High-throughput single-cell RNA-sequencing (scRNA-seq) methodologies enable characterization of complex biological samples by increasing the number of cells that can be profiled contemporaneously. Nevertheless, these approaches recover less information per cell than low-throughput strategies. To accurately report the expression of key phenotypic features of cells, scRNA-seq platforms are needed that are both high fidelity and high throughput. To address this need, we created Seq-Well S3 (“Second-Strand Synthesis”), a massively parallel scRNA-seq protocol that uses a randomly primed second-strand synthesis to recover complementary DNA (cDNA) molecules that were successfully reverse transcribed but to which a second oligonucleotide handle, necessary for subsequent whole transcriptome amplification, was not appended due to inefficient template switching. Seq-Well S3 increased the efficiency of transcript capture and gene detection compared with that of previous iterations by up to 10- and 5-fold, respectively. We used Seq-Well S3 to chart the transcriptional landscape of five human inflammatory skin diseases, thus providing a resource for the further study of human skin inflammation.
Barrier tissue epithelia play an essential role in maintaining organismal homeostasis, and changes in their cellular composition have been observed in multiple human diseases. Within the small intestinal epithelium, adult stem cells integrate diverse signals to regulate regeneration and differentiation, thereby establishing overall cellularity. Accordingly, directing stem cell differentiation could provide a tractable approach to alter the abundance or quality of specialized cells of the small intestinal epithelium, including the secretory Paneth, goblet, and enteroendocrine populations. Yet, to date, there has been a lack of suitable tools and rigorous approaches to identify biological targets and pharmacological agents that can modify epithelial composition to enable causal testing of disease-associated changes with novel therapeutic candidates. To empower the search for epithelia-modifying agents, we establish a first-of-its-kind high-throughput phenotypic organoid screen. We demonstrate the ability to screen thousands of samples and uncover biological targets and associated small molecule inhibitors which translate to in vivo. This approach is enabled by employing a functional, cell-type specific, scalable assay on an organoid model designed to represent the physiological cues of in vivo Paneth cell differentiation from adult intestinal stem cells. Further, we miniaturize and adapt the organoid culture system to enable automated plating and screening, thereby providing the ability to test thousands of samples. Strikingly, in our screen we identify inhibitors of the nuclear exporter Xpo1 modulate stem cell fate commitment by inducing a pan-epithelial stress response combined with an interruption of mitogen signaling in cycling intestinal progenitors, thereby significantly increasing the abundance of Paneth cells independent of known WNT and Notch differentiation cues. We extend our observation in vivo, demonstrating that oral administration of Xpo1 inhibitor KPT-330 at doses 1,000-fold lower than conventionally used in hematologic malignancies increases Paneth cell abundance. In total, we provide a framework to identify novel biological cues and therapeutic leads to rebalance intestinal stem cell differentiation and modulate epithelial tissue composition via high-throughput phenotypic screening in rationally-designed organoid model of differentiation.
The scale and capabilities of single-cell RNA-sequencing methods have expanded rapidly in recent years, enabling major dis- coveries and large-scale cell mapping efforts. However, these methods have not been systematically and comprehensively benchmarked. Here, we directly compare seven methods for single-cell and/or single-nucleus profiling—selecting representa- tive methods based on their usage and our expertise and resources to prepare libraries—including two low-throughput and five high-throughput methods. We tested the methods on three types of samples: cell lines, peripheral blood mononuclear cells and brain tissue, generating 36 libraries in six separate experiments in a single center. To directly compare the methods and avoid processing differences introduced by the existing pipelines, we developed scumi, a flexible computational pipeline that can be used with any single-cell RNA-sequencing method. We evaluated the methods for both basic performance, such as the structure and alignment of reads, sensitivity and extent of multiplets, and for their ability to recover known biological information in the samples.
Whether cultured in vitro or part of a complex tissue in vivo, a cell’s phenotype and function are significantly influenced by dynamic interactions with its microenvironment. To explicitly examine how a cell’s spatiotemporal activity impacts its behavior, we developed and validated a strategy termed SPACECAT – Spatially PhotoActivatable Color Encoded Cell Address Tags – to annotate, track, and isolate specific cells in a non-destructive, viability-preserving manner. In SPACECAT, a biological sample is immersed in a photocaged fluorescent molecule, and cells within a location of interest are labeled for further study by uncaging that molecule with user-patterned near-UV light. SPACECAT offers high spatial precision and temporal stability across diverse cell and tissue types, and is compatible with common downstream assays, including flow cytometry and single-cell RNA-Seq. Illustratively, we leveraged this approach in patient-derived intestinal organoids, a spatially complex system less amenable to genetic manipulations, to select for crypt-like regions enriched in stem-like and actively mitotic cells. Moreover, we demonstrate its applicability and utility on ex vivo tissue sections from four healthy organs and an autochthonous lung tumor model, uncovering spatially-biased gene expression patterns among immune cell subsets and identifying rare myeloid phenotypes enriched around tumor/healthy border regions. In sum, our method provides a minimally invasive and broadly applicable approach to link cellular spatiotemporal features and/or behavioral phenotypes with diverse downstream assays, enabling fundamental insights into the connections between tissue microenvironments and biological (dys)function.
By affinity capture and amplification of TCR transcripts from whole-transcriptome libraries, TCR CDR3 sequences can be recovered from 3′-barcoded scRNA-seq libraries (e.g. Seq-Well, Drop-seq, etc.). This method can be applied post-hoc, allowing for the capture of additional information from archived samples. The protocol can also be found here.
High-throughput 3′ single-cell RNA-sequencing (scRNA-seq) allows cost-effective, detailed characterization of individual immune cells from tissues. Current techniques, however, are limited in their ability to elucidate essential immune cell features, including variable sequences of T cell antigen receptors (TCRs) that confer antigen specificity. Here, we present a strategy that enables simultaneous analysis of TCR sequences and corresponding full transcriptomes from 3′-barcoded scRNA-seq samples. This approach is compatible with common 3′ scRNA-seq methods, and adaptable to processed samples post hoc. We applied the technique to identify transcriptional signatures associated with T cells sharing common TCRs from immunized mice and from patients with food allergy. We observed preferential phenotypes among subsets of expanded clonotypes, including type 2 helper CD4+ T cell (TH2) states associated with food allergy. These results demonstrate the utility of our method when studying diseases in which clonotype-driven responses are critical to understanding the underlying biology. The protocol can be found here.
To mark the 15th anniversary of Nature Methods, we asked scientists from across diverse fields of basic biology research for their views on the most exciting and essential methodological challenges that their communities are poised to tackle in the near future.
The development of high-throughput single-cell RNA-sequencing (scRNA-Seq) methodologies has empowered the characterization of complex biological samples by dramatically increasing the number of constituent cells that can be examined concurrently. Nevertheless, these approaches typically recover substantially less information per-cell as compared to lower-throughput microtiter plate-based strategies. To uncover critical phenotypic differences among cells and effectively link scRNA-Seq observations to legacy datasets, reliable detection of phenotype-defining transcripts – such as transcription factors, affinity receptors, and signaling molecules – by these methods is essential. Here, we describe a substantially improved massively-parallel scRNA-Seq protocol we term Seq-Well S^3 (“Second-Strand Synthesis”) that increases the efficiency of transcript capture and gene detection by up to 10- and 5-fold, respectively, relative to previous iterations, surpassing best-in-class commercial analogs. We first characterized the performance of Seq-Well S^3 in cell lines and PBMCs, and then examined five different inflammatory skin diseases, illustrative of distinct types of inflammation, to explore the breadth of potential immune and parenchymal cell states. Our work presents an essential methodological advance as well as a valuable resource for studying the cellular and molecular features that inform human skin inflammation.
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.
Circulating tumor cells (CTCs) play a fundamental role in cancer progression. However, in mice, limited blood volume and the rarity of CTCs in the bloodstream preclude longitudinal, in-depth studies of these cells using existing liquid biopsy techniques. Here, we present an optofluidic system that continuously collects fluorescently labeled CTCs from a genetically engineered mouse model (GEMM) for several hours per day over multiple days or weeks. The system is based on a microfluidic cell sorting chip connected serially to an unanesthetized mouse via an implanted arteriovenous shunt. Pneumatically controlled microfluidic valves capture CTCs as they flow through the device, and CTC-depleted blood is returned back to the mouse via the shunt. To demonstrate the utility of our system, we profile CTCs isolated longitudinally from animals over 4 days of treatment with the BET inhibitor JQ1 using single-cell RNA sequencing (scRNA-Seq) and show that our approach eliminates potential biases driven by intermouse heterogeneity that can occur when CTCs are collected across different mice. The CTC isolation and sorting technology presented here provides a research tool to help reveal details of how CTCs evolve over time, allowing studies to credential changes in CTCs as biomarkers of drug response and facilitating future studies to understand the role of CTCs in metastasis.
Mass and growth rate are highly integrative measures of cell physiology not discernable via genomic measurements. Here, we introduce a microfluidic platform enabling direct measurement of single-cell mass and growth rate upstream of highly multiplexed single-cell profiling such as single-cell RNA sequencing. We resolve transcriptional signatures associated with single-cell mass and growth rate in L1210 and FL5.12 cell lines and activated CD8+ T cells. Further, we demonstrate a framework using these linked measurements to characterize biophysical heterogeneity in a patient-derived glioblastoma cell line with and without drug treatment. Our results highlight the value of coupled phenotypic metrics in guiding single-cell genomics.
We develop single-cell genomic approaches to comprehensively profile complex biological ensembles. To date, the majority of our work has focused on establishing, validating, and scaling single-cell transcriptomics, often through the development of microdevices to enable genome-wide identification of the cell types/states that comprise functional or dysfunctional biological samples.
Most recently, we have developed Seq-Well, a portable, low-cost platform for high-throughput single-cell RNA-Seq (scRNA-Seq). By providing open access to resources and protocols, we hope to democratize access to cutting-edge approaches in single-cell genomics.
To complement and inform the analysis of scRNA-Seq datasets, we create methods to simultaneously profile additional cellular characteristics of interest (e.g. genome, epigenome, or proteome), independently, or in combination with, scRNA-Seq. For a given technique or system, we ask what additional information would help us better interpret our scRNA-Seq results and develop methods to collect these data. These novel methods often map ancillary information into a DNA-based readout that can be coanalyzed with cellular mRNA or developing/applying microdevices. Recently, we have developed a method for integrated mRNA and protein detection that leverages proximity extension assays (Genshaft et al. 2016). To extract the information content from these novel datasets more effectively, we also formulate new computational methods and analyses.
We explore how the extracellular milieu impacts intracellular decision-making by experimentally controlling the cellular microenvironment or leveraging naturally occurring sources of variation within a tissue. Here, we employ solutions that include controlled culture conditions with cells (Shalek et al., 2014) or organoids, chemical or genetic perturbations (Kumar et al., 2014), and constant microfluidic perfusion. We are also developing in silico approaches that are powered by in-situ cellular tagging techniques. In each instance, we aim to understand the degree to which extracellular environments modulate, and can be used to rationally control, the responses of individual cells or the overall distribution thereof, with an eye toward engineering ensemble responses.
We use microdevices, coupled with functional signal readouts, to create and study defined cell-cell interactions. By explicitly enumerating cell type, number, and additional functional properties (e.g., cytokine secretion), we model ensemble behaviors, looking for synergies and antagonisms. These genetic signatures, along with those collected via our other platforms, provide a unique and essential reference for deconvolving behaviors in complex ensembles. We are also using genetic tracing strategies to examine differences between interacting and random cell pairs in vivo, and are developing computational methods (Tirosh et al., 2016) to identify putative interactions from scRNA-Seq data.
As the amount of data we have relating to cells, properties, surroundings, and interactions increases exponentially, we are motivated to develop pan-system measurements and analyses to paint comprehensive pictures of immune response in health and disease. Relying on massive transcriptomic datasets generated from complex tissues, like melanoma tumors, inflamed human gut, M. tuberculosis (MTB)-induced granulomas, and healthy or SHIV-infected monkey tissues, we have begun to construct social networks of integrated responses to physiological perturbations. The technologies outlined above uniquely enable us to generate foundational datasets (e.g., transcriptomes from interacting cell pairs) for deconvolving and interpreting the potential drivers of observed ensemble behaviors, as well as for identifying which properties we cannot explain, and thus need to study. To date, our lab has generated over 2 million single-cell transcriptomes across multiple tissues, individuals, and species; we are utilizing this data, paired with metadata and additional characteristics, to look for common cellular network motifs, such as division of labor, quorum sensing, persistence, or bet-hedging.
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.
The immune system plays an important role in regulating homeostatic balance across tissues and individuals in the face of changing and challenging environments. Given the pivotal and outsized impact cell subsets (e.g., rare precocious DCs) can have on ensemble dynamics (e.g., global activation of an antiviral response and deactivation of inflammation), we aim to understand the functional consequences of variation in cellular composition across tissues, as well as how different immune cells adapt to changing environmental conditions.
Motivating questions in the lab include:
- How can we perform observational and experimental studies to understand the fundamental units of tissues structure and function?
- Can we derive basic principles governing homeostatic and pathogenic immune responses within tissues?
- What dictates the evolution of clonal antigen-specific T & B cell responses?
To this end, we are several multiple tissues from multiple organisms across common sources of variation. By examining consistent and unique themes that emerge across these systems, we aim to extract basic principles that govern homeostatic and pathogenic immune responses within tissues. Ultimately, we intend to leverage this information to rationally engineer immune responses (e.g., in vaccines and immunotherapies).
Our immune system collaborates with environment- and diet-dependent commensals to establish and maintain homeostasis, and to defend against pathogenic threats (e.g., viruses, bacteria, fungi). We are interested in understanding the nature and impact of these interactions on host tissues, as well as potential avenues to modulate them for therapeutic or prophylactic ends.
Illustrative questions and areas of study include:
- How do microbial composition and byproducts influence cellular differentiation and phenotypic diversity within the gut?
- How do pathogens (e.g. HIV and TB) impact target cell phenotypes and overall tissue function in the context of acute and systemic infection?
- To what degree can therapeutic intervention (e.g. cART for HIV-1) re-establish homeostatic setpoint (i.e. composition and function)?
We have several projects and collaborations (local and international) actively exploring these and related questions in vaccine design that have both inspired, and take advantage of, some of our unique tools to profile thousands of single cells from limited clinical samples anywhere in the world, and develop clinically relevant hypotheses.
A diverse array of mechanisms—including genetic mutations, environmental triggers, and diet—can alter cell function and reduce tissue stability, ultimately leading to malignancy, autoimmunity, or immunodeficiency. By identifying which cells these factors affect and in what ways, we aim to develop targeted therapeutic interventions in areas such as cancer, allergy, and inflammatory bowel disease.
Motivating questions that drive our research include:
- How do the coordinated interactions between epithelial and immune populations inform barrier tissue function in the context of homeostasis, inflammation and malignancy?
- How can we leverage information across systems to derive a set of unifying principles of cellular ecology in health and disease?
Current projects aim to contrast the cellular microenvironments of healthy, inflamed, and malignant (Tirosh et al., 2016; Patel et al., 2014) tissues to examine inflammation-induced changes and the drivers of malignant transformation, as well as to identify which cells remember prior insult. We are similarly profiling aberrant immune behaviors in immune privileged tissues, such as the nervous systems. As in our host-microbial studies, our goal is to identify common features shared across different immune-related diseases that we can probe further in natural (tissues, models) and engineered (patterned cells and cellular structures, organoids) ensembles.
We present a scalable, integrated strategy for coupled protein and RNA detection from single cells. Our approach leverages the DNA polymerase activity of reverse transcriptase to simultaneously perform proximity extension assays and complementary DNA synthesis in the same reaction. Using the Fluidigm C1TM system, we profile the transcriptomic and proteomic response of a human breast adenocarcinoma cell line to a chemical perturbation, benchmarking against in situ hybridizations and immunofluorescence staining, as well as recombinant proteins, ERCC Spike-Ins, and population lysate dilutions. Through supervised and unsupervised analyses, we demonstrate synergies enabled by simultaneous measurement of single-cell protein and RNA abundances. Collectively, our generalizable approach highlights the potential for molecular metadata to inform highly-multiplexed single-cell analyses.
We introduce a microfluidic platform that enables off-chip single-cell RNA-seq after multi-generational lineage tracking under controlled culture conditions. We use this platform to generate whole-transcriptome profiles of primary, activated murine CD8+ T-cell and lymphocytic leukemia cell line lineages. Here we report that both cell types have greater intra- than inter-lineage transcriptional similarity. For CD8+ T-cells, genes with functional annotation relating to lymphocyte differentiation and function—including Granzyme B—are enriched among the genes that demonstrate greater intra-lineage expression level similarity. Analysis of gene expression covariance with matched measurements of time since division reveals cell type-specific transcriptional signatures that correspond with cell cycle progression. We believe that the ability to directly measure the effects of lineage and cell cycle-dependent transcriptional profiles of single cells will be broadly useful to fields where heterogeneous populations of cells display distinct clonal trajectories, including immunology, cancer, and developmental biology.
An innovative, microwell-based platform for single-cell RNA sequencing (RNA-seq) combines cost efficiency, scalability and parallelizability, and will enable many new avenues of biological inquiry.
Cells, the basic units of biological structure and function, vary broadly in type and state. Single- cell genomics can characterize cell identity and function, but limitations of ease and scale have prevented its broad application. Here we describe Drop-seq, a strategy for quickly profiling thousands of individual cells by separating them into nanoliter-sized aqueous droplets, associating a different barcode with each cell’s RNAs, and sequencing them all together. Drop-seq analyzes mRNA transcripts from thousands of individual cells simultaneously while remembering transcripts’ cell of origin. We analyzed transcriptomes from 44,808 mouse retinal cells and identified 39 transcriptionally distinct cell populations, creating a molecular atlas of gene expression for known retinal cell classes and novel candidate cell subtypes. Drop-seq will accelerate biological discovery by enabling routine transcriptional profiling at single- cell resolution.
For the past several decades, due to technical limitations, the field of transcriptomics has focused on population-level measurements that can mask significant differences between individual cells. With the advent of single-cell RNA-Seq, it is now possible to profile the responses of individual cells at unprecedented depth and thereby uncover, transcriptome-wide, the heterogeneity that exists within these populations. This unit describes a method that merges several important technologies to produce, in highthroughput, single-cell RNA-Seq libraries. Complementary DNA (cDNA) is made from full-length mRNA transcripts using a reverse transcriptase that has terminal transferase activity. This,when combinedwith a second “template-switch” primer, allows for cDNAs to be constructed that have two universal priming sequences. Following preamplification from these common sequences, Nextera XT is used to prepare a pool of 96 uniquely indexed samples ready for Illumina sequencing.
A circuit level understanding of immune cells and hematological cancers has been severely impeded by a lack of techniques that enable intracellular perturbation without significantly altering cell viability and function. Here, we demonstrate that vertical silicon nanowires (NWs) enable gene-specific manipulation of diverse murine and human immune cells with negligible toxicity. To illustrate the power of the technique, we then apply NW-mediated gene silencing to investigate the role of the Wnt signaling pathway in chronic lymphocytic leukemia (CLL). Remarkably, CLL-B cells from different patients exhibit tremendous heterogeneity in their response to the knockdown of a single gene, LEF1. This functional heterogeneity defines three distinct patient groups not discernible by conventional CLL cytogenetic markers and provides a prognostic indicator for patients’ time to first therapy. Analyses of gene expression signatures associated with these functional patient subgroups reveal unique insights into the underlying molecular basis for disease heterogeneity. Overall, our findings suggest a functional classification that can potentially guide the selection of patient-specific therapies in CLL and highlight the opportunities for nanotechnology to drive biological inquiry.
A generalized platform for introducing a diverse range of biomolecules into living cells in high-throughput could transform how complex cellular processes are probed and analyzed. Here, we demonstrate spatially localized, efficient, and universal delivery of biomolecules into immortalized and primary mammalian cells using surface-modified vertical silicon nanowires. The method relies on the ability of the silicon nanowires to penetrate a cell’s membrane and subsequently release surface-bound molecules directly into the cell’s cytosol, thus allowing highly efficient delivery of biomolecules without chemical modification or viral packaging. This modality enables one to assess the phenotypic consequences of introducing a broad range of biological effectors (DNAs, RNAs, peptides, proteins, and small molecules) into almost any cell type. We show that this platform can be used to guide neuronal progenitor growth with small molecules, knock down transcript levels by delivering siRNAs, inhibit apoptosis using peptides, and introduce targeted proteins to specific organelles. We further demonstrate codelivery of siRNAs and proteins on a single substrate in a microarray format, highlighting this technology’s potential as a robust, monolithic platform for high-throughput, miniaturized bioassays.
Electrostatic force microscopy shows that the electric field gradients above pentacene monolayer islands on 2-nm SiO2/Si substrates, in a dark, dry nitrogen environment, display a wide distribution of signs and magnitude that is dependent on sample history. Under 12 mW/cm2 green (532 nm) illumination, pentacene islands accumulate positive charge because of photoexcited electron transfer across the oxide to the Si substrate. At a strong illumination of 60 mW/cm2, pentacene islands reform into small spherical particles, apparently because the positive charge Coulomb repulsion energy becomes comparable to the cohesive energy of the pentacene monolayer.