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Seq-Well provides similar scale and data quality to massively parallel, droplet-based single-cell RNA-seq methods in an easy to use, inexpensive and portable microwell format compatible with low-input samples. Single-cell RNA-seq can precisely resolve cellular states, but applying this method to low-input samples is challenging. Here, we present Seq-Well, a portable, low-cost platform for massively parallel single-cell RNA-seq. Barcoded mRNA capture beads and single cells are sealed in an array of subnanoliter wells using a semipermeable membrane, enabling efficient cell lysis and transcript capture. We use Seq-Well to profile thousands of primary human macrophages exposed to Mycobacterium tuberculosis .

Key Points There has been a renaissance in single-cell biology, facilitated in part by the rise of microfluidic devices that can facilitate easy capture, processing and profiling of single cells and their components, reducing labour and costs relative to conventional plate-based methods while also improving consistency. The three most common classes of microfluidic device are defined by their fundamental elements: valves, droplets or nanowells. Valve-based microfluidic devices afford control but have limited scale; droplet-based devices have high throughput but limited control; and nanowell-based methods have intermediate scale and control, but greater simplicity in operation. These factors influence the costs and benefits of porting any existing assay to each microfluidic device. Each of these three classes has been used to profile several cellular 'omics' — including the genome, epigenome, transcriptome and proteome — achieving different levels of throughput and efficiency, while leaving opportunities for future development. Emerging efforts are beginning to focus on measuring multiple cellular properties at once, such as the transcriptome and the proteome or the transcriptome and the epigenome, to obtain a more comprehensive picture of cellular phenotype and its drivers. Such comprehensive profiling is especially important when studying single cells owing to technical and biological noise sources, which limit the utility of any given measurement from any given cell. Sequencing is increasingly becoming the de facto method for profiling information from single cells given its bandwidth relative to the information content of a single cell and the growing ease of mapping information in a nucleic acid readout. Yet, given fixed sequencing bandwidth and the often limited utility of any one measurement, it is important to carefully consider how to most judiciously allocate reads over cells and their variables. As the genetic and phenotypic heterogeneities among cells become more appreciated, there is increasing demand for technologies that facilitate high-throughput and high-efficiency single-cell 'omic' analyses in miniaturized and automated formats. This Review discusses the diverse microfluidic methodologies — with a primary focus on valve-, droplet- and nanowell-based platforms — for characterization of the genomes, epigenomes, transcriptomes and proteomes of single cells, and addresses technical considerations and future opportunities. Recent advances in cellular profiling have demonstrated substantial heterogeneity in the behaviour of cells once deemed 'identical', challenging fundamental notions of cell 'type' and 'state'. Not surprisingly, these findings have elicited substantial interest in deeply characterizing the diversity, interrelationships and plasticity among cellular phenotypes. To explore these questions, experimental platforms are needed that can extensively and controllably profile many individual cells. Here, microfluidic structures — whether valve-, droplet- or nanowell-based — have an important role because they can facilitate easy capture and processing of single cells and their components, reducing labour and costs relative to conventional plate-based methods while also improving consistency. In this article, we review the current state-of-the-art methodologies with respect to microfluidics for mammalian single-cell 'omics' and discuss challenges and future opportunities.

Droplet-based single-cell RNA-seq has emerged as a powerful technique for massively parallel cellular profiling. While this approach offers the exciting promise to deconvolute cellular heterogeneity in diseased tissues, the lack of cost-effective and user-friendly instrumentation has hindered widespread adoption of droplet microfluidic techniques. To address this, we developed a 3D-printed, low-cost droplet microfluidic control instrument and deploy it in a clinical environment to perform single-cell transcriptome profiling of disaggregated synovial tissue from five rheumatoid arthritis patients. We sequence 20,387 single cells revealing 13 transcriptomically distinct clusters. These encompass an unsupervised draft atlas of the autoimmune infiltrate that contribute to disease biology. Additionally, we identify previously uncharacterized fibroblast subpopulations and discern their spatial location within the synovium. We envision that this instrument will have broad utility in both research and clinical settings, enabling low-cost and routine application of microfluidic techniques. Droplet-based single-cell RNA-seq is a powerful tool for cellular heterogeneity profiling in disease but is limited by instrumentation required. Here the authors develop a 3D printed microfluidic platform for massive parallel sequencing of rheumatoid arthritis tissues.

Single-cell analysis reveals aspects of cellular physiology not evident from population-based studies, particularly in the case of highly multiplexed methods such as mass cytometry (CyTOF) able to correlate the levels of multiple signalling, differentiation and cell fate markers. Immunofluorescence (IF) microscopy adds information on cell morphology and the microenvironment that are not obtained using flow-based techniques, but the multiplicity of conventional IF is limited. This has motivated development of imaging methods that require specialized instrumentation, exotic reagents or proprietary protocols that are difficult to reproduce in most laboratories. Here we report a public-domain method for achieving high multiplicity single-cell IF using cyclic immunofluorescence (CycIF), a simple and versatile procedure in which four-colour staining alternates with chemical inactivation of fluorophores to progressively build a multichannel image. Because CycIF uses standard reagents and instrumentation and is no more expensive than conventional IF, it is suitable for high-throughput assays and screening applications. Multiplexed single cell measurements provide insight into connections between cell state and phenotype. Here Lin et al. present CycIF, a high throughput, public domain immunofluorescence method for multiplexed single-cell analysis of adherent cells following live-cell imaging.

Simultaneous profiling of transcriptome and chromatin accessibility within single cells is a powerful approach to dissect gene regulatory programs in complex tissues. However, current tools are limited by modest throughput. We now describe an ultra high-throughput method, Paired-seq, for parallel analysis of transcriptome and accessible chromatin in millions of single cells. We demonstrate the utility of Paired-seq for analyzing the dynamic and cell-type-specific gene regulatory programs in complex tissues by applying it to mouse adult cerebral cortex and fetal forebrain. The joint profiles of a large number of single cells allowed us to deconvolute the transcriptome and open chromatin landscapes in the major cell types within these brain tissues, infer putative target genes of candidate enhancers, and reconstruct the trajectory of cellular lineages within the developing forebrain.

Cell atlas projects and high-throughput perturbation screens require single-cell sequencing at a scale that is challenging with current technology. To enable cost-effective single-cell sequencing for millions of individual cells, we developed ‘single-cell combinatorial fluidic indexing’ (scifi). The scifi-RNA-seq assay combines one-step combinatorial preindexing of entire transcriptomes inside permeabilized cells with subsequent single-cell RNA-seq using microfluidics. Preindexing allows us to load several cells per droplet and computationally demultiplex their individual expression profiles. Thereby, scifi-RNA-seq massively increases the throughput of droplet-based single-cell RNA-seq, and provides a straightforward way of multiplexing thousands of samples in a single experiment. Compared with multiround combinatorial indexing, scifi-RNA-seq provides an easy and efficient workflow. Compared to cell hashing methods, which flag and discard droplets containing more than one cell, scifi-RNA-seq resolves and retains individual transcriptomes from overloaded droplets. We benchmarked scifi-RNA-seq on various human and mouse cell lines, validated it for primary human T cells and applied it in a highly multiplexed CRISPR screen with single-cell transcriptome readout of T cell receptor activation.Combining whole-transcriptome preindexing with standard droplet microfluidics, scifi-RNA-seq enables single-cell RNA-seq with massive throughput and built-in sample multiplexing.

Single-cell RNA sequencing (scRNA-seq) requires high sensitivity, throughput, and broad compatibility across specimens. Current high-capacity methods lack sensitivity compared to low-capacity counterparts. Moreover, tissue-specific methods for collecting cells/nuclei limit unbiased comparisons across samples. Here, we propose a Unified framework by Split-Pool barcoding with optimal-efficiency PolydeoxyAdenylation for scRNA detection (USPPAR). Using short and long dsDNA substrates, low Co²⁺ concentration, while eliminating all other metal-ion components, enabled terminal deoxynucleotide transferase to efficiently polydeoxyadenylate intractable blunt and 3′ recessed dsDNA ends, which was unattainable with other systems. By benchmarking against six state-of-the-art technologies using HEK293, the efficient addition of PCR handles for cDNA amplification made USPPAR’s gene detection sensitivity comparable to high-sensitivity methods and significantly higher than existing high-cell-capacity platforms. In primary PBMCs, USPPAR enabled high-sensitivity, high-resolution scRNA-seq, and lysine conjugation improved sensitivity as an RNase inactivator. Based on nuclease reporter and mRNA protection assays, partially chelated Cu²⁺ served as a potent, non-precipitating, broad-spectrum nuclease inhibitor across various pH levels. Beyond demonstrating high sensitivity in liver tissue, an organ with low nuclease activity, single-nucleus RNA sequencing (snRNA-seq) with this inhibitor enabled one-pot extraction of RNA-stable nuclei from nuclease-rich tissues, such as the pancreas. Finally, comparisons with reference datasets from the 10× platform using mouse spleen and maize tissues showed that USPPAR matched cell-type coverage while achieving higher gene-detection efficiency. With five key enzymes available and quality-controlled, USPPAR provides a unified, cost-effective, sensitive method for high-cell-capacity scRNA profiling of diverse specimens without special equipment.

Single-cell RNA sequencing (scRNAseq) and flow cytometry studies in skin are methodologically complex and costly, limiting their accessibility to researchers worldwide. Ideally, RNA and protein-based analyses should be performed on the same lesion to obtain more comprehensive data. However, current protocols generally focus on either scRNAseq or flow cytometry of healthy skin. We present a novel label-free sample multiplexing strategy, building on the souporcell algorithm, which enables scRNAseq analysis of paired blood and skin samples. Additionally, we provide detailed instructions for simultaneous flow cytometry analysis from the same sample, with necessary adaptations for both healthy and inflamed skin specimens. This tissue multiplexing strategy mitigates technical batch effects and reduces costs by 2-4 times compared to existing protocols. We also demonstrate the effects of varying enzymatic incubation durations (1, 3, and 16 hours, with and without enzyme P) on flow cytometry outcomes. Comprehensive explanations of bioinformatic demultiplexing steps and a detailed step-by-step protocol of the entire experimental procedure are included. The protocol outlined in this article will make scRNAseq and flow cytometry analysis of skin samples more accessible to researchers, especially those new to these techniques.

Combining the data-analysis tool Starfish with technologies to pinpoint RNA’s cellular locations can add spatial detail to in situ transcriptomics. Combining the data-analysis tool Starfish with technologies to pinpoint RNA’s cellular locations can add spatial detail to in situ transcriptomics. Inferred large-scale DNA microscopy image