Get to Understand More from Single-Cells: Current Studies of Microfluidic-Based Techniques for Single-Cell Analysis

This review describes the microfluidic techniques developed for the analysis of a single cell. The characteristics of microfluidic (e.g., little sample amount required, high-throughput performance) make this tool suitable to answer and to solve biological questions of interest about a single cell. This review aims to introduce microfluidic related techniques for the isolation, trapping and manipulation of a single cell. The major approaches for detection in single-cell analysis are introduced; the applications of single-cell analysis are then summarized. The review concludes with discussions of the future directions and opportunities of microfluidic systems applied in analysis of a single cell.     

scAPAmod: Profiling Alternative Polyadenylation Modalities in Single Cells from Single-Cell RNA-Seq Data

Alternative polyadenylation (APA) is a key layer of gene expression regulation, and APA choice is finely modulated in cells. Advances in single-cell RNA-seq (scRNA-seq) have provided unprecedented opportunities to study APA in cell populations. However, existing studies that investigated APA in single cells were either confined to a few cells or focused on profiling APA dynamics between cell types or identifying APA sites. The diversity and pattern of APA usages on a genomic scale in single cells remains unappreciated. Here, we proposed an analysis framework based on a Gaussian mixture model, scAPAmod, to identify patterns of APA usage from homogeneous or heterogeneous cell populations at the single-cell level. We systematically evaluated the performance of scAPAmod using simulated data and scRNA-seq data. The results show that scAPAmod can accurately identify different patterns of APA usages at the single-cell level. We analyzed the dynamic changes in the pattern of APA usage using scAPAmod in different cell differentiation and developmental stages during mouse spermatogenesis and found that even the same gene has different patterns of APA usages in different differentiation stages. The preference of patterns of usages of APA sites in different genomic regions was also analyzed. We found that patterns of APA usages of the same gene in 3′ UTRs (3′ untranslated region) and non-3′ UTRs are different. Moreover, we analyzed cell-type-specific APA usage patterns and changes in patterns of APA usages across cell types. Different from the conventional analysis of single-cell heterogeneity based on gene expression profiling, this study profiled the heterogeneous pattern of APA isoforms, which contributes to revealing the heterogeneity of single-cell gene expression with higher resolution.     

Microfluidic Impedance Flow Cytometry Enabling High-Throughput Single-Cell Electrical Property Characterization

This article reviews recent developments in microfluidic impedance flow cytometry for high-throughput electrical property characterization of single cells. Four major perspectives of microfluidic impedance flow cytometry for single-cell characterization are included in this review: (1) early developments of microfluidic impedance flow cytometry for single-cell electrical property characterization; (2) microfluidic impedance flow cytometry with enhanced sensitivity; (3) microfluidic impedance and optical flow cytometry for single-cell analysis and (4) integrated point of care system based on microfluidic impedance flow cytometry. We examine the advantages and limitations of each technique and discuss future research opportunities from the perspectives of both technical innovation and clinical applications.     

单细胞测序技术研究进展

单细胞测序技术是能够在单个细胞的水平上,对基因组进行高通量测序分析的一项新技术。与传统高通量测序相比,单细胞测序不仅能够分析相同表型细胞的遗传异质性,还能获取难以培养微生物的遗传信息,具有广阔的应用前景。单细胞测序技术的流程主要包括单细胞分离、细胞溶解与基因组DNA获取、全基因组扩增、测序与数据分析4个方面,以该技术流程为主线,分析了现有技术的优缺点,并对最新的改进办法进行了综述。     

Single-Circulating Tumor Cell Whole Genome Amplification to Unravel Cancer Heterogeneity and Actionable Biomarkers

The field of single-cell analysis has advanced rapidly in the last decade and is providing new insights into the characterization of intercellular genetic heterogeneity and complexity, especially in human cancer. In this regard, analyzing single circulating tumor cells (CTCs) is becoming particularly attractive due to the easy access to CTCs from simple blood samples called “liquid biopsies”. Analysis of multiple single CTCs has the potential to allow the identification and characterization of cancer heterogeneity to guide best therapy and predict therapeutic response. However, single-CTC analysis is restricted by the low amounts of DNA in a single cell genome. Whole genome amplification (WGA) techniques have emerged as a key step, enabling single-cell downstream molecular analysis. Here, we provide an overview of recent advances in WGA and their applications in the genetic analysis of single CTCs, along with prospective views towards clinical applications. First, we focus on the technical challenges of isolating and recovering single CTCs and then explore different WGA methodologies and recent developments which have been utilized to amplify single cell genomes for further downstream analysis. Lastly, we list a portfolio of CTC studies which employ WGA and single-cell analysis for genetic heterogeneity and biomarker detection.     

The T Cell Repertoires from Nickel Sensitized Joint Implant Failure Patients

Nickel (Ni²⁺) is one of the most common allergens, affecting around 10–15% of the general population. As the demand for orthopedic implant surgery rises, the number of surgical revisions due to joint implant failure also increases. There is evidence that some patients develop joint failure due to an immune response to a component of the implant, and we have found that Ni²⁺ is an especially important cause. Hence, understanding the mechanisms by which Ni²⁺ allergy induces joint implant failure becomes a critical research question. The structural basis of Ni²⁺ activation of pathogenic T cells is still not clear. The purpose of this study was to characterize Ni²⁺-reactive T cell repertoires derived from the peripheral blood of joint failure patients due to Ni²⁺ sensitization using single-cell sequencing techniques. We stimulated the proliferation of Ni²⁺ -reactive T cells from two implant failure patients in vitro, and sorted them for single-cell VDJ sequencing (10× genomics). We identified 2650 productive V-J spanning pairs. Both TCR α chains and β chains were enriched. TRBV18 usage is the highest in the P7 CD4+ population (18.1%), and TRBV5-1 usage is the highest in the P7 CD8+ population (12.1%). TRBV19 and TRBV20-1 segments are present in a high percentage of both P7 and P9 sequenced T cells. Remarkably, the alpha and beta chain combination of TRAV41-TRBV18 accounts for 13.5% of the CD4+ population of P7 patient. Compared to current Ni specific T cell repertoire studies of contact dermatitis, the Vα and Vβ usages of these joint implant failure patients were different. This could be due to the different availability of self-peptides in these two different tissues. However, TRBV19 (Vβ17) was among frequently used TCR β chains, which are common in previous reports. This implies that some pathogenic T cells could be similar in Ni²⁺ hypersensitivities in skin and joints. The alignment of the TCR CDR3β sequences showed a conserved glutamic acid (Glu) that could potentially interact with Ni²⁺. The study of these Ni²⁺ specific TCRs may shed light on the molecular mechanism of T cell activation by low molecular weight chemical haptens.     

Integrated Workflow for the Label-Free Isolation and Genomic Analysis of Single Circulating Tumor Cells in Pancreatic Cancer

As pancreatic cancer is the third deadliest cancer in the U.S., the ability to study genetic alterations is necessary to provide further insight into potentially targetable regions for cancer treatment. Circulating tumor cells (CTCs) represent an especially aggressive subset of cancer cells, capable of causing metastasis and progressing the disease. Here, we present the Labyrinth–DEPArray pipeline for the isolation and analysis of single CTCs. Established cell lines, patient-derived CTC cell lines and freshly isolated CTCs were recovered and sequenced to reveal single-cell copy number variations (CNVs). The resulting CNV profiles of established cell lines showed concordance with previously reported data and highlight several gains and losses of cancer-related genes such as FGFR3 and GNAS. The novel sequencing of patient-derived CTC cell lines showed gains in chromosome 8q, 10q and 17q across both CTC cell lines. The pipeline was used to process and isolate single cells from a metastatic pancreatic cancer patient revealing a gain of chromosome 1q and a loss of chromosome 5q. Overall, the Labyrinth-DEPArray pipeline offers a validated workflow combining the benefits of antigen-free CTC isolation with single cell genomic analysis.     

Panoramic Manifold Projection (Panoramap) for Single-Cell Data Dimensionality Reduction and Visualization

Nonlinear dimensionality reduction (NLDR) methods such as t-Distributed Stochastic Neighbour Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP) have been widely used for biological data exploration, especially in single-cell analysis. However, the existing methods have drawbacks in preserving data’s geometric and topological structures. A high-dimensional data analysis method, called Panoramic manifold projection (Panoramap), was developed as an enhanced deep learning framework for structure-preserving NLDR. Panoramap enhances deep neural networks by using cross-layer geometry-preserving constraints. The constraints constitute the loss for deep manifold learning and serve as geometric regularizers for NLDR network training. Therefore, Panoramap has better performance in preserving global structures of the original data. Here, we apply Panoramap to single-cell datasets and show that Panoramap excels at delineating the cell type lineage/hierarchy and can reveal rare cell types. Panoramap can facilitate trajectory inference and has the potential to aid in the early diagnosis of tumors. Panoramap gives improved and more biologically plausible visualization and interpretation of single-cell data. Panoramap can be readily used in single-cell research domains and other research fields that involve high dimensional data analysis.     

Ultrasensitive and Multiplexed Protein Imaging with Cleavable Fluorescent Tyramide and Antibody Stripping

Multiplexed single-cell analysis of proteins in their native cellular contexts holds great promise to reveal the composition, interaction and function of the distinct cell types in complex biological systems. However, the existing multiplexed protein imaging technologies are limited by their detection sensitivity or technical demands. To address these issues, here, we develop an ultrasensitive and multiplexed in situ protein profiling approach by reiterative staining with off-the-shelf antibodies and cleavable fluorescent tyramide (CFT). In each cycle of this approach, the protein targets are recognized by antibodies labeled with horseradish peroxidase, which catalyze the covalent deposition of CFT on or close to the protein targets. After imaging, the fluorophores are chemically cleaved, and the antibodies are stripped. Through continuous cycles of staining, imaging, fluorophore cleavage and antibody stripping, a large number of proteins can be quantified in individual cells in situ. Applying this method, we analyzed 20 different proteins in each of ~67,000 cells in a human formalin-fixed paraffin-embedded (FFPE) tonsil tissue. Based on their unique protein expression profiles and microenvironment, these individual cells are partitioned into different cell clusters. We also explored the cell–cell interactions in the tissue by examining which specific cell clusters are selectively associating or avoiding each other.