence the library preparation (e.g., PCR handle inclusion, poly(dT) tail) and the quantification of the results (e.g., UMI addition). A comparative scheme of the different barcoding beads is presented in Figure 11. Sequencing paired heavy- and light-chain variable regions (VH and VL) of single B cells or alpha and beta chain variable regions (Vα and Vβ) of single T cells is desirable to further our understanding of the adaptive immune response, to study autoimmunity, and for therapeutic applications. However, because these variable regions are encoded by different mRNA transcripts, they are usually analyzed separately. A low throughput single-cell sequence analysis of VH:VL pairs can be performed by sorting individual cells in wells and subsequently performing single cell RT-PCR and sequencing. Attempts to increase the throughput of the characterization of VH:VL pairs were made by DeKosky et al.130 In their study, 104 single cells were compartmentalized in microwells together with magnetic beads labeled with poly(dT) primers. Subsequently, cells were lysed, and the mRNA annealed to the magnetic beads was collected and emulsified with PCR primers, reverse transcriptase and DNA polymerase. After reverse transcription and linkage PCR, the resulting linked VH:VL sequence was sequenced, enabling profiling of the complementarity-determining regions. The throughput of this method was later increased to 106 cells by single-cell isolation using droplet microfluidics instead of compartmentalization in microwells.131, 132 In parallel, this technology has been developed commercially by GigaGen.133, 134 AbVitro (later acquired by Juno Therapeutics) developed a similar approach for sequencing both BCR and TCR genes.135 Droplet microfluidics was used to compartmentalize single cells in droplets together with lysis/reaction mix containing a droplet barcode template molecule. After emulsification, cells are lysed, mRNA is reverse-transcribed and tagged with a molecule-specific and a droplet-specific barcode through PCR amplification. Finally, the resulting library is sequenced and the native receptor chain pairing can be reconstructed. The double-barcoding allows for the correction of the PCR amplification bias and for the clustering the reads to their molecules and cells of origin. Recently, 10X Genomics also commercialized a tool for single-cell immune profiling.136, 137 The method uses the Chromium platform for Single Cell Gene expression 5′ (as described in Section 6.1.4) but after reverse transcription and generation of the 5′ barcoded cDNA, the sample is divided in two aliquots and separately PCR amplified with custom primers. Finally, two separate 5′ mRNA and V(D)J enriched libraries are generated. The culmination of genome expression is the proteome, the cells repertoire of activated and non-activated proteins, which specifies the nature of the biochemical reactions that the cell is able to carry out. Capturing proteomic information from single cells has proven to be a substantial technical challenge. Multicolor (imaging) flow cytometry is an extremely powerful method for high-throughput analysis of single-cell protein levels but is limited to ≈15–30 proteins mostly due to the spectral overlap of the different fluorophores. By staining cells with isotope-labeled antibodies instead of fluorescent labels, CyTOF (cytometry by time of flight) allows simultaneous high-throughput (up to 1000 cells per second) quantification of up to 35 proteins (with the availability of more isotopes up to 100 markers could be measured).138-140 Briefly, cells with bound antibody-isotope conjugates are nebulized and their atomic constituents are ionized through an argon plasma. The resulting elements are analyzed by time-of-flight mass spectrometry to determine the properties of the cell. CyTOF has proven to be an extremely powerful technique; however, it is still limited in the number of proteins that can be analyzed and only membrane proteins can be characterized. In the following part of the review, technologies for high throughput single-cell proteomic analysis making use of droplet microfluidics technology for membrane protein analysis, as well as secreted proteins characterization are detailed. After the development of single-cell transcriptomic analysis tools such as inDrop and Drop-seq, the possibility of using similar or combined technologies (single-cell transcriptome analysis) platforms for single-cell proteomic analysis emerged. Stoeckius et al. introduced cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) which allows simultaneous single-cell transcriptome and proteome quantification by staining cells with DNA labeled antibodies prior to microfluidic processing.141 In particular, CITE-seq was shown to be compatible with Drop-seq protocol and with the commercial 10X Genomics Chromium platform. The general workflow of CITE-seq is shown in Figure 12a. Briefly, antibodies are conjugated by streptavidin-biotin binding to a DNA sequence containing: an antibody (Ab) barcode, a PCR handle, and a poly(dA) tail. Subsequently, cells are immunostained with DNA tagged Abs using traditional flow cytometry staining protocols. Single-cells are co-encapsulated in droplets with barcoding beads. After cell lysis and cleavage of the oligo sequence from the Abs by reduction of the disulfide bond in droplets, the poly(A) tail of the cellular mRNA and the poly(dA) tail of the DNA strands labeling the Abs anneal to the poly(dT) tail of the barcoding beads. During RT (which takes place in bulk in Drop-seq and in emulsion in the Chromium system) both the mRNA and the DNA Ab-labels are single-cell barcoded by extension, as the reverse transcriptase simultaneously extends the hybridized Ab DNA-tags and synthesizes complementary DNA from mRNA. Subsequently, the transcriptomic and proteomic-derived material are separated by size and PCR amplified separately, to ensure that the relative proportions of the libraries can be adjusted separately. Finally, the two libraries are sequenced, and during analysis the single-cell proteomic and transcriptomic data can be grouped. The proof of concept was demonstrated using 13 monoclonal antibodies used for staining. Figure 12Open in figure viewerPowerPoint Workflows of poly(dT)-based methods for membrane protein quantification. a) CITE-seq implemented with Drop-seq microfluidics and library preparation, b) REAP-seq implemented on 10X Genomics Chromium platform. R1: read 1. R2: read 2. An interesting development of CITE-seq is ECCITE-seq (Expanded CRISPR-compatible CITE-seq) allowing for multimodal single-cell assays.142 RNA expression and protein sequencing assay (REAP-seq) was introduced by Peterson et al. in 2017,143 almost at the same time as CITE-seq. The initial study used 82 antibodies for staining. The working principle of REAP-seq (Figure 12b) is very similar to CITE-seq and also allows the simultaneous quantification of surface proteins with mRNA at a single-cell level making use of droplet microfluidics. One difference between the two technologies is the conjugation chemistry of the oligonucleotides to the antibody. Instead of streptavidin-biotin binding, amine chemistry is used. Another difference is related to the (not)release of the antibody labels. In REAP-seq, after co-encapsulation in droplets of the stained cell and the barcoding bead, the oligos labeling the antibodies are not released into solution, and barcoding by RT is performed with the antibody DNA label still conjugated to the antibody. Both technologies can be adapted to work with all the droplet microfluidic technologies for single-cell transcriptomic analysis reviewed above. In particular, REAP-seq was shown to be compatible with the commercial 10X Genomics Chromium platform. One common limitation of CITE-seq and REAP-seq is their ability to characterize only surface proteins. Abseq was developed by the Abate group, also in 2017.144 The basic approach used is the same as CITE-seq and REAP-seq: labeling antibodies with DNA tags, staining cells with such labeled antibodies before microfluidic encapsulation, sequencing of libraries, and data analysis (Figure 13a). However, there are a few differences compared to the preceding ones. Briefly, in Abseq the microfluidic workflow consists of three devices instead of one for CITE-seq and REAP-seq. First, two emulsions are created independently: one with single cells and the second one with the barcoding primers. The barcode emulsion is produced by encapsulation of one DNA sequence, mixed with PCR reagents and primers that is further PCR amplified to obtain clonal populations. Each barcoding primer contains the cell barcode sequence and a capture sequence. The single cells droplets are obtained by emulsification of cells immunostained with DNA-tagged Abs (containing a PCR handle, a UMI, Ab barcode, capture sequence) with proteinase K (for cell lysis) following Poisson distribution. A third microfluidic device is used to merge by electrocoalescence one droplet from each emulsion with a third droplet containing reagents for PCR amplification. After mixing of the content, each (very large, unstable) droplet is split into four equal droplets, that are thermocycled and PCR amplified. Abseq has the advantage that cells are lysed with proteinase K, the proteinase is heat-deactivated, and then enzymes for PCR reaction are added, which prevents the inhibition of PCR reaction by the cell lysate. However, Abseq presents also some disadvantages compared to the techniques already presented. First, using a capture sequence different from a poly(dT) tail allows only for protein analysis and no mRNA. Second, as for Drop-seq, both the cells and the barcodes are encapsulated following Poisson distribution, consequently, the cell barcoding efficiency is relatively low. Finally, the use of three microfluidic devices introduces significant technological challenges. 10X Genomics commercialized, in collaboration with Biolegend, the characterization of both 3gene expression and surface protein expression at the single cells level as Feature Barcoding Technology,97 shown schematically in Figure 13b. This technology uses the Chromium System to encapsulate cells with barcoding beads as described above in Section 6.1.4. However, the hydrogel beads were redesigned (single-cell 3v3.1 Gel Beads) to contain three different types of primers. Each primer contains a cell barcode, a UMI sequence, and a poly(dT) sequence to bind poly-adenylated mRNA, or specific capture sequences for proteomic information. Cells are stained with DNA tagged Abs. However, the DNA tags do not contain a poly(dA) tail, in contrary to CITE-seq and REAP-seq, but a capture sequence. Tagged antibodies (with the capture sequence) compatible with the platform are available from their partner Biolegend: TotalSeq-B. The use of different capture sequences for mRNA and proteins has the advantage of independent barcoding of two analytes. mRNA and protein tags do not compete for the same barcoding primers. Comparison of the benefits and drawbacks of the different strategies discussed above for single-cell membrane protein analysis has to take into account differences related to the method itself, as well as to the microfluidic platform on which they are carried out (e.g., Drop-seq, Chromium platform). A comparison of the platforms is shown in Section 6.1.5. Importantly, to date, all these technologies are limited to membrane protein analysis. Recent work has demonstrated the possibility of extending this approach to include intracellular proteins as well.145, 146 However, these technologies have only been implemented in well plates with low cell throughput, and it would be desirable to use them with high-throughput droplet microfluidics. All the abovementioned techniques for single-cell proteomic analysis make use of DNA-tagged Abs (Figure 14). Multiple methods can be used to tag a DNA strand to the Abs: i) CITE-seq uses streptavidin-biotin binding, ii) REAP-seq: amine chemistry using the commercially available Thunder-Link PLUS Oligo Conjugation System, and iii) Abseq: a bifunctional crosslinker reactive toward thiol (via maleimide) and amine (via NHS) moieties. Figure 14Open in figure viewerPowerPoint Comparison of the labeled antibodies used for single-cell proteome analysis protocols (CITE-seq, REAP-seq, Abseq, and Feature Barcoding Technology, which uses TotalSeq-B antibodies). Different chemistries are adopted for the binding of the DNA sequence to the antibody: streptavidin-biotin binding (CITE-seq), bifunctional crosslinker (Abseq), and amine chemistry (REAP-seq and TotalSeq-B). Different DNA sequences are used: PCR handle, Ab barcode and poly(dA) tail for CITE-seq and REAP-seq, and PCR handle, Ab barcode, and capture sequence for Abseq and TotalSeq-B. Moreover, CITE-seq and REAP-seq use a poly(dA) tail as a capture sequence, in this way the single-cell proteome study can be easily integrated to the transcriptome study by mimicking the natural mRNAs poly(A) tail. In contrast, Abseq and Feature Barcoding Technology use a capture sequence homologous between the Ab-tag and the barcodes, specifically binding only to the proteins tags and not to the mRNA. DNA tagging of Abs is relatively straightforward, but oligonucleotide-conjugated antibodies compatible with CITE-seq and Feature Barcoding Technology are also commercially available. Droplet microfluidic technologies offer the unique advantage of confining individual cells with a stimulus and to ensure entrapment of the molecules secreted by the cell. Technologies using droplet microfluidics to analyze secreted proteins generally use fluorescence-based measurements instead of sequencing, making them low-cost, offering the possibility of kinetic studies, allowing phenotype visualization, and requiring no bioinformatics skills. Importantly, technologies for single-cell kinetic secretion measurements should have all reagents already present in the droplets to produce a fluorescent signal and should be coupled with an imaging chamber keeping the droplets fixed in one location over time. The study of immune cells, and in particular of their cytokine secretion at a single-cell level upon stimulation, is important to understand immune cell responses and cell-to-cell response heterogeneity upon exposure to a stimulus. Droplet microfluidics provides an ideal platform for the study of single-cell cytokine secretion by maintaining the cells under precisely defined conditions. To date, a number of tools have been developed using droplet microfluidics for single-cell cytokine secretion measurement.147-149 A comparison of the workflows for these techniques is depicted in Figure 15. In all cases, single cells are encapsulated in droplets at a limiting dilution with a capture construct specifically binding to cytokines. Additionally, a stimulus can be added in order to characterize the single-cell response. Once the cytokines bind to the capture construct and are optionally stained, a fluorescent signal is emitted that can be visualized by flow cytometry or fluorescence microscopy. Figure 15Open in figure viewerPowerPoint Comparison of droplet microfluidic technologies for single-cell cytokine secretion measurements. a) Membrane-anchored aptamer sensor, b) Catch reagent construct bound to the cell surface for cytokine capture, c). Antibody-labeled beads for cytokine capture. The main difference between the tools for cytokine secretion analysis is in the engineering of the capture reagent which contains two parts: one specifically binding to the cytokines and the other binding to a solid support (e.g., bead, cell surface). Antibodies148, 149 as well as aptamers147 have been used to specifically capture secreted cytokines. The capturing construct is directly bound to the cell surface147, 148 or to beads co-encapsulated in the droplet.149 After co-encapsulation of the single cells with the capture construct, the emulsion is incubated to allow secretion of cytokines in droplets. Qiu et al. reported the use of aptamers directly anchored on the cell surface (membrane-anchored aptamer sensor),147 which has the advantage that all the steps are performed in droplets and in principle, the cytokine production can be monitored over time (Figure 15a). Briefly, aptamers are linked to a cholesterol tail and anchored to the cell surface by hydrophobic interaction between the cholesterol and the cellular phospholipid layer. The two ends of the aptamers are labeled with a fluorophore and a quencher. In the absence of cytokines, the aptamers self-hybridize into a hairpin structure, keeping the fluorophore and the quencher in close proximity, resulting in quenched fluorescence. However, upon binding of the cytokines, the aptamers will switch into a specific tertiary structure, separating the fluorophore and the quencher, thus resulting in an increase in fluorescence signal. Instead of aptamers, Abs can directly bind to the cell surface as shown by Wimmers et al. who use a bifunctional construct labeled on the cell surface for cytokine capture.148 In this case, after emulsification, incubation, and binding of the cytokines to the cell surface, the emulsion is broken (Figure 15b). Thereafter, a fluorescently labeled antibody is introduced to bind to the secreted cytokines bound at the cell surface. Finally, Abs for the capture of cytokines have been immobilized on polystyrene beads.149 The workflow of this technique is shown in Figure 15c. Single cells are co-encapsulated with cytokine-capture beads in monodisperse agarose droplets. After co-encapsulation, incubation, and binding of the cytokines to the capture beads, the agarose droplets are gelated in agarose beads. Subsequently, the emulsion is broken and the obtained agarose beads are stained in bulk with fluorescently labeled antibodies against the secreted cytokines. The capability of B cells to secrete antibodies is a fundamental step in the immune response towards pathogens. Immunoglobulin G (IgG) is the most abundant type of antibody found in the blood.150, 151 The pool of antibody-secreting cells is heterogeneous152 and the study of the antibody secretion rate and of the antibody specificity is of particular interest for many applications, for example, to monitor immune responses during disease and therapy, to optimize immunization and vaccinations protocol, to facilitate the generation of high-quality monoclonal antibodies and for hybridoma selection.153-155 Droplet microfluidics offers the unique capability of entrapping single cells with their secreted antibodies and has been used to study single-cell antibody secretion over time,34 the secreted antibody affinity to specific antigens,156 and the secreted antibody functionality by binding and modulating (typically inhibiting) the activity of the target.157 Antibody secreting cells were sorted by Mazutis et al. by means of a fluorescence signal localized at the surface of capture beads34 as shown in Figure 16a. Briefly, individual cells are encapsulated in picoliter droplets together with single capture beads (coated with anti IgG antibodies) and fluorescently labeled Abs. After emulsification, droplets are incubated off-chip to allow the cells to secrete the antibodies of interest, the antibodies to bind to the capture bead, and the fluorescent probes to localize at the surface of the capture bead by binding to the captured antibodies. Subsequently, the emulsion is re-injected in a microfluidic sorting device. The localized fluorescent signal on the capture beads is then used for high-throughput screening and sorting of antibody-secreting cells. Figure 16Open in figure viewerPowerPoint Comparison of the different droplet microfluidic technologies for single-cell antibody secretion measurements. a) Sorter of secreting cells. b) DropMap for the study of antibody secretion and affinity to specific antigens. Secretion of antibodies binding to specific antigens by individual cells was studied over time using DropMap technology,156 Figure 16b. Single cells are compartmentalized in droplets together with ≈1300 magnetic nanoparticles pre-coated with a capturing molecule, fluorescently labeled detection antibodies, and fluorescently labeled antigens. The droplets are then immobilized in an observation chamber to form a 2D droplet array and imaged using fluorescence microscopy. The secreted antibodies bind to the capture beads and are visualized by the binding of a secondary fluorescently labeled antibody present in droplet. The application of a magnetic field induces the formation of elongated, easily observable nanoparticle aggregates in each droplet, termed beadlines. The single-cell antibody secretion rate is measured by fluorescence intensity of the detection antibody localized on the beadline and the antibodies affinity for the antigen by fluorescence intensity measurement of the antigen localized to the beadline. DropMap was commercialized by HiFiBio in 2019. Finally, Debs et al. developed an assay to test secreted antibody functionality—defined as their capability of binding and inhibiting the activity of a target,157 Figure 17. Single cells are co-encapsulated with the Ab target in droplets. After Ab secretion, these droplets are merged with a smaller droplet containing a FRET peptide which is the substrate for the target. A high fluorescent signal indicates the production of noninhibiting antibodies. Traditionally, the metabolome is studied using four major methods: mass spectrometry, fluorescence-based detection, fluorescence biosensors, and FRET biosensors. Measuring metabolites at the single-cell level is very difficult because of the very high diversity of compounds that encompass the metabolome, and their large dynamic range (from few molecules per cell to 1010 molecules for major metabolites in larger cells), which is highly variable (environmental cues vary its generation on a time scale of seconds or even milliseconds). Droplet microfluidics could play a role in single-cell metabolomics studies as they provide a controlled micro-environment. The main drawbacks of the technologies developed until now for single-cell metabolomics analysis using droplet microfluidics is that they are limited to the study of only one or a few metabolites (out of the 114 100 metabolites present in the Human Metabolome Database),158 and that they are not easily tunable to the study of other metabolites than the one they were designed to work for. Single-cell lactate secretion has been measured by making use of droplet microfluidics for circulating cancer cells detection due to their aberrant metabolism (Warburg effect),159 see Figure 18a. Single cells are emulsified in picoliter droplets with glucose and a ratiometric dye. Subsequently, the droplets are incubated to allow the cells to consume glucose and secrete lactate, consequently acidifying the droplets. The single-cell metabolism measurement is performed indirectly by measuring the pH of the cell-containing droplet as determined by the fluorescence intensity at two different wavelengths. Cancer cells, with higher lactate production compared to white blood cells, are detected by the higher fluorescence ratio of the droplets in which they are encapsulated. The main advantage of this method is that droplet pH is measured in real time and droplets of interest can be sorted for further downstream processing. However, it allows only for one time-point measurement of the single-cell metabolism. Combination of this technology with the DropMap visualization chamber could be interesting for capturing the single-cell metabolism over time. Figure 18Open in figure viewerPowerPoint Workflows of the droplet microfluidic technologies for single-cell metabolomics measurements. a) Detection of cancer cells (lactate secretion). b) Screening of bacterial mutant libraries (ethanol or lactate secretion). Droplet microfluidics has also been used to distinguish and sort genetically engineered bacteria, in particular genetically modified cyanobacteria from wild-type ethanol-producing160 and lactate-producing161 strains, as shown in Figure 18b. In both cases, after cell encapsulation in droplets, the emulsion is incubated to allow bacteria to secrete the metabolite of interest (ethanol or lactate). Subsequently, the emulsion is injected into another microfluidic device which fuses, by electrocoalescence, a second droplet that contains the assay enzyme solution. Upon incubation, the enzymatic assay activates a fluorescent substrate in the presence of the metabolite of interest. Finally, droplets are reinjected in a third microfluidic device where the presence of the metabolite is measured indirectly by fluorescence. This third device can also present a sorting unit to sort bacteria expressing high levels of the metabolite of interest as shown by Hammar et al.161 It is clear that droplet microfluidics has revolutionized our ability to perform quantitative measurements at the single-cell level. High-resolution data from individual cells provide completely new insights into cellular processes. Although enormous progress in single-cell analysis technologies has been made, there is still great potential for future technological developments. Most current approaches are limited to a single output type of information (e.g., DNA, RNA, or proteins). Quantitatively capturing multiple layers of information at the single-cell level is a highly attractive target, and some of the platforms described in this review (REAP-seq, CITE-seq, and Feature Barcoding Technology) are already moving in this direction. A major challenge in all single-cell approaches is a lack of information about dynamic processes (e.g., differentiation, proliferation), a challenge further complicated by the differences in state heterogeneity of the cells present in a sample. Experimental cell synchronization is challenging and can alter cell dynamics. New developments in computational analysis, making use of so-called “pseudotime” algorithms, have started to provide information about the changes in cellular composition (primarily transcriptome landscape) over time without the need of a priori knowledge of marker genes.162, 163 In order to reconstruct time ordering of the data, the basic assumption applied in such algorithms is that the transcriptomic changes are a continuous function with respect to time, allowing researchers to place cells within a sample on a virtual timeline. Another inherent characteristic of single-cell technologies is that they require tissue-dissociation, prior to single-cell encapsulation. This inevitably means that spatial information regarding the original position of the cells in the tissue is lost. Spatial transcriptomics aims to combine the cellular transcriptome information with their spatial organization within a tissue, adding an extra layer of information. Spatial transcriptomic information can be derived from whole-transcriptome profiling data by using new computational techniques, rather than experimental ones. An example is novoSpaRc164 which enables spatial reconstruction of single-cell gene expression de novo by assuming, similarly to the “pseudotime” algorithms, that gene expression between spatially adjacent cells is more similar than expression levels of cells separated by larger distances. Additionally, the Seurat package enables the addition of spatial information to the sc-RNA data by creating a spatial reference map by in situ hybridization for a subset of marker genes and by combining it with single-cell gene expression.165 This approach was successfully applied to study a developing zebrafish embryo; however, it presents limitations for tissues where spatial patterning varies between samples (e.g., solid tumors) or where expression patterns are similar in space. As already mentioned, there are several techniques designed for single-cell analysis that do not involve droplet microfluidics. We expect that future developments will attempt to adapt highly sensitive single-molecule protocols (such as nanopore sequencing of DNA, RNA, and possibly proteins) for high-throughput experiments using (droplet) microfluidics. In future developments, we see also promising applications for droplet microfluidic diagnostic techniques that exploit the “picoliter single-cell reaction flask” principle and screen single cells for secreted molecules (antibodies, cytokines, enzymes, metabolites), although we note that for large-scale, high-throughput screening, droplet generation, and interrogation speeds will need to be enhanced by 1–2 orders of magnitude. Finally, further integration of analytical techniques (one could think of chromatography and single cell mass spectrometry for the detection and characterization of small molecules and possibly proteins) and the sequencing methods described in this review with droplet microfluidics should bring us closer to the ultimate goal of measuring the complete molecular content of single cells. In space. And time. K.M. and F.R. contributed equally to this work. All authors were involved in the preparation of the manuscript and approved the final version. Kinga Matuła graduated maxima cum laude in biotechnology from the Rzeszow University of Technology (Poland). She studied at the Otto-von-Guericke University Magdeburg and worked at the Max Planck Institute for Dynamic and Complex Technical Systems in Magdeburg (Germany) where she prepared her master thesis in chemical and process engineering. Afterwards, she started her Ph.D. in soft condensed matter at the Institute of Physical Chemistry of the Polish Academy of Sciences in Warsaw (Poland). In 2018, Kinga joined the group of Professor Wilhelm Huck where she is working on development of droplet microfluidic platforms for single cell analysis. Francesca Rivello received an M.Sc. cum laude in nanotechnology at the University of Twente (The Netherlands) in 2015. She is currently a Ph.D. candidate under the supervision of Professor Wilhelm Huck in the Radboud University. Her project focuses on the study of single cells using droplet microfluidic technologies with particular focus on their kinetic responses and on their contribution to metastasis progression. Wilhelm Huck is Professor of Physical Organic Chemistry. 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2019年11月26日Abstract Droplet microfluidics has revolutionized the study of single cells. The ability to compartmentalize cells within picoliter droplets in micr...