Microfluidic design in single-cell sequencing and application to cancer precision medicine
Both intra-patient and intra-tumor heterogeneity have profound implications for the progression and treatment of cancers. Intra-tumor heterogeneity directly influences tumor proliferation, metastasis, and treatment resistance. The microenvironment of tumors and the heterogeneity of infiltrated immune cells determine the sensitivity of immunotherapy. Thus, the idea of precision medicine, i.e., therapies and treatment plans that are tailored to the genomic and cellular specificities of a given patient, has gained increased attention. An accurate characterization of specific molecular or cellular features of cancers and the surrounding microenvironment will be needed to provide therapeutic options of maximum benefit to individual patients. Standard oncology cell sequencing involves bulk profiling of a large sample consisting of thousands to millions of cells. This technique, however, is constrained by the loss of important information about the structural and functional properties of individual cells that may drive critical tumor processes. To address this dilemma, single-cell sequencing (SCS) has been developed to enable comprehension of the complex genetic variability within individual cells. Information derived from SCS potentially has a vital function in terms of including forecasting treatment response, tracking disease progression, prognosis, anticipating treatment efficacy, and predicting emerging drug resistance . The development of SCS platforms and workflows have developed hand in hand with microfluidics-based technologies. Microfluidics has helped in particular to solve the challenge of low throughput in SCS but also offers the potential for integration and automation to improve the performance of SCS. In recent years, a number of great reviews have been published to describe the combination of microfluidics and SCS techniques. , , , , , , Here, we aim to provide a thorough description of single-cell isolation techniques based on different microfluidics design principles, as well as an overview of the complementary sequencing or profiling technologies used with these. We also highlight recent applications in precision oncology research and challenges and perspective of microfluidics-based SCS in clinical settings. 

SCS can generally be described in the following steps: (1) sample acquisition; (2) cell isolation ; (3) cell lysis , ; (4) amplification (library preparation); (5) sequencing (e.g., Illumina, Ion Torrent, Pacific Biosciences, and Oxford Nanopore technologies) , ; and (6) cellular mono-omics or multiomics analysis. Microfluidics-based cell isolation combines small sample volume and high throughput, and it is therefore broadly employed in SCS research and in the development of commercialized products. According to different microfluidic technology principles, microfluidic chips for SCS can be categorized into two general types: devices based on microstructural entrapment and those with external forces. In this section, we outline six structures that are utilized in microfluidics-based SCS. Schematics and a summary of some of the key advantages and disadvantages of these designs are also summarized in . Droplet-based microfluidics Droplet-based microfluidics has become one of the most frequently used single-cell isolation methods owing to its simplicity of operation, high throughput, and low cost. Recently, there have been numerous review publications regarding the application of droplets in single-cell isolation and analysis. , , Droplets are generated by applying two immiscible fluids and could be generated by three classical geometries of microfluidic devices, which are T-junctions, co-flow, and flow focusing. The small volume of droplets co-encapsulated with cells and microbeads reduces the molecular reaction time and avoids external contamination. The aqueous phase of the droplet in SCS can also be replaced by hydrogel. Hydrogel droplet provides a 3D culture matrix to support single-cell cultures for longer periods and is flexible for downstream analysis and processing. Different microfluidics modules can subsequently be leveraged for highly flexible manipulation of droplets, such as incubation, splitting, merging, reloading, detection, and sorting. Notwithstanding the advantages of droplet microfluidics, the cells encapsulated in droplets are randomly distributed according to a Poisson distribution, risking the creation of empty droplets and droplets containing multiple cells. There are several solutions to this challenge. Fluorescence and magnetophoretic sorting have been adopted to screen the droplets themselves. Alternatively, a number of algorithms have been developed to statistically identify reads from empty or multicell droplets from sequencing datasets themselves. , In terms of droplet generation, fluorocarbon oil is the most commonly selected oil for single-cell assays because of its good permeability, and block copolymer of polyethylene glycol and perfluoropolyether (PEG-PFPE) is the most frequently chosen surfactant due to its good stability, but the risk of leakage due to the fragility of the droplets still cannot be overcome. , As such, the development of more high-performance surfactants may be meaningful for a wide range of applications of droplets. Valve-based microfluidics Microvalves for microfluidic control can be classified into four categories: active mechanical, active non-mechanical, passive mechanical, and passive non-mechanical. In general, active mechanical microvalves offer the best performance, whereas simple passive valves are more practical for applications. , Microvalves serve to isolate specific areas of the channel network, permitting the generation of reaction chambers and performing independent reactions. In order to achieve different needs, the valves are able to be opened or closed. For example, in a multilayer microfluidic structure designed by Chen and colleagues, a membrane is sandwiched between two layers, and pneumatic microvalves are able to regulate the membrane size and pressure, enabling dynamic control of the upper and lower size limits of the isolated cells. Hydrogels can also be involved in the construction of microvalves in microfluidics to regulate fluid flow by controlling the solubility of hydrogels in channels. Obst et al. developed a microfluidic hydrogel valve that is capable of visually switching on and off by ultraviolet (UV) spectroscopy. Valve-based microfluidics can achieve microfluidic large-scale integration, enabling the fabrication of microfluidic chips consisting of hundreds to thousands of pneumatic membrane valves similar to an array of flow-controlled multiplexers. This type of system allows precise manipulation of cells and reagents to automate more sophisticated analysis. Nevertheless, the higher manufacturing cost and complex operation of these devices may present barriers to widespread commercial applications. Hydrodynamic trap-based microfluidics Hydrodynamic traps are structures that are currently popular for passively trapping cells. This device is designed in a manner that allows individual cells to be effectively trapped in a mechanical barrier by the vortices induced by a fluid flow. , When a cell occupies a typical trap, the altered dynamic flow surrounding it has increased fluid resistance, which limits the access of other cells to the trap. Carlo et al. designed a U-shaped hydrodynamic cell trap to accomplish dynamic control of individual adherent cells for array culture and fluid perfusion. Besides U-shaped traps, other geometric traps are also available, such as hook-shaped traps, ratchet filters, and multibarrier structure-type filters. Microfluidics based on traps can achieve a large number of single-cell separations, but the efficiency of trapping and trap clogging are challenges to be considered. A proposed continuous hydrodynamic channel consisting of a main channel for loading cells, a narrow trap site, and a downstream bypass partly address the problem. A compact geometry designed by Jin et al. produced faster and higher capture efficiency compared to the serpentine cell-trapping structure. Apart from these, Lipp et al. proposed a planar hydrodynamic trap structure to overcome the problem of low trap density in the chip, which is composed of two superimposed microchannels with particles being trapped in the top channel. Overall, hydrodynamics-based single-cell trapping microfluidics demands meticulous design of structures and flow rates to improve trapping efficiency while reducing mechanical damage to cells and avoiding chip clogging. Microwell-based microfluidics Contrary to hydrodynamic traps, microfluidics based on microwell arrays for single-cell separation is mainly forced by gravity. Due to simplicity and scalability, microwell arrays are well accepted in the single-cell analysis field. Microwells are capable of being produced in a variety of geometric shapes with cross-sectional round, triangular, square, and rectangular shapes. , Indeed, the shape of the microwell array has an effect on the capture efficiency. Both Park et al. and Lai et al. have proven, by comparing different shapes of microwells, that triangular microwells are a more efficient geometry for capturing cells because the water phase generates a stronger backflow in the triangle. Surface functionalization of the microwells is also a critical factor to be taken into account regarding the capture efficiency of the cells, as is the size of the microwells. Microwell chips are easy to design, versatile in structure, and flexible in operation and thus are suitable for integration with other platforms to accomplish high-throughput single-cell analysis. However, applications can be restricted by the chip size and fabrication techniques. And when facing changes in experimental conditions, microwell chips will require redesign and manufacture. Cross-contamination is another thorny issue due to the difficulty of achieving complete isolation between microwells. In addition to this, microwell chips have the limitation of it being hard to retrieve the sample. Electrowetting-on-dielectric microfluidics The introduction of external forces in microfluidic devices has become an effective technique for single-cell trapping. Digital microfluidics (DMF) is a technology to actuate droplets based on the principle of electrowetting on dielectric (EWOD). It involves altering the surface wettability by applying an electrical potential to an array of electrodes underneath a hydrophobic surface, which ultimately pulls the droplet to the activated electrode. Different electrodes and a programmed sequence of applied voltages can cause the droplet to move in different directions on the DMF chip surface. For the EWOD effect, it can be described by contact-angle shift, which is quantitatively characterized by the Young-Lippmann equation. DMF features outstanding flexibility and simplicity in handling picoliter-to-microliter-volume samples and is widely employed for single-cell assays. The DMF chip designed by Ruan et al. contained 10 hydrophilic sites on an array of 95 electrodes, allowing cellular captured on the hydrophilic sites by precise manipulation. However, for this type of DMF chip, throughput is low, and its lifetime may be shortened because of the high drive voltage that is applied to the droplet. Zhai et al. have reported an alternative DMF chip with a 3D microstructure for cell capture and the combination of gas-soluble silicone oil with a fluorinated surfactant to reduce the drive voltage to 36 V. However, the complex manufacturing process and high cost of DMF chips are significant issues for wider adoption of these designs. Dielectrophoresis-based microfluidics Dielectrophoresis (DEP) is widely used in cellular manipulation techniques owing to its non-invasive, specific selectivity and high capture efficiency advantages. In DEP, a non-uniform electric field is applied, and the separation of the single cells is based on the relative difference in the polarization between the cell and the surrounding medium. Based on operating mode, DEP is mainly grouped into electrode-based DEP (eDEP), insulator-based DEP (iDEP), and contactless DEP (cDEP). In DEP, larger gradients can lead to joule heating, which can harm cells. And due to the short extension of the electric field gradient from the electrode or insulating structure, another critical difficulty of DEP-based single-cell isolation is the low volumetric throughput, especially when low-abundance cells need to be studied. Microfluidics with 3D electrodes would overcome these challenges to some extent. Puttaswamy et al. developed a 3D electrode with a height of 70 μm to enable capture and rotation of red blood cells. However, the fact that the electrodes have to be wired to the power supply causes complexity in the microfluidic device architecture. The integration of the wireless electrodes eliminates the branching microchannels of leads that are easily expanded into DEP devices. Li et al. developed an array of over 700 wireless bipolar electrodes (BPEs) that accomplished higher capture rates of single cells. DEP microfluidic platforms work as a highly usable platform for cell separation, but common problems include joule heating, bubble generation, and pearl-chain formation. 

Droplet-based microfluidics has become one of the most frequently used single-cell isolation methods owing to its simplicity of operation, high throughput, and low cost. Recently, there have been numerous review publications regarding the application of droplets in single-cell isolation and analysis. , , Droplets are generated by applying two immiscible fluids and could be generated by three classical geometries of microfluidic devices, which are T-junctions, co-flow, and flow focusing. The small volume of droplets co-encapsulated with cells and microbeads reduces the molecular reaction time and avoids external contamination. The aqueous phase of the droplet in SCS can also be replaced by hydrogel. Hydrogel droplet provides a 3D culture matrix to support single-cell cultures for longer periods and is flexible for downstream analysis and processing. Different microfluidics modules can subsequently be leveraged for highly flexible manipulation of droplets, such as incubation, splitting, merging, reloading, detection, and sorting. Notwithstanding the advantages of droplet microfluidics, the cells encapsulated in droplets are randomly distributed according to a Poisson distribution, risking the creation of empty droplets and droplets containing multiple cells. There are several solutions to this challenge. Fluorescence and magnetophoretic sorting have been adopted to screen the droplets themselves. Alternatively, a number of algorithms have been developed to statistically identify reads from empty or multicell droplets from sequencing datasets themselves. , In terms of droplet generation, fluorocarbon oil is the most commonly selected oil for single-cell assays because of its good permeability, and block copolymer of polyethylene glycol and perfluoropolyether (PEG-PFPE) is the most frequently chosen surfactant due to its good stability, but the risk of leakage due to the fragility of the droplets still cannot be overcome. , As such, the development of more high-performance surfactants may be meaningful for a wide range of applications of droplets. 

Microvalves for microfluidic control can be classified into four categories: active mechanical, active non-mechanical, passive mechanical, and passive non-mechanical. In general, active mechanical microvalves offer the best performance, whereas simple passive valves are more practical for applications. , Microvalves serve to isolate specific areas of the channel network, permitting the generation of reaction chambers and performing independent reactions. In order to achieve different needs, the valves are able to be opened or closed. For example, in a multilayer microfluidic structure designed by Chen and colleagues, a membrane is sandwiched between two layers, and pneumatic microvalves are able to regulate the membrane size and pressure, enabling dynamic control of the upper and lower size limits of the isolated cells. Hydrogels can also be involved in the construction of microvalves in microfluidics to regulate fluid flow by controlling the solubility of hydrogels in channels. Obst et al. developed a microfluidic hydrogel valve that is capable of visually switching on and off by ultraviolet (UV) spectroscopy. Valve-based microfluidics can achieve microfluidic large-scale integration, enabling the fabrication of microfluidic chips consisting of hundreds to thousands of pneumatic membrane valves similar to an array of flow-controlled multiplexers. This type of system allows precise manipulation of cells and reagents to automate more sophisticated analysis. Nevertheless, the higher manufacturing cost and complex operation of these devices may present barriers to widespread commercial applications. 

Hydrodynamic traps are structures that are currently popular for passively trapping cells. This device is designed in a manner that allows individual cells to be effectively trapped in a mechanical barrier by the vortices induced by a fluid flow. , When a cell occupies a typical trap, the altered dynamic flow surrounding it has increased fluid resistance, which limits the access of other cells to the trap. Carlo et al. designed a U-shaped hydrodynamic cell trap to accomplish dynamic control of individual adherent cells for array culture and fluid perfusion. Besides U-shaped traps, other geometric traps are also available, such as hook-shaped traps, ratchet filters, and multibarrier structure-type filters. Microfluidics based on traps can achieve a large number of single-cell separations, but the efficiency of trapping and trap clogging are challenges to be considered. A proposed continuous hydrodynamic channel consisting of a main channel for loading cells, a narrow trap site, and a downstream bypass partly address the problem. A compact geometry designed by Jin et al. produced faster and higher capture efficiency compared to the serpentine cell-trapping structure. Apart from these, Lipp et al. proposed a planar hydrodynamic trap structure to overcome the problem of low trap density in the chip, which is composed of two superimposed microchannels with particles being trapped in the top channel. Overall, hydrodynamics-based single-cell trapping microfluidics demands meticulous design of structures and flow rates to improve trapping efficiency while reducing mechanical damage to cells and avoiding chip clogging. 

Contrary to hydrodynamic traps, microfluidics based on microwell arrays for single-cell separation is mainly forced by gravity. Due to simplicity and scalability, microwell arrays are well accepted in the single-cell analysis field. Microwells are capable of being produced in a variety of geometric shapes with cross-sectional round, triangular, square, and rectangular shapes. , Indeed, the shape of the microwell array has an effect on the capture efficiency. Both Park et al. and Lai et al. have proven, by comparing different shapes of microwells, that triangular microwells are a more efficient geometry for capturing cells because the water phase generates a stronger backflow in the triangle. Surface functionalization of the microwells is also a critical factor to be taken into account regarding the capture efficiency of the cells, as is the size of the microwells. Microwell chips are easy to design, versatile in structure, and flexible in operation and thus are suitable for integration with other platforms to accomplish high-throughput single-cell analysis. However, applications can be restricted by the chip size and fabrication techniques. And when facing changes in experimental conditions, microwell chips will require redesign and manufacture. Cross-contamination is another thorny issue due to the difficulty of achieving complete isolation between microwells. In addition to this, microwell chips have the limitation of it being hard to retrieve the sample. 

The introduction of external forces in microfluidic devices has become an effective technique for single-cell trapping. Digital microfluidics (DMF) is a technology to actuate droplets based on the principle of electrowetting on dielectric (EWOD). It involves altering the surface wettability by applying an electrical potential to an array of electrodes underneath a hydrophobic surface, which ultimately pulls the droplet to the activated electrode. Different electrodes and a programmed sequence of applied voltages can cause the droplet to move in different directions on the DMF chip surface. For the EWOD effect, it can be described by contact-angle shift, which is quantitatively characterized by the Young-Lippmann equation. DMF features outstanding flexibility and simplicity in handling picoliter-to-microliter-volume samples and is widely employed for single-cell assays. The DMF chip designed by Ruan et al. contained 10 hydrophilic sites on an array of 95 electrodes, allowing cellular captured on the hydrophilic sites by precise manipulation. However, for this type of DMF chip, throughput is low, and its lifetime may be shortened because of the high drive voltage that is applied to the droplet. Zhai et al. have reported an alternative DMF chip with a 3D microstructure for cell capture and the combination of gas-soluble silicone oil with a fluorinated surfactant to reduce the drive voltage to 36 V. However, the complex manufacturing process and high cost of DMF chips are significant issues for wider adoption of these designs. 

Dielectrophoresis (DEP) is widely used in cellular manipulation techniques owing to its non-invasive, specific selectivity and high capture efficiency advantages. In DEP, a non-uniform electric field is applied, and the separation of the single cells is based on the relative difference in the polarization between the cell and the surrounding medium. Based on operating mode, DEP is mainly grouped into electrode-based DEP (eDEP), insulator-based DEP (iDEP), and contactless DEP (cDEP). In DEP, larger gradients can lead to joule heating, which can harm cells. And due to the short extension of the electric field gradient from the electrode or insulating structure, another critical difficulty of DEP-based single-cell isolation is the low volumetric throughput, especially when low-abundance cells need to be studied. Microfluidics with 3D electrodes would overcome these challenges to some extent. Puttaswamy et al. developed a 3D electrode with a height of 70 μm to enable capture and rotation of red blood cells. However, the fact that the electrodes have to be wired to the power supply causes complexity in the microfluidic device architecture. The integration of the wireless electrodes eliminates the branching microchannels of leads that are easily expanded into DEP devices. Li et al. developed an array of over 700 wireless bipolar electrodes (BPEs) that accomplished higher capture rates of single cells. DEP microfluidic platforms work as a highly usable platform for cell separation, but common problems include joule heating, bubble generation, and pearl-chain formation. 

Sequencing genomes, transcriptomes, epigenomes, proteomes, etc., of single cells facilitates a precise understanding of individual tumors and, therefore, achieving precision therapies. The incorporation of microfluidics with single-cell omics analysis offers several benefits, not only improving the throughput and sensitivity of cellular analysis but also reducing the possibility of contamination and bias during the amplification process. In the following sections, we discuss different sequencing modalities and the microfluidic designs that have been paired with these. Meanwhile, a summary of the partial methods and applications of SCS to cancer is shown in , and microfluidics-based SCS applications to cancer are presented in . Genome Single-cell DNA sequencing (scDNA-seq) provides insight into intercellular variation and heterogeneity of the genome at varying levels, including single-nucleotide variants (SNVs) and copy-number aberrations (CNAs). scDNA-seq is arguably the most direct way to reveal clonal substructures and lineages for tumor evolution, treatment, or metastasis. Sequencing the genome, however, is challenging due to the fact that there are only few copies of DNA molecules in a cell. Hence, whole-genome amplification (WGA) is a key part of scDNA-seq. The most frequent WGA methods currently utilized are multiple displacement amplification (MDA) and degenerated oligonucleotide primer-polymerase chain reaction (DOP-PCR). Newer methods include multiple annealing and looping-based amplification cycles (MALBACs) and linear amplification via transposon insertion (LIANTI). WGA generally presents several challenges, such as variability in sequencing depth, allele shedding (two alleles will not amplify at the same time), amplification bias, etc. Moreover, structural variants (SVs) of genes include deletions, duplications, insertions, and translocations, which can be difficult to recognize with the WGA approaches. Several technologies have been proposed to tackle these problems. Non-amplified Tn5-labeled scDNA-seq methodologies such as microfluidics-based DLP and DLP+ ( A) can avoid amplification based errors. Ruan et al. have developed a DMF chip that enabled the isolation of single cells with a butterfly structure of hydrodynamic traps ( B). In this design, cells were amplified by mixing them with lysate and MDA reagent via actuated droplets. The entire step of single-cell nanoliter-volume MDA processing was achieved in an integrated and non-contact manner. This could reduce amplification bias and errors in exponential amplification and provided excellent detection of CNVs and SNVs. A key contribution of scDNA-seq is to reveal the clonal evolution of drug resistance in tumor cells after targeted therapy. scDNA-seq together with microfluidics offers the possibility of achieving direct clinical relevance for the dynamic treatment of cancer. As an example, McMahon et al. performed high-throughput scDNA-seq of patients with FLT3-mutated acute myeloid leukemia (AML) who received gilteritinib clinical therapy by utilizing a commercial two-step droplet microfluidic platform (Tapestri). The results identified activation of RAS/MAPK pathway signaling by gilteritinib treatment, which is a clinically significant mechanism of drug resistance, and suggested that a combinatorial approach to enhance antileukemic cytotoxicity will be required to treat AML. Transcriptome Single-cell RNA sequencing (scRNA-seq) is the most popular sequencing modality in tumor research and therapy. In oncology therapeutic investigations, scRNA-seq provides a valuable perspective by analyzing the factors that determine cellular expression and functional heterogeneity within tumors. In scRNA-seq, RNA is first required to be converted into cDNA. In terms of different methods of reverse transcription and amplification, scRNA-seq can be broadly classified into three groups. Poly(A)-conjugated PCR is one option, though it is not often adopted due to reduced sequencing sensitivity and loss of low-abundance transcripts. In vitro transcription (IVT) is a linear amplification process that involves complex primers containing the T7 promoter at the 5′ end. The approach is applicable to the amplification step of cell expression by linear amplification and sequencing (CEL-seq). Lastly, reverse transcription of RNA by Moloney murine leukemia virus (MMLV) could enable synthesis of full-length cDNA by including the complete 5′ end, with reduced 3′ bias of transcription. MMLV-based reverse transcription has been utilized in Smart-Seq, Smart-Seq2, and single-cell tagged reverse transcription sequencing (STRT-seq) , workflows. In order to the overcome bias problem, a unique molecular identifier (UMI) is incorporated in the reverse transcription step to barcode each mRNA molecule in the cell, enhancing the quantitative nature of scRNA-seq and improving the accuracy of the reads. In scRNA-seq, the two most common approaches are droplet-based scRNA-seq and microwell-based scRNA-seq. The accuracy and sensitivity of microwell-based scRNA-seq may be superior, but the cost for commercialization is larger than droplet-based approaches. InDrop, Drop-seq, and 10× Genomics Chromium are the more common droplet-based technologies due to their low cost, easy operation, and high throughput. All three platforms involve the use of barcoded bead primers to distinguish individual cells and UMIs for bias correction. Beyond the more standard droplet-based methods, a number of groups have been working to leverage alternative microfluidic design principles in their scRNA-seq workflows. Li et al. achieved transcriptional gene analysis of tumor cells with different secretion efficiencies of MMP9 by using hydrodynamic capture of cells and virtual droplets not fully encapsulated by oil for selection and downstream mRNA sequencing. This microfluidic platform, which has 648 microchambers and a single-cell capture efficiency of up to 90%, as well as the ability to retrieve cells of interest, presents a new possibility for assessing tumor malignancy in clinical diagnostics. Further, Bai et al. demonstrated an integrated DEP-trapping-nanowell-transfer (dTNT) technique to break the Poisson limit and achieve scRNA-seq with a 91.84% single-cell capture rate, 82% transfer efficiency, and > 99% bead loading ( D). Valve-based scRNA-seq has been adopted increasingly due to its potential for efficient cell capture and selection of cells of interest for analysis. Zhang et al. leveraged hydrodynamic trapping and valve-based designs to create a highly parallel scRNA-seq platform called “Paired-seq.” Both individual cells and single barcode beads were trapped in paired chambers, and then the valve and pump combined system was capable of actively mixing them to allow lysis of cells and capture of mRNA. This system provides cell utilization efficiency of up to 95% and high mRNA detection accuracy ( E). Another valve-based microfluidic chip from Agnihotri et al. designed a three-layer microfluidic system for “bottom-up” SCS by co-encapsulating natural killer (NK) cells and tumor cells in droplets. It selectively released cells of interest for downstream sequencing analysis through usage of microvalves ( C). In this work, visual observation of immune cells with tumor cells, combined with selection of cells of interest for subsequent scRNA-seq, has provided new insights into immunotherapy. Temporally resolved transcriptomics scRNA-seq methods are also being developed to incorporate a temporal dimensions, enabling an understanding of the dynamic responses of cells to therapeutic stimuli and tumor progression. A single-cell, metabolically labeled new RNA tagging sequencing (scNT-seq) platform developed by Qiu et al. utilizes 4-thiouridine (4sU), a nucleotide analog that undergoes a chemical reaction resulting in a T-to-C switch in the sequencing data. Lin et al. demonstrated a Well-TEMP-seq assay with merits of low-cost ( < $0.1 per cell), high-throughput (8 parallel samples of thousands of cells each), and efficient cell loading ( A). This technique utilized a microfluidic chip with quasi-static hydrodynamic microwells and 4sU-labeled metabolic RNA. The authors used this method to investigate the transcriptional temporal dynamics of colorectal cancer cells exposed to a clinical drug (5-AZA-CdR) and discovered upregulation of tumor suppressor genes and downregulation of oncogenes. Another approach for temporal transcriptional sequencing is based on the introduction of mutations at specific sites through CRISPR-Cas9. Quinn et al. investigated the metastatic nature of tumor cells via temporal transcription of Cas9 on tens of thousands of cells. With this approach, they demonstrated that the metastatic capacity of tumor cells is heterogeneous and that it varies orthogonally to proliferative potential and characteristic transcriptional profiles. Molecular mechanisms underlying the metastasis-suppressive phenotype of KRT17 in vitro and in vivo were also elucidated. Spatial transcriptomics The spatial structure and arrangement of cell types within tumors is essential to cancer biology. However, in most scRNA-seq methods, the tissue would have to be dissociated prior to sequencing, which leads to a loss of spatial information. Spatial transcription techniques broadly belong to two categories. , One is imaging-based methods that are associated with the utilization of high-resolution imagers and in situ reagents, including in situ sequencing (ISS)-based methods (where transcripts are amplified and sequenced in tissue) and in situ hybridization (ISH)-based methods (where imaging probes are hybridized sequentially in tissue), such as single-molecule fluorescence ISH (smFISH), multiplexed error-robust fluorescence ISH (MERFISH), and sequential fluorescence ISH+ (seqFISH+). The second group is built on next-generation sequencing (NGS) technology and mainly relies on the spatial bar-code application prior to library preparation, such as Slide-seq. , The development of commercially available spatial transcriptomics such as Visium released by 10× Genomics, as well as GeoMx and CosMx released by Nanostring, enables spatial transcriptomics to become more accessible. Hirz et al. demonstrate that by combining droplet scRNA-seq and spatial transcriptomic data from Slide-seqV2, even more comprehensive information on tumor stromal cell and microenvironment interactions can be obtained. This study characterized tumor, immune, and non-immune stromal cell changes within the tumor microenvironment (TME) at high resolution for localized prostate cancer (PCa). They discovered that a suppressive immune microenvironment was established even in low-risk primary PCa. And cytotoxicity scores were significantly greater in “hot” tumors with high T cell infiltration, indicating that more functional T cells may influence the tumor response to immunotherapy. They further identified that the use of a CCL20-blocking antibody could block the communication between tumor-inflammatory monocytes and T-regulatory cells to significantly reduce tumor growth. Epigenome Epigenetic changes are thought to significantly contribute to tumor heterogeneity in response to drugs or the environment. At present, single-cell epigenetic analysis focuses on methylation patterns, histone modifications, and chromatin status. DNA methylation of cytosine (5mC) residues can be mapped genome wide with a variety of methods. Bisulfite conversion following sequencing (BS-seq) is a well-established approach that is regarded as the gold standard. , Histone markers are commonly mapped using chromatin immunoprecipitation (ChIP)-seq, where ChIP is performed with antibodies specific to such histone markers. High background noise and low efficiency of this assay is challenging for performing ChIP-seq at the single-cell level. Notably, Grosseli’s research team established a high-throughput droplet microfluidic scChIP-seq assay to characterize histone modifications with high coverage (averaging up to 10,000 loci per cell), uncovering the existence of relatively rare chromatin status in tumor samples. Transposase-accessible chromatin sequencing (ATAC-seq) cleaves only DNA regions not protected by binding proteins by using Tn5 transposase, which is already employed to measure dynamic changes in overall chromatin structure. Although ATAC-seq does not provide a complete picture of gene regulatory mechanisms, single-cell protocols for this type of profiling have proved to be relatively scalable. Satpathy et al. carried out droplet-based scATAC-seq for over 200,000 human blood and basal carcinoma cells. In addition to reconstructing cell-type-specific cis - and trans -regulatory profiles and B cell and dendritic cell (DC) developmental trajectories, the authors identified regulators of therapeutically responsive T cell subtypes and regulatory programs that control T cell depletion and shared programs with T follicular helper (Tfh) cells, possibly via interleukin-21 (IL-21). Proteome Assaying cancer proteomes at the single-cell level holds tremendous potential for understanding the ultimate, functional state and workings of an individual tumor. Currently, single-cell proteomic technologies mainly rely on mass spectrometry (MS)-based approaches to identify proteolytically digested peptides. The mass cytometry by time of flight (CyTOF) technique combines heavy-metal labeling of antibodies and MS. But in single cells, the protein content is extremely small. Two general approaches have been developed to overcome this challenge. One is through reducing the sample preparation volume to the nanoliter scale, for example using the nanoPOTS nanoliter dispensing system to increase peptide yields. Another method is multiplexing single cells with a carrier sample through chemical labeling using tandem mass tags (TMTs) to improve MS signaling and recognition. Microfluidics have contributed to the development of several cutting-edge protein profiling techniques. Similar to the other sequencing platforms discussed thus far, microfluidics has particularly contributed to the miniaturization of single-cell proteomics. As an example, Gebreyesus et al. proposed an automated, all-in-one proteomic sample preparation microfluidic chip to achieve cell capture, imaging and counting, lysis, protein digestion, desalting and peptide collection, and characterization ( B). This chip together with data-independent acquisition enabled profiling 1,500 ± 131 proteomes from single cells with a false discovery rate of 1% and identified protein abundances spanning five orders of magnitude. This microfluidic workflow improved proteomic sensitivity and provided good reproducibility compared with traditional single-cell proteomics assays. Microfluidic systems have also been designed to monitor specific protein markers, at the single-cell level, providing promising tools for clinical drug response monitoring for cancer precision medicine. A sophisticated example from Abdulla et al. entailed a multilayer structured, integrated multifunctional microfluidic device ( C). This platform combined inertial force-based cell sorting, a membrane filter for separation and enrichment, a gel, and a bottom-most array of microwells. It has accomplished rapid isolation and accurate monitoring of cisplatin-stimulated circulating tumor cells (CTCs), and dynamic detection of EpCAM expression on CTCs for patients with metastatic breast cancer to monitor their therapeutic response. Multiomics Combining sequencing modalities—i.e. multiomics approaches—has the potential to further reveal the complex regulatory mechanisms of tumors. For instance, combining genome and transcriptome sequencing can establish the correlation between genotype and phenotype. gDNA-mRNA sequencing (DR-seq) and genome and transcriptome sequencing (G&T-seq) are two such methods. DR-seq amplifies cDNA and gDNA by MALBAC and CEL-seq to construct DNA and RNA libraries. DR-seq reduces the loss of nucleic acids by eliminating the physical separation of DNA and RNA. G&T-seq involves the physical isolation of polyadenylated mRNA from DNA in cell lysates via oligo-d(T)-encapsulated magnetic beads, an approach that may result in the potential loss of mRNA and DNA molecules. The application of microfluidics can somewhat avoid the problems of cross-contamination and limited the sensitivity associated with simultaneous sequencing of DNA and RNA. Xu et al. developed DMF-DR-seq, a nanoliter-scale single-cell DNA and RNA detection platform based on DMF. DMF-DR-seq has shown a lower amplification bias, higher whole-genome coverage, and better gene detectability in DNA-/RNA-seq outcomes. By combining analyses of genomic and transcriptomic variants, the authors characterized aberrant transcriptome expression in CTCs and multiple myeloma cancer cells and identified genes participating in pathological progression, including the ABC transporter genes TAP1 and TAP2, involved in antigen processing. Joint analysis of transcriptome and proteome facilitates interpretation of how transcriptional diversity becomes functional phenotypic diversity. Cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) and RNA expression and protein sequencing (REAP-seq) assays are two methodologies that involve the simultaneous analysis of proteins and transcripts with the use of droplet microfluidics and DNA barcode-coupled antibodies. Liu et al. engineered a unique microfluidic platform, DBiT-seq, to produce 2D mosaics of tissue pixels for spatial transcriptomic analysis via cross-flow delivery of two sets of barcodes, A1-50 and B1-50, into tissue sections ( D). Tissue slides were stained by pre-introducing a mixture of 22 antibody-derived DNA tags (ADTs), each of which contains a unique barcode that allows for the co-detection of tissue transcriptomes and proteins. Xu et al. described a highly cell-utilized microfluidic platform—multiple pairing sequence—that enabled efficient single-cell/single-bead capture and pairing based on hydrodynamic differential flow resistance. They used this technique to classify approximately 1,000 different types of breast cancer cells and characterize expression dynamics. Correlation between mRNAs and proteins was quantified by DNA-encoded antibodies and barcoded beads incubated with single cells. The findings suggested that the correlation between mRNA and protein at the single-cell level did not match the high consistency exhibited at the population level. For example, it was observed that ErbB2 protein was reduced in SK-BR-3 cells before and after trastuzumab treatment, while ERBB2 mRNA remained unchanged. Reduction of ErbB2 protein was possibly ascribed to drug-induced protein endocytosis or ErbB2 shedding, which may prevent binding of trastuzumab and could lead to the development of drug resistance. Joint interrogation of the transcriptome and epigenome can provide insights into evolution or dynamics of gene regulatory networks within tumors. Single-cell methylation and transcriptome sequencing (scM&T-seq) is a method that involves a mild lysis protocol to prevent mixing of DNA and RNA. Methylation is analyzed by single-cell reduced-representation bisulfite sequencing (scRRBs) and RNA is analyzed by Smart-seq2. Chen et al. conducted a combined analysis of chromatin accessibility and mRNA based on droplet microfluidic co-encapsulation of Tn5 transposase-permeabilized nuclei and oligo-bearing barcoded beads. After the droplets heated, the fragmented accessible sites and mRNA were released and harvested, producing a pair of RNA-seq and chromatin-accessible libraries linked by their shared cellular barcodes. This droplet-based single-nucleus chromatin accessibility and mRNA expression sequencing (SNARE-seq) method provides better cell cluster separation with the ability to capture 4–5 times more accessible sites. 

Single-cell DNA sequencing (scDNA-seq) provides insight into intercellular variation and heterogeneity of the genome at varying levels, including single-nucleotide variants (SNVs) and copy-number aberrations (CNAs). scDNA-seq is arguably the most direct way to reveal clonal substructures and lineages for tumor evolution, treatment, or metastasis. Sequencing the genome, however, is challenging due to the fact that there are only few copies of DNA molecules in a cell. Hence, whole-genome amplification (WGA) is a key part of scDNA-seq. The most frequent WGA methods currently utilized are multiple displacement amplification (MDA) and degenerated oligonucleotide primer-polymerase chain reaction (DOP-PCR). Newer methods include multiple annealing and looping-based amplification cycles (MALBACs) and linear amplification via transposon insertion (LIANTI). WGA generally presents several challenges, such as variability in sequencing depth, allele shedding (two alleles will not amplify at the same time), amplification bias, etc. Moreover, structural variants (SVs) of genes include deletions, duplications, insertions, and translocations, which can be difficult to recognize with the WGA approaches. Several technologies have been proposed to tackle these problems. Non-amplified Tn5-labeled scDNA-seq methodologies such as microfluidics-based DLP and DLP+ ( A) can avoid amplification based errors. Ruan et al. have developed a DMF chip that enabled the isolation of single cells with a butterfly structure of hydrodynamic traps ( B). In this design, cells were amplified by mixing them with lysate and MDA reagent via actuated droplets. The entire step of single-cell nanoliter-volume MDA processing was achieved in an integrated and non-contact manner. This could reduce amplification bias and errors in exponential amplification and provided excellent detection of CNVs and SNVs. A key contribution of scDNA-seq is to reveal the clonal evolution of drug resistance in tumor cells after targeted therapy. scDNA-seq together with microfluidics offers the possibility of achieving direct clinical relevance for the dynamic treatment of cancer. As an example, McMahon et al. performed high-throughput scDNA-seq of patients with FLT3-mutated acute myeloid leukemia (AML) who received gilteritinib clinical therapy by utilizing a commercial two-step droplet microfluidic platform (Tapestri). The results identified activation of RAS/MAPK pathway signaling by gilteritinib treatment, which is a clinically significant mechanism of drug resistance, and suggested that a combinatorial approach to enhance antileukemic cytotoxicity will be required to treat AML. 

Single-cell RNA sequencing (scRNA-seq) is the most popular sequencing modality in tumor research and therapy. In oncology therapeutic investigations, scRNA-seq provides a valuable perspective by analyzing the factors that determine cellular expression and functional heterogeneity within tumors. In scRNA-seq, RNA is first required to be converted into cDNA. In terms of different methods of reverse transcription and amplification, scRNA-seq can be broadly classified into three groups. Poly(A)-conjugated PCR is one option, though it is not often adopted due to reduced sequencing sensitivity and loss of low-abundance transcripts. In vitro transcription (IVT) is a linear amplification process that involves complex primers containing the T7 promoter at the 5′ end. The approach is applicable to the amplification step of cell expression by linear amplification and sequencing (CEL-seq). Lastly, reverse transcription of RNA by Moloney murine leukemia virus (MMLV) could enable synthesis of full-length cDNA by including the complete 5′ end, with reduced 3′ bias of transcription. MMLV-based reverse transcription has been utilized in Smart-Seq, Smart-Seq2, and single-cell tagged reverse transcription sequencing (STRT-seq) , workflows. In order to the overcome bias problem, a unique molecular identifier (UMI) is incorporated in the reverse transcription step to barcode each mRNA molecule in the cell, enhancing the quantitative nature of scRNA-seq and improving the accuracy of the reads. In scRNA-seq, the two most common approaches are droplet-based scRNA-seq and microwell-based scRNA-seq. The accuracy and sensitivity of microwell-based scRNA-seq may be superior, but the cost for commercialization is larger than droplet-based approaches. InDrop, Drop-seq, and 10× Genomics Chromium are the more common droplet-based technologies due to their low cost, easy operation, and high throughput. All three platforms involve the use of barcoded bead primers to distinguish individual cells and UMIs for bias correction. Beyond the more standard droplet-based methods, a number of groups have been working to leverage alternative microfluidic design principles in their scRNA-seq workflows. Li et al. achieved transcriptional gene analysis of tumor cells with different secretion efficiencies of MMP9 by using hydrodynamic capture of cells and virtual droplets not fully encapsulated by oil for selection and downstream mRNA sequencing. This microfluidic platform, which has 648 microchambers and a single-cell capture efficiency of up to 90%, as well as the ability to retrieve cells of interest, presents a new possibility for assessing tumor malignancy in clinical diagnostics. Further, Bai et al. demonstrated an integrated DEP-trapping-nanowell-transfer (dTNT) technique to break the Poisson limit and achieve scRNA-seq with a 91.84% single-cell capture rate, 82% transfer efficiency, and > 99% bead loading ( D). Valve-based scRNA-seq has been adopted increasingly due to its potential for efficient cell capture and selection of cells of interest for analysis. Zhang et al. leveraged hydrodynamic trapping and valve-based designs to create a highly parallel scRNA-seq platform called “Paired-seq.” Both individual cells and single barcode beads were trapped in paired chambers, and then the valve and pump combined system was capable of actively mixing them to allow lysis of cells and capture of mRNA. This system provides cell utilization efficiency of up to 95% and high mRNA detection accuracy ( E). Another valve-based microfluidic chip from Agnihotri et al. designed a three-layer microfluidic system for “bottom-up” SCS by co-encapsulating natural killer (NK) cells and tumor cells in droplets. It selectively released cells of interest for downstream sequencing analysis through usage of microvalves ( C). In this work, visual observation of immune cells with tumor cells, combined with selection of cells of interest for subsequent scRNA-seq, has provided new insights into immunotherapy. Temporally resolved transcriptomics scRNA-seq methods are also being developed to incorporate a temporal dimensions, enabling an understanding of the dynamic responses of cells to therapeutic stimuli and tumor progression. A single-cell, metabolically labeled new RNA tagging sequencing (scNT-seq) platform developed by Qiu et al. utilizes 4-thiouridine (4sU), a nucleotide analog that undergoes a chemical reaction resulting in a T-to-C switch in the sequencing data. Lin et al. demonstrated a Well-TEMP-seq assay with merits of low-cost ( < $0.1 per cell), high-throughput (8 parallel samples of thousands of cells each), and efficient cell loading ( A). This technique utilized a microfluidic chip with quasi-static hydrodynamic microwells and 4sU-labeled metabolic RNA. The authors used this method to investigate the transcriptional temporal dynamics of colorectal cancer cells exposed to a clinical drug (5-AZA-CdR) and discovered upregulation of tumor suppressor genes and downregulation of oncogenes. Another approach for temporal transcriptional sequencing is based on the introduction of mutations at specific sites through CRISPR-Cas9. Quinn et al. investigated the metastatic nature of tumor cells via temporal transcription of Cas9 on tens of thousands of cells. With this approach, they demonstrated that the metastatic capacity of tumor cells is heterogeneous and that it varies orthogonally to proliferative potential and characteristic transcriptional profiles. Molecular mechanisms underlying the metastasis-suppressive phenotype of KRT17 in vitro and in vivo were also elucidated. Spatial transcriptomics The spatial structure and arrangement of cell types within tumors is essential to cancer biology. However, in most scRNA-seq methods, the tissue would have to be dissociated prior to sequencing, which leads to a loss of spatial information. Spatial transcription techniques broadly belong to two categories. , One is imaging-based methods that are associated with the utilization of high-resolution imagers and in situ reagents, including in situ sequencing (ISS)-based methods (where transcripts are amplified and sequenced in tissue) and in situ hybridization (ISH)-based methods (where imaging probes are hybridized sequentially in tissue), such as single-molecule fluorescence ISH (smFISH), multiplexed error-robust fluorescence ISH (MERFISH), and sequential fluorescence ISH+ (seqFISH+). The second group is built on next-generation sequencing (NGS) technology and mainly relies on the spatial bar-code application prior to library preparation, such as Slide-seq. , The development of commercially available spatial transcriptomics such as Visium released by 10× Genomics, as well as GeoMx and CosMx released by Nanostring, enables spatial transcriptomics to become more accessible. Hirz et al. demonstrate that by combining droplet scRNA-seq and spatial transcriptomic data from Slide-seqV2, even more comprehensive information on tumor stromal cell and microenvironment interactions can be obtained. This study characterized tumor, immune, and non-immune stromal cell changes within the tumor microenvironment (TME) at high resolution for localized prostate cancer (PCa). They discovered that a suppressive immune microenvironment was established even in low-risk primary PCa. And cytotoxicity scores were significantly greater in “hot” tumors with high T cell infiltration, indicating that more functional T cells may influence the tumor response to immunotherapy. They further identified that the use of a CCL20-blocking antibody could block the communication between tumor-inflammatory monocytes and T-regulatory cells to significantly reduce tumor growth. 

scRNA-seq methods are also being developed to incorporate a temporal dimensions, enabling an understanding of the dynamic responses of cells to therapeutic stimuli and tumor progression. A single-cell, metabolically labeled new RNA tagging sequencing (scNT-seq) platform developed by Qiu et al. utilizes 4-thiouridine (4sU), a nucleotide analog that undergoes a chemical reaction resulting in a T-to-C switch in the sequencing data. Lin et al. demonstrated a Well-TEMP-seq assay with merits of low-cost ( < $0.1 per cell), high-throughput (8 parallel samples of thousands of cells each), and efficient cell loading ( A). This technique utilized a microfluidic chip with quasi-static hydrodynamic microwells and 4sU-labeled metabolic RNA. The authors used this method to investigate the transcriptional temporal dynamics of colorectal cancer cells exposed to a clinical drug (5-AZA-CdR) and discovered upregulation of tumor suppressor genes and downregulation of oncogenes. Another approach for temporal transcriptional sequencing is based on the introduction of mutations at specific sites through CRISPR-Cas9. Quinn et al. investigated the metastatic nature of tumor cells via temporal transcription of Cas9 on tens of thousands of cells. With this approach, they demonstrated that the metastatic capacity of tumor cells is heterogeneous and that it varies orthogonally to proliferative potential and characteristic transcriptional profiles. Molecular mechanisms underlying the metastasis-suppressive phenotype of KRT17 in vitro and in vivo were also elucidated. 

The spatial structure and arrangement of cell types within tumors is essential to cancer biology. However, in most scRNA-seq methods, the tissue would have to be dissociated prior to sequencing, which leads to a loss of spatial information. Spatial transcription techniques broadly belong to two categories. , One is imaging-based methods that are associated with the utilization of high-resolution imagers and in situ reagents, including in situ sequencing (ISS)-based methods (where transcripts are amplified and sequenced in tissue) and in situ hybridization (ISH)-based methods (where imaging probes are hybridized sequentially in tissue), such as single-molecule fluorescence ISH (smFISH), multiplexed error-robust fluorescence ISH (MERFISH), and sequential fluorescence ISH+ (seqFISH+). The second group is built on next-generation sequencing (NGS) technology and mainly relies on the spatial bar-code application prior to library preparation, such as Slide-seq. , The development of commercially available spatial transcriptomics such as Visium released by 10× Genomics, as well as GeoMx and CosMx released by Nanostring, enables spatial transcriptomics to become more accessible. Hirz et al. demonstrate that by combining droplet scRNA-seq and spatial transcriptomic data from Slide-seqV2, even more comprehensive information on tumor stromal cell and microenvironment interactions can be obtained. This study characterized tumor, immune, and non-immune stromal cell changes within the tumor microenvironment (TME) at high resolution for localized prostate cancer (PCa). They discovered that a suppressive immune microenvironment was established even in low-risk primary PCa. And cytotoxicity scores were significantly greater in “hot” tumors with high T cell infiltration, indicating that more functional T cells may influence the tumor response to immunotherapy. They further identified that the use of a CCL20-blocking antibody could block the communication between tumor-inflammatory monocytes and T-regulatory cells to significantly reduce tumor growth. 

Epigenetic changes are thought to significantly contribute to tumor heterogeneity in response to drugs or the environment. At present, single-cell epigenetic analysis focuses on methylation patterns, histone modifications, and chromatin status. DNA methylation of cytosine (5mC) residues can be mapped genome wide with a variety of methods. Bisulfite conversion following sequencing (BS-seq) is a well-established approach that is regarded as the gold standard. , Histone markers are commonly mapped using chromatin immunoprecipitation (ChIP)-seq, where ChIP is performed with antibodies specific to such histone markers. High background noise and low efficiency of this assay is challenging for performing ChIP-seq at the single-cell level. Notably, Grosseli’s research team established a high-throughput droplet microfluidic scChIP-seq assay to characterize histone modifications with high coverage (averaging up to 10,000 loci per cell), uncovering the existence of relatively rare chromatin status in tumor samples. Transposase-accessible chromatin sequencing (ATAC-seq) cleaves only DNA regions not protected by binding proteins by using Tn5 transposase, which is already employed to measure dynamic changes in overall chromatin structure. Although ATAC-seq does not provide a complete picture of gene regulatory mechanisms, single-cell protocols for this type of profiling have proved to be relatively scalable. Satpathy et al. carried out droplet-based scATAC-seq for over 200,000 human blood and basal carcinoma cells. In addition to reconstructing cell-type-specific cis - and trans -regulatory profiles and B cell and dendritic cell (DC) developmental trajectories, the authors identified regulators of therapeutically responsive T cell subtypes and regulatory programs that control T cell depletion and shared programs with T follicular helper (Tfh) cells, possibly via interleukin-21 (IL-21). 

Assaying cancer proteomes at the single-cell level holds tremendous potential for understanding the ultimate, functional state and workings of an individual tumor. Currently, single-cell proteomic technologies mainly rely on mass spectrometry (MS)-based approaches to identify proteolytically digested peptides. The mass cytometry by time of flight (CyTOF) technique combines heavy-metal labeling of antibodies and MS. But in single cells, the protein content is extremely small. Two general approaches have been developed to overcome this challenge. One is through reducing the sample preparation volume to the nanoliter scale, for example using the nanoPOTS nanoliter dispensing system to increase peptide yields. Another method is multiplexing single cells with a carrier sample through chemical labeling using tandem mass tags (TMTs) to improve MS signaling and recognition. Microfluidics have contributed to the development of several cutting-edge protein profiling techniques. Similar to the other sequencing platforms discussed thus far, microfluidics has particularly contributed to the miniaturization of single-cell proteomics. As an example, Gebreyesus et al. proposed an automated, all-in-one proteomic sample preparation microfluidic chip to achieve cell capture, imaging and counting, lysis, protein digestion, desalting and peptide collection, and characterization ( B). This chip together with data-independent acquisition enabled profiling 1,500 ± 131 proteomes from single cells with a false discovery rate of 1% and identified protein abundances spanning five orders of magnitude. This microfluidic workflow improved proteomic sensitivity and provided good reproducibility compared with traditional single-cell proteomics assays. Microfluidic systems have also been designed to monitor specific protein markers, at the single-cell level, providing promising tools for clinical drug response monitoring for cancer precision medicine. A sophisticated example from Abdulla et al. entailed a multilayer structured, integrated multifunctional microfluidic device ( C). This platform combined inertial force-based cell sorting, a membrane filter for separation and enrichment, a gel, and a bottom-most array of microwells. It has accomplished rapid isolation and accurate monitoring of cisplatin-stimulated circulating tumor cells (CTCs), and dynamic detection of EpCAM expression on CTCs for patients with metastatic breast cancer to monitor their therapeutic response. 

Combining sequencing modalities—i.e. multiomics approaches—has the potential to further reveal the complex regulatory mechanisms of tumors. For instance, combining genome and transcriptome sequencing can establish the correlation between genotype and phenotype. gDNA-mRNA sequencing (DR-seq) and genome and transcriptome sequencing (G&T-seq) are two such methods. DR-seq amplifies cDNA and gDNA by MALBAC and CEL-seq to construct DNA and RNA libraries. DR-seq reduces the loss of nucleic acids by eliminating the physical separation of DNA and RNA. G&T-seq involves the physical isolation of polyadenylated mRNA from DNA in cell lysates via oligo-d(T)-encapsulated magnetic beads, an approach that may result in the potential loss of mRNA and DNA molecules. The application of microfluidics can somewhat avoid the problems of cross-contamination and limited the sensitivity associated with simultaneous sequencing of DNA and RNA. Xu et al. developed DMF-DR-seq, a nanoliter-scale single-cell DNA and RNA detection platform based on DMF. DMF-DR-seq has shown a lower amplification bias, higher whole-genome coverage, and better gene detectability in DNA-/RNA-seq outcomes. By combining analyses of genomic and transcriptomic variants, the authors characterized aberrant transcriptome expression in CTCs and multiple myeloma cancer cells and identified genes participating in pathological progression, including the ABC transporter genes TAP1 and TAP2, involved in antigen processing. Joint analysis of transcriptome and proteome facilitates interpretation of how transcriptional diversity becomes functional phenotypic diversity. Cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) and RNA expression and protein sequencing (REAP-seq) assays are two methodologies that involve the simultaneous analysis of proteins and transcripts with the use of droplet microfluidics and DNA barcode-coupled antibodies. Liu et al. engineered a unique microfluidic platform, DBiT-seq, to produce 2D mosaics of tissue pixels for spatial transcriptomic analysis via cross-flow delivery of two sets of barcodes, A1-50 and B1-50, into tissue sections ( D). Tissue slides were stained by pre-introducing a mixture of 22 antibody-derived DNA tags (ADTs), each of which contains a unique barcode that allows for the co-detection of tissue transcriptomes and proteins. Xu et al. described a highly cell-utilized microfluidic platform—multiple pairing sequence—that enabled efficient single-cell/single-bead capture and pairing based on hydrodynamic differential flow resistance. They used this technique to classify approximately 1,000 different types of breast cancer cells and characterize expression dynamics. Correlation between mRNAs and proteins was quantified by DNA-encoded antibodies and barcoded beads incubated with single cells. The findings suggested that the correlation between mRNA and protein at the single-cell level did not match the high consistency exhibited at the population level. For example, it was observed that ErbB2 protein was reduced in SK-BR-3 cells before and after trastuzumab treatment, while ERBB2 mRNA remained unchanged. Reduction of ErbB2 protein was possibly ascribed to drug-induced protein endocytosis or ErbB2 shedding, which may prevent binding of trastuzumab and could lead to the development of drug resistance. Joint interrogation of the transcriptome and epigenome can provide insights into evolution or dynamics of gene regulatory networks within tumors. Single-cell methylation and transcriptome sequencing (scM&T-seq) is a method that involves a mild lysis protocol to prevent mixing of DNA and RNA. Methylation is analyzed by single-cell reduced-representation bisulfite sequencing (scRRBs) and RNA is analyzed by Smart-seq2. Chen et al. conducted a combined analysis of chromatin accessibility and mRNA based on droplet microfluidic co-encapsulation of Tn5 transposase-permeabilized nuclei and oligo-bearing barcoded beads. After the droplets heated, the fragmented accessible sites and mRNA were released and harvested, producing a pair of RNA-seq and chromatin-accessible libraries linked by their shared cellular barcodes. This droplet-based single-nucleus chromatin accessibility and mRNA expression sequencing (SNARE-seq) method provides better cell cluster separation with the ability to capture 4–5 times more accessible sites. 

Tumors and their infiltrating environment (i.e., the TME) comprise a space with diverse biochemical and biomechanical properties as well as complex dynamics. Knowledge of tumor clonal diversity and kinetics can bring a valuable new perspective on its metastasis, drug treatment, and resistance. A growing proportion of SCS studies are therefore conducted from a spatial and temporal viewpoint. Multiomic profiling of cells is another area for future growth, providing a more multidimensional and comprehensive view of tumor biology. A growing number of scMultiomics data analysis tools , , can help deliver novel breakthroughs in the precision treatment of tumors by accelerating the discovery of pathogenic mechanisms and therapeutic approaches for a wide range of cancers. Meanwhile, more work is necessary to validate the results of the scMultiomics data in a consolidated assay. Single-cell CRISPR screening is one choice, with techniques that enable simultaneous phenotypic evaluation of target gene perturbations in combination with scMultiomics. In general, accurate and rapid analysis of massive numbers of cells at an affordable cost would be the ideal blueprint for bringing SCS technologies to applied medical contexts. The combination of microfluidics in SCS can improve sequencing throughput and reduce cost to a certain extent. Droplet- and microwell-based SCS are two microfluidic forms of SCS that have been commercialized. The cost of the experiment in the most popular droplet-based 10× Genomics is approximately $0.87 per cell. The droplet size and number are very flexible and controllable, but the efficiency of droplet co-encapsulation is not ideal, and the fragility of the droplet may interfere with the sequencing outcomes. Comparatively, microwell-based technology can be more productive. Smart-seq3 based microwells are priced at approximately $1.14 per cell for the experiment, an average price that is at an affordable level. An additional issue is that shallow sequencing of a large number of cell samples (e.g., tens of thousands or more cells) can minimize random variation and offer a more robust approach, but the overall cost of SCS sequencing remains prohibitively high at this scale. The combination of droplet- and microwell-based microfluidics with sample multiplexing technologies opens a further possibility to improve throughput and decrease cost. , In parallel, with the emergence of DMF as an innovative microfluidic technology, it brings the whole SCS implementation into the microfluidic platform, foreshadowing that automation is feasible. cell-in-library-out scRNA-seq (Cilo-seq), a platform of DMF designed by Zhang et al., is reusable and low cost. More importantly, this platform achieves outstanding accuracy with reduced nucleic acid loss and background interference. But the throughput of this assay is restricted to handle only one cell at a time, so high throughput at high sensitivity becomes another challenge. This is an era where SCS technology is flourishing. Sequencing of the transcriptome of individual oocytes and embryos was the first scRNA-seq in 2009, whereas in 2017, the 10× Genomics technology was advanced to sequence tens of thousands of cells. The introduction of microfluidics not only reduces costs by reducing sample volume but also helps to achieve high throughput. Integration of SCS procedures on a microfluidic platform promises to bring automation and better accuracy of sequencing. Therefore, there is a strong expectation that in the future, more automated and simplified SCS platforms will be available for clinical use. Current multidisciplinary developments in molecular biology, computer, and mechanical engineering will be needed to continue to address existing challenges and realize widespread, personalized profiling of patient cancers. 

