The major modules for realizing molecular biological assays in a micro total analysis system (μTAS) were developed for the detection of pathogenic organisms. The specific focus was the isolation and amplification of eukaryotic messenger RNA (mRNA) within a simple, single-channel device for very low RNA concentrations that could then be integrated with detection modules. The hsp70 mRNA from Cryptosporidium parvum was used as a model analyte. Important points of study were surface chemistries within poly(methyl methacrylate) (PMMA) microfluidic channels that enabled specific and sensitive mRNA isolation and amplification reactions for very low mRNA concentrations. Optimal conditions were achieved when the channel surface was carboxylated via UV/ozone treatment followed by the immobilization of polyamidoamine (PAMAM) dendrimers on the surface, thus increasing the immobilization efficiency of the thymidine oligonucleotide, oligo(dT)25, and providing a reliable surface for the amplification reaction, importantly, without the need for blocking agents. Additional chemical modifications of the remaining active surface groups were studied to avoid non-specific capturing of nucleic acids and hindering of the mRNA amplification at low RNA concentrations. Amplification of the mRNA was accomplished using nucleic acid sequence-based amplification (NASBA), an isothermal, primer-dependent technique. Positive controls consisting of previously generated NASBA amplicons could be diluted 1015 fold and still result in successful on-chip re-amplification. Finally, the successful isolation and amplification of mRNA from as few as 30 C. parvum oocysts was demonstrated directly on-chip and compared to bench-top devices. This is the first proof of successful mRNA isolation and NASBA-based amplification of mRNA within a simple microfluidic device in relevant analytical volumes.

The study of single cells has evolved over the past several years to include expression and genomic analysis of an increasing number of single cells. Several studies have demonstrated wide spread variation and heterogeneity within cell populations of similar phenotype. While the characterization of these populations will likely set the foundation for our understanding of genomic- and expression-based diversity, it will not be able to link the functional differences of a single cell to its underlying genomic structure and activity. Currently, it is difficult to perturb single cells in a controlled environment, monitor and measure the response due to perturbation, and link these response measurements to downstream genomic and transcriptomic analysis. In order to address this challenge, we developed a platform to integrate and miniaturize many of the experimental steps required to study single-cell function. The heart of this platform is an elastomer-based integrated fluidic circuit that uses fluidic logic to select and sequester specific single cells based on a phenotypic trait for downstream experimentation. Experiments with sequestered cells that have been performed include on-chip culture, exposure to various stimulants, and post-exposure image-based response analysis, followed by preparation of the mRNA transcriptome for massively parallel sequencing analysis. The flexible system embodies experimental design and execution that enable routine functional studies of single cells.

Background Label-free methods for isolating circulating tumor cells (CTCs) are attractive because they provide an opportunity to analyze a larger set of CTCs that may otherwise be missed due to variable or no expression of protein (label) markers. Understanding genetic and functional heterogeneity in CTCs allows us to gain insight into the mechanisms underscoring metastasis, drug resistance, and tumor aggressiveness. Currently, a simple workflow for isolation and molecular characterization of single CTCs by mRNA sequencing is lacking. In order to address this challenge, we developed a label-free workflow to isolate CTCs from breast cancer patients for full-length mRNA sequencing analysis by integrating the ClearCell® FX System with the Polaris™ system. The ClearCell FX system processes blood samples from cancer patients and enriches for CTCs in a label-free antibody-independent manner. The low level of nonspecifically isolated white blood cells from ClearCell FX is further depleted on the Polaris system by negative enrichment of viable CTCs. This unique integration of systems will enable researchers to perturb single CTCs in a controlled environment, monitor and measure the response due to perturbation, and link these response measurements to downstream genomic and transcriptomic analysis. Method and Results CTCs from 7.5 mL of peripheral blood sample from breast cancer patients were enriched using ClearCell FX. To differentiate larger blood cells from putative CTCs, we stained the enriched cells with Alexa Fluor® 647-conjugated CD45 and CD31 to identify leukocytes and endothelial cells, respectively. Calcein AM (live cell marker) and CellTracker™ Orange (universal cell marker) were added to identify live cells. Single CTCs were selected on Polaris (Fluidigm) system, lysed and reverse-transcribed, and cDNA were preamplified on the Polaris integrated fluidic circuit (IFC). Sequencing libraries were generated using the Nextera® kit and sequenced on Illumina® MiSeq™ and NextSeq™ systems. We successfully processed blood samples from four patients. Sequenced data showed high-quality metrics, with read depth of up to 2.5 million reads (MiSeq) or 60 million reads (NextSeq), with a low percentage of mapped reads to ribosomal RNA and mitochondrial RNA. Unsupervised hierarchical clustering of gene expression data showed clustering by patient, but considerable heterogeneity was also observed among the CTCs from the same patient. We will provide insights into full-length mRNA transcriptome of single CTCs from triple negative breast cancer patient. Conclusion We present the feasibility of integrating two microfluidics platforms to capture single CTCs for transcriptome and functional study. Our data suggests that the heterogeneity of tumor sample and characterization of metastatic processes can be elucidated from single-cell mRNA sequencing of CTCs. Citation Format: Naveen Ramalingam, Yi Fang Lee, Lukasz Szpankowski, Anne Leyrat, Brian Fowler, Jovina Tan, Chong Tracy Lu, Ninez Delos Angeles, Chad Sanada, Cassandra Greene, Kyle Hukari, Andrew Wu, Yoon-Sim Yap, Jay West, Ali Asgar Bhagat. Label-free enrichment and integrated full-length mRNA transcriptome analysis of single live circulating tumor cells from breast cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2923. doi:10.1158/1538-7445.AM2017-2923

Systematic delineation of complex biological systems is an ever-challenging and resource-intensive process. Single-cell transcriptomics allows us to study cell-to-cell variability in complex tissues at an unprecedented resolution. Accurate modeling of gene expression plays a critical role in the statistical determination of tissue-specific gene expression patterns. In the past few years, considerable efforts have been made to identify appropriate parametric models for single-cell expression data. The zero-inflated version of Poisson/negative binomial and log-normal distributions have emerged as the most popular alternatives owing to their ability to accommodate high dropout rates, as commonly observed in single-cell data. Although the majority of the parametric approaches directly model expression estimates, we explore the potential of modeling expression ranks, as robust surrogates for transcript abundance. Here we examined the performance of the discrete generalized beta distribution (DGBD) on real data and devised a Wald-type test for comparing gene expression across two phenotypically divergent groups of single cells. We performed a comprehensive assessment of the proposed method to understand its advantages compared with some of the existing best-practice approaches. We concluded that besides striking a reasonable balance between Type I and Type II errors, ROSeq, the proposed differential expression test, is exceptionally robust to expression noise and scales rapidly with increasing sample size. For wider dissemination and adoption of the method, we created an R package called ROSeq and made it available on the Bioconductor platform.

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