Single cell transcriptional analysis reveals novel innate immune cell types

Single-Cell Transcriptomics: Current Methods and Challenges in Data Acquisition and Analysis

Rapid cost drops and advancements in next-generation sequencing have made profiling of cells at individual level a conventional practice in scientific laboratories worldwide. Single-cell transcriptomics [single-cell RNA sequencing (SC-RNA-seq)] has an immense potential of uncovering the novel basis of human life. The well-known heterogeneity of cells at the individual level can be better studied by single-cell transcriptomics. Proper downstream analysis of this data will provide new insights into the scientific communities. However, due to low starting materials, the SC-RNA-seq data face various computational challenges: normalization, differential gene expression analysis, dimensionality reduction, etc. Additionally, new methods like 10× Chromium can profile millions of cells in parallel, which creates a considerable amount of data. Thus, single-cell data handling is another big challenge. This paper reviews the single-cell sequencing methods, library preparation, and data generation. We highlight some of the main computational challenges that require to be addressed by introducing new bioinformatics algorithms and tools for analysis. We also show single-cell transcriptomics data as a big data problem.

Single Cell Analysis of Neutrophils NETs by Microscopic LSPR Imaging System

A simple microengraving cell monitoring method for neutrophil extracellular traps (NETs) released from single neutrophils has been realized using a polydimethylsiloxane (PDMS) microwell array (MWA) sheet on a plasmon chip platform. An imbalance between NETs formation and the succeeding degradation (NETosis) are considered associated with autoimmune disease and its pathogenesis. Thus, an alternative platform that can conduct monitoring of this activity on single cell level at minimum cost but with great sensitivity is greatly desired. The developed MWA plasmon chips allow single cell isolation of neutrophils from 150 µL suspension (6.0 × 10⁵ cells/mL) with an efficiency of 36.3%; 105 microwells with single cell condition. To demonstrate the utility of the chip, trapped cells were incubated between 2 to 4 h after introducing with 100 nM phorbol 12-myristate 13-acetate (PMA) before measurement. Under observation using a hyperspectral imaging system that allows high-throughput screening, the neutrophils stimulated by PMA solution show a significant release of fibrils and NETs after 4 h, with observed maximum areas between 314–758 µm². An average absorption peak wavelength shows a redshift of Δλ = 1.5 nm as neutrophils release NETs.

Neutrophils in innate immunity and systems biology-level approaches

The innate immune system is the first line of host defense against invading microorganisms. Polymorphonuclear leukocytes (PMNs or neutrophils) are the most abundant leukocyte in humans and essential to the innate immune response against invading pathogens. Compared to the acquired immune response, which requires time to develop and is dependent on previous interaction with specific microbes, the ability of neutrophils to kill microorganisms is immediate, nonspecific, and not dependent on previous exposure to microorganisms. Historically, studies of PMN-pathogen interaction focused on the events leading to killing of microorganisms, such as recruitment/chemotaxis, transmigration, phagocytosis, and activation, whereas postphagocytosis sequelae were infrequently considered. In addition, it was widely accepted that human neutrophils possessed limited capacity for new gene transcription and thus, relatively little biosynthetic capacity. This notion has changed dramatically within the past 20 years. Further, there is now more effort directed to understand the events occurring in PMNs after killing of microbes. Herein, we give an updated review of the systems biology-level approaches that have been used to gain an enhanced view of the role of neutrophils during host-pathogen interaction and neutrophil-mediated diseases. We anticipate that these and future systems-level studies will continue to provide information important for understanding, treatment, and control of diseases caused by pathogenic microorganisms. This article is categorized under: Physiology > Organismal Responses to Environment Physiology > Mammalian Physiology in Health and Disease Biological Mechanisms > Cell Fates.

Oscillatory IL-2 stimulus reveals pertinent signaling timescales of T cell responsiveness

Cell response to extracellular ligand is affected not only by ligand availability, but also by pre-existing cell-to-cell variability that enables a range of responses within a cell population. We developed a computational model that incorporates cell heterogeneity in order to investigate Jurkat T cell response to time dependent extracellular IL-2 stimulation. Our model predicted preferred timing of IL-2 oscillatory input for maximizing downstream intracellular STAT5 nuclear translocation. The modeled cytokine exposure was replicated experimentally through the use of a microfluidic platform that enabled the parallelized capture of dynamic single cell response to precisely delivered pulses of IL-2 stimulus. The in vitro results demonstrate that single cell response profiles vary with pulsatile IL-2 input at pre-equilibrium levels. These observations confirmed our model predictions that Jurkat cells have a preferred range of extracellular IL-2 fluctuations, in which downstream response is rapidly initiated. Further investigation into this filtering behavior could increase our understanding of how pre-existing cellular states within immune cell populations enable a systems response within a preferred range of ligand fluctuations, and whether the observed cytokine range corresponds to in vivo conditions.

Single-cell analysis identifies cellular markers of the HIV permissive cell

Cellular permissiveness to HIV infection is highly heterogeneous across individuals. Heterogeneity is also found across CD4+ T cells from the same individual, where only a fraction of cells gets infected. To explore the basis of permissiveness, we performed single-cell RNA-seq analysis of non-infected CD4+ T cells from high and low permissive individuals. Transcriptional heterogeneity translated in a continuum of cell states, driven by T-cell receptor-mediated cell activation and was strongly linked to permissiveness. Proteins expressed at the cell surface and displaying the highest correlation with T cell activation were tested as biomarkers of cellular permissiveness to HIV. FACS sorting using antibodies against several biomarkers of permissiveness led to an increase of HIV cellular infection rates. Top candidate biomarkers included CD25, a canonical activation marker. The combination of CD25 high expression with other candidate biomarkers led to the identification of CD298, CD63 and CD317 as the best biomarkers for permissiveness. CD25highCD298highCD63highCD317high cell population showed an enrichment of HIV-infection of up to 28 fold as compared to the unsorted cell population. The purified hyper-permissive cell subpopulation was characterized by a downregulation of interferon-induced genes and several known restriction factors. Single-cell RNA-seq analysis coupled with functional characterization of cell biomarkers provides signatures of the "HIV-permissive cell".

Vertical flow array chips reliably identify cell types from single-cell mRNA sequencing experiments

Single-cell mRNA sequencing offers an unbiased approach to dissecting cell types as functional units in multicellular tissues. However, highly reliable cell typing based on single-cell gene expression analysis remains challenging because of the lack of methods for efficient sample preparation for high-throughput sequencing and evaluating the statistical reliability of the acquired cell types. Here, we present a highly efficient nucleic reaction chip (a vertical flow array chip (VFAC)) that uses porous materials to reduce measurement noise and improve throughput without a substantial increase in reagent. We also present a probabilistic evaluation method for cell typing depending on the amount of measurement noise. Applying the VFACs to 2580 monocytes provides 1967 single-cell expressions for 47 genes, including low-expression genes such as transcription factors. The statistical method can distinguish two cell types with probabilistic quality values, with the measurement noise level being considered for the first time. This approach enables the identification of various sub-types of cells in tissues and provides a foundation for subsequent analyses.

A Single-Cell Gene-Expression Profile Reveals Inter-Cellular Heterogeneity within Human Monocyte Subsets

Human monocytes are a heterogeneous cell population classified into three different subsets: Classical CD14++CD16-, intermediate CD14++CD16+, and non-classical CD14+CD16++ monocytes. These subsets are distinguished by their differential expression of CD14 and CD16, and unique gene expression profile. So far, the variation in inter-cellular gene expression within the monocyte subsets is largely unknown. In this study, the cellular variation within each human monocyte subset from a single healthy donor was described by using a novel single-cell PCR gene-expression analysis tool. We investigated 86 different genes mainly encoding cell surface markers, and proteins involved in immune regulation. Within the three human monocyte subsets, our descriptive findings show multimodal expression of key immune response genes, such as CD40, NFⱪB1, RELA, TLR4, TLR8 and TLR9. Furthermore, we discovered one subgroup of cells within the classical monocytes, which showed alterations of 22 genes e.g. IRF8, CD40, CSF1R, NFⱪB1, RELA and TNF. Additionally one subgroup within the intermediate and non-classical monocytes also displayed distinct gene signatures by altered expression of 8 and 6 genes, respectively. Hence the three monocyte subsets can be further subdivided according to activation status and differentiation, independently of the traditional classification based on cell surface markers. Demonstrating the use and the ability to discover cell heterogeneity within defined populations of human monocytes is of great importance, and can be useful in unravelling inter-cellular variation in leukocyte populations, identifying subpopulations involved in disease pathogenesis and help tailor new therapies.