Revealing allele-specific gene expression by single-cell transcriptomics

Single-cell sequencing has emerged as a revolutionary method that reveals biological processes with unprecedented resolution and scale, and has already greatly impacted biology and medicine. To investigate processes such as alternative splicing, novel exon detection and allele-specific expression (ASE), full-length based single-cell RNA-seq methods are required for broad sequence coverage and single nucleotide polymorphism (SNP) identification. In this review, we revisit recent achievements from studies that used single-cell RNA-seq to advance our understanding of ASE in the context of both autosomal and X-chromosome genes. We also recapitulate useful bioinformatic tools developed to identify haplotype phase.     

Single-cell analysis of tumors: Creating new value for molecular biomarker discovery of cancer stem cells and tumor-infiltrating immune cells

Biomarker-driven individualized treatment in oncology has made tremendous progress through technological developments, new therapeutic modalities and a deeper understanding of the molecular biology for tumors, cancer stem cells and tumor-infiltrating immune cells. Recent technical developments have led to the establishment of a variety of cancer-related diagnostic, prognostic and predictive biomarkers. In this regard, different modern OMICs approaches were assessed in order to categorize and classify prognostically different forms of neoplasia. Despite those technical advancements, the extent of molecular heterogeneity at the individual cell level in human tumors remains largely uncharacterized. Each tumor consists of a mixture of heterogeneous cell types. Therefore, it is important to quantify the dynamic cellular variations in order to predict clinical parameters, such as a response to treatment and or potential for disease recurrence. Recently, single-cell based methods have been developed to characterize the heterogeneity in seemingly homogenous cancer cell populations prior to and during treatment. In this review, we highlight the recent advances for single-cell analysis and discuss the challenges and prospects for molecular characterization of cancer cells, cancer stem cells and tumor-infiltrating immune cells.     

以葡萄渣为原料,固体发酵生产单细胞蛋白的初步研究

本文报道以葡萄渣为唯一碳源,利用微生物混合培养固体发酵技术生产单细胞蛋白的初步尝试。将自分的３株霉菌、（Ａｓｐｅｒｇｉｌｕｓ）４株酵母（Ｓａｃｈａｒｏｍｙｃｅｓ）和１株细菌（Ｃｅｌｕｌｏｍｏｎａｓ）进行了不同组合的培养,并用正交试验法作了培养基配方试验,同时对培养基含水量和发酵时间也进行了一些摸索。初步结果表明,培养基在未经灭菌的条件下,以尿素为氮源,培养基含水量６０％,于２８℃—３０℃,培养４８ｈ,发酵产物的粗蛋白含量可提高一倍。     

Dynamical features of higher-order correlation events: impact on cortical cells

Cortical neurons receive signals from thousands of other neurons. The statistical properties of the input spike trains substantially shape the output response properties of each neuron. Experimental and theoretical investigations have mostly focused on the second order statistical features of the input spike trains (mean firing rates and pairwise correlations). Little is known of how higher order correlations affect the integration and firing behavior of a cell independently of the second order statistics. To address this issue, we simulated the dynamics of a population of 5000 neurons, controlling both their second order and higher-order correlation properties to reflect physiological data. We then used these ensemble dynamics as the input stage to morphologically reconstructed cortical cells (layer 5 pyramidal, layer 4 spiny stellate cell), and to an integrate and fire neuron. Our results show that changes done solely to the higher-order correlation properties of the network’s dynamics significantly affect the response properties of a target neuron, both in terms of output rate and spike timing. Moreover, the neuronal morphology and voltage dependent mechanisms of the target neuron considerably modulate the quantitative aspects of these effects. Finally, we show how these results affect sparseness of neuronal representations, tuning properties, and feature selectivity of cortical cells. An erratum to this article can be found at     

单细胞技术进展及其在植物研究中的应用

多细胞生物体的生存依赖于不同类型细胞特异性的功能分工,不同类型的细胞尽管基因组相同,但有其独特的发育过程和应对环境变化的能力。生物学的一大挑战就是揭示基因如何在正确的位置、正确的时间表达到正确的水平,最近出现了很多通过细胞类型特异性方法研究单细胞组学的工具,这些新技术使我们能通过空前分辨率,理解多细胞生物体内不同类型的单个细胞基因表达特点及其适应环境变化的机制。单细胞样品的获取一直是单细胞研究的一大技术瓶颈,因此本文将以如何获得起始材料为重点,探讨单细胞研究的样品标记、单细胞分离及获取、组学数据分析和结果验证等技术方法及其在植物研究中的应用。     

Single-cell Analysis Technologies for Immuno-oncology Research: from Mechanistic Delineation to Biomarker Discovery

The successes with immune checkpoint blockade(ICB) and chimeric antigen receptor(CAR)-T-cell therapy in treating multiple cancer types have established immunotherapy as a powerful curative option for patients with advanced cancers. Unfortunately, many patients do not derive benefit or long-term responses, highlighting a pressing need to perform complete investigation of the underlying mechanisms and the immunotherapy-induced tumor regression or rejection.In recent years, a large number of single-cell technologies have leveraged advances in characterizing immune system, profiling tumor microenvironment, and identifying cellular heterogeneity, which establish the foundations for lifting the veil on the comprehensive crosstalk between cancer and immune system during immunotherapies. In this review, we introduce the applications of the most widely used single-cell technologies in furthering our understanding of immunotherapies in terms of underlying mechanisms and their association with therapeutic outcomes. We also discuss how single-cell analyses help to deliver new insights into biomarker discovery to predict patient response rate, monitor acquired resistance, and support prophylactic strategy development for toxicity management. Finally, we provide an overview of applying cutting-edge single-cell spatial-omics to point out the heterogeneity of tumor–immune interactions at higher level that can ultimately guide to the rational design of next-generation immunotherapies.     

DTFLOW: Inference and Visualization of Single-cell Pseudotime Trajectory Using Diffusion Propagation

One of the major challenges in single-cell data analysis is the determination of cellular developmental trajectories using single-cell data. Although substantial studies have been conducted in recent years, more effective methods are still strongly needed to infer the developmental processes accurately. This work devises a new method, named DTFLOW, for determining the pseudotemporal trajectories with multiple branches. DTFLOW consists of two major steps: a new method called Bhattacharyya kernel feature decomposition(BKFD) to reduce the data dimensions, and a novel approach named Reverse Searching on k-nearest neighbor graph(RSKG) to identify the multi-branching processes of cellular differentiation. In BKFD, we first establish a stationary distribution for each cell to represent the transition of cellular developmental states based on the random walk with restart algorithm, and then propose a new distance metric for calculating pseudotime of single cells by introducing the Bhattacharyya kernel matrix. The effectiveness of DTFLOW is rigorously examined by using four single-cell datasets. We compare the efficiency of DTFLOW with the published state-of-the-art methods. Simulation results suggest that DTFLOW has superior accuracy and strong robustness properties for constructing pseudotime trajectories. The Python source code of DTFLOW can be freely accessed at https://github.com/statway/DTFLOW.     

scLM: Automatic Detection of Consensus Gene Clusters Across Multiple Single-cell Datasets

In gene expression profiling studies, including single-cell RNA sequencing(sc RNA-seq)analyses, the identification and characterization of co-expressed genes provides critical information on cell identity and function. Gene co-expression clustering in sc RNA-seq data presents certain challenges. We show that commonly used methods for single-cell data are not capable of identifying co-expressed genes accurately, and produce results that substantially limit biological expectations of co-expressed genes. Herein, we present single-cell Latent-variable Model(sc LM), a gene coclustering algorithm tailored to single-cell data that performs well at detecting gene clusters with significant biologic context. Importantly, sc LM can simultaneously cluster multiple single-cell datasets, i.e., consensus clustering, enabling users to leverage single-cell data from multiple sources for novel comparative analysis. sc LM takes raw count data as input and preserves biological variation without being influenced by batch effects from multiple datasets. Results from both simulation data and experimental data demonstrate that sc LM outperforms the existing methods with considerably improved accuracy. To illustrate the biological insights of sc LM, we apply it to our in-house and public experimental sc RNA-seq datasets. sc LM identifies novel functional gene modules and refines cell states, which facilitates mechanism discovery and understanding of complex biosystems such as cancers. A user-friendly R package with all the key features of the sc LM method is available at https://github.com/QSong-github/sc LM.     

scLink: Inferring Sparse Gene Co-expression Networks from Single-cell Expression Data

A system-level understanding of the regulation and coordination mechanisms of gene expression is essential for studying the complexity of biological processes in health and disease. With the rapid development of single-cell RNA sequencing technologies, it is now possible to investigate gene interactions in a cell type-specific manner. Here we propose the scLink method, which uses statistical network modeling to understand the co-expression relationships among genes and construct sparse gene co-expression networks from single-cell gene expression data. We use both simulation and real data studies to demonstrate the advantages of scLink and its ability to improve single-cell gene network analysis. The scLink R package is available at https://github.com/Vivianstats/scLink.