Ultra-high-throughput single-cell RNA sequencing and perturbation screening with combinatorial fluidic indexing.

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.

FB5P-seq: FACS-Based 5-Prime End Single-Cell RNA-seq for Integrative Analysis of Transcriptome and Antigen Receptor Repertoire in B and T Cells.

Single-cell RNA sequencing (scRNA-seq) allows the identification, characterization, and quantification of cell types in a tissue. When focused on B and T cells of the adaptive immune system, scRNA-seq carries the potential to track the clonal lineage of each analyzed cell through the unique rearranged sequence of its antigen receptor (BCR or TCR, respectively) and link it to the functional state inferred from transcriptome analysis. Here we introduce FB5P-seq, a FACS-based 5'-end scRNA-seq method for cost-effective, integrative analysis of transcriptome and paired BCR or TCR repertoire in phenotypically defined B and T cell subsets. We describe in detail the experimental workflow and provide a robust bioinformatics pipeline for computing gene count matrices and reconstructing repertoire sequences from FB5P-seq data. We further present two applications of FB5P-seq for the analysis of human tonsil B cell subsets and peripheral blood antigen-specific CD4 T cells. We believe that our novel integrative scRNA-seq method will be a valuable option to study rare adaptive immune cell subsets in immunology research.

Ten Major Future Challenges in Single-Cell Metabolomics.

Information on cellular metabolism at the single-cell level can unravel countless biochemical process providing invaluable biomedical insight. Single-cell analysis field is at the very early stage at this moment, and all the work done so far are proof-of-principle work by early-stage researchers. In this chapter, I have outlined ten fundamental issues that are required for the development of robust single-cell metabolomics platform using mass spectrometry (MS).

An ultra high-throughput method for single-cell joint analysis of open chromatin and transcriptome.

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.

BART-Seq: cost-effective massively parallelized targeted sequencing for genomics, transcriptomics, and single-cell analysis.

We describe a highly sensitive, quantitative, and inexpensive technique for targeted sequencing of transcript cohorts or genomic regions from thousands of bulk samples or single cells in parallel. Multiplexing is based on a simple method that produces extensive matrices of diverse DNA barcodes attached to invariant primer sets, which are all pre-selected and optimized in silico. By applying the matrices in a novel workflow named Barcode Assembly foR Targeted Sequencing (BART-Seq), we analyze developmental states of thousands of single human pluripotent stem cells, either in different maintenance media or upon Wnt/ß-catenin pathway activation, which identifies the mechanisms of differentiation induction. Moreover, we apply BART-Seq to the genetic screening of breast cancer patients and identify BRCA mutations with very high precision. The processing of thousands of samples and dynamic range measurements that outperform global transcriptomics techniques makes BART-Seq first targeted sequencing technique suitable for numerous research applications.

CEL-Seq2-Single-Cell RNA Sequencing by Multiplexed Linear Amplification.

Single-cell RNA sequencing has revolutionized the way we look at cell populations. Of the methods available, CEL-Seq was the first to use linear RNA amplification. With early barcoding and 3' sequencing, it is sensitive, cost-effective and easy to perform. Here we describe a protocol for performing CEL-Seq2 on sorted cells, which can be performed without any special equipment.

Single-Cell RNA Sequencing with Drop-Seq.

Drop-Seq is a low-cost, high-throughput platform to profile thousands of cells by encapsualting them into individual droplets. Uniquely barcoded mRNA capture microparticles and cells are coconfined through a microfluidic device within the droplets where they undergo cell lysis and RNA hybridiztion. After breaking the droplets and pooling the hybridized particles, reverse transcription, PCR, and sequencing in single reactions allow to generate data from thousands of single-cell transcriptomes while maintaining information on the cellular origin of each transcript.

Seq-Well: A Sample-Efficient, Portable Picowell Platform for Massively Parallel Single-Cell RNA Sequencing.

Seq-Well is a low-cost picowell platform that can be used to simultaneously profile the transcriptomes of thousands of cells from diverse, low input clinical samples. In Seq-Well, uniquely barcoded mRNA capture beads and cells are co-confined in picowells that are sealed using a semipermeable membrane, enabling efficient cell lysis and mRNA capture. The beads are subsequently removed and processed in parallel for sequencing, with each transcript's cell of origin determined via the unique barcodes. Due to its simplicity and portability, Seq-Well can be performed almost anywhere.

High-Throughput Single-Cell Real-Time Quantitative PCR Analysis.

Examining transcriptomics of populations at the single-cell level allows for higher resolution when studying functionality in development, differentiation, and physiology. Real-time quantitative PCR (qPCR) enables a sensitive detection of specific gene expression; however, processing a large number of samples for single-cell research involves a time-consuming process and high reagent costs. Here we describe a protocol for single-cell qPCR using nanofluidic chips. This method reduces the number of handling steps and volumes per reaction, allowing for more samples and genes to be measured.

Sensitive high-throughput single-cell RNA-seq reveals within-clonal transcript correlations in yeast populations.

Single-cell RNA sequencing has revealed extensive cellular heterogeneity within many organisms, but few methods have been developed for microbial clonal populations. The yeast genome displays unusually dense transcript spacing, with interleaved and overlapping transcription from both strands, resulting in a minuscule but complex pool of RNA that is protected by a resilient cell wall. Here, we have developed a sensitive, scalable and inexpensive yeast single-cell RNA-seq (yscRNA-seq) method that digitally counts transcript start sites in a strand- and isoform-specific manner. YscRNA-seq detects the expression of low-abundance, noncoding RNAs and at least half of the protein-coding genome in each cell. In clonal cells, we observed a negative correlation for the expression of sense-antisense pairs, whereas paralogs and divergent transcripts co-expressed. By combining yscRNA-seq with index sorting, we uncovered a linear relationship between cell size and RNA content. Although we detected an average of ~3.5 molecules per gene, the number of expressed isoforms is restricted at the single-cell level. Remarkably, the expression of metabolic genes is highly variable, whereas their stochastic expression primes cells for increased fitness towards the corresponding environmental challenge. These findings suggest that functional transcript diversity acts as a mechanism that provides a selective advantage to individual cells within otherwise transcriptionally heterogeneous populations.

Comparative Analysis of Droplet-Based Ultra-High-Throughput Single-Cell RNA-Seq Systems.

Since its establishment in 2009, single-cell RNA sequencing (RNA-seq) has been a major driver behind progress in biomedical research. In developmental biology and stem cell studies, the ability to profile single cells confers particular benefits. Although most studies still focus on individual tissues or organs, the recent development of ultra-high-throughput single-cell RNA-seq has demonstrated potential power in characterizing more complex systems or even the entire body. However, although multiple ultra-high-throughput single-cell RNA-seq systems have attracted attention, no systematic comparison of these systems has been performed. Here, with the same cell line and bioinformatics pipeline, we developed directly comparable datasets for each of three widely used droplet-based ultra-high-throughput single-cell RNA-seq systems, inDrop, Drop-seq, and 10X Genomics Chromium. Although each system is capable of profiling single-cell transcriptomes, their detailed comparison revealed the distinguishing features and suitable applications for each system.

Interference Disturbance Analysis Enables Single-Cell Level Growth and Mobility Characterization for Rapid Antimicrobial Susceptibility Testing.

To support the emerging battle against antimicrobial resistance (AMR), detection methods that allow fast and accurate antimicrobial susceptibility testing (AST) are urgently needed. The early identification and application of an appropriate antibiotic treatment leads to lower mortality rates and substantial cost savings and prevents the development of resistant pathogens. In this work, we present a diffraction-based method, which is capable of quantitative bacterial growth, mobility, and susceptibility measurements. The method is based on the temporal analysis of the intensity of a light diffraction peak, which arises due to interference at a periodic pattern of gold nanostructures. The presence of bacteria disturbs the constructive interference, leading to an intensity decrease and thus allows the monitoring of bacterial growth in very low volumes. We demonstrate the direct correlation of the decrease in diffraction peak intensity with bacterial cell number starting from single cells and show the capability for rapid high-throughput AST measurements by determining the minimum inhibitory concentration for three different antimicrobials in less than 2-3 h as well as the susceptibility in less than 30-40 min. Furthermore, bacterial mobility is obtained from short-term fluctuations of the diffraction peak intensity and is shown to decrease by a factor of 3 during bacterial attachment to a surface. This multiparameter detection method allows for rapid AST of planktonic and of biofilm-forming bacterial strains in low volumes and in real-time without the need of high initial cell numbers.

Single-cell RNA-seq of rheumatoid arthritis synovial tissue using low-cost microfluidic instrumentation.

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.

Mapping the Mouse Cell Atlas by Microwell-Seq.

Single-cell RNA sequencing (scRNA-seq) technologies are poised to reshape the current cell-type classification system. However, a transcriptome-based single-cell atlas has not been achieved for complex mammalian systems. Here, we developed Microwell-seq, a high-throughput and low-cost scRNA-seq platform using simple, inexpensive devices. Using Microwell-seq, we analyzed more than 400,000 single cells covering all of the major mouse organs and constructed a basic scheme for a mouse cell atlas (MCA). We reveal a single-cell hierarchy for many tissues that have not been well characterized previously. We built a web-based "single-cell MCA analysis" pipeline that accurately defines cell types based on single-cell digital expression. Our study demonstrates the wide applicability of the Microwell-seq technology and MCA resource.

Scaling by shrinking: empowering single-cell 'omics' with microfluidic devices.

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.

Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput.

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.

Comparative Analysis of Single-Cell RNA Sequencing Methods.

Single-cell RNA sequencing (scRNA-seq) offers new possibilities to address biological and medical questions. However, systematic comparisons of the performance of diverse scRNA-seq protocols are lacking. We generated data from 583 mouse embryonic stem cells to evaluate six prominent scRNA-seq methods: CEL-seq2, Drop-seq, MARS-seq, SCRB-seq, Smart-seq, and Smart-seq2. While Smart-seq2 detected the most genes per cell and across cells, CEL-seq2, Drop-seq, MARS-seq, and SCRB-seq quantified mRNA levels with less amplification noise due to the use of unique molecular identifiers (UMIs). Power simulations at different sequencing depths showed that Drop-seq is more cost-efficient for transcriptome quantification of large numbers of cells, while MARS-seq, SCRB-seq, and Smart-seq2 are more efficient when analyzing fewer cells. Our quantitative comparison offers the basis for an informed choice among six prominent scRNA-seq methods, and it provides a framework for benchmarking further improvements of scRNA-seq protocols.