High-throughput phenotypic screens leveraging biochemical perturbations, high-content readouts, and complex multicellular models could advance therapeutic discovery yet remain constrained by limitations of scale. To address this, we establish a method for compressing screens by pooling perturbations followed by computational deconvolution. Conducting controlled benchmarks with a highly bioactive small molecule library and a high-content imaging readout, we demonstrate increased efficiency for compressed experimental designs compared to conventional approaches. To prove generalizability, we apply compressed screening to examine transcriptional responses of patient-derived pancreatic cancer organoids to a library of tumor-microenvironment (TME)-nominated recombinant protein ligands. Using single-cell RNA-seq as a readout, we uncover reproducible phenotypic shifts induced by ligands that correlate with clinical features in larger datasets and are distinct from reference signatures available in public databases. In sum, our approach enables phenotypic screens that interrogate complex multicellular models with rich phenotypic readouts to advance translatable drug discovery as well as basic biology.
Protocol for integrating CITE-seq with well-based scRNA-seq protocols.
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.
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.
We introduce a microfluidic platform that enables single-cell mass and growth rate measurements upstream of single-cell RNA-sequencing (scRNA-seq) to generate paired single-cell biophysical and transcriptional data sets. Biophysical measurements are collected with a serial suspended microchannel resonator platform (sSMR) that utilizes automated fluidic state switching to load individual cells at fixed intervals, achieving a throughput of 120 cells per hour. Each single-cell is subsequently captured downstream for linked molecular analysis using an automated collection system. From linked measurements of a murine leukemia (L1210) and pro-B cell line (FL5.12), we identify gene expression signatures that correlate significantly with cell mass and growth rate. In particular, we find that both cell lines display a cell-cycle signature that correlates with cell mass, with early and late cell-cycle signatures significantly enriched amongst genes with negative and positive correlations with mass, respectively. FL5.12 cells also show a significant correlation between single-cell growth efficiency and a G1-S transition signature, providing additional transcriptional evidence for a phenomenon previously observed through biophysical measurements alone. Importantly, the throughput and speed of our platform allows for the characterization of phenotypes in dynamic cellular systems. As a proof-ofprinciple, we apply our system to characterize activated murine CD8+ T cells and uncover two unique features of CD8+ T cells as they become proliferative in response to activation: i) the level of coordination between cell cycle gene expression and cell mass increases, and ii) translation-related gene expression increases and shows a correlation with single-cell growth efficiency. Overall, our approach provides a new means of characterizing the transcriptional mechanisms of normal and dysfunctional cellular mass and growth rate regulation across a range of biological contexts
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.
Seq-Well is a portable, low-cost platform for single-cell RNA sequencing designed to be compatible with low-input, clinical biopsies. We have recently published a manuscript detailing the development and validation of the Seq-Well plaftorm. Here, we provide an in-depth protocol and videos describing how to perform Seq-Well experiments.
We hope that you will use Seq-Well and apply it in your work.
CAD files for Seq-well devices and molds are provided here: CAD Files
Note: These files are made available under the “Attribution-NonCommercial-NoDerivatives 4.0 International” creative commons license.
Copyright 2017, Massachusetts Institute of Technology and the Broad Institute.
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).
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.
The orchestrated action of genes controls complex biological phenotypes, yet the systematic discovery of gene and drug combinations that modulate these phenotypes in human cells is labor intensive and challenging to scale. Here, we created a platform for the massively parallel screening of barcoded combinatorial gene perturbations in human cells and translated these hits into effective drug combinations. This technology leverages the simplicity of the CRISPR-Cas9 system for multiplexed targeting of specific genomic loci and the versatility of combinatorial genetics en masse (CombiGEM) to rapidly assemble barcoded combinatorial genetic libraries that can be tracked with high-throughput sequencing. We applied CombiGEM-CRISPR to create a library of 23,409 barcoded dual guide-RNA (gRNA) combinations and then perform a high-throughput pooled screen to identify gene pairs that inhibited ovarian cancer cell growth when they were targeted. We validated the growth-inhibiting effects of specific gene sets, including epigenetic regulators KDM4C/BRD4 and KDM6B/BRD4, via individual assays with CRISPR-Cas–based knockouts and RNA-interference–based knockdowns. We also tested small-molecule drug pairs directed against our pairwise hits and showed that they exerted synergistic antiproliferative effects against ovarian cancer cells. We envision that the CombiGEM-CRISPR platform will be applicable to a broad range of biological settings and will accelerate the systematic identification of genetic combinations and their translation into novel drug combinations that modulate complex human disease phenotypes.
Developing a detailed understanding of enzyme function in the context of an intracellular signal transduction pathway requires minimally invasive methods for probing enzyme activity in situ. Here, we describe a new method for monitoring enzyme activity in living cells by sandwiching live cells between two vertical silicon nanowire (NW) arrays. Specifically, we use the first NW array to immobilize the cells and then present enzymatic substrates intracellularly via the second NW array by utilizing the NWs’ ability to penetrate cellular membranes without affecting cells’ viability or function. This strategy, when coupled with fluorescence microscopy and mass spectrometry, enables intracellular examination of protease, phosphatase, and protein kinase activities, demonstrating the assay’s potential in uncovering the physiological roles of various enzymes.
Deciphering the neuronal code—the rules by which neuronal circuits store and process information—is a major scientific challenge. Currently, these efforts are impeded by a lack of experimental tools that are sensitive enough to quantify the strength of individual synaptic connections and also scalable enough to simultaneously measure and control a large number of mammalian neurons with single-cell resolution. Here, we report a scalable intracellular electrode platform based on vertical nanowires that allows parallel electrical interfacing to multiple mammalian neurons. Specifically, we show that our vertical nanowire electrode arrays can intracellularly record and stimulate neuronal activity in dissociated cultures of rat cortical neurons and can also be used to map multiple individual synaptic connections. The scalability of this platform, combined with its compatibility with silicon nanofabrication techniques, provides a clear path towards simultaneous, highfidelity interfacing with hundreds of individual neurons.
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.