Blood RNA alternative splicing events as diagnostic biomarkers for infectious disease.

Assays detecting blood transcriptome changes are studied for infectious disease diagnosis. Blood-based RNA alternative splicing (AS) events, which have not been well characterized in pathogen infection, have potential normalization and assay platform stability advantages over gene expression for diagnosis. Here, we present a computational framework for developing AS diagnostic biomarkers. Leveraging a large prospective cohort of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and whole-blood RNA sequencing (RNA-seq) data, we identify a major functional AS program switch upon viral infection. Using an independent cohort, we demonstrate the improved accuracy of AS biomarkers for SARS-CoV-2 diagnosis compared with six reported transcriptome signatures. We then optimize a subset of AS-based biomarkers to develop microfluidic PCR diagnostic assays. This assay achieves nearly perfect test accuracy (61/62 = 98.4%) using a naive principal component classifier, significantly more accurate than a gene expression PCR assay in the same cohort. Therefore, our RNA splicing computational framework enables a promising avenue for host-response diagnosis of infection.

Marker-free characterization of full-length transcriptomes of single live circulating tumor cells.

The identification and characterization of circulating tumor cells (CTCs) are important for gaining insights into the biology of metastatic cancers, monitoring disease progression, and medical management of the disease. The limiting factor in the enrichment of purified CTC populations is their sparse availability, heterogeneity, and altered phenotypes relative to the primary tumor. Intensive research both at the technical and molecular fronts led to the development of assays that ease CTC detection and identification from peripheral blood. Most CTC detection methods based on single-cell RNA sequencing (scRNA-seq) use a mix of size selection, marker-based white blood cell (WBC) depletion, and antibodies targeting tumor-associated antigens. However, the majority of these methods either miss out on atypical CTCs or suffer from WBC contamination. We present unCTC, an R package for unbiased identification and characterization of CTCs from single-cell transcriptomic data. unCTC features many standard and novel computational and statistical modules for various analyses. These include a novel method of scRNA-seq clustering, named deep dictionary learning using k-means clustering cost (DDLK), expression-based copy number variation (CNV) inference, and combinatorial, marker-based verification of the malignant phenotypes. DDLK enables robust segregation of CTCs and WBCs in the pathway space, as opposed to the gene expression space. We validated the utility of unCTC on scRNA-seq profiles of breast CTCs from six patients, captured and profiled using an integrated ClearCell FX and Polaris workflow that works by the principles of size-based separation of CTCs and marker-based WBC depletion.

Microfluidic live tracking and transcriptomics of cancer-immune cell doublets link intercellular proximity and gene regulation.

Cell-cell communication and physical interactions play a vital role in cancer initiation, homeostasis, progression, and immune response. Here, we report a system that combines live capture of different cell types, co-incubation, time-lapse imaging, and gene expression profiling of doublets using a microfluidic integrated fluidic circuit that enables measurement of physical distances between cells and the associated transcriptional profiles due to cell-cell interactions. We track the temporal variations in natural killer-triple-negative breast cancer cell distances and compare them with terminal cellular transcriptome profiles. The results show the time-bound activities of regulatory modules and allude to the existence of transcriptional memory. Our experimental and bioinformatic approaches serve as a proof of concept for interrogating live-cell interactions at doublet resolution. Together, our findings highlight the use of our approach across different cancers and cell types.

Single-cell analysis of circulating tumour cells: enabling technologies and clinical applications.

Multimodal analysis of circulating tumour cells (CTCs) has the potential to provide remarkable insight for cancer development and metastasis. CTCs and CTC clusters investigation using single-cell analysis, enables researchers to gain crucial information on metastatic mechanisms and the genomic alterations responsible for drug resistance, empowering treatment, and management of cancer. Despite a plethora of CTC isolation technologies, careful attention to the strengths and weaknesses of each method should be considered in order to isolate these rare cells. Here, we provide an overview of cutting-edge technologies used for single-cell isolation and analysis of CTCs. Additionally, we highlight the biological features, clinical application, and the therapeutic potential of CTCs and CTC clusters using single-cell analysis platforms for cancer management.

Simultaneous Quantification of Single-Cell Proteomes and Transcriptomes in Integrated Fluidic Circuits.

Understanding the principles of gene regulation at single-cell resolution would require measurement and integration of multiple methods such as DNA mutation profiling, open chromatin profiling, RNA expression, protein quantification, and DNA methylation. Recent developments in single-cell multi-omic technologies have enabled integration of these modes in various combinations.With the advent of RNA expression and protein sequencing assay (REAP-seq), researchers can simultaneously analyze protein and gene expression within the same cell. In REAP-seq , cells are labeled with antibodies conjugated to unique DNA sequences. A barcode of 8 nucleotides can allow up to 65,536 unique barcodes for multiplex analysis of proteins, circumventing the limitations of fluorescence (~17 targets). Here, we describe the implementation of REAP-seq assay in the Fluidigm® C1™ mRNA Seq HT (high-throughput) v2 system.

Modeling expression ranks for noise-tolerant differential expression analysis of scRNA-seq data.

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.

Erratum: Iyer, A., et al. Integrative Analysis and Machine Learning Based Characterization of Single Circulating Tumor Cells. J. Clin. Med. 2020, 9, 1206.

In the published manuscript [...].

Integrative Analysis and Machine Learning based Characterization of Single Circulating Tumor Cells.

We collated publicly available single-cell expression profiles of circulating tumor cells (CTCs) and showed that CTCs across cancers lie on a near-perfect continuum of epithelial to mesenchymal (EMT) transition. Integrative analysis of CTC transcriptomes also highlighted the inverse gene expression pattern between PD-L1 and MHC, which is implicated in cancer immunotherapy. We used the CTCs expression profiles in tandem with publicly available peripheral blood mononuclear cell (PBMC) transcriptomes to train a classifier that accurately recognizes CTCs of diverse phenotype. Further, we used this classifier to validate circulating breast tumor cells captured using a newly developed microfluidic system for label-free enrichment of CTCs.

Advances in the Characterization of Circulating Tumor Cells in Metastatic Breast Cancer: Single Cell Analyses and Interactions, and Patient-Derived Models for Drug Testing.

Metastasis is the major cause of breast cancer death worldwide. In metastatic breast cancer, circulating tumor cells (CTCs) can be captured from patient blood samples sequentially over time and thereby serve as surrogates to assess the biology of surviving cancer cells that may still persist in solitary or multiple metastatic sites following treatment. CTCs may thus function as potential real-time decision-making guides for selecting appropriate therapies during the course of disease or for the development and testing of new treatments. The heterogeneous nature of CTCs warrants the use of single cell platforms to better inform our understanding of these cancer cells. Current techniques for single cell analyses and techniques for investigating interactions between cancer and immune cells are discussed. In addition, methodologies for growing patient-derived CTCs in vitro or propagating them in vivo to facilitate CTC drug testing are reviewed. We advocate the use of CTCs in appropriate microenvironments to appraise the effectiveness of cancer chemotherapies, immunotherapies, and for the development of new cancer treatments, fundamental to personalizing and improving the clinical management of metastatic breast cancer.

Integrated Fluidic Circuits for Single-Cell Omics and Multi-omics Applications.

Single-cell genomics plays a crucial role in several aspects of biology, from developmental biology to mapping every cell in the human body through the Cell Atlas initiative. To meet these various applications, single-cell methods are rapidly evolving to increase throughput; improve sensitivity, quantification accuracy, and usability; and reduce nucleic-acid amplification bias and cost. In addition to improvement in single-cell methods, there is a huge interest in analyzing multiple analytes such as genome, epigenome, transcriptome, and protein from the same single cell. This approach is generalized as single-cell multi-omics. Automation of multi-step single-cell methods is highly desired to achieve a reproducible workflow; reduce human error and avoid contamination; and introduce technical variability to an existing stochastic process. Typically single-cell reactions start with a low level of nucleic acid, in the range of picograms. Miniaturization in microfluidic devices leads to a gain in reaction efficiency in Nanoliter or picoliter reaction volumes and active mixing help ensure that solid-state microfluidic devices provide the broadest flexibility and best sensitivity in single-cell reactions, compared to other methods. In this chapter, we will present integrated fluidic circuit (IFC) microfluidics for various single-cell multi-omics applications, and show how this technology fits into the current single-cell technology portfolio available from various vendors. We will then discuss possible uses for IFCs in multi-omics applications that are on the horizon.

C1 CAGE detects transcription start sites and enhancer activity at single-cell resolution.

Single-cell transcriptomic profiling is a powerful tool to explore cellular heterogeneity. However, most of these methods focus on the 3'-end of polyadenylated transcripts and provide only a partial view of the transcriptome. We introduce C1 CAGE, a method for the detection of transcript 5'-ends with an original sample multiplexing strategy in the C1TM microfluidic system. We first quantifiy the performance of C1 CAGE and find it as accurate and sensitive as other methods in the C1 system. We then use it to profile promoter and enhancer activities in the cellular response to TGF-ß of lung cancer cells and discover subpopulations of cells differing in their response. We also describe enhancer RNA dynamics revealing transcriptional bursts in subsets of cells with transcripts arising from either strand in a mutually exclusive manner, validated using single molecule fluorescence in situ hybridization.

Future of Liquid Biopsies With Growing Technological and Bioinformatics Studies: Opportunities and Challenges in Discovering Tumor Heterogeneity With Single-Cell Level Analysis.

Liquid biopsy provides minimally invasive and readily obtainable access to tumor-associated biological material in blood or other body fluids. These samples provide important insights into cancer biology, such as primary tumor heterogeneity; real-time tumor evolution; response to therapy, including immunotherapy; and mechanisms of cancer metastasis. Initial biological materials studied were circulating tumor cells and circulating nucleic acids, including circulating tumor DNA and microRNAs; more recently, studies have expanded to investigate extracellular vesicles, such as exosomes, microvesicles, and large oncosomes; tumor-derived circulating endothelial cells; and tumor-educated platelets. Even with an ongoing ambitious investment effort to develop liquid biopsy as an early cancer detection test in asymptomatic individuals, current challenges remain regarding how to access and analyze rare cells and tumor-derived nucleic acids in cancer patients. Technologies and associated bioinformatics tools are continuously evolving to capture these rare materials in an unbiased manner and to analyze them with high confidence. After first presenting recent applications of liquid biopsy, this review discusses aspects affecting the field, including tumor heterogeneity, single-cell analyses, and associated computational tools that will shape the future of liquid biopsy, with resultant opportunities and challenges.

Acetylated bovine serum albumin differentially inhibits polymerase chain reaction in microdevices.

Bovine serum albumin (BSA) is widely used as an additive in polymerase chain reaction (PCR)-based microfluidic devices to passivate reactors and alleviate nucleic-acid amplification. BSA is available commercially in two types: either acetylated or non-acetylated. A survey of literature indicates that both types of BSA are used in PCR-based microfluidic devices. Our study results reveal that the use of acetylated BSA in PCR micro-devices leads to differential inhibition of PCR, compared to non-acetylated BSA. This result is noticed for the first time, and the differential inhibition generally goes un-noticed, as compared to complete PCR inhibition.

Corrigendum: Fluidic Logic Used in a Systems Approach to Enable Integrated Single-Cell Functional Analysis.

[This corrects the article on p. 70 in vol. 4, PMID: 27709111.].

Fluidic Logic Used in a Systems Approach to Enable Integrated Single-Cell Functional Analysis.

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.

Numerical and experimental study of capillary-driven flow of PCR solution in hybrid hydrophobic microfluidic networks.

Capillary-driven microfluidics is essential for development of point-of-care diagnostic micro-devices. Polymerase chain reaction (PCR)-based micro-devices are widely developed and used in such point-of-care settings. It is imperative to characterize the fluid parameters of PCR solution for designing efficient capillary-driven microfluidic networks. Generally, for numeric modelling, the fluid parameters of PCR solution are approximated to that of water. This procedure leads to inaccurate results, which are discrepant to experimental data. This paper describes mathematical modeling and experimental validation of capillary-driven flow inside Poly-(dimethyl) siloxane (PDMS)-glass hybrid micro-channels. Using experimentally measured PCR fluid parameters, the capillary meniscus displacement in PDMS-glass microfluidic ladder network is simulated using computational fluid dynamic (CFD), and experimentally verified to match with the simulated data.

Single-cell profiling approaches to probing tumor heterogeneity.

Tumor heterogeneity is a major hindrance in cancer classification, diagnosis and treatment. Recent technological advances have begun to reveal the true extent of its heterogeneity. Single-cell analysis (SCA) is emerging as an important approach to detect variations in morphology, genetic or proteomic expression. In this review, we revisit the issue of inter- and intra-tumor heterogeneity, and list various modes of SCA techniques (cell-based, nucleic acid-based, protein-based, metabolite-based and lipid-based) presently used for cancer characterization. We further discuss the advantages of SCA over pooled cell analysis, as well as the limitations of conventional techniques. Emerging trends, such as high-throughput sequencing, are also mentioned as improved means for cancer profiling. Collectively, these applications have the potential for breakthroughs in cancer treatment.

Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex.

Large-scale surveys of single-cell gene expression have the potential to reveal rare cell populations and lineage relationships but require efficient methods for cell capture and mRNA sequencing. Although cellular barcoding strategies allow parallel sequencing of single cells at ultra-low depths, the limitations of shallow sequencing have not been investigated directly. By capturing 301 single cells from 11 populations using microfluidics and analyzing single-cell transcriptomes across downsampled sequencing depths, we demonstrate that shallow single-cell mRNA sequencing (~50,000 reads per cell) is sufficient for unbiased cell-type classification and biomarker identification. In the developing cortex, we identify diverse cell types, including multiple progenitor and neuronal subtypes, and we identify EGR1 and FOS as previously unreported candidate targets of Notch signaling in human but not mouse radial glia. Our strategy establishes an efficient method for unbiased analysis and comparison of cell populations from heterogeneous tissue by microfluidic single-cell capture and low-coverage sequencing of many cells.

Real-time PCR array chip with capillary-driven sample loading and reactor sealing for point-of-care applications.

A major challenge for the lab-on-a-chip (LOC) community is to develop point-of-care diagnostic chips that do not use instruments. Such instruments include pumping or liquid handling devices for distribution of patient's nucleic-acid test sample among an array of reactors and microvalves or mechanical parts to seal these reactors. In this paper, we report the development of a primer pair pre-loaded PCR array chip, in which the loading of the PCR mixture into an array of reactors and subsequent sealing of the reactors were realized by a novel capillary-based microfluidics with a manual two-step pipetting operations. The chip is capable of performing simultaneous (parallel) analyses of multiple gene targets and its performance was tested by amplifying twelve different gene targets against cDNA template from human hepatocellular carcinoma using SYBR Green I fluorescent dye. The versatility and reproducibility of the PCR-array chip are demonstrated by real-time PCR amplification of the BNI-1 fragment of SARS cDNA cloned in a plasmid vector. The reactor-to-reactor diffusion of the pre-loaded primer pairs in the chip is investigated to eliminate the possibility of primer cross-contamination. Key technical issues such as PCR mixture loss in gas-permeable PDMS chip layer and bubble generation due to different PDMS-glass bonding methods are investigated.