Aaron M. Streets

Microfluidic single-cell whole-transcriptome sequencing.

Significance RNA sequencing of single cells enables measurement of biological variation in heterogeneous cellular populations and dissection of transcriptome complexity that is masked in ensemble measurements of gene expression. The low quantity of RNA in a single cell, however, hinders efficient and consistent reverse transcription and amplification of cDNA, limiting accuracy and obscuring biological variation with high technical noise. We developed a microfluidic approach to prepare cDNA from single cells for high-throughput transcriptome sequencing. The microfluidic platform facilitates single-cell manipulation, minimizes contamination, and furthermore, provides improved detection sensitivity and measurement precision, which is necessary for differentiating biological variability from technical noise. Single-cell whole-transcriptome analysis is a powerful tool for quantifying gene expression heterogeneity in populations of cells. Many techniques have, thus, been recently developed to perform transcriptome sequencing (RNA-Seq) on individual cells. To probe subtle biological variation between samples with limiting amounts of RNA, more precise and sensitive methods are still required. We adapted a previously developed strategy for single-cell RNA-Seq that has shown promise for superior sensitivity and implemented the chemistry in a microfluidic platform for single-cell whole-transcriptome analysis. In this approach, single cells are captured and lysed in a microfluidic device, where mRNAs with poly(A) tails are reverse-transcribed into cDNA. Double-stranded cDNA is then collected and sequenced using a next generation sequencing platform. We prepared 94 libraries consisting of single mouse embryonic cells and technical replicates of extracted RNA and thoroughly characterized the performance of this technology. Microfluidic implementation increased mRNA detection sensitivity as well as improved measurement precision compared with tube-based protocols. With 0.2 M reads per cell, we were able to reconstruct a majority of the bulk transcriptome with 10 single cells. We also quantified variation between and within different types of mouse embryonic cells and found that enhanced measurement precision, detection sensitivity, and experimental throughput aided the distinction between biological variability and technical noise. With this work, we validated the advantages of an early approach to single-cell RNA-Seq and showed that the benefits of combining microfluidic technology with high-throughput sequencing will be valuable for large-scale efforts in single-cell transcriptome analysis.

Chip in a lab: Microfluidics for next generation life science research

Microfluidic circuits are characterized by fluidic channels and chambers with a linear dimension on the order of tens to hundreds of micrometers. Components of this size enable lab-on-a-chip technology that has much promise, for example, in the development of point-of-care diagnostics. Micro-scale fluidic circuits also yield practical, physical, and technological advantages for studying biological systems, enhancing the ability of researchers to make more precise quantitative measurements. Microfluidic technology has thus become a powerful tool in the life science research laboratory over the past decade. Here we focus on chip-in-a-lab applications of microfluidics and survey some examples of how small fluidic components have provided researchers with new tools for life science research.

High-throughput single-molecule optofluidic analysis

We describe a high-throughput, automated single-molecule measurement system, equipped with microfluidics. The microfluidic mixing device has integrated valves and pumps to accurately accomplish titration of biomolecules with picoliter resolution. We demonstrate that the approach enabled rapid sampling of biomolecule conformational landscape and of enzymatic activity, in the form of transcription by Escherichia coli RNA polymerase, as a function of the chemical environment.

Simultaneous measurement of amyloid fibril formation by dynamic light scattering and fluorescence reveals complex aggregation kinetics.

An apparatus that combines dynamic light scattering and Thioflavin T fluorescence detection is used to simultaneously probe fibril formation in polyglutamine peptides, the aggregating subunit associated with Huntingtons disease, in vitro. Huntingtons disease is a neurodegenerative disorder in a class of human pathologies that includes Alzheimers and Parkinsons disease. These pathologies are all related by the propensity of their associated protein or polypeptide to form insoluble, β-sheet rich, amyloid fibrils. Despite the wide range of amino acid sequence in the aggregation prone polypeptides associated with these diseases, the resulting amyloids display strikingly similar physical structure, an observation which suggests a physical basis for amyloid fibril formation. Thioflavin T fluorescence reports β-sheet fibril content while dynamic light scattering measures particle size distributions. The combined techniques allow elucidation of complex aggregation kinetics and are used to reveal multiple stages of amyloid fibril formation.

Imaging without Fluorescence: Nonlinear Optical Microscopy for Quantitative Cellular Imaging

Quantitative single-cell analysis enables the characterization of cellular systems with a level of detail that cannot be achieved with ensemble measurement. In this Feature we explore quantitative cellular imaging applications with nonlinear microscopy techniques. We first offer an introductory tutorial on nonlinear optical processes and then survey a range of techniques that have proven to be useful for quantitative live cell imaging without fluorescent labels.

How deep is enough in single-cell RNA-seq?

Guidelines for determining sequencing depth facilitate transcriptome profiling of single cells in heterogeneous populations.

Single-Cell Transcriptional Analysis

Despite being a relatively recent technological development, single-cell transcriptional analysis through high-throughput sequencing has already been used in hundreds of fruitful studies to make exciting new biological discoveries that would otherwise be challenging or even impossible. Consequently, this has fueled a virtuous cycle of even greater interest in the field and compelled development of further improved technical methodologies and approaches. Thanks to the combined efforts of the research community, including the fields of biochemistry and molecular biology, technology and instrumentation, data science, computational biology, and bioinformatics, the single-cell RNA-sequencing field is advancing at a pace that is both astounding and unprecedented. In this review, we provide a broad introduction to this revolutionary technology by presenting the state-of-the-art in sample preparation methodologies, technology platforms, and computational analysis methods, while highlighting the key considerations for designing, executing, and interpreting a study using single-cell RNA sequencing.

H3K4me3 epigenomic landscape derived from ChIP-Seq of 1 000 mouse early embryonic cells

Epigenetic regulation is crucial to the establishment and maintenance of the identity of a cell. Recent studies suggest that transcription is implemented amongst a mixture of various histone modifications [1]. It has also been recognized that to interrogate function of genetic information, comprehensively systematic profiling of the epigenome in multiple cell stages and types is required [2]. Chromatin immunoprecipitation (ChIP) has become one of the most critical assays to investigate the complex DNA-protein interactions [3]. Combined with profiling technologies such as microarrays (ChIP-on-chip) or high-throughput sequencing (ChIP-Seq), this assay becomes a great tool to study the epigenetic regulatory networks in cells [4-6]. However, the ChIP process produces limited amount of DNA due to the low yield of antibody pull-down, DNA damage during fragmentation and cleavage of DNA-protein complex, and complicated downstream analysis [7]. The conventional approaches have to consume a considerable amount of samples, typically 10 6-10 7 cells, to overcome this low-yield issue and obtain reliable results [5]. This limitation also restricts ChIP applications from precious primary tissue samples such as early embryonic cells or rare tumor stem cells. ChIP-Seq, compared with ChIP-on-Chip, deeply sequences the target DNA fragments and generates highly comprehensive data with higher resolution, fewer ar-tifacts, greater coverage and larger dynamic range [6]. Although recent application of automated microfluidic ChIP (AutoChIP) was successfully performed using 2 000 cells through locus-specific analysis by qPCR [8], such assays do not achieve the comprehensiveness afforded by DNA sequencing approaches. Recently, several approaches have been developed to perform ChIP-Seq using as low as 10 000 or even only 5 000 cells [7, 9-11, 14]. However, all of these methods rely on ChIP reactions in tens of microliters and preamplification of ChIP product before sequencing library preparation, either through linear amplification (by in vitro transcription) or exponential amplification (by PCR), both of which potentially introduce significant bias. Adli et al. [7] reported a modified protocol to realize the ChIP-Seq using 10 000 cells by revising the random primers used in amplification to reduce the primer self-annealing, with an optimized PCR condition to cover the GC-rich regions. Ng et al. [14] developed another protocol to perform ChIP-Seq of H3K4me3 modification using 10 000 mouse primor-dial germ cells, requiring pre-amplification before the sequencing library preparation. Sachs et al. [15] reported a chromatin immunoprecipitation study with low number of cells without pre-amplification, however, it needs at least 50 000 cells as starting material. Here …

Ultralarge Modulation of Single Wall Carbon Nanotube Fluorescence Mediated by Neuromodulators Adsorbed on Arrays of Oligonucleotide Rings

Non-covalent interactions between single-stranded DNA (ssDNA) oligonucleotides and single wall carbon nanotubes (SWNTs) have provided a unique class of tunable chemistries for a variety of applications. However, mechanistic insight into both the photophysical and intermolecular phenomena underlying their utility is lacking, resulting in obligate heuristic approaches for producing ssDNA-SWNT based technologies. In this work, we present an ultrasensitive “turn-on” nanosensor for neuromodulators dopamine and norepinephrine with strong ΔF/F0 of up to 3500%, a signal appropriate for in vivo imaging, and uncover the photophysical principles and intermolecular interactions that govern the molecular recognition and fluorescence modulation of this nanosensor synthesized from the non-covalent conjugation of (GT)6 ssDNA strands on SWNTs. The fluorescence modulation of the ssDNA-SWNT conjugate is shown to exhibit remarkable sensitivity to the ssDNA sequence chemistry, length, and surface density, providing a wealth of parameters with which to tune nanosensor dynamic range and strength of fluorescence turn-on. We employ classical and quantum mechanical molecular dynamics simulations to rationalize our experimental findings. Calculations show that (GT)6 ssDNA form ordered loops around SWNT, inducing periodic surface potentials that modulate exciton recombination lifetimes. Further evidence is presented to elucidate how analyte binding modulates SWNT fluorescence. We discuss the implications of our findings for SWNT-based molecular sensing applications.

Controller for microfluidic large-scale integration

Microfluidic devices with integrated valves provide precise, programmable fluid handling platforms for high-throughput biological or chemical assays. However, setting up the infrastructure to control such platforms often requires specific engineering expertise or expensive commercial solutions. To address these obstacles, we present a Kit for Arduino-based Transistor Array Actuation (KATARA), an open-source and low-cost Arduino-based controller that can drive 70 solenoid valves to pneumatically actuate integrated microfluidic valves. We include a python package with a GUI to control the KATARA from a personal computer. No programming experience is required.