Advanced sequencing-based high-throughput and long-read single-cell transcriptome analysis.

Cells are the fundamental building blocks of living systems, exhibiting significant heterogeneity. The transcriptome connects the cellular genotype and phenotype, and profiling single-cell transcriptomes is critical for uncovering distinct cell types, states, and the interplay between cells in development, health, and disease. Nevertheless, single-cell transcriptome analysis faces daunting challenges due to the low abundance and diverse nature of RNAs in individual cells, as well as their heterogeneous expression. The advent and continuous advancements of next-generation sequencing (NGS) and third-generation sequencing (TGS) technologies have solved these problems and facilitated the high-throughput, sensitive, full-length, and rapid profiling of single-cell RNAs. In this review, we provide a broad introduction to current methodologies for single-cell transcriptome sequencing. First, state-of-the-art advancements in high-throughput and full-length single-cell RNA sequencing (scRNA-seq) platforms using NGS are reviewed. Next, TGS-based long-read scRNA-seq methods are summarized. Finally, a brief conclusion and perspectives for comprehensive single-cell transcriptome analysis are discussed.

Well-Paired-Seq2: High-Throughput and High-Sensitivity Strategy for Characterizing Low RNA-Content Cell/Nucleus Transcriptomes.

Single-cell RNA sequencing (scRNA-seq) is a transformative technology that unravels the intricate cellular state heterogeneity. However, the Poisson-dependent cell capture and low sensitivity in scRNA-seq methods pose challenges for throughput and samples with a low RNA-content. Herein, to address these challenges, we present Well-Paired-Seq2 (WPS2), harnessing size-exclusion and quasi-static hydrodynamics for efficient cell capture. WPS2 exploits molecular crowding effect, tailing activity enhancement in reverse transcription, and homogeneous enzymatic reaction in the initial bead-based amplification to achieve 3116 genes and 8447 transcripts with an average of ∼20000 reads per cell. WPS2 detected 1420 more genes and 4864 more transcripts than our previous Well-Paired-Seq. It sensitively characterizes transcriptomes of low RNA-content single cells and nuclei, overcoming the Poisson limit for cell and barcoded bead capture. WPS2 also profiles transcriptomes from frozen clinical samples, revealing heterogeneous tumor copy number variations and intercellular crosstalk in clear cell renal cell carcinomas. Additionally, we provide the first single-cell-level characterization of rare metanephric adenoma (MA) and uncover potential specific markers. With the advantages of high sensitivity and high throughput, WPS2 holds promise for diverse basic and clinical research.

Highly Multiplexing, Throughput and Efficient Single-Cell Protein Analysis with Digital Microfluidics.

Proteins as crucial components of cells are responsible for the majority of cellular processes. Sensitive and efficient protein detection enables a more accurate and comprehensive investigation of cellular phenotypes and life activities. Here, a protein sequencing method with high multiplexing, high throughput, high cell utilization, and integration based on digital microfluidics (DMF-Protein-seq) is proposed, which transforms protein information into DNA sequencing readout via DNA-tagged antibodies and labels single cells with unique cell barcodes. In a 184-electrode DMF-Protein-seq system, ≈1800 cells are simultaneously detected per experimental run. The digital microfluidics device harnessing low-adsorbed hydrophobic surface and contaminants-isolated reaction space supports high cell utilization (>90%) and high mapping reads (>90%) with the input cells ranging from 140 to 2000. This system leverages split&pool strategy on the DMF chip for the first time to overcome DMF platform restriction in cell analysis throughput and replace the traditionally tedious bench-top combinatorial barcoding. With the benefits of high efficiency and sensitivity in protein analysis, the system offers great potential for cell classification and drug monitoring based on protein expression at the single-cell level.

In Situ Detection of Nucleic Acids in Extracellular Vesicles via Membrane Fusion.

Extracellular vesicles (EVs) carry diverse biomolecules (e. g., nucleic acids, proteins) for intercellular communication, serving as important markers for diseases. Analyzing nucleic acids derived from EVs enables non-invasive disease diagnosis and prognosis evaluation. Membrane fusion, a fundamental cellular process wherein two lipid membranes merge, facilitates cell communication and cargo transport. Building on this natural phenomenon, recent years have witnessed the emergence of membrane fusion-based strategies for the detection of nucleic acids within EVs. These strategies entail the encapsulation of detection probes within either artificial or natural vesicles, followed by the induction of membrane fusion with EVs to deliver probes. This innovative approach not only enables in situ detection of nucleic acids within EVs but also ensures the maintenance of structural integrity of EVs, thus preventing nucleic acid degradation and minimizing the interference from free nucleic acids. This concept categorizes approaches into universal and targeted membrane fusion strategies, and discusses their application potential, and challenges and future prospects.

Tumor Exosomal ENPP1 Hydrolyzes cGAMP to Inhibit cGAS-STING Signaling.

To evade immune surveillance, tumor cells express ectonucleotide pyrophosphatase phosphodiesterase 1 (ENPP1) on the surface of their membrane, which degrades extracellular cyclic GMP-AMP (cGAMP), thereby inhibiting the cyclic GMP-AMP synthase (cGAS) stimulator of interferon gene (STING) DNA-sensing pathway. To fully understand this tumor stealth mechanism, it is essential to determine whether other forms of ENPP1 with hydrolytic cGAMP activity also are present in the tumor microenvironment to regulate this innate immune pathway. Herein, it is reported that various tumor-derived exosomes carry ENPP1, and can hydrolyze synthetic 2'3'-cGAMP and endogenous 2'3'-cGAMP produced by cells to inhibit cGAS-STING pathway in immune cells. Moreover, tumor exosomal ENPP1 also can hydrolyze 2'3'-cGAMP bound to LL-37 (an effective transporter of 2'3'-cGAMP) to inhibit STING signaling. Furthermore, high expression of ENPP1 in exosomes is observed isolated from human breast and lung cancer tissue, and tumor exosomal ENPP1 inhibited the immune infiltration of CD8+ T cells and CD4+ T cells. The results elucidate the essential function of tumor exosomal ENPP1 in the cGAS-STING pathway, furthering understanding of the crosstalk between the tumor cells and immune system.

Domain generalization enables general cancer cell annotation in single-cell and spatial transcriptomics.

Single-cell and spatial transcriptome sequencing, two recently optimized transcriptome sequencing methods, are increasingly used to study cancer and related diseases. Cell annotation, particularly for malignant cell annotation, is essential and crucial for in-depth analyses in these studies. However, current algorithms lack accuracy and generalization, making it difficult to consistently and rapidly infer malignant cells from pan-cancer data. To address this issue, we present Cancer-Finder, a domain generalization-based deep-learning algorithm that can rapidly identify malignant cells in single-cell data with an average accuracy of 95.16%. More importantly, by replacing the single-cell training data with spatial transcriptomic datasets, Cancer-Finder can accurately identify malignant spots on spatial slides. Applying Cancer-Finder to 5 clear cell renal cell carcinoma spatial transcriptomic samples, Cancer-Finder demonstrates a good ability to identify malignant spots and identifies a gene signature consisting of 10 genes that are significantly co-localized and enriched at the tumor-normal interface and have a strong correlation with the prognosis of clear cell renal cell carcinoma patients. In conclusion, Cancer-Finder is an efficient and extensible tool for malignant cell annotation.

Characterization of the Nucleus Pulposus Progenitor Cells via Spatial Transcriptomics.

Loss of refreshment in nucleus pulposus (NP) cellularity leads to intervertebral disc (IVD) degeneration. Nevertheless, the cellular sequence of NP cell differentiation remains unclear, although an increasing body of literature has identified markers of NP progenitor cells (NPPCs). Notably, due to their fragility, the physical enrichment of NP-derived cells has limited conventional transcriptomic approaches in multiple studies. To overcome this limitation, a spatially resolved transcriptional atlas of the mouse IVD is generated via the 10x Genomics Visium platform dividing NP spots into two clusters. Based on this, most reported NPPC-markers, including Cathepsin K (Ctsk), are rare and predominantly located within the NP-outer subset. Cell lineage tracing further evidence that a small number of Ctsk-expressing cells generate the entire adult NP tissue. In contrast, Tie2, which has long suggested labeling NPPCs, is actually neither expressed in NP subsets nor labels NPPCs and their descendants in mouse models; consistent with this, an in situ sequencing (ISS) analysis validated the absence of Tie2 in NP tissue. Similarly, no Tie2-cre-mediated labeling of NPPCs is observed in an IVD degenerative mouse model. Altogether, in this study, the first spatial transcriptomic map of the IVD is established, thereby providing a public resource for bone biology.

Decoding Biomechanical Cues Based on DNA Sensors.

Biological systems perceive and respond to mechanical forces, generating mechanical cues to regulate life processes. Analyzing biomechanical forces has profound significance for understanding biological functions. Therefore, a series of molecular mechanical techniques have been developed, mainly including single-molecule force spectroscopy, traction force microscopy, and molecular tension sensor systems, which provide indispensable tools for advancing the field of mechanobiology. DNA molecules with a programmable structure and well-defined mechanical characteristics have attached much attention to molecular tension sensors as sensing elements, and are designed for the study of biomechanical forces to present biomechanical information with high sensitivity and resolution. In this work, a comprehensive overview of molecular mechanical technology is presented, with a particular focus on molecular tension sensor systems, specifically those based on DNA. Finally, the future development and challenges of DNA-based molecular tension sensor systems are looked upon.

Decoder-seq enhances mRNA capture efficiency in spatial RNA sequencing.

Spatial transcriptomics technologies with high resolution often lack high sensitivity in mRNA detection. Here we report a dendrimeric DNA coordinate barcoding design for spatial RNA sequencing (Decoder-seq), which offers both high sensitivity and high resolution. Decoder-seq combines dendrimeric nanosubstrates with microfluidic coordinate barcoding to generate spatial arrays with a DNA density approximately ten times higher than previously reported methods while maintaining flexibility in resolution. We show that the high RNA capture efficiency of Decoder-seq improved the detection of lowly expressed olfactory receptor (Olfr) genes in mouse olfactory bulbs and contributed to the discovery of a unique layer enrichment pattern for two Olfr genes. The near-cellular resolution provided by Decoder-seq has enabled the construction of a spatial single-cell atlas of the mouse hippocampus, revealing dendrite-enriched mRNAs in neurons. When applying Decoder-seq to human renal cell carcinomas, we dissected the heterogeneous tumor microenvironment across different cancer subtypes and identified spatial gradient-expressed genes related to epithelial-mesenchymal transition with the potential to predict tumor prognosis and progression.

DIRECT: Digital Microfluidics for Isolation-Free Shared Library Construction of Single-Cell DNA Methylome and Transcriptome.

Simultaneous profiling of DNA methylation and gene expression within single cells is a powerful technology to dissect complex gene regulatory network of cells. However, existing methods are based on picking a single-cell in a tube and split single-cell lysate into two parts for transcriptome and methylome library construction, respectively, which is costly and cumbersome. Here, DIRECT is proposed, a digital microfluidics-based method for high-efficiency single-cell isolation and simultaneous analysis of the methylome and transcriptome in a single library construction. The accuracy of DIRECT is demonstrated in comparison with bulk and single-omics data, and the high CpG site coverage of DIRECT allows for precise analysis of copy number variation information, enabling expansion of single cell analysis from two- to three-omics. By applying DIRECT to monitor the dynamics of mouse embryonic stem cell differentiation, the relationship between DNA methylation and changes in gene expression during differentiation is revealed. DIRECT enables accurate, robust, and reproducible single-cell DNA methylation and gene expression co-analysis in a more cost-effective, simpler library preparation and automated manner, broadening the application scenarios of single-cell multi-omics analysis and revealing a more comprehensive and fine-grained map of cellular regulatory landscapes.

Highly Multiplexed, Efficient, and Automated Single-Cell MicroRNA Sequencing with Digital Microfluidics.

Single-cell microRNA (miRNA) sequencing has allowed for comprehensively studying the abundance and complex networks of miRNAs, which provides insights beyond single-cell heterogeneity into the dynamic regulation of cellular events. Current benchtop-based technologies for single-cell miRNA sequencing are low throughput, limited reaction efficiency, tedious manual operations, and high reagent costs. Here, a highly multiplexed, efficient, integrated, and automated sample preparation platform is introduced for single-cell miRNA sequencing based on digital microfluidics (DMF), named Hiper-seq. The platform integrates major steps and automates the iterative operations of miRNA sequencing library construction by digital control of addressable droplets on the DMF chip. Based on the design of hydrophilic micro-structures and the capability of handling droplets of DMF, multiple single cells can be selectively isolated and subject to sample processing in a highly parallel way, thus increasing the throughput and efficiency for single-cell miRNA measurement. The nanoliter reaction volume of this platform enables a much higher miRNA detection ability and lower reagent cost compared to benchtop methods. It is further applied Hiper-seq to explore miRNAs involved in the ossification of mouse skeletal stem cells after bone fracture and discovered unreported miRNAs that regulate bone repairing.

Beyond single cells: microfluidics empowering multiomics analysis.

Single-cell multiomics technologies empower simultaneous measurement of multiple types of molecules within individual cells, providing a more profound comprehension compared with the analysis of discrete molecular layers from different cells. Microfluidic technology, on the other hand, has emerged as a pivotal facilitator for high-throughput single-cell analysis, offering precise control and manipulation of individual cells. The primary focus of this review encompasses an appraisal of cutting-edge microfluidic platforms employed in the realm of single-cell multiomics analysis. Furthermore, it discusses technological advancements in various single-cell omics such as genomics, transcriptomics, epigenomics, and proteomics, with their perspective applications. Finally, it provides future prospects of these integrated single-cell multiomics methodologies, shedding light on the possibilities for future biological research.

Aptamer-Assisted Blockade of the Immune Suppressor Sialic Acid-Binding Immunoglobulin-Like Lectin-15 for Cancer Immunotherapy.

The percentage of low response and adaptive resistance to current antibody-based immune checkpoint blockade (ICB) therapy requires the development of novel immunotherapy strategies. Here, we developed an aptamer-assisted immune checkpoint blockade (Ap-ICB) against sialic acid-binding immunoglobulin-like lectin-15 (Siglec-15), a novel immune suppressor broadly upregulated on cancer cells and tumor infiltrating myeloid cells, which is mutually exclusive of programmed cell death ligand 1 (PD-L1). Using protein aptamer selection, we identified WXY3 aptamer with high affinity against Siglec-15 protein/Siglec-15 positive cells. We demonstrated that WXY3 aptamer rescued antigen-specific T cell responses in vitro and in vivo. Importantly, the WXY3 Ap-ICB against Siglec-15 amplified anti-tumor immunity in the tumor microenvironment and inhibited tumor growth/metastasis in syngeneic mouse model, which may result from enhanced macrophage and T cell functionality. In addition, by using aptamer-based spherical nucleic acids, we developed a synergetic ICB strategy of multivalent binding and steric hindrance, which further improves the in vivo anti-tumor effect. Taken together, our results support Ap-ICB targeted Siglec-15 as a potential strategy for normalization cancer immunotherapy.

A Magnetically Driven Tandem Chip Enables Rapid Isolation and Multiplexed Profiling of Extracellular Vesicles.

The protein phenotypes of extracellular vesicles (EVs) have emerged as promising biomarkers for cancer diagnosis and treatment monitoring. However, the technical challenges in rapid isolation and multiplexed molecular detection of EVs have limited their clinical practice. Herein, we developed a magnetically driven tandem chip to achieve streamlined rapid isolation and multiplexed profiling of surface protein biomarkers of EVs. Driven by magnetic force, the magnetic nanomixers not only act as tiny stir bars to promote mass transfer and enhance reaction efficiency of EVs, but also transport on communicating vessels of the tandem chip continuously and expedite the assay workflow. We designed cyclic surface enhancement of Raman scattering (SERS) tags to bind with target EVs and then release them by exonuclease I, eliminating steric hindrance and amplifying the SERS signal of multiple protein biomarkers on EVs. Due to the excellent assay performance, six breast cancer biomarkers were detected simultaneously on EVs using only 10 µL plasma within 1.5 h. The unweighted SUM signature offers great accuracy in discriminating breast cancer patients from healthy donors. Overall, the dynamic magnetic driving tandem chip offers a new avenue to advance the clinical application of EV-based liquid biopsy.

Capturing nascent extracellular vesicles by metabolic glycan labeling-assisted microfluidics.

Extracellular vesicle (EV) secretion is a dynamic process crucial to cellular communication. Temporally sorting EVs, i.e., separating the newly-produced ones from the pre-existing, can allow not only deep understanding of EV dynamics, but also the discovery of potential EV biomarkers that are related to disease progression or responsible to drug intervention. However, the high similarity between the nascent and pre-existing EVs makes temporal separation extremely challenging. Here, by co-translational introduction of azido groups to act as a timestamp for click chemistry labelling, we develop a microfluidic-based strategy to enable selective isolation of nascent EVs stimulated by an external cue. In two mouse models of anti-PD-L1 immunotherapy, we demonstrate the strategy's feasibility and reveal the high positive correlation of nascent PD-L1+ EV level to tumor volume, suggesting an important role of nascent EVs in response to immunotherapy in cancer treatment.

Microfluidic Enrichment of Intact SARS-CoV-2 Viral Particles by Stoichiometric Balanced DNA Computation.

Health diagnostic tools for community safety and environmental monitoring require selective and quantitatively accurate active viral load assessment. Herein, we report a microfluidic enrichment strategy to separate intact SARS-CoV-2 particles by AND logic gate with inputs of cholesterol oligonucleotides for the envelope and aptamers for the spike viral proteins. Considering the unequal quantity of endogenous spikes and lipid membranes on SARS-CoV-2, a dual-domain binding strategy, with two aptamers targeting different spike domains, was applied to balance the spike-envelope stoichiometric ratio. By balancing the stoichiometric with DNA computation and promoting microscale mass transfer of the herringbone chip, the developed strategy enabled high sensitivity detection of pseudotyped SARS-CoV-2 with a limit of detection as low as 37 active virions/µL while distinguishing it from inactive counterparts, other nontarget viruses, and free spike protein. Moreover, the captured viral particles can be released through DNase I treatment with up to 90% efficiency, which is fully compatible with virus culture and sequencing. Overall, the developed strategy not only identified SARS-CoV-2-infected patients (n = 14) with 100% identification from healthy donors (n = 8) but also provided a fresh perspective on the regulation of stoichiometric ratio to achieve a more biologically relevant DNA computation.

Hierarchical Fluid Interface Enables Spatiotemporal Regulation of Ligand Distribution to Increase Kinetics and Thermodynamics of Interfacial Binding Reaction.

In nature, regulation of the spatiotemporal distribution of interfacial receptors and ligands leads to optimum binding kinetics and thermodynamics of receptor-ligand binding reactions within interfaces. Inspired by this, we report a hierarchical fluid interface (HieFluidFace) to regulate the spatiotemporal distribution of interfacial ligands to increase the rate and thermodynamic favorability of interfacial binding reactions. Each aptamer-functionalized gold nanoparticle, termed spherical aptamer (SAPT), is anchored on a supported lipid bilayer without fluidity, like an "island", and is surrounded by many fluorescent aptamers (FAPTs) with free fluidity, like "rafts". Such ligand "island-rafts" model provides a large reactive cross-section for rapid binding to cellular receptors. The synergistic multivalency of SAPTs and FAPTs improves interfacial affinity for tight capture. Moreover, FAPTs accumulate at binding sites to bind to cellular receptors with clustered fluorescence to "lighten" cells for direct identification. Thus, HieFluidFace in a microfluidic chip achieves high-performance capture and identification of circulating tumor cells from clinical samples, providing a new paradigm to optimize the kinetics and thermodynamics of interfacial binding reactions.

Simultaneous subset tracing and miRNA profiling of tumor-derived exosomes via dual-surface-protein orthogonal barcoding.

The clinical potential of miRNA-based liquid biopsy has been largely limited by the heterogeneous sources in plasma and tedious assay processes. Here, we develop a precise and robust one-pot assay called dual-surface-protein-guided orthogonal recognition of tumor-derived exosomes and in situ profiling of microRNAs (SORTER) to detect tumor-derived exosomal miRNAs and enhance the diagnostic accuracy of prostate cancer (PCa). The SORTER uses two allosteric aptamers against exosomal marker CD63 and tumor marker EpCAM to create an orthogonal labeling barcode and achieve selective sorting of tumor-specific exosome subtypes. Furthermore, the labeled barcode on tumor-derived exosomes initiated targeted membrane fusion with liposome probes to import miRNA detection reagents, enabling in situ sensitive profiling of tumor-derived exosomal miRNAs. With a signature of six miRNAs, SORTER differentiated PCa and benign prostatic hyperplasia with an accuracy of 100%. Notably, the diagnostic accuracy reached 90.6% in the classification of metastatic and nonmetastatic PCa. We envision that the SORTER will promote the clinical adaptability of miRNA-based liquid biopsy.

Biotemplated precise assembly approach toward ultra-scaled high-performance electronics.

Structural DNA nanotechnology can be programmed into complex designer structures with molecular precision for directing a wide range of inorganic and biological materials. However, the use of DNA-templated approaches for the fabrication and performance requirements of ultra-scaled semiconductor electronics is limited by its assembly disorder and destructive interface composition. In this protocol, using carbon nanotubes (CNTs) as model semiconductors, we provide a stepwise process to build ultra-scaled, high-performance field-effect transistors (FETs) from micron-scale three-dimensional DNA templates. We apply the approach to assemble CNT arrays with uniform pitches scaled between 24.1 and 10.4 nm with yields of more than 95%, which exceeds the resolution limits of conventional lithography. To achieve highly clean CNT interfaces, we detail a rinsing-after-fixing step to remove residual DNA template and salt contaminations present around the contact and the channel regions, without modifying the alignment of the CNT arrays. The DNA-templated CNT FETs display both high on-state current (4-15 µA per CNT) and small subthreshold swing (60-100 mV per decade), which are superior to previous examples of biotemplated electronics and match the performance metrics of high-performance, silicon-based electronics. The scalable assembly of defect-free three-dimensional DNA templates requires 1 week and the CNT arrays can be synthesized within half a day. The interface engineering requires 1-2 d, while the fabrication of high-performance FET and logic gate circuits requires 2-4 d. The structural and performance characterizations of molecular-precise DNA self-assembly and high-performance electronics requires 1-2 d. The protocol is suited for users with expertise in DNA nanotechnology and semiconductor electronics.

mitoSplitter: A mitochondrial variants-based method for efficient demultiplexing of pooled single-cell RNA-seq.

Single-cell RNA-seq (scRNA-seq) analysis of multiple samples separately can be costly and lead to batch effects. Exogenous barcodes or genome-wide RNA mutations can be used to demultiplex pooled scRNA-seq data, but they are experimentally or computationally challenging and limited in scope. Mitochondrial genomes are small but diverse, providing concise genotype information. We developed "mitoSplitter," an algorithm that demultiplexes samples using mitochondrial RNA (mtRNA) variants, and demonstrated that mtRNA variants can be used to demultiplex large-scale scRNA-seq data. Using affordable computational resources, mitoSplitter can accurately analyze 10 samples and 60,000 cells in 6 h. To avoid the batch effects from separated experiments, we applied mitoSplitter to analyze the responses of five non-small cell lung cancer cell lines to BET (Bromodomain and extraterminal) chemical degradation in a multiplexed fashion. We found the synthetic lethality of TOP2A inhibition and BET chemical degradation in BET inhibitor-resistant cells. The result indicates that mitoSplitter can accelerate the application of scRNA-seq assays in biomedical research.