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.
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.